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Add phosphorus to litter model #539

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merged 11 commits into from
Sep 10, 2024
Merged

Add phosphorus to litter model #539

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0c19765
Added phosphorus stochiometries for plant tissue turnovers
jacobcook1995 Aug 22, 2024
f1164ef
Updated plant tests to also test for phosphrus output
jacobcook1995 Aug 22, 2024
fadd9a4
Added phosphorus to litter partitioning calculation
jacobcook1995 Aug 22, 2024
dc95382
Added variables to track litter pool phosphorus concentrations
jacobcook1995 Aug 23, 2024
e8e2353
Added function to calculate phosphorus ratios of litter input flows
jacobcook1995 Aug 23, 2024
af82384
Added function to calculate changes in pool C:P ratios
jacobcook1995 Aug 27, 2024
603c5d9
Added calculation of phosphorus into function to calculate litter poo…
jacobcook1995 Aug 27, 2024
5c73e39
Added phosphorus update to litter model simulation flow
jacobcook1995 Aug 27, 2024
37cef39
Added a function to calculate P mineralisation rate from litter
jacobcook1995 Aug 27, 2024
a30889c
Added calculation of litter P mineralisation into the main model flow
jacobcook1995 Aug 27, 2024
76f76f9
Merge branch 'develop' into 512-litter-phosphorus
jacobcook1995 Sep 10, 2024
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20 changes: 19 additions & 1 deletion docs/source/refs.bib
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Original file line number Diff line number Diff line change
Expand Up @@ -839,4 +839,22 @@ @article{harfoot_madingley_2014
publisher={Public Library of Science},
doi={10.1371/journal.pbio.1001841},
url={https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001841}
}
}

@article{cleveland_cnp_2007,
title = {C:{N}:{P} stoichiometry in soil: is there a “{Redfield} ratio” for the microbial biomass?},
volume = {85},
copyright = {http://www.springer.com/tdm},
issn = {0168-2563, 1573-515X},
shorttitle = {C},
url = {http://link.springer.com/10.1007/s10533-007-9132-0},
doi = {10.1007/s10533-007-9132-0},
language = {en},
number = {3},
urldate = {2024-08-22},
journal = {Biogeochemistry},
author = {Cleveland, Cory C. and Liptzin, Daniel},
month = aug,
year = {2007},
pages = {235--252},
}
49 changes: 48 additions & 1 deletion tests/models/litter/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ def fixture_litter_model(dummy_litter_data, fixture_core_components):

# Build the config object
config = Config(
cfg_strings="[core]\n[core.timing]\nupdate_interval = '24 hours'\n[litter]\n"
cfg_strings="[core]\n[core.timing]\nupdate_interval = '48 hours'\n[litter]\n"
)
core_components = CoreComponents(config)

Expand Down Expand Up @@ -54,6 +54,11 @@ def dummy_litter_data(fixture_core_components):
"c_n_ratio_woody": [55.5, 63.3, 47.3, 59.1],
"c_n_ratio_below_metabolic": [10.7, 11.3, 15.2, 12.4],
"c_n_ratio_below_structural": [50.5, 55.6, 73.1, 61.2],
"c_p_ratio_above_metabolic": [57.3, 68.7, 100.1, 95.8],
"c_p_ratio_above_structural": [337.5, 473.2, 415.8, 570.2],
"c_p_ratio_woody": [555.5, 763.3, 847.3, 599.1],
"c_p_ratio_below_metabolic": [310.7, 411.3, 315.2, 412.4],
"c_p_ratio_below_structural": [550.5, 595.6, 773.1, 651.2],
"decomposed_excrement": [8e-07, 8.42857e-07, 3.28571e-05, 3.28571e-05],
"decomposed_carcasses": [1.0714e-4, 4.8571e-4, 1.15714e-3, 1.15714e-3],
"deadwood_production": [0.075, 0.099, 0.063, 0.033],
Expand All @@ -68,6 +73,10 @@ def dummy_litter_data(fixture_core_components):
"leaf_turnover_c_n_ratio": [15.0, 25.5, 43.1, 57.4],
"plant_reproductive_tissue_turnover_c_n_ratio": [12.5, 23.8, 15.7, 18.2],
"root_turnover_c_n_ratio": [30.3, 45.6, 43.3, 37.1],
"deadwood_c_p_ratio": [856.5, 675.4, 933.2, 888.8],
"leaf_turnover_c_p_ratio": [415.0, 327.4, 554.5, 380.9],
"plant_reproductive_tissue_turnover_c_p_ratio": [125.5, 105.0, 145.0, 189.2],
"root_turnover_c_p_ratio": [656.7, 450.6, 437.3, 371.9],
}

for var, vals in pool_values.items():
Expand Down Expand Up @@ -140,6 +149,11 @@ def metabolic_splits(dummy_litter_data):
"plant_reproductive_tissue_turnover_c_n_ratio"
].to_numpy(),
root_turnover_c_n_ratio=dummy_litter_data["root_turnover_c_n_ratio"].to_numpy(),
leaf_turnover_c_p_ratio=dummy_litter_data["leaf_turnover_c_p_ratio"].to_numpy(),
reproduct_turnover_c_p_ratio=dummy_litter_data[
"plant_reproductive_tissue_turnover_c_p_ratio"
].to_numpy(),
root_turnover_c_p_ratio=dummy_litter_data["root_turnover_c_p_ratio"].to_numpy(),
constants=LitterConsts,
)

Expand Down Expand Up @@ -197,3 +211,36 @@ def input_c_n_ratios(dummy_litter_data, metabolic_splits, litter_chemistry):
)

return input_c_n_ratios


@pytest.fixture
def input_c_p_ratios(dummy_litter_data, metabolic_splits, litter_chemistry):
"""Carbon:nitrogen ratio of each input flow."""

input_c_p_ratios = litter_chemistry.calculate_litter_input_phosphorus_ratios(
metabolic_splits=metabolic_splits,
struct_to_meta_phosphorus_ratio=LitterConsts.structural_to_metabolic_p_ratio,
)

return input_c_p_ratios


@pytest.fixture
def updated_pools(dummy_litter_data, decay_rates, plant_inputs):
"""Updated carbon mass of each pool."""
from virtual_ecosystem.models.litter.carbon import calculate_updated_pools

updated_pools = calculate_updated_pools(
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(),
below_metabolic=dummy_litter_data["litter_pool_below_metabolic"].to_numpy(),
below_structural=dummy_litter_data["litter_pool_below_structural"].to_numpy(),
decomposed_excrement=dummy_litter_data["decomposed_excrement"].to_numpy(),
decomposed_carcasses=dummy_litter_data["decomposed_carcasses"].to_numpy(),
decay_rates=decay_rates,
plant_inputs=plant_inputs,
update_interval=2.0,
)

return updated_pools
8 changes: 4 additions & 4 deletions tests/models/litter/test_carbon.py
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Expand Up @@ -105,11 +105,11 @@ def test_calculate_updated_pools(dummy_litter_data, decay_rates, plant_inputs):
from virtual_ecosystem.models.litter.carbon import calculate_updated_pools

expected_pools = {
"above_metabolic": [0.31632696, 0.15296346, 0.08537701, 0.08087947],
"above_structural": [0.50453639, 0.25006367, 0.09842669, 0.11162423],
"above_metabolic": [0.31567198, 0.1529074957, 0.0813030042, 0.0736771942],
"above_structural": [0.50519138, 0.25011962, 0.10250070, 0.11882651],
"woody": [4.77403361, 11.89845863, 7.3598224, 7.3298224],
"below_metabolic": [0.40174907, 0.36687303, 0.06792061, 0.08224246],
"below_structural": [0.60638765, 0.31821335, 0.02010401, 0.03038216],
"below_metabolic": [0.3976309, 0.3630269, 0.06787947, 0.07794085],
"below_structural": [0.61050583, 0.32205947352, 0.02014514530, 0.03468376530],
}

actual_pools = calculate_updated_pools(
Expand Down
143 changes: 94 additions & 49 deletions tests/models/litter/test_chemistry.py
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Expand Up @@ -27,27 +27,24 @@ def test_calculate_litter_chemistry_factor():


def test_calculate_new_pool_chemistries(
dummy_litter_data, plant_inputs, metabolic_splits, litter_chemistry
dummy_litter_data, plant_inputs, metabolic_splits, updated_pools, litter_chemistry
):
"""Test that function to calculate updated pool chemistries works correctly."""

updated_pools = {
"above_metabolic": np.array([0.32072786, 0.15473132, 0.08523907, 0.08074153]),
"above_structural": np.array([0.5047038, 0.25068224, 0.09843778, 0.11163532]),
"woody": np.array([4.774517, 11.898729, 7.361411, 7.331411]),
"below_metabolic": np.array([0.4090768, 0.37287148, 0.06883228, 0.08315412]),
"below_structural": np.array([0.6066315, 0.31860251, 0.02010566, 0.03038382]),
}

expected_chemistries = {
"lignin_above_structural": [0.49790843, 0.10067782, 0.70495536, 0.71045831],
"lignin_woody": [0.49580586, 0.79787834, 0.35224223, 0.35012603],
"lignin_below_structural": [0.50313604, 0.26586391, 0.7499951, 0.82142894],
"c_n_ratio_above_metabolic": [7.42828417, 8.93702902, 11.13974273, 10.28862942],
"c_n_ratio_above_structural": [37.5698310, 43.3465444, 49.0206010, 54.4471558],
"c_n_ratio_woody": [55.58168366, 63.25507083, 47.52080006, 59.08199528],
"c_n_ratio_below_metabolic": [10.9044015, 11.4675610, 15.2070612, 12.6623415],
"c_n_ratio_below_structural": [50.7755820, 56.387878, 73.1837156, 64.0424461],
"lignin_above_structural": [0.49726219, 0.10065698, 0.67693666, 0.6673972],
"lignin_woody": [0.49580543, 0.7978783, 0.35224272, 0.35012606],
"lignin_below_structural": [0.49974338, 0.26270806, 0.74846367, 0.71955592],
"c_n_ratio_above_metabolic": [7.39175978, 8.93054462, 10.40414542, 9.86041981],
"c_n_ratio_above_structural": [37.5547150, 43.3448492, 48.0974058, 52.0359678],
"c_n_ratio_woody": [55.5816919, 63.2550698, 47.5208477, 59.0819914],
"c_n_ratio_below_metabolic": [10.7299421, 11.3394567, 15.1984024, 12.2222413],
"c_n_ratio_below_structural": [50.6228215, 55.9998994, 73.0948342, 58.6661277],
"c_p_ratio_above_metabolic": [69.957176, 68.5502416, 107.1709994, 96.55826106],
"c_p_ratio_above_structural": [346.048307, 472.496124, 465.834123, 525.882608],
"c_p_ratio_woody": [560.22870571, 762.56863636, 848.03530307, 600.40427444],
"c_p_ratio_below_metabolic": [308.200782, 405.110726, 314.824814, 372.870229],
"c_p_ratio_below_structural": [563.06464, 597.68324, 772.78968, 609.82810],
}

actual_chemistries = litter_chemistry.calculate_new_pool_chemistries(
Expand All @@ -63,20 +60,14 @@ def test_calculate_new_pool_chemistries(


def test_calculate_lignin_updates(
dummy_litter_data, plant_inputs, input_lignin, litter_chemistry
dummy_litter_data, plant_inputs, input_lignin, updated_pools, litter_chemistry
):
"""Test that the function to calculate the lignin updates works as expected."""

updated_pools = {
"above_structural": np.array([0.5047038, 0.25068224, 0.09843778, 0.11163532]),
"woody": np.array([4.774517, 11.898729, 7.361411, 7.331411]),
"below_structural": np.array([0.6066315, 0.31860251, 0.02010566, 0.03038382]),
}

expected_lignin = {
"above_structural": [-0.00209157, 0.00067782, 0.00495532, 0.01045834],
"woody": [-0.00419414, -0.00212166, 0.00224223, 0.00012603],
"below_structural": [3.1360386e-3, 1.5863906e-2, -4.90160482e-6, 7.1428885e-2],
"above_structural": [-0.00273781, 0.00065698, -0.02306334, -0.03260280],
"woody": [-0.00419457, -0.0021217, 0.00224272, 0.00012606],
"below_structural": [-0.00025662, 0.01270806, -0.00153633, -0.03044408],
}

actual_lignin = litter_chemistry.calculate_lignin_updates(
Expand Down Expand Up @@ -113,24 +104,16 @@ def test_calculate_change_in_chemical_concentration(dummy_litter_data):


def test_calculate_c_n_ratio_updates(
dummy_litter_data, plant_inputs, input_c_n_ratios, litter_chemistry
dummy_litter_data, plant_inputs, input_c_n_ratios, updated_pools, litter_chemistry
):
"""Test that calculation of C:N ratio updates works properly."""

updated_pools = {
"above_metabolic": np.array([0.32072786, 0.15473132, 0.08523907, 0.08074153]),
"above_structural": np.array([0.5047038, 0.25068224, 0.09843778, 0.11163532]),
"woody": np.array([4.774517, 11.898729, 7.361411, 7.331411]),
"below_metabolic": np.array([0.4090768, 0.37287148, 0.06883228, 0.08315412]),
"below_structural": np.array([0.6066315, 0.31860251, 0.02010566, 0.03038382]),
}

expected_change = {
"above_metabolic": [0.12828416, 0.23702901, 1.03974239, 0.48862956],
"above_structural": [0.06983094, 0.14654437, 3.22060275, 4.24715499],
"woody": [0.081683655, -0.04492917, 0.220800061, -0.01800472],
"below_metabolic": [0.20440145, 0.16756069, 0.00706121, 0.26234147],
"below_structural": [0.27558203, 0.78787769, 0.08371555, 2.8424462],
"above_metabolic": [0.09175978, 0.23054462, 0.30414542, 0.06041981],
"above_structural": [0.05471499, 0.14484922, 2.29740576, 1.835967773],
"woody": [0.0816919, -0.0449302, 0.2208477, -0.0180086],
"below_metabolic": [0.02994209, 0.03945672, -0.00159759, -0.17775875],
"below_structural": [0.12282146, 0.39989943, -0.00516585, -2.53387232],
}

actual_change = litter_chemistry.calculate_c_n_ratio_updates(
Expand All @@ -145,6 +128,31 @@ def test_calculate_c_n_ratio_updates(
assert np.allclose(actual_change[key], expected_change[key])


def test_calculate_c_p_ratio_updates(
dummy_litter_data, plant_inputs, input_c_p_ratios, updated_pools, litter_chemistry
):
"""Test that calculation of C:P ratio updates works properly."""

expected_change = {
"above_metabolic": [12.657176, -0.14975840, 7.07099940, 0.75826106],
"above_structural": [8.5483073, -0.7038764, 50.034123, -44.317392],
"woody": [4.72870571, -0.73136364, 0.73530307, 1.30427444],
"below_metabolic": [-2.49921796, -6.18927446, -0.37518617, -39.52977135],
"below_structural": [12.56464272, 2.08324337, -0.31032454, -41.37190224],
}

actual_change = litter_chemistry.calculate_c_p_ratio_updates(
plant_inputs=plant_inputs,
input_c_p_ratios=input_c_p_ratios,
updated_pools=updated_pools,
)

assert set(expected_change.keys()) == set(actual_change.keys())

for key in actual_change.keys():
assert np.allclose(actual_change[key], expected_change[key])


def test_calculate_N_mineralisation(dummy_litter_data, decay_rates, litter_chemistry):
"""Test that function to calculate nitrogen mineralisation rate works properly."""

Expand All @@ -158,14 +166,27 @@ def test_calculate_N_mineralisation(dummy_litter_data, decay_rates, litter_chemi
assert np.allclose(actual_n_mineral, expected_n_mineral)


def test_calculate_P_mineralisation(dummy_litter_data, decay_rates, litter_chemistry):
"""Test that function to calculate phosphorus mineralisation rate works properly."""

expected_p_mineral = [4.39937479e-4, 2.13832149e-4, 6.40698004e-5, 6.56405873e-5]

actual_p_mineral = litter_chemistry.calculate_P_mineralisation(
decay_rates=decay_rates,
active_microbe_depth=CoreConsts.max_depth_of_microbial_activity,
)

assert np.allclose(actual_p_mineral, expected_p_mineral)


def test_calculate_litter_input_lignin_concentrations(
dummy_litter_data, plant_inputs, litter_chemistry
):
"""Check calculation of lignin concentrations of each plant flow to litter."""

expected_woody = [0.233, 0.545, 0.612, 0.378]
expected_concs_above_struct = [0.28329484, 0.23062465, 0.75773447, 0.75393599]
expected_concs_below_struct = [0.77196233, 0.80040249, 0.74908861, 0.95895666]
expected_concs_above_struct = [0.24971768, 0.22111396, 0.51122474, 0.56571041]
expected_concs_below_struct = [0.48590258, 0.56412613, 0.54265483, 0.67810978]

actual_concs = litter_chemistry.calculate_litter_input_lignin_concentrations(
plant_input_below_struct=plant_inputs["below_ground_structural"],
Expand All @@ -184,10 +205,10 @@ def test_calculate_litter_input_nitrogen_ratios(

expected_c_n_ratios = {
"woody": [60.7, 57.9, 73.1, 55.1],
"below_metabolic": [14.879783, 16.587126, 17.733169, 13.903046],
"below_structural": [74.398916, 82.935630, 88.665843, 69.515230],
"above_metabolic": [8.9373399, 14.343140, 15.968877, 13.520689],
"above_structural": [44.735092, 71.440811, 83.323241, 72.103527],
"below_metabolic": [11.449427, 13.09700, 14.48056, 11.04331],
"below_structural": [57.24714, 65.48498, 72.40281, 55.21655],
"above_metabolic": [8.48355299, 14.17116914, 12.3424635, 11.10877484],
"above_structural": [42.5018709, 69.9028550, 64.6044513, 57.7622747],
}

actual_c_n_ratios = litter_chemistry.calculate_litter_input_nitrogen_ratios(
Expand All @@ -201,6 +222,30 @@ def test_calculate_litter_input_nitrogen_ratios(
assert np.allclose(actual_c_n_ratios[key], expected_c_n_ratios[key])


def test_calculate_litter_input_phosphorus_ratios(
dummy_litter_data, metabolic_splits, litter_chemistry
):
"""Check function to calculate the C:P ratios of input to each litter pool works."""

expected_c_p_ratios = {
"woody": [856.5, 675.4, 933.2, 888.8],
"below_metabolic": [248.1465, 129.418998, 146.243645, 110.700999],
"below_structural": [1240.73249721, 647.09498874, 731.2182237, 553.50499377],
"above_metabolic": [220.55713162, 65.14600889, 152.23446238, 112.22496062],
"above_structural": [1118.95921, 343.440873, 825.333331, 387.658509],
}

actual_c_p_ratios = litter_chemistry.calculate_litter_input_phosphorus_ratios(
metabolic_splits=metabolic_splits,
struct_to_meta_phosphorus_ratio=LitterConsts.structural_to_metabolic_p_ratio,
)

assert set(expected_c_p_ratios.keys()) == set(actual_c_p_ratios.keys())

for key in actual_c_p_ratios.keys():
assert np.allclose(actual_c_p_ratios[key], expected_c_p_ratios[key])


def test_calculate_nutrient_split_between_litter_pools(
dummy_litter_data, metabolic_splits
):
Expand All @@ -209,8 +254,8 @@ def test_calculate_nutrient_split_between_litter_pools(
calculate_nutrient_split_between_litter_pools,
)

expected_meta_c_n = np.array([14.879783, 16.587126, 17.733169, 13.903046])
expected_struct_c_n = np.array([74.398916, 82.935630, 88.665843, 69.515230])
expected_meta_c_n = np.array([11.449427, 13.09700, 14.48056, 11.04331])
expected_struct_c_n = np.array([57.24714, 65.48498, 72.40281, 55.21655])

actual_meta_c_n, actual_struct_c_n = calculate_nutrient_split_between_litter_pools(
input_c_nut_ratio=dummy_litter_data["root_turnover_c_n_ratio"],
Expand Down
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