From 82e8df720cbd71ceaf181a24418c50c57dee0c25 Mon Sep 17 00:00:00 2001 From: Mattia Amadio <44863827+matamadio@users.noreply.github.com> Date: Thu, 13 Jun 2024 13:40:46 +0200 Subject: [PATCH] Script update and data push - Pushing datasets as md and json files (32 datasets) - Update script to work from _dataset/json folder on any .json found - Update import readme with WIN instructions --- _datasets/afghanistan-asset-exposure.md | 93 ++ _datasets/afghanistan-drought-hazard.md | 58 + _datasets/afghanistan-earthquake-hazard.md | 58 + _datasets/afghanistan-flood-hazard.md | 84 + _datasets/afghanistan-flood-risk.md | 5 +- _datasets/afghanistan-landslide-hazard.md | 77 + .../afghanistan-snow-avalanche-hazard.md | 75 + ...re-dataset-agriculture-wheat-and-cotton.md | 174 ++ .../central-asia-exposure-dataset-airports.md | 77 + ...al-asia-exposure-dataset-infrastructure.md | 77 + ...posure-dataset-nonresidential-buildings.md | 76 + ...entral-asia-exposure-dataset-population.md | 71 + ...dataset-residential-buildings-projected.md | 74 + ...-exposure-dataset-residential-buildings.md | 70 + ...central-asia-exposure-dataset-transport.md | 81 + .../central-asia-flood-hazard-maps-fluvial.md | 115 ++ .../central-asia-flood-hazard-maps-pluvial.md | 109 ++ .../central-asia-flood-risk-estimates.md | 405 +++++ ...central-asia-flood-vulnerability-curves.md | 83 + .../central-asia-seismic-risk-estimates.md | 144 ++ _datasets/global-extreme-heat-hazard.md | 59 + _datasets/global-landslide-hazard-maps.md | 76 + ...obal-tropical-cyclone-wind-speed-hazard.md | 58 + _datasets/json/rdls_exp-AFG_asset.json | 149 ++ _datasets/json/rdls_exp-OECS.json | 153 ++ _datasets/json/rdls_exp-SSD_asset.json | 121 ++ _datasets/json/rdls_exposure-SFRARR.json | 901 ++++++++++ _datasets/json/rdls_hazard-SFRARR_FL.json | 732 +++++++++ _datasets/json/rdls_hazard-SFRARR_PL.json | 330 ++++ .../json/rdls_hazard-SFRARR_Scenarios.json | 212 +++ _datasets/json/rdls_hzd-AFG_avalanche.json | 159 ++ _datasets/json/rdls_hzd-AFG_drought.json | 268 +++ _datasets/json/rdls_hzd-AFG_earthquake.json | 256 +++ _datasets/json/rdls_hzd-AFG_flood.json | 670 ++++++++ _datasets/json/rdls_hzd-AFG_landslide.json | 288 ++++ _datasets/json/rdls_hzd-ARUP-LS.json | 268 +++ _datasets/json/rdls_hzd-FTHv3.json | 934 +++++++++++ _datasets/json/rdls_hzd-SSD_drought.json | 139 ++ _datasets/json/rdls_hzd-SSD_earthquake.json | 187 +++ _datasets/json/rdls_hzd-SSD_flood.json | 251 +++ _datasets/json/rdls_hzd-STORM.json | 1109 +++++++++++++ .../json/rdls_hzd-SWIO_coastal_flood.json | 298 ++++ _datasets/json/rdls_hzd-SWIO_earthquake.json | 291 ++++ _datasets/json/rdls_hzd-SWIO_flood.json | 354 ++++ _datasets/json/rdls_hzd-SWIO_strong_wind.json | 295 ++++ _datasets/json/rdls_hzd-VITO.json | 161 ++ _datasets/json/rdls_loss-SFRARR_EQ.json | 228 +++ _datasets/json/rdls_loss-SFRARR_FL.json | 381 +++++ _datasets/json/rdls_lss-AFG_drought.json | 158 ++ _datasets/json/rdls_lss-AFG_flood.json | 195 +++ _datasets/json/rdls_vln-FL_JRC.json | 109 ++ .../json/rdls_vulnerability-SFRARR_EQ.json | 102 ++ .../json/rdls_vulnerability-SFRARR_FL.json | 102 ++ ...cation-map-of-dominica-and-saint-lucia.md} | 24 +- ...oftop-classification-map-of-saint-lucia.md | 72 - _datasets/south-sudan-asset-exposure.md | 70 + _datasets/south-sudan-drought-hazard.md | 53 + _datasets/south-sudan-earthquake-hazard.md | 54 + ...uth-west-indian-ocean-earthquake-hazard.md | 100 ++ .../south-west-indian-ocean-flood-hazard.md | 100 ++ import/README.md | 9 + import/rdl2jkan.py | 21 +- import/rdl_datasets.json | 1458 ----------------- 63 files changed, 12414 insertions(+), 1547 deletions(-) create mode 100644 _datasets/afghanistan-asset-exposure.md create mode 100644 _datasets/afghanistan-drought-hazard.md create mode 100644 _datasets/afghanistan-earthquake-hazard.md create mode 100644 _datasets/afghanistan-flood-hazard.md create mode 100644 _datasets/afghanistan-landslide-hazard.md create mode 100644 _datasets/afghanistan-snow-avalanche-hazard.md create mode 100644 _datasets/central-asia-exposure-dataset-agriculture-wheat-and-cotton.md create mode 100644 _datasets/central-asia-exposure-dataset-airports.md create mode 100644 _datasets/central-asia-exposure-dataset-infrastructure.md create mode 100644 _datasets/central-asia-exposure-dataset-nonresidential-buildings.md create mode 100644 _datasets/central-asia-exposure-dataset-population.md create mode 100644 _datasets/central-asia-exposure-dataset-residential-buildings-projected.md create mode 100644 _datasets/central-asia-exposure-dataset-residential-buildings.md create mode 100644 _datasets/central-asia-exposure-dataset-transport.md create mode 100644 _datasets/central-asia-flood-hazard-maps-fluvial.md create mode 100644 _datasets/central-asia-flood-hazard-maps-pluvial.md create mode 100644 _datasets/central-asia-flood-risk-estimates.md create mode 100644 _datasets/central-asia-flood-vulnerability-curves.md create mode 100644 _datasets/central-asia-seismic-risk-estimates.md create mode 100644 _datasets/global-extreme-heat-hazard.md create mode 100644 _datasets/global-landslide-hazard-maps.md create mode 100644 _datasets/global-tropical-cyclone-wind-speed-hazard.md create mode 100644 _datasets/json/rdls_exp-AFG_asset.json create mode 100644 _datasets/json/rdls_exp-OECS.json create mode 100644 _datasets/json/rdls_exp-SSD_asset.json create mode 100644 _datasets/json/rdls_exposure-SFRARR.json create mode 100644 _datasets/json/rdls_hazard-SFRARR_FL.json create mode 100644 _datasets/json/rdls_hazard-SFRARR_PL.json create mode 100644 _datasets/json/rdls_hazard-SFRARR_Scenarios.json create mode 100644 _datasets/json/rdls_hzd-AFG_avalanche.json create mode 100644 _datasets/json/rdls_hzd-AFG_drought.json create mode 100644 _datasets/json/rdls_hzd-AFG_earthquake.json create mode 100644 _datasets/json/rdls_hzd-AFG_flood.json create mode 100644 _datasets/json/rdls_hzd-AFG_landslide.json create mode 100644 _datasets/json/rdls_hzd-ARUP-LS.json create mode 100644 _datasets/json/rdls_hzd-FTHv3.json create mode 100644 _datasets/json/rdls_hzd-SSD_drought.json create mode 100644 _datasets/json/rdls_hzd-SSD_earthquake.json create mode 100644 _datasets/json/rdls_hzd-SSD_flood.json create mode 100644 _datasets/json/rdls_hzd-STORM.json create mode 100644 _datasets/json/rdls_hzd-SWIO_coastal_flood.json create mode 100644 _datasets/json/rdls_hzd-SWIO_earthquake.json create mode 100644 _datasets/json/rdls_hzd-SWIO_flood.json create mode 100644 _datasets/json/rdls_hzd-SWIO_strong_wind.json create mode 100644 _datasets/json/rdls_hzd-VITO.json create mode 100644 _datasets/json/rdls_loss-SFRARR_EQ.json create mode 100644 _datasets/json/rdls_loss-SFRARR_FL.json create mode 100644 _datasets/json/rdls_lss-AFG_drought.json create mode 100644 _datasets/json/rdls_lss-AFG_flood.json create mode 100644 _datasets/json/rdls_vln-FL_JRC.json create mode 100644 _datasets/json/rdls_vulnerability-SFRARR_EQ.json create mode 100644 _datasets/json/rdls_vulnerability-SFRARR_FL.json rename _datasets/{rooftop-classification-map-of-dominica.md => rooftop-classification-map-of-dominica-and-saint-lucia.md} (80%) delete mode 100644 _datasets/rooftop-classification-map-of-saint-lucia.md create mode 100644 _datasets/south-sudan-asset-exposure.md create mode 100644 _datasets/south-sudan-drought-hazard.md create mode 100644 _datasets/south-sudan-earthquake-hazard.md create mode 100644 _datasets/south-west-indian-ocean-earthquake-hazard.md create mode 100644 _datasets/south-west-indian-ocean-flood-hazard.md delete mode 100644 import/rdl_datasets.json diff --git a/_datasets/afghanistan-asset-exposure.md b/_datasets/afghanistan-asset-exposure.md new file mode 100644 index 000000000..15b9833bb --- /dev/null +++ b/_datasets/afghanistan-asset-exposure.md @@ -0,0 +1,93 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_exp-asset +description: 'Collection of exposure datasets for risk assessment purpose in Afghanistan. + Includes: + + - Location, area and USD value of rainfed and irrigated agricultural crops. + + - Total exposure value of buildings for different occupancy types: urban and rural + structures, residential, non-residential, and industrial area. Values expressed + as replacement cost (USD), area (m2), or number of elements (count). + + - Location, count and USD value (when available) for the following infrastructures + in Afghanistan: airports, bridges, dams, health centers, hospitals, power plants, + roads, schools and universities. + + - Population count and GDP value in USD for three macrosectors (Industry, Agriculture + and Services) in Afghanistan.' +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: + category: buildings; infrastructures; agriculture; population + dimension: content, structure + quantity_kind: area, count, currency + taxonomy: null +hazard: null +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: 'Buildings exposure for different occupancy types and materials: urban + and rural structures, residential, non-residential, and industrial area. Values + expressed as replacement cost (USD), area (m2), or number of elements (count).' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065490/exp-afg-buildings.zip + format: geotiff + id: '0' + spatial_resolution: 90 + title: Buildings +- coordinate_system: EPSG:32642 + description: Location, count and USD value (when available) for airports, bridges, + dams, health centers, hospitals, power plants, roads, schools and universities. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065491/exp-afg-infrastructures.zip + format: geotiff + id: '1' + spatial_resolution: 90 + title: Infrastructures +- coordinate_system: EPSG:32642 + description: Location, area and USD value of rainfed and irrigated agricultural + crops. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065489/exp-afg-agriculture.zip + format: geotiff + id: '2' + spatial_resolution: 90 + title: Agriculture +- coordinate_system: EPSG:32642 + description: Population count and GDP value in USD for three macrosectors (Industry, + Agriculture and Services). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065492/exp-afg-indicators.zip + format: geotiff + id: '3' + spatial_resolution: 90 + title: Population and GDP +risk_data_type: +- exposure +schema: rdl-02 +spatial: + countries: + - AFG + scale: national +title: Afghanistan Asset exposure +version: '2018' +vulnerability: null +--- diff --git a/_datasets/afghanistan-drought-hazard.md b/_datasets/afghanistan-drought-hazard.md new file mode 100644 index 000000000..9ba2f55da --- /dev/null +++ b/_datasets/afghanistan-drought-hazard.md @@ -0,0 +1,58 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_hzd-drought +description: Annual water availability per sub-catchment for baseline and projected + conditions (2050) according to seven return period scenarios. +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: null +hazard: + calculation_method: inferred, simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: drought + intensity: Water shortage (%) + occurrence_range: 10, 20, 100, 250, 500, 1000 years + processes: hydrological_drought +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: Water shortage is defined in terms of percentage deviation from the + baseline water demand due to rainfall deficit. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050633/DR0065474/hzd-afg-dr.zip + format: gpkg + id: '0' + spatial_resolution: null + title: Water shortage RP scenarios (historical baseline and 2050) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - AFG + scale: national +title: Afghanistan Drought hazard +version: '2018' +vulnerability: null +--- diff --git a/_datasets/afghanistan-earthquake-hazard.md b/_datasets/afghanistan-earthquake-hazard.md new file mode 100644 index 000000000..db0b2f786 --- /dev/null +++ b/_datasets/afghanistan-earthquake-hazard.md @@ -0,0 +1,58 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_hzd-earthquake +description: Earthquake hazard map representing Peak ground acceleration (PGA-g) for + seven return period scenarios. +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: earthquake + intensity: PGA:g + occurrence_range: 10, 50, 100, 250, 500, 1000, 2.500 years + processes: ground_motion +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: Peak ground acceleration (PGA-g) simulated for seven return period + scenarios (10, 50, 100, 250, 500 , 1000 and 2500 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050631/DR0065467/hzd-afg-eq.zip + format: geotiff + id: '0' + spatial_resolution: null + title: Earthquake ground shaking hazard scenarios +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - AFG + scale: national +title: Afghanistan Earthquake hazard +version: '2018' +vulnerability: null +--- diff --git a/_datasets/afghanistan-flood-hazard.md b/_datasets/afghanistan-flood-hazard.md new file mode 100644 index 000000000..687e44db8 --- /dev/null +++ b/_datasets/afghanistan-flood-hazard.md @@ -0,0 +1,84 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_hzd-flood +description: Fluvial flood hazard is calculated based on probabilistic hydrological + analysis models (precipitation into runoff) and hydrodynamic analysis (runoff into + river flow and inundation, and flow over floodplain areas). +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: empirical, probabilistic + hazard_type: flood + intensity: fl_wd:m + occurrence_range: 5, 10, 20, 50, 100, 250, 500, 1000 years, 5, 100, 1000 years + processes: fluvial_flood +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: Flood extent and water depth in Afghanistan for eight return period + scenarios (5, 10, 20, 50, 100, 250, 500 and 1000 years) based on historical baseline. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065469/hzd-afg-fl-baseline.zip + format: geotiff + id: '0' + spatial_resolution: 90 + title: Flood hazard scenarios - country (baseline) +- coordinate_system: EPSG:32642 + description: Flood extent and water depth in Afghanistan for eight return period + scenarios (5, 10, 20, 50, 100, 250, 500 and 1000 years) based on climate projections + for 2050. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065469/hzd-afg-fl-2050.zip + format: geotiff + id: '1' + spatial_resolution: 90 + title: Flood hazard scenarios - country (2050) +- coordinate_system: EPSG:32642 + description: Flood extent and water depth in Kabul for three return period scenarios + (5, 100 and 1000 years) based on historical baseline. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065472/hzd-afg-fl-kabul.zip + format: geotiff + id: '2' + spatial_resolution: 10 + title: Flood hazard scenarios - Kabul (baseline) +- coordinate_system: EPSG:32642 + description: 'Flood extent and water depth simulated for four historical events: + 1978, 1991 (2 river floods), 1992 (flash flood and landslide) event.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065471/hzd-afg-fl-hist_events.zip + format: geotiff + id: '3' + spatial_resolution: 90 + title: Flood hazard scenarios - country (2050) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - AFG + scale: national +title: Afghanistan Flood hazard +version: '2018' +vulnerability: null +--- diff --git a/_datasets/afghanistan-flood-risk.md b/_datasets/afghanistan-flood-risk.md index 90f0bd337..b23d8013b 100644 --- a/_datasets/afghanistan-flood-risk.md +++ b/_datasets/afghanistan-flood-risk.md @@ -6,9 +6,8 @@ creator: name: GFDRR url: https://www.gfdrr.org dataset_id: AFG_lss-flood -description: Average Annual Losses (AAL) for current population (AALpop), current - asset (AALnowUSD), population SSP scenarios at 2050 (AALpopSP1-5), asset SSP scenarios - at 2050 (AAL_usd_SP1-5). +description: Average Annual Losses (AAL) over population and asset under current conditions + and SSP scenarios at 2050. details: "To better understand natural hazard and disaster risk, the World Bank and\ \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ diff --git a/_datasets/afghanistan-landslide-hazard.md b/_datasets/afghanistan-landslide-hazard.md new file mode 100644 index 000000000..023ed0dee --- /dev/null +++ b/_datasets/afghanistan-landslide-hazard.md @@ -0,0 +1,77 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_hzd-landslide +description: Earthquake-induced landslide hazard measured as probability of occurrance + for seven return period scenarios. National hazard assessment and focus on two areas + (Kabul and Salang pass). +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: null +hazard: + calculation_method: inferred, simulated + disaster_identifiers: '' + hazard_analysis_type: deterministic, probabilistic + hazard_type: landslide + intensity: Debris flow intensity index + occurrence_range: 10, 50, 100, 250, 500, 1000, 2500 years + processes: landslide_general +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: Simulated Ground Motion process triggered by earthquake measured as + debris-flow intensity index + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050634/DR0065476/hzd-afg-ls-eq-rp.zip + format: geotiff + id: '0' + spatial_resolution: 30 + title: Landslide hazard RP scenarios +- coordinate_system: EPSG:32642 + description: 'Susceptibility map for bedrock landslides in slow evolution (S1), + bedrock landslides in rapid evolution (S2) and cover material landslides in rapid + evolution (S3) nationwide, including: rotational slides, translational slides, + earth flows and lateral spreading.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050634/DR0065476/hzd-afg-ls-eq-susceptibility.zip + format: geotiff + id: '1' + spatial_resolution: 30 + title: Landslide susceptibility +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - AFG + gazetteer_entries: + - description: Kabul District + id: AFG-KAB + scheme: ISO 3166-2 + - description: Salang pass + id: "K\u014Dtal-e S\u0101lang" + scheme: GEONAMES + uri: https://www.geonames.org/1127859/kotal-e-salang.html + scale: national +title: Afghanistan Landslide hazard +version: '2018' +vulnerability: null +--- diff --git a/_datasets/afghanistan-snow-avalanche-hazard.md b/_datasets/afghanistan-snow-avalanche-hazard.md new file mode 100644 index 000000000..8fdba835f --- /dev/null +++ b/_datasets/afghanistan-snow-avalanche-hazard.md @@ -0,0 +1,75 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: AFG_hzd-avalanche +description: Detailed avalanche study gathering historic avalanche data and performing + numerical modeling of the avalanche runout potential and dynamics nationwide. +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: landslide + intensity: kPa + occurrence_range: 100 years + processes: snow_avalanche +license: CC-BY-4.0 +loss: null +project: Afghanistan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: These maps have been derived on a nation-wide scale for the purpose of identifying + high risk- areas on the district and provincial scale, from which decisions can + be made on allocating efforts for more detailed site specific hazard and risk analysis. + Use of this information on smaller scales should be applied with care. Importantly + for on a local scale, it is often the case that more detailed case history and hazard + information is required to perform such hazard and risk modelling, particularly + where applied to dimension mitigation structures or strategies. +resources: +- coordinate_system: EPSG:32642 + description: Footprint masks for hazard exceeding 1kPa and 3 kPa. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065479/hzd-afg-ls-lav-kpa.zip + format: gpkg + id: '0' + spatial_resolution: null + title: Snow Avalanche hazard - 1kPa and 3 kPa +- coordinate_system: EPSG:32642 + description: Hazard map + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065478/hzd-afg-ls-lav.zip + format: geotiff + id: '1' + spatial_resolution: 20 + title: Snow Avalanche hazard +- coordinate_system: EPSG:32642 + description: Snow Water Equivalent (SWE) is calculated from EUWATCH data running + from 1958 to 2002. The max grid cell values of each hydrological year where the + accumulative SWE is taken. Then of the 44 years of modelled data the maximum of + the aformentioned maximum values is taken. Unit is kg/m2. Only the 100 year return + period scenario was computed. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065480/hzd-afg-ls-lav-rp100-swe.zip + format: geotiff + id: '2' + spatial_resolution: 2000 + title: Snow Avalanche hazard - Snow Water Equivalents +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - AFG + scale: national +title: Afghanistan Snow Avalanche hazard +version: '2018' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-agriculture-wheat-and-cotton.md b/_datasets/central-asia-exposure-dataset-agriculture-wheat-and-cotton.md new file mode 100644 index 000000000..b114e87bb --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-agriculture-wheat-and-cotton.md @@ -0,0 +1,174 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_agri +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: agriculture + dimension: product + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: "Cotton and wheat cropland in Kazakhstan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091987/SFRARR_exposure_croplands_KAZ_csvOnly.zip?versionId=2023-07-21T17:07:52.3193748Z + format: csv + id: CA_SFRARR_exp_agri_KAZ + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - KAZ +- coordinate_system: null + description: "Cotton and wheat cropland in Kyrgyz Republic, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091995/SFRARR_exposure_croplands_KGZ.zip?versionId=2023-07-21T17:07:56.1382112Z + format: csv + id: CA_SFRARR_exp_agri_KGZ + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - KGZ +- coordinate_system: null + description: "Cotton and wheat cropland in Turkmenistan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091651/SFRARR_exposure_croplands_TKM.zip?versionId=2023-07-21T17:07:54.2642733Z + format: csv + id: CA_SFRARR_exp_agri_TKM + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - TKM +- coordinate_system: null + description: "Cotton and wheat cropland in Tajikistan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091652/SFRARR_exposure_croplands_TJK.zip?versionId=2023-07-21T17:07:48.2706690Z + format: csv + id: CA_SFRARR_exp_agri_TJK + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - TJK +- coordinate_system: null + description: "Cotton and wheat cropland in Uzbekistan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091653/SFRARR_exposure_croplands_UZB_csvOnly.zip?versionId=2023-07-21T17:07:50.3494905Z + format: csv + id: CA_SFRARR_exp_agri_UZB + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - UZB +- coordinate_system: null + description: "Cotton and wheat cropland in Kyrgyz Republic, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091995/SFRARR_exposure_croplands_KGZ.zip?versionId=2023-07-21T17:07:56.1382112Z + format: shp + id: CA_SFRARR_exp_agri_KGZ_shp + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - KGZ +- coordinate_system: null + description: "Cotton and wheat cropland in Turkmenistan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091651/SFRARR_exposure_croplands_TKM.zip?versionId=2023-07-21T17:07:54.2642733Z + format: shp + id: CA_SFRARR_exp_agri_TKM_shp + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - TKM +- coordinate_system: null + description: "Cotton and wheat cropland in Tajikistan, Central Asia. The dataset\ + \ has been developed based on cropland data (cotton, wheat) collected from local\ + \ data provided at sub-national level. These data have been derived on a regional\ + \ scale for the purpose of consistent regional (multi-country hazard and risk\ + \ assessment). Application of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1\ + \ code\n\nCSV files only provided due to large size of GIS (.shp) files." + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091652/SFRARR_exposure_croplands_TJK.zip?versionId=2023-07-21T17:07:48.2706690Z + format: shp + id: CA_SFRARR_exp_agri_TJK_shp + spatial_resolution: null + title: Central Asia exposure dataset - agricultural crops - TJK +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - agriculture (wheat and cotton) +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-airports.md b/_datasets/central-asia-exposure-dataset-airports.md new file mode 100644 index 000000000..344f099a1 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-airports.md @@ -0,0 +1,77 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_airport +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: infrastructure + dimension: structure + quantity_kind: count + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: "Regional layer of airports in Central Asia. The dataset has been developed\ + \ based on global-scale airport layers, and validated locally based on visual\ + \ inspection. The folder contains one points shapefile of the airports locations,\ + \ and one polygon shapefile with the airport extent (assumed based on a circular\ + \ buffer of variable radius). The two shapefiles contain the same fields. These\ + \ data have been derived on a regional scale for the purpose of consistent regional\ + \ (multi-country hazard and risk assessment).\nApplication of this information\ + \ should on smaller scales should be done with care and taking into account the\ + \ limitations of the approach. \nFiles: Airports_Centralasia.shp; Airports_Centralasia_points.shp" + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064255/DR0091666/SFRARR_exposure_CA_Airports.zip?versionId=2023-07-05T13:40:53.8644184Z + format: shp + id: CA_SFRARR_exp_airport + spatial_resolution: null + title: Central Asia exposure dataset - airports +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - airports +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-infrastructure.md b/_datasets/central-asia-exposure-dataset-infrastructure.md new file mode 100644 index 000000000..b992a2fc1 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-infrastructure.md @@ -0,0 +1,77 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_infra +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: infrastructure + dimension: structure + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: "Regional layer of power and communications infrastructure in Central\ + \ Asia. The dataset has been developed based on infrastructure data collected\ + \ from global/regional layers and based on local data.\n\n\nThe layers contain\ + \ point infrastructure (extraction sites and mines, power plants, dams and reservoirs)\ + \ and lines infrastructure (oil, gas and water pipelines, electricity and communication\ + \ network).\n\n\nApplication of this information should on smaller scales should\ + \ be done with care and taking into account the limitations of the approach. \n\ + \n\nFiles: Infrastructure_lines.shp; Infrastructure_lines.csv: Infrastructure_points.shp;\ + \ Infrastructure_points.csv" + download_url: null + format: csv + id: CA_SFRARR_exp_infra + spatial_resolution: null + title: Central Asia exposure dataset - Other infrastructure +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - infrastructure +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-nonresidential-buildings.md b/_datasets/central-asia-exposure-dataset-nonresidential-buildings.md new file mode 100644 index 000000000..30cdb3105 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-nonresidential-buildings.md @@ -0,0 +1,76 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_nonres +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: buildings + dimension: structure + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: 'Central Asia exposure dataset - Non-residential buildings (education, + healthcare, industrial, commercial) + + Non-residential data provided as CSV files. + + File naming: COMMERCIAL_[OBLAST].csv, INDUSTRIAL_[OBLAST].csv, HEALTHCARE_[OBLAST].csv, + EDUCATION_[OBLAST].csv' + download_url: null + format: csv + id: CA_SFRARR_exp_nonres + spatial_resolution: null + title: Central Asia exposure dataset - Non-residential buildings (education, healthcare, + industrial, commercial) +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - nonresidential buildings +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-population.md b/_datasets/central-asia-exposure-dataset-population.md new file mode 100644 index 000000000..64e345fe8 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-population.md @@ -0,0 +1,71 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_pop +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: population + dimension: population + quantity_kind: count + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: 'Regional layer of population distribution in Central Asia. Files: + POPULATION_[OBLAST].shp, POPULATION_[OBLAST].csv. File names contain the Oblast + GAD_ID_1 code' + download_url: null + format: csv + id: CA_SFRARR_exp_pop + spatial_resolution: null + title: Central Asia exposure dataset - Population +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - population +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-residential-buildings-projected.md b/_datasets/central-asia-exposure-dataset-residential-buildings-projected.md new file mode 100644 index 000000000..6c73d9d6d --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-residential-buildings-projected.md @@ -0,0 +1,74 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_res_projected +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: buildings + dimension: structure + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: 'Regional layer of residential buildings in Central Asia.Central Asia + projected (2080) residential exposure - all countries, Oblast level: SSP4 + + Dataset includes SSP1, SSP4, SSP5 scenarios. + + Files: RESIDENTIAL_[OBLAST]._2080_[SSP].csv' + download_url: null + format: csv + id: CA_SFRARR_exp_res_projected + spatial_resolution: null + title: Central Asia exposure dataset - Projected residential exposure +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - residential buildings - projected +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-residential-buildings.md b/_datasets/central-asia-exposure-dataset-residential-buildings.md new file mode 100644 index 000000000..db747dfc4 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-residential-buildings.md @@ -0,0 +1,70 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_res +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: buildings + dimension: structure + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: "Central Asia exposure layers \u2013 Residential buildings.\nFiles:\ + \ RESIDENTIAL_[OBLAST].csv, RESIDENTIAL_[OBLAST].shp" + download_url: null + format: csv + id: CA_SFRARR_exp_res + spatial_resolution: null + title: Central Asia exposure dataset - Residential buildings +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - residential buildings +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-exposure-dataset-transport.md b/_datasets/central-asia-exposure-dataset-transport.md new file mode 100644 index 000000000..f28aaa418 --- /dev/null +++ b/_datasets/central-asia-exposure-dataset-transport.md @@ -0,0 +1,81 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + email: cscaini@inogs.it + name: Chiara Scaini +dataset_id: CA_SFRARR_exp_transport +description: 'Data from the EU-funded ''Strengthening Financial Resilience and Accelerating + Risk Reduction in Central Asia'' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + + Exposure data developed using high-resolution global and regional datasets and local + official data, harmonized to produce a regionally-consistent exposure database for + Central Asia. The exposure database includes: population, residential buildings, + non-residential buildings (schools, healthcare facilities, industrial and commercial + buildings), croplands, transportation system (roads, railways and bridges), airports + and airstrips, mines, and supply infrastructure. The exposure database developed + during this project can be used at regional scale, national scale or sub-national + scale (e.g., at Oblast scale). ' +details: null +exposure: + category: infrastructure + dimension: structure + quantity_kind: currency + taxonomy: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: "Regional layer of transportation infrastructure in Central Asia. The\ + \ dataset has been developed based on transportation data collected from Openstreetmap\ + \ and validated based on local data. Bridges location was derived based on spatial\ + \ analysis, in particular identifying the intersection of main roads/railways\ + \ and rivers. These data have been derived on a regional scale for the purpose\ + \ of consistent regional (multi-country hazard and risk assessment). The folder\ + \ includes one point layer containing Central Asia bridges, and one lines layer\ + \ containing roads and railways of different types. Note that minor roads (e.g.\ + \ residential, service) and railway types (e.g. subway) were not included in the\ + \ analysis. Application of this information should on smaller scales should be\ + \ done with care and taking into account the limitations of the approach. \n\n\ + \nCentral Asia exposure dataset - Transport - Bridges - UZB\n\nFiles: BRIDGES_[OBLAST].csv;\ + \ BRIDGES_[OBLAST].shp" + download_url: null + format: csv + id: CA_SFRARR_exp_transport + spatial_resolution: null + title: Central Asia exposure dataset - Transport +risk_data_type: +- exposure +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia exposure dataset - transport +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-flood-hazard-maps-fluvial.md b/_datasets/central-asia-flood-hazard-maps-fluvial.md new file mode 100644 index 000000000..dd949b178 --- /dev/null +++ b/_datasets/central-asia-flood-hazard-maps-fluvial.md @@ -0,0 +1,115 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + name: Gabriele Coccia +dataset_id: CA_SFRARR_FL +description: Fluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + period. +details: null +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: flood + intensity: fl_wd:m + occurrence_range: '' + processes: fluvial_flood +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: EPSG:4326 + description: 'Fluvial flood hazard maps for the undefended case for 5,10,20,50,100,200,500,1,000 + years return period. Each pixel represents the maximum water depth. In order to + derive the hazard maps over the entire domain, for each return period an hydraulic + simulation is performed for each simulation unit domain, feeding the model with + the hydrograph corresponding to the given return time. These data have been derived + on a regional scale for the purpose of consistent regional multi-country hazard + and risk assessment. Application of this information on smaller scales should + be done with care. Importantly on a local scale, it is often the case that more + detailed history and hazard information is required to perform such hazard and + risk modelling, particularly were applied to dimension mitigation structures or + strategies., it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies. FLU_UND_XXy_ZZZ.tif (XX= return + time, ZZZ= country initials) ' + download_url: https://datacatalog.worldbank.org/search/dataset/0064236 + format: geotiff + id: CA_SFRARR_FL_UND_Rpmaps + spatial_resolution: 90 + title: Central Asia undefended fluvial flood hazard maps - current climate +- coordinate_system: EPSG:4326 + description: 'Fluvial flood hazard maps for the defended case for 5,10,20,50,100,200,500,1,000 + years return period. Each pixel represents the maximum water depth. In order to + derive the hazard maps over the entire domain, for each return period an hydraulic + simulation is performed for each simulation unit domain, feeding the model with + the hydrograph corresponding to the given return time. These data have been derived + on a regional scale for the purpose of consistent regional multi-country hazard + and risk assessment. Application of this information on smaller scales should + be done with care. Importantly on a local scale, it is often the case that more + detailed history and hazard information is required to perform such hazard and + risk modelling, particularly were applied to dimension mitigation structures or + strategies., it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies. FLU_DEF_XXy_ZZZ.tif (XX= return + time, ZZZ= country initials) ' + download_url: https://datacatalog.worldbank.org/search/dataset/0064232 + format: geotiff + id: CA_SFRARR_FL_DEF_Rpmaps + spatial_resolution: 90 + title: Central Asia defended fluvial flood hazard maps - current climate +- coordinate_system: EPSG:4326 + description: 'Fluvial flood hazard maps for the 2080 scenario (climate change) for + 5,10,20,50,100,200,500,1,000 years return period. Each pixel represents the maximum + water depth. In order to derive the hazard maps over the entire domain, for each + return period an hydraulic simulation is performed for each simulation unit domain, + feeding the model with the hydrograph corresponding to the given return time. + These data have been derived on a regional scale for the purpose of consistent + regional multi-country hazard and risk assessment. Application of this information + on smaller scales should be done with care. Importantly on a local scale, it is + often the case that more detailed history and hazard information is required to + perform such hazard and risk modelling, particularly were applied to dimension + mitigation structures or strategies., it is often the case that more detailed + history and hazard information is required to perform such hazard and risk modelling, + particularly were applied to dimension mitigation structures or strategies. FLU_CC_XXy_ZZZ.tif + (XX= return time, ZZZ= country initials) ' + download_url: https://datacatalog.worldbank.org/search/dataset/0064238 + format: geotiff + id: CA_SFRARR_FL_CC_Rpmaps + spatial_resolution: 90 + title: Central Asia undefended fluvial flood hazard maps - 2080 cliamte projections +risk_data_type: +- hazard +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia flood hazard maps - fluvial +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-flood-hazard-maps-pluvial.md b/_datasets/central-asia-flood-hazard-maps-pluvial.md new file mode 100644 index 000000000..4e7573953 --- /dev/null +++ b/_datasets/central-asia-flood-hazard-maps-pluvial.md @@ -0,0 +1,109 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + name: Gabriele Coccia +dataset_id: CA_SFRARR_PL +description: 'Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. ' +details: null +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: flood + intensity: fl_wd:m, wd:m + occurrence_range: '' + processes: pluvial_flood +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com/ +purpose: Regional risk modelling +resources: +- coordinate_system: EPSG:4326 + description: Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. In order to derive the hazard maps over the entire domain, + for each return period an hydraulic simulation is performed for each simulation + unit domain feeding the model with the Intensity Duration Frequency curve at 6 + hours + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091741/PLU_KGZ.zip?versionId=2023-07-17T13:21:08.7536943Z + format: geotiff + id: CA_SFRARR_FL_PL_KAZ + spatial_resolution: 90 + title: Pluvial flood hazard maps - Kazakhstan +- coordinate_system: EPSG:4326 + description: Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. In order to derive the hazard maps over the entire domain, + for each return period an hydraulic simulation is performed for each simulation + unit domain feeding the model with the Intensity Duration Frequency curve at 6 + hours + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091741/PLU_KGZ.zip?versionId=2023-07-17T13:21:08.7536943Z + format: geotiff + id: CA_SFRARR_FL_PL_KGZ + spatial_resolution: 90 + title: Pluvial flood hazard maps - Kyrgyz Republic +- coordinate_system: EPSG:4326 + description: Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. In order to derive the hazard maps over the entire domain, + for each return period an hydraulic simulation is performed for each simulation + unit domain feeding the model with the Intensity Duration Frequency curve at 6 + hours + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091996/PLU_TJK.zip?versionId=2023-07-17T13:21:14.2955579Z + format: geotiff + id: CA_SFRARR_FL_PL_TJK + spatial_resolution: 90 + title: Pluvial flood hazard maps - Tajikistan +- coordinate_system: EPSG:4326 + description: Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. In order to derive the hazard maps over the entire domain, + for each return period an hydraulic simulation is performed for each simulation + unit domain feeding the model with the Intensity Duration Frequency curve at 6 + hours + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091742/PLU_TKM.zip?versionId=2023-07-17T13:21:18.4691974Z + format: geotiff + id: CA_SFRARR_FL_PL_TKM + spatial_resolution: 90 + title: Pluvial flood hazard maps - Turkmenistan +- coordinate_system: EPSG:4326 + description: Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return + time and 6 hours duration, i.e., a map for each return time where each pixel represents + the maximum water depth. In order to derive the hazard maps over the entire domain, + for each return period an hydraulic simulation is performed for each simulation + unit domain feeding the model with the Intensity Duration Frequency curve at 6 + hours + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091778/PLU_UZB.zip?versionId=2023-07-17T13:21:16.4303513Z + format: geotiff + id: CA_SFRARR_FL_PL_UZB + spatial_resolution: 90 + title: Pluvial flood hazard maps - Uzbekistan +risk_data_type: +- hazard +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia flood hazard maps - pluvial +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-flood-risk-estimates.md b/_datasets/central-asia-flood-risk-estimates.md new file mode 100644 index 000000000..d08bedaac --- /dev/null +++ b/_datasets/central-asia-flood-risk-estimates.md @@ -0,0 +1,405 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + name: Paolo Bazzuro +dataset_id: CA_SFRARR_FL_loss +description: 'Fluvial flood risk estimates, including return period loss estimates, + annual average loss estimates and event loss tables ' +details: null +exposure: null +hazard: null +license: CC-BY-SA-4.0 +loss: + approach: analytical + base_data_type: simulated + category: agriculture, buildings, infrastructure, population + description: Estimated economic loss per sector and for all sectors combined for + fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios), aggregated to the Oblast, national and regional level, Estimated + fatalities due to fluvial flood risk (current defended and undefended scenarios, + and for future climate scenarios), aggregated to the Oblast, national and regional + level + dimension: product, structure + exposure_id: CA_SFRARR_exp + hazard_analysis_type: probabilistic + hazard_id: CA_SFRARR_FL + hazard_process: fluvial_flood + hazard_type: flood + impact_metric: casualty_count, economic_loss_value + impact_type: direct + impact_unit: count + type: ground_up + vulnerability_id: CA_SFRARR_FL_vuln +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies. +resources: +- coordinate_system: null + description: 'Central Asia flood risk return period summaries. + + + Estimated economic loss and fatalities per sector and for all sectors combined + for fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios) + + + Summaries aggregated at ADM1 (Oblast) and country level. + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091702/RP_KZ.zip?versionId=2023-07-05T13:58:52.6031766Z + format: csv + id: CA_SFRARR_FL_loss_RP_KAZ + spatial_resolution: null + title: Central Asia flood risk return period summaries - KAZ +- coordinate_system: null + description: 'Central Asia flood risk return period summaries. + + + Estimated economic loss and fatalities per sector and for all sectors combined + for fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios) + + + Summaries aggregated at ADM1 (Oblast) and country level. + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091703/RP_TJ.zip?versionId=2023-07-05T13:58:48.7054093Z + format: csv + id: CA_SFRARR_FL_loss_RP_TJK + spatial_resolution: null + title: Central Asia flood risk return period summaries - TJK +- coordinate_system: null + description: 'Estimated economic loss and fatalities per sector and for all sectors + combined for fluvial flood risk (current defended and undefended scenarios, and + for future climate scenarios) + + + Summaries aggregated to the regional level (all CA countries combined). + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091700/RP_CA.zip?versionId=2023-07-05T13:58:54.3411816Z + format: csv + id: CA_SFRARR_FL_loss_RP_reg + spatial_resolution: null + title: Central Asia flood risk return period summaries - regional level +- coordinate_system: null + description: 'Central Asia flood risk return period summaries. + + + Estimated economic loss and fatalities per sector and for all sectors combined + for fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios) + + + Summaries aggregated at ADM1 (Oblast) and country level. + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091701/RP_KG.zip?versionId=2023-07-05T13:58:58.0210737Z + format: csv + id: CA_SFRARR_FL_loss_RP_KGZ + spatial_resolution: null + title: Central Asia flood risk return period summaries - KGZ +- coordinate_system: null + description: 'Central Asia flood risk return period summaries. + + + Estimated economic loss and fatalities per sector and for all sectors combined + for fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios) + + + Summaries aggregated at ADM1 (Oblast) and country level. + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091705/RP_UZ.zip?versionId=2023-07-05T13:58:56.1241597Z + format: csv + id: CA_SFRARR_FL_loss_RP_UZB + spatial_resolution: null + title: Central Asia flood risk return period summaries - UZB +- coordinate_system: null + description: 'Central Asia flood risk return period summaries. + + + Estimated economic loss and fatalities per sector and for all sectors combined + for fluvial flood risk (current defended and undefended scenarios, and for future + climate scenarios) + + + Summaries aggregated at ADM1 (Oblast) and country level. + + + One csv file per loss breakdown, giving the estimated loss per return period.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091704/RP_TM.zip?versionId=2023-07-05T13:58:50.7882165Z + format: csv + id: CA_SFRARR_FL_loss_RP_TKM + spatial_resolution: null + title: Central Asia flood risk return period summaries - TKM +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091710/EP_DEF_EC_CA.zip?versionId=2023-07-05T13:58:33.1952945Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_CA + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091713/EP_DEF_EC_KZ.zip?versionId=2023-07-05T13:58:08.8342487Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_KAZ + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended - KAZ +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091712/EP_DEF_EC_KG.zip?versionId=2023-07-05T13:58:14.4190494Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_KGZ + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended - KGZ +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091714/EP_DEF_EC_TJ.zip?versionId=2023-07-05T13:58:27.4545822Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_TJK + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended - TJK +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091715/EP_DEF_EC_TM.zip?versionId=2023-07-05T13:58:16.2689906Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_TKM + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended - TKM +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091716/EP_DEF_EC_UZ.zip?versionId=2023-07-05T13:58:18.0429734Z + format: csv + id: CA_SFRARR_FL_def_loss_EP_UZB + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood defended - UZB +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091711/EP_UND_EC_CA.zip?versionId=2023-07-05T13:58:35.0142527Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_CA + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091718/EP_UND_EC_KZ.zip?versionId=2023-07-05T13:58:21.7618436Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_KAZ + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended - KAZ +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091717/EP_UND_EC_KG.zip?versionId=2023-07-05T13:58:19.9968546Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_KGZ + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended - KGZ +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091719/EP_UND_EC_TJ.zip?versionId=2023-07-05T13:58:23.7826866Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_TJK + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended - TJK +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091720/EP_UND_EC_TM.zip?versionId=2023-07-05T13:58:25.5886514Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_TKM + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended - TKM +- coordinate_system: null + description: 'Estimated economic loss per sector and for all sectors combined, aggregated + at Oblast and national level. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091721/EP_UND_EC_UZ.zip?versionId=2023-07-05T13:58:10.7261652Z + format: csv + id: CA_SFRARR_FL_undef_loss_EP_UZB + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood undefended - UZB +- coordinate_system: null + description: 'Central Asia flood risk EP curves - population - flood defended + + + Estimated fatalities per sector and for all sectors combined, aggregated at ADM1, + country, and regional levels. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091707/EP_Def_Population.zip?versionId=2023-07-05T13:58:06.9733151Z + format: csv + id: CA_SFRARR_FL_def_population_EP + spatial_resolution: null + title: Central Asia flood risk EP curves - population - flood defended +- coordinate_system: null + description: 'Central Asia flood risk EP curves - population - flood undefended + + + Estimated fatalities per sector and for all sectors combined, aggregated at ADM1, + country, and regional levels. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091706/EP_UND_Population.zip?versionId=2023-07-05T13:58:29.6513239Z + format: csv + id: CA_SFRARR_FL_undef_population_EP + spatial_resolution: null + title: Central Asia flood risk EP curves - population - flood undefended +- coordinate_system: null + description: 'Central Asia flood risk EP curves - economic loss - flood climate + change scenario 2080. + + + Includes climate change and residential exposure change using SSP1, SSP4, and + SSP5. + + + Estimated economic loss per sector and for all sectors combined, aggregated at + ADM1, country, and regional levels. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091709/EP_CC_EC.zip?versionId=2023-07-05T13:58:31.4862739Z + format: csv + id: CA_SFRARR_FL_loss_EP_2080 + spatial_resolution: null + title: Central Asia flood risk EP curves - economic loss - flood climate change + scenario 2080 +- coordinate_system: null + description: 'Central Asia flood risk EP curves - population - flood climate change + scenario 2080. + + + Includes climate change and residential exposure change using SSP1, SSP4, and + SSP5. + + + Estimated fatalities per sector and for all sectors combined, aggregated at ADM1, + country, and regional levels. + + + One csv file per loss breakdown, giving the estimated loss per exceedance probability.' + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091708/EP_CC_Population.zip?versionId=2023-07-05T13:58:12.5291319Z + format: csv + id: CA_SFRARR_FL_population_EP_2080 + spatial_resolution: null + title: Central Asia flood risk EP curves - population - flood climate change scenario + 2080 +- coordinate_system: null + description: Event Loss Tables (ELTs) showing the severity and frequency of estimated + loss for each simulated flood event. + download_url: null + format: csv + id: CA_SFRARR_FL_ELTs + spatial_resolution: null + title: Central Asia flood risk - Event Loss Tables (ELTs) +- coordinate_system: null + description: Geospatial data layers describing estimated losses. Annual average + loss and probable maximum losses at Oblast level. + download_url: null + format: shp + id: CA_SFRARR_FL_RP_maps + spatial_resolution: null + title: Central Asia flood risk AAL and Return Period Loss maps +- coordinate_system: null + description: Tabulated summary of simulated fatalities and economic loss due to + three selected 1-in-100-year fluvial flood scenarios (Kara-Unkur River, Parkent + River, and Turkmenabat). + download_url: null + format: csv + id: CA_SFRARR_FL_scenarioLosses + spatial_resolution: null + title: Central Asia flood risk scenario losses +- coordinate_system: null + description: 'Tabulated and map summaries of fluvial flood hazard intensity for + selected infrastructure: airports, industrial sites, infrastructure, population + and transport.' + download_url: null + format: csv + id: CA_SFRARR_FL_hazardIntensity + spatial_resolution: null + title: Central Asia flood risk hazard intensity summaries +risk_data_type: +- loss +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia flood risk estimates +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-flood-vulnerability-curves.md b/_datasets/central-asia-flood-vulnerability-curves.md new file mode 100644 index 000000000..2b6b25f55 --- /dev/null +++ b/_datasets/central-asia-flood-vulnerability-curves.md @@ -0,0 +1,83 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + name: Gabriele Coccia +dataset_id: CA_SFRARR_FL_vuln +description: Vulnerability curves for buildings, crops, humans and infrastructure +details: null +exposure: null +hazard: null +license: CC-BY-SA-4.0 +loss: null +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies., it is often the case that more + detailed history and hazard information is required to perform such hazard and risk + modelling, particularly were applied to dimension mitigation structures or strategies +resources: +- coordinate_system: null + description: Tabulated vulnerability curves for each country in the Central Asia + region, and each type of building. Uses consistent Intensity Metrics and exposure + Taxonomies as accompanying hazard and exposure datasets. + download_url: null + format: csv + id: CA_SFRARR_FL_vulnCurves_buildings + spatial_resolution: null + title: Central Asia flood vulnerability curves - buildings +- coordinate_system: null + description: Tabulated vulnerability curves for each country in the Central Asia + region, and each type of crop (wheat and cotton). Uses consistent Intensity Metrics + and exposure Taxonomies as accompanying hazard and exposure datasets. + download_url: null + format: csv + id: CA_SFRARR_FL_vulnCurves_crops + spatial_resolution: null + title: Central Asia flood vulnerability curves - crops +- coordinate_system: null + description: Tabulated vulnerability curves for each country in the Central Asia + region, and multiple age/sex. Uses consistent Intensity Metrics and exposure Taxonomies + as accompanying hazard and exposure datasets. + download_url: null + format: csv + id: CA_SFRARR_FL_vulnCurves_human + spatial_resolution: null + title: Central Asia flood vulnerability curves - human +- coordinate_system: null + description: Tabulated vulnerability curves for each country in the Central Asia + region, and each type of infrastructure. Uses consistent Intensity Metrics and + exposure Taxonomies as accompanying hazard and exposure datasets. + download_url: null + format: csv + id: CA_SFRARR_FL_vulnCurves_infrastructure + spatial_resolution: null + title: Central Asia flood vulnerability curves - infrastructure +risk_data_type: +- vulnerability +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia flood vulnerability curves +version: '2022' +vulnerability: null +--- diff --git a/_datasets/central-asia-seismic-risk-estimates.md b/_datasets/central-asia-seismic-risk-estimates.md new file mode 100644 index 000000000..0569a0967 --- /dev/null +++ b/_datasets/central-asia-seismic-risk-estimates.md @@ -0,0 +1,144 @@ +--- +contact_point: + email: paola.ceresa@redrisk.com + name: Paola Ceresa +creator: + name: Paolo Bazzuro +dataset_id: CA_SFRARR_EQ_loss +description: 'Fluvial seismic risk estimates, including return period loss estimates, + annual average loss estimates and event loss tables ' +details: null +exposure: null +hazard: null +license: CC-BY-SA-4.0 +loss: + approach: analytical + base_data_type: simulated + category: agriculture, buildings, infrastructure, population + description: Estimated economic loss per sector and for all sectors combined for + seismic risk aggregated to the Oblast, national and regional level, Estimated + fatalities due to seismic risk, aggregated to the Oblast, national and regional + level + dimension: product, structure + exposure_id: CA_SFRARR_exp + hazard_analysis_type: probabilistic + hazard_id: CA_SFRARR_EQ + hazard_process: ground_motion + hazard_type: earthquake + impact_metric: casualty_count, economic_loss_value + impact_type: direct + impact_unit: count + type: ground_up + vulnerability_id: CA_SFRARR_EQ_vuln +project: World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk + Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia) +publisher: + name: RED - Risk, Engineering Development - Pavia (Italy) + url: https://www.redrisk.com +purpose: Regional risk modelling. These data have been derived on a regional scale + for the purpose of consistent regional multi-country hazard and risk assessment. + Application of this information on smaller scales should be done with care. Importantly + on a local scale, it is often the case that more detailed history and hazard information + is required to perform such hazard and risk modelling, particularly were applied + to dimension mitigation structures or strategies. +resources: +- coordinate_system: null + description: Tabulated return period loss estimates showing seismic risk at ADM1, + country and regional level. One csv file per loss breakdown, giving the estimated + loss per return period. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064273/DR0092053/SFRARR_EQ_RPsummaries_Economic_current.zip?versionId=2023-07-21T17:05:43.1885433Z + format: csv + id: CA_SFRARR_EQ_loss_RP + spatial_resolution: null + title: Central Asia seismic risk return period summaries - economic loss - current +- coordinate_system: null + description: Tabulated return period loss estimates showing seismic risk at ADM1, + country and regional level. One csv file per loss breakdown, giving the estimated + loss per return period. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0064273/DR0092055/SFRARR_EQ_RPsummaries_PopFatalities_current.zip?versionId=2023-07-21T17:05:47.0413599Z + format: csv + id: CA_SFRARR_EQ_fatalities_RP + spatial_resolution: null + title: Central Asia seismic risk return period summaries - population - current +- coordinate_system: null + description: Exceedance Probability (EP) loss curves showing the estimated severity + and frequency of earthquake losses (monetary loss and human fatalities). This + dataset includes risk estimates for the whole Central Asian region, each country + in the study, and each Oblast. Risk estimates are available for multiple sectors/asset + types individually, and all sectors combined. Asset types comprise residential, + commercial, education, and healthcare buildings, roads, bridges. Risk estimates + are provided for current exposure, and residential only and fatalities projected + using SSP1, SSP4 and SSP5. Estimated economic losses for each sector and all sectors + combined using current exposure. Losses are aggregated at regional, national and + Oblast levels in one csv file per ADM unit and sector giving the estimated loss + per selected exceedance probability (return period). + download_url: null + format: csv + id: CA_SFRARR_EQ_loss_EP + spatial_resolution: null + title: Central Asia seismic risk EP curves +- coordinate_system: null + description: 'Event Loss Tables (ELTs) showing the severity and frequency of estimated + loss for each simulated earthquake event. Event Loss Tables (ELTs) provide the + estimated economic loss per simulated event. The ELT is used to develop the EP + curves, AAL and return period loss estimates. An ELT is provided for losses aggregated + to regional, national and Oblast levels, for each sector sector and all sectors + combined using current exposure. One csv file per loss breakdown, giving the estimated + loss per event. ' + download_url: null + format: csv + id: CA_SFRARR_EQ_ELTs + spatial_resolution: null + title: Central Asia seismic risk - Event Loss Tables (ELTs) +- coordinate_system: null + description: Geospatial data layer describing estimated return period losses, Annual + aggregate and probable maximum losses at Oblast level per country per sectors, + current and projected exposure + download_url: null + format: shp + id: CA_SFRARR_EQ_RP_maps + spatial_resolution: null + title: Central Asia seismic risk AAL and Return Period Loss maps +- coordinate_system: null + description: 'Tabulated summary of simulated fatalities and economic loss due to + five hypothetical 1-in-100-year earthquake events impacting Almaty, Bishkek, Tashkent, + Ashgabat, and Dushanbe. EQ-Scenario Losses-Deterministic Analysis.csv: Table showing + the estimated fatalities, economic loss (million USD) and event parameters due + to each simulated scenario.' + download_url: null + format: csv + id: CA_SFRARR_EQ_scenarioLosses + spatial_resolution: null + title: Central Asia seismic risk scenario losses +- coordinate_system: null + description: 'Tabulated summary of seismic hazard intensity for selected infrastructure: + airports, industrial sites, infratructure, population and transport. Developed + as part of the Strengthening Financial Resilience and Accelerating Risk Reduction + in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). + Number of assets exposed to each maximum seismic ground shaking (pga) class for + each return period.' + download_url: null + format: csv + id: CA_SFRARR_EQ_hazardIntensity + spatial_resolution: null + title: Central Asia seismic risk hazard intensity summaries +risk_data_type: +- loss +schema: rdl-02 +spatial: + bbox: + - 46 + - 88 + - 34 + - 57 + countries: + - KAZ + - KGZ + - TKM + - TJK + - UZB + scale: regional +title: Central Asia seismic risk estimates +version: '2022' +vulnerability: null +--- diff --git a/_datasets/global-extreme-heat-hazard.md b/_datasets/global-extreme-heat-hazard.md new file mode 100644 index 000000000..b0275454a --- /dev/null +++ b/_datasets/global-extreme-heat-hazard.md @@ -0,0 +1,59 @@ +--- +contact_point: + email: mamadio@worldbank.org + name: Mattia Amadio +creator: + name: VITO + url: https://vito.be/en +dataset_id: VITO_WBGT +description: "Extreme Heat hazard described by the daily maximum Wet Bulb Globe Temperature\ + \ (WBGT \xB0C) for three return period scenarios." +details: "The WBGT is derived from global daily maximum air temperature contained\ + \ in ERA-Interim re-analysis fields for the period 1981-2010, which is considered\ + \ of sufficient length to provide robust climate statistics. The 0.75\xB0 lat/lon\ + \ fields are corrected for local-scale altitude effects by means of a high-resolution\ + \ global digital elevation model, resulting in global daily maximum WBGT fields\ + \ at a spatial resolution of approximately 10 km. These fields are temporally smoothed\ + \ using a 3-day filter, so as to account for the cumulative effects of prolonged\ + \ heat. These 30-year, 10-km resolution, 3-day smoothed daily maximum WBGT values\ + \ are then employed to fit a Generalized Extreme Value (GEV) probability distribution\ + \ function for each grid cell of the global domain. Considering return periods of\ + \ 5, 20, and 100 years, 10-km hazard intensity maps have been calculated for each\ + \ of these periods. To these hazard intensity maps, threshold values of 32\xB0C,\ + \ 28\xB0C and 25\xB0C, stemming from the scientific literature, subsequently are\ + \ applied, resulting in a global heat risk map." +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: extreme_temperature + intensity: WBGT:c + occurrence_range: 5, 20, 100 years + processes: extreme_heat +license: CC0-1.0 +loss: null +project: VITO_WBGT +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: null +resources: +- coordinate_system: EPSG:4326 + description: Global heat stress maps by return period (5, 20, 100 years) + download_url: null + format: geotiff + id: '0' + spatial_resolution: 10000 + title: Heat stress global maps +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - GLO + scale: global +title: Global extreme heat hazard +version: '2016' +vulnerability: null +--- diff --git a/_datasets/global-landslide-hazard-maps.md b/_datasets/global-landslide-hazard-maps.md new file mode 100644 index 000000000..05f06ec56 --- /dev/null +++ b/_datasets/global-landslide-hazard-maps.md @@ -0,0 +1,76 @@ +--- +contact_point: + email: mamadio@worldbank.org + name: Mattia Amadio +creator: + name: ARUP + url: https://www.arup.com +dataset_id: ARUP-LS +description: The Global Landslide hazard map is a gridded dataset of landslide hazard + produced at the global scale. Landslides happen around the world and have devastating + impacts on people and the built environment. To better understand the spatial and + temporal distribution of landslide hazard worldwide, the World Bank and the Global + Facility for Disaster Reduction and Recovery (GFDRR) commissioned Arup to undertake + a landslide hazard assessment at a global scale. Using a global landslide inventory, + landslide susceptibility information provided by NASA, and an innovative machine + learning model, our geohazard and risk management experts produced a state-of-the-art + quantitative landslide hazard map for the whole world. +details: "The dataset comprises gridded maps of estimated annual frequency of significant\ + \ landslides per square kilometre. Significant landslides are those which are likely\ + \ to have been reported had they occurred in a populated place; limited information\ + \ on reported landslide size makes it difficult to tie frequencies to size ranges\ + \ but broadly speaking would be at least greater than 100 m2. The data provides\ + \ frequency estimates for each grid cell on land between 60\xB0S and 72\xB0N for\ + \ landslides triggered by seismicity and rainfall. Applications of this dataset\ + \ include improved hazard screening based on frequency and severity, consistent\ + \ national, regional and global scale exposure assessment, estimates of annual expected\ + \ impact on population and the built environment." +exposure: null +hazard: + calculation_method: inferred + disaster_identifiers: '' + hazard_analysis_type: deterministic + hazard_type: landslide + intensity: ls_hzd:- + occurrence_range: '' + processes: landslide_general +license: CC-BY-4.0 +loss: null +project: null +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: null +resources: +- coordinate_system: EPSG:4326 + description: Median global landslide hazard triggered by heavy rainfall trigger + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045414/ls_rf_median_1980-2018.zip + format: geotiff + id: RF_trigger-med + spatial_resolution: 1000 + title: Median rainfall landslide hazard +- coordinate_system: EPSG:4326 + description: Mean global landslide hazard triggered by heavy rainfall trigger + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045413/ls_rf_mean_1980-2018.zip + format: geotiff + id: RF_trigger-mea + spatial_resolution: 1000 + title: Mean rainfall landslide hazard +- coordinate_system: EPSG:4326 + description: Mean global landslide hazard triggered by earthquake + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045412/ls_eq.zip + format: geotiff + id: EQ_trigger + spatial_resolution: 1000 + title: Global landslide hazard triggered by earthquake (median) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - GLO + scale: global +title: Global landslide hazard maps +version: '1' +vulnerability: null +--- diff --git a/_datasets/global-tropical-cyclone-wind-speed-hazard.md b/_datasets/global-tropical-cyclone-wind-speed-hazard.md new file mode 100644 index 000000000..f0ef47f15 --- /dev/null +++ b/_datasets/global-tropical-cyclone-wind-speed-hazard.md @@ -0,0 +1,58 @@ +--- +contact_point: + email: mamadio@worldbank.org + name: Mattia Amadio +creator: + name: Nadia Bloemendaal + url: https://data.4tu.nl/authors/8a084c6a-3315-4ba7-9768-dd1ba1825dbc +dataset_id: STORM +description: Datasets containing probabilistic analysis of tropical cyclone maximum + wind speed (in m/s). Return periods are generated using the STORM datasets. +details: Return periods were empirically calculated using Weibull's plotting formula. + The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set + of return periods at 10 km resolution in every ocean basin. +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: strong_wind + intensity: v_etc(10m):m/s + occurrence_range: 1/10 to 1/10000 years + processes: tropical_cyclone +license: CC0-1.0 +loss: null +project: STORM +publisher: + name: 4TU.Centre for Research Data + url: https://data.4tu.nl/ +purpose: null +resources: +- coordinate_system: EPSG:4326 + description: The GeoTIFFs provided in the original STORM datasets have been mosaicked + into single files with global extent for each climate model/return period. STORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif + download_url: null + format: geotiff + id: HST + spatial_resolution: 10000 + title: Global tropical cyclone wind speed maps by return period (historical 1979-2014) +- coordinate_system: EPSG:4326 + description: The GeoTIFFs provided in the original STORM datasets have been mosaicked + into single files with global extent for each climate model/return period. STORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif + download_url: null + format: geotiff + id: PRJ_MEDIAN + spatial_resolution: 10000 + title: Global tropical cyclone wind speed maps by return period (median projections + 2015-2050) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - GLO + scale: global +title: Global tropical cyclone (wind speed) hazard +version: '4' +vulnerability: null +--- diff --git a/_datasets/json/rdls_exp-AFG_asset.json b/_datasets/json/rdls_exp-AFG_asset.json new file mode 100644 index 000000000..a2e329d39 --- /dev/null +++ b/_datasets/json/rdls_exp-AFG_asset.json @@ -0,0 +1,149 @@ +{ + "datasets": [ + { + "id": "AFG_exp-asset", + "title": "Afghanistan Asset exposure", + "description": "Collection of exposure datasets for risk assessment purpose in Afghanistan. Includes:\n- Location, area and USD value of rainfed and irrigated agricultural crops.\n- Total exposure value of buildings for different occupancy types: urban and rural structures, residential, non-residential, and industrial area. Values expressed as replacement cost (USD), area (m2), or number of elements (count).\n- Location, count and USD value (when available) for the following infrastructures in Afghanistan: airports, bridges, dams, health centers, hospitals, power plants, roads, schools and universities.\n- Population count and GDP value in USD for three macrosectors (Industry, Agriculture and Services) in Afghanistan.", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "exposure": { + "category": "buildings; infrastructures; agriculture; population", + "metrics": [ + { + "id": "0", + "dimension": "structure", + "quantity_kind": "area" + }, + { + "id": "1", + "dimension": "structure", + "quantity_kind": "count" + }, + { + "id": "2", + "dimension": "structure", + "quantity_kind": "currency" + }, + { + "id": "3", + "dimension": "content", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Buildings", + "description": "Buildings exposure for different occupancy types and materials: urban and rural structures, residential, non-residential, and industrial area. Values expressed as replacement cost (USD), area (m2), or number of elements (count).", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065490/exp-afg-buildings.zip" + }, + { + "id": "1", + "title": "Infrastructures", + "description": "Location, count and USD value (when available) for airports, bridges, dams, health centers, hospitals, power plants, roads, schools and universities.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065491/exp-afg-infrastructures.zip" + }, + { + "id": "2", + "title": "Agriculture", + "description": "Location, area and USD value of rainfed and irrigated agricultural crops.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065489/exp-afg-agriculture.zip" + }, + { + "id": "3", + "title": "Population and GDP", + "description": "Population count and GDP value in USD for three macrosectors (Industry, Agriculture and Services).", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050638/DR0065492/exp-afg-indicators.zip" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_exp-OECS.json b/_datasets/json/rdls_exp-OECS.json new file mode 100644 index 000000000..9067930dc --- /dev/null +++ b/_datasets/json/rdls_exp-OECS.json @@ -0,0 +1,153 @@ +{ + "datasets": [ + { + "id": "ortho_OECS", + "title": "Rooftop classification map of Dominica and Saint Lucia", + "description": "Building footprint polygons in Dominica and Saint Lucia with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2018-2019, in the aftermath of Hurricane Maria.", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "GFDRR", + "email": "tisabelle@worldbank.org" + }, + "version": "1.0", + "project": "Digital Earth for Resilient Housing and Infrastructure in the Caribbean", + "details": "The vector dataset depicts building footprint polygons of Dominica with corresponding roof type and roof material attributes. The categories for roof type are FLAT, GABLE, HIP, and NO ROOF, and the categories for roof material are HEALTHY METAL, IRREGULAR METAL, CONCRETE/CEMENT, BLUE TARPAULIN, and INCOMPLETE. The roof classification map was derived using a convolutional neural network (CNN) model trained on ~15,000 labels across Dominica and Saint Lucia. The roof type and roof classification maps were predicted from nationwide very high-resolution RGB orthophotos with a spatial resolution of 20 cm/px taken in 2018-2019, in the aftermath of Hurricane Maria in 2017. The dataset also contains the predicted probabilities per category, suffixed by \\\"_PROB\\\". ", + "spatial": { + "countries": [ + "DOM", + "LCA" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Isabelle Tingzon", + "email": "tisabelle@worldbank.org", + "url": "https://issa-tingzon.github.io/" + }, + "creator": { + "name": "Isabelle Tingzon", + "email": "tisabelle@worldbank.org", + "url": "https://issa-tingzon.github.io/" + }, + "exposure": { + "category": "buildings", + "taxonomy": "Custom", + "metrics": [ + { + "id": "Roof type", + "dimension": "structure", + "quantity_kind": "area" + }, + { + "id": "Roof material", + "dimension": "structure", + "quantity_kind": "area" + } + ] + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Isabelle Tingzon", + "email": "tisabelle@worldbank.org", + "url": "https://issa-tingzon.github.io/" + }, + "role": "author" + }, + { + "id": "1", + "entity": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "sources": [ + { + "id": "0", + "name": "Orthophoto mosaic (20 cm/px) of Dominica from 2018-2019", + "type": "dataset", + "component": "exposure" + }, + { + "id": "1", + "name": "Orthophoto mosaic (10 cm/px) of Saint Lucia for 2022", + "type": "dataset", + "component": "exposure" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Can AI help build climate resilience in the Caribbean? Let’s look at housing.", + "author_names": [ + "Isabelle Tingzon", + "Nuala Margaret Cowan", + "Pierre Chrzanowski" + ], + "date_published": "2023-10-02", + "url": "https://blogs.worldbank.org/sustainablecities/can-ai-help-build-climate-resilience-caribbean-lets-look-housing" + }, + { + "id": "1", + "name": "Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean", + "author_names": [ + "Isabelle Tingzon", + "Nuala Margaret Cowan", + "Pierre Chrzanowski" + ], + "date_published": "2023-08-20", + "url": "https://arxiv.org/abs/2307.16177", + "doi": "https://doi.org/10.48550/arXiv.2307.16177" + } + ], + "resources": [ + { + "id": "0", + "title": "Dominica Rooftop Classification Map", + "description": "Building footprint polygons of Dominica with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2018-2019, in the aftermath of Hurricane Maria.", + "format": "gpkg", + "coordinate_system": "EPSG:32620", + "access_url": "https://drive.google.com/file/d/15_JAPZlxHaRw9ldMqYcwEC2xAlDmVD23/view?usp=drive_link", + "download_url": "https://drive.google.com/file/d/15_JAPZlxHaRw9ldMqYcwEC2xAlDmVD23/view?usp=drive_link", + "temporal": { + "start": "2018", + "end": "2019" + } + }, + { + "id": "1", + "title": "Saint Lucia Rooftop Classification Map", + "description": "Building footprint polygons of Saint Lucia with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2022.", + "format": "gpkg", + "coordinate_system": "EPSG:32620", + "access_url": "https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link", + "download_url": "https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link", + "temporal": { + "start": "2022", + "end": "2022" + } + }, + { + "id": "2", + "title": "Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean", + "description": "Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.", + "media_type": "application/pdf", + "format": "pdf", + "access_url": "https://arxiv.org/abs/2307.16177" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_exp-SSD_asset.json b/_datasets/json/rdls_exp-SSD_asset.json new file mode 100644 index 000000000..013d990cf --- /dev/null +++ b/_datasets/json/rdls_exp-SSD_asset.json @@ -0,0 +1,121 @@ +{ + "datasets": [ + { + "id": "SSD_exp-asset", + "title": "South Sudan Asset exposure", + "description": "Collection of exposure data from Open Street Map, OCHA and World Bank, representing location and type of settlments, land use, buildings, health facilities and roads.", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2019", + "purpose": "The results of the analysis contribute to the production of knowledge for disaster risk management (DRM) to support the World Bank’s operational teams in their in-country engagements. Specifcally, the key fndings of this study allow to rank South Sudan states in terms of natural disasters risk, and to identify the most critical components for each area. The output of this assessment includes a geodatabase which contains both the key primary data and all the resulting maps produced by the analysis, allowing risk analysts and managers to explore them in detail using GIS software.", + "project": "South Sudan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "SSD" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Lukas Loeschner", + "email": "lloeschner@worldbank.org" + }, + "creator": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "exposure": { + "category": "buildings", + "metrics": [ + { + "id": "0", + "dimension": "structure", + "quantity_kind": "area" + } + ] + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Lukas Loeschner", + "email": "lloeschner@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "role": "author" + } + ], + "sources": [ + { + "id": "0", + "name": "Open Street Map", + "type": "dataset", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Disasters, Conflict, and Displacement : Intersectional Risks in South Sudan (Vol. 2)", + "author_names": [ + "Rina Meutia", + "Lukas Loeschner", + "Makiko Watanabe", + "Meskerem Brhane", + "Mattia Amadio" + ], + "date_published": "2020-09-24", + "url": "http://documents1.worldbank.org/curated/en/570701601009027965/pdf/Summary.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Settlements", + "description": "Location and ranking of settlements from OCHA (2019)", + "format": "gpkg", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053214/exp-ssd-settlements_ocha.zip" + }, + { + "id": "1", + "title": "South Sudan buildings, land use and roads", + "description": "Buildings, land use, and roads polygons from OpenStreetMap", + "format": "gpkg", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053213/exp-ssd-osm.zip" + }, + { + "id": "2", + "title": "Health facilities", + "description": "Location and ranking of health facilities from World Bank (2009)", + "format": "gpkg", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053215/exp-ssd-health_wb.zip" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_exposure-SFRARR.json b/_datasets/json/rdls_exposure-SFRARR.json new file mode 100644 index 000000000..d34676305 --- /dev/null +++ b/_datasets/json/rdls_exposure-SFRARR.json @@ -0,0 +1,901 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_exp_airport", + "title": "Central Asia exposure dataset - airports", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "infrastructure", + "metrics": [ + { + "id": "CA_SFRARR_exp_airport", + "dimension": "structure", + "quantity_kind": "count" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_airport", + "title": "Central Asia exposure dataset - airports", + "description": "Regional layer of airports in Central Asia. The dataset has been developed based on global-scale airport layers, and validated locally based on visual inspection. The folder contains one points shapefile of the airports locations, and one polygon shapefile with the airport extent (assumed based on a circular buffer of variable radius). The two shapefiles contain the same fields. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment).\nApplication of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \nFiles: Airports_Centralasia.shp; Airports_Centralasia_points.shp", + "media_type": "application/vnd.shp", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064255", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064255/DR0091666/SFRARR_exposure_CA_Airports.zip?versionId=2023-07-05T13:40:53.8644184Z" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_agri", + "title": "Central Asia exposure dataset - agriculture (wheat and cotton)", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "agriculture", + "metrics": [ + { + "id": "CA_SFRARR_exp_agri", + "dimension": "product", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_agri_KAZ", + "title": "Central Asia exposure dataset - agricultural crops - KAZ", + "description": "Cotton and wheat cropland in Kazakhstan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091987/SFRARR_exposure_croplands_KAZ_csvOnly.zip?versionId=2023-07-21T17:07:52.3193748Z" + }, + { + "id": "CA_SFRARR_exp_agri_KGZ", + "title": "Central Asia exposure dataset - agricultural crops - KGZ", + "description": "Cotton and wheat cropland in Kyrgyz Republic, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091995/SFRARR_exposure_croplands_KGZ.zip?versionId=2023-07-21T17:07:56.1382112Z" + }, + { + "id": "CA_SFRARR_exp_agri_TKM", + "title": "Central Asia exposure dataset - agricultural crops - TKM", + "description": "Cotton and wheat cropland in Turkmenistan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091651/SFRARR_exposure_croplands_TKM.zip?versionId=2023-07-21T17:07:54.2642733Z" + }, + { + "id": "CA_SFRARR_exp_agri_TJK", + "title": "Central Asia exposure dataset - agricultural crops - TJK", + "description": "Cotton and wheat cropland in Tajikistan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091652/SFRARR_exposure_croplands_TJK.zip?versionId=2023-07-21T17:07:48.2706690Z" + }, + { + "id": "CA_SFRARR_exp_agri_UZB", + "title": "Central Asia exposure dataset - agricultural crops - UZB", + "description": "Cotton and wheat cropland in Uzbekistan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091653/SFRARR_exposure_croplands_UZB_csvOnly.zip?versionId=2023-07-21T17:07:50.3494905Z" + }, + { + "id": "CA_SFRARR_exp_agri_KGZ_shp", + "title": "Central Asia exposure dataset - agricultural crops - KGZ", + "description": "Cotton and wheat cropland in Kyrgyz Republic, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "application/vnd.shp", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091995/SFRARR_exposure_croplands_KGZ.zip?versionId=2023-07-21T17:07:56.1382112Z" + }, + { + "id": "CA_SFRARR_exp_agri_TKM_shp", + "title": "Central Asia exposure dataset - agricultural crops - TKM", + "description": "Cotton and wheat cropland in Turkmenistan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "application/vnd.shp", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091651/SFRARR_exposure_croplands_TKM.zip?versionId=2023-07-21T17:07:54.2642733Z" + }, + { + "id": "CA_SFRARR_exp_agri_TJK_shp", + "title": "Central Asia exposure dataset - agricultural crops - TJK", + "description": "Cotton and wheat cropland in Tajikistan, Central Asia. The dataset has been developed based on cropland data (cotton, wheat) collected from local data provided at sub-national level. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\n\nCOTTON_[OBLAST].csv, WHEAT_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code\n\nCSV files only provided due to large size of GIS (.shp) files.", + "media_type": "application/vnd.shp", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064248", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064248/DR0091652/SFRARR_exposure_croplands_TJK.zip?versionId=2023-07-21T17:07:48.2706690Z" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_nonres", + "title": "Central Asia exposure dataset - nonresidential buildings", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "buildings", + "metrics": [ + { + "id": "CA_SFRARR_exp_nonres", + "dimension": "structure", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_nonres", + "title": "Central Asia exposure dataset - Non-residential buildings (education, healthcare, industrial, commercial)", + "description": "Central Asia exposure dataset - Non-residential buildings (education, healthcare, industrial, commercial)\nNon-residential data provided as CSV files.\nFile naming: COMMERCIAL_[OBLAST].csv, INDUSTRIAL_[OBLAST].csv, HEALTHCARE_[OBLAST].csv, EDUCATION_[OBLAST].csv", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064288" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_pop", + "title": "Central Asia exposure dataset - population", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "population", + "metrics": [ + { + "id": "CA_SFRARR_exp_pop", + "dimension": "population", + "quantity_kind": "count" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_pop", + "title": "Central Asia exposure dataset - Population", + "description": "Regional layer of population distribution in Central Asia. Files: POPULATION_[OBLAST].shp, POPULATION_[OBLAST].csv. File names contain the Oblast GAD_ID_1 code", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064250" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_res", + "title": "Central Asia exposure dataset - residential buildings", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "buildings", + "metrics": [ + { + "id": "CA_SFRARR_exp_res", + "dimension": "structure", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_res", + "title": "Central Asia exposure dataset - Residential buildings", + "description": "Central Asia exposure layers – Residential buildings.\nFiles: RESIDENTIAL_[OBLAST].csv, RESIDENTIAL_[OBLAST].shp", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064251" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_transport", + "title": "Central Asia exposure dataset - transport", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "infrastructure", + "metrics": [ + { + "id": "CA_SFRARR_exp_transport", + "dimension": "structure", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_transport", + "title": "Central Asia exposure dataset - Transport", + "description": "Regional layer of transportation infrastructure in Central Asia. The dataset has been developed based on transportation data collected from Openstreetmap and validated based on local data. Bridges location was derived based on spatial analysis, in particular identifying the intersection of main roads/railways and rivers. These data have been derived on a regional scale for the purpose of consistent regional (multi-country hazard and risk assessment). The folder includes one point layer containing Central Asia bridges, and one lines layer containing roads and railways of different types. Note that minor roads (e.g. residential, service) and railway types (e.g. subway) were not included in the analysis. Application of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\nCentral Asia exposure dataset - Transport - Bridges - UZB\n\nFiles: BRIDGES_[OBLAST].csv; BRIDGES_[OBLAST].shp", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064252", + "temporal": { + "start": "2020", + "end": "2020" + } + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_infra", + "title": "Central Asia exposure dataset - infrastructure", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "infrastructure", + "metrics": [ + { + "id": "CA_SFRARR_exp_infra", + "dimension": "structure", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_infra", + "title": "Central Asia exposure dataset - Other infrastructure", + "description": "Regional layer of power and communications infrastructure in Central Asia. The dataset has been developed based on infrastructure data collected from global/regional layers and based on local data.\n\n\nThe layers contain point infrastructure (extraction sites and mines, power plants, dams and reservoirs) and lines infrastructure (oil, gas and water pipelines, electricity and communication network).\n\n\nApplication of this information should on smaller scales should be done with care and taking into account the limitations of the approach. \n\n\nFiles: Infrastructure_lines.shp; Infrastructure_lines.csv: Infrastructure_points.shp; Infrastructure_points.csv", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064253" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "CA_SFRARR_exp_res_projected", + "title": "Central Asia exposure dataset - residential buildings - projected", + "description": "Data from the EU-funded 'Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia' Program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia).\nExposure data developed using high-resolution global and regional datasets and local official data, harmonized to produce a regionally-consistent exposure database for Central Asia. The exposure database includes: population, residential buildings, non-residential buildings (schools, healthcare facilities, industrial and commercial buildings), croplands, transportation system (roads, railways and bridges), airports and airstrips, mines, and supply infrastructure. The exposure database developed during this project can be used at regional scale, national scale or sub-national scale (e.g., at Oblast scale). ", + "risk_data_type": [ + "exposure" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Chiara Scaini", + "email": "cscaini@inogs.it" + }, + "exposure": { + "category": "buildings", + "metrics": [ + { + "id": "CA_SFRARR_exp_res_projected", + "dimension": "structure", + "quantity_kind": "currency" + } + ] + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + }, + { + "id": "CA_SFRARR_OGS", + "entity": { + "name": "National Institute of Oceanography and Applied Geophysics, OGS, Italy" + }, + "role": "author" + }, + { + "id": "CA_SFRARR_WB", + "entity": { + "name": "World Bank" + }, + "role": "resource_provider" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_expReport_EN", + "name": "Central Asia exposure data development technical report - English version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091010/Task4_Exposure_Report_r6_EN.pdf?versionId=2023-07-21T17:33:32.2845222Z" + }, + { + "id": "CA_SFRARR_expReport_RU", + "name": "Central Asia exposure data development technical report - Russian version", + "date_published": "2022-11-16", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064117/DR0091011/Task4_Exposure_Report_r6_RU.pdf?versionId=2023-07-21T17:33:26.6527091Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_exp_res_projected", + "title": "Central Asia exposure dataset - Projected residential exposure", + "description": "Regional layer of residential buildings in Central Asia.Central Asia projected (2080) residential exposure - all countries, Oblast level: SSP4\nDataset includes SSP1, SSP4, SSP5 scenarios.\nFiles: RESIDENTIAL_[OBLAST]._2080_[SSP].csv", + "media_type": "text/csv", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064254", + "temporal": { + "start": "2080", + "end": "2080" + } + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hazard-SFRARR_FL.json b/_datasets/json/rdls_hazard-SFRARR_FL.json new file mode 100644 index 000000000..5f186bda4 --- /dev/null +++ b/_datasets/json/rdls_hazard-SFRARR_FL.json @@ -0,0 +1,732 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_FL", + "title": "Central Asia flood hazard maps - fluvial", + "description": "Fluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return period.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Gabriele Coccia" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "SFRARR_FloodReport_English", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "2022-09-02", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090774/Task3_FloodHazard_Report_r5_EN.pdf?versionId=2023-07-21T17:34:14.1038579Z" + }, + { + "id": "SFRARR_FloodReport_Russian", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "2022-09-02", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090783/Task3_FloodHazard_Report_r5_RU.pdf?versionId=2023-07-21T17:34:24.6069148Z" + }, + { + "id": "SFRARR_FloodReport_Annex_undefended_maps", + "name": "Task 3 Flood Hazard Assessment Report - annex 1 - Fluvial Flood Undefended Hazard Maps", + "date_published": "2022-09-02", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090775/T3_Annex1_Fluvial%20Undefended%20Hazard%20Maps_r5_EN.pdf?versionId=2023-07-21T17:34:15.9448173Z" + }, + { + "id": "SFRARR_FloodReport_Annex_defended_maps", + "name": "Task 3 Flood Hazard Assessment Report - annex 2 - Fluvial Flood Defended Hazard Maps", + "date_published": "2022-09-02", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090776/T3_Annex2_Fluvial%20Defended%20Hazard%20Maps_r5_EN.pdf?versionId=2023-07-21T17:34:17.6688407Z" + }, + { + "id": "SFRARR_FloodReport_Annex_climateProjection_maps", + "name": "Task 3 Flood Hazard Assessment Report - annex 3 - Fluvial Flood Undefended 2080 Climate Projections Hazard Maps", + "date_published": "2022-09-02", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090777/T3_Annex3_Fluvial%202080%20Scenario%20Hazard%20Maps_r5_EN.pdf?versionId=2023-07-21T17:34:05.1629203Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_FL_UND_Rpmaps", + "title": "Central Asia undefended fluvial flood hazard maps - current climate", + "description": "Fluvial flood hazard maps for the undefended case for 5,10,20,50,100,200,500,1,000 years return period. Each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain, feeding the model with the hydrograph corresponding to the given return time. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies. FLU_UND_XXy_ZZZ.tif (XX= return time, ZZZ= country initials) ", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalog.worldbank.org/search/dataset/0064236" + }, + { + "id": "CA_SFRARR_FL_DEF_Rpmaps", + "title": "Central Asia defended fluvial flood hazard maps - current climate", + "description": "Fluvial flood hazard maps for the defended case for 5,10,20,50,100,200,500,1,000 years return period. Each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain, feeding the model with the hydrograph corresponding to the given return time. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies. FLU_DEF_XXy_ZZZ.tif (XX= return time, ZZZ= country initials) ", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalog.worldbank.org/search/dataset/0064232" + }, + { + "id": "CA_SFRARR_FL_CC_Rpmaps", + "title": "Central Asia undefended fluvial flood hazard maps - 2080 cliamte projections", + "description": "Fluvial flood hazard maps for the 2080 scenario (climate change) for 5,10,20,50,100,200,500,1,000 years return period. Each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain, feeding the model with the hydrograph corresponding to the given return time. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies. FLU_CC_XXy_ZZZ.tif (XX= return time, ZZZ= country initials) ", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalog.worldbank.org/search/dataset/0064238" + } + ], + "hazard": { + "event_sets": [ + { + "id": "CA_SFRARR_FL_UND", + "analysis_type": "probabilistic", + "seasonality": "uniform", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TJK", + "TKM", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "hazards": [ + { + "id": "1", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "CA_SFRARR_UND5", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND5", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5, + "probability": { + "value": 0.2, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND10", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND10", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10, + "probability": { + "value": 0.1, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND20", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND20", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 20, + "probability": { + "value": 0.05, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND50", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND50", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50, + "probability": { + "value": 0.02, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND100", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND100", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100, + "probability": { + "value": 0.01, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND200", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND200", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 200, + "probability": { + "value": 0.005, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND500", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND500", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500, + "probability": { + "value": 0.002, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_UND1000", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_UND1000", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000, + "probability": { + "value": 0.001, + "span": 1 + } + } + } + } + ] + }, + { + "id": "CA_SFRARR_FL_DEF", + "analysis_type": "probabilistic", + "seasonality": "uniform", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TJK", + "TKM", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "hazards": [ + { + "id": "2", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "CA_SFRARR_DEF5", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF5", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5, + "probability": { + "value": 0.2, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF10", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF10", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10, + "probability": { + "value": 0.1, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF20", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF20", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 20, + "probability": { + "value": 0.05, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF50", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF50", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50, + "probability": { + "value": 0.02, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF100", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF100", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100, + "probability": { + "value": 0.01, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF200", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF200", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 200, + "probability": { + "value": 0.005, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF500", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF500", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500, + "probability": { + "value": 0.002, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_DEF1000", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_DEF1000", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000, + "probability": { + "value": 0.001, + "span": 1 + } + } + } + } + ] + }, + { + "id": "CA_SFRARR_FL_CC", + "analysis_type": "probabilistic", + "seasonality": "uniform", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TJK", + "TKM", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "hazards": [ + { + "id": "3", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "CA_SFRARR_CC5", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC5", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5, + "probability": { + "value": 0.2, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC10", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC10", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10, + "probability": { + "value": 0.1, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC20", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC20", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 20, + "probability": { + "value": 0.05, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC50", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC50", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50, + "probability": { + "value": 0.02, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC100", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC100", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100, + "probability": { + "value": 0.01, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC200", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC200", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 200, + "probability": { + "value": 0.005, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC500", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC500", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500, + "probability": { + "value": 0.002, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_CC1000", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_CC1000", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000, + "probability": { + "value": 0.001, + "span": 1 + } + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hazard-SFRARR_PL.json b/_datasets/json/rdls_hazard-SFRARR_PL.json new file mode 100644 index 000000000..549dd6e2f --- /dev/null +++ b/_datasets/json/rdls_hazard-SFRARR_PL.json @@ -0,0 +1,330 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_PL", + "title": "Central Asia flood hazard maps - pluvial", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. ", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com/" + }, + "version": "2022", + "purpose": "Regional risk modelling", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Gabriele Coccia" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "SFRARR_FloodReport_English", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "2022", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090774/Task3_FloodHazard_Report_r5_EN.pdf?versionId=2023-07-21T17:34:14.1038579Z" + }, + { + "id": "SFRARR_FloodReport_Russian", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "2022", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090783/Task3_FloodHazard_Report_r5_RU.pdf?versionId=2023-07-21T17:34:24.6069148Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_FL_PL_KAZ", + "title": "Pluvial flood hazard maps - Kazakhstan", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain feeding the model with the Intensity Duration Frequency curve at 6 hours", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091741/PLU_KGZ.zip?versionId=2023-07-17T13:21:08.7536943Z" + }, + { + "id": "CA_SFRARR_FL_PL_KGZ", + "title": "Pluvial flood hazard maps - Kyrgyz Republic", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain feeding the model with the Intensity Duration Frequency curve at 6 hours", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091741/PLU_KGZ.zip?versionId=2023-07-17T13:21:08.7536943Z" + }, + { + "id": "CA_SFRARR_FL_PL_TJK", + "title": "Pluvial flood hazard maps - Tajikistan", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain feeding the model with the Intensity Duration Frequency curve at 6 hours", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091996/PLU_TJK.zip?versionId=2023-07-17T13:21:14.2955579Z" + }, + { + "id": "CA_SFRARR_FL_PL_TKM", + "title": "Pluvial flood hazard maps - Turkmenistan", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain feeding the model with the Intensity Duration Frequency curve at 6 hours", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091742/PLU_TKM.zip?versionId=2023-07-17T13:21:18.4691974Z" + }, + { + "id": "CA_SFRARR_FL_PL_UZB", + "title": "Pluvial flood hazard maps - Uzbekistan", + "description": "Pluvial flood hazard maps for 5,10,20,50,100,200,500,1,000 years return time and 6 hours duration, i.e., a map for each return time where each pixel represents the maximum water depth. In order to derive the hazard maps over the entire domain, for each return period an hydraulic simulation is performed for each simulation unit domain feeding the model with the Intensity Duration Frequency curve at 6 hours", + "media_type": "image/tiff", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064155/DR0091778/PLU_UZB.zip?versionId=2023-07-17T13:21:16.4303513Z" + } + ], + "hazard": { + "event_sets": [ + { + "id": "CA_SFRARR_PL", + "analysis_type": "probabilistic", + "seasonality": "uniform", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TJK", + "TKM", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "hazards": [ + { + "id": "1", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + } + ], + "events": [ + { + "id": "CA_SFRARR_PL5", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL5", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5, + "probability": { + "value": 0.2, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL10", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL10", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10, + "probability": { + "value": 0.1, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL20", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL20", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 20, + "probability": { + "value": 0.05, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL50", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL50", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50, + "probability": { + "value": 0.02, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL100", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL100", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100, + "probability": { + "value": 0.01, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL200", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL200", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 200, + "probability": { + "value": 0.005, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL500", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL500", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500, + "probability": { + "value": 0.002, + "span": 1 + } + } + } + }, + { + "id": "CA_SFRARR_PL1000", + "calculation_method": "simulated", + "hazard": { + "id": "CA_SFRARR_PL1000", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000, + "probability": { + "value": 0.001, + "span": 1 + } + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hazard-SFRARR_Scenarios.json b/_datasets/json/rdls_hazard-SFRARR_Scenarios.json new file mode 100644 index 000000000..3e4b514ea --- /dev/null +++ b/_datasets/json/rdls_hazard-SFRARR_Scenarios.json @@ -0,0 +1,212 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_FL_scenarios", + "title": "Central Asia flood scenarios", + "description": "Fluvial flood hazard map for the historical scenario of the Hamadoni flood on the Panj River (Tajikistan, June-July 2005) and realistic scenarios for 100 years return period. ", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com/" + }, + "version": "2022", + "purpose": "Scenario modelling of selected floods representative of an extreme event in three countries (KGZ, TKM, UZB) and one historical scenario (TJK). Used for validation of flood modelling approach against historical observations and to communicate potential extreme flood impacts.", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 69.415864227, + 37.168360403, + 70.215864563, + 37.968361001 + ], + "scale": "sub-national" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Gabriele Coccia" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "sources": [ + { + "id": "Hamadoni Report", + "url": "https://openjicareport.jica.go.jp/pdf/11870748_01.pdf" + } + ], + "referenced_by": [ + { + "id": "SFRARR_FloodReport_English", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "1905-07-14", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090774/Task3_FloodHazard_Report_r5_EN.pdf?versionId=2023-07-21T17:34:14.1038579Z" + }, + { + "id": "SFRARR_FloodReport_Russian", + "name": "Task 3 Flood Hazard Assessment Report - Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia(SFRARR Central Asia disaster risk assessment)", + "date_published": "1905-07-14", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064084/DR0090783/Task3_FloodHazard_Report_r5_RU.pdf?versionId=2023-07-21T17:34:24.6069148Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_Hamadoni", + "title": "2005_Flood_Panj_Hamadoni_TJK ", + "description": "Fluvial flood hazard map ( i.e., a map where each pixel represents the maximum water depth ) for the historical scenario of the Hamadoni flood on the Panj River (Hamadoni, Tajikistan, June-July 2005). The simulated hydrograph was estimated from data reported by JICA (https://openjicareport.jica.go.jp/pdf/11870748_01.pdf). We assumed a bankfull discharge of 3-year return time.", + "media_type": "image/tiff", + "format": "geotiff", + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064165/DR0090876/maxwd_Hamadoni.tif?versionId=2023-07-21T17:20:16.5760431Z" + }, + { + "id": "CA_SFRARR_FL_100y", + "title": "100y_Fl_scenario_Turkmenabat_TKM", + "description": "Fluvial flood hazard maps for a realistic scenarios estiamted at 1-in-100 years return period for Turkmenabat, Turkmenistan. Realistic scenarios were identified to assess potential floods that have not happened in the past but may affect the region in the future. For this reason, we asked our local experts in the consortium to provide indications on the areas where our realistic scenarios should be implemented. Specifically, we applied the following criteria: (1) flood prone area; (2) populated and built area exposed to flood risk; (3) floods happened in the area in the past.", + "media_type": "image/tiff", + "format": "geotiff", + "coordinate_system": "EPSG:4326", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064166/DR0090880/Scenario_3_Turkmenabat_TKM_100y.tif?versionId=2023-07-21T17:20:00.4661664Z" + } + ], + "hazard": { + "event_sets": [ + { + "id": "CA_SFRARR_FL_hist", + "analysis_type": "deterministic", + "calculation_method": "inferred", + "event_count": 1, + "spatial": { + "countries": [ + "TJK" + ], + "bbox": [ + 69.415864227, + 37.168360403, + 70.215864563, + 37.968361001 + ], + "scale": "sub-national" + }, + "temporal": { + "start": "2005-06", + "end": "2005-07" + }, + "hazards": [ + { + "id": "1", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:m" + } + ], + "events": [ + { + "id": "TJK_Hamadoni", + "calculation_method": "inferred", + "hazard": { + "id": "1", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 3 + } + }, + "disaster_identifiers": [ + { + "scheme": "EMDAT", + "id": "2005-0370-TJK" + } + ] + } + ] + }, + { + "id": "CA_SFRARR_FL_100y", + "analysis_type": "deterministic", + "calculation_method": "simulated", + "event_count": 3, + "spatial": { + "countries": [ + "KGZ", + "TKM", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "sub-national" + }, + "hazards": [ + { + "id": "2", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:m" + } + ], + "events": [ + { + "id": "Three_100y_events", + "calculation_method": "simulated", + "hazard": { + "id": "2", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100, + "probability": { + "value": 0.01, + "span": 1 + } + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-AFG_avalanche.json b/_datasets/json/rdls_hzd-AFG_avalanche.json new file mode 100644 index 000000000..70b640e5d --- /dev/null +++ b/_datasets/json/rdls_hzd-AFG_avalanche.json @@ -0,0 +1,159 @@ +{ + "datasets": [ + { + "id": "AFG_hzd-avalanche", + "title": "Afghanistan Snow Avalanche hazard", + "description": "Detailed avalanche study gathering historic avalanche data and performing numerical modeling of the avalanche runout potential and dynamics nationwide.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Snow Avalanche hazard - 1kPa and 3 kPa", + "description": "Footprint masks for hazard exceeding 1kPa and 3 kPa.", + "format": "gpkg", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065479/hzd-afg-ls-lav-kpa.zip" + }, + { + "id": "1", + "title": "Snow Avalanche hazard", + "description": "Hazard map", + "format": "geotiff", + "spatial_resolution": 20, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065478/hzd-afg-ls-lav.zip" + }, + { + "id": "2", + "title": "Snow Avalanche hazard - Snow Water Equivalents", + "description": "Snow Water Equivalent (SWE) is calculated from EUWATCH data running from 1958 to 2002. The max grid cell values of each hydrological year where the accumulative SWE is taken. Then of the 44 years of modelled data the maximum of the aformentioned maximum values is taken. Unit is kg/m2. Only the 100 year return period scenario was computed.", + "format": "geotiff", + "spatial_resolution": 2000, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050635/DR0065480/hzd-afg-ls-lav-rp100-swe.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "simulated", + "occurrence_range": "100 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "0", + "type": "landslide", + "processes": [ + "snow_avalanche" + ], + "intensity_measure": "kPa" + } + ], + "events": [ + { + "id": "0", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "snow_avalanche" + ], + "intensity_measure": "kPa" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-AFG_drought.json b/_datasets/json/rdls_hzd-AFG_drought.json new file mode 100644 index 000000000..25cb9c991 --- /dev/null +++ b/_datasets/json/rdls_hzd-AFG_drought.json @@ -0,0 +1,268 @@ +{ + "datasets": [ + { + "id": "AFG_hzd-drought", + "title": "Afghanistan Drought hazard", + "description": "Annual water availability per sub-catchment for baseline and projected conditions (2050) according to seven return period scenarios.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "sources": [ + { + "id": "0", + "name": "WFLOW", + "type": "model", + "component": "hazard" + }, + { + "id": "1", + "name": "RIBASIM", + "type": "model", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Water shortage RP scenarios (historical baseline and 2050)", + "description": "Water shortage is defined in terms of percentage deviation from the baseline water demand due to rainfall deficit.", + "format": "gpkg", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050633/DR0065474/hzd-afg-dr.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "inferred", + "occurrence_range": "10, 20, 100, 250, 500, 1000 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "temporal": { + "start": "1958", + "end": "2001", + "duration": "P44Y" + }, + "hazards": [ + { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)", + "trigger": { + "type": "drought", + "processes": [ + "meteorological_drought" + ] + } + } + ], + "events": [ + { + "id": "0", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "1", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "2", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "3", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "4", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "6", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "drought", + "processes": [ + "hydrological_drought" + ], + "intensity_measure": "Water shortage (%)" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-AFG_earthquake.json b/_datasets/json/rdls_hzd-AFG_earthquake.json new file mode 100644 index 000000000..a88e2446f --- /dev/null +++ b/_datasets/json/rdls_hzd-AFG_earthquake.json @@ -0,0 +1,256 @@ +{ + "datasets": [ + { + "id": "AFG_hzd-earthquake", + "title": "Afghanistan Earthquake hazard", + "description": "Earthquake hazard map representing Peak ground acceleration (PGA-g) for seven return period scenarios.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "sources": [ + { + "id": "0", + "name": "CAPRA", + "url": "http://ecapra.org", + "type": "model", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Earthquake ground shaking hazard scenarios", + "description": "Peak ground acceleration (PGA-g) simulated for seven return period scenarios (10, 50, 100, 250, 500 , 1000 and 2500 years).", + "format": "geotiff", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050631/DR0065467/hzd-afg-eq.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "simulated", + "occurrence_range": "10, 50, 100, 250, 500, 1000, 2.500 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "temporal": { + "start": "0800", + "end": "2018" + }, + "hazards": [ + { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + } + ], + "events": [ + { + "id": "0", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "1", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "2", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "3", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "4", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + }, + { + "id": "6", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "earthquake", + "processes": [ + "ground_motion" + ], + "intensity_measure": "PGA:g" + }, + "occurrence": { + "probabilistic": { + "return_period": 2500 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-AFG_flood.json b/_datasets/json/rdls_hzd-AFG_flood.json new file mode 100644 index 000000000..46e773db0 --- /dev/null +++ b/_datasets/json/rdls_hzd-AFG_flood.json @@ -0,0 +1,670 @@ +{ + "datasets": [ + { + "id": "AFG_hzd-flood", + "title": "Afghanistan Flood hazard", + "description": "Fluvial flood hazard is calculated based on probabilistic hydrological analysis models (precipitation into runoff) and hydrodynamic analysis (runoff into river flow and inundation, and flow over floodplain areas).", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "sources": [ + { + "id": "0", + "name": "EUWATCH", + "type": "dataset", + "component": "hazard" + }, + { + "id": "1", + "name": "WFLOW", + "type": "model", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Flood hazard scenarios - country (baseline)", + "description": "Flood extent and water depth in Afghanistan for eight return period scenarios (5, 10, 20, 50, 100, 250, 500 and 1000 years) based on historical baseline.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065469/hzd-afg-fl-baseline.zip" + }, + { + "id": "1", + "title": "Flood hazard scenarios - country (2050)", + "description": "Flood extent and water depth in Afghanistan for eight return period scenarios (5, 10, 20, 50, 100, 250, 500 and 1000 years) based on climate projections for 2050.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065469/hzd-afg-fl-2050.zip" + }, + { + "id": "2", + "title": "Flood hazard scenarios - Kabul (baseline)", + "description": "Flood extent and water depth in Kabul for three return period scenarios (5, 100 and 1000 years) based on historical baseline.", + "format": "geotiff", + "spatial_resolution": 10, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065472/hzd-afg-fl-kabul.zip" + }, + { + "id": "3", + "title": "Flood hazard scenarios - country (2050)", + "description": "Flood extent and water depth simulated for four historical events: 1978, 1991 (2 river floods), 1992 (flash flood and landslide) event.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050632/DR0065471/hzd-afg-fl-hist_events.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "Baseline", + "analysis_type": "probabilistic", + "calculation_method": "simulated", + "occurrence_range": "5, 10, 20, 50, 100, 250, 500, 1000 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "temporal": { + "start": "1958", + "end": "2001", + "duration": "P44Y" + }, + "hazards": [ + { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "BL_5", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, 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"return_period": 100 + } + } + }, + { + "id": "BL_250", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "BL_500", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "BL_1000", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "Projections", + "analysis_type": "probabilistic", + "calculation_method": "simulated", + "occurrence_range": "5, 10, 20, 50, 100, 250, 500, 1000 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "temporal": { + "start": "2010", + "end": "2050", + "duration": "P40Y" + }, + "hazards": [ + { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "CP_5", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "CP_10", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "CP_20", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "CP_50", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "CP_100", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "CP_250", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "CP_500", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "CP_1000", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "Baseline-Kabul", + "analysis_type": "probabilistic", + "calculation_method": "simulated", + "occurrence_range": "5, 100, 1000 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "sub-national", + "gazetteer_entries": [ + { + "id": "AFG-KAB", + "scheme": "ISO 3166-2", + "description": "Kabul District" + } + ] + }, + "temporal": { + "start": "1958", + "end": "2001", + "duration": "P44Y" + }, + "hazards": [ + { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "BLK_5", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "BLK_100", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "BLK_1000", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "Historical events", + "analysis_type": "empirical", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "sub-national" + }, + "hazards": [ + { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + } + ], + "events": [ + { + "id": "HST_1978", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "empirical": { + "temporal": { + "start": "1978" + } + } + } + }, + { + "id": "HST_1991_1", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "empirical": { + "temporal": { + "start": "1991" + } + } + } + }, + { + "id": "HST_1991_2", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "empirical": { + "temporal": { + "start": "1991" + } + } + } + }, + { + "id": "HST_1992", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "empirical": { + "temporal": { + "start": "1992" + } + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "AFG_hzd-landslide", + "spatial": { + "gazetteer_entries": [ + { + "id": "AFG-KAB", + "scheme": "ISO 3166-2", + "description": "Kabul District" + } + ] + } + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-AFG_landslide.json b/_datasets/json/rdls_hzd-AFG_landslide.json new file mode 100644 index 000000000..5f3fb07df --- /dev/null +++ b/_datasets/json/rdls_hzd-AFG_landslide.json @@ -0,0 +1,288 @@ +{ + "datasets": [ + { + "id": "AFG_hzd-landslide", + "title": "Afghanistan Landslide hazard", + "description": "Earthquake-induced landslide hazard measured as probability of occurrance for seven return period scenarios. National hazard assessment and focus on two areas (Kabul and Salang pass).", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national", + "gazetteer_entries": [ + { + "id": "AFG-KAB", + "scheme": "ISO 3166-2", + "description": "Kabul District" + }, + { + "id": "Kōtal-e Sālang", + "scheme": "GEONAMES", + "description": "Salang pass", + "uri": "https://www.geonames.org/1127859/kotal-e-salang.html" + } + ] + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Landslide hazard RP scenarios", + "description": "Simulated Ground Motion process triggered by earthquake measured as debris-flow intensity index", + "format": "geotiff", + "spatial_resolution": 30, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050634/DR0065476/hzd-afg-ls-eq-rp.zip" + }, + { + "id": "1", + "title": "Landslide susceptibility", + "description": "Susceptibility map for bedrock landslides in slow evolution (S1), bedrock landslides in rapid evolution (S2) and cover material landslides in rapid evolution (S3) nationwide, including: rotational slides, translational slides, earth flows and lateral spreading.", + "format": "geotiff", + "spatial_resolution": 30, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050634/DR0065476/hzd-afg-ls-eq-susceptibility.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "inferred", + "event_count": 68, + "occurrence_range": "10, 50, 100, 250, 500, 1000, 2500 years", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + } + ], + "events": [ + { + "id": "0", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "1", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "2", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "3", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "4", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + }, + { + "id": "6", + "calculation_method": "simulated", + "hazard": { + "id": "0", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Debris flow intensity index" + }, + "occurrence": { + "probabilistic": { + "return_period": 2500 + } + } + } + ] + }, + { + "id": "1", + "analysis_type": "deterministic", + "calculation_method": "simulated", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "1", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "Susceptibility index" + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-ARUP-LS.json b/_datasets/json/rdls_hzd-ARUP-LS.json new file mode 100644 index 000000000..4599d98ce --- /dev/null +++ b/_datasets/json/rdls_hzd-ARUP-LS.json @@ -0,0 +1,268 @@ +{ + "datasets": [ + { + "id": "ARUP-LS", + "title": "Global landslide hazard maps", + "description": "The Global Landslide hazard map is a gridded dataset of landslide hazard produced at the global scale. Landslides happen around the world and have devastating impacts on people and the built environment. To better understand the spatial and temporal distribution of landslide hazard worldwide, the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) commissioned Arup to undertake a landslide hazard assessment at a global scale. Using a global landslide inventory, landslide susceptibility information provided by NASA, and an innovative machine learning model, our geohazard and risk management experts produced a state-of-the-art quantitative landslide hazard map for the whole world.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "1", + "details": "The dataset comprises gridded maps of estimated annual frequency of significant landslides per square kilometre. Significant landslides are those which are likely to have been reported had they occurred in a populated place; limited information on reported landslide size makes it difficult to tie frequencies to size ranges but broadly speaking would be at least greater than 100 m2. The data provides frequency estimates for each grid cell on land between 60°S and 72°N for landslides triggered by seismicity and rainfall. Applications of this dataset include improved hazard screening based on frequency and severity, consistent national, regional and global scale exposure assessment, estimates of annual expected impact on population and the built environment.", + "spatial": { + "scale": "global" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "creator": { + "name": "ARUP", + "url": "https://www.arup.com" + }, + "attributions": [ + { + "id": "1", + "entity": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "role": "resource_provider" + }, + { + "id": "2", + "entity": { + "name": "ARUP", + "url": "https://www.arup.com" + }, + "role": "author" + } + ], + "referenced_by": [ + { + "id": "1", + "name": "Global Landslide Hazard map - Project report", + "author_names": [ + "Peter Redshaw", + " James Bottomley", + " Matthew Free" + ], + "date_published": "2021-04-29", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045411/global-landslide-hazard-map-report.pdf?versionId=2023-01-18T20:43:00.6156776Z" + } + ], + "resources": [ + { + "id": "RF_trigger-med", + "title": "Median rainfall landslide hazard", + "description": "Median global landslide hazard triggered by heavy rainfall trigger", + "format": "geotiff", + "spatial_resolution": 1000, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0037584/global-landslide-hazard-map", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045414/ls_rf_median_1980-2018.zip" + }, + { + "id": "RF_trigger-mea", + "title": "Mean rainfall landslide hazard", + "description": "Mean global landslide hazard triggered by heavy rainfall trigger", + "format": "geotiff", + "spatial_resolution": 1000, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0037584/global-landslide-hazard-map", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045413/ls_rf_mean_1980-2018.zip" + }, + { + "id": "EQ_trigger", + "title": "Global landslide hazard triggered by earthquake (median)", + "description": "Mean global landslide hazard triggered by earthquake", + "format": "geotiff", + "spatial_resolution": 1000, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0037584/global-landslide-hazard-map", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0037584/DR0045412/ls_eq.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "RF_trigger-med", + "analysis_type": "deterministic", + "frequency_distribution": "user_defined", + "seasonality": "uniform", + "calculation_method": "inferred", + "event_count": 400000, + "spatial": { + "scale": "global" + }, + "temporal": { + "start": "1980", + "end": "2018", + "duration": "P39Y" + }, + "hazards": [ + { + "id": "RF", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "convective_storm" + } + } + ], + "events": [ + { + "id": "1", + "calculation_method": "inferred", + "hazard": { + "id": "RF", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "convective_storm" + } + }, + "occurrence": { + "deterministic": { + "index_criteria": "Median", + "thresholds": [ + "0.001", + "0.01" + ] + } + } + } + ] + }, + { + "id": "RF_trigger-mea", + "analysis_type": "deterministic", + "frequency_distribution": "user_defined", + "seasonality": "uniform", + "calculation_method": "inferred", + "event_count": 400000, + "spatial": { + "scale": "global" + }, + "temporal": { + "start": "1980", + "end": "2018", + "duration": "P39Y" + }, + "hazards": [ + { + "id": "RF", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "convective_storm" + } + } + ], + "events": [ + { + "id": "1", + "calculation_method": "inferred", + "hazard": { + "id": "RF", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "convective_storm" + } + }, + "occurrence": { + "deterministic": { + "index_criteria": "Mean", + "thresholds": [ + "0.001", + "0.01" + ] + } + } + } + ] + }, + { + "id": "EQ_trigger", + "analysis_type": "deterministic", + "frequency_distribution": "user_defined", + "seasonality": "uniform", + "calculation_method": "inferred", + "event_count": 130000, + "spatial": { + "scale": "global" + }, + "temporal": { + "start": "1980", + "end": "2018", + "duration": "P39Y" + }, + "hazards": [ + { + "id": "EQ", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "earthquake" + } + } + ], + "events": [ + { + "id": "1", + "calculation_method": "inferred", + "hazard": { + "id": "EQ", + "type": "landslide", + "processes": [ + "landslide_general" + ], + "intensity_measure": "ls_hzd:-", + "trigger": { + "type": "earthquake" + } + }, + "occurrence": { + "deterministic": { + "index_criteria": "Median", + "thresholds": [ + "0.001", + "0.01" + ] + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-FTHv3.json b/_datasets/json/rdls_hzd-FTHv3.json new file mode 100644 index 000000000..6372a388a --- /dev/null +++ b/_datasets/json/rdls_hzd-FTHv3.json @@ -0,0 +1,934 @@ +{ + "datasets": [ + { + "id": "FTH_v3", + "title": "Global flood hazard maps", + "description": "Probabilistic modelling of fluvial and pluvial flood hazard", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "Fathom", + "url": "https://www.fathom.global/" + }, + "version": "3", + "purpose": "Fathom Global hazard maps can support risk screening and analysis at the sub-national level. Caution is advised when using these data as the only source of flood hazard information for site-specific analysis. The model is driven by global assumptions; it can provide a useful overview of the likely hazard in a particular area, however local data should be sought out before detailed planning or operational decisions are made. The data are not suitable for engineering-level analysis (such as construction of bridges or flood defences).", + "details": "The FATHOM flood-hazard model is a global gridded dataset of flood hazard produced at the global scale. It provides flood extent and water depth to ground (in centimeters) for three types of flood phenomena:\n- Fluvial (or river) flooding occurs when a river exceeds its capacity and inundates surrounding areas.\n- Pluvial (or surface water) flooding occurs when extreme rainfall exceeds surface drainage capacity.\n- Coastal flooding occurs when a combination of storm-surge, tides and waves lead to water levels that submerge the coastal land.\nThere are two options for each flood type: Defended and Undefended (fluvial and coastal only). Defended scenarios account for protection standards in proportion to country wealth to reduce the chance of hazard occurrence. It does not take account location-specific physical protection measures.\nThe model covers 4 time periods:\n- 2020 (present baseline)\n- 2030 (near future)\n- 2050 (mid-century)\n- 2080 (far future)\nFuture periods include 4 model realizations, each one describing a different climate scenario:\n- SSP1 – 2.6 (limited emissions)\n- SSP2 – 4.5\n- SSP3 – 7.0\n- SSP5 – 8.5 (high emissions)\nEach scenario set is made of 10 events each representing a different intensity and probability of occurrence, expressed as “return period”. These are framed as 1 in 5, 10, 20, 50, 100, 200, 500 and 1,000 years. ", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "license": "Commercial", + "contact_point": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "creator": { + "name": "Fathom", + "url": "https://www.fathom.global/" + }, + "attributions": [ + { + "id": "1", + "entity": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "role": "distributor" + }, + { + "id": "2", + "entity": { + "name": "Fathom", + "url": "https://www.fathom.global" + }, + "role": "owner" + } + ], + "referenced_by": [ + { + "id": "1", + "name": "A high-resolution global flood hazard model", + "author_names": [ + "Christopher C. Sampson", + "Andrew M. Smith", + "Paul D. Bates", + "Jeffrey C. Neal", + "Lorenzo Alfieri", + "Jim E. Freer" + ], + "date_published": "2015-08-18", + "url": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015WR016954", + "doi": "10.1002/2015WR016954" + } + ], + "resources": [ + { + "id": "FTH_form_WB", + "title": "Fathom v3 global dataset", + "description": "Global country data - access limited to WB operations via request form", + "format": "geotiff", + "spatial_resolution": 30, + "coordinate_system": "EPSG:4326", + "access_url": "https://forms.office.com/r/sG0qWTnC0L" + } + ], + "hazard": { + "event_sets": [ + { + "id": "FFL_U", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "seasonality": "uniform", + "calculation_method": "simulated", + "occurrence_range": "1/10 to 1/1000 years", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "hazards": [ + { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + } + ], + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "200", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 200 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "FFL_D", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "seasonality": "uniform", + "calculation_method": "simulated", + "occurrence_range": "1/10 to 1/1000 years", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "hazards": [ + { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + } + ], + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "200", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 200 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "FFL", + "type": "flood", + "processes": [ + "fluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "PFL_D", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "seasonality": "uniform", + "calculation_method": "simulated", + "occurrence_range": "1/10 to 1/1000 years", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "hazards": [ + { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + } + ] + }, + { + "id": "CFL_U", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "seasonality": "uniform", + "calculation_method": "simulated", + "occurrence_range": "1/10 to 1/1000 years", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "hazards": [ + { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + } + ], + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "200", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 200 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "CFL_D", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "seasonality": "uniform", + "calculation_method": "simulated", + "occurrence_range": "1/10 to 1/1000 years", + "spatial": { + "bbox": [ + -180, + -90, + 180, + 90 + ], + "scale": "global" + }, + "hazards": [ + { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + } + ], + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "200", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 200 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "CFL", + "type": "flood", + "processes": [ + "coastal_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + }, + { + "id": "PFL_U", + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "200", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 200 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "PFL", + "type": "flood", + "processes": [ + "pluvial_flood" + ], + "intensity_measure": "wd:cm" + }, 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}, + { + "id": "9000", + "calculation_method": "simulated", + "hazard": { + "id": "TCW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):m/s" + }, + "occurrence": { + "probabilistic": { + "return_period": 9000 + } + } + }, + { + "id": "10000", + "calculation_method": "simulated", + "hazard": { + "id": "TCW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):m/s" + }, + "occurrence": { + "probabilistic": { + "return_period": 10000 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-SWIO_coastal_flood.json b/_datasets/json/rdls_hzd-SWIO_coastal_flood.json new file mode 100644 index 000000000..db4225515 --- /dev/null +++ b/_datasets/json/rdls_hzd-SWIO_coastal_flood.json @@ -0,0 +1,298 @@ +{ + "datasets": [ + { + "id": "SWIO_hzd-coastal_flood", + "title": "South West Indian Ocean Flood hazard", + "description": "Coastal flood hazard measured as the maximum water depth for six return periods. ", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2017", + "purpose": "The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.\nRegional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.", + "project": "SWIO-RAFI", + "details": "This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).", + "spatial": { + "countries": [ + "COM", + "MDG", + "MUS", + "SYC", + "TZA" + ], + "scale": "regional" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "role": "owner" + } + ], + "sources": [ + { + "id": "0", + "name": "AIR-Worldwide", + "url": "https://www.air-worldwide.com/", + "type": "model", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "South West Indian Ocean risk assessment and financing initiative (SWIO-RAFI): summary report ", + "author_names": [ + "Doekle Wielinga", + "Alanna Simpson", + "Julie Dana", + "Emily White", + "Emma Philips", + "Liana Razafindrazay", + "Luis Corrales", + "Richard Murnane", + "Richard Poulter", + "Samantha Cook", + "Simone Balog", + "Stuart Fraser", + "Vivien Deparday" + ], + "date_published": "2017-01-01", + "url": "http://documents1.worldbank.org/curated/en/951701497623912193/pdf/116342-WP-PUBLIC-52p-SWIO-RAFI-Summary-Report-2017-Publish-Version.pdf" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "inferred", + "occurrence_range": "10, 25, 50, 100, 250, 500 and 1000 years", + "spatial": { + "countries": [ + "COM", + "MDG", + "MUS", + "SYC", + "TZA" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m", + "trigger": { + "processes": [ + "tropical_cyclone" + ] + } + } + ], + "events": [ + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "25", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 25 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "250", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "CF", + "type": "coastal_flood", + "processes": [ + "storm_surge" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + }, + { + "id": "SWIO_hzd-flood", + "resources": [ + { + "id": "COM", + "title": "Comoros - 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Flood hazard scenarios (tropical cyclones)", + "description": "Flood hazard triggered by tropical cyclones over Mauritius measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038588/Mauritius-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038588/DR0054315/hzd-mus-fl-tcy.zip" + }, + { + "id": "MUS-etc", + "title": "Mauritius - Flood hazard scenarios (extra-tropical cyclones)", + "description": "Flood hazard triggered by extra-tropical cyclones over Mauritius measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038588/Mauritius-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038588/DR0054314/hzd-mus-fl-etc.zip" + }, + { + "id": "SYC-tcy", + "title": "Seychelles - Flood hazard scenarios (tropical cyclones)", + "description": "Flood hazard triggered by tropical cyclones over Seychelles measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038587/Seychelles-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038587/DR0054325/hzd-syc-fl-tcy.zip" + }, + { + "id": "SYC-etc", + "title": "Seychelles - Flood hazard scenarios (extra-tropical cyclones)", + "description": "Flood hazard triggered by extra-tropical cyclones over Seychelles measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038587/Seychelles-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038587/DR0054324/hzd-syc-fl-etc.zip" + }, + { + "id": "ZAN-tcy", + "title": "Zanzibar - Flood hazard scenarios (tropical cyclones)", + "description": "Flood hazard triggered by tropical cyclones over Zanzibar measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038586/Zanzibar-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038586/DR0054359/hzd-zan-fl-tcy.zip" + }, + { + "id": "ZAN-etc", + "title": "Zanzibar - Flood hazard scenarios (extra-tropical cyclones)", + "description": "Flood hazard triggered by extra-tropical cyclones over Zanzibar measured as the maximum water depth for six return periods.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038586/Zanzibar-flood-hazard", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038586/DR0054358/hzd-zan-fl-etc.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "inferred", + "occurrence_range": "10, 25, 50, 100, 250, 500 and 1000 years", + "spatial": { + "countries": [ + "COM", + "MDG", + "MUS", + "SYC", + "TZA" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m", + "trigger": { + "processes": [ + "tropical_cyclone", + "extratropical_cyclone" + ] + } + } + ], + "events": [ + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "25", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 25 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "250", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "FL", + "type": "flood", + "processes": [ + "fluvial_flood", + "pluvial_flood" + ], + "intensity_measure": "fl_wd:m" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-SWIO_strong_wind.json b/_datasets/json/rdls_hzd-SWIO_strong_wind.json new file mode 100644 index 000000000..19f6881b5 --- /dev/null +++ b/_datasets/json/rdls_hzd-SWIO_strong_wind.json @@ -0,0 +1,295 @@ +{ + "datasets": [ + { + "id": "SWIO_hzd-strong_wind", + "title": "South West Indian Ocean Flood hazard", + "description": "Strong Wind hazard caused by tropical cyclones measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2017", + "purpose": "The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.\nRegional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.", + "project": "SWIO-RAFI", + "details": "This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).", + "spatial": { + "countries": [ + "COM", + "MDG", + "MUS", + "SYC", + "TZA" + ], + "scale": "regional" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "role": "owner" + } + ], + "sources": [ + { + "id": "0", + "name": "AIR-Worldwide", + "url": "https://www.air-worldwide.com/", + "type": "model", + "component": "hazard" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "South West Indian Ocean risk assessment and financing initiative (SWIO-RAFI): summary report ", + "author_names": [ + "Doekle Wielinga", + "Alanna Simpson", + "Julie Dana", + "Emily White", + "Emma Philips", + "Liana Razafindrazay", + "Luis Corrales", + "Richard Murnane", + "Richard Poulter", + "Samantha Cook", + "Simone Balog", + "Stuart Fraser", + "Vivien Deparday" + ], + "date_published": "2017-01-01", + "url": "http://documents1.worldbank.org/curated/en/951701497623912193/pdf/116342-WP-PUBLIC-52p-SWIO-RAFI-Summary-Report-2017-Publish-Version.pdf" + } + ], + "resources": [ + { + "id": "COM", + "title": "Comoros - Flood hazard scenarios (tropical cyclones)", + "description": "Strong Wind hazard caused by tropical cyclones over Comoros measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038599/Comoros-strong-wind-hazard--tropical-cyclone-", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038599/DR0054350/hzd-com-wi.zip" + }, + { + "id": "MDG", + "title": "Madagascar - Flood hazard scenarios (tropical cyclones)", + "description": "Strong Wind hazard caused by tropical cyclones over Madagascar measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "format": "geotiff", + "spatial_resolution": 900, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038598/Madagascar-strong-wind-hazard--tropical-cyclone-", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038598/DR0054337/hzd-mdg-wi.zip" + }, + { + "id": "MUS", + "title": "Mauritius - Flood hazard scenarios (tropical cyclones)", + "description": "Strong Wind hazard caused by tropical cyclones over Mauritius measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038597/Mauritius-strong-wind-hazard--tropical-cyclone-", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038597/DR0054310/hzd-mus-wi.zip" + }, + { + "id": "SYC", + "title": "Seychelles - Flood hazard scenarios (tropical cyclones)", + "description": "Strong Wind hazard caused by tropical cyclones over Seychelles measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038596/Seychelles-strong-wind-hazard--tropical-cyclone-", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038596/DR0054318/hzd-syc-wi.zip" + }, + { + "id": "ZAN", + "title": "Zanzibar - Flood hazard scenarios (tropical cyclones)", + "description": "Strong Wind hazard caused by tropical cyclones over Zanzibar measured as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground surface.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0038595/Zanzibar-strong-wind-hazard--tropical-cyclone-", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0038595/DR0054365/hzd-zan-wi.zip" + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "calculation_method": "inferred", + "occurrence_range": "10, 25, 50, 100, 250, 500 and 1000 years", + "spatial": { + "countries": [ + "COM", + "MDG", + "MUS", + "SYC", + "TZA" + ], + "scale": "national" + }, + "hazards": [ + { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h", + "trigger": { + "processes": [ + "tropical_cyclone" + ] + } + } + ], + "events": [ + { + "id": "10", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 10 + } + } + }, + { + "id": "25", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 25 + } + } + }, + { + "id": "50", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 50 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + }, + { + "id": "250", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 250 + } + } + }, + { + "id": "500", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 500 + } + } + }, + { + "id": "1000", + "calculation_method": "simulated", + "hazard": { + "id": "SW", + "type": "strong_wind", + "processes": [ + "tropical_cyclone" + ], + "intensity_measure": "v_etc(10m):km/h" + }, + "occurrence": { + "probabilistic": { + "return_period": 1000 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_hzd-VITO.json b/_datasets/json/rdls_hzd-VITO.json new file mode 100644 index 000000000..d3c078a8c --- /dev/null +++ b/_datasets/json/rdls_hzd-VITO.json @@ -0,0 +1,161 @@ +{ + "datasets": [ + { + "id": "VITO_WBGT", + "title": "Global extreme heat hazard", + "description": "Extreme Heat hazard described by the daily maximum Wet Bulb Globe Temperature (WBGT °C) for three return period scenarios.", + "risk_data_type": [ + "hazard" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2016", + "project": "VITO_WBGT", + "details": "The WBGT is derived from global daily maximum air temperature contained in ERA-Interim re-analysis fields for the period 1981-2010, which is considered of sufficient length to provide robust climate statistics. The 0.75° lat/lon fields are corrected for local-scale altitude effects by means of a high-resolution global digital elevation model, resulting in global daily maximum WBGT fields at a spatial resolution of approximately 10 km. These fields are temporally smoothed using a 3-day filter, so as to account for the cumulative effects of prolonged heat. These 30-year, 10-km resolution, 3-day smoothed daily maximum WBGT values are then employed to fit a Generalized Extreme Value (GEV) probability distribution function for each grid cell of the global domain. Considering return periods of 5, 20, and 100 years, 10-km hazard intensity maps have been calculated for each of these periods. To these hazard intensity maps, threshold values of 32°C, 28°C and 25°C, stemming from the scientific literature, subsequently are applied, resulting in a global heat risk map.", + "spatial": { + "scale": "global" + }, + "license": "CC0-1.0", + "contact_point": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "creator": { + "name": "VITO", + "url": "https://vito.be/en" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "VITO", + "url": "https://vito.be/en" + }, + "role": "author" + }, + { + "id": "1", + "entity": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "role": "funder" + } + ], + "referenced_by": [ + { + "id": "1", + "name": "Development of a hazard screening protocol for Extreme Heat", + "author_names": [ + "Koen De Ridder", + "Dirk Lauwaet", + "Hans Hooyberghs", + "Filip Lefebre" + ], + "date_published": "2017-03-17", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0040194/DR0087127/VITO%20-%20Extreme%20heat%20Final_report_v2.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Heat stress global maps", + "description": "Global heat stress maps by return period (5, 20, 100 years)", + "format": "geotiff", + "spatial_resolution": 10000, + "coordinate_system": "EPSG:4326", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0040194/Global-extreme-heat-hazard", + "temporal": { + "start": "1981", + "end": "2010" + } + } + ], + "hazard": { + "event_sets": [ + { + "id": "0", + "analysis_type": "probabilistic", + "frequency_distribution": "generalized_extreme_value", + "calculation_method": "simulated", + "occurrence_range": "5, 20, 100 years", + "spatial": { + "scale": "global" + }, + "hazards": [ + { + "id": "HS", + "type": "extreme_temperature", + "processes": [ + "extreme_heat" + ], + "intensity_measure": "WBGT:c" + } + ], + "events": [ + { + "id": "5", + "calculation_method": "simulated", + "hazard": { + "id": "HS", + "type": "extreme_temperature", + "processes": [ + "extreme_heat" + ], + "intensity_measure": "WBGT:c" + }, + "occurrence": { + "probabilistic": { + "return_period": 5 + } + } + }, + { + "id": "20", + "calculation_method": "simulated", + "hazard": { + "id": "HS", + "type": "extreme_temperature", + "processes": [ + "extreme_heat" + ], + "intensity_measure": "WBGT:c" + }, + "occurrence": { + "probabilistic": { + "return_period": 20 + } + } + }, + { + "id": "100", + "calculation_method": "simulated", + "hazard": { + "id": "HS", + "type": "extreme_temperature", + "processes": [ + "extreme_heat" + ], + "intensity_measure": "WBGT:c" + }, + "occurrence": { + "probabilistic": { + "return_period": 100 + } + } + } + ] + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_loss-SFRARR_EQ.json b/_datasets/json/rdls_loss-SFRARR_EQ.json new file mode 100644 index 000000000..139741792 --- /dev/null +++ b/_datasets/json/rdls_loss-SFRARR_EQ.json @@ -0,0 +1,228 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_EQ_loss", + "title": "Central Asia seismic risk estimates", + "description": "Fluvial seismic risk estimates, including return period loss estimates, annual average loss estimates and event loss tables ", + "risk_data_type": [ + "loss" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies.", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Paolo Bazzuro" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "SFRARR_RiskReport_English", + "name": "Task 6 Earthquake and flood risk assessment", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091047/Task6_Risk_Report_r6_EN.pdf?versionId=2023-07-21T17:23:49.5763790Z" + }, + { + "id": "SFRARR_RiskReport_Russian", + "name": "Task 6 Earthquake and flood risk assessment", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091049/Task6_Risk_Report_r6_RU.pdf?versionId=2023-07-21T17:23:52.9654590Z" + }, + { + "id": "SFRARR_RiskAnnex_Russian", + "name": "Task 6 Earthquake and flood risk assessment - annex", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091050/Task6_Risk_Annex_r6_RU.pdf?versionId=2023-07-21T17:23:54.6115255Z" + }, + { + "id": "SFRARR_RiskAnnex_English", + "name": "Task 6 Earthquake and flood risk assessment - annex", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091048/Task6_Risk_Annex_r6_EN.pdf?versionId=2023-07-21T17:23:51.3253877Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_EQ_loss_RP", + "title": "Central Asia seismic risk return period summaries - economic loss - current", + "description": "Tabulated return period loss estimates showing seismic risk at ADM1, country and regional level. One csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064273", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064273/DR0092053/SFRARR_EQ_RPsummaries_Economic_current.zip?versionId=2023-07-21T17:05:43.1885433Z" + }, + { + "id": "CA_SFRARR_EQ_fatalities_RP", + "title": "Central Asia seismic risk return period summaries - population - current", + "description": "Tabulated return period loss estimates showing seismic risk at ADM1, country and regional level. One csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064273", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064273/DR0092055/SFRARR_EQ_RPsummaries_PopFatalities_current.zip?versionId=2023-07-21T17:05:47.0413599Z" + }, + { + "id": "CA_SFRARR_EQ_loss_EP", + "title": "Central Asia seismic risk EP curves", + "description": "Exceedance Probability (EP) loss curves showing the estimated severity and frequency of earthquake losses (monetary loss and human fatalities). This dataset includes risk estimates for the whole Central Asian region, each country in the study, and each Oblast. Risk estimates are available for multiple sectors/asset types individually, and all sectors combined. Asset types comprise residential, commercial, education, and healthcare buildings, roads, bridges. Risk estimates are provided for current exposure, and residential only and fatalities projected using SSP1, SSP4 and SSP5. Estimated economic losses for each sector and all sectors combined using current exposure. Losses are aggregated at regional, national and Oblast levels in one csv file per ADM unit and sector giving the estimated loss per selected exceedance probability (return period).", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064271" + }, + { + "id": "CA_SFRARR_EQ_ELTs", + "title": "Central Asia seismic risk - Event Loss Tables (ELTs)", + "description": "Event Loss Tables (ELTs) showing the severity and frequency of estimated loss for each simulated earthquake event. Event Loss Tables (ELTs) provide the estimated economic loss per simulated event. The ELT is used to develop the EP curves, AAL and return period loss estimates. An ELT is provided for losses aggregated to regional, national and Oblast levels, for each sector sector and all sectors combined using current exposure. One csv file per loss breakdown, giving the estimated loss per event. ", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064272" + }, + { + "id": "CA_SFRARR_EQ_RP_maps", + "title": "Central Asia seismic risk AAL and Return Period Loss maps", + "description": "Geospatial data layer describing estimated return period losses, Annual aggregate and probable maximum losses at Oblast level per country per sectors, current and projected exposure", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064275" + }, + { + "id": "CA_SFRARR_EQ_scenarioLosses", + "title": "Central Asia seismic risk scenario losses", + "description": "Tabulated summary of simulated fatalities and economic loss due to five hypothetical 1-in-100-year earthquake events impacting Almaty, Bishkek, Tashkent, Ashgabat, and Dushanbe. EQ-Scenario Losses-Deterministic Analysis.csv: Table showing the estimated fatalities, economic loss (million USD) and event parameters due to each simulated scenario.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064276" + }, + { + "id": "CA_SFRARR_EQ_hazardIntensity", + "title": "Central Asia seismic risk hazard intensity summaries", + "description": "Tabulated summary of seismic hazard intensity for selected infrastructure: airports, industrial sites, infratructure, population and transport. Developed as part of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia). Number of assets exposed to each maximum seismic ground shaking (pga) class for each return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064274" + } + ], + "loss": { + "losses": [ + { + "id": "EQ_loss_ec_bldg", + "hazard_type": "earthquake", + "hazard_process": "ground_motion", + "description": "Estimated economic loss per sector and for all sectors combined for seismic risk aggregated to the Oblast, national and regional level", + "category": "buildings", + "cost": { + "id": "EQ_cost_struct", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_EQ", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_EQ_vuln" + }, + { + "id": "EQ_loss_pop", + "hazard_type": "earthquake", + "hazard_process": "ground_motion", + "description": "Estimated fatalities due to seismic risk, aggregated to the Oblast, national and regional level", + "category": "population", + "impact": { + "type": "direct", + "metric": "casualty_count", + "unit": "count", + "base_data_type": "simulated" + }, + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_EQ", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_EQ_vuln" + }, + { + "id": "EQ_loss_ec_agri", + "hazard_type": "earthquake", + "hazard_process": "ground_motion", + "description": "Estimated economic loss per sector and for all sectors combined for seismic risk aggregated to the Oblast, national and regional level", + "category": "agriculture", + "cost": { + "id": "EQ_cost_struct", + "dimension": "product", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_EQ", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_EQ_vuln" + }, + { + "id": "EQ_loss_ec_infra", + "hazard_type": "earthquake", + "hazard_process": "ground_motion", + "description": "Estimated economic loss per sector and for all sectors combined for seismic risk aggregated to the Oblast, national and regional level", + "category": "infrastructure", + "cost": { + "id": "EQ_cost_struct", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_EQ", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_EQ_vuln" + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_loss-SFRARR_FL.json b/_datasets/json/rdls_loss-SFRARR_FL.json new file mode 100644 index 000000000..e91f35610 --- /dev/null +++ b/_datasets/json/rdls_loss-SFRARR_FL.json @@ -0,0 +1,381 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_FL_loss", + "title": "Central Asia flood risk estimates", + "description": "Fluvial flood risk estimates, including return period loss estimates, annual average loss estimates and event loss tables ", + "risk_data_type": [ + "loss" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies.", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Paolo Bazzuro" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "SFRARR_RiskReport_English", + "name": "Task 6 Earthquake and flood risk assessment", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091047/Task6_Risk_Report_r6_EN.pdf?versionId=2023-07-21T17:23:49.5763790Z" + }, + { + "id": "SFRARR_RiskReport_Russian", + "name": "Task 6 Earthquake and flood risk assessment", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091049/Task6_Risk_Report_r6_RU.pdf?versionId=2023-07-21T17:23:52.9654590Z" + }, + { + "id": "SFRARR_RiskAnnex_Russian", + "name": "Task 6 Earthquake and flood risk assessment - annex", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091050/Task6_Risk_Annex_r6_RU.pdf?versionId=2023-07-21T17:23:54.6115255Z" + }, + { + "id": "SFRARR_RiskAnnex_English", + "name": "Task 6 Earthquake and flood risk assessment - annex", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064118/DR0091048/Task6_Risk_Annex_r6_EN.pdf?versionId=2023-07-21T17:23:51.3253877Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_FL_loss_RP_KAZ", + "title": "Central Asia flood risk return period summaries - KAZ", + "description": "Central Asia flood risk return period summaries.\n\nEstimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated at ADM1 (Oblast) and country level.\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091702/RP_KZ.zip?versionId=2023-07-05T13:58:52.6031766Z" + }, + { + "id": "CA_SFRARR_FL_loss_RP_TJK", + "title": "Central Asia flood risk return period summaries - TJK", + "description": "Central Asia flood risk return period summaries.\n\nEstimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated at ADM1 (Oblast) and country level.\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091703/RP_TJ.zip?versionId=2023-07-05T13:58:48.7054093Z" + }, + { + "id": "CA_SFRARR_FL_loss_RP_reg", + "title": "Central Asia flood risk return period summaries - regional level", + "description": "Estimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated to the regional level (all CA countries combined).\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091700/RP_CA.zip?versionId=2023-07-05T13:58:54.3411816Z" + }, + { + "id": "CA_SFRARR_FL_loss_RP_KGZ", + "title": "Central Asia flood risk return period summaries - KGZ", + "description": "Central Asia flood risk return period summaries.\n\nEstimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated at ADM1 (Oblast) and country level.\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091701/RP_KG.zip?versionId=2023-07-05T13:58:58.0210737Z" + }, + { + "id": "CA_SFRARR_FL_loss_RP_UZB", + "title": "Central Asia flood risk return period summaries - UZB", + "description": "Central Asia flood risk return period summaries.\n\nEstimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated at ADM1 (Oblast) and country level.\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091705/RP_UZ.zip?versionId=2023-07-05T13:58:56.1241597Z" + }, + { + "id": "CA_SFRARR_FL_loss_RP_TKM", + "title": "Central Asia flood risk return period summaries - TKM", + "description": "Central Asia flood risk return period summaries.\n\nEstimated economic loss and fatalities per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios)\n\nSummaries aggregated at ADM1 (Oblast) and country level.\n\nOne csv file per loss breakdown, giving the estimated loss per return period.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064270", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064270/DR0091704/RP_TM.zip?versionId=2023-07-05T13:58:50.7882165Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_CA", + "title": "Central Asia flood risk EP curves - economic loss - flood defended", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091710/EP_DEF_EC_CA.zip?versionId=2023-07-05T13:58:33.1952945Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_KAZ", + "title": "Central Asia flood risk EP curves - economic loss - flood defended - KAZ", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091713/EP_DEF_EC_KZ.zip?versionId=2023-07-05T13:58:08.8342487Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_KGZ", + "title": "Central Asia flood risk EP curves - economic loss - flood defended - KGZ", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091712/EP_DEF_EC_KG.zip?versionId=2023-07-05T13:58:14.4190494Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_TJK", + "title": "Central Asia flood risk EP curves - economic loss - flood defended - TJK", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091714/EP_DEF_EC_TJ.zip?versionId=2023-07-05T13:58:27.4545822Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_TKM", + "title": "Central Asia flood risk EP curves - economic loss - flood defended - TKM", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091715/EP_DEF_EC_TM.zip?versionId=2023-07-05T13:58:16.2689906Z" + }, + { + "id": "CA_SFRARR_FL_def_loss_EP_UZB", + "title": "Central Asia flood risk EP curves - economic loss - flood defended - UZB", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091716/EP_DEF_EC_UZ.zip?versionId=2023-07-05T13:58:18.0429734Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_CA", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091711/EP_UND_EC_CA.zip?versionId=2023-07-05T13:58:35.0142527Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_KAZ", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended - KAZ", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091718/EP_UND_EC_KZ.zip?versionId=2023-07-05T13:58:21.7618436Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_KGZ", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended - KGZ", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091717/EP_UND_EC_KG.zip?versionId=2023-07-05T13:58:19.9968546Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_TJK", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended - TJK", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091719/EP_UND_EC_TJ.zip?versionId=2023-07-05T13:58:23.7826866Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_TKM", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended - TKM", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091720/EP_UND_EC_TM.zip?versionId=2023-07-05T13:58:25.5886514Z" + }, + { + "id": "CA_SFRARR_FL_undef_loss_EP_UZB", + "title": "Central Asia flood risk EP curves - economic loss - flood undefended - UZB", + "description": "Estimated economic loss per sector and for all sectors combined, aggregated at Oblast and national level.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091721/EP_UND_EC_UZ.zip?versionId=2023-07-05T13:58:10.7261652Z" + }, + { + "id": "CA_SFRARR_FL_def_population_EP", + "title": "Central Asia flood risk EP curves - population - flood defended", + "description": "Central Asia flood risk EP curves - population - flood defended\n\nEstimated fatalities per sector and for all sectors combined, aggregated at ADM1, country, and regional levels.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091707/EP_Def_Population.zip?versionId=2023-07-05T13:58:06.9733151Z" + }, + { + "id": "CA_SFRARR_FL_undef_population_EP", + "title": "Central Asia flood risk EP curves - population - flood undefended", + "description": "Central Asia flood risk EP curves - population - flood undefended\n\nEstimated fatalities per sector and for all sectors combined, aggregated at ADM1, country, and regional levels.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091706/EP_UND_Population.zip?versionId=2023-07-05T13:58:29.6513239Z" + }, + { + "id": "CA_SFRARR_FL_loss_EP_2080", + "title": "Central Asia flood risk EP curves - economic loss - flood climate change scenario 2080", + "description": "Central Asia flood risk EP curves - economic loss - flood climate change scenario 2080.\n\nIncludes climate change and residential exposure change using SSP1, SSP4, and SSP5.\n\nEstimated economic loss per sector and for all sectors combined, aggregated at ADM1, country, and regional levels.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091709/EP_CC_EC.zip?versionId=2023-07-05T13:58:31.4862739Z" + }, + { + "id": "CA_SFRARR_FL_population_EP_2080", + "title": "Central Asia flood risk EP curves - population - flood climate change scenario 2080", + "description": "Central Asia flood risk EP curves - population - flood climate change scenario 2080.\n\nIncludes climate change and residential exposure change using SSP1, SSP4, and SSP5.\n\nEstimated fatalities per sector and for all sectors combined, aggregated at ADM1, country, and regional levels.\n\nOne csv file per loss breakdown, giving the estimated loss per exceedance probability.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064268", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0064268/DR0091708/EP_CC_Population.zip?versionId=2023-07-05T13:58:12.5291319Z" + }, + { + "id": "CA_SFRARR_FL_ELTs", + "title": "Central Asia flood risk - Event Loss Tables (ELTs)", + "description": "Event Loss Tables (ELTs) showing the severity and frequency of estimated loss for each simulated flood event.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064269" + }, + { + "id": "CA_SFRARR_FL_RP_maps", + "title": "Central Asia flood risk AAL and Return Period Loss maps", + "description": "Geospatial data layers describing estimated losses. Annual average loss and probable maximum losses at Oblast level.", + "format": "shp", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064280" + }, + { + "id": "CA_SFRARR_FL_scenarioLosses", + "title": "Central Asia flood risk scenario losses", + "description": "Tabulated summary of simulated fatalities and economic loss due to three selected 1-in-100-year fluvial flood scenarios (Kara-Unkur River, Parkent River, and Turkmenabat).", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064267" + }, + { + "id": "CA_SFRARR_FL_hazardIntensity", + "title": "Central Asia flood risk hazard intensity summaries", + "description": "Tabulated and map summaries of fluvial flood hazard intensity for selected infrastructure: airports, industrial sites, infrastructure, population and transport.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064279" + } + ], + "loss": { + "losses": [ + { + "id": "FL_loss_ec_bldg", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Estimated economic loss per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios), aggregated to the Oblast, national and regional level", + "category": "buildings", + "cost": { + "id": "FL_cost_struct", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_FL", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_FL_vuln" + }, + { + "id": "FL_loss_pop", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Estimated fatalities due to fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios), aggregated to the Oblast, national and regional level", + "category": "population", + "impact": { + "type": "direct", + "metric": "casualty_count", + "unit": "count", + "base_data_type": "simulated" + }, + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_FL", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_FL_vuln" + }, + { + "id": "FL_loss_ec_agri", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Estimated economic loss per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios), aggregated to the Oblast, national and regional level", + "category": "agriculture", + "cost": { + "id": "FL_cost_struct", + "dimension": "product", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_FL", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_FL_vuln" + }, + { + "id": "FL_loss_ec_infra", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Estimated economic loss per sector and for all sectors combined for fluvial flood risk (current defended and undefended scenarios, and for future climate scenarios), aggregated to the Oblast, national and regional level", + "category": "infrastructure", + "cost": { + "id": "FL_cost_struct", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "economic_loss_value", + "base_data_type": "simulated" + }, + "type": "ground_up", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "CA_SFRARR_FL", + "exposure_id": "CA_SFRARR_exp", + "vulnerability_id": "CA_SFRARR_FL_vuln" + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_lss-AFG_drought.json b/_datasets/json/rdls_lss-AFG_drought.json new file mode 100644 index 000000000..c410bca26 --- /dev/null +++ b/_datasets/json/rdls_lss-AFG_drought.json @@ -0,0 +1,158 @@ +{ + "datasets": [ + { + "id": "AFG_lss-drought", + "title": "Afghanistan Drought risk", + "description": "Annual average losses in agricultural production (USD) and affected population, both for the baseline reference and future projections (2050)", + "risk_data_type": [ + "loss" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Afghanistan Drought risk: water per capita", + "description": "Water availability as m3 per capita at Administrative level (ADM1)", + "format": "gpkg", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065483/lss-afg-dr-dts.zip" + }, + { + "id": "1", + "title": "Afghanistan Drought risk: agriculture", + "description": "Agricultural losses (USD) aggregated for administrative boundaries (ADM1)", + "format": "gpkg", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065482/lss-afg-dr-dta.zip" + }, + { + "id": "2", + "title": "Afghanistan Drought risk: water per capita ", + "description": "Water availability per capita as tables, and reference threshold values.", + "format": "csv", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065484/lss-afg-dr-dts-tab.zip" + } + ], + "loss": { + "losses": [ + { + "id": "0", + "hazard_type": "drought", + "hazard_process": "socioeconomic_drought", + "description": "Water availability as m3 per capita at Administrative level (ADM1)", + "cost": { + "id": "0", + "dimension": "population" + }, + "impact": { + "type": "direct", + "metric": "at_risk_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-drought", + "exposure_id": "AFG_exp-asset" + }, + { + "id": "1", + "hazard_type": "drought", + "hazard_process": "agricultural_drought", + "description": "Agricultural losses (USD) aggregated for administrative boundaries (ADM1)", + "cost": { + "id": "1", + "dimension": "product", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "loss_annual_average_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-drought", + "exposure_id": "AFG_exp-asset" + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_lss-AFG_flood.json b/_datasets/json/rdls_lss-AFG_flood.json new file mode 100644 index 000000000..5546f0d28 --- /dev/null +++ b/_datasets/json/rdls_lss-AFG_flood.json @@ -0,0 +1,195 @@ +{ + "datasets": [ + { + "id": "AFG_lss-flood", + "title": "Afghanistan Flood risk", + "description": "Average Annual Losses (AAL) over population and asset under current conditions and SSP scenarios at 2050.", + "risk_data_type": [ + "loss" + ], + "publisher": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "version": "2018", + "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", + "project": "Afghanistan Multi-hazard risk assessment", + "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", + "spatial": { + "countries": [ + "AFG" + ], + "scale": "national" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Pierre Chrzanowski", + "email": "pchrzanowski@worldbank.org" + }, + "creator": { + "name": "GFDRR", + "url": "https://www.gfdrr.org" + }, + "attributions": [ + { + "id": "0", + "entity": { + "name": "Federica Ranghieri", + "email": "franghieri@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "1", + "entity": { + "name": "Ditte Fallesen", + "email": "dfallesen@worldbank.org" + }, + "role": "world_bank_team_lead" + }, + { + "id": "2", + "entity": { + "name": "Brandan Jongman", + "email": "bjongman@worldbank.org" + }, + "role": "world_bank_team_lead" + } + ], + "referenced_by": [ + { + "id": "0", + "name": "Afghanistan - Multi-hazard risk assessment", + "author_names": [ + "Federica Ranghieri", + "Ditte Fallesen", + "Brenden Jongman", + "Guillermo Siercke", + "Abdul Azim Doosti", + "Julian Palma", + "Simone Balog", + "Sayed Sharifullah Mashahid", + "Erika Vargas" + ], + "date_published": "2018-12-18", + "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" + } + ], + "resources": [ + { + "id": "0", + "title": "Afghanistan AAL and RPs (baseline and 2050)", + "description": "Average Annual Losses (AAL) for current population (AALpop), current asset (AALnowUSD), population SSP scenarios at 2050 (AALpopSP1-5), asset SSP scenarios at 2050 (AAL_usd_SP1-5). Aggregated ad Administrative level (ADM1).", + "format": "gpkg", + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050637/DR0065486/lss-afg-fl-adm.zip" + }, + { + "id": "1", + "title": "Afghanistan AAL and RPs (baseline)", + "description": "Average Annual Losses (AAL) over physical asset in USD for baseline scenario.", + "format": "geotiff", + "spatial_resolution": 90, + "coordinate_system": "EPSG:32642", + "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050637/DR0065487/lss-afg-fl.zip" + } + ], + "loss": { + "losses": [ + { + "id": "0", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Average Annual Losses (AAL) for current population (AALpop)", + "cost": { + "id": "0", + "dimension": "population" + }, + "impact": { + "type": "direct", + "metric": "at_risk_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-flood", + "exposure_id": "AFG_exp-asset" + }, + { + "id": "1", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Average Annual Losses (AAL) for current asset (AALnowUSD)", + "cost": { + "id": "1", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "loss_annual_average_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-flood", + "exposure_id": "AFG_exp-asset" + }, + { + "id": "2", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Average Annual Losses (AAL) for population SSP scenarios at 2050 (AALpopSP1-5)", + "cost": { + "id": "2", + "dimension": "population" + }, + "impact": { + "type": "direct", + "metric": "at_risk_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-flood", + "exposure_id": "AFG_exp-asset" + }, + { + "id": "3", + "hazard_type": "flood", + "hazard_process": "fluvial_flood", + "description": "Average Annual Losses (AAL) for asset SSP scenarios at 2050 (AAL_usd_SP1-5)", + "cost": { + "id": "3", + "dimension": "structure", + "unit": "USD" + }, + "impact": { + "type": "direct", + "metric": "loss_annual_average_value", + "unit": "count", + "base_data_type": "inferred" + }, + "type": "gross", + "approach": "analytical", + "hazard_analysis_type": "probabilistic", + "hazard_id": "AFG_hzd-flood", + "exposure_id": "AFG_exp-asset" + } + ] + }, + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_vln-FL_JRC.json b/_datasets/json/rdls_vln-FL_JRC.json new file mode 100644 index 000000000..6a2c65888 --- /dev/null +++ b/_datasets/json/rdls_vln-FL_JRC.json @@ -0,0 +1,109 @@ +{ + "datasets": [ + { + "id": "https://publications.jrc.ec.europa.eu/repository/handle/JRC105688", + "title": "Global flood depth-damage functions", + "description": "This dataset contains damage curves depicting fractional damage function of water depth as well as maximum damage values for a variety of assets and land use classes.", + "risk_data_type": [ + "vulnerability" + ], + "publisher": { + "name": "EU Joint Research Centre (JRC)", + "url": "https://joint-research-centre.ec.europa.eu/index_en" + }, + "version": "2.0", + "purpose": "Assessing potential damage of flood events is an important component in flood risk management. Determining direct flood damage is commonly done using depth-damage curves, which denote the flood damage that would occur at specific water depths per asset or per land-use class. Many countries have developed flood damage models using depth-damage curves based on analysis of past flood events and on expert judgement. However, the fact that such damage curves are not available for all regions hampers damage assessments in some areas. Moreover, due to different methodologies employed for various damage models in different countries, damage assessments cannot be directly compared with each other, obstructing also supra-national flood damage assessments.", + "details": "Based on an extensive literature survey concave damage curves have been developed for each continent, while differentiation in flood damage between countries is established by determining maximum damage values at the country scale. These maximum damage values are based on construction cost surveys from multinational construction companies, which provide a coherent set of detailed building cost data across dozens of countries. A consistent set of maximum flood damage values for all countries was computed using statistical regressions with socio-economic World Development Indicators. Further, based on insights from the literature survey, guidance is also given on how the damage curves and maximum damage values can be adjusted for specific local circumstances, such as urban vs. rural locations or use of specific building material. This dataset can be used for consistent supra-national scale flood damage assessments, and guide assessment in countries where no damage model is currently available.", + "spatial": { + "scale": "global" + }, + "license": "CC-BY-4.0", + "contact_point": { + "name": "Mattia Amadio", + "email": "mamadio@worldbank.org" + }, + "creator": { + "name": "Huizinga, J." + }, + "vulnerability": { + "hazard_primary": "flood", + "hazard_process_primary": "fluvial_flood", + "hazard_analysis_type": "empirical", + "intensity": "fl_wd:m", + "category": "buildings", + "impact": { + "type": "direct", + "metric": "mean_damage_ratio", + "unit": "percentage", + "base_data_type": "inferred" + }, + "spatial": { + "scale": "global" + }, + "functions": { + "fragility": { + "approach": "hybrid" + } + }, + "cost": [ + { + "id": "1", + "dimension": "structure", + "unit": "EUR" + }, + { + "id": "2", + "dimension": "content", + "unit": "EUR" + } + ] + }, + "attributions": [ + { + "id": "1", + "entity": { + "name": "JRC", + "url": "https://publications.jrc.ec.europa.eu/repository/handle/JRC105688" + }, + "role": "distributor" + }, + { + "id": "2", + "entity": { + "name": "Jan Huizinga" + }, + "role": "author" + } + ], + "referenced_by": [ + { + "id": "1", + "name": "JRC Technical report - Global flood depth-damage functions", + "author_names": [ + "Jan Huizinga", + "Hans de Moel", + "Wojciech Szewczyk" + ], + "date_published": "2017-04-12", + "url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/global_flood_depth-damage_functions__10042017.pdf", + "doi": "10.2760/16510" + } + ], + "resources": [ + { + "id": "1", + "title": "Global flood depth-damage functions database", + "description": "This spreadsheet contains two components required for flood damage assessment: fractional depth-damage functions and maximum damage values. The damage functions provide the share of asset that is damaged at a given flood depth, while the maximum damage values provide the associated maximum damage value for the given asset and, when combined together, they yield the monetary value of the damage.", + "format": "csv", + "download_url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/copy_of_global_flood_depth-damage_functions__30102017.xlsx" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_vulnerability-SFRARR_EQ.json b/_datasets/json/rdls_vulnerability-SFRARR_EQ.json new file mode 100644 index 000000000..e047ba93e --- /dev/null +++ b/_datasets/json/rdls_vulnerability-SFRARR_EQ.json @@ -0,0 +1,102 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_FL_vuln", + "title": "Central Asia flood vulnerability curves", + "description": "Vulnerability curves for buildings, crops, humans and infrastructure", + "risk_data_type": [ + "vulnerability" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Gabriele Coccia" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_FL_vuln_report_EN", + "name": "Central Asia Flood vulnerability technical report - English version", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064115/DR0090975/Task5b_FL_Vulnerability_Report_r6_EN.pdf?versionId=2023-07-21T17:33:11.5312644Z" + }, + { + "id": "CA_SFRARR_FL_vuln_report_RU", + "name": "Central Asia Flood vulnerability technical report - Russian version", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064115/DR0090976/Task5b_FL_Vulnerability_Report_r6_RU.pdf?versionId=2023-07-21T17:33:13.2273042Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_FL_vulnCurves_buildings", + "title": "Central Asia flood vulnerability curves - buildings", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of building. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064239" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_crops", + "title": "Central Asia flood vulnerability curves - crops", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of crop (wheat and cotton). Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064241" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_human", + "title": "Central Asia flood vulnerability curves - human", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and multiple age/sex. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064240" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_infrastructure", + "title": "Central Asia flood vulnerability curves - infrastructure", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of infrastructure. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064242" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/json/rdls_vulnerability-SFRARR_FL.json b/_datasets/json/rdls_vulnerability-SFRARR_FL.json new file mode 100644 index 000000000..e047ba93e --- /dev/null +++ b/_datasets/json/rdls_vulnerability-SFRARR_FL.json @@ -0,0 +1,102 @@ +{ + "datasets": [ + { + "id": "CA_SFRARR_FL_vuln", + "title": "Central Asia flood vulnerability curves", + "description": "Vulnerability curves for buildings, crops, humans and infrastructure", + "risk_data_type": [ + "vulnerability" + ], + "publisher": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)", + "url": "https://www.redrisk.com" + }, + "version": "2022", + "purpose": "Regional risk modelling. These data have been derived on a regional scale for the purpose of consistent regional multi-country hazard and risk assessment. Application of this information on smaller scales should be done with care. Importantly on a local scale, it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies., it is often the case that more detailed history and hazard information is required to perform such hazard and risk modelling, particularly were applied to dimension mitigation structures or strategies", + "project": "World Bank SFRARR - Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia program. (https://www.gfdrr.org/en/program/SFRARR-Central-Asia)", + "spatial": { + "countries": [ + "KAZ", + "KGZ", + "TKM", + "TJK", + "UZB" + ], + "bbox": [ + 46, + 88, + 34, + 57 + ], + "scale": "regional" + }, + "license": "CC-BY-SA-4.0", + "contact_point": { + "name": "Paola Ceresa", + "email": "paola.ceresa@redrisk.com" + }, + "creator": { + "name": "Gabriele Coccia" + }, + "attributions": [ + { + "id": "CA_SFRARR_RED", + "entity": { + "name": "RED - Risk, Engineering Development - Pavia (Italy)" + }, + "role": "principal_investigator" + } + ], + "referenced_by": [ + { + "id": "CA_SFRARR_FL_vuln_report_EN", + "name": "Central Asia Flood vulnerability technical report - English version", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064115/DR0090975/Task5b_FL_Vulnerability_Report_r6_EN.pdf?versionId=2023-07-21T17:33:11.5312644Z" + }, + { + "id": "CA_SFRARR_FL_vuln_report_RU", + "name": "Central Asia Flood vulnerability technical report - Russian version", + "date_published": "2022-12-08", + "url": "https://datacatalogfiles.worldbank.org/ddh-published/0064115/DR0090976/Task5b_FL_Vulnerability_Report_r6_RU.pdf?versionId=2023-07-21T17:33:13.2273042Z" + } + ], + "resources": [ + { + "id": "CA_SFRARR_FL_vulnCurves_buildings", + "title": "Central Asia flood vulnerability curves - buildings", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of building. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064239" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_crops", + "title": "Central Asia flood vulnerability curves - crops", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of crop (wheat and cotton). Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064241" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_human", + "title": "Central Asia flood vulnerability curves - human", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and multiple age/sex. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064240" + }, + { + "id": "CA_SFRARR_FL_vulnCurves_infrastructure", + "title": "Central Asia flood vulnerability curves - infrastructure", + "description": "Tabulated vulnerability curves for each country in the Central Asia region, and each type of infrastructure. Uses consistent Intensity Metrics and exposure Taxonomies as accompanying hazard and exposure datasets.", + "format": "csv", + "access_url": "https://datacatalog.worldbank.org/search/dataset/0064242" + } + ], + "links": [ + { + "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", + "rel": "describedby" + } + ] + } + ] +} \ No newline at end of file diff --git a/_datasets/rooftop-classification-map-of-dominica.md b/_datasets/rooftop-classification-map-of-dominica-and-saint-lucia.md similarity index 80% rename from _datasets/rooftop-classification-map-of-dominica.md rename to _datasets/rooftop-classification-map-of-dominica-and-saint-lucia.md index 957f278ae..1e4f587eb 100644 --- a/_datasets/rooftop-classification-map-of-dominica.md +++ b/_datasets/rooftop-classification-map-of-dominica-and-saint-lucia.md @@ -7,9 +7,9 @@ creator: email: tisabelle@worldbank.org name: Isabelle Tingzon url: https://issa-tingzon.github.io/ -dataset_id: ortho_DOM -description: Building footprint polygons of Dominica with corresponding roof type - and roof material attributes predicted from RGB orthophotos taken in 2018-2019, +dataset_id: ortho_OECS +description: Building footprint polygons in Dominica and Saint Lucia with corresponding + roof type and roof material attributes predicted from RGB orthophotos taken in 2018-2019, in the aftermath of Hurricane Maria. details: 'The vector dataset depicts building footprint polygons of Dominica with corresponding roof type and roof material attributes. The categories for roof type @@ -20,12 +20,12 @@ details: 'The vector dataset depicts building footprint polygons of Dominica wit classification maps were predicted from nationwide very high-resolution RGB orthophotos with a spatial resolution of 20 cm/px taken in 2018-2019, in the aftermath of Hurricane Maria in 2017. The dataset also contains the predicted probabilities per category, - suffixed by "_PROB". ' + suffixed by \"_PROB\". ' exposure: category: buildings dimension: structure quantity_kind: area - taxonomy: Custom roof type and roof material taxonomy + taxonomy: Custom hazard: null license: CC-BY-4.0 loss: null @@ -44,6 +44,14 @@ resources: id: '0' spatial_resolution: null title: Dominica Rooftop Classification Map +- coordinate_system: EPSG:32620 + description: Building footprint polygons of Saint Lucia with corresponding roof + type and roof material attributes predicted from RGB orthophotos taken in 2022. + download_url: https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link + format: gpkg + id: '1' + spatial_resolution: null + title: Saint Lucia Rooftop Classification Map - coordinate_system: null description: Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes @@ -69,7 +77,9 @@ schema: rdl-02 spatial: countries: - DOM -title: Rooftop classification map of Dominica -version: version_01 + - LCA + scale: national +title: Rooftop classification map of Dominica and Saint Lucia +version: '1.0' vulnerability: null --- diff --git a/_datasets/rooftop-classification-map-of-saint-lucia.md b/_datasets/rooftop-classification-map-of-saint-lucia.md deleted file mode 100644 index f2f7c86ae..000000000 --- a/_datasets/rooftop-classification-map-of-saint-lucia.md +++ /dev/null @@ -1,72 +0,0 @@ ---- -contact_point: - email: tisabelle@worldbank.org - name: Isabelle Tingzon - url: https://issa-tingzon.github.io/ -creator: - email: tisabelle@worldbank.org - name: Isabelle Tingzon - url: https://issa-tingzon.github.io/ -dataset_id: ortho_LCA -description: 'Building footprint polygons of Saint Lucia with corresponding roof type - and roof material attributes predicted from RGB orthophotos taken in 2022. ' -details: 'The vector dataset depicts building footprint polygons of Saint Lucia with - corresponding roof type and roof material attributes. The categories for roof type - are FLAT, GABLE, HIP, and NO ROOF, and the categories for roof material are HEALTHY - METAL, IRREGULAR METAL, CONCRETE/CEMENT, BLUE TARPAULIN, and INCOMPLETE. The roof - classification map was derived using a convolutional neural network (CNN) model - trained on ~15,000 labels across Dominica and Saint Lucia. The roof type and roof - classification maps were predicted from nationwide very high-resolution RGB orthophotos - with a spatial resolution of 10 cm/px taken in 2022. The dataset also contains the - predicted probabilities per category, suffixed by "_PROB". ' -exposure: - category: buildings - dimension: structure - quantity_kind: area - taxonomy: Custom roof type and roof material taxonomy -hazard: null -license: CC-BY-4.0 -loss: null -project: Digital Earth for Resilient Housing and Infrastructure in the Caribbean -publisher: - email: tisabelle@worldbank.org - name: GFDRR -purpose: null -resources: -- coordinate_system: EPSG:32620 - description: 'Building footprint polygons of Saint Lucia with corresponding roof - type and roof material attributes predicted from RGB orthophotos taken in 2022. ' - download_url: https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link - format: gpkg - id: '1' - spatial_resolution: null - title: Saint Lucia Rooftop Classification Map -- coordinate_system: null - description: Accurate and up-to-date information on building characteristics is - essential for vulnerability assessment; however, the high costs and long timeframes - associated with conducting traditional field surveys can be an obstacle to obtaining - critical exposure datasets needed for disaster risk management. In this work, - we leverage deep learning techniques for the automated classification of roof - characteristics from very high-resolution orthophotos and airborne LiDAR data - obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the - fusion of multimodal earth observation data performs better than using any single - data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and - 0.92 for roof type and roof material classification, respectively. This work is - intended to help governments produce more timely building information to improve - resilience and disaster response in the Caribbean. - download_url: null - format: pdf - id: '3' - spatial_resolution: null - title: Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification - in the Caribbean -risk_data_type: -- exposure -schema: rdl-02 -spatial: - countries: - - LCA -title: Rooftop classification map of Saint Lucia -version: version_01 -vulnerability: null ---- diff --git a/_datasets/south-sudan-asset-exposure.md b/_datasets/south-sudan-asset-exposure.md new file mode 100644 index 000000000..8c72c700f --- /dev/null +++ b/_datasets/south-sudan-asset-exposure.md @@ -0,0 +1,70 @@ +--- +contact_point: + email: lloeschner@worldbank.org + name: Lukas Loeschner +creator: + email: mamadio@worldbank.org + name: Mattia Amadio +dataset_id: SSD_exp-asset +description: Collection of exposure data from Open Street Map, OCHA and World Bank, + representing location and type of settlments, land use, buildings, health facilities + and roads. +details: "To better understand natural hazard and disaster risk, the World Bank and\ + \ Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development\ + \ of new \uFB02uvial \uFB02ood, \uFB02ash \uFB02ood, drought, landslide, avalanche\ + \ and seismic risk information in Afghanistan, as well as a frst-order analysis\ + \ of the costs and benefts of resilient reconstruction and risk reduction strategies.\ + \ This publication describes the applied methods and main results of the project." +exposure: + category: buildings + dimension: structure + quantity_kind: area + taxonomy: null +hazard: null +license: CC-BY-4.0 +loss: null +project: South Sudan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: "The results of the analysis contribute to the production of knowledge for\ + \ disaster risk management (DRM) to support the World Bank\u2019s operational teams\ + \ in their in-country engagements. Specifcally, the key fndings of this study allow\ + \ to rank South Sudan states in terms of natural disasters risk, and to identify\ + \ the most critical components for each area. The output of this assessment includes\ + \ a geodatabase which contains both the key primary data and all the resulting maps\ + \ produced by the analysis, allowing risk analysts and managers to explore them\ + \ in detail using GIS software." +resources: +- coordinate_system: EPSG:4326 + description: Location and ranking of settlements from OCHA (2019) + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053214/exp-ssd-settlements_ocha.zip + format: gpkg + id: '0' + spatial_resolution: 90 + title: Settlements +- coordinate_system: EPSG:4326 + description: Buildings, land use, and roads polygons from OpenStreetMap + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053213/exp-ssd-osm.zip + format: gpkg + id: '1' + spatial_resolution: 90 + title: South Sudan buildings, land use and roads +- coordinate_system: EPSG:4326 + description: Location and ranking of health facilities from World Bank (2009) + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0042416/DR0053215/exp-ssd-health_wb.zip + format: gpkg + id: '2' + spatial_resolution: 90 + title: Health facilities +risk_data_type: +- exposure +schema: rdl-02 +spatial: + countries: + - SSD + scale: national +title: South Sudan Asset exposure +version: '2019' +vulnerability: null +--- diff --git a/_datasets/south-sudan-drought-hazard.md b/_datasets/south-sudan-drought-hazard.md new file mode 100644 index 000000000..6eb97bdad --- /dev/null +++ b/_datasets/south-sudan-drought-hazard.md @@ -0,0 +1,53 @@ +--- +contact_point: + email: lloeschner@worldbank.org + name: Lukas Loeschner +creator: + email: mamadio@worldbank.org + name: Mattia Amadio +dataset_id: SSD_hzd-drought +description: Drought hazard for South Sudan measured as Agricultural Stress Index + (ASI) over a period of 30 years. +details: null +exposure: null +hazard: + calculation_method: inferred + disaster_identifiers: '' + hazard_analysis_type: deterministic + hazard_type: drought + intensity: ASI:per + occurrence_range: '' + processes: agricultural_drought +license: CC-BY-4.0 +loss: null +project: South Sudan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: "The results of the analysis contribute to the production of knowledge for\ + \ disaster risk management (DRM) to support the World Bank\u2019s operational teams\ + \ in their in-country engagements. Specifcally, the key fndings of this study allow\ + \ to rank South Sudan states in terms of natural disasters risk, and to identify\ + \ the most critical components for each area. The output of this assessment includes\ + \ a geodatabase which contains both the key primary data and all the resulting maps\ + \ produced by the analysis, allowing risk analysts and managers to explore them\ + \ in detail using GIS software." +resources: +- coordinate_system: EPSG:4326 + description: Agricultural drought hazard derived from FAO-GIEWS ASI (30 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0042412/DR0053203/hzd-ssd-dr-asi.zip + format: gpkg + id: '0' + spatial_resolution: null + title: Agricultural Stress Index (ASI) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - SSD + scale: national +title: South Sudan Drought hazard +version: '2019' +vulnerability: null +--- diff --git a/_datasets/south-sudan-earthquake-hazard.md b/_datasets/south-sudan-earthquake-hazard.md new file mode 100644 index 000000000..54b8428ce --- /dev/null +++ b/_datasets/south-sudan-earthquake-hazard.md @@ -0,0 +1,54 @@ +--- +contact_point: + email: lloeschner@worldbank.org + name: Lukas Loeschner +creator: + email: mamadio@worldbank.org + name: Mattia Amadio +dataset_id: SSD_hzd-earthquake +description: Earthquake hazard intensity (g) for Sub Saharan Africa from Global Earthquake + Model (GEM) 2016 +details: null +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: earthquake + intensity: PGA:g + occurrence_range: 100, 475, 975, 2475 years + processes: ground_motion +license: CC-BY-4.0 +loss: null +project: South Sudan Multi-hazard risk assessment +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: "The results of the analysis contribute to the production of knowledge for\ + \ disaster risk management (DRM) to support the World Bank\u2019s operational teams\ + \ in their in-country engagements. Specifcally, the key fndings of this study allow\ + \ to rank South Sudan states in terms of natural disasters risk, and to identify\ + \ the most critical components for each area. The output of this assessment includes\ + \ a geodatabase which contains both the key primary data and all the resulting maps\ + \ produced by the analysis, allowing risk analysts and managers to explore them\ + \ in detail using GIS software." +resources: +- coordinate_system: EPSG:4326 + description: Earthquake hazard intensity (g) for Sub Saharan Africa from Global + Earthquake Model (GEM) 2016 for RP 100, 475, 975, 2475 years. + download_url: null + format: geotiff + id: '0' + spatial_resolution: null + title: Earthquake hazard scenarios +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - SSD + scale: national +title: South Sudan Earthquake hazard +version: '2019' +vulnerability: null +--- diff --git a/_datasets/south-west-indian-ocean-earthquake-hazard.md b/_datasets/south-west-indian-ocean-earthquake-hazard.md new file mode 100644 index 000000000..6ad8d7c3a --- /dev/null +++ b/_datasets/south-west-indian-ocean-earthquake-hazard.md @@ -0,0 +1,100 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: SWIO_hzd-earthquake +description: Earthquake hazard map representing Peak ground acceleration (PGA-g) for + six return period scenarios. +details: This data set was produced with financial support from the European Union + in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed + by the Global Facility for Disaster Reduction and Recovery (GFDRR). +exposure: null +hazard: + calculation_method: simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: earthquake + intensity: PGA:g + occurrence_range: 10, 25, 50, 100, 250, 500 and 1000 years + processes: ground_motion +license: CC-BY-4.0 +loss: null +project: SWIO-RAFI +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: 'The goal of the South West Indian Ocean Risk Assessment and Financing Initiative + (SWIO RAFI) is to improve the resiliency and capacity of the island states through + the creation of disaster risk financing strategies. A key component of this effort + involves the quantification of site specific risk from the perils of flood, earthquakes, + and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. + + Regional hazard intensity calculations were applied to 10,000 years of Stochastic + catalogs derived from the historical records to produce hazard intensity profiles + at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are + at their original resolution (0.00083) except for Madagascar (0.0032) which was + resampled to reduce file sizes.' +resources: +- coordinate_system: EPSG:4326 + description: Earthquake hazard map representing Peak ground acceleration (PGA) measured + in units of g for seven return period scenarios (10, 25, 50, 100, 250, 500 and + 1000 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038594/DR0054346/hzd-com-eq.zip + format: geotiff + id: COM + spatial_resolution: 1000 + title: Comoros - Earthquake ground shaking hazard scenarios +- coordinate_system: EPSG:4326 + description: Earthquake hazard map representing Peak ground acceleration (PGA) measured + in units of g for seven return period scenarios (10, 25, 50, 100, 250, 500 and + 1000 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038594/DR0054346/hzd-mdg-eq.zip + format: geotiff + id: MDG + spatial_resolution: 1000 + title: Madagascar - Earthquake ground shaking hazard scenarios +- coordinate_system: EPSG:4326 + description: Earthquake hazard map representing Peak ground acceleration (PGA) measured + in units of g for seven return period scenarios (10, 25, 50, 100, 250, 500 and + 1000 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038594/DR0054346/hzd-mus-eq.zip + format: geotiff + id: MUS + spatial_resolution: 1000 + title: Mauritius - Earthquake ground shaking hazard scenarios +- coordinate_system: EPSG:4326 + description: Earthquake hazard map representing Peak ground acceleration (PGA) measured + in units of g for seven return period scenarios (10, 25, 50, 100, 250, 500 and + 1000 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038594/DR0054346/hzd-syc-eq.zip + format: geotiff + id: SYC + spatial_resolution: 1000 + title: Seychelles - Earthquake ground shaking hazard scenarios +- coordinate_system: EPSG:4326 + description: Earthquake hazard map representing Peak ground acceleration (PGA) measured + in units of g for seven return period scenarios (10, 25, 50, 100, 250, 500 and + 1000 years). + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038594/DR0054346/hzd-zan-eq.zip + format: geotiff + id: ZAN + spatial_resolution: 1000 + title: Zanzibar - Earthquake ground shaking hazard scenarios +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - COM + - MDG + - MUS + - SYC + - TZA + scale: regional +title: South West Indian Ocean Earthquake hazard +version: '2017' +vulnerability: null +--- diff --git a/_datasets/south-west-indian-ocean-flood-hazard.md b/_datasets/south-west-indian-ocean-flood-hazard.md new file mode 100644 index 000000000..6be24d2b8 --- /dev/null +++ b/_datasets/south-west-indian-ocean-flood-hazard.md @@ -0,0 +1,100 @@ +--- +contact_point: + email: pchrzanowski@worldbank.org + name: Pierre Chrzanowski +creator: + name: GFDRR + url: https://www.gfdrr.org +dataset_id: SWIO_hzd-strong_wind +description: Strong Wind hazard caused by tropical cyclones measured as the maximum + one-minute sustained wind speed (kph) at 10 meters above the ground surface. +details: This data set was produced with financial support from the European Union + in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed + by the Global Facility for Disaster Reduction and Recovery (GFDRR). +exposure: null +hazard: + calculation_method: inferred, simulated + disaster_identifiers: '' + hazard_analysis_type: probabilistic + hazard_type: strong_wind + intensity: v_etc(10m):km/h + occurrence_range: 10, 25, 50, 100, 250, 500 and 1000 years + processes: tropical_cyclone +license: CC-BY-4.0 +loss: null +project: SWIO-RAFI +publisher: + name: GFDRR + url: https://www.gfdrr.org +purpose: 'The goal of the South West Indian Ocean Risk Assessment and Financing Initiative + (SWIO RAFI) is to improve the resiliency and capacity of the island states through + the creation of disaster risk financing strategies. A key component of this effort + involves the quantification of site specific risk from the perils of flood, earthquakes, + and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. + + Regional hazard intensity calculations were applied to 10,000 years of Stochastic + catalogs derived from the historical records to produce hazard intensity profiles + at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are + at their original resolution (0.00083) except for Madagascar (0.0032) which was + resampled to reduce file sizes.' +resources: +- coordinate_system: EPSG:4326 + description: Strong Wind hazard caused by tropical cyclones over Comoros measured + as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground + surface. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038599/DR0054350/hzd-com-wi.zip + format: geotiff + id: COM + spatial_resolution: 90 + title: Comoros - Flood hazard scenarios (tropical cyclones) +- coordinate_system: EPSG:4326 + description: Strong Wind hazard caused by tropical cyclones over Madagascar measured + as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground + surface. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038598/DR0054337/hzd-mdg-wi.zip + format: geotiff + id: MDG + spatial_resolution: 900 + title: Madagascar - Flood hazard scenarios (tropical cyclones) +- coordinate_system: EPSG:4326 + description: Strong Wind hazard caused by tropical cyclones over Mauritius measured + as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground + surface. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038597/DR0054310/hzd-mus-wi.zip + format: geotiff + id: MUS + spatial_resolution: 90 + title: Mauritius - Flood hazard scenarios (tropical cyclones) +- coordinate_system: EPSG:4326 + description: Strong Wind hazard caused by tropical cyclones over Seychelles measured + as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground + surface. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038596/DR0054318/hzd-syc-wi.zip + format: geotiff + id: SYC + spatial_resolution: 90 + title: Seychelles - Flood hazard scenarios (tropical cyclones) +- coordinate_system: EPSG:4326 + description: Strong Wind hazard caused by tropical cyclones over Zanzibar measured + as the maximum one-minute sustained wind speed (kph) at 10 meters above the ground + surface. + download_url: https://datacatalogfiles.worldbank.org/ddh-published/0038595/DR0054365/hzd-zan-wi.zip + format: geotiff + id: ZAN + spatial_resolution: 90 + title: Zanzibar - Flood hazard scenarios (tropical cyclones) +risk_data_type: +- hazard +schema: rdl-02 +spatial: + countries: + - COM + - MDG + - MUS + - SYC + - TZA + scale: regional +title: South West Indian Ocean Flood hazard +version: '2017' +vulnerability: null +--- diff --git a/import/README.md b/import/README.md index 609c86d70..e7d1e476c 100644 --- a/import/README.md +++ b/import/README.md @@ -4,12 +4,21 @@ From `/import`, run: +Linux: + ``` python3 -m venv .venv source .venv/bin/activate python3 -m pip install -r requirements.txt ``` +Windows: +``` +python -m venv .venv +.venv\Scripts\activate +python -m pip install -r requirements.txt +``` + If PyYAML gives you trouble, try [these steps](https://github.com/yaml/pyyaml/issues/736#issuecomment-1653209769) ### Generate datasets diff --git a/import/rdl2jkan.py b/import/rdl2jkan.py index 767a97d7a..467e7c3bf 100644 --- a/import/rdl2jkan.py +++ b/import/rdl2jkan.py @@ -327,9 +327,9 @@ def write_frontmatter(metadata, output_path): description="Convert RDL JSON datasets into JKAN frontmatter" ) parser.add_argument( - "--input_file", - help="Path to a dump of RDL datasets, in JSON format", - default="rdl_datasets.json", + "--input_folder", + help="Path to the folder containing RDL datasets in JSON format", + default=".", action="store", ) args = parser.parse_args() @@ -339,8 +339,11 @@ def write_frontmatter(metadata, output_path): if not Path(datasets_output_dir).is_dir(): os.makedirs(datasets_output_dir) - # Open input - with open(args.input_file) as input_file: + +# Iterate over all JSON files in the input folder +input_path = Path(args.input_folder) +for json_file in input_path.glob("../_datasets/json/*.json"): + with open(json_file, encoding='utf-8') as input_file: datasets_json = json.load(input_file) for dataset_json in datasets_json["datasets"]: # Generate output @@ -349,8 +352,12 @@ def write_frontmatter(metadata, output_path): dataset_frontmatter = make_dataset_frontmatter(dataset_json) write_frontmatter(dataset_frontmatter, datasets_output_dir) except Exception as e: - logging.error(f"While writing {dataset_json.get("title", "a dataset with a missing title")} (dataset_id: {dataset_json.get("dataset_id", "missing")})",exc_info=e) - + logging.error( + f"While writing {dataset_json.get('title', 'a dataset with a missing title')} " + f"(dataset_id: {dataset_json.get('dataset_id', 'missing')})", + exc_info=e + ) + print("\nAll done! Please enjoy your datasets :)\n", "Datasets have been generated in: `import/generated/_datasets`", diff --git a/import/rdl_datasets.json b/import/rdl_datasets.json deleted file mode 100644 index 5be31c190..000000000 --- a/import/rdl_datasets.json +++ /dev/null @@ -1,1458 +0,0 @@ -{ - "datasets": [ - { - "id": "ortho_DOM", - "title": "Rooftop classification map of Dominica", - "description": "Building footprint polygons of Dominica with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2018-2019, in the aftermath of Hurricane Maria.", - "risk_data_type": ["exposure"], - "publisher": { - "name": "GFDRR", - "email": "tisabelle@worldbank.org" - }, - "version": "version_01", - "project": "Digital Earth for Resilient Housing and Infrastructure in the Caribbean", - "details": "The vector dataset depicts building footprint polygons of Dominica with corresponding roof type and roof material attributes. The categories for roof type are FLAT, GABLE, HIP, and NO ROOF, and the categories for roof material are HEALTHY METAL, IRREGULAR METAL, CONCRETE/CEMENT, BLUE TARPAULIN, and INCOMPLETE. The roof classification map was derived using a convolutional neural network (CNN) model trained on ~15,000 labels across Dominica and Saint Lucia. The roof type and roof classification maps were predicted from nationwide very high-resolution RGB orthophotos with a spatial resolution of 20 cm/px taken in 2018-2019, in the aftermath of Hurricane Maria in 2017. The dataset also contains the predicted probabilities per category, suffixed by \"_PROB\". ", - "spatial": { - "countries": ["DOM"] - }, - "license": "CC-BY-4.0", - "contact_point": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "creator": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "exposure": { - "category": "buildings", - "taxonomy": "Custom roof type and roof material taxonomy", - "metrics": [ - { - "id": "Roof Type", - "dimension": "structure", - "quantity_kind": "area" - }, - { - "id": "Roof Material", - "dimension": "structure", - "quantity_kind": "area" - } - ] - }, - "attributions": [ - { - "id": "1", - "entity": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "role": "author" - }, - { - "id": "2", - "entity": { - "name": "Pierre Chrzanowski", - "email": "pchrzanowski@worldbank.org" - }, - "role": "world_bank_team_lead" - } - ], - "sources": [ - { - "id": "1", - "name": "Orthophoto mosaic (20 cm/px) of Dominica from 2018-2019", - "type": "dataset", - "component": "exposure" - } - ], - "referenced_by": [ - { - "id": "0", - "name": "Can AI help build climate resilience in the Caribbean? Let’s look at housing.", - "author_names": [ - "Isabelle Tingzon", - " Nuala Margaret Cowan", - " Pierre Chrzanowski" - ], - "date_published": "2023-10-02", - "url": "https://blogs.worldbank.org/sustainablecities/can-ai-help-build-climate-resilience-caribbean-lets-look-housing" - }, - { - "id": "1", - "name": "Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean", - "author_names": [ - "Isabelle Tingzon", - " Nuala Margaret Cowan", - " Pierre Chrzanowski" - ], - "date_published": "2023-08-20", - "url": "https://arxiv.org/abs/2307.16177", - "doi": "https://doi.org/10.48550/arXiv.2307.16177" - } - ], - "resources": [ - { - "id": "0", - "title": "Dominica Rooftop Classification Map", - "description": "Building footprint polygons of Dominica with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2018-2019, in the aftermath of Hurricane Maria.", - "format": "gpkg", - "coordinate_system": "EPSG:32620", - "access_url": "https://drive.google.com/file/d/15_JAPZlxHaRw9ldMqYcwEC2xAlDmVD23/view?usp=drive_link", - "download_url": "https://drive.google.com/file/d/15_JAPZlxHaRw9ldMqYcwEC2xAlDmVD23/view?usp=drive_link", - "temporal": { - "start": "2018", - "end": "2019" - } - }, - { - "id": "2", - "title": "Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean", - "description": "Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.", - "media_type": "application/pdf", - "format": "pdf", - "access_url": "https://arxiv.org/abs/2307.16177" - } - ], - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - }, - { - "id": "ortho_LCA", - "title": "Rooftop classification map of Saint Lucia", - "description": "Building footprint polygons of Saint Lucia with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2022. ", - "risk_data_type": ["exposure"], - "publisher": { - "name": "GFDRR", - "email": "tisabelle@worldbank.org" - }, - "version": "version_01", - "project": "Digital Earth for Resilient Housing and Infrastructure in the Caribbean", - "details": "The vector dataset depicts building footprint polygons of Saint Lucia with corresponding roof type and roof material attributes. The categories for roof type are FLAT, GABLE, HIP, and NO ROOF, and the categories for roof material are HEALTHY METAL, IRREGULAR METAL, CONCRETE/CEMENT, BLUE TARPAULIN, and INCOMPLETE. The roof classification map was derived using a convolutional neural network (CNN) model trained on ~15,000 labels across Dominica and Saint Lucia. The roof type and roof classification maps were predicted from nationwide very high-resolution RGB orthophotos with a spatial resolution of 10 cm/px taken in 2022. The dataset also contains the predicted probabilities per category, suffixed by \"_PROB\". ", - "spatial": { - "countries": ["LCA"] - }, - "license": "CC-BY-4.0", - "contact_point": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "creator": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "exposure": { - "category": "buildings", - "taxonomy": "Custom roof type and roof material taxonomy", - "metrics": [ - { - "id": "Roof Type", - "dimension": "structure", - "quantity_kind": "area" - }, - { - "id": "Roof Material", - "dimension": "structure", - "quantity_kind": "area" - } - ] - }, - "attributions": [ - { - "id": "1", - "entity": { - "name": "Isabelle Tingzon", - "email": "tisabelle@worldbank.org", - "url": "https://issa-tingzon.github.io/" - }, - "role": "author" - }, - { - "id": "2", - "entity": { - "name": "Pierre Chrzanowski", - "email": "pchrzanowski@worldbank.org" - }, - "role": "world_bank_team_lead" - } - ], - "sources": [ - { - "id": "2", - "name": "Orthophoto mosaic (10 cm/px) of Saint Lucia for 2022", - "type": "dataset", - "component": "exposure" - } - ], - "resources": [ - { - "id": "1", - "title": "Saint Lucia Rooftop Classification Map", - "description": "Building footprint polygons of Saint Lucia with corresponding roof type and roof material attributes predicted from RGB orthophotos taken in 2022. ", - "format": "gpkg", - "coordinate_system": "EPSG:32620", - "access_url": "https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link", - "download_url": "https://drive.google.com/file/d/1VjaGp_Hhh7urqJsWU3QxYHirqQzReT8y/view?usp=drive_link", - "temporal": { - "start": "2022", - "end": "2022" - } - }, - { - "id": "3", - "title": "Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean", - "description": "Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.", - "media_type": "application/pdf", - "format": "pdf", - "access_url": "https://arxiv.org/abs/2307.16177" - } - ], - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - }, - { - "id": "FTH_v3", - "title": "Global flood hazard maps", - "description": "Probabilistic modelling of fluvial and pluvial flood hazard", - "risk_data_type": ["hazard"], - "publisher": { - "name": "Fathom", - "url": "https://www.fathom.global/" - }, - "version": "3", - "purpose": "Fathom Global hazard maps can support risk screening and analysis at the sub-national level. Caution is advised when using these data as the only source of flood hazard information for site-specific analysis. The model is driven by global assumptions; it can provide a useful overview of the likely hazard in a particular area, however local data should be sought out before detailed planning or operational decisions are made. The data are not suitable for engineering-level analysis (such as construction of bridges or flood defences).", - "details": "The FATHOM flood-hazard model is a global gridded dataset of flood hazard produced at the global scale. It provides flood extent and water depth to ground (in centimeters) for three types of flood phenomena:\n- Fluvial (or river) flooding occurs when a river exceeds its capacity and inundates surrounding areas.\n- Pluvial (or surface water) flooding occurs when extreme rainfall exceeds surface drainage capacity.\n- Coastal flooding occurs when a combination of storm-surge, tides and waves lead to water levels that submerge the coastal land.\nThere are two options for each flood type: Defended and Undefended (fluvial and coastal only). Defended scenarios account for protection standards in proportion to country wealth to reduce the chance of hazard occurrence. It does not take account location-specific physical protection measures.\nThe model covers 4 time periods:\n- 2020 (present baseline)\n- 2030 (near future)\n- 2050 (mid-century)\n- 2080 (far future)\nFuture periods include 4 model realizations, each one describing a different climate scenario:\n- SSP1 – 2.6 (limited emissions)\n- SSP2 – 4.5\n- SSP3 – 7.0\n- SSP5 – 8.5 (high emissions)\nEach scenario set is made of 10 events each representing a different intensity and probability of occurrence, expressed as “return period”. These are framed as 1 in 5, 10, 20, 50, 100, 200, 500 and 1,000 years. ", - "spatial": { - "bbox": [-180, -90, 180, 90], - "scale": "global" - }, - "license": "Commercial", - "contact_point": { - "name": "Mattia Amadio", - "email": "mamadio@worldbank.org" - }, - "creator": { - "name": "Fathom", - "url": "https://www.fathom.global/" - }, - "attributions": [ - { - "id": "1", - "entity": { - "name": "GFDRR", - "url": "https://www.gfdrr.org" - }, - "role": "distributor" - }, - { - "id": "2", - "entity": { - "name": "Fathom", - "url": "https://www.fathom.global" - }, - "role": "owner" - } - ], - "referenced_by": [ - { - "id": "1", - "name": "A high-resolution global flood hazard model", - "author_names": [ - "Christopher C. Sampson", - "Andrew M. Smith", - "Paul D. Bates", - "Jeffrey C. Neal", - "Lorenzo Alfieri", - "Jim E. Freer" - ], - "date_published": "2015-08-18", - "url": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015WR016954", - "doi": "10.1002/2015WR016954" - } - ], - "resources": [ - { - "id": "FTH_form_WB", - "title": "Fathom v3 global dataset", - "description": "Global country data - access limited to WB operations via request form", - "format": "geotiff", - "spatial_resolution": 30, - "coordinate_system": "EPSG:4326", - "access_url": "https://forms.office.com/r/sG0qWTnC0L" - } - ], - "hazard": { - "event_sets": [ - { - "id": "FFL_U", - "analysis_type": "probabilistic", - "frequency_distribution": "generalized_extreme_value", - "seasonality": "uniform", - "calculation_method": "simulated", - "occurrence_range": "1/10 to 1/1000 years", - "spatial": { - "bbox": [-180, -90, 180, 90], - "scale": "global" - }, - "hazards": [ - { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - } - ], - "events": [ - { - "id": "5", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 5 - } - } - }, - { - "id": "10", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 10 - } - } - }, - { - "id": "20", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 20 - } - } - }, - { - "id": "50", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 50 - } - } - }, - { - "id": "100", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 100 - } - } - }, - { - "id": "200", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 200 - } - } - }, - { - "id": "500", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 500 - } - } - }, - { - "id": "1000", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 1000 - } - } - } - ] - }, - { - "id": "FFL_D", - "analysis_type": "probabilistic", - "frequency_distribution": "generalized_extreme_value", - "seasonality": "uniform", - "calculation_method": "simulated", - "occurrence_range": "1/10 to 1/1000 years", - "spatial": { - "bbox": [-180, -90, 180, 90], - "scale": "global" - }, - "hazards": [ - { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - } - ], - "events": [ - { - "id": "5", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 5 - } - } - }, - { - "id": "10", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 10 - } - } - }, - { - "id": "20", - "calculation_method": "simulated", - "hazard": { - "id": "FFL", - "type": "flood", - "processes": ["fluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - 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}, - "hazards": [ - { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - } - ], - "events": [ - { - "id": "5", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 5 - } - } - }, - { - "id": "10", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 10 - } - } - }, - { - "id": "20", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 20 - } - } - }, - { - "id": "50", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - 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}, - "occurrence": { - "probabilistic": { - "return_period": 1000 - } - } - } - ] - }, - { - "id": "CFL_D", - "analysis_type": "probabilistic", - "frequency_distribution": "generalized_extreme_value", - "seasonality": "uniform", - "calculation_method": "simulated", - "occurrence_range": "1/10 to 1/1000 years", - "spatial": { - "bbox": [-180, -90, 180, 90], - "scale": "global" - }, - "hazards": [ - { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - } - ], - "events": [ - { - "id": "5", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 5 - } - } - }, - { - "id": "10", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 10 - } - } - }, - { - "id": "20", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 20 - } - } - }, - { - "id": "50", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 50 - } - } - }, - { - "id": "100", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 100 - } - } - }, - { - "id": "200", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 200 - } - } - }, - { - "id": "500", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 500 - } - } - }, - { - "id": "1000", - "calculation_method": "simulated", - "hazard": { - "id": "CFL", - "type": "flood", - "processes": ["coastal_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 1000 - } - } - } - ] - }, - { - "id": "PFL_U", - "events": [ - { - "id": "5", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 5 - } - } - }, - { - "id": "10", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 10 - } - } - }, - { - "id": "20", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 20 - } - } - }, - { - "id": "50", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 50 - } - } - }, - { - "id": "100", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 100 - } - } - }, - { - "id": "200", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 200 - } - } - }, - { - "id": "500", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 500 - } - } - }, - { - "id": "1000", - "calculation_method": "simulated", - "hazard": { - "id": "PFL", - "type": "flood", - "processes": ["pluvial_flood"], - "intensity_measure": "wd:cm" - }, - "occurrence": { - "probabilistic": { - "return_period": 1000 - } - } - } - ] - } - ] - }, - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - }, - { - "id": "https://publications.jrc.ec.europa.eu/repository/handle/JRC105688", - "title": "Global flood depth-damage functions", - "description": "This dataset contains damage curves depicting fractional damage function of water depth as well as maximum damage values for a variety of assets and land use classes.", - "risk_data_type": ["vulnerability"], - "publisher": { - "name": "EU Joint Research Centre (JRC)", - "url": "https://joint-research-centre.ec.europa.eu/index_en" - }, - "version": "2.0", - "purpose": "Assessing potential damage of flood events is an important component in flood risk management. Determining direct flood damage is commonly done using depth-damage curves, which denote the flood damage that would occur at specific water depths per asset or per land-use class. Many countries have developed flood damage models using depth-damage curves based on analysis of past flood events and on expert judgement. However, the fact that such damage curves are not available for all regions hampers damage assessments in some areas. Moreover, due to different methodologies employed for various damage models in different countries, damage assessments cannot be directly compared with each other, obstructing also supra-national flood damage assessments.", - "details": "Based on an extensive literature survey concave damage curves have been developed for each continent, while differentiation in flood damage between countries is established by determining maximum damage values at the country scale. These maximum damage values are based on construction cost surveys from multinational construction companies, which provide a coherent set of detailed building cost data across dozens of countries. A consistent set of maximum flood damage values for all countries was computed using statistical regressions with socio-economic World Development Indicators. Further, based on insights from the literature survey, guidance is also given on how the damage curves and maximum damage values can be adjusted for specific local circumstances, such as urban vs. rural locations or use of specific building material. This dataset can be used for consistent supra-national scale flood damage assessments, and guide assessment in countries where no damage model is currently available.", - "spatial": { - "scale": "global" - }, - "license": "CC-BY-4.0", - "contact_point": { - "name": "Mattia Amadio", - "email": "mamadio@worldbank.org" - }, - "creator": { - "name": "Huizinga, J." - }, - "vulnerability": { - "hazard_primary": "flood", - "hazard_process_primary": "fluvial_flood", - "hazard_analysis_type": "empirical", - "intensity": "fl_wd:m", - "category": "buildings", - "impact": { - "type": "direct", - "metric": "mean_damage_ratio", - "unit": "percentage", - "base_data_type": "inferred" - }, - "spatial": { - "scale": "global" - }, - "functions": { - "fragility": { - "approach": "hybrid" - } - }, - "cost": [ - { - "id": "1", - "dimension": "structure", - "unit": "EUR" - }, - { - "id": "2", - "dimension": "content", - "unit": "EUR" - } - ] - }, - "attributions": [ - { - "id": "1", - "entity": { - "name": "JRC", - "url": "https://publications.jrc.ec.europa.eu/repository/handle/JRC105688" - }, - "role": "distributor" - }, - { - "id": "2", - "entity": { - "name": "Jan Huizinga" - }, - "role": "author" - } - ], - "referenced_by": [ - { - "id": "1", - "name": "JRC Technical report - Global flood depth-damage functions", - "author_names": ["Jan Huizinga", "Hans de Moel", "Wojciech Szewczyk"], - "date_published": "2017-04-12", - "url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/global_flood_depth-damage_functions__10042017.pdf", - "doi": "10.2760/16510" - } - ], - "resources": [ - { - "id": "1", - "title": "Global flood depth-damage functions database", - "description": "This spreadsheet contains two components required for flood damage assessment: fractional depth-damage functions and maximum damage values. The damage functions provide the share of asset that is damaged at a given flood depth, while the maximum damage values provide the associated maximum damage value for the given asset and, when combined together, they yield the monetary value of the damage.", - "format": "csv", - "download_url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/copy_of_global_flood_depth-damage_functions__30102017.xlsx" - } - ], - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - }, - { - "id": "AFG_lss-drought", - "title": "Afghanistan Drought risk", - "description": "Annual average losses in agricultural production (USD) and affected population, both for the baseline reference and future projections (2050)", - "risk_data_type": ["loss"], - "publisher": { - "name": "GFDRR", - "url": "https://www.gfdrr.org" - }, - "version": "2018", - "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", - "project": "Afghanistan Multi-hazard risk assessment", - "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", - "spatial": { - "countries": ["AFG"], - "scale": "national" - }, - "license": "CC-BY-4.0", - "contact_point": { - "name": "Pierre Chrzanowski", - "email": "pchrzanowski@worldbank.org" - }, - "creator": { - "name": "GFDRR", - "url": "https://www.gfdrr.org" - }, - "attributions": [ - { - "id": "0", - "entity": { - "name": "Federica Ranghieri", - "email": "franghieri@worldbank.org" - }, - "role": "world_bank_team_lead" - }, - { - "id": "1", - "entity": { - "name": "Ditte Fallesen", - "email": "dfallesen@worldbank.org" - }, - "role": "world_bank_team_lead" - }, - { - "id": "2", - "entity": { - "name": "Brandan Jongman", - "email": "bjongman@worldbank.org" - }, - "role": "world_bank_team_lead" - } - ], - "referenced_by": [ - { - "id": "0", - "name": "Afghanistan - Multi-hazard risk assessment", - "author_names": [ - "Federica Ranghieri", - "Ditte Fallesen", - "Brenden Jongman", - "Guillermo Siercke", - "Abdul Azim Doosti", - "Julian Palma", - "Simone Balog", - "Sayed Sharifullah Mashahid", - "Erika Vargas" - ], - "date_published": "2018-12-18", - "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" - } - ], - "resources": [ - { - "id": "0", - "title": "Afghanistan Drought risk: water per capita", - "description": "Water availability as m3 per capita at Administrative level (ADM1)", - "format": "gpkg", - "coordinate_system": "EPSG:32642", - "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065483/lss-afg-dr-dts.zip" - }, - { - "id": "1", - "title": "Afghanistan Drought risk: agriculture", - "description": "Agricultural losses (USD) aggregated for administrative boundaries (ADM1)", - "format": "gpkg", - "coordinate_system": "EPSG:32642", - "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065482/lss-afg-dr-dta.zip" - }, - { - "id": "2", - "title": "Afghanistan Drought risk: water per capita ", - "description": "Water availability per capita as tables, and reference threshold values.", - "format": "csv", - "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050636/DR0065484/lss-afg-dr-dts-tab.zip" - } - ], - "loss": { - "losses": [ - { - "id": "0", - "hazard_type": "drought", - "hazard_process": "socioeconomic_drought", - "description": "Water availability as m3 per capita at Administrative level (ADM1)", - "cost": { - "id": "0", - "dimension": "population" - }, - "impact": { - "type": "direct", - "metric": "at_risk_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-drought", - "exposure_id": "AFG_exp-asset" - }, - { - "id": "1", - "hazard_type": "drought", - "hazard_process": "agricultural_drought", - "description": "Agricultural losses (USD) aggregated for administrative boundaries (ADM1)", - "cost": { - "id": "1", - "dimension": "product", - "unit": "USD" - }, - "impact": { - "type": "direct", - "metric": "loss_annual_average_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-drought", - "exposure_id": "AFG_exp-asset" - } - ] - }, - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - }, - { - "id": "AFG_lss-flood", - "title": "Afghanistan Flood risk", - "description": "Average Annual Losses (AAL) for current population (AALpop), current asset (AALnowUSD), population SSP scenarios at 2050 (AALpopSP1-5), asset SSP scenarios at 2050 (AAL_usd_SP1-5).", - "risk_data_type": ["loss"], - "publisher": { - "name": "GFDRR", - "url": "https://www.gfdrr.org" - }, - "version": "2018", - "purpose": "These maps have been derived on a nation-wide scale for the purpose of identifying high risk- areas on the district and provincial scale, from which decisions can be made on allocating efforts for more detailed site specific hazard and risk analysis. Use of this information on smaller scales should be applied with care. Importantly for on a local scale, it is often the case that more detailed case history and hazard information is required to perform such hazard and risk modelling, particularly where applied to dimension mitigation structures or strategies.", - "project": "Afghanistan Multi-hazard risk assessment", - "details": "To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) supported the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a frst-order analysis of the costs and benefts of resilient reconstruction and risk reduction strategies. This publication describes the applied methods and main results of the project.", - "spatial": { - "countries": ["AFG"], - "scale": "national" - }, - "license": "CC-BY-4.0", - "contact_point": { - "name": "Pierre Chrzanowski", - "email": "pchrzanowski@worldbank.org" - }, - "creator": { - "name": "GFDRR", - "url": "https://www.gfdrr.org" - }, - "attributions": [ - { - "id": "0", - "entity": { - "name": "Federica Ranghieri", - "email": "franghieri@worldbank.org" - }, - "role": "world_bank_team_lead" - }, - { - "id": "1", - "entity": { - "name": "Ditte Fallesen", - "email": "dfallesen@worldbank.org" - }, - "role": "world_bank_team_lead" - }, - { - "id": "2", - "entity": { - "name": "Brandan Jongman", - "email": "bjongman@worldbank.org" - }, - "role": "world_bank_team_lead" - } - ], - "referenced_by": [ - { - "id": "0", - "name": "Afghanistan - Multi-hazard risk assessment", - "author_names": [ - "Federica Ranghieri", - "Ditte Fallesen", - "Brenden Jongman", - "Guillermo Siercke", - "Abdul Azim Doosti", - "Julian Palma", - "Simone Balog", - "Sayed Sharifullah Mashahid", - "Erika Vargas" - ], - "date_published": "2018-12-18", - "url": "https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf" - } - ], - "resources": [ - { - "id": "0", - "title": "Afghanistan AAL and RPs (baseline and 2050)", - "description": "Average Annual Losses (AAL) for current population (AALpop), current asset (AALnowUSD), population SSP scenarios at 2050 (AALpopSP1-5), asset SSP scenarios at 2050 (AAL_usd_SP1-5). Aggregated ad Administrative level (ADM1).", - "format": "gpkg", - "coordinate_system": "EPSG:32642", - "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050637/DR0065486/lss-afg-fl-adm.zip" - }, - { - "id": "1", - "title": "Afghanistan AAL and RPs (baseline)", - "description": "Average Annual Losses (AAL) over physical asset in USD for baseline scenario.", - "format": "geotiff", - "spatial_resolution": 90, - "coordinate_system": "EPSG:32642", - "download_url": "https://datacatalogfiles.worldbank.org/ddh-published/0050637/DR0065487/lss-afg-fl.zip" - } - ], - "loss": { - "losses": [ - { - "id": "0", - "hazard_type": "flood", - "hazard_process": "fluvial_flood", - "description": "Average Annual Losses (AAL) for current population (AALpop)", - "cost": { - "id": "0", - "dimension": "population" - }, - "impact": { - "type": "direct", - "metric": "at_risk_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-flood", - "exposure_id": "AFG_exp-asset" - }, - { - "id": "1", - "hazard_type": "flood", - "hazard_process": "fluvial_flood", - "description": "Average Annual Losses (AAL) for current asset (AALnowUSD)", - "cost": { - "id": "1", - "dimension": "structure", - "unit": "USD" - }, - "impact": { - "type": "direct", - "metric": "loss_annual_average_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-flood", - "exposure_id": "AFG_exp-asset" - }, - { - "id": "2", - "hazard_type": "flood", - "hazard_process": "fluvial_flood", - "description": "Average Annual Losses (AAL) for population SSP scenarios at 2050 (AALpopSP1-5)", - "cost": { - "id": "2", - "dimension": "population" - }, - "impact": { - "type": "direct", - "metric": "at_risk_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-flood", - "exposure_id": "AFG_exp-asset" - }, - { - "id": "3", - "hazard_type": "flood", - "hazard_process": "fluvial_flood", - "description": "Average Annual Losses (AAL) for asset SSP scenarios at 2050 (AAL_usd_SP1-5)", - "cost": { - "id": "3", - "dimension": "structure", - "unit": "USD" - }, - "impact": { - "type": "direct", - "metric": "loss_annual_average_value", - "unit": "count", - "base_data_type": "inferred" - }, - "type": "gross", - "approach": "analytical", - "hazard_analysis_type": "probabilistic", - "hazard_id": "AFG_hzd-flood", - "exposure_id": "AFG_exp-asset" - } - ] - }, - "links": [ - { - "href": "https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json", - "rel": "describedby" - } - ] - } - ] -}