Update paper.md

joss_submission/paper.md

@@ -77,7 +77,7 @@ The `MultiHazard` package was developed in collaberation with the South Florida
 
 The package possesses the functionaity to compute the desired isoline by interpolating joint return periods calculated over a user-defined grid which can result in smoother curves than overlaying the partial isolines. Isoline generation for time series comprising events from multiple (independent) populations e.g. different types of storms is faciliated by multiplying independent annual-non excedanc probabilities associated with each poopulation as outlined in @Maduwantha:2024. Also coded in the package are the methods proposed in @Barltrop:2023 to derive isolines using the @Heffernan:2004 model and the angular dependence function [@Wadsworth:2013] models along with the novel bootstrap procedure for calculating sample uncertainties, see Figure \autoref{fig:isolines}c,d.
 
-![The 100-year isoline at case study site derived using two methods. Two-sided consitoina sample - copula theory method: (a) Partial isolines from the samples conditioned on rainfall (red line) and water level (blue line) whose outer envelopen when overlaid gives (b) the full isoline. Heffernan and Tawn model method: (c) Sample uncertainties are calculated along 100 rays eminating from the point comprising the mimum obseved values of both drivers. (d) The Isoline (red line) and associated 90% confidence interval (dashed black lines) obtained via a block bootstrap procedure . \label{fig:isolines}](Figure_1.png){width=20%}
+![The 100-year isoline at case study site derived using two methods. Two-sided consitoina sample - copula theory method: (a) Partial isolines from the samples conditioned on rainfall (red line) and water level (blue line) whose outer envelopen when overlaid gives (b) the full isoline. Heffernan and Tawn model method: (c) Sample uncertainties are calculated along 100 rays eminating from the point comprising the mimum obseved values of both drivers. (d) The Isoline (red line) and associated 90% confidence interval (dashed black lines) obtained via a block bootstrap procedure . \label{fig:isolines}](Figure_1.png)
 
 Compound events are increasingly concern for entities responsible for managing flood risk. `MultiHazard` is designed as a comprehensive user-friendly tool for copula-based joint probability analysis in R. As such the package provides functions for pre-processing data including imputing missing values, detrending and declustering time series as well as exploratory data analysis e.g., analyzing pairwise correlations over a range of time-lags between the two drivers/hazards. The package also contains an automated threshold selection method for the Generalized Pareto distribution [@Solari:2017] and approaches for robustly capturing the dependence structure when there are more than two relevant drivers/hazards,namely, standard (elliptic/Archimedean) copulas, Pair Copula Constructions (PCCs) and the conditional threshold exceedance approach of @Heffernan:2004. For the analysis undertaken for the SFWMD, the higher dimensional approaches enabled groundwater level to be included in the analysis.  More recently, an approach to generate time varying synthetic events, i.e., hyetographs and hydrographs was added. Time varying condition are a prerequisite for non-steady state hydrodynamic modeling.