Add soil enzymes to model

docs/source/api/soil/env_factors.md

@@ -0,0 +1,23 @@
+---
+jupytext:
+  cell_metadata_filter: -all
+  formats: md:myst
+  main_language: python
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.13.8
+kernelspec:
+  display_name: vr_python3
+  language: python
+  name: vr_python3
+---
+
+# API documentation for the {mod}`~virtual_rainforest.models.soil.env_factors` module
+
+```{eval-rst}
+.. automodule:: virtual_rainforest.models.soil.env_factors
+    :autosummary:
+    :members:
+```

docs/source/data_recipes/soil_dummy.md

@@ -22,85 +22,42 @@ this data is a set of plausible values for which the soil model absolutely has t
 function sensibly for.
 
 ```{code-cell} ipython3
+import numpy as np
 from xarray import Dataset
 
-def generate_pH_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of pH values.
-
-    We're looking at acidic soils so a range of 3.5-4.5 seems plausible.
-    """
-    return 3.5 + (x * y) / (64)
-
-
-def generate_BD_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of bulk density values.
-
-    Bulk density can vary quite a lot so a range of 1200-1800 kg m^-3 seems sensible.
-    """
-    return 1200.0 + 600.0 * (x * y) / (64)
-
-
-def generate_clay_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of clay content values.
-
-    We're considering fairly clayey soils, so look at a range of 27.0-40.0 % clay.
-    """
-    return 27.0 + 13.0 * (x * y) / (64)
-
-
-def generate_lmwc_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of lmwc values.
-
-    LMWC generally a very small carbon pool, so a range of 0.005-0.01 kg C m^-3 is used.
-    """
-    return 0.005 + 0.005 * (x * y) / (64)
-
-
-def generate_maom_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of maom values.
-
-    A huge amount of carbon can be locked away as MAOM, so a range of 1.0-3.0 kg C m^-3
-    is used.
-    """
-    return 1.0 + 2.0 * (x * y) / (64)
-
-
-def generate_microbial_C_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of microbial C values.
-
-    The carbon locked up as microbial biomass is tiny, so a range of 0.0015-0.005
-    kg C m^-3 is used.
-    """
-    return 0.0015 + 0.0035 * (x * y) / (64)
-
-def generate_pom_values(x: float, y: float) -> float:
-    """Function to generate a reasonable range of pom values.
-
-    A reasonable amount of carbon is stored as particulate organic matter (POM), so a
-    range of 0.1-1.0 kg C m^-3 is used.
-    """
-    return 0.1 + 0.9 * (x * y) / (64)
-
-
-# Generate range of cell numbers in the a x and y directions. Here we have a 9x9 grid,
-# so cells are numbered from 0 to 8 in each direction.
-x_cell_ids = range(0, 9)
-y_cell_ids = range(0, 9)
-
-# Make matrices containing all the relevant values
-pH_values = [[generate_pH_values(x, y) for y in y_cell_ids] for x in x_cell_ids]
-bulk_density_values = [
-    [generate_BD_values(x, y) for y in y_cell_ids] for x in x_cell_ids
-]
-percent_clay_values = [
-    [generate_clay_values(x, y) for y in y_cell_ids] for x in x_cell_ids
-]
-lmwc_values = [[generate_lmwc_values(x, y) for y in y_cell_ids] for x in x_cell_ids]
-maom_values = [[generate_maom_values(x, y) for y in y_cell_ids] for x in x_cell_ids]
-microbial_C_values = [
-    [generate_microbial_C_values(x, y) for y in y_cell_ids] for x in x_cell_ids
-]
-pom_values = [[generate_pom_values(x, y) for y in y_cell_ids] for x in x_cell_ids]
+# How far the center of each cell is from the origin. This applies to both the x and y
+# direction independently, so cell (0,0) is at the origin, whereas cell (2,3) is 180m
+# from the origin in the x direction and 270m in the y direction.
+cell_spacing = np.arange(0, 721, 90)
+gradient = np.multiply.outer(cell_spacing/90, cell_spacing/90)
+
+# Generate a range of plausible values (3.5-4.5) for the soil pH [unitless].
+pH_values = 3.5 + 1.00 * (gradient) / (64)
+
+# Generate a range of plausible values (1200-1800) for the bulk density [kg m^-3].
+bulk_density_values = 1200.0 + 600.0 * (gradient) / (64)
+
+# Generate a range of plausible values (0.27-0.40) for the clay fraction [fraction].
+clay_fraction_values = 0.27 + 0.13 * (gradient) / (64)
+
+# Generate a range of plausible values (0.005-0.01) for the lmwc pool [kg C m^-3].
+lmwc_values = 0.005 + 0.005 * (gradient) / (64)
+
+# Generate a range of plausible values (1.0-3.0) for the maom pool [kg C m^-3].
+maom_values = 1.0 + 2.0 * (gradient) / (64)
+
+# Generate a range of plausible values (0.0015-0.005) for the microbial C pool
+# [kg C m^-3].
+microbial_C_values = 0.0015 + 0.0035 * (gradient) / (64)
+
+# Generate a range of plausible values (0.1-1.0) for the POM pool [kg C m^-3].
+pom_values = 0.1 + 0.9 * (gradient) / (64)
+
+# Generate a range of plausible values (0.01-0.5) for the POM enzyme pool [kg C m^-3].
+pom_enzyme_values = 0.01 + 0.49 * (gradient) / (64)
+
+# Generate a range of plausible values (0.01-0.5) for the MAOM enzyme pool [kg C m^-3].
+maom_enzyme_values = 0.01 + 0.49 * (gradient) / (64)
 
 # How far the center of each cell is from the origin. This applies to both the x and y
 # direction independently, so cell (0,0) is at the origin, whereas cell (2,3) is 180m
@@ -112,11 +69,13 @@ dummy_soil_data = Dataset(
     data_vars=dict(
         pH=(["x", "y"], pH_values),
         bulk_density=(["x", "y"], bulk_density_values),
-        percent_clay=(["x", "y"], percent_clay_values),
+        clay_fraction=(["x", "y"], clay_fraction_values),
         soil_c_pool_lmwc=(["x", "y"], lmwc_values),
         soil_c_pool_maom=(["x", "y"], maom_values),
         soil_c_pool_microbe=(["x", "y"], microbial_C_values),
         soil_c_pool_pom=(["x", "y"], pom_values),
+        soil_enzyme_pom = (["x", "y"], pom_enzyme_values),
+        soil_enzyme_maom = (["x", "y"], maom_enzyme_values),
     ),
     coords=dict(
         x=(["x"], cell_displacements),

docs/source/index.md

@@ -99,6 +99,7 @@ team.
   Soil Overview <api/soil.md>
   Soil Model <api/soil/soil_model.md>
   Soil Carbon <api/soil/carbon.md>
+  Soil Environmental Factors <api/soil/env_factors.md>
   Soil Constants <api/soil/constants.md>
   Abiotic Simple Overview <api/abiotic_simple.md>
   Abiotic Simple Model <api/abiotic_simple/abiotic_simple_model.md>

docs/source/refs.bib

@@ -1,3 +1,36 @@
+@article{porporato_hydrologic_2003,
+	title = {Hydrologic controls on soil carbon and nitrogen cycles. {I}. {Modeling} scheme},
+	volume = {26},
+	issn = {03091708},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S0309170802000945},
+	doi = {10.1016/S0309-1708(02)00094-5},
+	language = {en},
+	number = {1},
+	urldate = {2023-11-14},
+	journal = {Advances in Water Resources},
+	author = {Porporato, A and D’Odorico, P and Laio, F and Rodriguez-Iturbe, I},
+	month = jan,
+	year = {2003},
+	pages = {45--58},
+}
+
+@article{orwin_organic_2011,
+	title = {Organic nutrient uptake by mycorrhizal fungi enhances ecosystem carbon storage: a model-based assessment},
+	volume = {14},
+	issn = {1461023X},
+	shorttitle = {Organic nutrient uptake by mycorrhizal fungi enhances ecosystem carbon storage},
+	url = {https://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2011.01611.x},
+	doi = {10.1111/j.1461-0248.2011.01611.x},
+	language = {en},
+	number = {5},
+	urldate = {2023-10-18},
+	journal = {Ecology Letters},
+	author = {Orwin, Kate H. and Kirschbaum, Miko U. F. and St John, Mark G. and Dickie, Ian A.},
+	month = may,
+	year = {2011},
+	pages = {493--502},
+}
+
 
 @book{monteith_light_1969,
 	address = {Madison, Wisconsin, U.S.A.},
@@ -333,22 +366,6 @@ @book{campbell_introduction_1998
 	year = {1998},
 }
 
-@article{mayes_relation_2012,
-	title = {Relation between {Soil} {Order} and {Sorption} of {Dissolved} {Organic} {Carbon} in {Temperate} {Subsoils}},
-	volume = {76},
-	issn = {0361-5995, 1435-0661},
-	url = {https://onlinelibrary.wiley.com/doi/10.2136/sssaj2011.0340},
-	doi = {10.2136/sssaj2011.0340},
-	language = {en},
-	number = {3},
-	urldate = {2023-02-22},
-	journal = {Soil Science Society of America Journal},
-	author = {Mayes, Melanie A. and Heal, Katherine R. and Brandt, Craig C. and Phillips, Jana R. and Jardine, Philip M.},
-	month = may,
-	year = {2012},
-	pages = {1027--1037},
-}
-
 @article{davis_simple_2017,
 	title = {Simple process-led algorithms for simulating habitats ({SPLASH} v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture},
 	volume = {10},

tests/conftest.py

@@ -172,12 +172,22 @@ def dummy_carbon_data(layer_roles_fixture):
     """Microbial biomass (carbon) pool (kg C m^-3)"""
     data["soil_c_pool_pom"] = DataArray([0.1, 1.0, 0.7, 0.35], dims=["cell_id"])
     """Particulate organic matter pool (kg C m^-3)"""
+    data["soil_enzyme_pom"] = DataArray(
+        [0.022679, 0.009576, 0.050051, 0.003010], dims=["cell_id"]
+    )
+    """Soil enzyme that breaks down particulate organic matter (kg C m^-3)"""
+    data["soil_enzyme_maom"] = DataArray(
+        [0.0356, 0.0117, 0.02509, 0.00456], dims=["cell_id"]
+    )
+    """Soil enzyme that breaks down mineral associated organic matter (kg C m^-3)"""
     data["pH"] = DataArray([3.0, 7.5, 9.0, 5.7], dims=["cell_id"])
     data["bulk_density"] = DataArray([1350.0, 1800.0, 1000.0, 1500.0], dims=["cell_id"])
-    data["percent_clay"] = DataArray([80.0, 30.0, 10.0, 90.0], dims=["cell_id"])
+    data["clay_fraction"] = DataArray([0.8, 0.3, 0.1, 0.9], dims=["cell_id"])
     data["litter_C_mineralisation_rate"] = DataArray(
         [0.00212106, 0.00106053, 0.00049000, 0.0055], dims=["cell_id"]
     )
+    # Data for combined vertical flow (for entire timestep)
+    data["vertical_flow"] = DataArray([3.0, 15.0, 75.0, 47.7], dims=["cell_id"])
 
     # The layer dependant data has to be handled separately
     data["soil_moisture"] = xr.concat(
@@ -186,7 +196,7 @@ def dummy_carbon_data(layer_roles_fixture):
             # At present the soil model only uses the top soil layer, so this is the
             # only one with real test values in
             DataArray(
-                [[0.472467929, 0.399900047, 0.256053640, 0.153616897]],
+                [[0.9304620050, 0.787549327, 0.504263188, 0.302527807]],
                 dims=["layers", "cell_id"],
             ),
             DataArray(np.full((1, 4), np.nan), dims=["layers", "cell_id"]),
@@ -200,7 +210,6 @@ 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"]),

tests/core/test_constants_class.py

@@ -29,14 +29,14 @@ class Test(ConstantsDataclass):  # type: ignore [misc]
         pytest.param(
             {},
             does_not_raise(),
-            123.4,
+            0.25,
             (),
             id="defaults_with_no_config",
         ),
         pytest.param(
-            {"placeholder": 432.1},
+            {"depth_of_active_soil_layer": 1.55},
             does_not_raise(),
-            432.1,
+            1.55,
             (),
             id="configured",
         ),
@@ -70,6 +70,6 @@ def test_ConstantsDataclass_from_config(caplog, config, raises, exp_val, exp_log
         constants_instance = CoreConsts.from_config(config)
 
         if isinstance(raises, does_not_raise):
-            assert constants_instance.placeholder == exp_val
+            assert constants_instance.depth_of_active_soil_layer == exp_val
 
         log_check(caplog=caplog, expected_log=exp_log)

tests/core/test_constants_loader.py

@@ -17,16 +17,16 @@
             "core",
             "CoreConsts",
             does_not_raise(),
-            123.4,
+            0.25,
             ((INFO, "Initialised core.CoreConsts from config"),),
             id="default_values",
         ),
         pytest.param(
-            "[core.constants.CoreConsts]\nplaceholder=432.1",
+            "[core.constants.CoreConsts]\ndepth_of_active_soil_layer=1.5",
             "core",
             "CoreConsts",
             does_not_raise(),
-            432.1,
+            1.5,
             ((INFO, "Initialised core.CoreConsts from config"),),
             id="configured_value",
         ),
@@ -96,7 +96,7 @@ def test_load_constants(
             assert isinstance(constants_instance, CoreConsts)
             # The unconfigurable zero_Celsius should take the default value
             assert constants_instance.zero_Celsius == constants.zero_Celsius
-            # Check the placeholder constant has been configured
-            assert constants_instance.placeholder == exp_val
+            # Check the depth_of_active_soil_layer constant has been configured
+            assert constants_instance.depth_of_active_soil_layer == exp_val
 
         log_check(caplog=caplog, expected_log=exp_log)

tests/core/test_data.py

@@ -986,6 +986,8 @@ def test_output_current_state(mocker, dummy_carbon_data, time_index):
             "soil_c_pool_lmwc",
             "soil_c_pool_microbe",
             "soil_c_pool_pom",
+            "soil_enzyme_pom",
+            "soil_enzyme_maom",
         ],
         time_index,
     )