diff --git a/tests/conftest.py b/tests/conftest.py
index f9db7c36e..7bd1956ad 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -139,7 +139,10 @@ def dummy_carbon_data(layer_roles_fixture):
             DataArray(np.full((13, 4), np.nan), dims=["layers", "cell_id"]),
             # At present the soil model only uses the top soil layer, so this is the
             # only one with real test values in
-            DataArray([[0.5, 0.7, 0.6, 0.2]], dims=["layers", "cell_id"]),
+            DataArray(
+                [[0.472467929, 0.399900047, 0.256053640, 0.153616897]],
+                dims=["layers", "cell_id"],
+            ),
             DataArray(np.full((1, 4), np.nan), dims=["layers", "cell_id"]),
         ],
         dim="layers",
@@ -151,6 +154,23 @@ def dummy_carbon_data(layer_roles_fixture):
             "cell_id": data.grid.cell_id,
         }
     )
+    # TODO - Eventually this should replace the dummy soil moisture entirely
+    data["matric_potential"] = xr.concat(
+        [
+            DataArray(np.full((13, 4), np.nan), dims=["layers", "cell_id"]),
+            # At present the soil model only uses the top soil layer, so this is the
+            # only one with real test values in
+            DataArray([[-3.0, -10.0, -250.0, -10000.0]], dims=["layers", "cell_id"]),
+            DataArray(np.full((1, 4), np.nan), dims=["layers", "cell_id"]),
+        ],
+        dim="layers",
+    ).assign_coords(
+        {
+            "layers": np.arange(0, 15),
+            "layer_roles": ("layers", layer_roles_fixture),
+            "cell_id": data.grid.cell_id,
+        }
+    )
     data["soil_temperature"] = xr.concat(
         [
             DataArray(np.full((13, 4), np.nan), dims=["dim_0", "cell_id"]),
diff --git a/tests/models/litter/conftest.py b/tests/models/litter/conftest.py
index 9d9d1c5cb..fa6e2586c 100644
--- a/tests/models/litter/conftest.py
+++ b/tests/models/litter/conftest.py
@@ -68,12 +68,15 @@ def dummy_litter_data(layer_roles_fixture):
     )
 
     # The layer dependant data has to be handled separately
-    data["soil_moisture"] = concat(
+    data["matric_potential"] = concat(
         [
             DataArray(np.full((13, 3), np.nan), dims=["layers", "cell_id"]),
             # At present the soil model only uses the top soil layer, so this is the
             # only one with real test values in
-            DataArray([[0.25, 0.45, 0.3]], dims=["layers", "cell_id"]),
+            DataArray(
+                [[-10.0, -25.0, -100.0]],
+                dims=["layers", "cell_id"],
+            ),
             DataArray(np.full((1, 3), np.nan), dims=["layers", "cell_id"]),
         ],
         dim="layers",
diff --git a/tests/models/litter/test_litter_model.py b/tests/models/litter/test_litter_model.py
index 6d2797657..790e5964b 100644
--- a/tests/models/litter/test_litter_model.py
+++ b/tests/models/litter/test_litter_model.py
@@ -441,12 +441,12 @@ def test_update(litter_model_fixture, dummy_litter_data):
     end_above_meta = [0.29587973, 0.14851276, 0.07041856]
     end_above_struct = [0.50055126, 0.25010012, 0.0907076]
     end_woody = [4.702103, 11.802315, 7.300997]
-    end_below_meta = [0.394145, 0.35923, 0.069006]
-    end_below_struct = [0.60027118, 0.30975403, 0.02047743]
+    end_below_meta = [0.38949196, 0.36147436, 0.06906041]
+    end_below_struct = [0.60011634, 0.30989963, 0.02047753]
     end_lignin_above_struct = [0.4996410, 0.1004310, 0.6964345]
     end_lignin_woody = [0.49989001, 0.79989045, 0.34998229]
     end_lignin_below_struct = [0.499760108, 0.249922519, 0.737107757]
-    c_mineral = [0.0212182, 0.02746286, 0.00796359]
+    c_mineral = [0.02987233, 0.02316114, 0.00786517]
 
     litter_model_fixture.update(time_index=0)
 
@@ -468,23 +468,3 @@ def test_update(litter_model_fixture, dummy_litter_data):
         dummy_litter_data["lignin_below_structural"], end_lignin_below_struct
     )
     assert np.allclose(dummy_litter_data["litter_C_mineralisation_rate"], c_mineral)
-
-
-def test_convert_soil_moisture_to_water_potential(
-    dummy_litter_data, top_soil_layer_index
-):
-    """Test that function to convert soil moisture to a water potential works."""
-    from virtual_rainforest.models.litter.litter_model import (
-        convert_soil_moisture_to_water_potential,
-    )
-
-    expected_potentials = [-297.14104, -4.2647655, -79.666189]
-
-    actual_potentials = convert_soil_moisture_to_water_potential(
-        dummy_litter_data["soil_moisture"][top_soil_layer_index].to_numpy(),
-        air_entry_water_potential=LitterConsts.air_entry_water_potential,
-        water_retention_curvature=LitterConsts.water_retention_curvature,
-        saturated_water_content=LitterConsts.saturated_water_content,
-    )
-
-    assert np.allclose(actual_potentials, expected_potentials)
diff --git a/tests/models/litter/test_litter_pools.py b/tests/models/litter/test_litter_pools.py
index 3632989b7..447cc86de 100644
--- a/tests/models/litter/test_litter_pools.py
+++ b/tests/models/litter/test_litter_pools.py
@@ -14,30 +14,25 @@ def temp_and_water_factors(
     dummy_litter_data, surface_layer_index, top_soil_layer_index
 ):
     """Temperature and water factors for the various litter layers."""
-    from virtual_rainforest.models.litter.litter_model import (
-        convert_soil_moisture_to_water_potential,
-    )
     from virtual_rainforest.models.litter.litter_pools import (
         calculate_environmental_factors,
     )
 
-    water_potentials = convert_soil_moisture_to_water_potential(
-        dummy_litter_data["soil_moisture"][top_soil_layer_index],
-        air_entry_water_potential=LitterConsts.air_entry_water_potential,
-        water_retention_curvature=LitterConsts.water_retention_curvature,
-        saturated_water_content=LitterConsts.saturated_water_content,
-    )
-
     environmental_factors = calculate_environmental_factors(
         surface_temp=dummy_litter_data["air_temperature"][surface_layer_index],
         topsoil_temp=dummy_litter_data["soil_temperature"][top_soil_layer_index],
-        water_potential=water_potentials,
+        water_potential=dummy_litter_data["matric_potential"][top_soil_layer_index],
         constants=LitterConsts,
     )
 
     return environmental_factors
 
 
+# TODO - Compare the below
+# [-297.1410435034187, -4.264765510307134, -79.66618999943468]
+# [-10.0, -25.0, -100.0]
+
+
 @pytest.fixture
 def decay_rates(dummy_litter_data, temp_and_water_factors):
     """Decay rates for the various litter pools."""
@@ -59,9 +54,6 @@ def test_calculate_environmental_factors(
         calculate_environmental_factors,
     )
 
-    # TODO - hardcoding this in, but eventually this should be added to the data object.
-    water_potentials = [-10.0, -25.0, -100.0]
-
     expected_water_factors = [1.0, 0.88496823, 0.71093190]
     expected_temp_above_factors = [0.1878681, 0.1878681, 0.1878681]
     expected_temp_below_factors = [0.2732009, 0.2732009, 0.2732009]
@@ -69,7 +61,7 @@ def test_calculate_environmental_factors(
     environmental_factors = calculate_environmental_factors(
         surface_temp=dummy_litter_data["air_temperature"][surface_layer_index],
         topsoil_temp=dummy_litter_data["soil_temperature"][top_soil_layer_index],
-        water_potential=water_potentials,
+        water_potential=dummy_litter_data["matric_potential"][top_soil_layer_index],
         constants=LitterConsts,
     )
 
@@ -104,7 +96,7 @@ def test_calculate_moisture_effect_on_litter_decomp(top_soil_layer_index):
         calculate_moisture_effect_on_litter_decomp,
     )
 
-    water_potentials = [-10.0, -25.0, -100.0, -400.0]
+    water_potentials = np.array([-10.0, -25.0, -100.0, -400.0])
 
     expected_factor = [1.0, 0.88496823, 0.71093190, 0.53689556]
 
@@ -139,9 +131,6 @@ def test_calculate_change_in_litter_variables(
     dummy_litter_data, surface_layer_index, top_soil_layer_index
 ):
     """Test that litter pool update calculation is correct."""
-    from virtual_rainforest.models.litter.litter_model import (
-        convert_soil_moisture_to_water_potential,
-    )
     from virtual_rainforest.models.litter.litter_pools import (
         calculate_change_in_litter_variables,
     )
@@ -150,22 +139,14 @@ def test_calculate_change_in_litter_variables(
         "litter_pool_above_metabolic": [0.29587973, 0.14851276, 0.07041856],
         "litter_pool_above_structural": [0.50055126, 0.25010012, 0.0907076],
         "litter_pool_woody": [4.702103, 11.802315, 7.300997],
-        "litter_pool_below_metabolic": [0.394145, 0.35923, 0.069006],
-        "litter_pool_below_structural": [0.60027118, 0.30975403, 0.02047743],
+        "litter_pool_below_metabolic": [0.38949196, 0.36147436, 0.06906041],
+        "litter_pool_below_structural": [0.60011634, 0.30989963, 0.02047753],
         "lignin_above_structural": [0.4996410, 0.1004310, 0.6964345],
         "lignin_woody": [0.49989001, 0.79989045, 0.34998229],
         "lignin_below_structural": [0.499760108, 0.249922519, 0.737107757],
-        "litter_C_mineralisation_rate": [0.0212182, 0.02746286, 0.00796359],
+        "litter_C_mineralisation_rate": [0.02987233, 0.02316114, 0.00786517],
     }
 
-    # Calculate water potential
-    water_potential = convert_soil_moisture_to_water_potential(
-        dummy_litter_data["soil_moisture"][top_soil_layer_index].to_numpy(),
-        air_entry_water_potential=LitterConsts.air_entry_water_potential,
-        water_retention_curvature=LitterConsts.water_retention_curvature,
-        saturated_water_content=LitterConsts.saturated_water_content,
-    )
-
     result = calculate_change_in_litter_variables(
         surface_temp=dummy_litter_data["air_temperature"][
             surface_layer_index
@@ -173,7 +154,9 @@ def test_calculate_change_in_litter_variables(
         topsoil_temp=dummy_litter_data["soil_temperature"][
             top_soil_layer_index
         ].to_numpy(),
-        water_potential=water_potential,
+        water_potential=dummy_litter_data["matric_potential"][
+            top_soil_layer_index
+        ].to_numpy(),
         above_metabolic=dummy_litter_data["litter_pool_above_metabolic"].to_numpy(),
         above_structural=dummy_litter_data["litter_pool_above_structural"].to_numpy(),
         woody=dummy_litter_data["litter_pool_woody"].to_numpy(),
@@ -200,8 +183,8 @@ def test_calculate_decay_rates(dummy_litter_data, temp_and_water_factors):
         "metabolic_above": [0.00450883, 0.00225442, 0.00105206],
         "structural_above": [1.67429665e-4, 6.18573593e-4, 1.10869077e-5],
         "woody": [0.0004832, 0.00027069, 0.0015888],
-        "metabolic_below": [0.00627503, 0.01118989, 0.00141417],
-        "structural_below": [2.08818451e-4, 7.25965456e-4, 2.56818870e-6],
+        "metabolic_below": [0.01092804, 0.00894564, 0.00135959],
+        "structural_below": [3.63659952e-04, 5.80365659e-04, 2.46907410e-06],
     }
 
     actual_decay = calculate_decay_rates(
@@ -362,7 +345,7 @@ def test_calculate_litter_decay_metabolic_below(
         calculate_litter_decay_metabolic_below,
     )
 
-    expected_decay = [0.00627503, 0.01118989, 0.00141417]
+    expected_decay = [0.01092804, 0.00894564, 0.00135959]
 
     actual_decay = calculate_litter_decay_metabolic_below(
         temperature_factor=temp_and_water_factors["temp_below"],
@@ -382,7 +365,7 @@ def test_calculate_litter_decay_structural_below(
         calculate_litter_decay_structural_below,
     )
 
-    expected_decay = [2.08818451e-04, 7.25965456e-04, 2.56818870e-06]
+    expected_decay = [3.63659952e-04, 5.80365659e-04, 2.46907410e-06]
 
     actual_decay = calculate_litter_decay_structural_below(
         temperature_factor=temp_and_water_factors["temp_below"],
diff --git a/tests/models/soil/test_carbon.py b/tests/models/soil/test_carbon.py
index a700f2259..8c7da068a 100644
--- a/tests/models/soil/test_carbon.py
+++ b/tests/models/soil/test_carbon.py
@@ -14,41 +14,48 @@
 
 
 @pytest.fixture
-def moist_temp_scalars(dummy_carbon_data, top_soil_layer_index):
-    """Combined moisture and temperature scalars based on dummy carbon data."""
-    from virtual_rainforest.models.soil.carbon import (
-        convert_moisture_to_scalar,
-        convert_temperature_to_scalar,
-    )
+def moist_scalars(dummy_carbon_data, top_soil_layer_index):
+    """Moisture scalars based on dummy carbon data."""
+    from virtual_rainforest.models.soil.carbon import convert_moisture_to_scalar
 
     moist_scalars = convert_moisture_to_scalar(
-        dummy_carbon_data["soil_moisture"][top_soil_layer_index],
+        np.array([0.5, 0.7, 0.6, 0.2]),
         SoilConsts.moisture_scalar_coefficient,
         SoilConsts.moisture_scalar_exponent,
     )
-    temp_scalars = convert_temperature_to_scalar(
-        dummy_carbon_data["soil_temperature"][top_soil_layer_index],
-        SoilConsts.temp_scalar_coefficient_1,
-        SoilConsts.temp_scalar_coefficient_2,
-        SoilConsts.temp_scalar_coefficient_3,
-        SoilConsts.temp_scalar_coefficient_4,
-        SoilConsts.temp_scalar_reference_temp,
+
+    return moist_scalars
+
+
+@pytest.fixture
+def water_factors(dummy_carbon_data, top_soil_layer_index):
+    """Water potential factors based on dummy carbon data."""
+    from virtual_rainforest.models.soil.carbon import (
+        calculate_water_potential_impact_on_microbes,
+    )
+
+    moist_scalars = calculate_water_potential_impact_on_microbes(
+        water_potential=dummy_carbon_data["matric_potential"][top_soil_layer_index],
+        water_potential_halt=SoilConsts.soil_microbe_water_potential_halt,
+        water_potential_opt=SoilConsts.soil_microbe_water_potential_optimum,
+        moisture_response_curvature=SoilConsts.moisture_response_curvature,
     )
 
-    return moist_scalars * temp_scalars
+    return moist_scalars
 
 
 def test_top_soil_data_extraction(dummy_carbon_data, top_soil_layer_index):
     """Test that top soil data can be extracted from the data object correctly."""
 
     top_soil_temps = [35.0, 37.5, 40.0, 25.0]
-    top_soil_moistures = [0.5, 0.7, 0.6, 0.2]
+    top_soil_water_potentials = [-3.0, -10.0, -250.0, -10000.0]
 
     assert np.allclose(
         dummy_carbon_data["soil_temperature"][top_soil_layer_index], top_soil_temps
     )
     assert np.allclose(
-        dummy_carbon_data["soil_moisture"][top_soil_layer_index], top_soil_moistures
+        dummy_carbon_data["matric_potential"][top_soil_layer_index],
+        top_soil_water_potentials,
     )
 
 
@@ -58,10 +65,10 @@ def test_calculate_soil_carbon_updates(dummy_carbon_data, top_soil_layer_index):
     from virtual_rainforest.models.soil.carbon import calculate_soil_carbon_updates
 
     change_in_pools = {
-        "soil_c_pool_lmwc": [0.00509349, 0.02038796, 0.71353723, 0.00125521],
-        "soil_c_pool_maom": [0.13088391, 0.05654771, -0.39962841, 0.00533357],
-        "soil_c_pool_microbe": [-0.33131188, -0.16636299, -0.76078599, -0.01275669],
-        "soil_c_pool_pom": [-0.00168464, -0.00936813, -0.00873008, 0.00403342],
+        "soil_c_pool_lmwc": [0.25847352, 0.59685734, 0.97122869, 0.02112077],
+        "soil_c_pool_maom": [0.10296645, 0.04445693, -0.31401747, 0.00422143],
+        "soil_c_pool_microbe": [-0.16002809, -0.07621711, -0.36452355, -0.01140071],
+        "soil_c_pool_pom": [-0.25377786, -0.58704913, -0.41245236, -0.01566563],
     }
 
     # Make order of pools object
@@ -70,17 +77,20 @@ def test_calculate_soil_carbon_updates(dummy_carbon_data, top_soil_layer_index):
         pool_order[pool] = np.array([])
 
     delta_pools = calculate_soil_carbon_updates(
-        dummy_carbon_data["soil_c_pool_lmwc"].to_numpy(),
-        dummy_carbon_data["soil_c_pool_maom"].to_numpy(),
-        dummy_carbon_data["soil_c_pool_microbe"].to_numpy(),
-        dummy_carbon_data["soil_c_pool_pom"].to_numpy(),
-        dummy_carbon_data["pH"],
-        dummy_carbon_data["bulk_density"],
-        dummy_carbon_data["soil_moisture"][top_soil_layer_index],
-        dummy_carbon_data["soil_temperature"][top_soil_layer_index],
-        dummy_carbon_data["percent_clay"],
-        dummy_carbon_data["litter_C_mineralisation_rate"],
-        pool_order,
+        soil_c_pool_lmwc=dummy_carbon_data["soil_c_pool_lmwc"].to_numpy(),
+        soil_c_pool_maom=dummy_carbon_data["soil_c_pool_maom"].to_numpy(),
+        soil_c_pool_microbe=dummy_carbon_data["soil_c_pool_microbe"].to_numpy(),
+        soil_c_pool_pom=dummy_carbon_data["soil_c_pool_pom"].to_numpy(),
+        pH=dummy_carbon_data["pH"],
+        bulk_density=dummy_carbon_data["bulk_density"],
+        soil_moisture=np.array([0.5, 0.7, 0.6, 0.2]),
+        soil_water_potential=dummy_carbon_data["matric_potential"][
+            top_soil_layer_index
+        ].to_numpy(),
+        soil_temp=dummy_carbon_data["soil_temperature"][top_soil_layer_index],
+        percent_clay=dummy_carbon_data["percent_clay"],
+        mineralisation_rate=dummy_carbon_data["litter_C_mineralisation_rate"],
+        delta_pools_ordered=pool_order,
         constants=SoilConsts,
     )
 
@@ -90,12 +100,12 @@ def test_calculate_soil_carbon_updates(dummy_carbon_data, top_soil_layer_index):
         assert np.allclose(delta_pools[i * 4 : (i + 1) * 4], change_in_pools[pool])
 
 
-def test_calculate_mineral_association(dummy_carbon_data, moist_temp_scalars):
+def test_calculate_mineral_association(dummy_carbon_data, moist_scalars):
     """Test that mineral_association runs and generates the correct values."""
 
     from virtual_rainforest.models.soil.carbon import calculate_mineral_association
 
-    output_l_to_m = [-7.35822655e-03, -1.27716013e-02, -7.16245859e-01, 3.29436494e-05]
+    output_l_to_m = [-5.78872135e-03, -1.00408341e-02, -5.62807109e-01, 2.60743689e-05]
 
     # Then calculate mineral association rate
     lmwc_to_maom = calculate_mineral_association(
@@ -103,7 +113,7 @@ def test_calculate_mineral_association(dummy_carbon_data, moist_temp_scalars):
         dummy_carbon_data["soil_c_pool_maom"],
         dummy_carbon_data["pH"],
         dummy_carbon_data["bulk_density"],
-        moist_temp_scalars,
+        moist_scalars,
         dummy_carbon_data["percent_clay"],
         constants=SoilConsts,
     )
@@ -199,22 +209,30 @@ def test_calculate_binding_coefficient(dummy_carbon_data):
     assert np.allclose(binding_coefs, output_coefs)
 
 
-def test_convert_temperature_to_scalar(dummy_carbon_data, top_soil_layer_index):
-    """Test that scalar_temperature runs and generates the correct value."""
-    from virtual_rainforest.models.soil.carbon import convert_temperature_to_scalar
-
-    output_scalars = [1.27113, 1.27196, 1.27263, 1.26344]
+@pytest.mark.parametrize(
+    "activation_energy,expected_factors",
+    [
+        (30000.0, [2.57153601, 2.82565326, 3.10021393, 1.73629781]),
+        (45000.0, [4.12371761, 4.74983258, 5.45867825, 2.28789625]),
+        (57000.0, [6.01680536, 7.19657491, 8.58309980, 2.85289648]),
+    ],
+)
+def calculate_temperature_effect_on_microbes(
+    dummy_carbon_data, top_soil_layer_index, activation_energy, expected_factors
+):
+    """Test function to calculate microbial temperature response."""
+    from virtual_rainforest.models.soil.carbon import (
+        calculate_temperature_effect_on_microbes,
+    )
 
-    temp_scalar = convert_temperature_to_scalar(
-        dummy_carbon_data["soil_temperature"][top_soil_layer_index],
-        SoilConsts.temp_scalar_coefficient_1,
-        SoilConsts.temp_scalar_coefficient_2,
-        SoilConsts.temp_scalar_coefficient_3,
-        SoilConsts.temp_scalar_coefficient_4,
-        SoilConsts.temp_scalar_reference_temp,
+    actual_factors = calculate_temperature_effect_on_microbes(
+        soil_temperature=dummy_carbon_data["soil_temperature"][top_soil_layer_index],
+        activation_energy=activation_energy,
+        reference_temperature=SoilConsts.arrhenius_reference_temp,
+        gas_constant=SoilConsts.universal_gas_constant,
     )
 
-    assert np.allclose(temp_scalar, output_scalars)
+    assert np.allclose(expected_factors, actual_factors)
 
 
 @pytest.mark.parametrize(
@@ -257,7 +275,7 @@ def test_convert_moisture_to_scalar(
             )
         else:
             moist_scalar = convert_moisture_to_scalar(
-                dummy_carbon_data["soil_moisture"][top_soil_layer_index],
+                np.array([0.5, 0.7, 0.6, 0.2]),
                 SoilConsts.moisture_scalar_coefficient,
                 SoilConsts.moisture_scalar_exponent,
             )
@@ -267,30 +285,52 @@ def test_convert_moisture_to_scalar(
     log_check(caplog, expected_log_entries)
 
 
-def test_calculate_maintenance_respiration(dummy_carbon_data, moist_temp_scalars):
+def test_calculate_water_potential_impact_on_microbes():
+    """Test the calculation of the impact of soil water on microbial rates."""
+    from virtual_rainforest.models.soil.carbon import (
+        calculate_water_potential_impact_on_microbes,
+    )
+
+    water_potentials = np.array([-3.0, -10.0, -250.0, -10000.0])
+
+    expected_factor = [1.0, 0.94414168, 0.62176357, 0.07747536]
+
+    actual_factor = calculate_water_potential_impact_on_microbes(
+        water_potential=water_potentials,
+        water_potential_halt=SoilConsts.soil_microbe_water_potential_halt,
+        water_potential_opt=SoilConsts.soil_microbe_water_potential_optimum,
+        moisture_response_curvature=SoilConsts.moisture_response_curvature,
+    )
+
+    assert np.allclose(actual_factor, expected_factor)
+
+
+def test_calculate_maintenance_respiration(
+    dummy_carbon_data, moist_scalars, top_soil_layer_index
+):
     """Check maintenance respiration cost calculates correctly."""
     from virtual_rainforest.models.soil.carbon import calculate_maintenance_respiration
 
-    expected_resps = [0.19906823, 0.0998193, 0.45592854, 0.00763283]
+    expected_resps = [0.05443078, 0.02298407, 0.12012258, 0.00722288]
 
     main_resps = calculate_maintenance_respiration(
-        dummy_carbon_data["soil_c_pool_microbe"],
-        moist_temp_scalars,
-        SoilConsts.microbial_turnover_rate,
+        soil_c_pool_microbe=dummy_carbon_data["soil_c_pool_microbe"],
+        soil_temp=dummy_carbon_data["soil_temperature"][top_soil_layer_index],
+        constants=SoilConsts,
     )
 
     assert np.allclose(main_resps, expected_resps)
 
 
-def test_calculate_necromass_adsorption(dummy_carbon_data, moist_temp_scalars):
+def test_calculate_necromass_adsorption(dummy_carbon_data, moist_scalars):
     """Check maintenance respiration cost calculates correctly."""
     from virtual_rainforest.models.soil.carbon import calculate_necromass_adsorption
 
-    expected_adsorps = [0.13824183, 0.06931897, 0.31661708, 0.00530057]
+    expected_adsorps = [0.10875517, 0.05449776, 0.24878964, 0.00419536]
 
     actual_adsorps = calculate_necromass_adsorption(
         dummy_carbon_data["soil_c_pool_microbe"],
-        moist_temp_scalars,
+        moist_scalars,
         SoilConsts.necromass_adsorption_rate,
     )
 
@@ -359,49 +399,52 @@ def test_calculate_pom_decomposition_saturation(dummy_carbon_data):
 
 
 def test_calculate_microbial_carbon_uptake(
-    dummy_carbon_data, top_soil_layer_index, moist_temp_scalars
+    dummy_carbon_data, top_soil_layer_index, water_factors
 ):
     """Check microbial carbon uptake calculates correctly."""
     from virtual_rainforest.models.soil.carbon import calculate_microbial_carbon_uptake
 
-    expected_uptake = [0.00599894, 0.00277614, 0.01176059, 0.00017683]
+    expected_uptake = [3.15786124e-3, 1.26472838e-3, 4.38868085e-3, 1.75270792e-5]
 
     actual_uptake = calculate_microbial_carbon_uptake(
-        dummy_carbon_data["soil_c_pool_lmwc"],
-        dummy_carbon_data["soil_c_pool_microbe"],
-        moist_temp_scalars,
-        dummy_carbon_data["soil_temperature"][top_soil_layer_index],
+        soil_c_pool_lmwc=dummy_carbon_data["soil_c_pool_lmwc"],
+        soil_c_pool_microbe=dummy_carbon_data["soil_c_pool_microbe"],
+        water_factor=water_factors,
+        soil_temp=dummy_carbon_data["soil_temperature"][top_soil_layer_index],
         constants=SoilConsts,
     )
 
     assert np.allclose(actual_uptake, expected_uptake)
 
 
-def test_calculate_labile_carbon_leaching(dummy_carbon_data, moist_temp_scalars):
+def test_calculate_labile_carbon_leaching(dummy_carbon_data, moist_scalars):
     """Check leaching of labile carbon is calculated correctly."""
     from virtual_rainforest.models.soil.carbon import calculate_labile_carbon_leaching
 
-    expected_leaching = [7.15045537e-05, 3.61665981e-05, 1.68115460e-04, 1.59018704e-06]
+    expected_leaching = [5.62526764e-05, 2.84336164e-05, 1.32100695e-04, 1.25860748e-06]
 
     actual_leaching = calculate_labile_carbon_leaching(
         dummy_carbon_data["soil_c_pool_lmwc"],
-        moist_temp_scalars,
+        moist_scalars,
         SoilConsts.leaching_rate_labile_carbon,
     )
 
     assert np.allclose(actual_leaching, expected_leaching)
 
 
-def test_calculate_pom_decomposition(dummy_carbon_data, moist_temp_scalars):
+def test_calculate_pom_decomposition(
+    dummy_carbon_data, top_soil_layer_index, water_factors
+):
     """Check that particulate organic matter decomposition is calculated correctly."""
     from virtual_rainforest.models.soil.carbon import calculate_pom_decomposition
 
-    expected_decomp = [0.0038056940, 0.0104286084, 0.0092200655, 0.0014665616]
+    expected_decomp = [0.25589892, 0.58810966, 0.41294236, 0.02116563]
 
     actual_decomp = calculate_pom_decomposition(
         dummy_carbon_data["soil_c_pool_pom"],
         dummy_carbon_data["soil_c_pool_microbe"],
-        moist_temp_scalars,
+        water_factor=water_factors,
+        soil_temp=dummy_carbon_data["soil_temperature"][top_soil_layer_index],
         constants=SoilConsts,
     )
 
diff --git a/tests/models/soil/test_soil_model.py b/tests/models/soil/test_soil_model.py
index ac8688620..ef8a8f4aa 100644
--- a/tests/models/soil/test_soil_model.py
+++ b/tests/models/soil/test_soil_model.py
@@ -222,7 +222,7 @@ def test_soil_model_initialization(
                 },
             },
             pint.Quantity("12 hours"),
-            0.01,
+            0.2,
             does_not_raise(),
             (
                 (
@@ -369,6 +369,7 @@ def test_update(mocker, soil_model_fixture, dummy_carbon_data):
     end_lmwc = [0.04980117, 0.01999411, 0.09992829, 0.00499986]
     end_maom = [2.50019883, 1.70000589, 4.50007171, 0.50000014]
     end_microbe = [5.8, 2.3, 11.3, 1.0]
+    end_pom = [0.25, 2.34, 0.746, 0.3467]
 
     mock_integrate = mocker.patch.object(soil_model_fixture, "integrate")
 
@@ -377,6 +378,7 @@ def test_update(mocker, soil_model_fixture, dummy_carbon_data):
             soil_c_pool_lmwc=DataArray(end_lmwc, dims="cell_id"),
             soil_c_pool_maom=DataArray(end_maom, dims="cell_id"),
             soil_c_pool_microbe=DataArray(end_microbe, dims="cell_id"),
+            soil_c_pool_pom=DataArray(end_pom, dims="cell_id"),
         )
     )
 
@@ -388,6 +390,8 @@ def test_update(mocker, soil_model_fixture, dummy_carbon_data):
     # Check that data fixture has been updated correctly
     assert np.allclose(dummy_carbon_data["soil_c_pool_lmwc"], end_lmwc)
     assert np.allclose(dummy_carbon_data["soil_c_pool_maom"], end_maom)
+    assert np.allclose(dummy_carbon_data["soil_c_pool_microbe"], end_microbe)
+    assert np.allclose(dummy_carbon_data["soil_c_pool_pom"], end_pom)
 
 
 @pytest.mark.parametrize(
@@ -399,17 +403,17 @@ def test_update(mocker, soil_model_fixture, dummy_carbon_data):
             Dataset(
                 data_vars=dict(
                     lmwc=DataArray(
-                        [0.05283173, 0.03076934, 1.87751275, 0.00561615], dims="cell_id"
+                        [0.13122264, 0.30432201, 0.50601794, 0.01541629], dims="cell_id"
                     ),
                     maom=DataArray(
-                        [2.56409185, 1.72670677, 2.84168443, 0.50266489], dims="cell_id"
+                        [2.54647825, 1.71347325, 4.32317632, 0.50157362], dims="cell_id"
                     ),
                     microbe=DataArray(
-                        [5.63681284, 2.21869505, 10.96048678, 0.99364838],
+                        [5.72558633, 2.27805994, 11.21111949, 0.99495352],
                         dims="cell_id",
                     ),
                     pom=DataArray(
-                        [0.09916254, 0.99531802, 0.69563758, 0.35201611], dims="cell_id"
+                        [0.02068157, 0.71317448, 0.50004912, 0.34220329], dims="cell_id"
                     ),
                 )
             ),
@@ -475,6 +479,7 @@ def test_order_independance(dummy_carbon_data, soil_model_fixture):
         "pH",
         "bulk_density",
         "soil_moisture",
+        "matric_potential",
         "soil_temperature",
         "percent_clay",
         "litter_C_mineralisation_rate",
@@ -516,24 +521,26 @@ def test_construct_full_soil_model(dummy_carbon_data, top_soil_layer_index):
 
     from virtual_rainforest.models.soil.soil_model import construct_full_soil_model
 
-    delta_pools = [
-        0.00509349,
-        0.02038796,
-        0.71353723,
-        0.00125521,
-        0.13088391,
-        0.05654771,
-        -0.39962841,
-        0.00533357,
-        -0.33131188,
-        -0.16636299,
-        -0.76078599,
-        -0.01275669,
-        -0.00168464,
-        -0.00936813,
-        -0.00873008,
-        0.00403342,
-    ]
+    delta_pools = (
+        [
+            0.25809707,
+            0.59264789,
+            0.56851013,
+            0.02112901,
+            0.0962045,
+            0.02576618,
+            -0.08926844,
+            0.0029497,
+            -0.15288599,
+            -0.05330495,
+            -0.18645947,
+            -0.01013683,
+            -0.25377786,
+            -0.58704913,
+            -0.41245236,
+            -0.01566563,
+        ],
+    )
 
     # make pools
     pools = np.concatenate(
@@ -557,7 +564,7 @@ def test_construct_full_soil_model(dummy_carbon_data, top_soil_layer_index):
         dummy_carbon_data,
         4,
         top_soil_layer_index,
-        delta_pools_ordered,
+        delta_pools_ordered=delta_pools_ordered,
         constants=SoilConsts,
     )
 
diff --git a/virtual_rainforest/models/litter/constants.py b/virtual_rainforest/models/litter/constants.py
index ca2fc4490..4d402dc34 100644
--- a/virtual_rainforest/models/litter/constants.py
+++ b/virtual_rainforest/models/litter/constants.py
@@ -162,38 +162,6 @@ class LitterConsts:
     being.
     """
 
-    air_entry_water_potential: float = -3.815
-    """Water potential at which soil pores begin to aerate [kPa].
-
-    The constant is used to estimate soil water potential from soil moisture. As this
-    estimation is a stopgap this constant probably shouldn't become a core constant. The
-    value is the average across all soil types found in
-    :cite:t:`cosby_statistical_1984`. In future, this could be calculated based on soil
-    texture.
-
-    TODO: This estimation of water potential will either be moved to or superseded by
-    the hydrology model. So these constants should **not** remain here in the long term.
-    """
-
-    water_retention_curvature: float = -7.22
-    """Curvature of the water retention curve.
-
-    The value is the average across all soil types found in
-    :cite:t:`cosby_statistical_1984`; see documentation for
-    :attr:`air_entry_water_potential` for further details.
-    """
-
-    saturated_water_content: float = 0.457
-    """Relative water content at which the soil is completely saturated [unitless].
-
-    The value is the average across all soil types found in
-    :cite:t:`cosby_statistical_1984`; see documentation for
-    :attr:`air_entry_water_potential` for further details.
-
-    TODO: This constant already exists within the hydrology model, once the hydrology
-    model handles soil water potential, this constant **must** be deleted.
-    """
-
     lignin_inhibition_factor: float = -5.0
     """Exponential factor expressing the extent that lignin inhibits litter breakdown.
 
diff --git a/virtual_rainforest/models/litter/litter_model.py b/virtual_rainforest/models/litter/litter_model.py
index 94d9f20da..fe0cf870e 100644
--- a/virtual_rainforest/models/litter/litter_model.py
+++ b/virtual_rainforest/models/litter/litter_model.py
@@ -36,7 +36,6 @@ class instance. If errors crop here when converting the information from the con
 from typing import Any
 
 import numpy as np
-from numpy.typing import NDArray
 from pint import Quantity
 from xarray import DataArray
 
@@ -97,6 +96,7 @@ class LitterModel(BaseModel):
         "lignin_above_structural",
         "lignin_woody",
         "lignin_below_structural",
+        "litter_C_mineralisation_rate",
     )
     """Variables updated by the litter model."""
 
@@ -208,16 +208,6 @@ def update(self, time_index: int, **kwargs: Any) -> None:
             time_index: The index representing the current time step in the data object.
         """
 
-        # Estimate water potentials based on soil moistures
-        water_potential = convert_soil_moisture_to_water_potential(
-            soil_moisture=self.data["soil_moisture"][
-                self.top_soil_layer_index
-            ].to_numpy(),
-            air_entry_water_potential=self.constants.air_entry_water_potential,
-            water_retention_curvature=self.constants.water_retention_curvature,
-            saturated_water_content=self.constants.saturated_water_content,
-        )
-
         # Find change in litter variables using the function
         updated_variables = calculate_change_in_litter_variables(
             surface_temp=self.data["air_temperature"][
@@ -226,7 +216,9 @@ def update(self, time_index: int, **kwargs: Any) -> None:
             topsoil_temp=self.data["soil_temperature"][
                 self.top_soil_layer_index
             ].to_numpy(),
-            water_potential=water_potential,
+            water_potential=self.data["matric_potential"][
+                self.top_soil_layer_index
+            ].to_numpy(),
             constants=self.constants,
             update_interval=self.update_interval.to("day").magnitude,
             above_metabolic=self.data["litter_pool_above_metabolic"].to_numpy(),
@@ -252,35 +244,3 @@ def update(self, time_index: int, **kwargs: Any) -> None:
 
     def cleanup(self) -> None:
         """Placeholder function for litter model cleanup."""
-
-
-def convert_soil_moisture_to_water_potential(
-    soil_moisture: NDArray[np.float32],
-    air_entry_water_potential: float,
-    water_retention_curvature: float,
-    saturated_water_content: float,
-) -> NDArray[np.float32]:
-    """Convert soil moisture into an estimate of water potential.
-
-    This function provides a coarse estimate of soil water potential. It is taken from
-    :cite:t:`campbell_simple_1974`.
-
-    TODO - This is a stopgap solution until we decide on a systematic way of handling
-    water potentials across the relevant models (`soil`, `litter`, `plants` and
-    `hydrology`).
-
-    Args:
-        soil_moisture: Volumetric relative water content [unitless]
-        air_entry_water_potential: Water potential at which soil pores begin to aerate
-            [kPa]
-        water_retention_curvature: Curvature of water retention curve [unitless]
-        saturated_water_content: The relative water content at which the soil is fully
-            saturated [unitless].
-
-    Returns:
-        An estimate of the water potential of the soil [kPa]
-    """
-
-    return air_entry_water_potential * (
-        (soil_moisture / saturated_water_content) ** water_retention_curvature
-    )
diff --git a/virtual_rainforest/models/litter/litter_pools.py b/virtual_rainforest/models/litter/litter_pools.py
index 0b1e55001..130389431 100644
--- a/virtual_rainforest/models/litter/litter_pools.py
+++ b/virtual_rainforest/models/litter/litter_pools.py
@@ -497,10 +497,13 @@ def calculate_moisture_effect_on_litter_decomp(
         decomposition [unitless]
     """
 
+    # TODO - Need to make sure that this function is properly defined for a plausible
+    # range of matric potentials.
+
     # Calculate how much moisture suppresses microbial activity
     supression = (
-        (np.log10(np.abs(water_potential)) - np.log10(abs(water_potential_opt)))
-        / (np.log10(abs(water_potential_halt)) - np.log10(abs(water_potential_opt)))
+        (np.log10(-water_potential) - np.log10(-water_potential_opt))
+        / (np.log10(-water_potential_halt) - np.log10(-water_potential_opt))
     ) ** moisture_response_curvature
 
     return 1 - supression
diff --git a/virtual_rainforest/models/soil/carbon.py b/virtual_rainforest/models/soil/carbon.py
index 49f657772..f1ef4d2a4 100644
--- a/virtual_rainforest/models/soil/carbon.py
+++ b/virtual_rainforest/models/soil/carbon.py
@@ -6,10 +6,14 @@
 
 import numpy as np
 from numpy.typing import NDArray
+from scipy.constants import convert_temperature, gas_constant
 
 from virtual_rainforest.core.logger import LOGGER
 from virtual_rainforest.models.soil.constants import SoilConsts
 
+# TODO - Once enzymes are added, temperature dependence of saturation constants should
+# be added.
+
 
 def calculate_soil_carbon_updates(
     soil_c_pool_lmwc: NDArray[np.float32],
@@ -19,6 +23,7 @@ def calculate_soil_carbon_updates(
     pH: NDArray[np.float32],
     bulk_density: NDArray[np.float32],
     soil_moisture: NDArray[np.float32],
+    soil_water_potential: NDArray[np.float32],
     soil_temp: NDArray[np.float32],
     percent_clay: NDArray[np.float32],
     mineralisation_rate: NDArray[np.float32],
@@ -39,6 +44,7 @@ def calculate_soil_carbon_updates(
         pH: pH values for each soil grid cell
         bulk_density: bulk density values for each soil grid cell [kg m^-3]
         soil_moisture: relative water content for each soil grid cell [unitless]
+        soil_water_potential: Soil water potential for each grid cell [kPa]
         soil_temp: soil temperature for each soil grid cell [degrees C]
         percent_clay: Percentage clay for each soil grid cell
         mineralisation_rate: Amount of litter mineralised into POM pool [kg C m^-3
@@ -51,46 +57,63 @@ def calculate_soil_carbon_updates(
         A vector containing net changes to each pool. Order [lmwc, maom].
     """
 
-    # Find scalar factors that multiple rates
-    temp_scalar = convert_temperature_to_scalar(
-        soil_temp,
-        constants.temp_scalar_coefficient_1,
-        constants.temp_scalar_coefficient_2,
-        constants.temp_scalar_coefficient_3,
-        constants.temp_scalar_coefficient_4,
-        constants.temp_scalar_reference_temp,
-    )
+    # TODO - At present we have two different factors that capture the impact of soil
+    # water. water_factor is based on soil water potential and applies to the microbial
+    # processes, whereas moist_scalar is based on soil water content and applies to the
+    # chemical processes. In future, all processes will be microbially mediated, meaning
+    # that moist_scalar can be removed.
+
+    # Find the impact of soil water content on chemical soil processes
     moist_scalar = convert_moisture_to_scalar(
         soil_moisture,
         constants.moisture_scalar_coefficient,
         constants.moisture_scalar_exponent,
     )
-    moist_temp_scalar = moist_scalar * temp_scalar
-
+    # Find the impact of soil water potential on the biochemical soil processes
+    water_factor = calculate_water_potential_impact_on_microbes(
+        water_potential=soil_water_potential,
+        water_potential_halt=constants.soil_microbe_water_potential_halt,
+        water_potential_opt=constants.soil_microbe_water_potential_optimum,
+        moisture_response_curvature=constants.moisture_response_curvature,
+    )
     # Calculate transfers between pools
     lmwc_to_maom = calculate_mineral_association(
-        soil_c_pool_lmwc,
-        soil_c_pool_maom,
-        pH,
-        bulk_density,
-        moist_temp_scalar,
-        percent_clay,
-        constants,
+        soil_c_pool_lmwc=soil_c_pool_lmwc,
+        soil_c_pool_maom=soil_c_pool_maom,
+        pH=pH,
+        bulk_density=bulk_density,
+        moisture_scalar=moist_scalar,
+        percent_clay=percent_clay,
+        constants=constants,
     )
     microbial_uptake = calculate_microbial_carbon_uptake(
-        soil_c_pool_lmwc, soil_c_pool_microbe, moist_temp_scalar, soil_temp, constants
+        soil_c_pool_lmwc=soil_c_pool_lmwc,
+        soil_c_pool_microbe=soil_c_pool_microbe,
+        water_factor=water_factor,
+        soil_temp=soil_temp,
+        constants=constants,
     )
     microbial_respiration = calculate_maintenance_respiration(
-        soil_c_pool_microbe, moist_temp_scalar, constants.microbial_turnover_rate
+        soil_c_pool_microbe=soil_c_pool_microbe,
+        soil_temp=soil_temp,
+        constants=constants,
     )
     necromass_adsorption = calculate_necromass_adsorption(
-        soil_c_pool_microbe, moist_temp_scalar, constants.necromass_adsorption_rate
+        soil_c_pool_microbe=soil_c_pool_microbe,
+        moisture_scalar=moist_scalar,
+        necromass_adsorption_rate=constants.necromass_adsorption_rate,
     )
     labile_carbon_leaching = calculate_labile_carbon_leaching(
-        soil_c_pool_lmwc, moist_temp_scalar, constants.leaching_rate_labile_carbon
+        soil_c_pool_lmwc=soil_c_pool_lmwc,
+        moisture_scalar=moist_scalar,
+        leaching_rate=constants.leaching_rate_labile_carbon,
     )
     pom_decomposition_to_lmwc = calculate_pom_decomposition(
-        soil_c_pool_pom, soil_c_pool_microbe, moist_temp_scalar, constants
+        soil_c_pool_pom=soil_c_pool_pom,
+        soil_c_pool_microbe=soil_c_pool_microbe,
+        water_factor=water_factor,
+        soil_temp=soil_temp,
+        constants=constants,
     )
 
     # Determine net changes to the pools
@@ -117,7 +140,7 @@ def calculate_mineral_association(
     soil_c_pool_maom: NDArray[np.float32],
     pH: NDArray[np.float32],
     bulk_density: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
+    moisture_scalar: NDArray[np.float32],
     percent_clay: NDArray[np.float32],
     constants: SoilConsts,
 ) -> NDArray[np.float32]:
@@ -135,8 +158,8 @@ def calculate_mineral_association(
         soil_c_pool_maom: Mineral associated organic matter pool [kg C m^-3]
         pH: pH values for each soil grid cell
         bulk_density: bulk density values for each soil grid cell [kg m^-3]
-        moist_temp_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
+        moisture_scalar: A scalar capturing the impact of soil moisture and on process
+            rates [unitless]
         percent_clay: Percentage clay for each soil grid cell
         constants: Set of constants for the soil model.
 
@@ -153,9 +176,7 @@ def calculate_mineral_association(
     )
     equib_maom = calculate_equilibrium_maom(pH, Q_max, soil_c_pool_lmwc, constants)
 
-    return (
-        moist_temp_scalar * soil_c_pool_lmwc * (equib_maom - soil_c_pool_maom) / Q_max
-    )
+    return moisture_scalar * soil_c_pool_lmwc * (equib_maom - soil_c_pool_maom) / Q_max
 
 
 def calculate_max_sorption_capacity(
@@ -251,48 +272,80 @@ def calculate_binding_coefficient(
     return 10.0 ** (binding_with_ph_slope * pH + binding_with_ph_intercept)
 
 
-def convert_temperature_to_scalar(
-    soil_temp: NDArray[np.float32],
-    temp_scalar_coefficient_1: float,
-    temp_scalar_coefficient_2: float,
-    temp_scalar_coefficient_3: float,
-    temp_scalar_coefficient_4: float,
-    temp_scalar_reference_temp: float,
+def calculate_temperature_effect_on_microbes(
+    soil_temperature: NDArray[np.float32],
+    activation_energy: float,
+    reference_temperature: float,
 ) -> NDArray[np.float32]:
-    """Convert soil temperature into a factor to multiply rates by.
+    """Calculate the effect that temperature has on microbial metabolic rates.
 
-    This form is used in :cite:t:`abramoff_millennial_2018` to minimise differences with
-    the CENTURY model. We very likely want to define our own functional form here. I'm
-    also a bit unsure how this form was even obtained, so further work here is very
-    needed.
+    This uses a standard Arrhenius equation to calculate the impact of temperature.
+
+    This function takes temperatures in Celsius as inputs and converts them into Kelvin
+    for use in the Arrhenius equation. TODO - review this after we have decided how to
+    handle these conversions in general.
 
     Args:
-        soil_temp: soil temperature for each soil grid cell [degrees C]
-        temp_scalar_coefficient_1: Unclear exactly what this parameter is [degrees C]
-        temp_scalar_coefficient_2: Unclear exactly what this parameter is [unclear]
-        temp_scalar_coefficient_3: Unclear exactly what this parameter is [unclear]
-        temp_scalar_coefficient_4: Unclear exactly what this parameter is [unclear]
-        temp_scalar_reference_temp: Reference temperature for temperature scalar
-            [degrees C]
+        soil_temperature: The temperature of the soil [C]
+        activation_energy: Energy of activation [J mol^-1]
+        soil_temperature: The reference temperature of the Arrhenius equation [C]
 
     Returns:
-        A scalar that captures the impact of soil temperature on process rates
+        A multiplicative factor capturing the effect of temperature on microbial rates
     """
 
-    # This expression is drawn from Abramoff et al. (2018)
-    numerator = temp_scalar_coefficient_2 + (
-        temp_scalar_coefficient_3 / np.pi
-    ) * np.arctan(np.pi * (soil_temp - temp_scalar_coefficient_1))
-
-    denominator = temp_scalar_coefficient_2 + (
-        temp_scalar_coefficient_3 / np.pi
-    ) * np.arctan(
-        np.pi
-        * temp_scalar_coefficient_4
-        * (temp_scalar_reference_temp - temp_scalar_coefficient_1)
+    # Convert the temperatures to Kelvin
+    soil_temp_in_kelvin = convert_temperature(
+        soil_temperature, old_scale="Celsius", new_scale="Kelvin"
+    )
+    ref_temp_in_kelvin = convert_temperature(
+        reference_temperature, old_scale="Celsius", new_scale="Kelvin"
     )
 
-    return np.divide(numerator, denominator)
+    return np.exp(
+        (-activation_energy / gas_constant)
+        * ((1 / (soil_temp_in_kelvin)) - (1 / (ref_temp_in_kelvin)))
+    )
+
+
+def calculate_water_potential_impact_on_microbes(
+    water_potential: NDArray[np.float32],
+    water_potential_halt: float,
+    water_potential_opt: float,
+    moisture_response_curvature: float,
+) -> NDArray[np.float32]:
+    """Calculate the effect that soil water potential has on microbial rates.
+
+    This function only returns valid output for soil water potentials that are less than
+    the optimal water potential.
+
+    Args:
+        water_potential: Soil water potential [kPa]
+        water_potential_halt: Water potential at which all microbial activity stops
+            [kPa]
+        water_potential_opt: Optimal water potential for microbial activity [kPa]
+        moisture_response_curvature: Parameter controlling the curvature of the moisture
+            response function [unitless]
+
+    Returns:
+        A multiplicative factor capturing the impact of moisture on soil microbe rates
+        decomposition [unitless]
+    """
+
+    # If the water potential is greater than the optimal then the function produces NaNs
+    # so the simulation should be interrupted
+    if any(water_potential > water_potential_opt):
+        err = ValueError("Water potential greater than minimum value")
+        LOGGER.critical(err)
+        raise err
+
+    # Calculate how much moisture suppresses microbial activity
+    supression = (
+        (np.log10(-water_potential) - np.log10(-water_potential_opt))
+        / (np.log10(-water_potential_halt) - np.log10(-water_potential_opt))
+    ) ** moisture_response_curvature
+
+    return 1 - supression
 
 
 def convert_moisture_to_scalar(
@@ -333,42 +386,48 @@ def convert_moisture_to_scalar(
 
 def calculate_maintenance_respiration(
     soil_c_pool_microbe: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
-    microbial_turnover_rate: float,
+    soil_temp: NDArray[np.float32],
+    constants: SoilConsts,
 ) -> NDArray[np.float32]:
     """Calculate the maintenance respiration of the microbial pool.
 
     Args:
         soil_c_pool_microbe: Microbial biomass (carbon) pool [kg C m^-3]
-        moist_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
-        microbial_turnover_rate: Rate of microbial biomass turnover [day^-1]
+        soil_temp: soil temperature for each soil grid cell [degrees C]
+        constants: Set of constants for the soil model.
 
     Returns:
-        Total respiration for all microbial biomass
+        Total maintenance respiration for all microbial biomass
     """
 
-    return microbial_turnover_rate * moist_temp_scalar * soil_c_pool_microbe
+    temp_factor = calculate_temperature_effect_on_microbes(
+        soil_temperature=soil_temp,
+        activation_energy=constants.activation_energy_microbial_turnover,
+        reference_temperature=constants.arrhenius_reference_temp,
+    )
+
+    return constants.microbial_turnover_rate * temp_factor * soil_c_pool_microbe
 
 
 def calculate_necromass_adsorption(
     soil_c_pool_microbe: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
+    moisture_scalar: NDArray[np.float32],
     necromass_adsorption_rate: float,
 ) -> NDArray[np.float32]:
     """Calculate adsorption of microbial necromass to soil minerals.
 
     Args:
         soil_c_pool_microbe: Microbial biomass (carbon) pool [kg C m^-3]
-        moist_temp_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
+        moisture_scalar: A scalar capturing the impact of soil moisture on process rates
+            [unitless]
         necromass_adsorption_rate: Rate at which necromass is adsorbed by soil minerals
+            [day^-1]
 
     Returns:
         Adsorption of microbial biomass to mineral associated organic matter (MAOM)
     """
 
-    return necromass_adsorption_rate * moist_temp_scalar * soil_c_pool_microbe
+    return necromass_adsorption_rate * moisture_scalar * soil_c_pool_microbe
 
 
 def calculate_carbon_use_efficiency(
@@ -379,6 +438,8 @@ def calculate_carbon_use_efficiency(
 ) -> NDArray[np.float32]:
     """Calculate the (temperature dependant) carbon use efficiency.
 
+    TODO - This should be adapted to use an Arrhenius function at some point.
+
     Args:
         soil_temp: soil temperature for each soil grid cell [degrees C]
         reference_cue: Carbon use efficiency at reference temp [unitless]
@@ -468,7 +529,7 @@ def calculate_pom_decomposition_saturation(
 def calculate_microbial_carbon_uptake(
     soil_c_pool_lmwc: NDArray[np.float32],
     soil_c_pool_microbe: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
+    water_factor: NDArray[np.float32],
     soil_temp: NDArray[np.float32],
     constants: SoilConsts,
 ) -> NDArray[np.float32]:
@@ -477,8 +538,8 @@ def calculate_microbial_carbon_uptake(
     Args:
         soil_c_pool_lmwc: Low molecular weight carbon pool [kg C m^-3]
         soil_c_pool_microbe: Microbial biomass (carbon) pool [kg C m^-3]
-        moist_temp_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
+        water_factor: A factor capturing the impact of soil water potential on microbial
+            rates [unitless]
         soil_temp: soil temperature for each soil grid cell [degrees C]
         constants: Set of constants for the soil model.
 
@@ -496,6 +557,11 @@ def calculate_microbial_carbon_uptake(
     microbial_saturation = calculate_microbial_saturation(
         soil_c_pool_microbe, constants.half_sat_microbial_activity
     )
+    temp_factor = calculate_temperature_effect_on_microbes(
+        soil_temperature=soil_temp,
+        activation_energy=constants.activation_energy_microbial_uptake,
+        reference_temperature=constants.arrhenius_reference_temp,
+    )
 
     # TODO - the quantities calculated above can be used to calculate the carbon
     # respired instead of being uptaken. This isn't currently of interest, but will be
@@ -503,7 +569,8 @@ def calculate_microbial_carbon_uptake(
 
     return (
         constants.max_uptake_rate_labile_C
-        * moist_temp_scalar
+        * water_factor
+        * temp_factor
         * soil_c_pool_lmwc
         * microbial_saturation
         * carbon_use_efficency
@@ -512,7 +579,7 @@ def calculate_microbial_carbon_uptake(
 
 def calculate_labile_carbon_leaching(
     soil_c_pool_lmwc: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
+    moisture_scalar: NDArray[np.float32],
     leaching_rate: float,
 ) -> NDArray[np.float32]:
     """Calculate rate at which labile carbon is leached.
@@ -522,21 +589,22 @@ def calculate_labile_carbon_leaching(
 
     Args:
         soil_c_pool_lmwc: Low molecular weight carbon pool [kg C m^-3]
-        moist_temp_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
+        moisture_scalar: A scalar capturing the impact of soil moisture on process rates
+            [unitless]
         leaching_rate: The rate at which labile carbon leaches from the soil [day^-1]
 
     Returns:
         The amount of labile carbon leached
     """
 
-    return leaching_rate * moist_temp_scalar * soil_c_pool_lmwc
+    return leaching_rate * moisture_scalar * soil_c_pool_lmwc
 
 
 def calculate_pom_decomposition(
     soil_c_pool_pom: NDArray[np.float32],
     soil_c_pool_microbe: NDArray[np.float32],
-    moist_temp_scalar: NDArray[np.float32],
+    water_factor: NDArray[np.float32],
+    soil_temp: NDArray[np.float32],
     constants: SoilConsts,
 ) -> NDArray[np.float32]:
     """Calculate decomposition of particulate organic matter into labile carbon (LMWC).
@@ -547,8 +615,9 @@ def calculate_pom_decomposition(
     Args:
         soil_c_pool_pom: Particulate organic matter pool [kg C m^-3]
         soil_c_pool_microbe: Microbial biomass (carbon) pool [kg C m^-3]
-        moist_temp_scalar: A scalar capturing the combined impact of soil moisture and
-            temperature on process rates
+        water_factor: A factor capturing the impact of soil water potential on microbial
+            rates [unitless]
+        soil_temp: soil temperature for each soil grid cell [degrees C]
         constants: Set of constants for the soil model.
 
     Returns:
@@ -564,9 +633,17 @@ def calculate_pom_decomposition(
         soil_c_pool_pom, constants.half_sat_pom_decomposition
     )
 
+    # Calculate the impact of temperature on the rate
+    temp_factor = calculate_temperature_effect_on_microbes(
+        soil_temperature=soil_temp,
+        activation_energy=constants.activation_energy_pom_decomp,
+        reference_temperature=constants.arrhenius_reference_temp,
+    )
+
     return (
         constants.max_decomp_rate_pom
         * saturation_with_pom
         * saturation_with_biomass
-        * moist_temp_scalar
+        * water_factor
+        * temp_factor
     )
diff --git a/virtual_rainforest/models/soil/constants.py b/virtual_rainforest/models/soil/constants.py
index b9640e22c..1ec32dca0 100644
--- a/virtual_rainforest/models/soil/constants.py
+++ b/virtual_rainforest/models/soil/constants.py
@@ -7,9 +7,6 @@
 
 # TODO - Need to figure out a sensible area to volume conversion
 
-# TODO - Need to either work out what the temp and moisture scalars are, or find an
-# alternative way of capturing temperature and moisture effects
-
 
 @dataclass(frozen=True)
 class SoilConsts:
@@ -56,86 +53,125 @@ class SoilConsts:
     Units of [(RWC)^-1]
     """
 
-    temp_scalar_coefficient_1: float = 15.4
-    """Used in :cite:t:`abramoff_millennial_2018`, can't find original source.
+    reference_cue: float = 0.6
+    """Carbon use efficiency of community at the reference temperature [no units].
 
-    Unclear exactly what this parameter is [degrees C]
+    Default value taken from :cite:t:`abramoff_millennial_2018`.
     """
 
-    temp_scalar_coefficient_2: float = 11.75
-    """Used in :cite:t:`abramoff_millennial_2018`, can't find original source.
+    cue_reference_temp: float = 15.0
+    """Reference temperature for carbon use efficiency [degrees C].
 
-    Unclear exactly what this parameter is [units unclear]
+    Default value taken from :cite:t:`abramoff_millennial_2018`.
     """
 
-    temp_scalar_coefficient_3: float = 29.7
-    """Used in :cite:t:`abramoff_millennial_2018`, can't find original source.
+    cue_with_temperature: float = 0.012
+    """Change in carbon use efficiency with increasing temperature [degree C^-1].
 
-    Unclear exactly what this parameter is [units unclear]
+    Default value taken from :cite:t:`abramoff_millennial_2018`.
     """
 
-    temp_scalar_coefficient_4: float = 0.031
-    """Used in :cite:t:`abramoff_millennial_2018`, can't find original source.
+    necromass_adsorption_rate: float = 0.025
+    """Rate at which necromass is adsorbed by soil minerals [day^-1].
 
-    Unclear exactly what this parameter is [units unclear]
+    Taken from :cite:t:`abramoff_millennial_2018`, where it was obtained by calibration.
     """
 
-    temp_scalar_reference_temp: float = 30.0
-    """Used in :cite:t:`abramoff_millennial_2018`, can't find original source.
+    half_sat_microbial_activity: float = 0.0072
+    """Half saturation constant for microbial activity (with increasing biomass).
 
-    Reference temperature [degrees C]
+    Units of [kg C m^-2].
     """
 
-    reference_cue: float = 0.6
-    """Carbon use efficiency of community at the reference temperature [no units].
+    half_sat_microbial_pom_mineralisation: float = 0.012
+    """Half saturation constant for microbial POM mineralisation [kg C m^-2]."""
 
-    Default value taken from :cite:t:`abramoff_millennial_2018`.
+    leaching_rate_labile_carbon: float = 1.5e-3
+    """Leaching rate for labile carbon (lmwc) [day^-1]."""
+
+    half_sat_pom_decomposition: float = 0.150
+    """Half saturation constant for POM decomposition to LMWC [kg C m^-2]."""
+
+    soil_microbe_water_potential_optimum: float = -3.0
+    """The water potential at which soil microbial rates are maximised [kPa].
+
+    Value is taken from :cite:t`moyano_responses_2013`.
     """
 
-    cue_reference_temp: float = 15.0
-    """Reference temperature for carbon use efficiency [degrees C].
+    soil_microbe_water_potential_halt: float = -15800.0
+    """The water potential at which soil microbial activity stops entirely [kPa].
 
-    Default value taken from :cite:t:`abramoff_millennial_2018`.
+    Value is taken from :cite:t`moyano_responses_2013`.
     """
 
-    cue_with_temperature: float = 0.012
-    """Change in carbon use efficiency with increasing temperature [degree C^-1].
+    moisture_response_curvature: float = 1.47
+    """Curvature of the soil microbial moisture response function [unitless].
 
-    Default value taken from :cite:t:`abramoff_millennial_2018`.
+    Value is taken from :cite:t`moyano_responses_2013`.
     """
 
-    microbial_turnover_rate: float = 0.036
-    """Microbial turnover rate [day^-1], this isn't a constant but often treated as one.
+    arrhenius_reference_temp: float = 12.0
+    """Reference temperature for the Arrhenius equation [C].
+
+    This is the reference temperature used in :cite:t:`wang_development_2013`, which is
+    the source of the activation energies and corresponding rates.
     """
 
-    max_uptake_rate_labile_C: float = 0.35
-    """Maximum (theoretical) rate at which microbes can take up labile carbon [day^-1].
+    # TODO - Split this and the following into 2 constants once fungi are introduced
+    max_uptake_rate_labile_C: float = 0.04
+    """Maximum rate at the reference temperature of labile carbon uptake [day^-1].
+
+    The reference temperature is given
+    by :attr:`arrhenius_reference_temp`, and the corresponding activation energy is
+    given by :attr:`activation_energy_microbial_uptake`.
+
+    TODO - Source of this constant is not completely clear, investigate this further
+    once fungi are added.
     """
 
-    necromass_adsorption_rate: float = 0.025
-    """Rate at which necromass is adsorbed by soil minerals [day^-1].
+    activation_energy_microbial_uptake = 47000
+    """Activation energy for microbial uptake of low molecular weight carbon [J K^-1].
 
-    Taken from :cite:t:`abramoff_millennial_2018`, where it was obtained by calibration.
+    Value taken from :cite:t:`wang_development_2013`. The maximum labile carbon uptake
+    rate that this activation energy corresponds to is given by
+    :attr:`max_uptake_rate_labile_C`.
     """
 
-    half_sat_microbial_activity: float = 0.0072
-    """Half saturation constant for microbial activity (with increasing biomass).
+    # TODO - Add another set of constants once we start tracking lignin
+    max_decomp_rate_pom: float = 0.2
+    """Maximum rate for particulate organic matter break down (at reference temp).
 
-    Units of [kg C m^-2].
+    Units of [day^-1]. The reference temperature is given by
+    :attr:`arrhenius_reference_temp`, and the corresponding activation energy is given
+    by :attr:`activation_energy_pom_decomp`.
+
+    TODO - Once enzymes are included this should take the value of 200.0 [day^-1].
+
+    TODO - Source of this constant is not completely clear, investigate this further
+    once lignin chemistry is added.
     """
 
-    half_sat_microbial_pom_mineralisation: float = 0.012
-    """Half saturation constant for microbial POM mineralisation [kg C m^-2]."""
+    activation_energy_pom_decomp = 37000
+    """Activation energy for decomposition of particulate organic matter [J K^-1].
 
-    max_decomp_rate_pom: float = 0.01
-    """Maximum (theoretical) rate for particulate organic matter break down.
+    Taken from :cite:t:`wang_development_2013`.
+    """
 
-    Units of [kg C m^-2 day^-1]. Taken from :cite:t:`abramoff_millennial_2018`, where it
-    was obtained by calibration.
+    # TODO - Split this and the following into 2 constants once fungi are introduced
+    microbial_turnover_rate: float = 0.005
+    """Microbial turnover rate at reference temperature [day^-1].
+
+    The reference temperature is given by :attr:`arrhenius_reference_temp`, and the
+    corresponding activation energy is given by
+    :attr:`activation_energy_microbial_turnover`.
+
+    TODO - Source of this constant is not completely clear, investigate this further
+    once fungi are added.
     """
 
-    leaching_rate_labile_carbon: float = 1.5e-3
-    """Leaching rate for labile carbon (lmwc) [day^-1]."""
+    activation_energy_microbial_turnover = 20000
+    """Activation energy for microbial maintenance turnover rate [J K^-1].
 
-    half_sat_pom_decomposition: float = 0.150
-    """Half saturation constant for POM decomposition to LMWC [kg C m^-2]."""
+    Value taken from :cite:t:`wang_development_2013`. The microbial turnover rate that
+    this activation energy corresponds to is given by :attr:`microbial_turnover_rate`.
+    """
diff --git a/virtual_rainforest/models/soil/soil_model.py b/virtual_rainforest/models/soil/soil_model.py
index 400136e4b..5855635eb 100644
--- a/virtual_rainforest/models/soil/soil_model.py
+++ b/virtual_rainforest/models/soil/soil_model.py
@@ -288,6 +288,7 @@ def construct_full_soil_model(
         pH=data["pH"].to_numpy(),
         bulk_density=data["bulk_density"].to_numpy(),
         soil_moisture=data["soil_moisture"][top_soil_layer_index].to_numpy(),
+        soil_water_potential=data["matric_potential"][top_soil_layer_index].to_numpy(),
         soil_temp=data["soil_temperature"][top_soil_layer_index].to_numpy(),
         percent_clay=data["percent_clay"].to_numpy(),
         mineralisation_rate=data["litter_C_mineralisation_rate"].to_numpy(),