From 7886411e1af6ab5f6c6ae2e748c5a29738af3a51 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 25 Jul 2024 16:35:38 +0100 Subject: [PATCH 01/28] Initial draft of new quantum yield module: some mypy issues --- pyrealm/pmodel/functions.py | 75 +- pyrealm/pmodel/quantum_yield.py | 255 + pyrealm_build_data/sandoval_kphio/calc_phi0.R | 91 + .../sandoval_kphio/sandoval_kphio.csv | 8001 +++++++++++++++++ 4 files changed, 8420 insertions(+), 2 deletions(-) create mode 100644 pyrealm/pmodel/quantum_yield.py create mode 100644 pyrealm_build_data/sandoval_kphio/calc_phi0.R create mode 100644 pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 91d921d3..f666794a 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -14,6 +14,7 @@ def calc_ftemp_arrh( tk: NDArray, ha: float, + tk_ref: NDArray | None = None, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ) -> NDArray: @@ -47,6 +48,7 @@ def calc_ftemp_arrh( Args: tk: Temperature (in Kelvin) ha: Activation energy (in :math:`J \text{mol}^{-1}`) + tk_ref: The reference temperature for the reaction. pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. core_const: Instance of :class:`~pyrealm.constants.core_const.CoreConst`. @@ -69,9 +71,10 @@ def calc_ftemp_arrh( # exp( ha * (tc - 25.0)/(298.15 * kR * (tc + 273.15)) ) # exp( (ha/kR) * (1/298.15 - 1/tk) ) - tkref = pmodel_const.plant_T_ref + core_const.k_CtoK + if tk_ref is None: + tk_ref = np.array([pmodel_const.plant_T_ref + core_const.k_CtoK]) - return np.exp(ha * (tk - tkref) / (tkref * core_const.k_R * tk)) + return np.exp(ha * (tk - tk_ref) / (tk_ref * core_const.k_R * tk)) def calc_ftemp_inst_rd( @@ -194,6 +197,74 @@ def calc_ftemp_inst_vcmax( return fva * fvb +def calc_modified_arrhenius_factor( + tk: NDArray, + Ha: NDArray, + Hd: NDArray, + deltaS: NDArray, + mode: str = "M2002", + tk_ref: NDArray | None = None, + pmodel_const: PModelConst = PModelConst(), + core_const: CoreConst = CoreConst(), +) -> NDArray: + r"""Calculate the modified Arrhenius factor with temperature for an enzyme. + + This function returns a temperature-determined factor expressing the rate of an + enzymatic process relative to the rate at a given reference temperature. This is + used in the calculation of :math:`V_{cmax}` but also other temperature dependent + enzymatic processes. + + The function can operate in one of two modes ("M2002" or "J1942") using the two + alternative derivations of the modified Arrhenius relationship presented in + :cite:`murphy:2021a`. + + Args: + tk: The temperature at which to calculate the factor (K) + Ha: The activation energy of the enzyme (:math:`H_a`) + Hd: The deactivation energy of the enzyme (:math:`H_d`) + deltaS: The entropy of the process (:math:`\Delta S`) + tk_ref: The reference temperature for the process (K) + mode: The calculation mode. + pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. + core_const: Instance of :class:`~pyrealm.constants.core_const.CoreConst`. + + PModel Parameters: + To: The standard reference temperature expressed in Kelvin (`T_0`, ``k_To``) + R: the universal gas constant (:math:`R`, ``k_R``) + + Returns: + Values for :math:`f` + + Examples: + >>> # Relative change in Vcmax going (instantaneously, i.e. not + >>> # not acclimatedly) from 10 to 25 degrees (percent change): + >>> val = ((calc_ftemp_inst_vcmax(25)/calc_ftemp_inst_vcmax(10)-1) * 100) + >>> round(val, 4) + np.float64(283.1775) + """ + + if mode not in ["M2002", "J1942"]: + raise ValueError( + f"Unknown mode option for calc_modified_arrhenius_factor: {mode}" + ) + + # Convert temperatures to Kelvin + if tk_ref is None: + tk_ref = pmodel_const.plant_T_ref + core_const.k_CtoK + + # Calculate Arrhenius components + fva = calc_ftemp_arrh(tk=tk, ha=Ha, tk_ref=tk_ref) + + fvb = (1 + np.exp((tk_ref * deltaS - Hd) / (core_const.k_R * tk_ref))) / ( + 1 + np.exp((tk * deltaS - Hd) / (core_const.k_R * tk)) + ) + + if mode == "M2002": + return fva * fvb + elif mode == "J1942": + return fva * (tk / tk_ref) * fvb + + def calc_ftemp_kphio( tc: NDArray, c4: bool = False, pmodel_const: PModelConst = PModelConst() ) -> NDArray: diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py new file mode 100644 index 00000000..04fc9338 --- /dev/null +++ b/pyrealm/pmodel/quantum_yield.py @@ -0,0 +1,255 @@ +r"""The module :mod:`~pyrealm.pmodel.quantum_yield` provides +the abstract base class :class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC`, +which is used to support different implementations of the calculation of the intrinsic +quantum yield of photosynthesis. +""" # noqa D210, D415 + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any +from warnings import warn + +import numpy as np +from numpy.typing import NDArray + +from pyrealm import ExperimentalFeatureWarning +from pyrealm.constants import CoreConst, PModelConst +from pyrealm.core.utilities import ( + check_input_shapes, + evaluate_horner_polynomial, + summarize_attrs, +) +from pyrealm.pmodel.functions import calc_modified_arrhenius_factor +from pyrealm.pmodel.pmodel_environment import PModelEnvironment + +QUANTUM_YIELD_CLASS_REGISTRY: dict[str, type[QuantumYieldABC]] = {} +r"""A registry for intrinsic quantum yield of photosynthesis calculation classes. + +Different implementations of the calculation of the intrinsic quantum yield of +photosynthesis (:math:`\phi_{0}`) must all be subclasses of +:class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC` abstract base class. This +dictionary is used as a registry for defined subclasses and a method name is used to +retrieve a particular implementation from this registry. For example: + +.. code:: python + + bianchi_phi_0 = QUANTUM_YIELD_CLASS_REGISTRY['bianchi'] +""" + + +class QuantumYieldABC(ABC): + r"""ABC for calculating the intrinsic quantum yield of photosynthesis. + + This provides an abstract base class for the implementation of alternative + approaches to calculating the the intrinsic quantum yield of photosynthesis. All + implementations estimate the :math:`\phi_{0} following values, which is then stored + in the ``kphio`` attribute of the resulting class instance. + + The abstract base class requires that implementations of specific approaches defines + the `calculate_kphio` method. The provides the approach specific calculation of + ``kphio`` and is automatically called by the ``__init__`` method when a subclass + instance is created. + + Args: + env: An instance of + :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` providing the + photosynthetic environment for the model. + pmodel_const: An instance of + :class:`~pyrealm.constants.pmodel_const.PModelConst`. + + Returns: + Instances of the abstract base class should not be created - use instances of + specific subclasses. + """ + + method: str + """A short method name used to identify the class in + :data:`~pyrealm.pmodel.quantum_yield.QUANTUM_YIELD_CLASS_REGISTRY`. + """ + is_c4: bool + """A flag indicating if the method captures the C4 photosynthetic pathway.""" + requires: list[str] + """A list of names of optional attributes of + :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` that must be populated + to use a method. + """ + + def __init__( + self, + env: PModelEnvironment, + pmodel_const: PModelConst = PModelConst(), + core_const: CoreConst = CoreConst(), + ): + self.env: PModelEnvironment = env + """The PModelEnvironment containing the photosynthetic environment for the + model.""" + self.shape: tuple[int, ...] = env.shape + """The shape of the input environment data.""" + self.pmodel_const: PModelConst = pmodel_const + """The PModelConst used for calculating quantum yield""" + self.core_const: CoreConst = core_const + """The CoreConst used for calculating quantum yield""" + + # Declare attributes populated by methods. These are typed but not assigned a + # default value as they must are populated by the subclass specific + # calculate_kphio method, which is called below to populate the values. + self.kphio: NDArray + """The intrinsic quantum yield of photosynthesis.""" + + # Run the calculation methods after checking for any required variables + self._check_requires() + self._calculate_kphio() + + # Validate that the subclass methods populate the attributes correctly. + _ = check_input_shapes(env.ca, self.kphio) + + @abstractmethod + def _calculate_kphio(self, **kwargs: Any) -> None: + """Calculate the intrinsic quantum yield of photosynthesis.""" + + def _check_requires(self) -> None: + """Check additional required variables are present.""" + + for required_var in self.requires: + if getattr(self.env, required_var) is None: + raise ValueError( + f"{self.__class__.__name__} (method {self.method}) requires " + f"{required_var} to be provided in the PModelEnvironment." + ) + + def __repr__(self) -> str: + """Generates a string representation of an QuantumYield instance.""" + return f"{type(self).__name__}(shape={self.shape})" + + def summarize(self, dp: int = 2) -> None: + """Print QuantumYield summary. + + Prints a summary of the variables calculated within an instance + of QuantumYield including the mean, range and number of nan values. + + Args: + dp: The number of decimal places used in rounding summary stats. + """ + + attrs = [("kphio", "-")] + + summarize_attrs(self, attrs, dp=dp) + + @classmethod + def __init_subclass__(cls, method: str, is_c4: bool, requires: list[str]) -> None: + """Initialise a subclass deriving from this ABC.""" + + cls.method = method + cls.is_c4 = is_c4 + cls.requires = requires + QUANTUM_YIELD_CLASS_REGISTRY[cls.method] = cls + + +class QuantumYieldConstant( + QuantumYieldABC, + method="constant", + is_c4=False, + requires=[], +): + """Constant kphio.""" + + def _calculate_kphio(self, **kwargs: Any) -> None: + """Constant kphio.""" + + if "init_kphio" not in kwargs: + raise ValueError("Missing definition of initial kphio.") + + self.kphio = np.array([kwargs["init_kphio"]]) + + +class QuantumYieldBernacchiC3( + QuantumYieldABC, + method="bernacchi_c3", + is_c4=False, + requires=[], +): + """Calculate kphio following Bernacchi for C3 plants.""" + + def _calculate_kphio(self, **kwargs: Any) -> None: + """Calculate kphio.""" + + if "init_kphio" not in kwargs: + raise ValueError("Missing definition of constant kphio.") + + ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C3) + ftemp = np.clip(ftemp, 0.0, None) + + self.kphio = ftemp * kwargs["init_kphio"] + + +class QuantumYieldBernacchiC4( + QuantumYieldABC, + method="bernacchi_c4", + is_c4=True, + requires=[], +): + """Calculate kphio following Bernacchi.""" + + def _calculate_kphio(self, **kwargs: Any) -> None: + """Calculate kphio.""" + + if "init_kphio" not in kwargs: + raise ValueError("Missing definition of constant kphio.") + + ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C4) + ftemp = np.clip(ftemp, 0.0, None) + + self.kphio = ftemp * kwargs["init_kphio"] + + +class QuantumYieldSandoval( + QuantumYieldABC, + method="sandoval", + is_c4=False, + requires=["aridity_index", "mean_growth_temperature"], +): + """Calculate kphio following Sandoval.""" + + def _calculate_kphio(self, **kwargs: Any) -> None: + """Constant kphio.""" + + # Warn that this is an experimental feature. + warn( + "The sandoval method for calculating kphi0 is experimental, " + "see the class documentation", + ExperimentalFeatureWarning, + ) + + # Calculate activation entropy as a function of mean growth temperature, J/mol/K + deltaS = 1558.853 - 50.223 * self.env.mean_growth_temperature + # Calculate deaactivation energy J/mol + Hd = 294.804 * deltaS + # activation energy J/mol + Ha = 75000 + + # theoretical maximum phi0 and curvature parameters (Long, 1993;Sandoval et al., + # in.prep.) + phi_o_theo = 0.111 + m = 6.8681 + n = 0.07956432 + + # Calculate the optimal temperature to be used as the reference temperature in + # the modified Arrhenius calculation + Topt = Hd / (deltaS - self.core_const.k_R * np.log(Ha / (Hd - Ha))) + + # Calculate peak kphio given the aridity index + kphio_peak = phi_o_theo / (1 + (self.env.aridity_index) ** m) ** n + + # Calculate the modified Arrhenius factor using the + f_kphio = calc_modified_arrhenius_factor( + tk=self.env.tc + self.core_const.k_CtoK, + Ha=Ha, + Hd=Hd, + deltaS=deltaS, + mode="J1942", + tk_ref=Topt, + ) + + # Apply the factor and store it. + self.kphio = kphio_peak * f_kphio diff --git a/pyrealm_build_data/sandoval_kphio/calc_phi0.R b/pyrealm_build_data/sandoval_kphio/calc_phi0.R new file mode 100644 index 00000000..125f504d --- /dev/null +++ b/pyrealm_build_data/sandoval_kphio/calc_phi0.R @@ -0,0 +1,91 @@ +# calc_phi0 +calc_phi0 <- function(AI, tc, mGDD0 = NA) { + # ************************************************************************ + # Name: calc_phi0 + # Inputs: - double - scalar (AI), climatological aridity index, defined as PET/P + # - double - vector (tc), air temperature, degrees C + # - double - scalar (mGDD0), mean temperature during growing degree days with tc>0 + # Returns: double, intrinsic quantum yield at temperature tc, mol CO2/mol photon + # Features: This function calculates the temperature and aridity dependence of the + # Intrinsic quantum Yield + # * Ref: Sandoval, Flo, Morfopoulus and Prentice + # The temperature effect on the intrinsic quantum yield at the ecosystem level + # in prep.; + # doi: + # ************************************************************************ + ############################################################################################### + # 01.define the parameters/constants + ############################################################################################### + phi_o_theo <- 0.111 # theoretical maximum phi0 (Long, 1993;Sandoval et al., in.prep.) + m <- 6.8681 # curvature parameter phio max (Sandoval et al., in.prep.) + n <- 0.07956432 # curvature parameter phio max (Sandoval et al., in.prep.) + Rgas <- 8.3145 # ideal gas constant J/mol/K + Ha <- 75000 # activation energy J/mol (Sandoval et al., in.prep.) + # if mGDD0 is missing, calculate + if (is.na(mGDD0)) { + mGDD0 <- mean(tc[tc > 0], na.rm = T) + } + ## calc activation entropy, J/mol/K (Sandoval et al., in.prep.) + DeltaS <- 1558.853 - 50.223 * mGDD0 + ## calc deaactivation energy J/mol (Sandoval et al., in.prep.) + Hd <- 294.804 * DeltaS + + ############################################################################################### + # 02.define the functions + ############################################################################################### + + no_acc_f_arr <- function(tcleaf, Ha = 71513, Hd = 2e+05, dent = 649) { + ### 10.1111/nph.16883 + + ## fix for optimization + if (!is.na(Ha) & !is.na(Hd) & Ha > Hd) { + Ha <- Hd - 1 + } + + Top <- Hd / (dent - Rgas * log(Ha / (Hd - Ha))) + tkleaf <- tcleaf + 273.15 + + f1 <- (tkleaf / Top) * exp((Ha * (tkleaf - Top)) / (Top * Rgas * tkleaf)) + f2 <- 1 + exp((Top * dent - Hd) / (Top * Rgas)) + f3 <- 1 + exp((tkleaf * dent - Hd) / (tkleaf * Rgas)) + + farr <- f1 * (f2 / f3) + + return(farr) + } + ############################################################################################### + # 03.calculate maximum phi0 + ############################################################################################### + phi_o_peak <- (phi_o_theo / (1 + (AI)^m)^n) + ############################################################################################### + # 04.calculate temperature dependence of phi0 + ############################################################################################### + phi0_fT <- no_acc_f_arr(tcleaf = tc, Ha = Ha, Hd = Hd, dent = DeltaS) + ############################################################################################### + # 05.calculate phi0 + ############################################################################################### + phi0 <- phi_o_peak * phi0_fT + return(phi0) +} + + +aridity_index <- exp(seq(log(0.2), log(30), length = 20)) +temp <- seq(5, 45, length = 20) +mean_gdd_temp <- seq(5, 45, length = 20) + +data <- expand.grid( + aridity_index = aridity_index, + temp = temp, + mean_gdd_temp = mean_gdd_temp +) + +data$phio <- NA + +for (row_idx in seq_along(data$aridity_index)) { + data$phio[row_idx] <- with( + data[row_idx, ], + calc_phi0(AI = aridity_index, tc = temp, mGDD0 = mean_gdd_temp) + ) +} + +write.csv(data, "sandoval_kphio.csv") diff --git a/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv new file mode 100644 index 00000000..1555dbdb --- /dev/null +++ b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv @@ -0,0 +1,8001 @@ +"","aridity_index","temp","mean_gdd_temp","phio" +"1",0.2,5,5,0.0276881368424111 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pyrealm/pmodel/pmodel_environment.py | 30 ++++++++++++++++++++++++++-- pyrealm/pmodel/quantum_yield.py | 2 +- 3 files changed, 38 insertions(+), 10 deletions(-) diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index f666794a..3fed726f 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -13,7 +13,7 @@ def calc_ftemp_arrh( tk: NDArray, - ha: float, + ha: float | NDArray, tk_ref: NDArray | None = None, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), @@ -199,9 +199,9 @@ def calc_ftemp_inst_vcmax( def calc_modified_arrhenius_factor( tk: NDArray, - Ha: NDArray, - Hd: NDArray, - deltaS: NDArray, + Ha: float | NDArray, + Hd: float | NDArray, + deltaS: float | NDArray, mode: str = "M2002", tk_ref: NDArray | None = None, pmodel_const: PModelConst = PModelConst(), @@ -250,7 +250,7 @@ def calc_modified_arrhenius_factor( # Convert temperatures to Kelvin if tk_ref is None: - tk_ref = pmodel_const.plant_T_ref + core_const.k_CtoK + tk_ref = np.array([pmodel_const.plant_T_ref + core_const.k_CtoK]) # Calculate Arrhenius components fva = calc_ftemp_arrh(tk=tk, ha=Ha, tk_ref=tk_ref) @@ -260,9 +260,11 @@ def calc_modified_arrhenius_factor( ) if mode == "M2002": + # Medlyn et al. 2002 simplification return fva * fvb - elif mode == "J1942": - return fva * (tk / tk_ref) * fvb + + # Johnson et al 1942 + return fva * (tk / tk_ref) * fvb def calc_ftemp_kphio( diff --git a/pyrealm/pmodel/pmodel_environment.py b/pyrealm/pmodel/pmodel_environment.py index 3b8be62d..14ff2f9b 100644 --- a/pyrealm/pmodel/pmodel_environment.py +++ b/pyrealm/pmodel/pmodel_environment.py @@ -81,6 +81,8 @@ def __init__( patm: NDArray, theta: NDArray | None = None, rootzonestress: NDArray | None = None, + aridity_index: NDArray | None = None, + mean_growth_temperature: NDArray | None = None, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ): @@ -132,10 +134,20 @@ def __init__( temperature and pressure, unitless""" # Optional variables - self.theta: NDArray | None = None + + # TODO - could this be done flexibly using kwargs and then setting the requires + # attributes for the downstream classes that use the PModelEnvironment. + # Easy to add the attributes dynamically, but bounds checking less + # obvious. + + self.theta: NDArray """Volumetric soil moisture (m3/m3)""" - self.rootzonestress: NDArray | None = None + self.rootzonestress: NDArray """Rootzone stress factor (experimental) (-)""" + self.aridity_index: NDArray + """Climatological aridity index as PET/P (-)""" + self.mean_growth_temperature: NDArray + """Mean temperature > 0°C during growing degree days (°C)""" if theta is not None: # Is the input congruent with the other variables and in bounds. @@ -149,6 +161,20 @@ def __init__( rootzonestress, 0, 1, "[]", "rootzonestress", "-" ) + if aridity_index is not None: + # Is the input congruent with the other variables and in bounds. + _ = check_input_shapes(tc, aridity_index) + self.aridity_index = bounds_checker( + aridity_index, 0, 50, "[]", "aridity_index", "-" + ) + + if mean_growth_temperature is not None: + # Is the input congruent with the other variables and in bounds. + _ = check_input_shapes(tc, mean_growth_temperature) + self.mean_growth_temperature = bounds_checker( + mean_growth_temperature, 0, 50, "[]", "mean_growth_temperature", "-" + ) + # Store constant settings self.pmodel_const = pmodel_const """PModel constants used to calculate environment""" diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 04fc9338..c3fc7b1d 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -226,7 +226,7 @@ def _calculate_kphio(self, **kwargs: Any) -> None: # Calculate deaactivation energy J/mol Hd = 294.804 * deltaS # activation energy J/mol - Ha = 75000 + Ha = 75000.0 # theoretical maximum phi0 and curvature parameters (Long, 1993;Sandoval et al., # in.prep.) From 07a9912868b9fe72c745ce05b216c22a249e696f Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 25 Jul 2024 19:58:19 +0100 Subject: [PATCH 03/28] Regression test for QuantumYieldSandoval --- tests/regression/pmodel/test_quantum_yield.py | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 tests/regression/pmodel/test_quantum_yield.py diff --git a/tests/regression/pmodel/test_quantum_yield.py b/tests/regression/pmodel/test_quantum_yield.py new file mode 100644 index 00000000..231567b2 --- /dev/null +++ b/tests/regression/pmodel/test_quantum_yield.py @@ -0,0 +1,45 @@ +"""Test quantum yield calculations against benchmarks.""" + +from importlib import resources + +import numpy as np +import pandas as pd +import pytest + + +@pytest.fixture(scope="module") +def values(): + """Fixture to load test inputs and expected rpmodel outputs from file.""" + + datapath = ( + resources.files("pyrealm_build_data.sandoval_kphio") / "sandoval_kphio.csv" + ) + + with open(str(datapath)) as infile: + values = pd.read_csv(infile) + + return values + + +def test_QuantumYieldSandoval(values): + """Check implementation against values from original R code.""" + from pyrealm.pmodel import PModelEnvironment + from pyrealm.pmodel.quantum_yield import QuantumYieldSandoval + + env = PModelEnvironment( + tc=values["temp"].to_numpy(), + patm=101325, + vpd=820, + co2=400, + mean_growth_temperature=values["mean_gdd_temp"].to_numpy(), + aridity_index=values["aridity_index"].to_numpy(), + ) + + # Calculate kphio for that environment + qy = QuantumYieldSandoval(env) + + # Get expected kphio, masking negative values from the reference implementation + expected = values["phio"].to_numpy() + expected = np.where(expected < 0, np.nan, expected) + + assert np.allclose(expected, qy.kphio, equal_nan=True) From 425b24855df3e2250f1e736acb081ca818b9dd1b Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 25 Jul 2024 20:33:05 +0100 Subject: [PATCH 04/28] Simpler sandoval test data set and test fixes --- pyrealm/pmodel/functions.py | 16 +- pyrealm/pmodel/optimal_chi.py | 2 +- pyrealm_build_data/sandoval_kphio/calc_phi0.R | 11 +- .../sandoval_kphio/sandoval_kphio.csv | 9838 +++-------------- 4 files changed, 1852 insertions(+), 8015 deletions(-) diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 3fed726f..84692690 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -62,8 +62,8 @@ def calc_ftemp_arrh( Examples: >>> # Relative rate change from 25 to 10 degrees Celsius (percent change) - >>> round((1.0-calc_ftemp_arrh( 283.15, 100000)) * 100, 4) - np.float64(88.1991) + >>> np.round((1.0-calc_ftemp_arrh( 283.15, 100000)) * 100, 4) + array([88.1991]) """ # Note that the following forms are equivalent: @@ -174,8 +174,8 @@ def calc_ftemp_inst_vcmax( >>> # Relative change in Vcmax going (instantaneously, i.e. not >>> # not acclimatedly) from 10 to 25 degrees (percent change): >>> val = ((calc_ftemp_inst_vcmax(25)/calc_ftemp_inst_vcmax(10)-1) * 100) - >>> round(val, 4) - np.float64(283.1775) + >>> np.round(val, 4) + array([283.1775]) """ # Convert temperatures to Kelvin @@ -239,8 +239,8 @@ def calc_modified_arrhenius_factor( >>> # Relative change in Vcmax going (instantaneously, i.e. not >>> # not acclimatedly) from 10 to 25 degrees (percent change): >>> val = ((calc_ftemp_inst_vcmax(25)/calc_ftemp_inst_vcmax(10)-1) * 100) - >>> round(val, 4) - np.float64(283.1775) + >>> np.round(val, 4) + array([283.1775]) """ if mode not in ["M2002", "J1942"]: @@ -477,8 +477,8 @@ def calc_kmm( Examples: >>> # Michaelis-Menten coefficient at 20 degrees Celsius and standard >>> # atmosphere (in Pa): - >>> round(calc_kmm(20, 101325), 5) - np.float64(46.09928) + >>> np.round(calc_kmm(20, 101325), 5) + array([46.09928]) """ # Check inputs, return shape not used diff --git a/pyrealm/pmodel/optimal_chi.py b/pyrealm/pmodel/optimal_chi.py index 2a028a0f..3b5b5ba7 100644 --- a/pyrealm/pmodel/optimal_chi.py +++ b/pyrealm/pmodel/optimal_chi.py @@ -151,7 +151,7 @@ def _check_requires(self) -> None: """Check additional required variables are present.""" for required_var in self.requires: - if getattr(self.env, required_var) is None: + if not hasattr(self.env, required_var): raise ValueError( f"{self.__class__.__name__} (method {self.method}) requires " f"{required_var} to be provided in the PModelEnvironment." diff --git a/pyrealm_build_data/sandoval_kphio/calc_phi0.R b/pyrealm_build_data/sandoval_kphio/calc_phi0.R index 125f504d..836c74e1 100644 --- a/pyrealm_build_data/sandoval_kphio/calc_phi0.R +++ b/pyrealm_build_data/sandoval_kphio/calc_phi0.R @@ -69,16 +69,17 @@ calc_phi0 <- function(AI, tc, mGDD0 = NA) { } -aridity_index <- exp(seq(log(0.2), log(30), length = 20)) -temp <- seq(5, 45, length = 20) -mean_gdd_temp <- seq(5, 45, length = 20) +aridity_index <- c(0.2, 0.5, 0.8, 1.0, 5.0, 10.0) +temp <- seq(0, 50, by = 1) +mean_gdd_temp <- seq(5, 30, by = 5) data <- expand.grid( - aridity_index = aridity_index, temp = temp, + aridity_index = aridity_index, mean_gdd_temp = mean_gdd_temp ) +# Function is not parallelised so loop over inputs data$phio <- NA for (row_idx in seq_along(data$aridity_index)) { @@ -88,4 +89,4 @@ for (row_idx in seq_along(data$aridity_index)) { ) } -write.csv(data, "sandoval_kphio.csv") +write.csv(data, "sandoval_kphio.csv", row.names = FALSE) diff --git a/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv index 1555dbdb..bb4af59e 100644 --- a/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv +++ b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv @@ -1,8001 +1,1837 @@ -"","aridity_index","temp","mean_gdd_temp","phio" -"1",0.2,5,5,0.0276881368424111 -"2",0.260352117694686,5,5,0.0276879584022285 -"3",0.338916125940539,5,5,0.0276868670405322 -"4",0.441187655547492,5,5,0.0276802027440709 -"5",0.574320702112717,5,5,0.0276398979166404 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+50,10,30,-0.0117748942966451 From 29b6959814a938a8236a7199337df085387b37c1 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 16:06:43 +0100 Subject: [PATCH 05/28] Added sandoval parameters to PModelConsts, update regresssion test values --- pyrealm/constants/pmodel_const.py | 16 + pyrealm/pmodel/quantum_yield.py | 83 +- pyrealm_build_data/sandoval_kphio/calc_phi0.R | 6 +- .../sandoval_kphio/sandoval_kphio.csv | 3672 ++++++++--------- 4 files changed, 1903 insertions(+), 1874 deletions(-) diff --git a/pyrealm/constants/pmodel_const.py b/pyrealm/constants/pmodel_const.py index 970179d3..817636e0 100644 --- a/pyrealm/constants/pmodel_const.py +++ b/pyrealm/constants/pmodel_const.py @@ -89,6 +89,22 @@ class PModelConst(ConstantsClass): """ + sandoval_peak_phio: tuple[float, float] = (6.8681, 0.07956432) + """Curvature parameters for calculation of peak phio in the Sandoval method for + estimation of quantum yield efficiency.""" + + sandoval_kinetics: tuple[float, float, float, float] = ( + 1558.853, + -50.223, + 294.804, + 75000.0, + ) + """Enzyme kinetics parameters for estimation of kphion from mean growth temperature + in the Sandoval method for estimation of quantum yield efficiency. Values are: the + intercept and slope of activation entropy as a function of the mean growth + temperature (J/mol/K), the deactivation energy constant (J/mol) and the activation + energy (J/mol). """ + plant_T_ref: float = 25.0 """Standard baseline reference temperature of photosynthetic processes (Prentice, unpublished) (:math:`T_o` , 25.0, °C)""" diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index c3fc7b1d..41895561 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -7,7 +7,6 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any from warnings import warn import numpy as np @@ -74,10 +73,14 @@ class QuantumYieldABC(ABC): :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` that must be populated to use a method. """ + default_reference_kphio: float + """A default value for the reference kphio value for use with a given + implementation.""" def __init__( self, env: PModelEnvironment, + reference_kphio: float | None = None, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ): @@ -90,12 +93,14 @@ def __init__( """The PModelConst used for calculating quantum yield""" self.core_const: CoreConst = core_const """The CoreConst used for calculating quantum yield""" + self.reference_kphio: float = reference_kphio or self.default_reference_kphio + """The reference value for kphio for the method.""" # Declare attributes populated by methods. These are typed but not assigned a # default value as they must are populated by the subclass specific # calculate_kphio method, which is called below to populate the values. self.kphio: NDArray - """The intrinsic quantum yield of photosynthesis.""" + """The calculated intrinsic quantum yield of photosynthesis.""" # Run the calculation methods after checking for any required variables self._check_requires() @@ -105,14 +110,14 @@ def __init__( _ = check_input_shapes(env.ca, self.kphio) @abstractmethod - def _calculate_kphio(self, **kwargs: Any) -> None: + def _calculate_kphio(self) -> None: """Calculate the intrinsic quantum yield of photosynthesis.""" def _check_requires(self) -> None: """Check additional required variables are present.""" for required_var in self.requires: - if getattr(self.env, required_var) is None: + if not hasattr(self.env, required_var): raise ValueError( f"{self.__class__.__name__} (method {self.method}) requires " f"{required_var} to be provided in the PModelEnvironment." @@ -137,12 +142,19 @@ def summarize(self, dp: int = 2) -> None: summarize_attrs(self, attrs, dp=dp) @classmethod - def __init_subclass__(cls, method: str, is_c4: bool, requires: list[str]) -> None: + def __init_subclass__( + cls, + method: str, + is_c4: bool, + requires: list[str], + default_reference_kphio: float, + ) -> None: """Initialise a subclass deriving from this ABC.""" cls.method = method cls.is_c4 = is_c4 cls.requires = requires + cls.default_reference_kphio = default_reference_kphio QUANTUM_YIELD_CLASS_REGISTRY[cls.method] = cls @@ -151,16 +163,14 @@ class QuantumYieldConstant( method="constant", is_c4=False, requires=[], + default_reference_kphio=0.049977, ): """Constant kphio.""" - def _calculate_kphio(self, **kwargs: Any) -> None: + def _calculate_kphio(self) -> None: """Constant kphio.""" - if "init_kphio" not in kwargs: - raise ValueError("Missing definition of initial kphio.") - - self.kphio = np.array([kwargs["init_kphio"]]) + self.kphio = np.array([self.reference_kphio]) class QuantumYieldBernacchiC3( @@ -168,19 +178,19 @@ class QuantumYieldBernacchiC3( method="bernacchi_c3", is_c4=False, requires=[], + default_reference_kphio=0.081785, ): """Calculate kphio following Bernacchi for C3 plants.""" - def _calculate_kphio(self, **kwargs: Any) -> None: + def _calculate_kphio( + self, + ) -> None: """Calculate kphio.""" - if "init_kphio" not in kwargs: - raise ValueError("Missing definition of constant kphio.") - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C3) ftemp = np.clip(ftemp, 0.0, None) - self.kphio = ftemp * kwargs["init_kphio"] + self.kphio = ftemp * self.reference_kphio class QuantumYieldBernacchiC4( @@ -188,19 +198,17 @@ class QuantumYieldBernacchiC4( method="bernacchi_c4", is_c4=True, requires=[], + default_reference_kphio=0.081785, ): """Calculate kphio following Bernacchi.""" - def _calculate_kphio(self, **kwargs: Any) -> None: + def _calculate_kphio(self) -> None: """Calculate kphio.""" - if "init_kphio" not in kwargs: - raise ValueError("Missing definition of constant kphio.") - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C4) ftemp = np.clip(ftemp, 0.0, None) - self.kphio = ftemp * kwargs["init_kphio"] + self.kphio = ftemp * self.reference_kphio class QuantumYieldSandoval( @@ -208,38 +216,39 @@ class QuantumYieldSandoval( method="sandoval", is_c4=False, requires=["aridity_index", "mean_growth_temperature"], + default_reference_kphio=1.0 / 9.0, ): - """Calculate kphio following Sandoval.""" + """Calculate kphio following Sandoval. - def _calculate_kphio(self, **kwargs: Any) -> None: - """Constant kphio.""" + Reference kphio is the theoretical maximum quantum yield, defaulting to the ratio of + 1/9 in the absence of a Q cycle (Long, 1993). + """ + + def _calculate_kphio(self) -> None: + """Calculate kphio.""" # Warn that this is an experimental feature. warn( - "The sandoval method for calculating kphi0 is experimental, " + "The sandoval method for calculating kphio is experimental, " "see the class documentation", ExperimentalFeatureWarning, ) - # Calculate activation entropy as a function of mean growth temperature, J/mol/K - deltaS = 1558.853 - 50.223 * self.env.mean_growth_temperature + # Calculate enzyme kinetics + a_ent, b_ent, Hd_base, Ha = self.pmodel_const.sandoval_kinetics + # Calculate activation entropy as a linear function of + # mean growth temperature, J/mol/K + deltaS = a_ent + b_ent * self.env.mean_growth_temperature # Calculate deaactivation energy J/mol - Hd = 294.804 * deltaS - # activation energy J/mol - Ha = 75000.0 - - # theoretical maximum phi0 and curvature parameters (Long, 1993;Sandoval et al., - # in.prep.) - phi_o_theo = 0.111 - m = 6.8681 - n = 0.07956432 + Hd = Hd_base * deltaS # Calculate the optimal temperature to be used as the reference temperature in # the modified Arrhenius calculation Topt = Hd / (deltaS - self.core_const.k_R * np.log(Ha / (Hd - Ha))) # Calculate peak kphio given the aridity index - kphio_peak = phi_o_theo / (1 + (self.env.aridity_index) ** m) ** n + m, n = self.pmodel_const.sandoval_peak_phio + kphio_peak = self.reference_kphio / (1 + (self.env.aridity_index) ** m) ** n # Calculate the modified Arrhenius factor using the f_kphio = calc_modified_arrhenius_factor( @@ -252,4 +261,4 @@ def _calculate_kphio(self, **kwargs: Any) -> None: ) # Apply the factor and store it. - self.kphio = kphio_peak * f_kphio + self.kphio = np.array([kphio_peak * f_kphio]) diff --git a/pyrealm_build_data/sandoval_kphio/calc_phi0.R b/pyrealm_build_data/sandoval_kphio/calc_phi0.R index 836c74e1..6be1fc27 100644 --- a/pyrealm_build_data/sandoval_kphio/calc_phi0.R +++ b/pyrealm_build_data/sandoval_kphio/calc_phi0.R @@ -16,7 +16,10 @@ calc_phi0 <- function(AI, tc, mGDD0 = NA) { ############################################################################################### # 01.define the parameters/constants ############################################################################################### - phi_o_theo <- 0.111 # theoretical maximum phi0 (Long, 1993;Sandoval et al., in.prep.) + + # DO change here to avoid imprecision in theoretical maxmimum + # phi_o_theo <- 0.111 # theoretical maximum phi0 (Long, 1993;Sandoval et al., in.prep.) + phi_o_theo <- 1 / 9 m <- 6.8681 # curvature parameter phio max (Sandoval et al., in.prep.) n <- 0.07956432 # curvature parameter phio max (Sandoval et al., in.prep.) Rgas <- 8.3145 # ideal gas constant J/mol/K @@ -89,4 +92,5 @@ for (row_idx in seq_along(data$aridity_index)) { ) } +data$phio <- round(data$phio, digits = 8) write.csv(data, "sandoval_kphio.csv", row.names = FALSE) diff --git a/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv index bb4af59e..b9adec24 100644 --- a/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv +++ b/pyrealm_build_data/sandoval_kphio/sandoval_kphio.csv @@ -1,1837 +1,1837 @@ "temp","aridity_index","mean_gdd_temp","phio" -0,0.2,5,0.0150189600590777 -1,0.2,5,0.0170035547170646 -2,0.2,5,0.0192332352709473 -3,0.2,5,0.0217360643227769 -4,0.2,5,0.024543006070574 -5,0.2,5,0.0276881368424111 -6,0.2,5,0.0312088037406028 -7,0.2,5,0.0351456582790139 -8,0.2,5,0.0395424206049159 -9,0.2,5,0.0444450927333507 -10,0.2,5,0.0499000783715251 -11,0.2,5,0.0559501789959781 -12,0.2,5,0.0626265467550122 -13,0.2,5,0.0699331259947415 -14,0.2,5,0.077817639344329 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+36,10,30,-0.010175 +37,10,30,-0.010292 +38,10,30,-0.01040875 +39,10,30,-0.01052525 +40,10,30,-0.01064147 +41,10,30,-0.01075741 +42,10,30,-0.01087306 +43,10,30,-0.01098841 +44,10,30,-0.01110345 +45,10,30,-0.01121816 +46,10,30,-0.01133255 +47,10,30,-0.01144661 +48,10,30,-0.01156032 +49,10,30,-0.01167368 +50,10,30,-0.01178668 From 0ad2fb536d6be1b9461b71eaac36936a150233f3 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 17:08:26 +0100 Subject: [PATCH 06/28] Rework reference code files, initial unit test suite --- pyproject.toml | 1 + pyrealm_build_data/sandoval_kphio/calc_phi0.R | 168 ++++++++---------- .../sandoval_kphio/create_test_inputs.R | 24 +++ tests/unit/pmodel/test_quantum_yield.py | 147 +++++++++++++++ 4 files changed, 244 insertions(+), 96 deletions(-) create mode 100644 pyrealm_build_data/sandoval_kphio/create_test_inputs.R create mode 100644 tests/unit/pmodel/test_quantum_yield.py diff --git a/pyproject.toml b/pyproject.toml index 8c28b9aa..4e22dcb1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,6 +106,7 @@ addopts = """ --doctest-modules --ignore=pyrealm/__main__.py --ignore=tests/pmodel/generate_test_inputs.py + --import-mode importlib """ python_files = 'test_*.py' testpaths = ['tests', 'pyrealm'] diff --git a/pyrealm_build_data/sandoval_kphio/calc_phi0.R b/pyrealm_build_data/sandoval_kphio/calc_phi0.R index 6be1fc27..e688ee28 100644 --- a/pyrealm_build_data/sandoval_kphio/calc_phi0.R +++ b/pyrealm_build_data/sandoval_kphio/calc_phi0.R @@ -1,96 +1,72 @@ -# calc_phi0 -calc_phi0 <- function(AI, tc, mGDD0 = NA) { - # ************************************************************************ - # Name: calc_phi0 - # Inputs: - double - scalar (AI), climatological aridity index, defined as PET/P - # - double - vector (tc), air temperature, degrees C - # - double - scalar (mGDD0), mean temperature during growing degree days with tc>0 - # Returns: double, intrinsic quantum yield at temperature tc, mol CO2/mol photon - # Features: This function calculates the temperature and aridity dependence of the - # Intrinsic quantum Yield - # * Ref: Sandoval, Flo, Morfopoulus and Prentice - # The temperature effect on the intrinsic quantum yield at the ecosystem level - # in prep.; - # doi: - # ************************************************************************ - ############################################################################################### - # 01.define the parameters/constants - ############################################################################################### - - # DO change here to avoid imprecision in theoretical maxmimum - # phi_o_theo <- 0.111 # theoretical maximum phi0 (Long, 1993;Sandoval et al., in.prep.) - phi_o_theo <- 1 / 9 - m <- 6.8681 # curvature parameter phio max (Sandoval et al., in.prep.) - n <- 0.07956432 # curvature parameter phio max (Sandoval et al., in.prep.) - Rgas <- 8.3145 # ideal gas constant J/mol/K - Ha <- 75000 # activation energy J/mol (Sandoval et al., in.prep.) - # if mGDD0 is missing, calculate - if (is.na(mGDD0)) { - mGDD0 <- mean(tc[tc > 0], na.rm = T) - } - ## calc activation entropy, J/mol/K (Sandoval et al., in.prep.) - DeltaS <- 1558.853 - 50.223 * mGDD0 - ## calc deaactivation energy J/mol (Sandoval et al., in.prep.) - Hd <- 294.804 * DeltaS - - ############################################################################################### - # 02.define the functions - ############################################################################################### - - no_acc_f_arr <- function(tcleaf, Ha = 71513, Hd = 2e+05, dent = 649) { - ### 10.1111/nph.16883 - - ## fix for optimization - if (!is.na(Ha) & !is.na(Hd) & Ha > Hd) { - Ha <- Hd - 1 - } - - Top <- Hd / (dent - Rgas * log(Ha / (Hd - Ha))) - tkleaf <- tcleaf + 273.15 - - f1 <- (tkleaf / Top) * exp((Ha * (tkleaf - Top)) / (Top * Rgas * tkleaf)) - f2 <- 1 + exp((Top * dent - Hd) / (Top * Rgas)) - f3 <- 1 + exp((tkleaf * dent - Hd) / (tkleaf * Rgas)) - - farr <- f1 * (f2 / f3) - - return(farr) - } - ############################################################################################### - # 03.calculate maximum phi0 - ############################################################################################### - phi_o_peak <- (phi_o_theo / (1 + (AI)^m)^n) - ############################################################################################### - # 04.calculate temperature dependence of phi0 - ############################################################################################### - phi0_fT <- no_acc_f_arr(tcleaf = tc, Ha = Ha, Hd = Hd, dent = DeltaS) - ############################################################################################### - # 05.calculate phi0 - ############################################################################################### - phi0 <- phi_o_peak * phi0_fT - return(phi0) -} - - -aridity_index <- c(0.2, 0.5, 0.8, 1.0, 5.0, 10.0) -temp <- seq(0, 50, by = 1) -mean_gdd_temp <- seq(5, 30, by = 5) - -data <- expand.grid( - temp = temp, - aridity_index = aridity_index, - mean_gdd_temp = mean_gdd_temp -) - -# Function is not parallelised so loop over inputs -data$phio <- NA - -for (row_idx in seq_along(data$aridity_index)) { - data$phio[row_idx] <- with( - data[row_idx, ], - calc_phi0(AI = aridity_index, tc = temp, mGDD0 = mean_gdd_temp) - ) -} - -data$phio <- round(data$phio, digits = 8) -write.csv(data, "sandoval_kphio.csv", row.names = FALSE) +# calc_phi0 +calc_phi0 <- function(AI, tc, mGDD0 = NA, phi_o_theo = 1 / 9) { + # ************************************************************************ + # Name: calc_phi0 + # Inputs: - double - scalar (AI), climatological aridity index, defined as PET/P + # - double - vector (tc), air temperature, degrees C + # - double - scalar (mGDD0), mean temperature during growing degree days with tc>0 + # Returns: double, intrinsic quantum yield at temperature tc, mol CO2/mol photon + # Features: This function calculates the temperature and aridity dependence of the + # Intrinsic quantum Yield + # * Ref: Sandoval, Flo, Morfopoulus and Prentice + # The temperature effect on the intrinsic quantum yield at the ecosystem level + # in prep.; + # doi: + # ************************************************************************ + ############################################################################################### + # 01.define the parameters/constants + ############################################################################################### + + # DO change here to avoid imprecision in theoretical maxmimum and to allow alternatives + # phi_o_theo <- 0.111 # theoretical maximum phi0 (Long, 1993;Sandoval et al., in.prep.) + + m <- 6.8681 # curvature parameter phio max (Sandoval et al., in.prep.) + n <- 0.07956432 # curvature parameter phio max (Sandoval et al., in.prep.) + Rgas <- 8.3145 # ideal gas constant J/mol/K + Ha <- 75000 # activation energy J/mol (Sandoval et al., in.prep.) + # if mGDD0 is missing, calculate + if (is.na(mGDD0)) { + mGDD0 <- mean(tc[tc > 0], na.rm = T) + } + ## calc activation entropy, J/mol/K (Sandoval et al., in.prep.) + DeltaS <- 1558.853 - 50.223 * mGDD0 + ## calc deaactivation energy J/mol (Sandoval et al., in.prep.) + Hd <- 294.804 * DeltaS + + ############################################################################################### + # 02.define the functions + ############################################################################################### + + no_acc_f_arr <- function(tcleaf, Ha = 71513, Hd = 2e+05, dent = 649) { + ### 10.1111/nph.16883 + + ## fix for optimization + if (!is.na(Ha) & !is.na(Hd) & Ha > Hd) { + Ha <- Hd - 1 + } + + Top <- Hd / (dent - Rgas * log(Ha / (Hd - Ha))) + tkleaf <- tcleaf + 273.15 + + f1 <- (tkleaf / Top) * exp((Ha * (tkleaf - Top)) / (Top * Rgas * tkleaf)) + f2 <- 1 + exp((Top * dent - Hd) / (Top * Rgas)) + f3 <- 1 + exp((tkleaf * dent - Hd) / (tkleaf * Rgas)) + + farr <- f1 * (f2 / f3) + + return(farr) + } + ############################################################################################### + # 03.calculate maximum phi0 + ############################################################################################### + phi_o_peak <- (phi_o_theo / (1 + (AI)^m)^n) + ############################################################################################### + # 04.calculate temperature dependence of phi0 + ############################################################################################### + phi0_fT <- no_acc_f_arr(tcleaf = tc, Ha = Ha, Hd = Hd, dent = DeltaS) + ############################################################################################### + # 05.calculate phi0 + ############################################################################################### + phi0 <- phi_o_peak * phi0_fT + return(phi0) +} diff --git a/pyrealm_build_data/sandoval_kphio/create_test_inputs.R b/pyrealm_build_data/sandoval_kphio/create_test_inputs.R new file mode 100644 index 00000000..79adb1a7 --- /dev/null +++ b/pyrealm_build_data/sandoval_kphio/create_test_inputs.R @@ -0,0 +1,24 @@ +source("calc_phi0.R") + +aridity_index <- c(0.2, 0.5, 0.8, 1.0, 5.0, 10.0) +temp <- seq(0, 50, by = 1) +mean_gdd_temp <- seq(5, 30, by = 5) + +data <- expand.grid( + temp = temp, + aridity_index = aridity_index, + mean_gdd_temp = mean_gdd_temp +) + +# Function is not parallelised so loop over inputs +data$phio <- NA + +for (row_idx in seq_along(data$aridity_index)) { + data$phio[row_idx] <- with( + data[row_idx, ], + calc_phi0(AI = aridity_index, tc = temp, mGDD0 = mean_gdd_temp) + ) +} + +data$phio <- round(data$phio, digits = 8) +write.csv(data, "sandoval_kphio.csv", row.names = FALSE) diff --git a/tests/unit/pmodel/test_quantum_yield.py b/tests/unit/pmodel/test_quantum_yield.py new file mode 100644 index 00000000..ece4682e --- /dev/null +++ b/tests/unit/pmodel/test_quantum_yield.py @@ -0,0 +1,147 @@ +"""Test the quantum yield implementations.""" + +import numpy as np +import pytest + + +@pytest.fixture +def quantum_yield_env(): + """Simple fixture for providing the quantum yield inputs.""" + from pyrealm.pmodel import PModelEnvironment + + return PModelEnvironment( + tc=np.array([5, 10, 15, 20, 25, 30]), + patm=101325, + vpd=820, + co2=400, + mean_growth_temperature=np.array([10, 20, 10, 20, 10, 20]), + aridity_index=np.array([0.9, 0.9, 2, 2, 5, 5]), + ) + + +@pytest.mark.parametrize( + argnames="reference_kphio, expected_kphio", + argvalues=( + pytest.param(None, 0.049977, id="default"), + pytest.param(0.1, 0.1, id="provided"), + ), +) +def test_QuantumYieldConstant(quantum_yield_env, reference_kphio, expected_kphio): + """Test the constant method.""" + + from pyrealm.pmodel.quantum_yield import QuantumYieldConstant + + qy = QuantumYieldConstant( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + # Should be a scalar + assert np.allclose(qy.kphio, np.array([expected_kphio])) + + +@pytest.mark.parametrize( + argnames="reference_kphio, expected_kphio_factor", + argvalues=( + pytest.param( + None, np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), id="default" + ), + pytest.param( + 0.1, np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), id="provided" + ), + ), +) +def test_QuantumYieldBernacchiC3( + quantum_yield_env, reference_kphio, expected_kphio_factor +): + """Test the Bernacchi temperature method for C3 plants.""" + + from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC3 + + qy = QuantumYieldBernacchiC3( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + # The expected_kphio_factor values are the output of the previous implementation + # (calc_ftemp_kphio), which returned the temperature factors that then needed + # multiplying by the reference kphio. + assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) + + +@pytest.mark.parametrize( + argnames="reference_kphio, expected_kphio_factor", + argvalues=( + pytest.param( + None, + np.array([0.0744, 0.1896, 0.2816, 0.3504, 0.396, 0.4184]), + id="default", + ), + pytest.param( + 0.1, + np.array([0.0744, 0.1896, 0.2816, 0.3504, 0.396, 0.4184]), + id="provided", + ), + ), +) +def test_QuantumYieldBernacchiC4( + quantum_yield_env, reference_kphio, expected_kphio_factor +): + """Test the Bernacchi temperature method for C4 plants.""" + + from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC4 + + qy = QuantumYieldBernacchiC4( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + # The expected_kphio_factor values are the output of the previous implementation + # (calc_ftemp_kphio), which returned the temperature factors that then needed + # multiplying by the reference kphio. + assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) + + +@pytest.mark.parametrize( + argnames="reference_kphio, expected_kphio", + argvalues=( + pytest.param( + None, + np.array( + [0.02848466, 0.05510828, 0.06099888, 0.07537036, 0.02231382, 0.03026185] + ), + id="default", + ), + pytest.param( + 1 / 8, + np.array( + [0.03204524, 0.06199681, 0.06862374, 0.08479165, 0.02510305, 0.03404458] + ), + id="provided", + ), + ), +) +def test_QuantumYieldSandoval(quantum_yield_env, reference_kphio, expected_kphio): + """Test the Sandoval temperature and aridity method. + + The test values here have been calculated using the original R implementation + provided in pyrealm_build_data/sandoval_kphio/calc_phi0.R. A more complete check + across a wider range of values is provided in the regression tests but this also + tests the provisision of alternative reference_kphio. + + > source('calc_phi0.R') + > tc <- c(5, 10, 15, 20, 25, 30) + > ai <- c(0.9, 0.9, 2, 2, 5, 5) + > gdd0 <- c(10, 20, 10, 20, 10, 20) + > round(mapply(calc_phi0, ai, tc, gdd0), 5) + > round(mapply(calc_phi0, ai, tc, gdd0, MoreArgs=list(phi_o_theo=1/8)), 5) + """ + + from pyrealm.pmodel.quantum_yield import QuantumYieldSandoval + + qy = QuantumYieldSandoval( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + assert np.allclose(qy.kphio, expected_kphio) From 8dd18bac81641e5c223dd32d829d9f0edea0b554 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 17:53:10 +0100 Subject: [PATCH 07/28] Added use of QuantumYield registry to tests --- tests/unit/pmodel/test_quantum_yield.py | 51 +++++++++++++++++++++++-- 1 file changed, 47 insertions(+), 4 deletions(-) diff --git a/tests/unit/pmodel/test_quantum_yield.py b/tests/unit/pmodel/test_quantum_yield.py index ece4682e..165d6d3f 100644 --- a/tests/unit/pmodel/test_quantum_yield.py +++ b/tests/unit/pmodel/test_quantum_yield.py @@ -29,7 +29,10 @@ def quantum_yield_env(): def test_QuantumYieldConstant(quantum_yield_env, reference_kphio, expected_kphio): """Test the constant method.""" - from pyrealm.pmodel.quantum_yield import QuantumYieldConstant + from pyrealm.pmodel.quantum_yield import ( + QUANTUM_YIELD_CLASS_REGISTRY, + QuantumYieldConstant, + ) qy = QuantumYieldConstant( env=quantum_yield_env, @@ -39,6 +42,13 @@ def test_QuantumYieldConstant(quantum_yield_env, reference_kphio, expected_kphio # Should be a scalar assert np.allclose(qy.kphio, np.array([expected_kphio])) + qy2 = QUANTUM_YIELD_CLASS_REGISTRY["constant"]( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + assert np.allclose(qy2.kphio, np.array([expected_kphio])) + @pytest.mark.parametrize( argnames="reference_kphio, expected_kphio_factor", @@ -56,7 +66,10 @@ def test_QuantumYieldBernacchiC3( ): """Test the Bernacchi temperature method for C3 plants.""" - from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC3 + from pyrealm.pmodel.quantum_yield import ( + QUANTUM_YIELD_CLASS_REGISTRY, + QuantumYieldBernacchiC3, + ) qy = QuantumYieldBernacchiC3( env=quantum_yield_env, @@ -68,6 +81,13 @@ def test_QuantumYieldBernacchiC3( # multiplying by the reference kphio. assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) + qy2 = QUANTUM_YIELD_CLASS_REGISTRY["bernacchi_c3"]( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + assert np.allclose(qy2.kphio, qy2.reference_kphio * expected_kphio_factor) + @pytest.mark.parametrize( argnames="reference_kphio, expected_kphio_factor", @@ -89,7 +109,10 @@ def test_QuantumYieldBernacchiC4( ): """Test the Bernacchi temperature method for C4 plants.""" - from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC4 + from pyrealm.pmodel.quantum_yield import ( + QUANTUM_YIELD_CLASS_REGISTRY, + QuantumYieldBernacchiC4, + ) qy = QuantumYieldBernacchiC4( env=quantum_yield_env, @@ -101,6 +124,16 @@ def test_QuantumYieldBernacchiC4( # multiplying by the reference kphio. assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) + qy2 = QUANTUM_YIELD_CLASS_REGISTRY["bernacchi_c4"]( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + # The expected_kphio_factor values are the output of the previous implementation + # (calc_ftemp_kphio), which returned the temperature factors that then needed + # multiplying by the reference kphio. + assert np.allclose(qy2.kphio, qy2.reference_kphio * expected_kphio_factor) + @pytest.mark.parametrize( argnames="reference_kphio, expected_kphio", @@ -137,7 +170,10 @@ def test_QuantumYieldSandoval(quantum_yield_env, reference_kphio, expected_kphio > round(mapply(calc_phi0, ai, tc, gdd0, MoreArgs=list(phi_o_theo=1/8)), 5) """ - from pyrealm.pmodel.quantum_yield import QuantumYieldSandoval + from pyrealm.pmodel.quantum_yield import ( + QUANTUM_YIELD_CLASS_REGISTRY, + QuantumYieldSandoval, + ) qy = QuantumYieldSandoval( env=quantum_yield_env, @@ -145,3 +181,10 @@ def test_QuantumYieldSandoval(quantum_yield_env, reference_kphio, expected_kphio ) assert np.allclose(qy.kphio, expected_kphio) + + qy2 = QUANTUM_YIELD_CLASS_REGISTRY["sandoval"]( + env=quantum_yield_env, + reference_kphio=reference_kphio, + ) + + assert np.allclose(qy2.kphio, expected_kphio) From 14a9c86e52d24e3eb15a58ccaec0f1ef4721ce46 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 18:49:33 +0100 Subject: [PATCH 08/28] Use QuantumYieldABC implementation in PModel ! SubdailyPModel not implemented --- pyrealm/pmodel/pmodel.py | 97 ++++++++++---------------- pyrealm/pmodel/quantum_yield.py | 8 ++- tests/regression/pmodel/test_pmodel.py | 43 ++++++++---- 3 files changed, 70 insertions(+), 78 deletions(-) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 4498b2c1..2971c4f2 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -20,25 +20,7 @@ from pyrealm.pmodel.jmax_limitation import JmaxLimitation from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY, OptimalChiABC from pyrealm.pmodel.pmodel_environment import PModelEnvironment - -# Design notes on PModel (0.3.1 -> 0.4.0) -# The PModel until 0.3.1 was a single class taking tc etc. as inputs. However -# a common use case would be to look at how the PModel predictions change with -# different options. I (DO) did consider retaining the single class and having -# PModel __init__ create the environment and then have PModel.fit(), but -# having two classes seemed to better separate the physiological model (PModel -# class and attributes) from the environment the model is being fitted to and -# also creates _separate_ PModel objects. - -# Design notes on PModel (0.4.0 -> 0.5.0) -# In separating PPFD and FAPAR into a second step, I had created the IabsScaled -# class to store variables that scale linearly with Iabs. That class held and -# exposed scaled and unscaled versions of several parameteres. For a start, that -# was a bad class name since the values could be unscaled, but also most of the -# unscaled versions are never really used. Really (hat tip, Keith Bloomfield), -# there are two meaningful _efficiency_ variables (LUE, IWUE) and then a set of -# productivity variables. The new structure reflects that, removing the -# un-needed unscaled variables and simplifying the structure. +from pyrealm.pmodel.quantum_yield import QUANTUM_YIELD_CLASS_REGISTRY, QuantumYieldABC class PModel: @@ -141,20 +123,20 @@ class PModel: Args: env: An instance of - :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` - kphio: (Optional) The quantum yield efficiency of photosynthesis - (:math:`\phi_0`, unitless). Note that :math:`\phi_0` is sometimes used to - refer to the quantum yield of electron transfer, which is exactly four times - larger, so check definitions here. + :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` + method_kphio: The method to use for calculating the quantum yield + efficiency of photosynthesis (:math:`\phi_0`, unitless). The method name + must be included in the + :data:`~pyrealm.pmodel.quantum_yield.QUANTUM_YIELD_CLASS_REGISTRY`. method_optchi: (Optional, default=`prentice14`) Selects the method to be used for calculating optimal :math:`chi`. The choice of method also sets the choice of C3 or C4 photosynthetic pathway (see :class:`~pyrealm.pmodel.optimal_chi.OptimalChiABC`). method_jmaxlim: (Optional, default=`wang17`) Method to use for - :math:`J_{max}` limitation - do_ftemp_kphio: (Optional, default=True) Include the temperature- - dependence of quantum yield efficiency (see - :func:`~pyrealm.pmodel.functions.calc_ftemp_kphio`). + :math:`J_{max}` limitation. + reference_kphio: An optional alternative reference value to be passed to the + kphio calculation method. + Examples: >>> import numpy as np @@ -186,10 +168,10 @@ class PModel: def __init__( self, env: PModelEnvironment, - kphio: float | None = None, - do_ftemp_kphio: bool = True, + method_kphio: str = "bernacchi_c3", method_optchi: str = "prentice14", method_jmaxlim: str = "wang17", + reference_kphio: float | None = None, ): self.shape: tuple = env.shape """Records the common numpy array shape of array inputs.""" @@ -204,23 +186,6 @@ def __init__( self.core_const: CoreConst = env.core_const """The CoreConst instance used to create the model environment.""" - # kphio calculation: - self.init_kphio: float - r"""The initial value of :math:`\phi_0` (``kphio``)""" - self.kphio: NDArray - r"""The value of :math:`\phi_0` used with any temperature correction applied.""" - self.do_ftemp_kphio: bool = do_ftemp_kphio - r"""Records if :math:`\phi_0` (``kphio``) is temperature corrected.""" - - # Set context specific defaults for kphio to match Stocker paper - if kphio is None: - if not self.do_ftemp_kphio: - self.init_kphio = 0.049977 - else: - self.init_kphio = 0.081785 - else: - self.init_kphio = kphio - # ----------------------------------------------------------------------- # Optimal ci # The heart of the P-model: calculate ci:ca ratio (chi) and additional terms @@ -243,15 +208,22 @@ def __init__( """Does the OptimalChi method approximate a C3 or C4 pathway.""" # ----------------------------------------------------------------------- - # Temperature dependence of quantum yield efficiency + # Calculate the quantum yield of photosynthesis (kphio) # ----------------------------------------------------------------------- - if self.do_ftemp_kphio: - ftemp_kphio = calc_ftemp_kphio( - env.tc, self.c4, pmodel_const=self.pmodel_const - ) - self.kphio = self.init_kphio * ftemp_kphio - else: - self.kphio = np.array([self.init_kphio]) + self.method_kphio: str = method_kphio + try: + kphio_class = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio] + except KeyError: + raise ValueError(f"Unknown kphio calculation method: {method_kphio}") + + self.kphio: QuantumYieldABC = kphio_class( + env=env, + pmodel_const=self.pmodel_const, + core_const=self.core_const, + reference_kphio=reference_kphio, + ) + """A subclass of QuantumYieldABC, implementing the requested kphio calculation + method.""" # ----------------------------------------------------------------------- # Calculation of Jmax limitation terms @@ -263,6 +235,7 @@ def __init__( self.optchi, method=self.method_jmaxlim, pmodel_const=self.pmodel_const ) """Details of the Jmax limitation calculation for the model""" + # ----------------------------------------------------------------------- # Store the two efficiency predictions # ----------------------------------------------------------------------- @@ -279,7 +252,10 @@ def __init__( # The basic calculation of LUE = phi0 * M_c * m with an added penalty term # for jmax limitation self.lue: NDArray = ( - self.kphio * self.optchi.mj * self.jmaxlim.f_v * self.core_const.k_c_molmass + self.kphio.kphio + * self.optchi.mj + * self.jmaxlim.f_v + * self.core_const.k_c_molmass ) """Light use efficiency (LUE, g C mol-1)""" @@ -401,7 +377,7 @@ def estimate_productivity( self._gpp = self.lue * iabs # V_cmax - self._vcmax = self.kphio * iabs * self.optchi.mjoc * self.jmaxlim.f_v + self._vcmax = self.kphio.kphio * iabs * self.optchi.mjoc * self.jmaxlim.f_v # V_cmax25 (vcmax normalized to const.k_To) ftemp25_inst_vcmax = calc_ftemp_inst_vcmax( @@ -418,10 +394,10 @@ def estimate_productivity( ) # Calculate Jmax - self._jmax = 4 * self.kphio * iabs * self.jmaxlim.f_j + self._jmax = 4 * self.kphio.kphio * iabs * self.jmaxlim.f_j # AJ and AC - a_j = self.kphio * iabs * self.optchi.mj * self.jmaxlim.f_v + a_j = self.kphio.kphio * iabs * self.optchi.mj * self.jmaxlim.f_v a_c = self._vcmax * self.optchi.mc assim = np.minimum(a_j, a_c) @@ -448,11 +424,10 @@ def __repr__(self) -> str: return ( f"PModel(" f"shape={self.shape}, " - f"initial kphio={self.init_kphio}, " - f"ftemp_kphio={self.do_ftemp_kphio}, " f"method_optchi={self.method_optchi}, " f"c4={self.c4}, " f"method_jmaxlim={self.method_jmaxlim}, " + f"method_kphio={self.method_kphio}, " ) def summarize(self, dp: int = 2) -> None: diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 41895561..6a108209 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -1,7 +1,9 @@ r"""The module :mod:`~pyrealm.pmodel.quantum_yield` provides -the abstract base class :class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC`, -which is used to support different implementations of the calculation of the intrinsic -quantum yield of photosynthesis. +the abstract base class :class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC`, which is +used to support different implementations of the calculation of the intrinsic quantum +yield efficiency of photosynthesis (:math:`\phi_0`, unitless). Note that :math:`\phi_0` +is sometimes used to refer to the quantum yield of electron transfer, which is exactly +four times larger, so check definitions here. """ # noqa D210, D415 from __future__ import annotations diff --git a/tests/regression/pmodel/test_pmodel.py b/tests/regression/pmodel/test_pmodel.py index e07db945..e46c1386 100644 --- a/tests/regression/pmodel/test_pmodel.py +++ b/tests/regression/pmodel/test_pmodel.py @@ -602,13 +602,15 @@ def pmodelenv(values): @pytest.mark.parametrize("soilmstress", [False, True], ids=["sm-off", "sm-on"]) -@pytest.mark.parametrize("ftemp_kphio", [True, False], ids=["fkphio-on", "fkphio-off"]) +@pytest.mark.parametrize( + "method_kphio", ["bernacchi_c3", "constant"], ids=["fkphio-on", "fkphio-off"] +) @pytest.mark.parametrize( "luevcmax_method", ["wang17", "smith19", "none"], ids=["wang17", "smith19", "none"] ) @pytest.mark.parametrize("environ", ["sc", "ar"], ids=["sc", "ar"]) def test_pmodel_class_c3( - request, values, pmodelenv, soilmstress, ftemp_kphio, luevcmax_method, environ + request, values, pmodelenv, soilmstress, method_kphio, luevcmax_method, environ ): """Test the PModel class for C3 plants.""" @@ -625,9 +627,9 @@ def test_pmodel_class_c3( ret = PModel( pmodelenv[environ], - kphio=0.05, - do_ftemp_kphio=ftemp_kphio, + method_kphio=method_kphio, method_jmaxlim=luevcmax_method, + reference_kphio=0.05, ) # Estimate productivity @@ -704,12 +706,21 @@ def test_pmodel_class_c3( @pytest.mark.parametrize("soilmstress", [False, True], ids=["sm-off", "sm-on"]) -@pytest.mark.parametrize("ftemp_kphio", [True, False], ids=["fkphio-on", "fkphio-off"]) +@pytest.mark.parametrize( + "method_kphio", ["bernacchi_c4", "constant"], ids=["fkphio-on", "fkphio-off"] +) @pytest.mark.parametrize("environ", ["sc", "ar"], ids=["sc", "ar"]) -def test_pmodel_class_c4(request, values, pmodelenv, soilmstress, ftemp_kphio, environ): +def test_pmodel_class_c4( + request, values, pmodelenv, soilmstress, method_kphio, environ +): """Test the PModel class for C4 plants.""" - from pyrealm.pmodel import PModel, calc_ftemp_kphio, calc_soilmstress_stocker + from pyrealm.pmodel import ( + PModel, + PModelEnvironment, + calc_soilmstress_stocker, + ) + from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC4 if soilmstress: soilmstress = calc_soilmstress_stocker( @@ -718,19 +729,23 @@ def test_pmodel_class_c4(request, values, pmodelenv, soilmstress, ftemp_kphio, e else: soilmstress = np.array([1.0]) - # TODO bug in rpmodel 1.2.2 forces an odd downscaling of kphio when - # do_ftemp_kphio is False, so this is scaling back up to match. - if ftemp_kphio: + # TODO bug in rpmodel 1.2.2 forces an odd downscaling of kphio when do_ftemp_kphio + # is False, so the kf factor is calculated to scale the reference kphio back up to + # match. + + if method_kphio == "bernacchi_c4": kf = 1 else: - kf = calc_ftemp_kphio(15, c4=True) + bug_env = PModelEnvironment(tc=15, patm=101325, vpd=800, co2=400) + correction = QuantumYieldBernacchiC4(env=bug_env, reference_kphio=0.05) + kf = correction.kphio / 0.05 ret = PModel( pmodelenv[environ], - kphio=0.05 * kf, # See note above - do_ftemp_kphio=ftemp_kphio, + method_kphio=method_kphio, method_jmaxlim="simple", # enforced in rpmodel. method_optchi="c4", + reference_kphio=0.05 * kf, # See note above ) # Estimate productivity @@ -797,7 +812,7 @@ def test_pmodel_summarise(capsys, values, pmodelenv): from pyrealm.pmodel import PModel - ret = PModel(pmodelenv["sc"], kphio=0.05) + ret = PModel(pmodelenv["sc"], reference_kphio=0.05) # Test what comes back before estimate_productivity ret.summarize() From 39cb8322e1538b10526a940bc93d27862d6d0207 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 18:49:38 +0100 Subject: [PATCH 09/28] Use QuantumYieldABC implementation in PModel ! SubdailyPModel not implemented --- pyrealm/pmodel/pmodel.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 2971c4f2..87d080d6 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -15,7 +15,6 @@ from pyrealm.pmodel.functions import ( calc_ftemp_inst_rd, calc_ftemp_inst_vcmax, - calc_ftemp_kphio, ) from pyrealm.pmodel.jmax_limitation import JmaxLimitation from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY, OptimalChiABC From a81d0fe71c9cedda05b90cdeb1cbf2b6fc2585e4 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 31 Jul 2024 21:16:29 +0100 Subject: [PATCH 10/28] Moving C3/C4 handling within different kphio approaches ! Subdaily still not updated --- pyrealm/pmodel/pmodel.py | 3 +- pyrealm/pmodel/quantum_yield.py | 42 +++++---------- tests/regression/pmodel/test_pmodel.py | 12 +++-- tests/unit/pmodel/test_quantum_yield.py | 72 ++++++++----------------- 4 files changed, 43 insertions(+), 86 deletions(-) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 87d080d6..bcbb965d 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -167,7 +167,7 @@ class PModel: def __init__( self, env: PModelEnvironment, - method_kphio: str = "bernacchi_c3", + method_kphio: str = "temperature", method_optchi: str = "prentice14", method_jmaxlim: str = "wang17", reference_kphio: float | None = None, @@ -217,6 +217,7 @@ def __init__( self.kphio: QuantumYieldABC = kphio_class( env=env, + use_c4=self.c4, pmodel_const=self.pmodel_const, core_const=self.core_const, reference_kphio=reference_kphio, diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 6a108209..a18da211 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -68,8 +68,6 @@ class QuantumYieldABC(ABC): """A short method name used to identify the class in :data:`~pyrealm.pmodel.quantum_yield.QUANTUM_YIELD_CLASS_REGISTRY`. """ - is_c4: bool - """A flag indicating if the method captures the C4 photosynthetic pathway.""" requires: list[str] """A list of names of optional attributes of :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` that must be populated @@ -83,6 +81,7 @@ def __init__( self, env: PModelEnvironment, reference_kphio: float | None = None, + use_c4: bool = False, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ): @@ -97,6 +96,8 @@ def __init__( """The CoreConst used for calculating quantum yield""" self.reference_kphio: float = reference_kphio or self.default_reference_kphio """The reference value for kphio for the method.""" + self.use_c4: bool = use_c4 + """Use a C4 parameterisation if available.""" # Declare attributes populated by methods. These are typed but not assigned a # default value as they must are populated by the subclass specific @@ -147,14 +148,12 @@ def summarize(self, dp: int = 2) -> None: def __init_subclass__( cls, method: str, - is_c4: bool, requires: list[str], default_reference_kphio: float, ) -> None: """Initialise a subclass deriving from this ABC.""" cls.method = method - cls.is_c4 = is_c4 cls.requires = requires cls.default_reference_kphio = default_reference_kphio QUANTUM_YIELD_CLASS_REGISTRY[cls.method] = cls @@ -163,7 +162,6 @@ def __init_subclass__( class QuantumYieldConstant( QuantumYieldABC, method="constant", - is_c4=False, requires=[], default_reference_kphio=0.049977, ): @@ -175,48 +173,34 @@ def _calculate_kphio(self) -> None: self.kphio = np.array([self.reference_kphio]) -class QuantumYieldBernacchiC3( +class QuantumYieldTemperature( QuantumYieldABC, - method="bernacchi_c3", - is_c4=False, + method="temperature", requires=[], default_reference_kphio=0.081785, ): - """Calculate kphio following Bernacchi for C3 plants.""" + """Calculate temperature modulated kphio. + + This method follows for C3 plants. + """ def _calculate_kphio( self, ) -> None: """Calculate kphio.""" - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C3) - ftemp = np.clip(ftemp, 0.0, None) - - self.kphio = ftemp * self.reference_kphio - + if self.use_c4: + ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C4) + else: + ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C3) -class QuantumYieldBernacchiC4( - QuantumYieldABC, - method="bernacchi_c4", - is_c4=True, - requires=[], - default_reference_kphio=0.081785, -): - """Calculate kphio following Bernacchi.""" - - def _calculate_kphio(self) -> None: - """Calculate kphio.""" - - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C4) ftemp = np.clip(ftemp, 0.0, None) - self.kphio = ftemp * self.reference_kphio class QuantumYieldSandoval( QuantumYieldABC, method="sandoval", - is_c4=False, requires=["aridity_index", "mean_growth_temperature"], default_reference_kphio=1.0 / 9.0, ): diff --git a/tests/regression/pmodel/test_pmodel.py b/tests/regression/pmodel/test_pmodel.py index e46c1386..3a377c58 100644 --- a/tests/regression/pmodel/test_pmodel.py +++ b/tests/regression/pmodel/test_pmodel.py @@ -603,7 +603,7 @@ def pmodelenv(values): @pytest.mark.parametrize("soilmstress", [False, True], ids=["sm-off", "sm-on"]) @pytest.mark.parametrize( - "method_kphio", ["bernacchi_c3", "constant"], ids=["fkphio-on", "fkphio-off"] + "method_kphio", ["temperature", "constant"], ids=["fkphio-on", "fkphio-off"] ) @pytest.mark.parametrize( "luevcmax_method", ["wang17", "smith19", "none"], ids=["wang17", "smith19", "none"] @@ -707,7 +707,7 @@ def test_pmodel_class_c3( @pytest.mark.parametrize("soilmstress", [False, True], ids=["sm-off", "sm-on"]) @pytest.mark.parametrize( - "method_kphio", ["bernacchi_c4", "constant"], ids=["fkphio-on", "fkphio-off"] + "method_kphio", ["temperature", "constant"], ids=["fkphio-on", "fkphio-off"] ) @pytest.mark.parametrize("environ", ["sc", "ar"], ids=["sc", "ar"]) def test_pmodel_class_c4( @@ -720,7 +720,7 @@ def test_pmodel_class_c4( PModelEnvironment, calc_soilmstress_stocker, ) - from pyrealm.pmodel.quantum_yield import QuantumYieldBernacchiC4 + from pyrealm.pmodel.quantum_yield import QuantumYieldTemperature if soilmstress: soilmstress = calc_soilmstress_stocker( @@ -733,11 +733,13 @@ def test_pmodel_class_c4( # is False, so the kf factor is calculated to scale the reference kphio back up to # match. - if method_kphio == "bernacchi_c4": + if method_kphio == "temperature": kf = 1 else: bug_env = PModelEnvironment(tc=15, patm=101325, vpd=800, co2=400) - correction = QuantumYieldBernacchiC4(env=bug_env, reference_kphio=0.05) + correction = QuantumYieldTemperature( + env=bug_env, reference_kphio=0.05, use_c4=True + ) kf = correction.kphio / 0.05 ret = PModel( diff --git a/tests/unit/pmodel/test_quantum_yield.py b/tests/unit/pmodel/test_quantum_yield.py index 165d6d3f..aaf8e204 100644 --- a/tests/unit/pmodel/test_quantum_yield.py +++ b/tests/unit/pmodel/test_quantum_yield.py @@ -51,72 +51,46 @@ def test_QuantumYieldConstant(quantum_yield_env, reference_kphio, expected_kphio @pytest.mark.parametrize( - argnames="reference_kphio, expected_kphio_factor", + argnames="use_c4, reference_kphio, expected_kphio_factor", argvalues=( pytest.param( - None, np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), id="default" + False, + None, + np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), + id="default_c3", ), pytest.param( - 0.1, np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), id="provided" + False, + 0.1, + np.array([0.4535, 0.538, 0.6055, 0.656, 0.6895, 0.706]), + id="provided_c3", ), - ), -) -def test_QuantumYieldBernacchiC3( - quantum_yield_env, reference_kphio, expected_kphio_factor -): - """Test the Bernacchi temperature method for C3 plants.""" - - from pyrealm.pmodel.quantum_yield import ( - QUANTUM_YIELD_CLASS_REGISTRY, - QuantumYieldBernacchiC3, - ) - - qy = QuantumYieldBernacchiC3( - env=quantum_yield_env, - reference_kphio=reference_kphio, - ) - - # The expected_kphio_factor values are the output of the previous implementation - # (calc_ftemp_kphio), which returned the temperature factors that then needed - # multiplying by the reference kphio. - assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) - - qy2 = QUANTUM_YIELD_CLASS_REGISTRY["bernacchi_c3"]( - env=quantum_yield_env, - reference_kphio=reference_kphio, - ) - - assert np.allclose(qy2.kphio, qy2.reference_kphio * expected_kphio_factor) - - -@pytest.mark.parametrize( - argnames="reference_kphio, expected_kphio_factor", - argvalues=( pytest.param( + True, None, np.array([0.0744, 0.1896, 0.2816, 0.3504, 0.396, 0.4184]), - id="default", + id="default_c4", ), pytest.param( + True, 0.1, np.array([0.0744, 0.1896, 0.2816, 0.3504, 0.396, 0.4184]), - id="provided", + id="provided_c4", ), ), ) -def test_QuantumYieldBernacchiC4( - quantum_yield_env, reference_kphio, expected_kphio_factor +def test_QuantumYieldTemperature( + quantum_yield_env, use_c4, reference_kphio, expected_kphio_factor ): - """Test the Bernacchi temperature method for C4 plants.""" + """Test the temperature kphio method.""" from pyrealm.pmodel.quantum_yield import ( QUANTUM_YIELD_CLASS_REGISTRY, - QuantumYieldBernacchiC4, + QuantumYieldTemperature, ) - qy = QuantumYieldBernacchiC4( - env=quantum_yield_env, - reference_kphio=reference_kphio, + qy = QuantumYieldTemperature( + env=quantum_yield_env, reference_kphio=reference_kphio, use_c4=use_c4 ) # The expected_kphio_factor values are the output of the previous implementation @@ -124,14 +98,10 @@ def test_QuantumYieldBernacchiC4( # multiplying by the reference kphio. assert np.allclose(qy.kphio, qy.reference_kphio * expected_kphio_factor) - qy2 = QUANTUM_YIELD_CLASS_REGISTRY["bernacchi_c4"]( - env=quantum_yield_env, - reference_kphio=reference_kphio, + qy2 = QUANTUM_YIELD_CLASS_REGISTRY["temperature"]( + env=quantum_yield_env, reference_kphio=reference_kphio, use_c4=use_c4 ) - # The expected_kphio_factor values are the output of the previous implementation - # (calc_ftemp_kphio), which returned the temperature factors that then needed - # multiplying by the reference kphio. assert np.allclose(qy2.kphio, qy2.reference_kphio * expected_kphio_factor) From 84a1d67d848ae8d10dd10426f8f9a41d947a4e2a Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 1 Aug 2024 11:01:00 +0100 Subject: [PATCH 11/28] QuantumYield should inherit constants from PModelEnvironment, not directlyas an argument ! Subdaily still not updated --- pyrealm/pmodel/pmodel.py | 34 ++++++++++++++++----------------- pyrealm/pmodel/quantum_yield.py | 23 ++++++++++------------ 2 files changed, 26 insertions(+), 31 deletions(-) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index bcbb965d..561efe96 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -133,8 +133,10 @@ class PModel: :class:`~pyrealm.pmodel.optimal_chi.OptimalChiABC`). method_jmaxlim: (Optional, default=`wang17`) Method to use for :math:`J_{max}` limitation. - reference_kphio: An optional alternative reference value to be passed to the - kphio calculation method. + reference_kphio: An optional alternative reference value for the quantum yield + efficiency of photosynthesis (:math:`\phi_0`, -) to be passed to the kphio + calculation method. + Examples: @@ -189,19 +191,18 @@ def __init__( # Optimal ci # The heart of the P-model: calculate ci:ca ratio (chi) and additional terms # ----------------------------------------------------------------------- - self.method_optchi: str = method_optchi - """Records the method used to calculate optimal chi.""" - try: - opt_chi_class = OPTIMAL_CHI_CLASS_REGISTRY[method_optchi] - except KeyError: + if method_optchi not in OPTIMAL_CHI_CLASS_REGISTRY: raise ValueError(f"Unknown optimal chi estimation method: {method_optchi}") - self.optchi: OptimalChiABC = opt_chi_class( + self.method_optchi: str = method_optchi + """The method used to calculate optimal chi.""" + + self.optchi: OptimalChiABC = OPTIMAL_CHI_CLASS_REGISTRY[method_optchi]( env=env, pmodel_const=self.pmodel_const, ) - """An subclass OptimalChi, implementing the requested chi calculation method""" + """A subclass of OptimalChiABC, providing optimal chi calculation.""" self.c4: bool = self.optchi.is_c4 """Does the OptimalChi method approximate a C3 or C4 pathway.""" @@ -209,21 +210,18 @@ def __init__( # ----------------------------------------------------------------------- # Calculate the quantum yield of photosynthesis (kphio) # ----------------------------------------------------------------------- - self.method_kphio: str = method_kphio - try: - kphio_class = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio] - except KeyError: + if method_kphio not in QUANTUM_YIELD_CLASS_REGISTRY: raise ValueError(f"Unknown kphio calculation method: {method_kphio}") - self.kphio: QuantumYieldABC = kphio_class( + self.method_kphio: str = method_kphio + """The method used to calculate kphio.""" + + self.kphio: QuantumYieldABC = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio]( env=env, use_c4=self.c4, - pmodel_const=self.pmodel_const, - core_const=self.core_const, reference_kphio=reference_kphio, ) - """A subclass of QuantumYieldABC, implementing the requested kphio calculation - method.""" + """A subclass of QuantumYieldABC, providing kphio calculation.""" # ----------------------------------------------------------------------- # Calculation of Jmax limitation terms diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index a18da211..0f076d99 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -15,7 +15,6 @@ from numpy.typing import NDArray from pyrealm import ExperimentalFeatureWarning -from pyrealm.constants import CoreConst, PModelConst from pyrealm.core.utilities import ( check_input_shapes, evaluate_horner_polynomial, @@ -82,18 +81,12 @@ def __init__( env: PModelEnvironment, reference_kphio: float | None = None, use_c4: bool = False, - pmodel_const: PModelConst = PModelConst(), - core_const: CoreConst = CoreConst(), ): self.env: PModelEnvironment = env """The PModelEnvironment containing the photosynthetic environment for the model.""" self.shape: tuple[int, ...] = env.shape """The shape of the input environment data.""" - self.pmodel_const: PModelConst = pmodel_const - """The PModelConst used for calculating quantum yield""" - self.core_const: CoreConst = core_const - """The CoreConst used for calculating quantum yield""" self.reference_kphio: float = reference_kphio or self.default_reference_kphio """The reference value for kphio for the method.""" self.use_c4: bool = use_c4 @@ -190,9 +183,13 @@ def _calculate_kphio( """Calculate kphio.""" if self.use_c4: - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C4) + ftemp = evaluate_horner_polynomial( + self.env.tc, self.env.pmodel_const.kphio_C4 + ) else: - ftemp = evaluate_horner_polynomial(self.env.tc, self.pmodel_const.kphio_C3) + ftemp = evaluate_horner_polynomial( + self.env.tc, self.env.pmodel_const.kphio_C3 + ) ftemp = np.clip(ftemp, 0.0, None) self.kphio = ftemp * self.reference_kphio @@ -221,7 +218,7 @@ def _calculate_kphio(self) -> None: ) # Calculate enzyme kinetics - a_ent, b_ent, Hd_base, Ha = self.pmodel_const.sandoval_kinetics + a_ent, b_ent, Hd_base, Ha = self.env.pmodel_const.sandoval_kinetics # Calculate activation entropy as a linear function of # mean growth temperature, J/mol/K deltaS = a_ent + b_ent * self.env.mean_growth_temperature @@ -230,15 +227,15 @@ def _calculate_kphio(self) -> None: # Calculate the optimal temperature to be used as the reference temperature in # the modified Arrhenius calculation - Topt = Hd / (deltaS - self.core_const.k_R * np.log(Ha / (Hd - Ha))) + Topt = Hd / (deltaS - self.env.core_const.k_R * np.log(Ha / (Hd - Ha))) # Calculate peak kphio given the aridity index - m, n = self.pmodel_const.sandoval_peak_phio + m, n = self.env.pmodel_const.sandoval_peak_phio kphio_peak = self.reference_kphio / (1 + (self.env.aridity_index) ** m) ** n # Calculate the modified Arrhenius factor using the f_kphio = calc_modified_arrhenius_factor( - tk=self.env.tc + self.core_const.k_CtoK, + tk=self.env.tc + self.env.core_const.k_CtoK, Ha=Ha, Hd=Hd, deltaS=deltaS, From 1cadd6b089a434df24fdf64f905c85fa81bb134e Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 1 Aug 2024 12:24:05 +0100 Subject: [PATCH 12/28] Updating subdaily --- pyrealm/pmodel/subdaily.py | 101 +++++++++++++++++++---------- tests/unit/pmodel/test_subdaily.py | 8 ++- 2 files changed, 72 insertions(+), 37 deletions(-) diff --git a/pyrealm/pmodel/subdaily.py b/pyrealm/pmodel/subdaily.py index b7d15d01..d438fdd4 100644 --- a/pyrealm/pmodel/subdaily.py +++ b/pyrealm/pmodel/subdaily.py @@ -31,9 +31,13 @@ PModelEnvironment, SubdailyScaler, calc_ftemp_arrh, - calc_ftemp_kphio, ) -from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY +from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY, OptimalChiABC +from pyrealm.pmodel.quantum_yield import ( + QUANTUM_YIELD_CLASS_REGISTRY, + QuantumYieldABC, + QuantumYieldTemperature, +) def memory_effect( @@ -210,7 +214,9 @@ class SubdailyPModel: function be allowed to hold over values to fill missing values. allow_partial_data: Should estimates of daily optimal conditions be calculated with missing values in the acclimation window. - kphio: The quantum yield efficiency of photosynthesis (:math:`\phi_0`, -). + reference_kphio: An optional alternative reference value for the quantum yield + efficiency of photosynthesis (:math:`\phi_0`, -) to be passed to the kphio + calculation method. fill_kind: The approach used to fill daily realised values to the subdaily timescale, currently one of 'previous' or 'linear'. """ @@ -221,10 +227,10 @@ def __init__( fs_scaler: SubdailyScaler, fapar: NDArray, ppfd: NDArray, - kphio: float = 1 / 8, - do_ftemp_kphio: bool = True, method_optchi: str = "prentice14", method_jmaxlim: str = "wang17", + method_kphio: str = "temperature", + reference_kphio: float | None = None, alpha: float = 1 / 15, allow_holdover: bool = False, allow_partial_data: bool = False, @@ -252,23 +258,42 @@ def __init__( self.datetimes = fs_scaler.datetimes """The datetimes of the observations used in the subdaily model.""" - # Set up kphio attributes + # Populate PModel attributes and type unpopulated attributes self.env: PModelEnvironment = env - self.init_kphio: float = kphio - self.do_ftemp_kphio = do_ftemp_kphio - self.kphio: NDArray + + # Validate the method choices for kphio and optimal chi + if method_optchi not in OPTIMAL_CHI_CLASS_REGISTRY: + raise ValueError(f"Unknown optimal chi estimation method: {method_optchi}") + + self.method_optchi: str = method_optchi + """The method used to calculate optimal chi.""" + self.c4: bool = OPTIMAL_CHI_CLASS_REGISTRY[method_optchi].is_c4 + """Does the optimal chi method represent a C4 pathway.""" + + if method_kphio not in QUANTUM_YIELD_CLASS_REGISTRY: + raise ValueError(f"Unknown kphio calculation method: {method_kphio}") + + self.method_kphio: str = method_kphio + """The method used to calculate kphio.""" # 1) Generate a PModelEnvironment containing the average conditions within the - # daily acclimation window, including any optional variables required by the - # optimal chi calculations used in the model. - optimal_chi_class = OPTIMAL_CHI_CLASS_REGISTRY[method_optchi] + # daily acclimation window. This daily average environment also needs to also + # pass through any optional variables required by the optimal chi and kphio + # method set for the model, which can be accessed via the class requires + # attribute. + + # Get the list of variables for which to calculate daily acclimation conditions. daily_environment_vars = [ "tc", "co2", "patm", "vpd", - *optimal_chi_class.requires, + *OPTIMAL_CHI_CLASS_REGISTRY[method_optchi].requires, + *QUANTUM_YIELD_CLASS_REGISTRY[method_kphio].requires, ] + + # Construct a dictionary of daily acclimation variables, handling optional + # choices which can be None. daily_environment: dict[str, NDArray] = {} for env_var_name in daily_environment_vars: env_var = getattr(self.env, env_var_name) @@ -277,7 +302,8 @@ def __init__( env_var, allow_partial_data=allow_partial_data ) - pmodel_env_acclim = PModelEnvironment( + # Calculate the acclimation environment passing on the constants definitions. + pmodel_env_acclim: PModelEnvironment = PModelEnvironment( **daily_environment, pmodel_const=self.env.pmodel_const, core_const=self.env.core_const, @@ -286,11 +312,11 @@ def __init__( # 2) Fit a PModel to those environmental conditions, using the supplied settings # for the original model. self.pmodel_acclim: PModel = PModel( - pmodel_env_acclim, - kphio=kphio, - do_ftemp_kphio=do_ftemp_kphio, + env=pmodel_env_acclim, + method_kphio=method_kphio, method_optchi=method_optchi, method_jmaxlim=method_jmaxlim, + reference_kphio=reference_kphio, ) r"""P Model predictions for the daily acclimation conditions. @@ -357,29 +383,28 @@ def __init__( """Estimated subdaily :math:`J_{max}`.""" # 8) Recalculate chi using the OptimalChi class from the provided method. - self.optimal_chi = optimal_chi_class( + self.optimal_chi: OptimalChiABC = OPTIMAL_CHI_CLASS_REGISTRY[method_optchi]( env=self.env, pmodel_const=env.pmodel_const ) self.optimal_chi.estimate_chi(xi_values=self.subdaily_xi) """Estimated subdaily :math:`c_i`.""" - # Calculate Ac, J and Aj at subdaily scale to calculate assimilation - if self.do_ftemp_kphio: - ftemp_kphio = calc_ftemp_kphio( - env.tc, optimal_chi_class.is_c4, pmodel_const=env.pmodel_const - ) - self.kphio = self.init_kphio * ftemp_kphio - else: - self.kphio = np.array([self.init_kphio]) + # Calculate kphio at the subdaily scale. + self.kphio: QuantumYieldABC = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio]( + env=env, + use_c4=self.c4, + reference_kphio=reference_kphio, + ) + # Calculate Ac, J and Aj at subdaily scale to calculate assimilation self.subdaily_Ac: NDArray = self.subdaily_vcmax * self.optimal_chi.mc """Estimated subdaily :math:`A_c`.""" iabs = fapar * ppfd - subdaily_J = (4 * self.kphio * iabs) / np.sqrt( - 1 + ((4 * self.kphio * iabs) / self.subdaily_jmax) ** 2 + subdaily_J = (4 * self.kphio.kphio * iabs) / np.sqrt( + 1 + ((4 * self.kphio.kphio * iabs) / self.subdaily_jmax) ** 2 ) self.subdaily_Aj: NDArray = (subdaily_J / 4) * self.optimal_chi.mj @@ -426,10 +451,10 @@ def convert_pmodel_to_subdaily( fs_scaler=fs_scaler, fapar=pmodel.fapar, ppfd=pmodel.ppfd, - kphio=pmodel.init_kphio, - do_ftemp_kphio=pmodel.do_ftemp_kphio, method_optchi=pmodel.method_optchi, method_jmaxlim=pmodel.method_jmaxlim, + method_kphio=pmodel.method_kphio, + reference_kphio=pmodel.kphio.reference_kphio, alpha=alpha, allow_holdover=allow_holdover, fill_kind=fill_kind, @@ -441,7 +466,7 @@ class SubdailyPModel_JAMES: This is alternative implementation of the P Model incorporating slow responses that duplicates the original implementation of the weighted-average approach of - {cite:t}`mengoli:2022a`. + {cite:t}`mengoli:2022a` for C3 plants. The key difference is that :math:`\xi` does not have a slow response, with :math:`c_i` calculated using the daily optimal values during the acclimation window @@ -541,7 +566,11 @@ def __init__( pmodel_const=self.env.pmodel_const, core_const=self.env.core_const, ) - self.pmodel_acclim: PModel = PModel(pmodel_env_acclim, kphio=kphio) + self.pmodel_acclim: PModel = PModel( + env=pmodel_env_acclim, + reference_kphio=kphio, + method_kphio="temperature", + ) r"""P Model predictions for the daily acclimation conditions. A :class:`~pyrealm.pmodel.pmodel.PModel` instance providing the predictions of @@ -627,11 +656,13 @@ def __init__( ) """Estimated subdaily :math:`A_c`.""" - kphio_tc = kphio * calc_ftemp_kphio(tc=self.env.tc) + self.kphio: QuantumYieldABC = QuantumYieldTemperature( + env=env, reference_kphio=kphio + ) iabs = fapar * ppfd - subdaily_J = (4 * kphio_tc * iabs) / np.sqrt( - 1 + ((4 * kphio_tc * iabs) / self.subdaily_jmax) ** 2 + subdaily_J = (4 * self.kphio.kphio * iabs) / np.sqrt( + 1 + ((4 * self.kphio.kphio * iabs) / self.subdaily_jmax) ** 2 ) self.subdaily_Aj: NDArray = ( diff --git a/tests/unit/pmodel/test_subdaily.py b/tests/unit/pmodel/test_subdaily.py index e6ed5c0e..3d43515b 100644 --- a/tests/unit/pmodel/test_subdaily.py +++ b/tests/unit/pmodel/test_subdaily.py @@ -196,11 +196,12 @@ def test_FSPModel_corr(be_vie_data_components, data_args): half_width=np.timedelta64(30, "m"), ) - # Run as a subdaily model + # Run as a subdaily model using the kphio used in the reference implementation. subdaily_pmodel = SubdailyPModel( env=env, ppfd=ppfd, fapar=fapar, + reference_kphio=1 / 8, fs_scaler=fsscaler, allow_holdover=True, ) @@ -330,7 +331,10 @@ def test_convert_pmodel_to_subdaily(be_vie_data_components, method_optchi): ) # Convert a standard model - standard_model = PModel(env=env, kphio=1 / 8, method_optchi=method_optchi) + standard_model = PModel( + env=env, + method_optchi=method_optchi, + ) standard_model.estimate_productivity(fapar=fapar, ppfd=ppfd) converted = convert_pmodel_to_subdaily( From fce92d9e73d8c02081af21b2466eba26b81c0ea0 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 1 Aug 2024 12:32:04 +0100 Subject: [PATCH 13/28] Better R in sandoval_kphio --- .../sandoval_kphio/create_test_inputs.R | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/pyrealm_build_data/sandoval_kphio/create_test_inputs.R b/pyrealm_build_data/sandoval_kphio/create_test_inputs.R index 79adb1a7..5a3bd0eb 100644 --- a/pyrealm_build_data/sandoval_kphio/create_test_inputs.R +++ b/pyrealm_build_data/sandoval_kphio/create_test_inputs.R @@ -1,24 +1,23 @@ source("calc_phi0.R") +# Generate all combinations of some simple input values aridity_index <- c(0.2, 0.5, 0.8, 1.0, 5.0, 10.0) temp <- seq(0, 50, by = 1) mean_gdd_temp <- seq(5, 30, by = 5) data <- expand.grid( - temp = temp, - aridity_index = aridity_index, - mean_gdd_temp = mean_gdd_temp + temp = temp, + aridity_index = aridity_index, + mean_gdd_temp = mean_gdd_temp ) -# Function is not parallelised so loop over inputs -data$phio <- NA - -for (row_idx in seq_along(data$aridity_index)) { - data$phio[row_idx] <- with( - data[row_idx, ], - calc_phi0(AI = aridity_index, tc = temp, mGDD0 = mean_gdd_temp) - ) -} +# Run the reference implementation, which is not parallelised so need mapply +data$phio <- round( + mapply( + calc_phi0, data$aridity_index, data$temp, data$mean_gdd_temp + ), + digits = 8 +) -data$phio <- round(data$phio, digits = 8) +# Save reference dataset. write.csv(data, "sandoval_kphio.csv", row.names = FALSE) From 2495d841c7b7a4dd9110c5aed23688015ec3c303 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 1 Aug 2024 13:59:23 +0100 Subject: [PATCH 14/28] pytest importlib mode not behaving: https://github.com/pytest-dev/pytest/issues/11475 --- pyproject.toml | 1 - .../{test_quantum_yield.py => test_sandoval_quantum_yield.py} | 0 2 files changed, 1 deletion(-) rename tests/regression/pmodel/{test_quantum_yield.py => test_sandoval_quantum_yield.py} (100%) diff --git a/pyproject.toml b/pyproject.toml index 4e22dcb1..8c28b9aa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,7 +106,6 @@ addopts = """ --doctest-modules --ignore=pyrealm/__main__.py --ignore=tests/pmodel/generate_test_inputs.py - --import-mode importlib """ python_files = 'test_*.py' testpaths = ['tests', 'pyrealm'] diff --git a/tests/regression/pmodel/test_quantum_yield.py b/tests/regression/pmodel/test_sandoval_quantum_yield.py similarity index 100% rename from tests/regression/pmodel/test_quantum_yield.py rename to tests/regression/pmodel/test_sandoval_quantum_yield.py From dfb23e2278c8e5bb715e4cf4ce2e8530640a8151 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 1 Aug 2024 14:38:02 +0100 Subject: [PATCH 15/28] doctest output format fixes --- pyrealm/core/utilities.py | 12 ++++++------ pyrealm/core/water.py | 10 +++++----- pyrealm/pmodel/functions.py | 10 +++++----- 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/pyrealm/core/utilities.py b/pyrealm/core/utilities.py index 7d6370f2..9491ebf8 100644 --- a/pyrealm/core/utilities.py +++ b/pyrealm/core/utilities.py @@ -24,10 +24,10 @@ >>> import numpy as np >>> x = np.ma.masked_array([1,2,np.nan, 4], mask=[1,0,0,1]) >>> x.mean() - np.float64(nan) + nan >>> x = np.ma.masked_array([1,2,np.nan, 4], mask=[1,0,1,0]) >>> x.mean() - np.float64(3.0) + 3.0 So the general approach here is now: @@ -282,16 +282,16 @@ def bounds_mask( Examples: >>> vals = np.array([-15, 20, 30, 124], dtype=float) >>> np.nansum(vals) - np.float64(159.0) + 159.0 >>> vals_c = bounds_mask(vals, 0, 100, label='temperature') >>> np.nansum(vals_c) - np.float64(50.0) + 50.0 >>> vals_c = bounds_mask(vals, 0, 124, interval_type='[]', label='temperature') >>> np.nansum(vals_c) - np.float64(174.0) + 174.0 >>> vals_c = bounds_mask(vals, 0, 124, interval_type='[)', label='temperature') >>> np.nansum(vals_c) - np.float64(50.0) + 50.0 """ # Get the interval functions diff --git a/pyrealm/core/water.py b/pyrealm/core/water.py index 72c7568b..1d4a9ecc 100644 --- a/pyrealm/core/water.py +++ b/pyrealm/core/water.py @@ -37,7 +37,7 @@ def calc_density_h2o_chen( Examples: >>> round(calc_density_h2o_chen(20, 101325), 3) - np.float64(998.25) + 998.25 """ # Calculate density at 1 atm (kg/m^3): @@ -93,7 +93,7 @@ def calc_density_h2o_fisher( Examples: >>> round(calc_density_h2o_fisher(20, 101325), 3) - np.float64(998.206) + 998.206 """ # Check input shapes, shape not used @@ -156,7 +156,7 @@ def calc_density_h2o( Examples: >>> round(calc_density_h2o(20, 101325), 3) - np.float64(998.206) + 998.206 """ # Safe guard against instability in functions at low temperature. @@ -201,7 +201,7 @@ def calc_viscosity_h2o( Examples: >>> # Density of water at 20 degrees C and standard atmospheric pressure: >>> round(calc_viscosity_h2o(20, 101325), 7) - np.float64(0.0010016) + 0.0010016 """ # Check inputs, return shape not used @@ -269,7 +269,7 @@ def calc_viscosity_h2o_matrix( Examples: >>> # Density of water at 20 degrees C and standard atmospheric pressure: >>> round(calc_viscosity_h2o(20, 101325), 7) - np.float64(0.0010016) + 0.0010016 """ # Check inputs, return shape not used diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 84692690..e5a198ab 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -110,7 +110,7 @@ def calc_ftemp_inst_rd( >>> # Relative percentage instantaneous change in Rd going from 10 to 25 degrees >>> val = (calc_ftemp_inst_rd(25) / calc_ftemp_inst_rd(10) - 1) * 100 >>> round(val, 4) - np.float64(250.9593) + 250.9593 """ return np.exp( @@ -306,13 +306,13 @@ def calc_ftemp_kphio( >>> # degrees celsius (percent change): >>> val = (calc_ftemp_kphio(25.0) / calc_ftemp_kphio(5.0) - 1) * 100 >>> round(val, 5) - np.float64(52.03969) + 52.03969 >>> # Relative change in the quantum yield efficiency between 5 and 25 >>> # degrees celsius (percent change) for a C4 plant: >>> val = (calc_ftemp_kphio(25.0, c4=True) / ... calc_ftemp_kphio(5.0, c4=True) - 1) * 100 >>> round(val, 5) - np.float64(432.25806) + 432.25806 """ if c4: @@ -411,7 +411,7 @@ def calc_ns_star( >>> # Relative viscosity at 20 degrees Celsius and standard >>> # atmosphere (in Pa): >>> round(calc_ns_star(20, 101325), 5) - np.float64(1.12536) + 1.12536 """ visc_env = calc_viscosity_h2o(tc, patm, core_const=core_const) @@ -729,7 +729,7 @@ def calc_co2_to_ca(co2: NDArray, patm: NDArray) -> NDArray: Examples: >>> np.round(calc_co2_to_ca(413.03, 101325), 6) - np.float64(41.850265) + 41.850265 """ return 1.0e-6 * co2 * patm # Pa, atms. CO2 From 0e23f5f5756beac8fd6ebc9754cd12da95dd7431 Mon Sep 17 00:00:00 2001 From: David Orme Date: Fri, 2 Aug 2024 08:37:07 +0100 Subject: [PATCH 16/28] =?UTF-8?q?=C2=A0Cleaner=20calculation=20of=20kphio?= =?UTF-8?q?=20in=20subdaily=20P=20model,=20mypy=20fixes?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyrealm/pmodel/pmodel.py | 2 +- pyrealm/pmodel/quantum_yield.py | 2 +- pyrealm/pmodel/subdaily.py | 14 +++----------- 3 files changed, 5 insertions(+), 13 deletions(-) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 561efe96..8db8b81e 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -172,7 +172,7 @@ def __init__( method_kphio: str = "temperature", method_optchi: str = "prentice14", method_jmaxlim: str = "wang17", - reference_kphio: float | None = None, + reference_kphio: float | NDArray | None = None, ): self.shape: tuple = env.shape """Records the common numpy array shape of array inputs.""" diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index d5b215f9..78138a78 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -106,7 +106,7 @@ def __init__( ) else: reference_kphio = reference_kphio or self.default_reference_kphio - self.reference_kphio: NDArray = np.array([reference_kphio]) + self.reference_kphio = np.array([reference_kphio]) """The kphio reference value for the method.""" self.use_c4: bool = use_c4 diff --git a/pyrealm/pmodel/subdaily.py b/pyrealm/pmodel/subdaily.py index 694c2d3c..42a85755 100644 --- a/pyrealm/pmodel/subdaily.py +++ b/pyrealm/pmodel/subdaily.py @@ -236,7 +236,7 @@ def __init__( method_optchi: str = "prentice14", method_jmaxlim: str = "wang17", method_kphio: str = "temperature", - reference_kphio: float | None = None, + reference_kphio: float | NDArray | None = None, alpha: float = 1 / 15, allow_holdover: bool = False, allow_partial_data: bool = False, @@ -318,21 +318,12 @@ def __init__( # 2) Fit a PModel to those environmental conditions, using the supplied settings # for the original model. - # If the kphio is a non-scalar array, use the mean kphio within the window to - # calculate the daily optimal behaviour. - if np.ndim(reference_kphio) > 0: - daily_kphio = fs_scaler.get_daily_means( - reference_kphio, allow_partial_data=allow_partial_data - ) - else: - daily_kphio = reference_kphio - self.pmodel_acclim: PModel = PModel( env=pmodel_env_acclim, method_kphio=method_kphio, method_optchi=method_optchi, method_jmaxlim=method_jmaxlim, - reference_kphio=daily_kphio, + reference_kphio=reference_kphio, ) r"""P Model predictions for the daily acclimation conditions. @@ -412,6 +403,7 @@ def __init__( use_c4=self.c4, reference_kphio=reference_kphio, ) + """Subdaily kphio values.""" # Calculate Ac, J and Aj at subdaily scale to calculate assimilation self.subdaily_Ac: NDArray = self.subdaily_vcmax * self.optimal_chi.mc From 758b70abc86fba8437f7a0bc925e901e3bf540d6 Mon Sep 17 00:00:00 2001 From: David Orme Date: Fri, 2 Aug 2024 10:16:13 +0100 Subject: [PATCH 17/28] Rethink of SubdailyPModel behaviour with array kphio --- pyrealm/core/utilities.py | 2 +- pyrealm/pmodel/quantum_yield.py | 21 +++++++++++--------- pyrealm/pmodel/subdaily.py | 35 +++++++++++++++++++++++---------- 3 files changed, 38 insertions(+), 20 deletions(-) diff --git a/pyrealm/core/utilities.py b/pyrealm/core/utilities.py index 9491ebf8..03fa90f8 100644 --- a/pyrealm/core/utilities.py +++ b/pyrealm/core/utilities.py @@ -88,7 +88,7 @@ def check_input_shapes(*args: float | int | np.generic | np.ndarray | None) -> t if isinstance(val, np.ndarray): # Note that 0-dim ndarrays (which are scalars) pass through as do # one dimensional arrays with a single value (also a scalar) - if not (val.ndim == 0 or val.shape == (1,)): + if val.size > 1: shapes.add(val.shape) # elif isinstance(val, Series): # # Note that 0-dim ndarrays (which are scalars) pass through diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 78138a78..357ec350 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -90,24 +90,27 @@ def __init__( model.""" self.shape: tuple[int, ...] = env.shape """The shape of the input environment data.""" - self.reference_kphio: NDArray - """The kphio reference value for the method.""" - # If the reference kphio value is an array check if it is allowed and that it - # matches the shape of the environment. - if isinstance(reference_kphio, np.ndarray): + # Set the reference kphio to the class default value if not provided and convert + # the value to np.array if needed + if reference_kphio is None: + reference_kphio = self.default_reference_kphio + if isinstance(reference_kphio, float | int): + reference_kphio = np.array([reference_kphio]) + + # Now check - if the reference_kphio value is a non-scalar array - that array + # inputs are handled by the kphio method and that the shape matches the shape of + # the environment. + if isinstance(reference_kphio, np.ndarray) and reference_kphio.size > 1: if self.array_reference_kphio_ok: check_input_shapes(self.env.tc, reference_kphio) - self.reference_kphio = reference_kphio else: raise ValueError( f"The {self.method} method for kphio does not support arrays " "of reference kphio values" ) - else: - reference_kphio = reference_kphio or self.default_reference_kphio - self.reference_kphio = np.array([reference_kphio]) + self.reference_kphio: NDArray = reference_kphio """The kphio reference value for the method.""" self.use_c4: bool = use_c4 """Use a C4 parameterisation if available.""" diff --git a/pyrealm/pmodel/subdaily.py b/pyrealm/pmodel/subdaily.py index 42a85755..0367ce38 100644 --- a/pyrealm/pmodel/subdaily.py +++ b/pyrealm/pmodel/subdaily.py @@ -315,15 +315,38 @@ def __init__( core_const=self.env.core_const, ) + # Handle the kphio settings. First, calculate kphio at the subdaily scale. + self.kphio: QuantumYieldABC = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio]( + env=env, + use_c4=self.c4, + reference_kphio=reference_kphio, + ) + """Subdaily kphio values.""" + + # If the kphio method takes a single reference value then we can simply + # recalculate the kphio using the same method for the daily acclimation + # conditions but if the reference value is an array then the correct behaviour + # is not obvious: currently, use the mean calculated kphio within the window to + # calculate the daily acclimation value behaviour and set the kphio method to be + # fixed to avoid altering the inputs. + if self.kphio.reference_kphio.size > 1: + daily_reference_kphio = fs_scaler.get_daily_means( + self.kphio.kphio, allow_partial_data=allow_partial_data + ) + daily_method_kphio = "fixed" + else: + daily_reference_kphio = self.kphio.reference_kphio + daily_method_kphio = self.method_kphio + # 2) Fit a PModel to those environmental conditions, using the supplied settings # for the original model. self.pmodel_acclim: PModel = PModel( env=pmodel_env_acclim, - method_kphio=method_kphio, + method_kphio=daily_method_kphio, method_optchi=method_optchi, method_jmaxlim=method_jmaxlim, - reference_kphio=reference_kphio, + reference_kphio=daily_reference_kphio, ) r"""P Model predictions for the daily acclimation conditions. @@ -397,14 +420,6 @@ def __init__( """Estimated subdaily :math:`c_i`.""" - # Calculate kphio at the subdaily scale. - self.kphio: QuantumYieldABC = QUANTUM_YIELD_CLASS_REGISTRY[method_kphio]( - env=env, - use_c4=self.c4, - reference_kphio=reference_kphio, - ) - """Subdaily kphio values.""" - # Calculate Ac, J and Aj at subdaily scale to calculate assimilation self.subdaily_Ac: NDArray = self.subdaily_vcmax * self.optimal_chi.mc """Estimated subdaily :math:`A_c`.""" 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mask=[1,0,1,0]) >>> x.mean() - 3.0 + np.float64(3.0) So the general approach here is now: @@ -282,16 +282,16 @@ def bounds_mask( Examples: >>> vals = np.array([-15, 20, 30, 124], dtype=float) >>> np.nansum(vals) - 159.0 + np.float64(159.0) >>> vals_c = bounds_mask(vals, 0, 100, label='temperature') >>> np.nansum(vals_c) - 50.0 + np.float64(50.0) >>> vals_c = bounds_mask(vals, 0, 124, interval_type='[]', label='temperature') >>> np.nansum(vals_c) - 174.0 + np.float64(174.0) >>> vals_c = bounds_mask(vals, 0, 124, interval_type='[)', label='temperature') >>> np.nansum(vals_c) - 50.0 + np.float64(50.0) """ # Get the interval functions diff --git a/pyrealm/core/water.py b/pyrealm/core/water.py index 1d4a9ecc..72c7568b 100644 --- a/pyrealm/core/water.py +++ b/pyrealm/core/water.py @@ -37,7 +37,7 @@ def calc_density_h2o_chen( Examples: >>> round(calc_density_h2o_chen(20, 101325), 3) - 998.25 + np.float64(998.25) """ # Calculate density at 1 atm (kg/m^3): @@ -93,7 +93,7 @@ def calc_density_h2o_fisher( Examples: >>> round(calc_density_h2o_fisher(20, 101325), 3) - 998.206 + np.float64(998.206) """ # Check input shapes, shape not used @@ -156,7 +156,7 @@ def calc_density_h2o( Examples: >>> round(calc_density_h2o(20, 101325), 3) - 998.206 + np.float64(998.206) """ # Safe guard against instability in functions at low temperature. @@ -201,7 +201,7 @@ def calc_viscosity_h2o( Examples: >>> # Density of water at 20 degrees C and standard atmospheric pressure: >>> round(calc_viscosity_h2o(20, 101325), 7) - 0.0010016 + np.float64(0.0010016) """ # Check inputs, return shape not used @@ -269,7 +269,7 @@ def calc_viscosity_h2o_matrix( Examples: >>> # Density of water at 20 degrees C and standard atmospheric pressure: >>> round(calc_viscosity_h2o(20, 101325), 7) - 0.0010016 + np.float64(0.0010016) """ # Check inputs, return shape not used diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index e5a198ab..84692690 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -110,7 +110,7 @@ def calc_ftemp_inst_rd( >>> # Relative percentage instantaneous change in Rd going from 10 to 25 degrees >>> val = (calc_ftemp_inst_rd(25) / calc_ftemp_inst_rd(10) - 1) * 100 >>> round(val, 4) - 250.9593 + np.float64(250.9593) """ return np.exp( @@ -306,13 +306,13 @@ def calc_ftemp_kphio( >>> # degrees celsius (percent change): >>> val = (calc_ftemp_kphio(25.0) / calc_ftemp_kphio(5.0) - 1) * 100 >>> round(val, 5) - 52.03969 + np.float64(52.03969) >>> # Relative change in the quantum yield efficiency between 5 and 25 >>> # degrees celsius (percent change) for a C4 plant: >>> val = (calc_ftemp_kphio(25.0, c4=True) / ... calc_ftemp_kphio(5.0, c4=True) - 1) * 100 >>> round(val, 5) - 432.25806 + np.float64(432.25806) """ if c4: @@ -411,7 +411,7 @@ def calc_ns_star( >>> # Relative viscosity at 20 degrees Celsius and standard >>> # atmosphere (in Pa): >>> round(calc_ns_star(20, 101325), 5) - 1.12536 + np.float64(1.12536) """ visc_env = calc_viscosity_h2o(tc, patm, core_const=core_const) @@ -729,7 +729,7 @@ def calc_co2_to_ca(co2: NDArray, patm: NDArray) -> NDArray: Examples: >>> np.round(calc_co2_to_ca(413.03, 101325), 6) - 41.850265 + np.float64(41.850265) """ return 1.0e-6 * co2 * patm # Pa, atms. CO2 From e758592b2c0131dff12d4790680237936f4bb912 Mon Sep 17 00:00:00 2001 From: David Orme Date: Tue, 6 Aug 2024 14:30:31 +0100 Subject: [PATCH 19/28] Doc updates --- docs/source/conf.py | 2 +- docs/source/refs.bib | 1566 +++++++++-------- .../pmodel/pmodel_details/lue_limitation.md | 257 ++- .../pmodel/pmodel_details/pmodel_overview.md | 33 +- pyrealm/pmodel/quantum_yield.py | 14 +- 5 files changed, 995 insertions(+), 877 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a9905789..73f896ad 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -82,7 +82,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle): ) bibtex_reference_style = "author_year_round" - +bibtex_default_style = "plain" # Cross-reference checking # TODO - find some better solution than this to all of these bizarre cross reference diff --git a/docs/source/refs.bib b/docs/source/refs.bib index 4a8b91e0..75945668 100644 --- a/docs/source/refs.bib +++ b/docs/source/refs.bib @@ -1,860 +1,886 @@ - -@article{Wang:2020ik, - title = {Acclimation of leaf respiration consistent with optimal photosynthetic capacity}, - volume = {26}, - url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.14980}, - doi = {10.1111/gcb.14980}, - language = {english}, - number = {4}, - journal = {Global Change Biology}, - author = {Wang, Han and Atkin, Owen K and Keenan, Trevor F and Smith, Nicholas G and Wright, Ian J. and Bloomfield, Keith J and Kattge, Jens and Reich, Peter B and Prentice, I. Colin}, - month = feb, - year = {2020}, - note = {tex.date-added: 2020-11-30T12:23:58GMT - tex.date-modified: 2021-01-22T13:54:24GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Wang/Global\%20Change\%20Biol\%202020\%20Wang.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1111/gcb.14980}, - pages = {2573--2583}, - file = {gcb15314-sup-0004-Supinfo.pdf:/Users/dorme/Zotero/storage/NSCU7SWY/gcb15314-sup-0004-Supinfo.pdf:application/pdf;Global Change Biol 2020 Wang.pdf:/Users/dorme/Zotero/storage/4C9SVQUB/Global Change Biol 2020 Wang.pdf:application/pdf} +@techreport{allen:1998a, + title = {Crop Evapotranspiration - {{Guidelines}} for Computing Crop Water Requirements}, + author = {Allen, Richard G. and Pereira, Luis S and Raes, Dirk and Smith, Martin}, + year = {1998}, + number = {56}, + address = {Rome}, + institution = {FAO}, + file = {/Users/dorme/Zotero/storage/UW9UBHX2/Allen et al. - 1998 - Crop evapotranspiration - Guidelines for computing.pdf} } -@article{Wang:2017go, - title = {Towards a universal model for carbon dioxide uptake by plants}, - url = {http://dx.doi.org/10.1038/s41477-017-0006-8}, - doi = {10.1038/s41477-017-0006-8}, - abstract = {Nature Plants, doi:10.1038/s41477-017-0006-8}, - journal = {Nature Plants}, - author = {Wang, Han and Prentice, I. Colin and Keenan, Trevor F and Davis, Tyler W and Wright, Ian J. and Cornwell, William K and Evans, Bradley J and Peng, Changhui}, - month = sep, - year = {2017}, - note = {Publisher: Springer US - tex.date-added: 2020-11-30T12:27:00GMT - tex.date-modified: 2021-01-28T09:14:19GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2017/Wang/Nature\%20Plants\%202017\%20Wang.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1038/s41477-017-0006-8}, - pages = {1--8}, - file = {Nature Plants 2017 Wang.pdf:/Users/dorme/Zotero/storage/XIP7696Y/Nature Plants 2017 Wang.pdf:application/pdf} +@article{Argles:2020cy, + title = {Robust {{Ecosystem Demography}} ({{RED}} Version 1.0): A Parsimonious Approach to Modelling Vegetation Dynamics in {{Earth}} System Models}, + author = {Argles, Arthur P K and Moore, Jonathan R and Huntingford, Chris and Wiltshire, Andrew J and Harper, Anna B and Jones, Chris D and Cox, Peter M}, + year = {2020}, + journal = {Geoscientific Model Development}, + volume = {13}, + number = {9}, + pages = {4067--4089}, + doi = {10.5194/gmd-13-4067-2020}, + date-added = {2020-12-01T16:58:12GMT}, + date-modified = {2021-07-22T14:23:35GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Argles/Geoscientific\%20Model\%20Development\%202020\%20Argles.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.5194/gmd-13-4067-2020}, + file = {/Users/dorme/Zotero/storage/WKQGYNRT/Geoscientific Model Development 2020 Argles.pdf} } -@article{Huber:2009fy, - title = {New international formulation for the viscosity of {H2O}}, - volume = {38}, - url = {http://aip.scitation.org/doi/10.1063/1.3088050}, - doi = {10.1063/1.3088050}, - language = {english}, - number = {2}, - journal = {Journal of Physical and Chemical Reference Data}, - author = {Huber, M L and Perkins, R A and Laesecke, A and Friend, D G and Sengers, J V and Assael, M J and Metaxa, I N and Vogel, E and Mareš, R and Miyagawa, K}, - month = jun, - year = {2009}, - note = {tex.date-added: 2020-12-02T09:52:51GMT - tex.date-modified: 2020-12-17T08:58:40GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2009/Huber/Journal\%20of\%20Physical\%20and\%20Chemical\%20Reference\%20Data\%202009\%20Huber.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1063/1.3088050}, - pages = {101--125}, - file = {Journal of Physical and Chemical Reference Data 2009 Huber.pdf:/Users/dorme/Zotero/storage/D2W4H4S4/Journal of Physical and Chemical Reference Data 2009 Huber.pdf:application/pdf} +@article{Atkin:2015hk, + title = {Global Variability in Leaf Respiration in Relation to Climate, Plant Functional Types and Leaf Traits.}, + author = {Atkin, Owen K and Bloomfield, Keith J and Reich, Peter B and Tjoelker, Mark G and Asner, Gregory P. and Bonal, Damien and B{\"o}nisch, Gerhard and Bradford, Matt G and Cernusak, Lucas A and Cosio, Eric G and Creek, Danielle and Crous, Kristine Y and Domingues, Tomas F and Dukes, Jeffrey S and Egerton, John J G and Evans, John R and Farquhar, Graham D and Fyllas, Nikolaos M and Gauthier, Paul P G and Gloor, Emanuel and Gimeno, Teresa E and Griffin, Kevin L and Guerrieri, Rossella and Heskel, Mary A and Huntingford, Chris and Ishida, Fran{\c c}oise Yoko and Kattge, Jens and Lambers, Hans and Liddell, Michael J and Lloyd, Jon and Lusk, Christopher H and Martin, Roberta E and Maksimov, Ayal P and Maximov, Trofim C and Malhi, Yadvinder and Medlyn, Belinda E and Meir, Patrick and Mercado, Lina M and Mirotchnick, Nicholas and Ng, Desmond and Niinemets, {\"U}lo and O'Sullivan, Odhran S and Phillips, Oliver L and Poorter, Lourens and Poot, Pieter and Prentice, I. Colin and Salinas, Norma and Rowland, Lucy M and Ryan, Michael G and Sitch, Stephen and Slot, Martijn and Smith, Nicholas G and Turnbull, Matthew H and VanderWel, Mark C and Valladares, Fernando and Veneklaas, Erik J and Weerasinghe, Lasantha K and Wirth, Christian and Wright, Ian J. and Wythers, Kirk R and Xiang, Jen and Xiang, Shuang and {Zaragoza-Castells}, Joana}, + year = {2015}, + month = apr, + journal = {New Phytologist}, + volume = {206}, + number = {2}, + pages = {614--636}, + doi = {10.1111/nph.13253}, + abstract = {Leaf dark respiration (Rdark ) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark . Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20{$^\circ$}C increase in growing T (8-28{$^\circ$}C). By contrast, Rdark at a standard T (25{$^\circ$}C, Rdark (25) ) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark (25) at a given photosynthetic capacity (Vcmax (25) ) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark (25) values at any given Vcmax (25) or [N] were higher in herbs than in woody plants. The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs).}, + affiliation = {ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University, Building 134, Canberra, ACT, 0200, Australia; Division of Plant Sciences, Research School of Biology, The Australian National University, Building 46, Canberra, ACT, 0200, Australia.}, + date-added = {2021-01-28T11:56:12GMT}, + date-modified = {2021-01-28T15:58:45GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2015/Atkin/New\%20Phytol.\%202015\%20Atkin.pdf}, + pmid = {25581061}, + rating = {0}, + uri = {papers3://publication/doi/10.1111/nph.13253}, + file = {/Users/dorme/Zotero/storage/JCS7TM7H/New Phytol. 2015 Atkin.pdf} } -@article{Argles:2020cy, - title = {Robust {Ecosystem} {Demography} ({RED} version 1.0): a parsimonious approach to modelling vegetation dynamics in {Earth} system models}, - volume = {13}, - url = {https://gmd.copernicus.org/articles/13/4067/2020/}, - doi = {10.5194/gmd-13-4067-2020}, - language = {english}, - number = {9}, - journal = {Geoscientific Model Development}, - author = {Argles, Arthur P K and Moore, Jonathan R and Huntingford, Chris and Wiltshire, Andrew J and Harper, Anna B and Jones, Chris D and Cox, Peter M}, - year = {2020}, - note = {tex.date-added: 2020-12-01T16:58:12GMT - tex.date-modified: 2021-07-22T14:23:35GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Argles/Geoscientific\%20Model\%20Development\%202020\%20Argles.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.5194/gmd-13-4067-2020}, - pages = {4067--4089}, - file = {Geoscientific Model Development 2020 Argles.pdf:/Users/dorme/Zotero/storage/6WTVNVIZ/Geoscientific Model Development 2020 Argles.pdf:application/pdf} +@article{badeck:2005a, + title = {Post-Photosynthetic Fractionation of Stable Carbon Isotopes between Plant Organs---a Widespread Phenomenon}, + author = {Badeck, Franz-W. and Tcherkez, Guillaume and Nogu{\'e}s, Salvador and Piel, Cl{\'e}ment and Ghashghaie, Jaleh}, + year = {2005}, + journal = {Rapid Communications in Mass Spectrometry}, + volume = {19}, + number = {11}, + eprint = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/rcm.1912}, + pages = {1381--1391}, + doi = {10.1002/rcm.1912}, + abstract = {Abstract Discrimination against 13C during photosynthesis is a well-characterised phenomenon. It leaves behind distinct signatures in organic matter of plants and in the atmosphere. The former is depleted in 13C, the latter is enriched during periods of preponderant photosynthetic activity of terrestrial ecosystems. The intra-annual cycle and latitudinal gradient in atmospheric 13C resulting from photosynthetic and respiratory activities of terrestrial plants have been exploited for the reconstruction of sources and sinks through deconvolution by inverse modelling. Here, we compile evidence for widespread post-photosynthetic fractionation that further modifies the isotopic signatures of individual plant organs and consequently leads to consistent differences in {$\delta$}13C between plant organs. Leaves were on average 0.96‰ and 1.91‰ more depleted than roots and woody stems, respectively. This phenomenon is relevant if the isotopic signature of CO2-exchange fluxes at the ecosystem level is used for the reconstruction of individual sources and sinks. It may also modify the parameterisation of inverse modelling approaches if it leads to different isotopic signatures of organic matter with different residence times within the ecosystems and to a respiratory contribution to the average difference between the isotopic composition of plant organic matter and the atmosphere. We discuss the main hypotheses that can explain the observed inter-organ differences in {$\delta$}13C. Copyright {\copyright} 2005 John Wiley \& Sons, Ltd.}, + file = {/Users/dorme/Zotero/storage/2NXLVXHK/Badeck et al. - 2005 - Post-photosynthetic fractionation of stable carbon.pdf} } -@article{Atkin:2015hk, - title = {Global variability in leaf respiration in relation to climate, plant functional types and leaf traits.}, - volume = {206}, - url = {http://doi.wiley.com/10.1111/nph.13253}, - doi = {10.1111/nph.13253}, - abstract = {Leaf dark respiration (Rdark ) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark . Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20°C increase in growing T (8-28°C). By contrast, Rdark at a standard T (25°C, Rdark (25) ) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark (25) at a given photosynthetic capacity (Vcmax (25) ) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark (25) values at any given Vcmax (25) or [N] were higher in herbs than in woody plants. The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs).}, - language = {english}, - number = {2}, - journal = {New Phytologist}, - author = {Atkin, Owen K and Bloomfield, Keith J and Reich, Peter B and Tjoelker, Mark G and Asner, Gregory P. and Bonal, Damien and Bönisch, Gerhard and Bradford, Matt G and Cernusak, Lucas A and Cosio, Eric G and Creek, Danielle and Crous, Kristine Y and Domingues, Tomas F and Dukes, Jeffrey S and Egerton, John J G and Evans, John R and Farquhar, Graham D and Fyllas, Nikolaos M and Gauthier, Paul P G and Gloor, Emanuel and Gimeno, Teresa E and Griffin, Kevin L and Guerrieri, Rossella and Heskel, Mary A and Huntingford, Chris and Ishida, Françoise Yoko and Kattge, Jens and Lambers, Hans and Liddell, Michael J and Lloyd, Jon and Lusk, Christopher H and Martin, Roberta E and Maksimov, Ayal P and Maximov, Trofim C and Malhi, Yadvinder and Medlyn, Belinda E and Meir, Patrick and Mercado, Lina M and Mirotchnick, Nicholas and Ng, Desmond and Niinemets, Ülo and O’Sullivan, Odhran S and Phillips, Oliver L and Poorter, Lourens and Poot, Pieter and Prentice, I. Colin and Salinas, Norma and Rowland, Lucy M and Ryan, Michael G and Sitch, Stephen and Slot, Martijn and Smith, Nicholas G and Turnbull, Matthew H and VanderWel, Mark C and Valladares, Fernando and Veneklaas, Erik J and Weerasinghe, Lasantha K and Wirth, Christian and Wright, Ian J. and Wythers, Kirk R and Xiang, Jen and Xiang, Shuang and Zaragoza-Castells, Joana}, - month = apr, - year = {2015}, - pmid = {25581061}, - note = {tex.affiliation: ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University, Building 134, Canberra, ACT, 0200, Australia; Division of Plant Sciences, Research School of Biology, The Australian National University, Building 46, Canberra, ACT, 0200, Australia. - tex.date-added: 2021-01-28T11:56:12GMT - tex.date-modified: 2021-01-28T15:58:45GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2015/Atkin/New\%20Phytol.\%202015\%20Atkin.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1111/nph.13253}, - pages = {614--636}, - file = {New Phytol. 2015 Atkin.pdf:/Users/dorme/Zotero/storage/JEF8GCAD/New Phytol. 2015 Atkin.pdf:application/pdf} +@article{BerberanSantos:2009bk, + title = {On the Barometric Formula inside the {{Earth}}}, + author = {{Berberan-Santos}, Mario N and Bodunov, Evgeny N and Pogliani, Lionello}, + year = {2009}, + month = oct, + journal = {Journal of Mathematical Chemistry}, + volume = {47}, + number = {3}, + pages = {990--1004}, + doi = {10.1007/s10910-009-9620-7}, + date-added = {2020-11-30T16:18:45GMT}, + date-modified = {2020-11-30T16:19:02GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2009/Berberan-Santos/J\%20Math\%20Chem\%202009\%20Berberan-Santos.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1007/s10910-009-9620-7}, + file = {/Users/dorme/Zotero/storage/EN33QA2H/J Math Chem 2009 Berberan-Santos.pdf} } -@article{Stocker:2020dh, - title = {P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production}, - volume = {13}, - url = {https://gmd.copernicus.org/articles/13/1545/2020/}, - doi = {10.5194/gmd-13-1545-2020}, - language = {english}, - number = {3}, - journal = {Geoscientific Model Development}, - author = {Stocker, Benjamin D and Wang, Han and Smith, Nicholas G and Harrison, Sandy P and Keenan, Trevor F and Sandoval, David and Davis, Tyler and Prentice, I. Colin}, - year = {2020}, - note = {tex.date-added: 2020-11-30T12:24:06GMT - tex.date-modified: 2021-01-28T11:55:51GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Stocker/Geoscientific\%20Model\%20Development\%202020\%20Stocker.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.5194/gmd-13-1545-2020}, - pages = {1545--1581}, - file = {Geoscientific Model Development 2020 Stocker.pdf:/Users/dorme/Zotero/storage/87D3WF3G/Geoscientific Model Development 2020 Stocker.pdf:application/pdf} +@article{berger:1978a, + title = {Long-{{Term Variations}} of {{Daily Insolation}} and {{Quaternary Climatic Changes}}}, + author = {Berger, Andr{\'e}L}, + year = {1978}, + month = dec, + journal = {Journal of the Atmospheric Sciences}, + volume = {35}, + number = {12}, + pages = {2362--2367}, + publisher = {American Meteorological Society}, + issn = {0022-4928, 1520-0469}, + doi = {10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2}, + urldate = {2023-07-13}, + abstract = {Abstract The first part of this note provides all trigonometrical formulas which allow the direct spectral analysis and the computation of those long-term variations of the earth's orbital elements which are of primary interest for the computation of the insolation. The elements are the eccentricity, the longitude of the perihelion, the processional parameter and the obliquity. This new formulary is much more simple to use than the ones previously designed and still provides excellent accuracy, mainly because it takes into account the influence of the most important higher order terms in the series expansions. The second part is devoted to the computation of the daily insolation both for calendar and solar dates.}, + chapter = {Journal of the Atmospheric Sciences}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/DPM5WN32/Berger - 1978 - Long-Term Variations of Daily Insolation and Quate.pdf} +} + +@article{berger:1993a, + title = {Insolation and {{Earth}}'s Orbital Periods}, + author = {Berger, Andr{\'e} and Loutre, Marie-France and Tricot, Christian}, + year = {1993}, + journal = {Journal of Geophysical Research: Atmospheres}, + volume = {98}, + number = {D6}, + pages = {10341--10362}, + issn = {2156-2202}, + doi = {10.1029/93JD00222}, + urldate = {2024-08-02}, + abstract = {Solar irradiance received on a horizontal surface depends on the solar output, the semimajor axis of the elliptical orbit of the Earth around the sun (a), the distance from the Earth to the sun (r), and the zenith distance (z). The spectrum of the distance, r, for a given value of the true longitude, {$\lambda$}, displays mainly the precessional periods and, with much less power, half precession periods, eccentricity periods, and some combination tones. The zenith distance or its equivalent, the elevation angle (E), is only a function of obliquity ({$\epsilon$}) for a given latitude, {$\phi$}, true longitude, and hour angle, H. Therefore the insolation at a given constant value of z is only a function of precession and eccentricity. On the other hand, the value of the hour angle, H, corresponding to this fixed value of z varies with {$\varepsilon$}, except for the equinoxes, where H corresponding to a constant z also remains constant through time. Three kinds of insolation have been computed both analytically and numerically: the instantaneous insolation (irradiance) at noon, the daily irradiation, and the irradiations received during particular time intervals of the day defined by two constant values of the zenith distance (diurnal irradiations). Mean irradiances (irradiations divided by the length of the time interval over which they are calculated) are also computed for different time intervals, like the interval between sunrise and sunset, in particular. Examples of these insolations are given in this paper for the equinoxes and the solstices. At the equinoxes, for each latitude, all insolations are only a function of precession (this invalidates the results obtained by Cerveny [1991]). At the solstices, both precession and obliquity are present, although precession dominates for most of the latitudes. Because the lengths of the astronomical seasons are secularly variable (in terms of precession only), a particular calendar day does not always correspond to the same position relative to the sun through geological time. Similarly, a given longitude of the Sun on its orbit does not correspond to the same calendar day. For example, 103 kyr ago, assuming arbitrarily that the spring equinox is always on March 21, autumn began on September 13, and 114 kyr ago, it began on September 27, the length of the summer season being 85 and 98 calendar days, respectively, at these remote times in the past.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/ALTXDVSM/Berger et al. - 1993 - Insolation and Earth's orbital periods.pdf;/Users/dorme/Zotero/storage/HWQ4XSZN/93JD00222.html} } -@article{BerberanSantos:2009bk, - title = {On the barometric formula inside the {Earth}}, - volume = {47}, - url = {http://link.springer.com/10.1007/s10910-009-9620-7}, - doi = {10.1007/s10910-009-9620-7}, - language = {english}, - number = {3}, - journal = {Journal of Mathematical Chemistry}, - author = {Berberan-Santos, Mario N and Bodunov, Evgeny N and Pogliani, Lionello}, - month = oct, - year = {2009}, - note = {tex.date-added: 2020-11-30T16:18:45GMT - tex.date-modified: 2020-11-30T16:19:02GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2009/Berberan-Santos/J\%20Math\%20Chem\%202009\%20Berberan-Santos.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1007/s10910-009-9620-7}, - pages = {990--1004}, - file = {J Math Chem 2009 Berberan-Santos.pdf:/Users/dorme/Zotero/storage/7WD7PHD7/J Math Chem 2009 Berberan-Santos.pdf:application/pdf} +@article{Bernacchi:2001kg, + title = {Improved Temperature Response Functions for Models of {{Rubisco-limited}} Photosynthesis}, + author = {Bernacchi, C J and Singsaas, E L and Pimentel, C and Portis Jr, A R and Long, S P}, + year = {2001}, + journal = {Plant, Cell \& Environment}, + volume = {24}, + number = {2}, + pages = {253--259}, + doi = {10.1111/j.1365-3040.2001.00668.x}, + date-added = {2020-11-30T12:24:10GMT}, + date-modified = {2020-11-30T14:42:53GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2001/Bernacchi/Plant\%20Cell\%20\&\%20Environment\%202001\%20Bernacchi.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1111/j.1365-3040.2001.00668.x}, + file = {/Users/dorme/Zotero/storage/FI9562Z3/Plant Cell & Environment 2001 Bernacchi.pdf} } -@book{Fisher:1975tm, - title = {Equation of state of pure water and sea water}, - url = {https://apps.dtic.mil/dtic/tr/fulltext/u2/a017775.pdf}, - publisher = {Scripps Institution of Oceanography}, - author = {Fisher, F H and Dial Jr, O E}, - year = {1975}, - note = {tex.date-added: 2020-11-30T12:27:10GMT - tex.date-modified: 2021-01-21T15:04:43GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Books/1975/Fisher/1975\%20Fisher.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/uuid/5D2C2C96-7975-4AB9-8314-A988EFA489FF}, - file = {1975 Fisher.pdf:/Users/dorme/Zotero/storage/MNQYGV9P/1975 Fisher.pdf:application/pdf} +@article{Bernacchi:2003dc, + title = {In Vivo Temperature Response Functions of Parameters Required to Model {{RuBP-limited}} Photosynthesis}, + author = {Bernacchi, C J and Pimentel, C and Long, S P}, + year = {2003}, + month = sep, + journal = {Plant, Cell \& Environment}, + volume = {26}, + number = {9}, + pages = {1419--1430}, + publisher = {John Wiley \& Sons, Ltd}, + doi = {10.1046/j.0016-8025.2003.01050.x}, + abstract = {The leaf model of C3 photosynthesis of Farquhar, von Caemmerer \& Berry (Planta 149, 78--90, 1980) provides the basis for scaling carbon exchange from leaf to canopy and Earth-System models, and is wid...}, + date-added = {2020-11-30T14:15:56GMT}, + date-modified = {2020-11-30T14:42:52GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2003/Bernacchi/Plant\%20Cell\%20\&\%20Environment\%202003\%20Bernacchi.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1046/j.0016-8025.2003.01050.x}, + file = {/Users/dorme/Zotero/storage/7J2I98DM/Plant Cell & Environment 2003 Bernacchi.pdf} } -@article{DeKauwe:2015im, - title = {A test of an optimal stomatal conductance scheme within the {CABLE} land surface model}, - volume = {8}, - url = {https://gmd.copernicus.org/articles/8/431/2015/gmd-8-431-2015.pdf}, - doi = {10.5194/gmd-8-431-2015}, - abstract = {¡p¿¡strong class="journal-contentHeaderColor"¿Abstract.¡/strong¿ Stomatal conductance (¡i¿g¡/i¿¡sub¿s¡/sub¿) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between ¡i¿g¡/i¿¡sub¿s¡/sub¿ model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "¡i¿g¡/i¿¡sub¿1¡/sub¿", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the ¡i¿g¡/i¿¡sub¿1¡/sub¿ parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration ({\textasciitilde} 30\% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model–data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.¡/p¿}, - language = {english}, - number = {2}, - journal = {Geoscientific Model Development}, - author = {De Kauwe, M G and Kala, J and Lin, Y S and Pitman, A J and Medlyn, B E and Duursma, R A and Abramowitz, G and Wang, Y P and Miralles, D G}, - month = feb, - year = {2015}, - note = {Publisher: Copernicus GmbH - tex.date-added: 2021-11-11T14:04:21GMT - tex.date-modified: 2021-11-11T14:13:27GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2015/De\%20Kauwe/Geoscientific\%20Model\%20Development\%202015\%20De\%20Kauwe.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.5194/gmd-8-431-2015}, - pages = {431--452}, - file = {Geoscientific Model Development 2015 De Kauwe.pdf:/Users/dorme/Zotero/storage/2SQR2JU6/Geoscientific Model Development 2015 De Kauwe.pdf:application/pdf;Geoscientific Model Development 2015 De Kauwe.pdf:/Users/dorme/Zotero/storage/Y65YLM8W/Geoscientific Model Development 2015 De Kauwe.pdf:application/pdf} +@article{boyd:2015a, + title = {Temperature Response of {{C4}} Photosynthesis: {{Biochemical}} Analysis of {{Rubisco}}, {{Phosphoenolpyruvate Carboxylase}} and {{Carbonic Anhydrase}} in {{Setaria}} Viridis.}, + shorttitle = {Temperature Response of {{C4}} Photosynthesis}, + author = {Boyd, Ryan Allen and Gandin, Anthony and Cousins, Asaph B}, + year = {2015}, + month = sep, + journal = {Plant Physiology}, + pages = {pp.00586.2015}, + issn = {0032-0889, 1532-2548}, + doi = {10.1104/pp.15.00586}, + urldate = {2022-05-20}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/P866H95S/Boyd et al. - 2015 - Temperature response of C4 photosynthesis Biochem.pdf} } -@article{Moore:2018dv, - title = {Equilibrium forest demography explains the distribution of tree sizes across {North} {America}}, - volume = {13}, - url = {https://iopscience.iop.org/article/10.1088/1748-9326/aad6d1}, - doi = {10.1088/1748-9326/aad6d1}, - abstract = {Environmental Research Letters, 13(2018) 084019. doi:10.1088/1748-9326/aad6d1}, - number = {8}, - journal = {Environmental Research Letters}, - author = {Moore, Jonathan R and Zhu, Kai and Huntingford, Chris and Cox, Peter M}, - month = aug, - year = {2018}, - note = {Publisher: IOP Publishing - tex.date-added: 2020-12-01T16:56:53GMT - tex.date-modified: 2020-12-17T08:58:39GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Moore/Environ\%20Res\%20Lett\%202018\%20Moore.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1088/1748-9326/aad6d1}, - pages = {084019--10}, - file = {Environ Res Lett 2018 Moore.pdf:/Users/dorme/Zotero/storage/XT8SWLIA/Environ Res Lett 2018 Moore.pdf:application/pdf} +@article{cai:2020a, + title = {Recent Trends in Gross Primary Production and Their Drivers: Analysis and Modelling at Flux-Site and Global Scales}, + shorttitle = {Recent Trends in Gross Primary Production and Their Drivers}, + author = {Cai, Wenjia and Prentice, Iain Colin}, + year = {2020}, + month = dec, + journal = {Environmental Research Letters}, + volume = {15}, + number = {12}, + pages = {124050}, + issn = {1748-9326}, + doi = {10.1088/1748-9326/abc64e}, + urldate = {2022-05-16}, + abstract = {Abstract Gross primary production (GPP) by terrestrial ecosystems is the largest flux in the global carbon cycle, and its continuing increase in response to environmental changes is key to land ecosystems' capacity to offset anthropogenic CO 2 emissions. However, the CO 2 - and climate-sensitivities of GPP vary among models. We applied the `P model'---a parameter-sparse and extensively tested light use efficiency (LUE) model, driven by CO 2 , climate and remotely sensed greenness data---at 29 sites with multi-year eddy-covariance flux measurements. Observed (both positive and negative) GPP trends at these sites were predicted, albeit with some bias. Increasing LUE (due to rising atmospheric CO 2 concentration) and green vegetation cover were the primary controls of modelled GPP trends across sites. Global GPP simulated by the same model increased by 0.46 {\textpm} 0.09 Pg C yr --2 during 1982--2016. This increase falls in the mid-range rate of simulated increase by the TRENDY v8 ensemble of state-of-the-art ecosystem models. The modelled LUE increase during 1900--2013 was 15\%, similar to a published estimate based on deuterium isotopomers. Rising CO 2 was the largest contributor to the modelled GPP increase. Greening, which may in part be caused by rising CO 2 , ranked second but dominated the modelled GPP change over large areas, including semi-arid vegetation on all continents. Warming caused a small net reduction in modelled global GPP, but dominated the modelled GPP increase in high northern latitudes. These findings strengthen the evidence that rising LUE due to rising CO 2 level and increased green vegetation cover (fAPAR) are the main causes of increasing GPP, and thereby, the terrestrial carbon sink.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/9YJ8TV4Y/Cai and Prentice - 2020 - Recent trends in gross primary production and thei.pdf} } -@article{Smith:2019dv, - title = {Global photosynthetic capacity is optimized to the environment}, - volume = {22}, - url = {https://onlinelibrary.wiley.com/doi/10.1111/ele.13210}, - doi = {10.1111/ele.13210}, - language = {english}, - number = {3}, - journal = {Ecology Letters}, - author = {Smith, Nicholas G and Keenan, Trevor F and Colin Prentice, I and Wang, Han and Wright, Ian J. and Niinemets, Ülo and Crous, Kristine Y and Domingues, Tomas F and Guerrieri, Rossella and Yoko Ishida, F and Kattge, Jens and Kruger, Eric L and Maire, Vincent and Rogers, Alistair and Serbin, Shawn P and Tarvainen, Lasse and Togashi, Henrique F and Townsend, Philip A and Wang, Meng and Weerasinghe, Lasantha K and Zhou, Shuang Xi}, - month = jan, - year = {2019}, - note = {tex.date-added: 2020-12-02T15:16:49GMT - tex.date-modified: 2021-01-25T10:03:32GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2019/Smith/Ecol\%20Lett\%202019\%20Smith.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1111/ele.13210}, - pages = {506--517}, - file = {Ecol Lett 2019 Smith.pdf:/Users/dorme/Zotero/storage/3YQVDBCU/Ecol Lett 2019 Smith.pdf:application/pdf} +@article{chen:2008a, + title = {The Equation of State of Pure Water Determined from Sound Speeds}, + author = {Chen, Chen-Tung and Fine, Rana A. and Millero, Frank J.}, + year = {2008}, + month = aug, + journal = {The Journal of Chemical Physics}, + volume = {66}, + number = {5}, + pages = {2142--2144}, + issn = {0021-9606}, + doi = {10.1063/1.434179}, + urldate = {2023-07-04}, + abstract = {The equation of state of water valid over the range 0--100\,{$^\circ$}C and 0--1000 bar has been determined from the high pressure sound velocities of Wilson, which were reanalyzed by Chen and Millero. The equation of state has a maximum error of {\textpm}0.01 bar-1 in isothermal compressibility and is in the form of a secant bulk modulus: K=V0P/(V0-V) =K0+AP+BP2, where K, K0, and V, V0 are the secant bulk moduli and specific volumes at applied pressures P and 0 (1 atm), respectively; A and B are temperature dependent parameters. The good agreement (to within 20{\texttimes}10-6 cm3\,g-1) of specific volumes calculated using the above equation with those obtained from other modifications of the Wilson sound velocity data demonstrates the reliability of the sound velocity method for determining equations of state.}, + file = {/Users/dorme/Zotero/storage/HHYLGDG4/Chen et al. - 2008 - The equation of state of pure water determined fro.pdf;/Users/dorme/Zotero/storage/M6MBWTPL/The-equation-of-state-of-pure-water-determined.html} } -@phdthesis{Lancelot:2020tm, - title = {A novel computational model to predict forest dynamics}, - url = {https://imperialcollegelondon.app.box.com/}, - author = {Lancelot, Maxime}, - month = aug, - year = {2020}, - note = {tex.affiliation: Imperial College London - tex.date-added: 2020-11-30T12:25:21GMT - tex.date-modified: 2020-11-30T14:42:52GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Books/2020/Lancelot/2020\%20Lancelot.pdf - tex.rating: 0 - tex.uri: papers3://publication/uuid/D73D9C6F-36C9-461D-A90C-784809C52BCB}, - file = {2020 Lancelot.pdf:/Users/dorme/Zotero/storage/YGXR7FCI/2020 Lancelot.pdf:application/pdf} +@article{davis:2017a, + title = {Simple Process-Led Algorithms for Simulating Habitats ({{SPLASH}} v.1.0): Robust Indices of Radiation, Evapotranspiration and Plant-Available Moisture}, + shorttitle = {Simple Process-Led Algorithms for Simulating Habitats ({{SPLASH}} v.1.0)}, + author = {Davis, Tyler W. and Prentice, I. Colin and Stocker, Benjamin D. and Thomas, Rebecca T. and Whitley, Rhys J. and Wang, Han and Evans, Bradley J. and {Gallego-Sala}, Angela V. and Sykes, Martin T. and Cramer, Wolfgang}, + year = {2017}, + month = feb, + journal = {Geoscientific Model Development}, + volume = {10}, + number = {2}, + pages = {689--708}, + publisher = {Copernicus GmbH}, + issn = {1991-959X}, + doi = {10.5194/gmd-10-689-2017}, + urldate = {2023-07-05}, + abstract = {Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley--Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/CDUHE8TA/Davis et al. - 2017 - Simple process-led algorithms for simulating habit.pdf} } -@article{Kattge:2007db, - title = {Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species}, - volume = {30}, - url = {http://doi.wiley.com/10.1111/j.1365-3040.2007.01690.x}, - doi = {10.1111/j.1365-3040.2007.01690.x}, - abstract = {The Farquhar et al. model of C3 photosynthesis is frequently used to study the effect of global changes on the biosphere. Its two main parameters representing photosynthetic capacity, Vcmax and Jmax, have been observed to acclimate to plant growth temperature for single species, but a general formulation has never been derived. Here, we present a reanalysis of data from 36 plant species to quantify the temperature dependence of Vcmax and Jmax with a focus on plant growth temperature, ie the plants' average ambient …}, - language = {english}, - number = {9}, - journal = {Plant, Cell \& Environment}, - author = {Kattge, Jens and Knorr, Wolfgang}, - month = sep, - year = {2007}, - note = {tex.date-added: 2020-11-30T14:04:07GMT - tex.date-modified: 2020-12-02T16:14:05GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2007/Kattge/Plant\%20Cell\%20\&\%20Environment\%202007\%20Kattge.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1111/j.1365-3040.2007.01690.x}, - pages = {1176--1190}, - file = {Plant Cell & Environment 2007 Kattge.pdf:/Users/dorme/Zotero/storage/UTXF2JRP/Plant Cell & Environment 2007 Kattge.pdf:application/pdf} +@article{DeKauwe:2015im, + title = {A Test of an Optimal Stomatal Conductance Scheme within the {{CABLE}} Land Surface Model}, + author = {De Kauwe, M G and Kala, J and Lin, Y S and Pitman, A J and Medlyn, B E and Duursma, R A and Abramowitz, G and Wang, Y P and Miralles, D G}, + year = {2015}, + month = feb, + journal = {Geoscientific Model Development}, + volume = {8}, + number = {2}, + pages = {431--452}, + publisher = {Copernicus GmbH}, + doi = {10.5194/gmd-8-431-2015}, + abstract = {{\textexclamdown}p{\textquestiondown}{\textexclamdown}strong class="journal-contentHeaderColor"{\textquestiondown}Abstract.{\textexclamdown}/strong{\textquestiondown} Stomatal conductance ({\textexclamdown}i{\textquestiondown}g{\textexclamdown}/i{\textquestiondown}{\textexclamdown}sub{\textquestiondown}s{\textexclamdown}/sub{\textquestiondown}) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between {\textexclamdown}i{\textquestiondown}g{\textexclamdown}/i{\textquestiondown}{\textexclamdown}sub{\textquestiondown}s{\textexclamdown}/sub{\textquestiondown} model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "{\textexclamdown}i{\textquestiondown}g{\textexclamdown}/i{\textquestiondown}{\textexclamdown}sub{\textquestiondown}1{\textexclamdown}/sub{\textquestiondown}", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the {\textexclamdown}i{\textquestiondown}g{\textexclamdown}/i{\textquestiondown}{\textexclamdown}sub{\textquestiondown}1{\textexclamdown}/sub{\textquestiondown} parameter can be estimated using two long-term average (1960--1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration ({\textasciitilde} 30\% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model--data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.{\textexclamdown}/p{\textquestiondown}}, + date-added = {2021-11-11T14:04:21GMT}, + date-modified = {2021-11-11T14:13:27GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2015/De\%20Kauwe/Geoscientific\%20Model\%20Development\%202015\%20De\%20Kauwe.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.5194/gmd-8-431-2015}, + file = {/Users/dorme/Zotero/storage/8WDYDVKQ/Geoscientific Model Development 2015 De Kauwe.pdf;/Users/dorme/Zotero/storage/WXI7ASHC/Geoscientific Model Development 2015 De Kauwe.pdf} } -@article{Heskel:2016fg, - title = {Convergence in the temperature response of leaf respiration across biomes and plant functional types}, - volume = {113}, - url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1520282113}, - doi = {10.1073/pnas.1520282113}, - language = {english}, - number = {14}, - journal = {Proceedings of the National Academy of Sciences of the United States of America}, - author = {Heskel, Mary A and O’Sullivan, Odhran S and Reich, Peter B and Tjoelker, Mark G and Weerasinghe, Lasantha K and Penillard, Aurore and Egerton, John J G and Creek, Danielle and Bloomfield, Keith J and Xiang, Jen and Sinca, Felipe and Stangl, Zsofia R and Martinez-de la Torre, Alberto and Griffin, Kevin L and Huntingford, Chris and Hurry, Vaughan and Meir, Patrick and Turnbull, Matthew H and Atkin, Owen K}, - month = apr, - year = {2016}, - note = {tex.date-added: 2020-11-30T13:55:54GMT - tex.date-modified: 2020-11-30T14:42:52GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2016/Heskel/PNAS\%202016\%20Heskel.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1073/pnas.1520282113}, - pages = {3832--3837}, - file = {PNAS 2016 Heskel.pdf:/Users/dorme/Zotero/storage/YR5JESPC/PNAS 2016 Heskel.pdf:application/pdf;PNAS 2016 Heskel.pdf:/Users/dorme/Zotero/storage/ZTIEDG3M/PNAS 2016 Heskel.pdf:application/pdf} +@article{Diaz:2016by, + title = {The Global Spectrum of Plant Form and Function}, + author = {D{\'i}az, Sandra and Kattge, Jens and Cornelissen, Johannes H C and Wright, Ian J. and Lavorel, Sandra and Dray, St{\'e}phane and Reu, Bj{\"o}rn and Kleyer, Michael and Wirth, Christian and Prentice, I. Colin and Garnier, Eric and B{\"o}nisch, Gerhard and Westoby, Mark and Poorter, Hendrik and Reich, Peter B and Moles, Angela T and Dickie, John and Gillison, Andrew N and Zanne, Amy E and Chave, J{\'e}r{\^o}me and Wright, S Joseph and Sheremet'ev, Serge N and Jactel, Herv{\'e} and Baraloto, Christopher and Cerabolini, Bruno and Pierce, Simon and Shipley, Bill and Kirkup, Donald and Casanoves, Fernando and Joswig, Julia S and G{\"u}nther, Angela and Falczuk, Valeria and R{\"u}ger, Nadja and Mahecha, Miguel D and Gorn{\'e}, Lucas D}, + year = {2016}, + month = jan, + journal = {Nature}, + volume = {529}, + number = {7585}, + pages = {167--171}, + publisher = {Nature Publishing Group}, + doi = {10.1038/nature16489}, + abstract = {Nature 529, 167 (2016). doi:10.1038/nature16489}, + date-added = {2021-06-14T12:23:21GMT}, + date-modified = {2021-11-11T14:13:27GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2016/D\%C3\%ADaz/Nature\%202016\%20D\%C3\%ADaz.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1038/nature16489}, + file = {/Users/dorme/Zotero/storage/RGG46LIG/Nature 2016 Díaz.pdf} +} + +@book{duffie:2013a, + title = {Solar {{Engineering}} of {{Thermal Processes}}}, + author = {Duffie, John A. and Beckman, William A.}, + year = {2013}, + month = apr, + publisher = {John Wiley \& Sons}, + abstract = {The updated fourth edition of the "bible" of solar energy theory and applications Over several editions, Solar Engineering of Thermal Processes has become a classic solar engineering text and reference. This revised Fourth Edition offers current coverage of solar energy theory, systems design, and applications in different market sectors along with an emphasis on solar system design and analysis using simulations to help readers translate theory into practice. An important resource for students of solar engineering, solar energy, and alternative energy as well as professionals working in the power and energy industry or related fields, Solar Engineering of Thermal Processes, Fourth Edition features: Increased coverage of leading-edge topics such as photovoltaics and the design of solar cells and heaters A brand-new chapter on applying CombiSys (a readymade TRNSYS simulation program available for free download) to simulate a solar heated house with solar- heated domestic hot water Additional simulation problems available through a companion website An extensive array of homework problems and exercises}, + googlebooks = {5uDdUfMgXYQC}, + isbn = {978-1-118-41541-2}, + langid = {english}, + keywords = {Technology & Engineering / Electronics / General,Technology & Engineering / Mechanical,Technology & Engineering / Power Resources / General} } -@article{Prentice:2014bc, - title = {Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology}, - volume = {17}, - url = {http://doi.wiley.com/10.1111/ele.12211}, - doi = {10.1111/ele.12211}, - language = {english}, - number = {1}, - journal = {Ecology Letters}, - author = {Prentice, I. Colin and Dong, Ning and Gleason, Sean M and Maire, Vincent and Wright, Ian J.}, - year = {2014}, - note = {tex.date-added: 2020-12-01T13:22:48GMT - tex.date-modified: 2021-01-21T15:04:43GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Prentice/Ecol\%20Lett\%202014\%20Prentice.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1111/ele.12211}, - pages = {82--91}, - file = {Ecol Lett 2014 Prentice.pdf:/Users/dorme/Zotero/storage/SZVZVTJS/Ecol Lett 2014 Prentice.pdf:application/pdf} +@article{Farquhar:1980ft, + title = {A Biochemical Model of Photosynthetic {{CO2}} Assimilation in Leaves of {{C3}} Species.}, + author = {Farquhar, G D and {von Caemmerer}, S and Berry, J A}, + year = {1980}, + month = jun, + journal = {Planta}, + volume = {149}, + number = {1}, + pages = {78--90}, + doi = {10.1007/BF00386231}, + abstract = {Various aspects of the biochemistry of photosynthetic carbon assimilation in C3 plants are integrated into a form compatible with studies of gas exchange in leaves. These aspects include the kinetic properties of ribulose bisphosphate carboxylase-oxygenase; the requirements of the photosynthetic carbon reduction and photorespiratory carbon oxidation cycles for reduced pyridine nucleotides; the dependence of electron transport on photon flux and the presence of a temperature dependent upper limit to electron transport. The measurements of gas exchange with which the model outputs may be compared include those of the temperature and partial pressure of CO2(p(CO2)) dependencies of quantum yield, the variation of compensation point with temperature and partial pressure of O2(p(O2)), the dependence of net CO2 assimilation rate on p(CO2) and irradiance, and the influence of p(CO2) and irradiance on the temperature dependence of assimilation rate.}, + affiliation = {Department of Environmental Biology, Research School of Biological Sciences, Australian National University, P.O. Box 475, 2601, Canberra City, ACT, Australia.}, + date-added = {2020-12-17T09:47:58GMT}, + date-modified = {2020-12-17T09:49:44GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/1980/Farquhar/Planta\%201980\%20Farquhar.pdf}, + pmid = {24306196}, + rating = {0}, + uri = {papers3://publication/doi/10.1007/BF00386231}, + file = {/Users/dorme/Zotero/storage/PGDMWNFK/Planta 1980 Farquhar.pdf;/Users/dorme/Zotero/storage/YNCRDMLJ/Planta 1980 Farquhar.pdf} } -@article{Stocker:2018be, - title = {Quantifying soil moisture impacts on light use efficiency across biomes}, - volume = {218}, - url = {http://doi.wiley.com/10.1111/nph.15123}, - doi = {10.1111/nph.15123}, - abstract = {Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained …}, - language = {english}, - number = {4}, - journal = {New Phytologist}, - author = {Stocker, Benjamin D and Zscheischler, Jakob and Keenan, Trevor F and Prentice, I. Colin and Penuelas, Josep and Seneviratne, Sonia I}, - month = mar, - year = {2018}, - note = {tex.date-added: 2021-01-25T09:23:00GMT - tex.date-modified: 2021-01-28T15:58:46GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Stocker/New\%20Phytol.\%202018\%20Stocker.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1111/nph.15123}, - pages = {1430--1449}, - file = {New Phytol. 2018 Stocker.pdf:/Users/dorme/Zotero/storage/5AZW6ZK9/New Phytol. 2018 Stocker.pdf:application/pdf} +@article{farquhar:1982a, + title = {On the Relationship between Carbon Isotope Discrimination and the Intercellular Carbon Dioxide Concentration in Leaves}, + author = {Farquhar, Graham D and O'Leary, Marion H and Berry, Joseph A}, + year = {1982}, + journal = {Australian Journal of Plant Physiology}, + volume = {9}, + number = {2}, + pages = {121}, + issn = {1445-4408}, + doi = {10.1071/PP9820121}, + urldate = {2022-05-20}, + abstract = {Theory is developed to explain the carbon isotopic composition of plants. It is shown how diffusion of gaseous COz can significantly affect carbon isotopic discrimination. The effects on discrimination by diffusion and carboxylation are integrated, yielding a simple relationship between discrimination and the ratio of the intercellular and atmospheric partial pressures of COZ. The effects of dark respiration and photorespiration are also considered, and it is suggested that they have relatively little effect on discrimination other than cia their effects on intercellular p(COz). It is also suggested that various environmental factors such as light, temperature, salinity and drought will also have effects via changes in intercellular p(C0,). A simple method is suggested for assessing water use efficiencies in the field.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/8ACJPME5/Farquhar et al. - 1982 - On the Relationship Between Carbon Isotope Discrim.pdf} } -@book{HEllenbergBerlinGeobotanicalInstituteETH:fe, - title = {A key to {Raunkiaer} plant life forms with revised subdivision.}, - url = {https://ci.nii.ac.jp/naid/10004142697/}, - abstract = {CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ]. メニュー 検索 …}, - author = {{H Ellenberg - Berlin Geobotanical Institute ETH} and {Stiftung} and {1967}}, - doi = {10.5169/seals-377651}, - note = {tex.date-added: 2020-12-07T11:13:07GMT - tex.date-modified: 2020-12-17T08:58:40GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Books/Unknown/H\%20Ellenberg\%C2\%A0-\%20Berlin\%20Geobotanical\%20Institute\%20ETH/H\%20Ellenberg\%C2\%A0-\%20Berlin\%20Geobotanical\%20Institute\%20ETH.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.5169/seals-377651} +@book{Fisher:1975tm, + title = {Equation of State of Pure Water and Sea Water}, + author = {Fisher, F H and Dial Jr, O E}, + year = {1975}, + publisher = {Scripps Institution of Oceanography}, + date-added = {2020-11-30T12:27:10GMT}, + date-modified = {2021-01-21T15:04:43GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Books/1975/Fisher/1975\%20Fisher.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/uuid/5D2C2C96-7975-4AB9-8314-A988EFA489FF}, + file = {/Users/dorme/Zotero/storage/3K2X7BU7/1975 Fisher.pdf} } -@article{Walker:2014ce, - title = {The relationship of leaf photosynthetic traits - {Vcmaxand} {Jmax}- to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study}, - volume = {4}, - url = {http://doi.wiley.com/10.1002/ece3.1173}, - doi = {10.1002/ece3.1173}, - language = {english}, - number = {16}, - journal = {Ecology and Evolution}, - author = {Walker, Anthony P and Beckerman, Andrew P and Gu, Lianhong and Kattge, Jens and Cernusak, Lucas A and Domingues, Tomas F and Scales, Joanna C and Wohlfahrt, Georg and Wullschleger, Stan D and Woodward, F Ian}, - month = jul, - year = {2014}, - note = {tex.date-added: 2020-12-07T11:43:53GMT - tex.date-modified: 2020-12-17T08:58:39GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Walker/Ecology\%20and\%20Evolution\%202014\%20Walker.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1002/ece3.1173}, - pages = {3218--3235}, - file = {Ecology and Evolution 2014 Walker.pdf:/Users/dorme/Zotero/storage/WVL2ZGGZ/Ecology and Evolution 2014 Walker.pdf:application/pdf} +@article{frank:2015a, + title = {Water-Use Efficiency and Transpiration across {{European}} Forests during the {{Anthropocene}}}, + author = {Frank, D. C. and Poulter, B. and Saurer, M. and Esper, J. and Huntingford, C. and Helle, G. and Treydte, K. and Zimmermann, N. E. and Schleser, G.~H. and Ahlstr{\"o}m, A. and Ciais, P. and Friedlingstein, P. and Levis, S. and Lomas, M. and Sitch, S. and Viovy, N. and {Andreu-Hayles}, L. and Bednarz, Z. and Berninger, F. and Boettger, T. and D`Alessandro, C. M. and Daux, V. and Filot, M. and Grabner, M. and Gutierrez, E. and Haupt, M. and Hilasvuori, E. and Jungner, H. and {Kalela-Brundin}, M. and Krapiec, M. and Leuenberger, M. and Loader, N. J. and Marah, H. and {Masson-Delmotte}, V. and Pazdur, A. and Pawelczyk, S. and Pierre, M. and Planells, O. and Pukiene, R. and {Reynolds-Henne}, C. E. and Rinne, K. T. and Saracino, A. and Sonninen, E. and Stievenard, M. and Switsur, V. R. and Szczepanek, M. and {Szychowska-Krapiec}, E. and Todaro, L. and Waterhouse, J.~S. and Weigl, M.}, + year = {2015}, + month = jun, + journal = {Nature Climate Change}, + volume = {5}, + number = {6}, + pages = {579--583}, + issn = {1758-6798}, + doi = {10.1038/nclimate2614}, + abstract = {Considering the combined effects of CO2 fertilization and climate change drivers on plant physiology leads to a modest increase in simulated European forest transpiration in spite of the effects of CO2-induced stomatal closure.}, + file = {/Users/dorme/Zotero/storage/CNDFZFBW/Frank et al. - 2015 - Water-use efficiency and transpiration across Euro.pdf} } -@article{Peng:2021ky, - title = {Global climate and nutrient controls of photosynthetic capacity}, - url = {http://dx.doi.org/10.1038/s42003-021-01985-7}, - doi = {10.1038/s42003-021-01985-7}, - abstract = {Communications Biology, doi:10.1038/s42003-021-01985-7}, - journal = {Communications Biology}, - author = {Peng, Yunke and Bloomfield, Keith J and Cernusak, Lucas A and Domingues, Tomas F and Prentice, I. Colin}, - month = apr, - year = {2021}, - note = {Publisher: Springer US - tex.date-added: 2021-06-14T08:31:31GMT - tex.date-modified: 2021-11-11T14:13:27GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2021/Peng/Communications\%20Biology\%202021\%20Peng.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1038/s42003-021-01985-7}, - pages = {1--9}, - file = {Communications Biology 2021 Peng.pdf:/Users/dorme/Zotero/storage/U8Q5LA52/Communications Biology 2021 Peng.pdf:application/pdf} +@article{graven:2020a, + title = {Changes to {{Carbon Isotopes}} in {{Atmospheric CO}} {\textsubscript{2}} {{Over}} the {{Industrial Era}} and {{Into}} the {{Future}}}, + author = {Graven, Heather and Keeling, Ralph F. and Rogelj, Joeri}, + year = {2020}, + month = nov, + journal = {Global Biogeochemical Cycles}, + volume = {34}, + number = {11}, + issn = {0886-6236, 1944-9224}, + doi = {10.1029/2019GB006170}, + urldate = {2022-05-23}, + abstract = {In this ``Grand Challenges'' paper, we review how the carbon isotopic composition of atmospheric CO2 has changed since the Industrial Revolution due to human activities and their influence on the natural carbon cycle, and we provide new estimates of possible future changes for a range of scenarios. Emissions of CO2 from fossil fuel combustion and land use change reduce the ratio of 13C/12C in atmospheric CO2 ({$\delta$}13CO2). This is because 12C is preferentially assimilated during photosynthesis and {$\delta$}13C in plant-derived carbon in terrestrial ecosystems and fossil fuels is lower than atmospheric {$\delta$}13CO2. Emissions of CO2 from fossil fuel combustion also reduce the ratio of 14C/C in atmospheric CO2 ({$\Delta$}14CO2) because 14C is absent in million-year-old fossil fuels, which have been stored for much longer than the radioactive decay time of 14C. Atmospheric {$\Delta$}14CO2 rapidly increased in the 1950s to 1960s because of 14C produced during nuclear bomb testing. The resulting trends in {$\delta$}13C and {$\Delta$}14C in atmospheric CO2 are influenced not only by these human emissions but also by natural carbon exchanges that mix carbon between the atmosphere and ocean and terrestrial ecosystems. This mixing caused {$\Delta$}14CO2 to return toward preindustrial levels in the first few decades after the spike from nuclear testing. More recently, as the bomb 14C excess is now mostly well mixed with the decadally overturning carbon reservoirs, fossil fuel emissions have become the main factor driving further decreases in atmospheric {$\Delta$}14CO2. For {$\delta$}13CO2, in addition to exchanges between reservoirs, the extent to which 12C is preferentially assimilated during photosynthesis appears to have increased, slowing down the recent {$\delta$}13CO2 trend slightly. A new compilation of ice core and flask {$\delta$}13CO2 observations indicates that the decline in {$\delta$}13CO2 since the preindustrial period is less than some prior estimates, which may have incorporated artifacts owing to offsets from different laboratories' measurements. Atmospheric observations of {$\delta$}13CO2 have been used to investigate carbon fluxes and the functioning of plants, and they are used for comparison with {$\delta$}13C in other materials such as tree rings. Atmospheric observations of {$\Delta$}14CO2 have been used to quantify the rate of air-sea gas exchange and ocean circulation, and the rate of net primary production and the turnover time of carbon in plant material and soils. Atmospheric observations of {$\Delta$}14CO2 are also used for comparison with {$\Delta$}14C in other materials in many fields such as archaeology, forensics, and physiology. Another major application is the assessment of regional emissions of CO2 from fossil fuel combustion using {$\Delta$}14CO2 observations and models. In the future, {$\delta$}13CO2 and {$\Delta$}14CO2 will continue to change. The sign and magnitude of the changes are mainly determined by global fossil fuel emissions. We present here simulations of future {$\delta$}13CO2 and {$\Delta$}14CO2 for six scenarios based on the shared socioeconomic pathways (SSPs) from the 6th Coupled Model Intercomparison Project (CMIP6). Applications using atmospheric {$\delta$}13CO2 and {$\Delta$}14CO2 observations in carbon cycle science and many other fields will be affected by these future changes. We recommend an increased effort toward making coordinated measurements of {$\delta$}13C and {$\Delta$}14C across the Earth System and for further development of isotopic modeling and model-data analysis tools.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/BFT23X8N/Graven et al. - 2020 - Changes to Carbon Isotopes in Atmospheric CO .pdf} } -@article{Bernacchi:2001kg, - title = {Improved temperature response functions for models of {Rubisco}-limited photosynthesis}, - volume = {24}, - url = {http://doi.wiley.com/10.1111/j.1365-3040.2001.00668.x}, - doi = {10.1111/j.1365-3040.2001.00668.x}, - language = {english}, - number = {2}, - journal = {Plant, Cell \& Environment}, - author = {Bernacchi, C J and Singsaas, E L and Pimentel, C and Portis Jr, A R and Long, S P}, - year = {2001}, - note = {tex.date-added: 2020-11-30T12:24:10GMT - tex.date-modified: 2020-11-30T14:42:53GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2001/Bernacchi/Plant\%20Cell\%20\&\%20Environment\%202001\%20Bernacchi.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1111/j.1365-3040.2001.00668.x}, - pages = {253--259}, - file = {Plant Cell & Environment 2001 Bernacchi.pdf:/Users/dorme/Zotero/storage/NQZRL4RI/Plant Cell & Environment 2001 Bernacchi.pdf:application/pdf} +@book{HEllenbergBerlinGeobotanicalInstituteETH:fe, + title = {A Key to {{Raunkiaer}} Plant Life Forms with Revised Subdivision.}, + author = {{H Ellenberg - Berlin Geobotanical Institute ETH} and {Stiftung} and {1967}}, + doi = {10.5169/seals-377651}, + abstract = {CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ]. メニュー 検索 {\dots}}, + date-added = {2020-12-07T11:13:07GMT}, + date-modified = {2020-12-17T08:58:40GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Books/Unknown/H\%20Ellenberg\%C2\%A0-\%20Berlin\%20Geobotanical\%20Institute\%20ETH/H\%20Ellenberg\%C2\%A0-\%20Berlin\%20Geobotanical\%20Institute\%20ETH.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.5169/seals-377651} } -@article{Li:2014bc, - title = {Simulation of tree-ring widths with a model for primary production, carbon allocation, and growth}, - volume = {11}, - url = {https://bg.copernicus.org/articles/11/6711/2014/}, - doi = {10.5194/bg-11-6711-2014}, - language = {english}, - number = {23}, - journal = {Biogeosciences (Online)}, - author = {Li, G and Harrison, S P and Prentice, I. C. and Falster, D}, - year = {2014}, - note = {tex.date-added: 2020-12-01T16:59:12GMT - tex.date-modified: 2021-11-11T13:59:21GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Li/Biogeosciences\%202014\%20Li.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.5194/bg-11-6711-2014}, - pages = {6711--6724}, - file = {Biogeosciences 2014 Li.pdf:/Users/dorme/Zotero/storage/346S9AQK/Biogeosciences 2014 Li.pdf:application/pdf} +@article{henderson-sellers:1984a, + title = {A New Formula for Latent Heat of Vaporization of Water as a Function of Temperature}, + author = {{Henderson-Sellers}, B.}, + year = {1984}, + journal = {Quarterly Journal of the Royal Meteorological Society}, + volume = {110}, + number = {466}, + pages = {1186--1190}, + issn = {1477-870X}, + doi = {10.1002/qj.49711046626}, + urldate = {2023-07-04}, + abstract = {Existing formulae and approximations for the latent heat of vaporization of water, Lv, are reviewed. Using an analytical approximation to the saturated vapour pressure as a function of temperature, a new, temperature-dependent function for Lv is derived.}, + copyright = {Copyright {\copyright} 1984 Royal Meteorological Society}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/RHK394FU/qj.html} } -@article{Bernacchi:2003dc, - title = {In vivo temperature response functions of parameters required to model {RuBP}-limited photosynthesis}, - volume = {26}, - url = {https://onlinelibrary-wiley-com.iclibezp1.cc.ic.ac.uk/doi/full/10.1046/j.0016-8025.2003.01050.x}, - doi = {10.1046/j.0016-8025.2003.01050.x}, - abstract = {The leaf model of C3 photosynthesis of Farquhar, von Caemmerer \& Berry (Planta 149, 78–90, 1980) provides the basis for scaling carbon exchange from leaf to canopy and Earth-System models, and is wid...}, - language = {english}, - number = {9}, - journal = {Plant, Cell \& Environment}, - author = {Bernacchi, C J and Pimentel, C and Long, S P}, - month = sep, - year = {2003}, - note = {Publisher: John Wiley \& Sons, Ltd - tex.date-added: 2020-11-30T14:15:56GMT - tex.date-modified: 2020-11-30T14:42:52GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2003/Bernacchi/Plant\%20Cell\%20\&\%20Environment\%202003\%20Bernacchi.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1046/j.0016-8025.2003.01050.x}, - pages = {1419--1430}, - file = {Plant Cell & Environment 2003 Bernacchi.pdf:/Users/dorme/Zotero/storage/K2EX6ZIL/Plant Cell & Environment 2003 Bernacchi.pdf:application/pdf} +@article{hengl:2017a, + title = {{{SoilGrids250m}}: {{Global}} Gridded Soil Information Based on Machine Learning}, + shorttitle = {{{SoilGrids250m}}}, + author = {Hengl, Tomislav and de Jesus, Jorge Mendes and Heuvelink, Gerard B. M. and Gonzalez, Maria Ruiperez and Kilibarda, Milan and Blagoti{\'c}, Aleksandar and Shangguan, Wei and Wright, Marvin N. and Geng, Xiaoyuan and {Bauer-Marschallinger}, Bernhard and Guevara, Mario Antonio and Vargas, Rodrigo and MacMillan, Robert A. and Batjes, Niels H. and Leenaars, Johan G. B. and Ribeiro, Eloi and Wheeler, Ichsani and Mantel, Stephan and Kempen, Bas}, + year = {2017}, + month = feb, + journal = {PLOS ONE}, + volume = {12}, + number = {2}, + pages = {e0169748}, + publisher = {Public Library of Science}, + issn = {1932-6203}, + doi = {10.1371/journal.pone.0169748}, + urldate = {2023-07-05}, + abstract = {This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods---random forest and gradient boosting and/or multinomial logistic regression---as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10--fold cross-validation show that the ensemble models explain between 56\% (coarse fragments) and 83\% (pH) of variation with an overall average of 61\%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230\%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.}, + langid = {english}, + keywords = {Agricultural soil science,Forecasting,Glaciers,Machine learning,Remote sensing,Shannon index,Soil pH,Trees}, + file = {/Users/dorme/Zotero/storage/2YL6J66K/Hengl et al. - 2017 - SoilGrids250m Global gridded soil information bas.pdf} } -@article{Diaz:2016by, - title = {The global spectrum of plant form and function}, - volume = {529}, - url = {http://dx.doi.org/10.1038/nature16489}, - doi = {10.1038/nature16489}, - abstract = {Nature 529, 167 (2016). doi:10.1038/nature16489}, - number = {7585}, - journal = {Nature}, - author = {Díaz, Sandra and Kattge, Jens and Cornelissen, Johannes H C and Wright, Ian J. and Lavorel, Sandra and Dray, Stéphane and Reu, Björn and Kleyer, Michael and Wirth, Christian and Prentice, I. Colin and Garnier, Eric and Bönisch, Gerhard and Westoby, Mark and Poorter, Hendrik and Reich, Peter B and Moles, Angela T and Dickie, John and Gillison, Andrew N and Zanne, Amy E and Chave, Jérôme and Wright, S Joseph and Sheremet’ev, Serge N and Jactel, Hervé and Baraloto, Christopher and Cerabolini, Bruno and Pierce, Simon and Shipley, Bill and Kirkup, Donald and Casanoves, Fernando and Joswig, Julia S and Günther, Angela and Falczuk, Valeria and Rüger, Nadja and Mahecha, Miguel D and Gorné, Lucas D}, - month = jan, - year = {2016}, - note = {Publisher: Nature Publishing Group - tex.date-added: 2021-06-14T12:23:21GMT - tex.date-modified: 2021-11-11T14:13:27GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2016/D\%C3\%ADaz/Nature\%202016\%20D\%C3\%ADaz.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1038/nature16489}, - pages = {167--171}, - file = {Nature 2016 Díaz.pdf:/Users/dorme/Zotero/storage/5DWC9AW2/Nature 2016 Díaz.pdf:application/pdf} +@article{Heskel:2016fg, + title = {Convergence in the Temperature Response of Leaf Respiration across Biomes and Plant Functional Types}, + author = {Heskel, Mary A and O'Sullivan, Odhran S and Reich, Peter B and Tjoelker, Mark G and Weerasinghe, Lasantha K and Penillard, Aurore and Egerton, John J G and Creek, Danielle and Bloomfield, Keith J and Xiang, Jen and Sinca, Felipe and Stangl, Zsofia R and {Martinez-de la Torre}, Alberto and Griffin, Kevin L and Huntingford, Chris and Hurry, Vaughan and Meir, Patrick and Turnbull, Matthew H and Atkin, Owen K}, + year = {2016}, + month = apr, + journal = {Proceedings of the National Academy of Sciences of the United States of America}, + volume = {113}, + number = {14}, + pages = {3832--3837}, + doi = {10.1073/pnas.1520282113}, + date-added = {2020-11-30T13:55:54GMT}, + date-modified = {2020-11-30T14:42:52GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2016/Heskel/PNAS\%202016\%20Heskel.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1073/pnas.1520282113}, + file = {/Users/dorme/Zotero/storage/22X4KLAX/PNAS 2016 Heskel.pdf;/Users/dorme/Zotero/storage/LC3HMVT7/PNAS 2016 Heskel.pdf} } -@article{Lin:2015wh, - title = {Optimal stomatal behaviour around the world}, - volume = {5}, - url = {https://doi.org/10.1038/nclimate2550}, - abstract = {Stomatal conductance is a land-surface attribute that links the water and carbon cycles. Analysis of a global database covering a wide range of plant functional types and biomes now provides a framework for predicting the behaviour of stomatal conductance that can be applied to model ecosystem productivity, energy balance and ecohydrological processes in a changing climate.}, - number = {5}, - journal = {Nature Climate Change}, - author = {Lin, Yan-Shih and Medlyn, Belinda E and Duursma, Remko A and Prentice, I. Colin and Wang, Han and Baig, Sofia and Eamus, Derek and de Dios, Victor Resco and Mitchell, Patrick and Ellsworth, David S and de Beeck, Maarten Op and Wallin, Goran and Uddling, Johan and Tarvainen, Lasse and Linderson, Maj-Lena and Cernusak, Lucas A and Nippert, Jesse B and Ocheltree, Troy W and Tissue, David T and Martin-StPaul, Nicolas K and Rogers, Alistair and Warren, Jeff M and De Angelis, Paolo and Hikosaka, Kouki and Han, Qingmin and Onoda, Yusuke and Gimeno, Teresa E and Barton, Craig V M and Bennie, Jonathan and Bonal, Damien and Bosc, Alexandre and Low, Markus and Macinins-Ng, Cate and Rey, Ana and Rowland, Lucy and Setterfield, Samantha A and Tausz-Posch, Sabine and Zaragoza-Castells, Joana and Broadmeadow, Mark S J and Drake, John E and Freeman, Michael and Ghannoum, Oula and Hutley, Lindsay B and Kelly, Jeff W and Kikuzawa, Kihachiro and Kolari, Pasi and Koyama, Kohei and Limousin, Jean-Marc and Meir, Patrick and Lola da Costa, Antonio C and Mikkelsen, Teis N and Salinas, Norma and Sun, Wei and Wingate, Lisa}, - year = {2015}, - note = {tex.date-added: 2021-11-11T14:12:05GMT - tex.date-modified: 2021-11-11T14:13:27GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2015/Lin/2015\%20Lin-2.pdf - tex.rating: 0 - tex.uri: papers3://publication/uuid/61D6E8CB-F257-4021-AC24-2DF4D72C298E}, - pages = {459--464}, - file = {2015 Lin-2.pdf:/Users/dorme/Zotero/storage/WV3XV4SD/2015 Lin-2.pdf:application/pdf} +@article{Huber:2009fy, + title = {New International Formulation for the Viscosity of {{H2O}}}, + author = {Huber, M L and Perkins, R A and Laesecke, A and Friend, D G and Sengers, J V and Assael, M J and Metaxa, I N and Vogel, E and Mare{\v s}, R and Miyagawa, K}, + year = {2009}, + month = jun, + journal = {Journal of Physical and Chemical Reference Data}, + volume = {38}, + number = {2}, + pages = {101--125}, + doi = {10.1063/1.3088050}, + date-added = {2020-12-02T09:52:51GMT}, + date-modified = {2020-12-17T08:58:40GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2009/Huber/Journal\%20of\%20Physical\%20and\%20Chemical\%20Reference\%20Data\%202009\%20Huber.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1063/1.3088050}, + file = {/Users/dorme/Zotero/storage/HREIVFC4/Journal of Physical and Chemical Reference Data 2009 Huber.pdf} } -@article{Farquhar:1980ft, - title = {A biochemical model of photosynthetic {CO2} assimilation in leaves of {C3} species.}, - volume = {149}, - url = {http://link.springer.com/10.1007/BF00386231}, - doi = {10.1007/BF00386231}, - abstract = {Various aspects of the biochemistry of photosynthetic carbon assimilation in C3 plants are integrated into a form compatible with studies of gas exchange in leaves. These aspects include the kinetic properties of ribulose bisphosphate carboxylase-oxygenase; the requirements of the photosynthetic carbon reduction and photorespiratory carbon oxidation cycles for reduced pyridine nucleotides; the dependence of electron transport on photon flux and the presence of a temperature dependent upper limit to electron transport. The measurements of gas exchange with which the model outputs may be compared include those of the temperature and partial pressure of CO2(p(CO2)) dependencies of quantum yield, the variation of compensation point with temperature and partial pressure of O2(p(O2)), the dependence of net CO2 assimilation rate on p(CO2) and irradiance, and the influence of p(CO2) and irradiance on the temperature dependence of assimilation rate.}, - language = {english}, - number = {1}, - journal = {Planta}, - author = {Farquhar, G D and von Caemmerer, S and Berry, J A}, - month = jun, - year = {1980}, - pmid = {24306196}, - note = {tex.affiliation: Department of Environmental Biology, Research School of Biological Sciences, Australian National University, P.O. Box 475, 2601, Canberra City, ACT, Australia. - tex.date-added: 2020-12-17T09:47:58GMT - tex.date-modified: 2020-12-17T09:49:44GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/1980/Farquhar/Planta\%201980\%20Farquhar.pdf - tex.rating: 0 - tex.uri: papers3://publication/doi/10.1007/BF00386231}, - pages = {78--90}, - file = {Planta 1980 Farquhar.pdf:/Users/dorme/Zotero/storage/WFTRGJRB/Planta 1980 Farquhar.pdf:application/pdf;Planta 1980 Farquhar.pdf:/Users/dorme/Zotero/storage/FP3457LT/Planta 1980 Farquhar.pdf:application/pdf} +@article{Kattge:2007db, + title = {Temperature Acclimation in a Biochemical Model of Photosynthesis: A Reanalysis of Data from 36 Species}, + author = {Kattge, Jens and Knorr, Wolfgang}, + year = {2007}, + month = sep, + journal = {Plant, Cell \& Environment}, + volume = {30}, + number = {9}, + pages = {1176--1190}, + doi = {10.1111/j.1365-3040.2007.01690.x}, + abstract = {The Farquhar et al. model of C3 photosynthesis is frequently used to study the effect of global changes on the biosphere. Its two main parameters representing photosynthetic capacity, Vcmax and Jmax, have been observed to acclimate to plant growth temperature for single species, but a general formulation has never been derived. Here, we present a reanalysis of data from 36 plant species to quantify the temperature dependence of Vcmax and Jmax with a focus on plant growth temperature, ie the plants' average ambient {\dots}}, + date-added = {2020-11-30T14:04:07GMT}, + date-modified = {2020-12-02T16:14:05GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2007/Kattge/Plant\%20Cell\%20\&\%20Environment\%202007\%20Kattge.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1111/j.1365-3040.2007.01690.x}, + file = {/Users/dorme/Zotero/storage/WCL92RHJ/Plant Cell & Environment 2007 Kattge.pdf} } -@article{Togashi:2018es, - title = {Functional trait variation related to gap dynamics in tropical moist forests$_{\textrm{{A}}}$ vegetation modelling perspective}, - volume = {35}, - url = {https://doi.org/10.1016/j.ppees.2018.10.004}, - doi = {10.1016/j.ppees.2018.10.004}, - abstract = {Perspectives in Plant Ecology, Evolution and Systematics, 35 (2018) 52-64. doi:10.1016/j.ppees.2018.10.004}, - journal = {Perspectives in Plant Ecology, Evolution and Systematics}, - author = {Togashi, Henrique Fürstenau and Atkin, Owen K and Bloomfield, Keith J and Bradford, Matt and Cao, Kunfang and Dong, Ning and Evans, Bradley J and Fan, Zexin and Harrison, Sandy P and Hua, Zhu and Liddell, Michael J and Lloyd, Jon and Ni, Jian and Wang, Han and Weerasinghe, Lasantha K and Prentice, Iain Colin}, - month = dec, - year = {2018}, - note = {Publisher: Elsevier - tex.date-added: 2020-12-01T17:02:03GMT - tex.date-modified: 2020-12-17T08:58:40GMT - tex.local-url: file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Togashi/Perspectives\%20in\%20Plant\%20Ecology\%20Evolution\%20and\%20Systematics\%202018\%20Togashi.pdf - tex.rating: 0 - tex.read: Yes - tex.uri: papers3://publication/doi/10.1016/j.ppees.2018.10.004}, - pages = {52--64}, - file = {Perspectives in Plant Ecology Evolution and Systematics 2018 Togashi.pdf:/Users/dorme/Zotero/storage/7T29HQCL/Perspectives in Plant Ecology Evolution and Systematics 2018 Togashi.pdf:application/pdf} +@article{kennedy:2019a, + title = {Implementing {{Plant Hydraulics}} in the {{Community Land Model}}, {{Version}} 5}, + author = {Kennedy, Daniel and Swenson, Sean and Oleson, Keith W. and Lawrence, David M. and Fisher, Rosie and {Lola da Costa}, Antonio Carlos and Gentine, Pierre}, + year = {2019}, + month = feb, + journal = {Journal of Advances in Modeling Earth Systems}, + volume = {11}, + number = {2}, + pages = {485--513}, + issn = {19422466}, + doi = {10.1029/2018MS001500}, + urldate = {2022-07-21}, + abstract = {Version 5 of the Community Land Model (CLM5) introduces the plant hydraulic stress (PHS) configuration of vegetation water use, which is described and compared with the corresponding parameterization from CLM4.5. PHS updates vegetation water stress and root water uptake to better reflect plant hydraulic theory, advancing the physical basis of the model. The new configuration introduces prognostic vegetation water potential, modeled at the root, stem, and leaf levels. Leaf water potential replaces soil potential as the basis for stomatal conductance water stress, and root water potential is used to implement hydraulic root water uptake, replacing a transpiration partitioning function. Point simulations of a tropical forest site (Caxiuan{\~a}, Brazil) under ambient conditions and partial precipitation exclusion highlight the differences between PHS and the previous CLM implementation. Model description and simulation results are contextualized with a list of benefits and limitations of the new model formulation, including hypotheses that were not testable in previous versions of the model. Key results include reductions in transpiration and soil moisture biases relative to a control model under both ambient and exclusion conditions, correcting excessive dry season soil moisture stress in the control model. PHS implements hydraulic gradient root water uptake, which allows hydraulic redistribution and compensatory root water uptake and results in PHS utilizing a larger portion of the soil column to buffer shortfalls in precipitation. The new model structure, which bases water stress on leaf water potential, could have significant implications for vegetation-climate feedbacks, including increased sensitivity of photosynthesis to atmospheric vapor pressure deficit.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/X6XIHAEF/Kennedy et al. - 2019 - Implementing Plant Hydraulics in the Community Lan.pdf} } -@article{cai:2020a, - title = {Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales}, - volume = {15}, - issn = {1748-9326}, - shorttitle = {Recent trends in gross primary production and their drivers}, - url = {https://iopscience.iop.org/article/10.1088/1748-9326/abc64e}, - doi = {10.1088/1748-9326/abc64e}, - abstract = {Abstract - - Gross primary production (GPP) by terrestrial ecosystems is the largest flux in the global carbon cycle, and its continuing increase in response to environmental changes is key to land ecosystems’ capacity to offset anthropogenic CO - 2 - emissions. However, the CO - 2 - - and climate-sensitivities of GPP vary among models. We applied the ‘P model’—a parameter-sparse and extensively tested light use efficiency (LUE) model, driven by CO - 2 - , climate and remotely sensed greenness data—at 29 sites with multi-year eddy-covariance flux measurements. Observed (both positive and negative) GPP trends at these sites were predicted, albeit with some bias. Increasing LUE (due to rising atmospheric CO - 2 - concentration) and green vegetation cover were the primary controls of modelled GPP trends across sites. Global GPP simulated by the same model increased by 0.46 ± 0.09 Pg C yr - –2 - during 1982–2016. This increase falls in the mid-range rate of simulated increase by the TRENDY v8 ensemble of state-of-the-art ecosystem models. The modelled LUE increase during 1900–2013 was 15\%, similar to a published estimate based on deuterium isotopomers. Rising CO - 2 - was the largest contributor to the modelled GPP increase. Greening, which may in part be caused by rising CO - 2 - , ranked second but dominated the modelled GPP change over large areas, including semi-arid vegetation on all continents. Warming caused a small net reduction in modelled global GPP, but dominated the modelled GPP increase in high northern latitudes. These findings strengthen the evidence that rising LUE due to rising CO - 2 - level and increased green vegetation cover (fAPAR) are the main causes of increasing GPP, and thereby, the terrestrial carbon sink.}, - language = {en}, - number = {12}, - urldate = {2022-05-16}, - journal = {Environmental Research Letters}, - author = {Cai, Wenjia and Prentice, Iain Colin}, - month = dec, - year = {2020}, - pages = {124050}, - file = {Cai and Prentice - 2020 - Recent trends in gross primary production and thei.pdf:/Users/dorme/Zotero/storage/NGHELDVI/Cai and Prentice - 2020 - Recent trends in gross primary production and thei.pdf:application/pdf} +@phdthesis{Lancelot:2020tm, + title = {A Novel Computational Model to Predict Forest Dynamics}, + author = {Lancelot, Maxime}, + year = {2020}, + month = aug, + affiliation = {Imperial College London}, + date-added = {2020-11-30T12:25:21GMT}, + date-modified = {2020-11-30T14:42:52GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Books/2020/Lancelot/2020\%20Lancelot.pdf}, + rating = {0}, + uri = {papers3://publication/uuid/D73D9C6F-36C9-461D-A90C-784809C52BCB}, + file = {/Users/dorme/Zotero/storage/86NBGKCA/2020 Lancelot.pdf} } @article{lavergne:2020a, - title = {Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: {Insights} from stable carbon isotope data}, - volume = {26}, - issn = {1354-1013, 1365-2486}, - shorttitle = {Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis}, - url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15364}, - doi = {10.1111/gcb.15364}, - abstract = {Atmospheric aridity and drought both influence physiological function in plant leaves, but their relative contributions to changes in the ratio of leaf internal to ambient partial pressure of CO2 (χ) – an index of adjustments in both stomatal conductance and photosynthetic rate to environmental conditions – are difficult to disentangle. Many stomatal models predicting χ include the influence of only one of these drivers. In particular, the least-cost optimality hypothesis considers the effect of atmospheric demand for water on χ but does not predict how soils with reduced water further influence χ, potentially leading to an overestimation of χ under dry conditions. Here, we use a large network of stable carbon isotope measurements in C3 woody plants to examine the acclimated response of χ to soil water stress. We estimate the ratio of cost factors for carboxylation and transpiration (β) expected from the theory to explain the variance in the data, and investigate the responses of β (and thus χ) to soil water content and suction across seed plant groups, leaf phenological types and regions. Overall, β decreases linearly with soil drying, implying that the cost of water transport along the soil–plant–atmosphere continuum increases as water available in the soil decreases. However, despite contrasting hydraulic strategies, the stomatal responses of angiosperms and gymnosperms to soil water tend to converge, consistent with the optimality theory. The prediction of β as a simple, empirical function of soil water significantly improves χ predictions by up to 6.3 ± 2.3\% (mean ± SD of adjusted-R2) over 1980–2018 and results in a reduction of around 2\% of mean χ values across the globe. Our results highlight the importance of soil water status on stomatal functions and plant water-use efficiency, and suggest the implementation of trait-based hydraulic functions into the model to account for soil water stress.}, - language = {en}, - number = {12}, - urldate = {2022-05-19}, - journal = {Global Change Biology}, - author = {Lavergne, Aliénor and Sandoval, David and Hare, Vincent J. and Graven, Heather and Prentice, Iain Colin}, - month = dec, - year = {2020}, - pages = {7158--7172}, - file = {Lavergne et al. - 2020 - Impacts of soil water stress on the acclimated sto.pdf:/Users/dorme/Zotero/storage/ULV9VU2Y/Lavergne et al. - 2020 - Impacts of soil water stress on the acclimated sto.pdf:application/pdf} + title = {Impacts of Soil Water Stress on the Acclimated Stomatal Limitation of Photosynthesis: {{Insights}} from Stable Carbon Isotope Data}, + shorttitle = {Impacts of Soil Water Stress on the Acclimated Stomatal Limitation of Photosynthesis}, + author = {Lavergne, Ali{\'e}nor and Sandoval, David and Hare, Vincent J. and Graven, Heather and Prentice, Iain Colin}, + year = {2020}, + month = dec, + journal = {Global Change Biology}, + volume = {26}, + number = {12}, + pages = {7158--7172}, + issn = {1354-1013, 1365-2486}, + doi = {10.1111/gcb.15364}, + urldate = {2022-05-19}, + abstract = {Atmospheric aridity and drought both influence physiological function in plant leaves, but their relative contributions to changes in the ratio of leaf internal to ambient partial pressure of CO2 ({$\chi$}) -- an index of adjustments in both stomatal conductance and photosynthetic rate to environmental conditions -- are difficult to disentangle. Many stomatal models predicting {$\chi$} include the influence of only one of these drivers. In particular, the least-cost optimality hypothesis considers the effect of atmospheric demand for water on {$\chi$} but does not predict how soils with reduced water further influence {$\chi$}, potentially leading to an overestimation of {$\chi$} under dry conditions. Here, we use a large network of stable carbon isotope measurements in C3 woody plants to examine the acclimated response of {$\chi$} to soil water stress. We estimate the ratio of cost factors for carboxylation and transpiration ({$\beta$}) expected from the theory to explain the variance in the data, and investigate the responses of {$\beta$} (and thus {$\chi$}) to soil water content and suction across seed plant groups, leaf phenological types and regions. Overall, {$\beta$} decreases linearly with soil drying, implying that the cost of water transport along the soil--plant--atmosphere continuum increases as water available in the soil decreases. However, despite contrasting hydraulic strategies, the stomatal responses of angiosperms and gymnosperms to soil water tend to converge, consistent with the optimality theory. The prediction of {$\beta$} as a simple, empirical function of soil water significantly improves {$\chi$} predictions by up to 6.3 {\textpm} 2.3\% (mean {\textpm} SD of adjusted-R2) over 1980--2018 and results in a reduction of around 2\% of mean {$\chi$} values across the globe. Our results highlight the importance of soil water status on stomatal functions and plant water-use efficiency, and suggest the implementation of trait-based hydraulic functions into the model to account for soil water stress.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/ECFRUWX5/Lavergne et al. - 2020 - Impacts of soil water stress on the acclimated sto.pdf} } -@article{farquhar:1982a, - title = {On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves}, - volume = {9}, - issn = {1445-4408}, - url = {http://www.publish.csiro.au/?paper=PP9820121}, - doi = {10.1071/PP9820121}, - abstract = {Theory is developed to explain the carbon isotopic composition of plants. It is shown how diffusion of gaseous COz can significantly affect carbon isotopic discrimination. The effects on discrimination by diffusion and carboxylation are integrated, yielding a simple relationship between discrimination and the ratio of the intercellular and atmospheric partial pressures of COZ. The effects of dark respiration and photorespiration are also considered, and it is suggested that they have relatively little effect on discrimination other than cia their effects on intercellular p(COz). It is also suggested that various environmental factors such as light, temperature, salinity and drought will also have effects via changes in intercellular p(C0,). A simple method is suggested for assessing water use efficiencies in the field.}, - language = {en}, - number = {2}, - urldate = {2022-05-20}, - journal = {Australian Journal of Plant Physiology}, - author = {Farquhar, Graham D and O'Leary, Marion H and Berry, Joseph A}, - year = {1982}, - pages = {121}, - file = {Farquhar et al. - 1982 - On the Relationship Between Carbon Isotope Discrim.pdf:/Users/dorme/Zotero/storage/QMMIYWRA/Farquhar et al. - 1982 - On the Relationship Between Carbon Isotope Discrim.pdf:application/pdf} +@article{lavergne:2022a, + title = {A Semi-Empirical Model for Primary Production, Isotopic Discrimination and Competition of {{C3}} and {{C4}} Plants}, + author = {Lavergne, Ali{\'e}nor and Harrison, Sandy P. and Atsawawaranunt, Kamolphat and Dong, Ning and Prentice, Iain Colin}, + year = {2022}, + journal = {Global Ecology and Biogeography (submitted)}, + file = {/Users/dorme/Zotero/storage/I797PRT2/Lavergneetal_GEB.docx} } -@article{voncaemmerer:2014a, - title = {Carbon isotope discrimination as a tool to explore {C4} photosynthesis}, - volume = {65}, - issn = {1460-2431, 0022-0957}, - url = {https://academic.oup.com/jxb/article-lookup/doi/10.1093/jxb/eru127}, - doi = {10.1093/jxb/eru127}, - abstract = {Photosynthetic carbon isotope discrimination is a non-destructive tool for investigating C4 metabolism. Tuneable diode laser absorption spectroscopy provides new opportunities for making rapid, concurrent measurements of carbon isotope discrimination and CO2 assimilation over a range of environmental conditions, and this has facilitated the use of carbon isotope discrimination as a probe of C4 metabolism. In spite of the significant progress made in recent years, understanding how photosynthetic carbon isotope discrimination measured concurrently with gas exchange relates to carbon isotope composition of leaf and plant dry matter remains a challenge that requires resolution if this technique is to be successfully applied as a screening tool in crop breeding and phylogenetic research. In this review, we update our understanding of the factors and assumptions that underlie variations in photosynthetic carbon isotope discrimination in C4 leaves. Closing the main gaps in our understanding of carbon isotope discrimination during C4 photosynthesis may help advance research aimed at developing higher productivity and efficiency in key C4 food, feed, and biofuel crops.}, - language = {en}, - number = {13}, - urldate = {2022-05-20}, - journal = {Journal of Experimental Botany}, - author = {von Caemmerer, Susanne and Ghannoum, Oula and Pengelly, Jasper J. L. and Cousins, Asaph B.}, - month = jul, - year = {2014}, - pages = {3459--3470}, - file = {Caemmerer et al. - 2014 - Carbon isotope discrimination as a tool to explore.pdf:/Users/dorme/Zotero/storage/EVAEICEV/Caemmerer et al. - 2014 - Carbon isotope discrimination as a tool to explore.pdf:application/pdf} +@article{Li:2014bc, + title = {Simulation of Tree-Ring Widths with a Model for Primary Production, Carbon Allocation, and Growth}, + author = {Li, G and Harrison, S P and Prentice, I. C. and Falster, D}, + year = {2014}, + journal = {Biogeosciences (Online)}, + volume = {11}, + number = {23}, + pages = {6711--6724}, + doi = {10.5194/bg-11-6711-2014}, + date-added = {2020-12-01T16:59:12GMT}, + date-modified = {2021-11-11T13:59:21GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Li/Biogeosciences\%202014\%20Li.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.5194/bg-11-6711-2014}, + file = {/Users/dorme/Zotero/storage/HAR4D2NE/Biogeosciences 2014 Li.pdf} } -@article{boyd:2015a, - title = {Temperature response of {C4} photosynthesis: {Biochemical} analysis of {Rubisco}, {Phosphoenolpyruvate} {Carboxylase} and {Carbonic} {Anhydrase} in {Setaria} viridis.}, - issn = {0032-0889, 1532-2548}, - shorttitle = {Temperature response of {C4} photosynthesis}, - url = {https://academic.oup.com/plphys/article/169/3/1850-1861/6114196}, - doi = {10.1104/pp.15.00586}, - language = {en}, - urldate = {2022-05-20}, - journal = {Plant Physiology}, - author = {Boyd, Ryan Allen and Gandin, Anthony and Cousins, Asaph B}, - month = sep, - year = {2015}, - pages = {pp.00586.2015}, - file = {Boyd et al. - 2015 - Temperature response of C4 photosynthesis Biochem.pdf:/Users/dorme/Zotero/storage/RHH98FWY/Boyd et al. - 2015 - Temperature response of C4 photosynthesis Biochem.pdf:application/pdf} +@article{Lin:2015wh, + title = {Optimal Stomatal Behaviour around the World}, + author = {Lin, Yan-Shih and Medlyn, Belinda E and Duursma, Remko A and Prentice, I. Colin and Wang, Han and Baig, Sofia and Eamus, Derek and {de Dios}, Victor Resco and Mitchell, Patrick and Ellsworth, David S and {de Beeck}, Maarten Op and Wallin, Goran and Uddling, Johan and Tarvainen, Lasse and Linderson, Maj-Lena and Cernusak, Lucas A and Nippert, Jesse B and Ocheltree, Troy W and Tissue, David T and {Martin-StPaul}, Nicolas K and Rogers, Alistair and Warren, Jeff M and De Angelis, Paolo and Hikosaka, Kouki and Han, Qingmin and Onoda, Yusuke and Gimeno, Teresa E and Barton, Craig V M and Bennie, Jonathan and Bonal, Damien and Bosc, Alexandre and Low, Markus and {Macinins-Ng}, Cate and Rey, Ana and Rowland, Lucy and Setterfield, Samantha A and {Tausz-Posch}, Sabine and {Zaragoza-Castells}, Joana and Broadmeadow, Mark S J and Drake, John E and Freeman, Michael and Ghannoum, Oula and Hutley, Lindsay B and Kelly, Jeff W and Kikuzawa, Kihachiro and Kolari, Pasi and Koyama, Kohei and Limousin, Jean-Marc and Meir, Patrick and {Lola da Costa}, Antonio C and Mikkelsen, Teis N and Salinas, Norma and Sun, Wei and Wingate, Lisa}, + year = {2015}, + journal = {Nature Climate Change}, + volume = {5}, + number = {5}, + pages = {459--464}, + abstract = {Stomatal conductance is a land-surface attribute that links the water and carbon cycles. Analysis of a global database covering a wide range of plant functional types and biomes now provides a framework for predicting the behaviour of stomatal conductance that can be applied to model ecosystem productivity, energy balance and ecohydrological processes in a changing climate.}, + date-added = {2021-11-11T14:12:05GMT}, + date-modified = {2021-11-11T14:13:27GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2015/Lin/2015\%20Lin-2.pdf}, + rating = {0}, + uri = {papers3://publication/uuid/61D6E8CB-F257-4021-AC24-2DF4D72C298E}, + file = {/Users/dorme/Zotero/storage/J7WANJNI/2015 Lin-2.pdf} +} + +@article{linacre:1968a, + title = {Estimating the Net-Radiation Flux}, + author = {Linacre, E. T.}, + year = {1968}, + month = jan, + journal = {Agricultural Meteorology}, + volume = {5}, + number = {1}, + pages = {49--63}, + issn = {0002-1571}, + doi = {10.1016/0002-1571(68)90022-8}, + urldate = {2024-08-02}, + abstract = {A major influence controlling the water loss from irrigated crops is the net-radiation intensity Qn, but measurements of this are not normally available, and so attempts are often made to deduce it from other climatic data, such as the solar-radiation. Here it is shown that the relationship between net and solar-radiation intensities depends on the degree of cloudiness and the ambient temperature. By making appropriate assumptions, a series of expressions for Qn is derived, with decreasing accuracy but increasing simplicity of estimation. It appears that clouds lower the net-radiation intensity when it exceeds a critical value in the region of 0.02 cal./cm2 min, but increase it when the intensity is lower.}, + file = {/Users/dorme/Zotero/storage/UY4NRV4W/0002157168900228.html} +} + +@article{long:1993a, + title = {Quantum Yields for Uptake of Carbon Dioxide in {{C3}} Vascular Plants of Contrasting Habitats and Taxonomic Groupings}, + author = {Long, S. P. and Postl, W. F. and {Bolh{\'a}r-Nordenkampf}, H. R.}, + year = {1993}, + month = feb, + journal = {Planta}, + volume = {189}, + number = {2}, + pages = {226--234}, + issn = {1432-2048}, + doi = {10.1007/BF00195081}, + urldate = {2024-07-19}, + abstract = {The maximum quantum yields ({$\phi$}a,c) for CO2 uptake in low-oxygen atmospheres were determined for 11 species of C3 vascular plants of diverse taxa, habitat and life form using an Ulbricht-sphere leaf chamber. Comparisons were also made between tissues of varied age within species. The species examined were Psilotum nudum (L.) P. Beauv., Davallia bullata Wall. ex Hook., Cycas revoluta Thunb., Araucaria heterophylla (Salisb.) Franco, Picea abies (L.) Karst., Nerium oleander L., Ruellia humilis Nutt., Pilea microphylla (L.) Karst., Beaucarnea stricta Lem., Oplismenus hirtellus (L.) P. Beauv. and Poa annua L. Quantum yields were calculated from the initial slopes of the response of CO2 uptake to the quantity of photons absorbed in conditions of diffuse lighting. Regression analysis of variance of the initial slopes of the response of CO2 uptake to photon absorption failed to show any statistically significant differences between age classes within species or between the mature photosynthetic organs of different species. The constancy of {$\phi$}a,c was apparent despite marked variation in the light-saturated rates of CO2 uptake within and between species. The mean {$\phi$}a,c was 0.093{\textpm}0.003 for 11 species. By contrast, surface absorptance varied markedly between species from 0.90 to 0.60, producing proportional variation in the quantum yield calculated on an incidentlight basis. The ratio of variable to maximum fluorescence emission at 695 nm for the same tissues also failed to show any statistically significant variation between species, with a mean of 0.838{\textpm}0.008. Mean values of {$\phi$}a,c reported here for C3 species, in the absence of photorespiration, are higher than reported in previous surveys of vascular plants, but consistent with recent estimates of the quantum yields of O2 evolution.}, + langid = {english}, + keywords = {C3 plant,Photosynthesis,Quantum yield (O2 CO2 uptake)}, + file = {/Users/dorme/Zotero/storage/88PTVDYD/Long et al. - 1993 - Quantum yields for uptake of carbon dioxide in C3 .pdf} } -@article{graven:2020a, - title = {Changes to {Carbon} {Isotopes} in {Atmospheric} {CO} $_{\textrm{2}}$ {Over} the {Industrial} {Era} and {Into} the {Future}}, - volume = {34}, - issn = {0886-6236, 1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/10.1029/2019GB006170}, - doi = {10.1029/2019GB006170}, - abstract = {In this “Grand Challenges” paper, we review how the carbon isotopic composition of atmospheric CO2 has changed since the Industrial Revolution due to human activities and their influence on the natural carbon cycle, and we provide new estimates of possible future changes for a range of scenarios. Emissions of CO2 from fossil fuel combustion and land use change reduce the ratio of 13C/12C in atmospheric CO2 (δ13CO2). This is because 12C is preferentially assimilated during photosynthesis and δ13C in plant‐derived carbon in terrestrial ecosystems and fossil fuels is lower than atmospheric δ13CO2. Emissions of CO2 from fossil fuel combustion also reduce the ratio of 14C/C in atmospheric CO2 (Δ14CO2) because 14C is absent in million‐year‐old fossil fuels, which have been stored for much longer than the radioactive decay time of 14C. Atmospheric Δ14CO2 rapidly increased in the 1950s to 1960s because of 14C produced during nuclear bomb testing. The resulting trends in δ13C and Δ14C in atmospheric CO2 are influenced not only by these human emissions but also by natural carbon exchanges that mix carbon between the atmosphere and ocean and terrestrial ecosystems. This mixing caused Δ14CO2 to return toward preindustrial levels in the first few decades after the spike from nuclear testing. More recently, as the bomb 14C excess is now mostly well mixed with the decadally overturning carbon reservoirs, fossil fuel emissions have become the main factor driving further decreases in atmospheric Δ14CO2. For δ13CO2, in addition to exchanges between reservoirs, the extent to which 12C is preferentially assimilated during photosynthesis appears to have increased, slowing down the recent δ13CO2 trend slightly. A new compilation of ice core and flask δ13CO2 observations indicates that the decline in δ13CO2 since the preindustrial period is less than some prior estimates, which may have incorporated artifacts owing to offsets from different laboratories' measurements. Atmospheric observations of δ13CO2 have been used to investigate carbon fluxes and the functioning of plants, and they are used for comparison with δ13C in other materials such as tree rings. Atmospheric observations of Δ14CO2 have been used to quantify the rate of air‐sea gas exchange and ocean circulation, and the rate of net primary production and the turnover time of carbon in plant material and soils. Atmospheric observations of Δ14CO2 are also used for comparison with Δ14C in other materials in many fields such as archaeology, forensics, and physiology. Another major application is the assessment of regional emissions of CO2 from fossil fuel combustion using Δ14CO2 observations and models. In the future, δ13CO2 and Δ14CO2 will continue to change. The sign and magnitude of the changes are mainly determined by global fossil fuel emissions. We present here simulations of future δ13CO2 and Δ14CO2 for six scenarios based on the shared socioeconomic pathways (SSPs) from the 6th Coupled Model Intercomparison Project (CMIP6). Applications using atmospheric δ13CO2 and Δ14CO2 observations in carbon cycle science and many other fields will be affected by these future changes. We recommend an increased effort toward making coordinated measurements of δ13C and Δ14C across the Earth System and for further development of isotopic modeling and model‐data analysis tools.}, - language = {en}, - number = {11}, - urldate = {2022-05-23}, - journal = {Global Biogeochemical Cycles}, - author = {Graven, Heather and Keeling, Ralph F. and Rogelj, Joeri}, - month = nov, - year = {2020}, - file = {Graven et al. - 2020 - Changes to Carbon Isotopes in Atmospheric CO .pdf:/Users/dorme/Zotero/storage/XUCC46IU/Graven et al. - 2020 - Changes to Carbon Isotopes in Atmospheric CO .pdf:application/pdf} +@article{mengoli:2022a, + title = {Ecosystem {{Photosynthesis}} in {{Land}}-{{Surface Models}}: {{A First}}-{{Principles Approach Incorporating Acclimation}}}, + shorttitle = {Ecosystem {{Photosynthesis}} in {{Land}}-{{Surface Models}}}, + author = {Mengoli, Giulia and Agust{\'i}-Panareda, Anna and Boussetta, Souhail and Harrison, Sandy P. and Trotta, Carlo and Prentice, I. Colin}, + year = {2022}, + month = jan, + journal = {Journal of Advances in Modeling Earth Systems}, + volume = {14}, + number = {1}, + issn = {1942-2466, 1942-2466}, + doi = {10.1029/2021MS002767}, + urldate = {2022-05-26}, + abstract = {Vegetation regulates land-atmosphere, water, and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to the environment with the fast responses observable in the laboratory. The effects of acclimation can be taken into account by including PFT-specific values of photosynthetic parameters, but at the cost of increasing parameter requirements. Here, we develop an alternative approach for including acclimation in LSMs by adopting the P model, an existing light-use efficiency model for gross primary production (GPP) that implicitly predicts the acclimation of photosynthetic parameters on a weekly to monthly timescale via optimality principles. We demonstrate that it is possible to explicitly separate the fast and slow photosynthetic responses to environmental conditions, allowing the simulation of GPP at the sub-daily timesteps required for coupling in an LSM. The resulting model reproduces the diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests. The best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. Comparison between this model and the operational LSM in the European Centre for Medium-range Weather Forecasts climate model shows that the new model has better predictive power in most of the sites and years analyzed, particularly in summer and autumn. Our analyses suggest a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with potential applications in weather, climate, and carbon-cycle modeling.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/KAFPVD6H/2021ms002767-sup-0002-supporting information si-s02.pdf;/Users/dorme/Zotero/storage/VU8B6HP8/Mengoli et al. - 2022 - Ecosystem Photosynthesis in Land‐Surface Models A.pdf;/Users/dorme/Zotero/storage/ZQBUWXEK/2021ms002767-sup-0001-supporting information si-s01.pdf} } -@article{mengoli:2022a, - title = {Ecosystem {Photosynthesis} in {Land}‐{Surface} {Models}: {A} {First}‐{Principles} {Approach} {Incorporating} {Acclimation}}, - volume = {14}, - issn = {1942-2466, 1942-2466}, - shorttitle = {Ecosystem {Photosynthesis} in {Land}‐{Surface} {Models}}, - url = {https://onlinelibrary.wiley.com/doi/10.1029/2021MS002767}, - doi = {10.1029/2021MS002767}, - abstract = {Vegetation regulates land-atmosphere, water, and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to the environment with the fast responses observable in the laboratory. The effects of acclimation can be taken into account by including PFT-specific values of photosynthetic parameters, but at the cost of increasing parameter requirements. Here, we develop an alternative approach for including acclimation in LSMs by adopting the P model, an existing light-use efficiency model for gross primary production (GPP) that implicitly predicts the acclimation of photosynthetic parameters on a weekly to monthly timescale via optimality principles. We demonstrate that it is possible to explicitly separate the fast and slow photosynthetic responses to environmental conditions, allowing the simulation of GPP at the sub-daily timesteps required for coupling in an LSM. The resulting model reproduces the diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests. The best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. Comparison between this model and the operational LSM in the European Centre for Medium-range Weather Forecasts climate model shows that the new model has better predictive power in most of the sites and years analyzed, particularly in summer and autumn. Our analyses suggest a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with potential applications in weather, climate, and carbon-cycle modeling.}, - language = {en}, - number = {1}, - urldate = {2022-05-26}, - journal = {Journal of Advances in Modeling Earth Systems}, - author = {Mengoli, Giulia and Agustí‐Panareda, Anna and Boussetta, Souhail and Harrison, Sandy P. and Trotta, Carlo and Prentice, I. Colin}, - month = jan, - year = {2022}, - file = {2021ms002767-sup-0001-supporting information si-s01.pdf:/Users/dorme/Zotero/storage/YC3IEKUM/2021ms002767-sup-0001-supporting information si-s01.pdf:application/pdf;2021ms002767-sup-0002-supporting information si-s02.pdf:/Users/dorme/Zotero/storage/TP5WW2YH/2021ms002767-sup-0002-supporting information si-s02.pdf:application/pdf;Mengoli et al. - 2022 - Ecosystem Photosynthesis in Land‐Surface Models A.pdf:/Users/dorme/Zotero/storage/JU7AZTI4/Mengoli et al. - 2022 - Ecosystem Photosynthesis in Land‐Surface Models A.pdf:application/pdf} +@article{mengoli:2023a, + title = {A Global Function of Climatic Aridity Accounts for Soil Moisture Stress on Carbon Assimilation}, + author = {Mengoli, Giulia and Harrison, Sandy P. and Prentice, I. Colin}, + year = {2023}, + month = jun, + journal = {EGUsphere}, + pages = {1--19}, + publisher = {Copernicus GmbH}, + doi = {10.5194/egusphere-2023-1261}, + urldate = {2023-07-03}, + abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} The coupling between carbon uptake and water loss through stomata implies that gross primary production (GPP) can be limited by soil water availability through reduced leaf area and/or reduced stomatal conductance. Vegetation and land-surface models typically assume that GPP is highest under well-watered conditions and apply a stress function to reduce GPP with declining soil moisture below a critical threshold, which may be universal or prescribed by vegetation type. It is unclear how well current schemes represent the water conservation strategies of plants in different climates. Here eddy-covariance flux data are used to investigate empirically how soil moisture influences the light-use efficiency (LUE) of GPP. Well-watered GPP is estimated using the P model, a first-principles LUE model driven by atmospheric data and remotely sensed green vegetation cover. Breakpoint regression is used to relate the daily value of the ratio \β(\θ) (flux-derived GPP/modelled well-watered GPP) to soil moisture, which is estimated using a generic water-balance model. Maximum LUE, even during wetter periods, is shown to decline with increasing climatic aridity index (AI). The critical soil-moisture threshold also declines with AI. Moreover, for any AI, there is a value of soil moisture at which \β(\θ) is maximized, and this value declines with increasing AI. Thus, ecosystems adapted to seasonally dry conditions use water more conservatively (relative to well-watered ecosystems) when soil moisture is high, but maintain higher GPP when soil moisture is low. An empirical non-linear function of AI expressing these relationships is derived by non-linear regression, and used to generate a \β(\θ) function that provides a multiplier for well-watered GPP as simulated by the P model. Substantially improved GPP simulation is shown during both unstressed and water-stressed conditions, compared to the reference model version that ignores soil-moisture stress, and to an earlier formulation in which maximum LUE was not reduced. This scheme may provide a step towards better-founded representations of carbon-water cycle coupling in vegetation and land-surface models.{$<$}/p{$>$}}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/GMFKIMVQ/Mengoli et al. - 2023 - A global function of climatic aridity accounts for.pdf} } -@article{lavergne:2022a, - title = {A semi-empirical model for primary production, isotopic discrimination and competition of {C3} and {C4} plants}, - journal = {Global Ecology and Biogeography (submitted)}, - author = {Lavergne, Aliénor and Harrison, Sandy P. and Atsawawaranunt, Kamolphat and Dong, Ning and Prentice, Iain Colin}, - year = {2022}, - file = {Lavergneetal_GEB.docx:/Users/dorme/Zotero/storage/C4HSADV3/Lavergneetal_GEB.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document} +@article{Moore:2018dv, + title = {Equilibrium Forest Demography Explains the Distribution of Tree Sizes across {{North America}}}, + author = {Moore, Jonathan R and Zhu, Kai and Huntingford, Chris and Cox, Peter M}, + year = {2018}, + month = aug, + journal = {Environmental Research Letters}, + volume = {13}, + number = {8}, + pages = {084019--10}, + publisher = {IOP Publishing}, + doi = {10.1088/1748-9326/aad6d1}, + abstract = {Environmental Research Letters, 13(2018) 084019. doi:10.1088/1748-9326/aad6d1}, + date-added = {2020-12-01T16:56:53GMT}, + date-modified = {2020-12-17T08:58:39GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Moore/Environ\%20Res\%20Lett\%202018\%20Moore.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1088/1748-9326/aad6d1}, + file = {/Users/dorme/Zotero/storage/9Z93DUPX/Environ Res Lett 2018 Moore.pdf} +} + +@article{murphy:2021a, + title = {A Derivation Error That Affects Carbon Balance Models Exists in the Current Implementation of the Modified {{Arrhenius}} Function}, + author = {Murphy, Bridget K. and Stinziano, Joseph R.}, + year = {2021}, + journal = {New Phytologist}, + volume = {231}, + number = {6}, + pages = {2371--2381}, + issn = {1469-8137}, + doi = {10.1111/nph.16883}, + urldate = {2024-07-25}, + abstract = {Understanding biological temperature responses is crucial to predicting global carbon fluxes. The current approach to modelling temperature responses of photosynthetic capacity in large scale modelling efforts uses a modified Arrhenius equation. We rederived the modified Arrhenius equation from the source publication from 1942 and uncovered a missing term that was dropped by 2002. We compare fitted temperature response parameters between the correct and incorrect derivation of the modified Arrhenius equation. We find that most parameters are minimally affected, though activation energy is impacted quite substantially. We then scaled the impact of these small errors to whole plant carbon balance and found that the impact of the rederivation of the modified Arrhenius equation on modelled daily carbon gain causes a meaningful deviation of c. 18\% day-1. This suggests that the error in the derivation of the modified Arrhenius equation has impacted the accuracy of predictions of carbon fluxes at larger scales since {$>$} 40\% of Earth System Models contain the erroneous derivation. We recommend that the derivation error be corrected in modelling efforts moving forward.}, + copyright = {{\copyright} 2020 The Authors New Phytologist {\copyright} 2020 New Phytologist Trust}, + langid = {english}, + keywords = {Arrhenius,carbon balance,gas exchange,modelling,photosynthesis,temperature}, + file = {/Users/dorme/Zotero/storage/NZTU5A6H/Murphy and Stinziano - 2021 - A derivation error that affects carbon balance mod.pdf;/Users/dorme/Zotero/storage/5Y2669L6/nph.html} } -@article{kennedy:2019a, - title = {Implementing {Plant} {Hydraulics} in the {Community} {Land} {Model}, {Version} 5}, - volume = {11}, - issn = {19422466}, - url = {http://doi.wiley.com/10.1029/2018MS001500}, - doi = {10.1029/2018MS001500}, - abstract = {Version 5 of the Community Land Model (CLM5) introduces the plant hydraulic stress (PHS) configuration of vegetation water use, which is described and compared with the corresponding parameterization from CLM4.5. PHS updates vegetation water stress and root water uptake to better reflect plant hydraulic theory, advancing the physical basis of the model. The new configuration introduces prognostic vegetation water potential, modeled at the root, stem, and leaf levels. Leaf water potential replaces soil potential as the basis for stomatal conductance water stress, and root water potential is used to implement hydraulic root water uptake, replacing a transpiration partitioning function. Point simulations of a tropical forest site (Caxiuanã, Brazil) under ambient conditions and partial precipitation exclusion highlight the differences between PHS and the previous CLM implementation. Model description and simulation results are contextualized with a list of benefits and limitations of the new model formulation, including hypotheses that were not testable in previous versions of the model. Key results include reductions in transpiration and soil moisture biases relative to a control model under both ambient and exclusion conditions, correcting excessive dry season soil moisture stress in the control model. PHS implements hydraulic gradient root water uptake, which allows hydraulic redistribution and compensatory root water uptake and results in PHS utilizing a larger portion of the soil column to buffer shortfalls in precipitation. The new model structure, which bases water stress on leaf water potential, could have significant implications for vegetation-climate feedbacks, including increased sensitivity of photosynthesis to atmospheric vapor pressure deficit.}, - language = {en}, - number = {2}, - urldate = {2022-07-21}, - journal = {Journal of Advances in Modeling Earth Systems}, - author = {Kennedy, Daniel and Swenson, Sean and Oleson, Keith W. and Lawrence, David M. and Fisher, Rosie and Lola da Costa, Antonio Carlos and Gentine, Pierre}, - month = feb, - year = {2019}, - pages = {485--513}, - file = {Kennedy et al. - 2019 - Implementing Plant Hydraulics in the Community Lan.pdf:/Users/dorme/Zotero/storage/DK2KUV85/Kennedy et al. - 2019 - Implementing Plant Hydraulics in the Community Lan.pdf:application/pdf} +@article{Peng:2021ky, + title = {Global Climate and Nutrient Controls of Photosynthetic Capacity}, + author = {Peng, Yunke and Bloomfield, Keith J and Cernusak, Lucas A and Domingues, Tomas F and Prentice, I. Colin}, + year = {2021}, + month = apr, + journal = {Communications Biology}, + pages = {1--9}, + publisher = {Springer US}, + doi = {10.1038/s42003-021-01985-7}, + abstract = {Communications Biology, doi:10.1038/s42003-021-01985-7}, + date-added = {2021-06-14T08:31:31GMT}, + date-modified = {2021-11-11T14:13:27GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2021/Peng/Communications\%20Biology\%202021\%20Peng.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1038/s42003-021-01985-7}, + file = {/Users/dorme/Zotero/storage/ZPIDVNUB/Communications Biology 2021 Peng.pdf} } -@article{badeck:2005a, - title = {Post-photosynthetic fractionation of stable carbon isotopes between plant organs—a widespread phenomenon}, - volume = {19}, - url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/rcm.1912}, - doi = {https://doi.org/10.1002/rcm.1912}, - abstract = {Abstract Discrimination against 13C during photosynthesis is a well-characterised phenomenon. It leaves behind distinct signatures in organic matter of plants and in the atmosphere. The former is depleted in 13C, the latter is enriched during periods of preponderant photosynthetic activity of terrestrial ecosystems. The intra-annual cycle and latitudinal gradient in atmospheric 13C resulting from photosynthetic and respiratory activities of terrestrial plants have been exploited for the reconstruction of sources and sinks through deconvolution by inverse modelling. Here, we compile evidence for widespread post-photosynthetic fractionation that further modifies the isotopic signatures of individual plant organs and consequently leads to consistent differences in δ13C between plant organs. Leaves were on average 0.96‰ and 1.91‰ more depleted than roots and woody stems, respectively. This phenomenon is relevant if the isotopic signature of CO2-exchange fluxes at the ecosystem level is used for the reconstruction of individual sources and sinks. It may also modify the parameterisation of inverse modelling approaches if it leads to different isotopic signatures of organic matter with different residence times within the ecosystems and to a respiratory contribution to the average difference between the isotopic composition of plant organic matter and the atmosphere. We discuss the main hypotheses that can explain the observed inter-organ differences in δ13C. Copyright © 2005 John Wiley \& Sons, Ltd.}, - number = {11}, - journal = {Rapid Communications in Mass Spectrometry}, - author = {Badeck, Franz-W. and Tcherkez, Guillaume and Nogués, Salvador and Piel, Clément and Ghashghaie, Jaleh}, - year = {2005}, - note = {tex.eprint: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/rcm.1912}, - pages = {1381--1391}, - file = {Badeck et al. - 2005 - Post-photosynthetic fractionation of stable carbon.pdf:/Users/dorme/Zotero/storage/A8TG832F/Badeck et al. - 2005 - Post-photosynthetic fractionation of stable carbon.pdf:application/pdf} +@article{Prentice:2014bc, + title = {Balancing the Costs of Carbon Gain and Water Transport: Testing a New Theoretical Framework for Plant Functional Ecology}, + author = {Prentice, I. Colin and Dong, Ning and Gleason, Sean M and Maire, Vincent and Wright, Ian J.}, + year = {2014}, + journal = {Ecology Letters}, + volume = {17}, + number = {1}, + pages = {82--91}, + doi = {10.1111/ele.12211}, + date-added = {2020-12-01T13:22:48GMT}, + date-modified = {2021-01-21T15:04:43GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Prentice/Ecol\%20Lett\%202014\%20Prentice.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1111/ele.12211}, + file = {/Users/dorme/Zotero/storage/MDRBDVTF/Ecol Lett 2014 Prentice.pdf} } -@article{frank:2015a, - title = {Water-use efficiency and transpiration across {European} forests during the {Anthropocene}}, - volume = {5}, - issn = {1758-6798}, - url = {https://doi.org/10.1038/nclimate2614}, - doi = {10.1038/nclimate2614}, - abstract = {Considering the combined effects of CO2 fertilization and climate change drivers on plant physiology leads to a modest increase in simulated European forest transpiration in spite of the effects of CO2-induced stomatal closure.}, - number = {6}, - journal = {Nature Climate Change}, - author = {Frank, D. C. and Poulter, B. and Saurer, M. and Esper, J. and Huntingford, C. and Helle, G. and Treydte, K. and Zimmermann, N. E. and Schleser, G. H. and Ahlström, A. and Ciais, P. and Friedlingstein, P. and Levis, S. and Lomas, M. and Sitch, S. and Viovy, N. and Andreu-Hayles, L. and Bednarz, Z. and Berninger, F. and Boettger, T. and D‘Alessandro, C. M. and Daux, V. and Filot, M. and Grabner, M. and Gutierrez, E. and Haupt, M. and Hilasvuori, E. and Jungner, H. and Kalela-Brundin, M. and Krapiec, M. and Leuenberger, M. and Loader, N. J. and Marah, H. and Masson-Delmotte, V. and Pazdur, A. and Pawelczyk, S. and Pierre, M. and Planells, O. and Pukiene, R. and Reynolds-Henne, C. E. and Rinne, K. T. and Saracino, A. and Sonninen, E. and Stievenard, M. and Switsur, V. R. and Szczepanek, M. and Szychowska-Krapiec, E. and Todaro, L. and Waterhouse, J. S. and Weigl, M.}, - month = jun, - year = {2015}, - pages = {579--583}, - file = {Frank et al. - 2015 - Water-use efficiency and transpiration across Euro.pdf:/Users/dorme/Zotero/storage/DQE7I4U4/Frank et al. - 2015 - Water-use efficiency and transpiration across Euro.pdf:application/pdf} +@article{Smith:2019dv, + title = {Global Photosynthetic Capacity Is Optimized to the Environment}, + author = {Smith, Nicholas G and Keenan, Trevor F and Colin Prentice, I and Wang, Han and Wright, Ian J. and Niinemets, {\"U}lo and Crous, Kristine Y and Domingues, Tomas F and Guerrieri, Rossella and Yoko Ishida, F and Kattge, Jens and Kruger, Eric L and Maire, Vincent and Rogers, Alistair and Serbin, Shawn P and Tarvainen, Lasse and Togashi, Henrique F and Townsend, Philip A and Wang, Meng and Weerasinghe, Lasantha K and Zhou, Shuang Xi}, + year = {2019}, + month = jan, + journal = {Ecology Letters}, + volume = {22}, + number = {3}, + pages = {506--517}, + doi = {10.1111/ele.13210}, + date-added = {2020-12-02T15:16:49GMT}, + date-modified = {2021-01-25T10:03:32GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2019/Smith/Ecol\%20Lett\%202019\%20Smith.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1111/ele.13210}, + file = {/Users/dorme/Zotero/storage/EIWDBL6T/Ecol Lett 2019 Smith.pdf} } -@article{mengoli:2023a, - title = {A global function of climatic aridity accounts for soil moisture stress on carbon assimilation}, - url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1261/}, - doi = {10.5194/egusphere-2023-1261}, - abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} The coupling between carbon uptake and water loss through stomata implies that gross primary production (GPP) can be limited by soil water availability through reduced leaf area and/or reduced stomatal conductance. Vegetation and land-surface models typically assume that GPP is highest under well-watered conditions and apply a stress function to reduce GPP with declining soil moisture below a critical threshold, which may be universal or prescribed by vegetation type. It is unclear how well current schemes represent the water conservation strategies of plants in different climates. Here eddy-covariance flux data are used to investigate empirically how soil moisture influences the light-use efficiency (LUE) of GPP. Well-watered GPP is estimated using the P model, a first-principles LUE model driven by atmospheric data and remotely sensed green vegetation cover. Breakpoint regression is used to relate the daily value of the ratio \β(\θ) (flux-derived GPP/modelled well-watered GPP) to soil moisture, which is estimated using a generic water-balance model. Maximum LUE, even during wetter periods, is shown to decline with increasing climatic aridity index (AI). The critical soil-moisture threshold also declines with AI. Moreover, for any AI, there is a value of soil moisture at which \β(\θ) is maximized, and this value declines with increasing AI. Thus, ecosystems adapted to seasonally dry conditions use water more conservatively (relative to well-watered ecosystems) when soil moisture is high, but maintain higher GPP when soil moisture is low. An empirical non-linear function of AI expressing these relationships is derived by non-linear regression, and used to generate a \β(\θ) function that provides a multiplier for well-watered GPP as simulated by the P model. Substantially improved GPP simulation is shown during both unstressed and water-stressed conditions, compared to the reference model version that ignores soil-moisture stress, and to an earlier formulation in which maximum LUE was not reduced. This scheme may provide a step towards better-founded representations of carbon-water cycle coupling in vegetation and land-surface models.{\textless}/p{\textgreater}}, - language = {English}, - urldate = {2023-07-03}, - journal = {EGUsphere}, - author = {Mengoli, Giulia and Harrison, Sandy P. and Prentice, I. Colin}, - month = jun, - year = {2023}, - note = {Publisher: Copernicus GmbH}, - pages = {1--19}, - file = {Full Text PDF:/Users/dorme/Zotero/storage/JVIB9NS3/Mengoli et al. - 2023 - A global function of climatic aridity accounts for.pdf:application/pdf} +@article{Stocker:2018be, + title = {Quantifying Soil Moisture Impacts on Light Use Efficiency across Biomes}, + author = {Stocker, Benjamin D and Zscheischler, Jakob and Keenan, Trevor F and Prentice, I. Colin and Penuelas, Josep and Seneviratne, Sonia I}, + year = {2018}, + month = mar, + journal = {New Phytologist}, + volume = {218}, + number = {4}, + pages = {1430--1449}, + doi = {10.1111/nph.15123}, + abstract = {Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained {\dots}}, + date-added = {2021-01-25T09:23:00GMT}, + date-modified = {2021-01-28T15:58:46GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Stocker/New\%20Phytol.\%202018\%20Stocker.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1111/nph.15123}, + file = {/Users/dorme/Zotero/storage/E6PPD38Z/New Phytol. 2018 Stocker.pdf} } -@techreport{allen:1998a, - address = {Rome}, - title = {Crop evapotranspiration - {Guidelines} for computing crop water requirements}, - number = {56}, - institution = {FAO}, - author = {Allen, Richard G. and Pereira, Luis S and Raes, Dirk and Smith, Martin}, - year = {1998}, - file = {Allen et al. - 1998 - Crop evapotranspiration - Guidelines for computing.pdf:/Users/dorme/Zotero/storage/68AVA64R/Allen et al. - 1998 - Crop evapotranspiration - Guidelines for computing.pdf:application/pdf} +@article{Stocker:2020dh, + title = {P-Model v1.0: An Optimality-Based Light Use Efficiency Model for Simulating Ecosystem Gross Primary Production}, + author = {Stocker, Benjamin D and Wang, Han and Smith, Nicholas G and Harrison, Sandy P and Keenan, Trevor F and Sandoval, David and Davis, Tyler and Prentice, I. Colin}, + year = {2020}, + journal = {Geoscientific Model Development}, + volume = {13}, + number = {3}, + pages = {1545--1581}, + doi = {10.5194/gmd-13-1545-2020}, + date-added = {2020-11-30T12:24:06GMT}, + date-modified = {2021-01-28T11:55:51GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Stocker/Geoscientific\%20Model\%20Development\%202020\%20Stocker.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.5194/gmd-13-1545-2020}, + file = {/Users/dorme/Zotero/storage/J4YM5X2R/Geoscientific Model Development 2020 Stocker.pdf} } -@article{tsilingiris:2008a, - title = {Thermophysical and transport properties of humid air at temperature range between 0 and 100°{C}}, - volume = {49}, - issn = {0196-8904}, - url = {https://www.sciencedirect.com/science/article/pii/S0196890407003329}, - doi = {10.1016/j.enconman.2007.09.015}, - abstract = {The aim of the present investigation is evaluation of the thermophysical and transport properties of moist air as a function of mixture temperature with relative humidity as a parameter, ranging between dry air and saturation conditions. Based on a literature review of the most widely available analytical procedures and methods, a number of developed correlations are presented, which are employed with recent gas mixture component properties as input parameters, to derive the temperature and humidity dependence of mixture density, viscosity, specific heat capacity, thermal conductivity, thermal diffusivity and Prandtl number under conditions corresponding to the total barometric pressure of 101.3kPa. The derived results at an accuracy level suitable for engineering calculations were plotted and compared with adequate accuracy with existing results from previous analytical calculations and measured data from earlier experimental investigations. The saturated mixture properties were also appropriately fitted, and the fitting expressions suitable for computer calculations are also presented.}, - language = {en}, - number = {5}, - urldate = {2023-07-04}, - journal = {Energy Conversion and Management}, - author = {Tsilingiris, P. T.}, - month = may, - year = {2008}, - keywords = {Density, Prandtl number, Specific heat capacity, Thermal conductivity, Thermal diffusivity, Thermophysical properties, Viscosity}, - pages = {1098--1110}, - file = {ScienceDirect Full Text PDF:/Users/dorme/Zotero/storage/EZSDLB2V/Tsilingiris - 2008 - Thermophysical and transport properties of humid a.pdf:application/pdf;ScienceDirect Snapshot:/Users/dorme/Zotero/storage/IZWWYPC8/S0196890407003329.html:text/html} +@article{Togashi:2018es, + title = {Functional Trait Variation Related to Gap Dynamics in Tropical Moist forests{{{\textsubscript{A}}}} Vegetation Modelling Perspective}, + author = {Togashi, Henrique F{\"u}rstenau and Atkin, Owen K and Bloomfield, Keith J and Bradford, Matt and Cao, Kunfang and Dong, Ning and Evans, Bradley J and Fan, Zexin and Harrison, Sandy P and Hua, Zhu and Liddell, Michael J and Lloyd, Jon and Ni, Jian and Wang, Han and Weerasinghe, Lasantha K and Prentice, Iain Colin}, + year = {2018}, + month = dec, + journal = {Perspectives in Plant Ecology, Evolution and Systematics}, + volume = {35}, + pages = {52--64}, + publisher = {Elsevier}, + doi = {10.1016/j.ppees.2018.10.004}, + abstract = {Perspectives in Plant Ecology, Evolution and Systematics, 35 (2018) 52-64. doi:10.1016/j.ppees.2018.10.004}, + date-added = {2020-12-01T17:02:03GMT}, + date-modified = {2020-12-17T08:58:40GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2018/Togashi/Perspectives\%20in\%20Plant\%20Ecology\%20Evolution\%20and\%20Systematics\%202018\%20Togashi.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1016/j.ppees.2018.10.004}, + file = {/Users/dorme/Zotero/storage/L4HJJSBU/Perspectives in Plant Ecology Evolution and Systematics 2018 Togashi.pdf} } -@article{henderson-sellers:1984a, - title = {A new formula for latent heat of vaporization of water as a function of temperature}, - volume = {110}, - copyright = {Copyright © 1984 Royal Meteorological Society}, - issn = {1477-870X}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/qj.49711046626}, - doi = {10.1002/qj.49711046626}, - abstract = {Existing formulae and approximations for the latent heat of vaporization of water, Lv, are reviewed. Using an analytical approximation to the saturated vapour pressure as a function of temperature, a new, temperature-dependent function for Lv is derived.}, - language = {en}, - number = {466}, - urldate = {2023-07-04}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Henderson-Sellers, B.}, - year = {1984}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.49711046626}, - pages = {1186--1190}, - file = {Snapshot:/Users/dorme/Zotero/storage/WJAU4R6F/qj.html:text/html} +@article{tsilingiris:2008a, + title = {Thermophysical and Transport Properties of Humid Air at Temperature Range between 0 and 100{$^\circ$}{{C}}}, + author = {Tsilingiris, P. T.}, + year = {2008}, + month = may, + journal = {Energy Conversion and Management}, + volume = {49}, + number = {5}, + pages = {1098--1110}, + issn = {0196-8904}, + doi = {10.1016/j.enconman.2007.09.015}, + urldate = {2023-07-04}, + abstract = {The aim of the present investigation is evaluation of the thermophysical and transport properties of moist air as a function of mixture temperature with relative humidity as a parameter, ranging between dry air and saturation conditions. Based on a literature review of the most widely available analytical procedures and methods, a number of developed correlations are presented, which are employed with recent gas mixture component properties as input parameters, to derive the temperature and humidity dependence of mixture density, viscosity, specific heat capacity, thermal conductivity, thermal diffusivity and Prandtl number under conditions corresponding to the total barometric pressure of 101.3kPa. The derived results at an accuracy level suitable for engineering calculations were plotted and compared with adequate accuracy with existing results from previous analytical calculations and measured data from earlier experimental investigations. The saturated mixture properties were also appropriately fitted, and the fitting expressions suitable for computer calculations are also presented.}, + langid = {english}, + keywords = {Density,Prandtl number,Specific heat capacity,Thermal conductivity,Thermal diffusivity,Thermophysical properties,Viscosity}, + file = {/Users/dorme/Zotero/storage/CUISZDZT/Tsilingiris - 2008 - Thermophysical and transport properties of humid a.pdf;/Users/dorme/Zotero/storage/RFS82MFU/S0196890407003329.html} } -@article{chen:2008a, - title = {The equation of state of pure water determined from sound speeds}, - volume = {66}, - issn = {0021-9606}, - url = {https://doi.org/10.1063/1.434179}, - doi = {10.1063/1.434179}, - abstract = {The equation of state of water valid over the range 0–100 °C and 0–1000 bar has been determined from the high pressure sound velocities of Wilson, which were reanalyzed by Chen and Millero. The equation of state has a maximum error of ±0.01 bar−1 in isothermal compressibility and is in the form of a secant bulk modulus: K=V0P/(V0−V) =K0+AP+BP2, where K, K0, and V, V0 are the secant bulk moduli and specific volumes at applied pressures P and 0 (1 atm), respectively; A and B are temperature dependent parameters. The good agreement (to within 20×10−6 cm3 g−1) of specific volumes calculated using the above equation with those obtained from other modifications of the Wilson sound velocity data demonstrates the reliability of the sound velocity method for determining equations of state.}, - number = {5}, - urldate = {2023-07-04}, - journal = {The Journal of Chemical Physics}, - author = {Chen, Chen‐Tung and Fine, Rana A. and Millero, Frank J.}, - month = aug, - year = {2008}, - pages = {2142--2144}, - file = {Full Text PDF:/Users/dorme/Zotero/storage/2WBI492T/Chen et al. - 2008 - The equation of state of pure water determined fro.pdf:application/pdf;Snapshot:/Users/dorme/Zotero/storage/IRHA5IDN/The-equation-of-state-of-pure-water-determined.html:text/html} +@article{voncaemmerer:2014a, + title = {Carbon Isotope Discrimination as a Tool to Explore {{C4}} Photosynthesis}, + author = {{von Caemmerer}, Susanne and Ghannoum, Oula and Pengelly, Jasper J. L. and Cousins, Asaph B.}, + year = {2014}, + month = jul, + journal = {Journal of Experimental Botany}, + volume = {65}, + number = {13}, + pages = {3459--3470}, + issn = {1460-2431, 0022-0957}, + doi = {10.1093/jxb/eru127}, + urldate = {2022-05-20}, + abstract = {Photosynthetic carbon isotope discrimination is a non-destructive tool for investigating C4 metabolism. Tuneable diode laser absorption spectroscopy provides new opportunities for making rapid, concurrent measurements of carbon isotope discrimination and CO2 assimilation over a range of environmental conditions, and this has facilitated the use of carbon isotope discrimination as a probe of C4 metabolism. In spite of the significant progress made in recent years, understanding how photosynthetic carbon isotope discrimination measured concurrently with gas exchange relates to carbon isotope composition of leaf and plant dry matter remains a challenge that requires resolution if this technique is to be successfully applied as a screening tool in crop breeding and phylogenetic research. In this review, we update our understanding of the factors and assumptions that underlie variations in photosynthetic carbon isotope discrimination in C4 leaves. Closing the main gaps in our understanding of carbon isotope discrimination during C4 photosynthesis may help advance research aimed at developing higher productivity and efficiency in key C4 food, feed, and biofuel crops.}, + langid = {english}, + file = {/Users/dorme/Zotero/storage/DBGBCVUB/Caemmerer et al. - 2014 - Carbon isotope discrimination as a tool to explore.pdf} } -@article{hengl:2017a, - title = {{SoilGrids250m}: {Global} gridded soil information based on machine learning}, - volume = {12}, - issn = {1932-6203}, - shorttitle = {{SoilGrids250m}}, - url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169748}, - doi = {10.1371/journal.pone.0169748}, - abstract = {This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56\% (coarse fragments) and 83\% (pH) of variation with an overall average of 61\%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230\%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.}, - language = {en}, - number = {2}, - urldate = {2023-07-05}, - journal = {PLOS ONE}, - author = {Hengl, Tomislav and Jesus, Jorge Mendes de and Heuvelink, Gerard B. M. and Gonzalez, Maria Ruiperez and Kilibarda, Milan and Blagotić, Aleksandar and Shangguan, Wei and Wright, Marvin N. and Geng, Xiaoyuan and Bauer-Marschallinger, Bernhard and Guevara, Mario Antonio and Vargas, Rodrigo and MacMillan, Robert A. and Batjes, Niels H. and Leenaars, Johan G. B. and Ribeiro, Eloi and Wheeler, Ichsani and Mantel, Stephan and Kempen, Bas}, - month = feb, - year = {2017}, - note = {Publisher: Public Library of Science}, - keywords = {Agricultural soil science, Forecasting, Glaciers, Machine learning, Remote sensing, Shannon index, Soil pH, Trees}, - pages = {e0169748}, - file = {Full Text PDF:/Users/dorme/Zotero/storage/VS5FRVSK/Hengl et al. - 2017 - SoilGrids250m Global gridded soil information bas.pdf:application/pdf} +@article{Walker:2014ce, + title = {The Relationship of Leaf Photosynthetic Traits - {{Vcmaxand Jmax-}} to Leaf Nitrogen, Leaf Phosphorus, and Specific Leaf Area: A Meta-Analysis and Modeling Study}, + author = {Walker, Anthony P and Beckerman, Andrew P and Gu, Lianhong and Kattge, Jens and Cernusak, Lucas A and Domingues, Tomas F and Scales, Joanna C and Wohlfahrt, Georg and Wullschleger, Stan D and Woodward, F Ian}, + year = {2014}, + month = jul, + journal = {Ecology and Evolution}, + volume = {4}, + number = {16}, + pages = {3218--3235}, + doi = {10.1002/ece3.1173}, + date-added = {2020-12-07T11:43:53GMT}, + date-modified = {2020-12-17T08:58:39GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2014/Walker/Ecology\%20and\%20Evolution\%202014\%20Walker.pdf}, + rating = {0}, + uri = {papers3://publication/doi/10.1002/ece3.1173}, + file = {/Users/dorme/Zotero/storage/N8LKZ4EP/Ecology and Evolution 2014 Walker.pdf} } -@article{davis:2017a, - title = {Simple process-led algorithms for simulating habitats ({SPLASH} v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture}, - volume = {10}, - issn = {1991-959X}, - shorttitle = {Simple process-led algorithms for simulating habitats ({SPLASH} v.1.0)}, - url = {https://gmd.copernicus.org/articles/10/689/2017/}, - doi = {10.5194/gmd-10-689-2017}, - abstract = {Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley–Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.}, - language = {English}, - number = {2}, - urldate = {2023-07-05}, - journal = {Geoscientific Model Development}, - author = {Davis, Tyler W. and Prentice, I. Colin and Stocker, Benjamin D. and Thomas, Rebecca T. and Whitley, Rhys J. and Wang, Han and Evans, Bradley J. and Gallego-Sala, Angela V. and Sykes, Martin T. and Cramer, Wolfgang}, - month = feb, - year = {2017}, - note = {Publisher: Copernicus GmbH}, - pages = {689--708}, - file = {Full Text PDF:/Users/dorme/Zotero/storage/RUZWLGHT/Davis et al. - 2017 - Simple process-led algorithms for simulating habit.pdf:application/pdf} +@article{Wang:2017go, + title = {Towards a Universal Model for Carbon Dioxide Uptake by Plants}, + author = {Wang, Han and Prentice, I. Colin and Keenan, Trevor F and Davis, Tyler W and Wright, Ian J. and Cornwell, William K and Evans, Bradley J and Peng, Changhui}, + year = {2017}, + month = sep, + journal = {Nature Plants}, + pages = {1--8}, + publisher = {Springer US}, + doi = {10.1038/s41477-017-0006-8}, + abstract = {Nature Plants, doi:10.1038/s41477-017-0006-8}, + date-added = {2020-11-30T12:27:00GMT}, + date-modified = {2021-01-28T09:14:19GMT}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2017/Wang/Nature\%20Plants\%202017\%20Wang.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1038/s41477-017-0006-8}, + file = {/Users/dorme/Zotero/storage/D4RYI3NP/Nature Plants 2017 Wang.pdf} } -@article{berger:1978a, - title = {Long-{Term} {Variations} of {Daily} {Insolation} and {Quaternary} {Climatic} {Changes}}, - volume = {35}, - issn = {0022-4928, 1520-0469}, - url = {https://journals.ametsoc.org/view/journals/atsc/35/12/1520-0469_1978_035_2362_ltvodi_2_0_co_2.xml}, - doi = {10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2}, - abstract = {Abstract The first part of this note provides all trigonometrical formulas which allow the direct spectral analysis and the computation of those long-term variations of the earth’s orbital elements which are of primary interest for the computation of the insolation. The elements are the eccentricity, the longitude of the perihelion, the processional parameter and the obliquity. This new formulary is much more simple to use than the ones previously designed and still provides excellent accuracy, mainly because it takes into account the influence of the most important higher order terms in the series expansions. The second part is devoted to the computation of the daily insolation both for calendar and solar dates.}, - language = {EN}, - number = {12}, - urldate = {2023-07-13}, - journal = {Journal of the Atmospheric Sciences}, - author = {Berger, AndréL}, - month = dec, - year = {1978}, - note = {Publisher: American Meteorological Society - Section: Journal of the Atmospheric Sciences}, - pages = {2362--2367}, - file = {Full Text PDF:/Users/dorme/Zotero/storage/M9UHD7J8/Berger - 1978 - Long-Term Variations of Daily Insolation and Quate.pdf:application/pdf} +@article{Wang:2020ik, + title = {Acclimation of Leaf Respiration Consistent with Optimal Photosynthetic Capacity}, + author = {Wang, Han and Atkin, Owen K and Keenan, Trevor F and Smith, Nicholas G and Wright, Ian J. and Bloomfield, Keith J and Kattge, Jens and Reich, Peter B and Prentice, I. Colin}, + year = {2020}, + month = feb, + journal = {Global Change Biology}, + volume = {26}, + number = {4}, + pages = {2573--2583}, + doi = {10.1111/gcb.14980}, + date-added = {2020-11-30T12:23:58GMT}, + date-modified = {2021-01-22T13:54:24GMT}, + langid = {english}, + local-url = {file://localhost/Users/dorme/References/Library.papers3/Articles/2020/Wang/Global\%20Change\%20Biol\%202020\%20Wang.pdf}, + rating = {0}, + read = {Yes}, + uri = {papers3://publication/doi/10.1111/gcb.14980}, + file = {/Users/dorme/Zotero/storage/2XYW4BF4/gcb15314-sup-0004-Supinfo.pdf;/Users/dorme/Zotero/storage/RAGU56IZ/Global Change Biol 2020 Wang.pdf} +} + +@book{woolf:1968a, + title = {On the {{Computation}} of {{Solar Elevation Angles}} and the {{Determination}} of {{Sunrise}} and {{Sunset Times}}}, + author = {Woolf, Harold M.}, + year = {1968}, + publisher = {{National Aeronautics and Space Administration}}, + langid = {english} } diff --git a/docs/source/users/pmodel/pmodel_details/lue_limitation.md b/docs/source/users/pmodel/pmodel_details/lue_limitation.md index 86edacac..4a2ee005 100644 --- a/docs/source/users/pmodel/pmodel_details/lue_limitation.md +++ b/docs/source/users/pmodel/pmodel_details/lue_limitation.md @@ -7,7 +7,7 @@ jupytext: format_version: 0.13 jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -26,7 +26,8 @@ kernelspec: from matplotlib import pyplot import numpy as np -from pyrealm.pmodel import PModel, PModelEnvironment, calc_ftemp_kphio +from pyrealm.pmodel import PModel, PModelEnvironment +from pyrealm.pmodel.quantum_yield import QuantumYieldTemperature, QuantumYieldSandoval %matplotlib inline @@ -44,9 +45,9 @@ meanalpha_2d = np.broadcast_to(meanalpha_1d, (n_pts, n_pts)) co2_2d = np.broadcast_to(co2_1d, (n_pts, n_pts)) ``` -Once key [photosynthetic parameters](photosynthetic_environment) and -[optimal chi](optimal_chi.md) have been calculated, the -{class}`~pyrealm.pmodel.pmodel.PModel` class can report estimates of: +Once key [photosynthetic parameters](photosynthetic_environment) and [optimal +chi](optimal_chi.md) have been calculated, the {class}`~pyrealm.pmodel.pmodel.PModel` +class can report estimates of: * the light use efficiency (LUE), as grams of carbon per mole of photons, and * the intrinsic water use efficiency (IWUE), as micromoles per mole of photons. @@ -59,100 +60,93 @@ $$ \text{LUE} = \phi_0 \cdot M_C \cdot m_j $$ -where $\phi_0$ is the quantum yield efficiency of photosynthesis, $M_C$ is the -molar mass of carbon and $m_j$ is the $\ce{CO2}$ limitation term of light use -efficiency from the calculation of optimal $\chi$. +where $\phi_0$ is the quantum yield efficiency of photosynthesis, $M_C$ is the molar +mass of carbon and $m_j$ is the $\ce{CO2}$ limitation term of light use efficiency from +the calculation of optimal $\chi$. However, the light use efficiency may be adjusted by +different approaches to estimation of $\phi_0$ and limitation of $J_{max}$, adding +terms for: -```{warning} - -Note that $\phi_0$ is sometimes used to refer to the quantum yield of electron -transfer, which is exactly four times larger than the quantum yield of -photosynthesis. - -``` - -However, the {class}`pyrealm.pmodel.pmodel.PModel` class also incorporates two further -factors: - -* temperature (t) dependence of $\phi_0$, -* $J_{max}$ limitation of $m_j$ by a factor $f_v$ and +* method dependent modulation of $\phi_0$ ($\phi_0^{\prime}$), and +* $J_{max}$ limitation of $m_j$ by a factor $f_v$. $$ - \text{LUE} = \phi_0(t) \cdot M_C \cdot m_j \cdot f_v + \text{LUE} = \phi_0^{\prime} \cdot M_C \cdot m_j \cdot f_v $$ -### $\phi_0$ and temperature dependency +### Quantum yield efficiency of photosynthesis -The {class}`~pyrealm.pmodel.pmodel.PModel` uses a single variable to capture the -apparent quantum yield efficiency of photosynthesis (`kphio`, $\phi_0$). The value of -$\phi_0$ shows temperature dependence, which is modelled following -{cite:t}`Bernacchi:2003dc` for C3 plants and {cite:t}`cai:2020a` for C4 plants (see -{func}`~pyrealm.pmodel.functions.calc_ftemp_kphio`). The temperature dependency is -applied by default but can be turned off using the -{class}`~pyrealm.pmodel.pmodel.PModel` argument `do_ftemp_kphio=False`. +:::{warning} -The default values of `kphio` vary with the model options, corresponding -to the empirically fitted values from {cite:t}`Stocker:2020dh`. If the temperature -dependence of $\phi_0$ is applied, $\phi_0 = 0.081785$, otherwise $\phi_0 = 0.049977$. +Note that $\phi_0$ is also sometimes used to refer to the quantum yield of electron +transfer, which is exactly four times larger than the quantum yield of photosynthesis. -The initial value of $\phi_0$ and the values used in calculations are stored in -the `init_kphio` and `kphio` attributes of the {class}`~pyrealm.pmodel.pmodel.PModel` -object. The code examples compare models with and without temperature -dependency of $\phi_0$. +::: -```{code-cell} -env = PModelEnvironment(tc=30, patm=101325, vpd=820, co2=400) -model_fixkphio = PModel(env, kphio=0.08, do_ftemp_kphio=False) -model_fixkphio.init_kphio -``` +The value of $\phi_0$ captures the conversion rate of moles photosynthetically active +photons into moles of $\ce{CO2}$. The theoretical maximum for this value is 1/9, in the +absence of a Q cycle, or 1/8 when a Q cycle is operating {cite}`long:1993a`. These +theoretical maxima are not necessarily directly used in calculating light use +efficiency: -```{code-cell} -model_fixkphio.kphio -``` +* The values of $\phi_0$ are often adjusted to include other components of light +capture. For example, {cite:t}`Stocker:2020dh` include a factor for incomplete leaf +absorptance in their estimation of $\phi_0$ and argue that $\phi_0$ should be treated as +a parameter representing canopy-scale effective quantum yield. -```{code-cell} -model_tempkphio = PModel(env, kphio=0.08, do_ftemp_kphio=True) -model_fixkphio.init_kphio -``` +* The maximum quantum yield can vary with environmental conditions, such as temperature +variation in $\phi_0$ {cite}`Bernacchi:2003dc`. -```{code-cell} -model_fixkphio.kphio -``` +For these reasons, the {class}`~pyrealm.pmodel.pmodel.PModel` provides alternative +approaches to estimating the value of $\phi{0}$, using the `method_kphio` argument. The +currently implemented approaches are described below. Note that each approach has a +specific **reference value for $\phi_{0}$**, which is used as the baseline for further +calculations. This value can be altered via the `reference_kphio` argument. + +### Temperature dependent $\phi_0$ -The scaling of temperature dependence varies for C3 and C4 plants and the function -{func}`~pyrealm.pmodel.functions.calc_ftemp_kphio` is used to calculate a limitation -factor that is applied to $\phi_0$. +The default approach (`method_kphio='temperature'`) applies a temperature dependent +estimate of $\phi_0$, following {cite:t}`Bernacchi:2003dc` for C3 plants and +{cite:t}`cai:2020a` for C4 plants. The default reference value for this approach is +$\phi_0 = 0.081785$, following the BRC parameterisation in Table 1. of +{cite:t}`Stocker:2020dh`. ```{code-cell} :tags: [hide-input] # Calculate temperature dependence of quantum yield efficiency -fkphio_c3 = calc_ftemp_kphio(tc_1d, c4=False) -fkphio_c4 = calc_ftemp_kphio(tc_1d, c4=True) +env = PModelEnvironment(tc=tc_1d, patm=101325, vpd=820, co2=400) + +fkphio_c3 = QuantumYieldTemperature(env=env, use_c4=False) +fkphio_c4 = QuantumYieldTemperature(env=env, use_c4=True) # Create a line plot of ftemp kphio -pyplot.plot(tc_1d, fkphio_c3, label="C3") -pyplot.plot(tc_1d, fkphio_c4, label="C4") +pyplot.plot(tc_1d, fkphio_c3.kphio, label="C3") +pyplot.plot(tc_1d, fkphio_c4.kphio, label="C4") pyplot.title("Temperature dependence of quantum yield efficiency") pyplot.xlabel("Temperature °C") -pyplot.ylabel("Limitation factor") +pyplot.ylabel("Quantum yield efficiency ($\phi_0$)") pyplot.legend() pyplot.show() ``` -### Setting $\phi_0$ directly +#### Fixed $\phi_0$ -In addition to fixed or temperature dependent $\phi_0$, it is also possible to provide -$\phi_0$ values for each observation in the P Model. In this case, you will need to -provide an array of values that has the same shape as the other driver variables and -these values are then used within the calculations for each observation. +This approach (`method_kphio='fixed'`) applies a fixed value of $\phi_0$ in the +calculation of light use efficiency. The default reference value used in this case is +$\phi_0 = 0.049977$, following the ORG settings parameterisation in Table 1. of +{cite:t}`Stocker:2020dh`. -This option is provided to allow users to experiment with alternative per-observation -calculations of $\phi_0$ limitation - for example, modulation of $\phi_0$ by temperature -and aridity - that are not implemented within the `PModel` or `SubdailyPModel` classes. -This approach is used in the plot below to show the simple linear scaling of LUE with -$\phi_0$ for a constant environment. +However, the fixed method will also accept $\phi_0$ values for each observation being +fitted in the PModel. This option is provided to allow users to experiment with +alternative per-observation estimation of $\phi_0$ that are not currently implemented. +You will need to provide an array of values that has the same shape as the other driver +variables and these values are then used within the calculations for each observation. + +In the code and plot below, this approach is used to provide a simple linear series of +$\phi_0$ values to an otherwise constant environment. As you would expect given +$\text{LUE} = \phi_0 \cdot M_C \cdot m_j$, light use efficiency changes linearly along +this gradient of $\phi_0$ values. ```{code-cell} :tags: [hide-input] @@ -167,7 +161,7 @@ env = PModelEnvironment( vpd=np.repeat(820, n_vals), co2=np.repeat(400, n_vals), ) -model_var_kphio = PModel(env, kphio=kphio_values, do_ftemp_kphio=False) +model_var_kphio = PModel(env, method_kphio="fixed", reference_kphio=kphio_values) # Create a line plot of ftemp kphio pyplot.plot(kphio_values, model_var_kphio.lue) @@ -177,6 +171,93 @@ pyplot.ylabel("LUE") pyplot.show() ``` +#### Temperature and aridity effects on $\phi_0$ + +The option `method_kphio='sandoval'` implements an experimental calculation +{cite}`sandoval:in_prep` of $\phi_0$ as a function of a local aridity index (P/PET), the +mean growth temperature and the air temperature {cite}`sandoval:in_prep`. This approach +uses the theoretical maximum value of $\phi_0 = 1/9$ as the reference value. You will +need to provide the aridity index and mean growing temperature for observations when +creating the `PModelEnvironment`. + +First, the aridity index is used to adjust the reference value ($\phi_{0R}$) using a +double exponential function to calculate a new maximum value given the climatological +aridity ($\phi_{0A}$): + +$\phi_{0A} = \dfrac{\phi_{0R}}{(1 + \textrm{AI}^m) ^ n}$ + +This captures a decrease in maximum $\phi_0$ in arid conditions, as shown below. + +```{code-cell} +n_vals = 51 +aridity_index = np.logspace(-2, 1.5, num=n_vals) + +env = PModelEnvironment( + tc=np.repeat(20, n_vals), + patm=np.repeat(101325, n_vals), + vpd=np.repeat(820, n_vals), + co2=np.repeat(400, n_vals), + aridity_index=aridity_index, + mean_growth_temperature=np.repeat(20, n_vals), +) + +sandoval_kphio = QuantumYieldSandoval(env) + +fig, ax = pyplot.subplots(1, 1) +ax.plot(aridity_index, sandoval_kphio.kphio) +ax.set_title("Change in $\phi_0$ with aridity index (P/PET).") +ax.set_ylabel("$\phi_0$") +ax.set_xlabel("Aridity Index") +ax.set_xscale("log") +pyplot.show() +``` + +In addition to capping the peak $\phi_0$ as a function of the aridity index, this +approach also alters the temperature at which $\phi_0$ is maximised as a function of the +mean growth temperature ($T_g$) in a location. The plot below shows how aridity and mean +growth temperature interact to change the location and height of the peak $\phi_0$. + +```{code-cell} +n_vals = 51 +mean_growth_values = np.array([10, 22, 24, 25]) +aridity_values = np.array([1.0, 1.5, 5.0]) +tc_values = np.linspace(0, 40, n_vals) + +shape = (n_vals, len(aridity_values), len(mean_growth_values)) + +ai3, tc3, mg3 = np.meshgrid(aridity_values, tc_values, mean_growth_values) + + +env = PModelEnvironment( + tc=tc3, + patm=np.full(shape, 101325), + vpd=np.full(shape, 820), + co2=np.full(shape, 400), + aridity_index=ai3, + mean_growth_temperature=mg3, +) + +sandoval_kphio = QuantumYieldSandoval(env) + +fig, axes = pyplot.subplots(ncols=3, nrows=1, sharey=True, figsize=(10, 6)) + +for ai_idx, (ax, ai_val) in enumerate(zip(axes, aridity_values)): + + for mg_idx, mg_val in enumerate(mean_growth_values): + ax.plot( + env.tc[:, ai_idx, mg_idx], + sandoval_kphio.kphio[:, ai_idx, mg_idx], + label=f"$T_{{g}}$ = {mg_val}", + ) + ax.set_title(f"AI = {ai_val}") + ax.set_ylabel("$\phi_0$") + ax.set_xlabel("Observed temperature") + + +ax.legend(frameon=False) +pyplot.show() +``` + ### Limitation of electron transfer rate ($J_{max}$) and carboxylation capacity ($V_{cmax}$) The {class}`~pyrealm.pmodel.pmodel.PModel` implements three alternative approaches to @@ -195,33 +276,31 @@ options for this setting are: and $V_{cmax}$ described in {cite:t}`Smith:2019dv`. The calculation details can be seen in the {meth}`~pyrealm.pmodel.jmax_limitation.JmaxLimitation.smith19` method. -```{code-cell} -model_jmax_simple = PModel(env, kphio=0.08, method_jmaxlim="simple") -model_jmax_wang17 = PModel(env, kphio=0.08, method_jmaxlim="wang17") -model_jmax_smith19 = PModel(env, kphio=0.08, method_jmaxlim="smith19") - -# Compare LUE from the three methods -np.array([model_jmax_simple.lue, model_jmax_wang17.lue, model_jmax_smith19.lue]) -``` ++++ The plot below shows the effects of each method on the LUE across a temperature -gradient ($P=101325.0 , \ce{CO2}= 400 \text{ppm}, \text{VPD}=820$) and $\phi_0=0.05$). +gradient ($P=101325.0 , \ce{CO2}= 400 \text{ppm}, \text{VPD}=820$) and fixed $\phi_0=0.08$. ```{code-cell} :tags: [hide-input] # Calculate variation in m_jlim with temperature env = PModelEnvironment(tc=tc_1d, patm=101325, vpd=820, co2=400) -model_tc_wang17 = PModel(env, kphio=0.08, do_ftemp_kphio=False) -model_tc_simple = PModel(env, kphio=0.08, do_ftemp_kphio=False, method_jmaxlim="simple") -model_tc_smith19 = PModel( - env, kphio=0.08, do_ftemp_kphio=False, method_jmaxlim="smith19" + +model_jmax_simple = PModel( + env, method_jmaxlim="simple", method_kphio="fixed", reference_kphio=0.08 +) +model_jmax_wang17 = PModel( + env, method_jmaxlim="wang17", method_kphio="fixed", reference_kphio=0.08 +) +model_jmax_smith19 = PModel( + env, method_jmaxlim="smith19", method_kphio="fixed", reference_kphio=0.08 ) # Create a line plot of the resulting values of m_j -pyplot.plot(tc_1d, model_tc_simple.lue, label="simple") -pyplot.plot(tc_1d, model_tc_wang17.lue, label="wang17") -pyplot.plot(tc_1d, model_tc_smith19.lue, label="smith19") +pyplot.plot(tc_1d, model_jmax_simple.lue, label="simple") +pyplot.plot(tc_1d, model_jmax_wang17.lue, label="wang17") +pyplot.plot(tc_1d, model_jmax_smith19.lue, label="smith19") pyplot.title("Effects of J_max limitation") pyplot.xlabel("Temperature °C") @@ -229,3 +308,7 @@ pyplot.ylabel("Light Use Efficiency (g C mol-1)") pyplot.legend() pyplot.show() ``` + +```{code-cell} + +``` diff --git a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md index ef50e665..6963ef28 100644 --- a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md +++ b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md @@ -26,16 +26,16 @@ reference documentation](../../../api/pmodel_api) ## Overview -The P Model is a model of carbon capture and water use by plants. Four forcing variables -are used to define the environment that the plant experiences: +The P Model is a model of carbon capture and water use by plants. Four core forcing +variables are used to define the environment that the plant experiences: * temperature (`tc`, °C), * vapor pressure deficit (`vpd`, Pa), * atmospheric $\ce{CO2}$ concentration (`co2`, ppm), and * atmospheric pressure (`patm`, Pa). -From these inputs, the model breaks down into four broad stages, each of which is -described in more detail in the link for each stage +From these inputs, the model breaks down into the following broad stages, each of which +is described in more detail in the link for each stage The main steps are: @@ -43,22 +43,21 @@ The main steps are: environmental variables are used to calculate four key variables describing the photosynthetic environment of a plant. -* Calculation of [leaf $\ce{CO2}$ variables](../pmodel_details/optimal_chi). The photosynthetic - environment is then used to calculate the optimal ratio of internal to external CO2 - concentration ($chi$), along with $\ce{CO2}$ partial pressures and limitation factors. - This step also governs the main differences between C3 and C4 photosynthesis. +* Calculation of [leaf $\ce{CO2}$ variables](../pmodel_details/optimal_chi). The + photosynthetic environment is then used to calculate the optimal ratio of internal to + external CO2 concentration ($chi$), along with $\ce{CO2}$ partial pressures and + limitation factors. This step also governs the main differences between C3 and C4 + photosynthesis. -* Constraints on [light use efficiency (LUE)](lue_limitation). The calculation of light - use efficiency can be subjected to a number of constraints: +* Determination of the temperature sensitivity of the [quantum yield + efficiency](quantum_yield) of photosynthesis (`kphio`, $\phi_0$). - * Theoretical limitations to the maximum rates of Rubsico regeneration - ($J_{max}$) and maximum carboxylation capacity ($V_{cmax}$) +* Theoretical limitations to the maximum rates of Rubsico regeneration $J_{max}$) and + maximum carboxylation capacity ($V_{cmax}$) that can act as further constraints on + [light use efficiency (LUE)](lue_limitation). - * Temperature sensitivity of the quantum yield efficiency of photosynthesis - (`kphio`, $\phi_0$). - - * Different approaches to incorporating effects of [soil moisture - stress](soil_moisture) on productivity. +* Different approaches to incorporating effects of [soil moisture stress](soil_moisture) + on productivity. * Estimation of [gross primary productivity](estimating-productivity). Once LUE has been calculated, estimates of absorbed photosynthetically active radiation, can be used to diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 357ec350..a3f3a6c6 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -235,6 +235,17 @@ class QuantumYieldSandoval( 1/9 in the absence of a Q cycle (Long, 1993). """ + def peak_quantum_yield(self, aridity: NDArray) -> NDArray: + """Calculate the peak quantum yield as a function of the aridity index. + + Args: + aridity: An array of aridity index values. + """ + + # Calculate peak kphio given the aridity index + m, n = self.env.pmodel_const.sandoval_peak_phio + return self.reference_kphio / (1 + (self.env.aridity_index) ** m) ** n + def _calculate_kphio(self) -> None: """Calculate kphio.""" @@ -258,8 +269,7 @@ def _calculate_kphio(self) -> None: Topt = Hd / (deltaS - self.env.core_const.k_R * np.log(Ha / (Hd - Ha))) # Calculate peak kphio given the aridity index - m, n = self.env.pmodel_const.sandoval_peak_phio - kphio_peak = self.reference_kphio / (1 + (self.env.aridity_index) ** m) ** n + kphio_peak = self.peak_quantum_yield(aridity=self.env.aridity_index) # Calculate the modified Arrhenius factor using the f_kphio = calc_modified_arrhenius_factor( From 2246a613781efb62988b3cffe580a98e8616a783 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 7 Aug 2024 11:37:16 +0100 Subject: [PATCH 20/28] P model user docs update --- .../pmodel/pmodel_details/jmax_limitation.md | 79 +++++++++++++ .../pmodel/pmodel_details/pmodel_overview.md | 107 ++++++++++++------ .../{lue_limitation.md => quantum_yield.md} | 98 ++-------------- pyproject.toml | 6 + 4 files changed, 168 insertions(+), 122 deletions(-) create mode 100644 docs/source/users/pmodel/pmodel_details/jmax_limitation.md rename docs/source/users/pmodel/pmodel_details/{lue_limitation.md => quantum_yield.md} (71%) diff --git a/docs/source/users/pmodel/pmodel_details/jmax_limitation.md b/docs/source/users/pmodel/pmodel_details/jmax_limitation.md new file mode 100644 index 00000000..5e07802f --- /dev/null +++ b/docs/source/users/pmodel/pmodel_details/jmax_limitation.md @@ -0,0 +1,79 @@ +--- +jupytext: + formats: md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# $J_{max}$ limitation + +```{code-cell} +:tags: [hide-input] + +from matplotlib import pyplot +import numpy as np +from pyrealm.pmodel import PModel, PModelEnvironment + +%matplotlib inline + +# Set the resolution of examples +n_pts = 201 + +# Create a range of representative values for key inputs. +tc_1d = np.linspace(-25, 50, n_pts) +``` + +Environmental conditions can also lead to limitation of both the electron transfer rate +and the carboxylation capacity ($V_{cmax}$) of leaves. The +{class}`~pyrealm.pmodel.pmodel.PModel` implements three alternative approaches to the +calculation of $J_{max}$ and $V_{cmax}$, using the argument `method_jmaxlim`. These +options set the calculation of two factor ($f_j$ and $f_v$) which are applied to the +calculation of $J_{max}$ and $V_{cmax}$. The options for this setting are: + +* `simple`: This approach implements the 'simple' formulations of the P Model, with no + limitations and hence $f_j = f_v = 1$. +* `wang17`: This is the default setting for `method_jmaxlim` and applies the + calculations describe in {cite:t}`Wang:2017go`. The calculation details can be + seen in the {meth}`~pyrealm.pmodel.jmax_limitation.JmaxLimitation.wang17` method. + +* `smith19`: This is an alternate calculation for optimal values of $J_{max}$ + and $V_{cmax}$ described in {cite:t}`Smith:2019dv`. The calculation details can be + seen in the {meth}`~pyrealm.pmodel.jmax_limitation.JmaxLimitation.smith19` method. + +The plot below shows the effects of each method on the light use efficienct across a +temperature gradient. The other forcing variables are fixed ($P=101325.0 , \ce{CO2}= 400 +\text{ppm}, \text{VPD}=820$) and $\phi_0$ is also fixed ($\phi_0=0.08$). + +```{code-cell} +:tags: [hide-input] + +# Calculate variation in m_jlim with temperature +env = PModelEnvironment(tc=tc_1d, patm=101325, vpd=820, co2=400) + +model_jmax_simple = PModel( + env, method_jmaxlim="simple", method_kphio="fixed", reference_kphio=0.08 +) +model_jmax_wang17 = PModel( + env, method_jmaxlim="wang17", method_kphio="fixed", reference_kphio=0.08 +) +model_jmax_smith19 = PModel( + env, method_jmaxlim="smith19", method_kphio="fixed", reference_kphio=0.08 +) + +# Create a line plot of the resulting values of m_j +pyplot.plot(tc_1d, model_jmax_simple.lue, label="simple") +pyplot.plot(tc_1d, model_jmax_wang17.lue, label="wang17") +pyplot.plot(tc_1d, model_jmax_smith19.lue, label="smith19") + +pyplot.title("Effects of J_max limitation") +pyplot.xlabel("Temperature °C") +pyplot.ylabel("Light Use Efficiency (g C mol-1)") +pyplot.legend() +pyplot.show() +``` diff --git a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md index 6963ef28..5bf8192f 100644 --- a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md +++ b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md @@ -5,9 +5,8 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -26,42 +25,86 @@ reference documentation](../../../api/pmodel_api) ## Overview -The P Model is a model of carbon capture and water use by plants. Four core forcing -variables are used to define the environment that the plant experiences: +The P Model is a model of carbon capture and water use by plants. There are four core +forcing variables that define the photosynthetic environment of the plant, although +extensions of the basic P model may add other required variables such as aridity or soil +moisture content. The four core variables are: * temperature (`tc`, °C), * vapor pressure deficit (`vpd`, Pa), * atmospheric $\ce{CO2}$ concentration (`co2`, ppm), and * atmospheric pressure (`patm`, Pa). -From these inputs, the model breaks down into the following broad stages, each of which -is described in more detail in the link for each stage - -The main steps are: - -* Calculation of the [photosynthetic environment](photosynthetic_environment). The - environmental variables are used to calculate four key variables describing the - photosynthetic environment of a plant. - -* Calculation of [leaf $\ce{CO2}$ variables](../pmodel_details/optimal_chi). The - photosynthetic environment is then used to calculate the optimal ratio of internal to - external CO2 concentration ($chi$), along with $\ce{CO2}$ partial pressures and - limitation factors. This step also governs the main differences between C3 and C4 - photosynthesis. - -* Determination of the temperature sensitivity of the [quantum yield - efficiency](quantum_yield) of photosynthesis (`kphio`, $\phi_0$). - -* Theoretical limitations to the maximum rates of Rubsico regeneration $J_{max}$) and - maximum carboxylation capacity ($V_{cmax}$) that can act as further constraints on - [light use efficiency (LUE)](lue_limitation). - -* Different approaches to incorporating effects of [soil moisture stress](soil_moisture) - on productivity. - -* Estimation of [gross primary productivity](estimating-productivity). Once LUE has been - calculated, estimates of absorbed photosynthetically active radiation, can be used to - predict gross primary productivity (GPP) and other key rates within the leaf. +Those forcing variables are then used to calculate four further variables that capture +the [photosynthetic environment](photosynthetic_environment) of a leaf. These are the: + +1. photorespiratory compensation point ($\Gamma^*$), +2. Michaelis-Menten coefficient for photosynthesis ($K_{mm}$), +3. relative viscosity of water, given a standard at 25°C ($\eta^*$), and +4. partial pressure of $\ce{CO2}$ in ambient air ($c_a$). + +A P Model can then be fitted to calculate the expected optimal light use efficiency +given the environment. The basic calculation captures how many moles of carbon can be +captured for each mole of photosynthetically active photons captured by the leaf. In its +simplest form, this equation is: + +$$ + \text{LUE} = M_C \cdot \phi_0 \cdot m_j, +$$ + +where $M_C$ is the molar mass of carbon and $\phi_0$ (the quantum yield efficiency of +photosynthesis) captures how many moles of photons are required to capture one mole of +carbon dioxide. + +The term $m_j$ is at the heart of the P model and describes the trade off between +[carbon dioxide capture and water loss in photosynthesis](optimal_chi). Given the +environmental conditions, a leaf will adjust its stomata, setting a ratio of internal to +external carbon dioxide partial pressure ($\chi$) that optimises this trade off. Under +adverse conditions, this limits the partial pressure of carbon dioxide within the leaf +($c_i = c_a \chi$) and gives rise to the limitation term $m_j$ that describes the +resulting loss in light use efficiency. This calculation also allows the P model to +estimate the intrinsic water use efficiency of photosynthesis (IWUE) as micromoles per +mole of photons. + +There are several methods for the calculation of $\chi$ and $m_j$, which are selected +using the `method_optchi` argument when fitting a P model. These methods specify the +choice of C3 and C4 photosynthetic pathways but also environmental modification of +optimal $\chi$. + +The P model can optionally include further limitations to the light use efficiency of +photosynthesis. The extended equation shows two modifications: + +$$ + \text{LUE} = M_C \cdot \phi_0(E) \cdot m_j \cdot f_J +$$ + +* The function $\phi_0(E)$ captures methods that introduce environmental modulation of + the [quantum yield efficiency of photosynthesis](quantum_yield), such as variation in + $\phi_0$ with air temperature. These methods are selected using the `method_kphio` + argument when fitting a P model. + +* The additional term $f_j$ describes further [limitation of the electron transfer + rate](jmax_limitation) of photosynthesis ($J_{max}$ limitation). These methods are + selected using the `method_jmaxlim` argument when fitting a P model. + +:::{warning} + +Several of the approaches implemented within the P model in `pyrealm` may be estimating +the same underlying environmental limitation of light use efficiency via different +pathways. As an example, there are multiple approaches to incorporating effects of [soil +moisture stress](soil_moisture) on productivity, via modulation of $\phi_0$, $m_j$ and +the calculation of GPP penalty factors. + +Some combinations of methods may therefore result in multiple corrections for the same +limitation. At present, `pyrealm` does not automatically detect such over-correction, so +take care when selecting methods for fitting the P model. + +::: + +As a final stage, once the LUE has been calculated, then estimates of the actual +absorbed irradiance for a section of canopy (µmols of photons per m2 per second) can +then be used to estimate [gross primary productivity](estimating-productivity) (GPP) and +other key rates within the leaf. ## Variable graph diff --git a/docs/source/users/pmodel/pmodel_details/lue_limitation.md b/docs/source/users/pmodel/pmodel_details/quantum_yield.md similarity index 71% rename from docs/source/users/pmodel/pmodel_details/lue_limitation.md rename to docs/source/users/pmodel/pmodel_details/quantum_yield.md index 4a2ee005..92a3ae8c 100644 --- a/docs/source/users/pmodel/pmodel_details/lue_limitation.md +++ b/docs/source/users/pmodel/pmodel_details/quantum_yield.md @@ -5,14 +5,13 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- -# LUE Limitation +# Quantum yield efficiency of photosynthesis ```{code-cell} :tags: [hide-input] @@ -45,36 +44,6 @@ meanalpha_2d = np.broadcast_to(meanalpha_1d, (n_pts, n_pts)) co2_2d = np.broadcast_to(co2_1d, (n_pts, n_pts)) ``` -Once key [photosynthetic parameters](photosynthetic_environment) and [optimal -chi](optimal_chi.md) have been calculated, the {class}`~pyrealm.pmodel.pmodel.PModel` -class can report estimates of: - -* the light use efficiency (LUE), as grams of carbon per mole of photons, and -* the intrinsic water use efficiency (IWUE), as micromoles per mole of photons. - -## Light use efficiency - -In its simplest form: - -$$ - \text{LUE} = \phi_0 \cdot M_C \cdot m_j -$$ - -where $\phi_0$ is the quantum yield efficiency of photosynthesis, $M_C$ is the molar -mass of carbon and $m_j$ is the $\ce{CO2}$ limitation term of light use efficiency from -the calculation of optimal $\chi$. However, the light use efficiency may be adjusted by -different approaches to estimation of $\phi_0$ and limitation of $J_{max}$, adding -terms for: - -* method dependent modulation of $\phi_0$ ($\phi_0^{\prime}$), and -* $J_{max}$ limitation of $m_j$ by a factor $f_v$. - -$$ - \text{LUE} = \phi_0^{\prime} \cdot M_C \cdot m_j \cdot f_v -$$ - -### Quantum yield efficiency of photosynthesis - :::{warning} Note that $\phi_0$ is also sometimes used to refer to the quantum yield of electron @@ -102,7 +71,7 @@ currently implemented approaches are described below. Note that each approach ha specific **reference value for $\phi_{0}$**, which is used as the baseline for further calculations. This value can be altered via the `reference_kphio` argument. -### Temperature dependent $\phi_0$ +## Temperature dependent $\phi_0$ The default approach (`method_kphio='temperature'`) applies a temperature dependent estimate of $\phi_0$, following {cite:t}`Bernacchi:2003dc` for C3 plants and @@ -130,7 +99,7 @@ pyplot.legend() pyplot.show() ``` -#### Fixed $\phi_0$ +## Fixed $\phi_0$ This approach (`method_kphio='fixed'`) applies a fixed value of $\phi_0$ in the calculation of light use efficiency. The default reference value used in this case is @@ -171,7 +140,7 @@ pyplot.ylabel("LUE") pyplot.show() ``` -#### Temperature and aridity effects on $\phi_0$ +## Temperature and aridity effects on $\phi_0$ The option `method_kphio='sandoval'` implements an experimental calculation {cite}`sandoval:in_prep` of $\phi_0$ as a function of a local aridity index (P/PET), the @@ -189,6 +158,8 @@ $\phi_{0A} = \dfrac{\phi_{0R}}{(1 + \textrm{AI}^m) ^ n}$ This captures a decrease in maximum $\phi_0$ in arid conditions, as shown below. ```{code-cell} +:tags: [hide-input] + n_vals = 51 aridity_index = np.logspace(-2, 1.5, num=n_vals) @@ -218,6 +189,8 @@ mean growth temperature ($T_g$) in a location. The plot below shows how aridity growth temperature interact to change the location and height of the peak $\phi_0$. ```{code-cell} +:tags: [hide-input] + n_vals = 51 mean_growth_values = np.array([10, 22, 24, 25]) aridity_values = np.array([1.0, 1.5, 5.0]) @@ -257,58 +230,3 @@ for ai_idx, (ax, ai_val) in enumerate(zip(axes, aridity_values)): ax.legend(frameon=False) pyplot.show() ``` - -### Limitation of electron transfer rate ($J_{max}$) and carboxylation capacity ($V_{cmax}$) - -The {class}`~pyrealm.pmodel.pmodel.PModel` implements three alternative approaches to -the calculation of $J_{max}$ and $V_{cmax}$, using the argument -`method_jmaxlim`. These options set the calculation of two factor ($f_j$ and -$f_v$) which are applied to the calculation of $J_{max}$ and $V_{cmax}$. The -options for this setting are: - -* `simple`: These are the 'simple' formulations of the P Model, with $f_j = f_v - = 1$. -* `wang17`: This is the default setting for `method_jmaxlim` and applies the - calculations describe in {cite:t}`Wang:2017go`. The calculation details can be - seen in the {meth}`~pyrealm.pmodel.jmax_limitation.JmaxLimitation.wang17` method. - -* `smith19`: This is an alternate calculation for optimal values of $J_{max}$ - and $V_{cmax}$ described in {cite:t}`Smith:2019dv`. The calculation details can be - seen in the {meth}`~pyrealm.pmodel.jmax_limitation.JmaxLimitation.smith19` method. - -+++ - -The plot below shows the effects of each method on the LUE across a temperature -gradient ($P=101325.0 , \ce{CO2}= 400 \text{ppm}, \text{VPD}=820$) and fixed $\phi_0=0.08$. - -```{code-cell} -:tags: [hide-input] - -# Calculate variation in m_jlim with temperature -env = PModelEnvironment(tc=tc_1d, patm=101325, vpd=820, co2=400) - -model_jmax_simple = PModel( - env, method_jmaxlim="simple", method_kphio="fixed", reference_kphio=0.08 -) -model_jmax_wang17 = PModel( - env, method_jmaxlim="wang17", method_kphio="fixed", reference_kphio=0.08 -) -model_jmax_smith19 = PModel( - env, method_jmaxlim="smith19", method_kphio="fixed", reference_kphio=0.08 -) - -# Create a line plot of the resulting values of m_j -pyplot.plot(tc_1d, model_jmax_simple.lue, label="simple") -pyplot.plot(tc_1d, model_jmax_wang17.lue, label="wang17") -pyplot.plot(tc_1d, model_jmax_smith19.lue, label="smith19") - -pyplot.title("Effects of J_max limitation") -pyplot.xlabel("Temperature °C") -pyplot.ylabel("Light Use Efficiency (g C mol-1)") -pyplot.legend() -pyplot.show() -``` - -```{code-cell} - -``` diff --git a/pyproject.toml b/pyproject.toml index 772a40c7..7110af96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -140,3 +140,9 @@ ignore = [ [tool.ruff.lint.pydocstyle] convention = "google" + +[tool.jupytext] +# Stop jupytext from removing mystnb and other settings in MyST Notebook YAML headers +notebook_metadata_filter = "-jupytext.text_representation.jupytext_version,settings,mystnb" +# Also stop it from stripping cell metadata. +cell_metadata_filter = "all" \ No newline at end of file From 3239cb4a48046363367c34d75bbdf73bb8c86188 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 7 Aug 2024 12:30:13 +0100 Subject: [PATCH 21/28] Fixing issue with typed but not populated attributes in summarise --- pyrealm/pmodel/pmodel_environment.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/pyrealm/pmodel/pmodel_environment.py b/pyrealm/pmodel/pmodel_environment.py index 14ff2f9b..34722c4c 100644 --- a/pyrealm/pmodel/pmodel_environment.py +++ b/pyrealm/pmodel/pmodel_environment.py @@ -211,7 +211,17 @@ def summarize(self, dp: int = 2) -> None: ("ns_star", "-"), ] - if self.theta is not None: - attrs += [("theta", "m3/m3")] + # Add any optional variables - need to check here if these attributes actually + # exist because if they are not provided they are typed but not populated. + optional_vars = [ + ("theta", "m3/m3"), + ("rootzonestress", "-"), + ("aridity_index", "-"), + ("mean_growth_temperature", "°C"), + ] + + for opt_var, unit in optional_vars: + if getattr(self, opt_var, None) is not None: + attrs += [(opt_var, unit)] summarize_attrs(self, attrs, dp=dp) From ed5ad067092144fe5fe6b7db688a5a5436873a04 Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 7 Aug 2024 12:46:06 +0100 Subject: [PATCH 22/28] Doc fixing and notebook updates --- docs/source/_toc.yml | 3 +- docs/source/api/pmodel_api.md | 9 ++++- docs/source/refs.bib | 8 ++++ docs/source/users/pmodel/module_overview.md | 8 ++-- .../pmodel/pmodel_details/pmodel_overview.md | 8 ++-- .../pmodel/pmodel_details/worked_examples.md | 27 +++++++++++++- .../subdaily_details/subdaily_calculations.md | 3 +- .../pmodel/subdaily_details/worked_example.md | 37 ++++++++++++++++--- pyrealm/pmodel/pmodel.py | 2 - 9 files changed, 84 insertions(+), 21 deletions(-) diff --git a/docs/source/_toc.yml b/docs/source/_toc.yml index 51ceec9f..1c587e0e 100644 --- a/docs/source/_toc.yml +++ b/docs/source/_toc.yml @@ -12,7 +12,8 @@ subtrees: - file: users/pmodel/pmodel_details/worked_examples.md - file: users/pmodel/pmodel_details/photosynthetic_environment.md - file: users/pmodel/pmodel_details/optimal_chi.md - - file: users/pmodel/pmodel_details/lue_limitation.md + - file: users/pmodel/pmodel_details/quantum_yield.md + - file: users/pmodel/pmodel_details/jmax_limitation.md - file: users/pmodel/pmodel_details/envt_variation_outputs.md - file: users/pmodel/pmodel_details/soil_moisture.md - file: users/pmodel/pmodel_details/extreme_values.md diff --git a/docs/source/api/pmodel_api.md b/docs/source/api/pmodel_api.md index ca593ae4..9bb4f669 100644 --- a/docs/source/api/pmodel_api.md +++ b/docs/source/api/pmodel_api.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python @@ -50,6 +49,14 @@ kernelspec: :members: ``` +## The {mod}`~pyrealm.pmodel.quantum_yield` submodule + +```{eval-rst} +.. automodule:: pyrealm.pmodel.quantum_yield + :autosummary: + :members: +``` + ## The {mod}`~pyrealm.pmodel.functions` submodule ```{eval-rst} diff --git a/docs/source/refs.bib b/docs/source/refs.bib index 75945668..f7dc458b 100644 --- a/docs/source/refs.bib +++ b/docs/source/refs.bib @@ -705,6 +705,14 @@ @article{Prentice:2014bc file = {/Users/dorme/Zotero/storage/MDRBDVTF/Ecol Lett 2014 Prentice.pdf} } +@article{sandoval:in_prep, + title = {Aridity and Growth Temperature Effects on Phio Manuscript}, + author = {Sandoval, David}, + year = {in\_prep}, + volume = {?}, + pages = {??-??} +} + @article{Smith:2019dv, title = {Global Photosynthetic Capacity Is Optimized to the Environment}, author = {Smith, Nicholas G and Keenan, Trevor F and Colin Prentice, I and Wang, Han and Wright, Ian J. and Niinemets, {\"U}lo and Crous, Kristine Y and Domingues, Tomas F and Guerrieri, Rossella and Yoko Ishida, F and Kattge, Jens and Kruger, Eric L and Maire, Vincent and Rogers, Alistair and Serbin, Shawn P and Tarvainen, Lasse and Togashi, Henrique F and Townsend, Philip A and Wang, Meng and Weerasinghe, Lasantha K and Zhou, Shuang Xi}, diff --git a/docs/source/users/pmodel/module_overview.md b/docs/source/users/pmodel/module_overview.md index 5be8782d..04c29687 100644 --- a/docs/source/users/pmodel/module_overview.md +++ b/docs/source/users/pmodel/module_overview.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python @@ -27,8 +26,11 @@ ecophysiological model of optimal carbon dioxide uptake by plants * Details of calculations of: * the [photosynthetic_environment](pmodel_details/photosynthetic_environment), * [optimal chi](pmodel_details/optimal_chi) values, - * limits on [light use efficiency](pmodel_details/lue_limitation), and - * the estimation of [gross primary productivity](pmodel_details/envt_variation_outputs.md#estimating-productivity). + * estimation of quantum yield efficiency (pmodel_details/quantum_yield), + * estimation of [electron transfer rate limitation](pmodel_details/jmax_limitation), + and + * the estimation of [gross primary + productivity](pmodel_details/envt_variation_outputs.md#estimating-productivity). * Approaches to the impacts of [soil moisture stress](pmodel_details/soil_moisture). * The behaviour of P Model equations with [extreme forcing diff --git a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md index 5bf8192f..9626ee98 100644 --- a/docs/source/users/pmodel/pmodel_details/pmodel_overview.md +++ b/docs/source/users/pmodel/pmodel_details/pmodel_overview.md @@ -57,7 +57,7 @@ photosynthesis) captures how many moles of photons are required to capture one m carbon dioxide. The term $m_j$ is at the heart of the P model and describes the trade off between -[carbon dioxide capture and water loss in photosynthesis](optimal_chi). Given the +[carbon dioxide capture and water loss in photosynthesis](./optimal_chi). Given the environmental conditions, a leaf will adjust its stomata, setting a ratio of internal to external carbon dioxide partial pressure ($\chi$) that optimises this trade off. Under adverse conditions, this limits the partial pressure of carbon dioxide within the leaf @@ -79,12 +79,12 @@ $$ $$ * The function $\phi_0(E)$ captures methods that introduce environmental modulation of - the [quantum yield efficiency of photosynthesis](quantum_yield), such as variation in - $\phi_0$ with air temperature. These methods are selected using the `method_kphio` + the [quantum yield efficiency of photosynthesis](./quantum_yield), such as variation + in $\phi_0$ with air temperature. These methods are selected using the `method_kphio` argument when fitting a P model. * The additional term $f_j$ describes further [limitation of the electron transfer - rate](jmax_limitation) of photosynthesis ($J_{max}$ limitation). These methods are + rate](./jmax_limitation) of photosynthesis ($J_{max}$ limitation). These methods are selected using the `method_jmaxlim` argument when fitting a P model. :::{warning} diff --git a/docs/source/users/pmodel/pmodel_details/worked_examples.md b/docs/source/users/pmodel/pmodel_details/worked_examples.md index 4fe2823a..f3db6947 100644 --- a/docs/source/users/pmodel/pmodel_details/worked_examples.md +++ b/docs/source/users/pmodel/pmodel_details/worked_examples.md @@ -5,9 +5,8 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -40,6 +39,8 @@ The example shows the steps required using a single site with: ### Estimate photosynthetic environment ```{code-cell} +:trusted: true + from importlib import resources from matplotlib import pyplot as plt @@ -60,10 +61,14 @@ terse - just the shape of the data - but the more detailed summary of the attributes. ```{code-cell} +:trusted: true + env ``` ```{code-cell} +:trusted: true + env.summarize() ``` @@ -73,6 +78,8 @@ Next, the P Model can be fitted to the photosynthetic environment using the ({class}`~pyrealm.pmodel.pmodel.PModel`) class: ```{code-cell} +:trusted: true + model = PModel(env) ``` @@ -80,6 +87,8 @@ The returned model object holds a lot of information. The representation of the model object shows a terse display of the settings used to run the model: ```{code-cell} +:trusted: true + model ``` @@ -90,6 +99,8 @@ photosynthetic efficiency: the intrinsic water use efficiency (``iwue``) and the use efficiency (``lue``). ```{code-cell} +:trusted: true + model.summarize() ``` @@ -102,6 +113,8 @@ This object also has a {meth}`~pyrealm.pmodel.optimal_chi.OptimalChiABC.summariz method: ```{code-cell} +:trusted: true + model.optchi.summarize() ``` @@ -118,6 +131,8 @@ Here we are using: * a PPFD of 834 µmol m-2 s-1. ```{code-cell} +:trusted: true + model.estimate_productivity(fapar=0.91, ppfd=834) model.summarize() ``` @@ -150,6 +165,8 @@ to be the same size so some of the variables have repeated data across dimension * Elevation is constant across months, so the data for each month is repeated. ```{code-cell} +:trusted: true + # Load an example dataset containing the forcing variables. data_path = resources.files("pyrealm_build_data.rpmodel") / "pmodel_global.nc" ds = xarray.load_dataset(data_path) @@ -169,6 +186,8 @@ data to atmospheric pressure, and then this is used to set the photosynthetic environment for the model: ```{code-cell} +:trusted: true + # Convert elevation to atmospheric pressure patm = calc_patm(elev) @@ -187,6 +206,8 @@ That environment can then be run to calculate the P model predictions for light efficiency: ```{code-cell} +:trusted: true + # Run the P model model = PModel(env) @@ -200,6 +221,8 @@ Finally, the light use efficiency can be used to calculate GPP given the photosynthetic photon flux density and fAPAR. ```{code-cell} +:trusted: true + # Scale the outputs from values per unit iabs to realised values model.estimate_productivity(fapar, ppfd) diff --git a/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md b/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md index 661fd643..21664b8b 100644 --- a/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md +++ b/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python @@ -78,7 +77,7 @@ subdaily_env = PModelEnvironment( ) # Fit the standard P Model -pmodel_standard = PModel(subdaily_env, kphio=1 / 8) +pmodel_standard = PModel(subdaily_env, method_kphio="fixed", reference_kphio=1 / 8) pmodel_standard.estimate_productivity(ppfd=ppfd_subdaily, fapar=fapar_subdaily) pmodel_standard.summarize() ``` diff --git a/docs/source/users/pmodel/subdaily_details/worked_example.md b/docs/source/users/pmodel/subdaily_details/worked_example.md index e53936e5..fac12b6a 100644 --- a/docs/source/users/pmodel/subdaily_details/worked_example.md +++ b/docs/source/users/pmodel/subdaily_details/worked_example.md @@ -5,9 +5,8 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -15,6 +14,8 @@ kernelspec: # Worked example of the Subdaily P Model ```{code-cell} +:trusted: true + from importlib import resources import xarray @@ -60,6 +61,8 @@ The test data use some UK WFDE data for three sites in order to compare predicti over a time series. ```{code-cell} +:trusted: true + # Loading the example dataset: dpath = ( resources.files("pyrealm_build_data.uk_data") / "UK_WFDE5_FAPAR_2018_JuneJuly.nc" @@ -81,6 +84,8 @@ The WFDE data need some conversion for use in the PModel, along with the definit the atmospheric CO2 concentration. ```{code-cell} +:trusted: true + # Variable set up # Air temperature in °C from Tair in Kelvin tc = (ds["Tair"] - 273.15).to_numpy() @@ -100,6 +105,8 @@ co2 = np.ones_like(tc) * 400 The code below then calculates the photosynthetic environment. ```{code-cell} +:trusted: true + # Generate and check the PModelEnvironment pm_env = PModelEnvironment(tc=tc, patm=patm, vpd=vpd, co2=co2) pm_env.summarize() @@ -111,14 +118,22 @@ The standard implementation of the P Model used below assumes that plants can instantaneously adopt optimal behaviour. ```{code-cell} +:trusted: true + # Standard PModels -pmodC3 = PModel(env=pm_env, kphio=1 / 8, method_optchi="prentice14") +pmodC3 = PModel( + env=pm_env, method_kphio="fixed", reference_kphio=1 / 8, method_optchi="prentice14" +) pmodC3.estimate_productivity(fapar=fapar, ppfd=ppfd) pmodC3.summarize() ``` ```{code-cell} -pmodC4 = PModel(env=pm_env, kphio=1 / 8, method_optchi="c4_no_gamma") +:trusted: true + +pmodC4 = PModel( + env=pm_env, method_kphio="fixed", reference_kphio=1 / 8, method_optchi="c4_no_gamma" +) pmodC4.estimate_productivity(fapar=fapar, ppfd=ppfd) pmodC4.summarize() ``` @@ -132,6 +147,8 @@ calculations: essentially the plant does not acclimate until the optimal values calculated again to update those realised estimates. ```{code-cell} +:trusted: true + # Set the acclimation window to an hour either side of noon fsscaler = SubdailyScaler(datetimes) fsscaler.set_window( @@ -142,7 +159,8 @@ fsscaler.set_window( # Fit C3 and C4 with the new implementation subdailyC3 = SubdailyPModel( env=pm_env, - kphio=1 / 8, + method_kphio="fixed", + reference_kphio=1 / 8, method_optchi="prentice14", fapar=fapar, ppfd=ppfd, @@ -152,7 +170,8 @@ subdailyC3 = SubdailyPModel( ) subdailyC4 = SubdailyPModel( env=pm_env, - kphio=1 / 8, + method_kphio="fixed", + reference_kphio=1 / 8, method_optchi="c4_no_gamma", fapar=fapar, ppfd=ppfd, @@ -169,6 +188,8 @@ shown above and plots the instantaneous predictions against predictions includin photosynthetic responses. ```{code-cell} +:trusted: true + # Store the predictions in the xarray Dataset to use indexing ds["GPP_pmodC3"] = (ds["Tair"].dims, pmodC3.gpp) ds["GPP_subdailyC3"] = (ds["Tair"].dims, subdailyC3.gpp) @@ -226,6 +247,8 @@ The subdaily models can also be obtained directly from the standard models, usin `convert_pmodel_to_subdaily` method: ```{code-cell} +:trusted: true + # Convert standard C3 model converted_C3 = convert_pmodel_to_subdaily( pmodel=pmodC3, @@ -247,6 +270,8 @@ This produces the same outputs as the `SubdailyPModel` class, but is convenient compact when the two models are going to be compared. ```{code-cell} +:trusted: true + # Models have identical GPP - maximum absolute difference is zero. print(np.nanmax(abs(subdailyC3.gpp.flatten() - converted_C3.gpp.flatten()))) print(np.nanmax(abs(subdailyC4.gpp.flatten() - converted_C4.gpp.flatten()))) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 8db8b81e..354a4e9f 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -137,8 +137,6 @@ class PModel: efficiency of photosynthesis (:math:`\phi_0`, -) to be passed to the kphio calculation method. - - Examples: >>> import numpy as np >>> from pyrealm.pmodel.pmodel_environment import PModelEnvironment From 387d3c959ae832cdf5cf42d98c2103b3184cfd4b Mon Sep 17 00:00:00 2001 From: David Orme Date: Wed, 7 Aug 2024 13:56:45 +0100 Subject: [PATCH 23/28] Docstring update for quantum yield --- pyrealm/pmodel/quantum_yield.py | 97 +++++++++++++++++++++++++++------ 1 file changed, 81 insertions(+), 16 deletions(-) diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index a3f3a6c6..2ed797f6 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -1,9 +1,11 @@ -r"""The module :mod:`~pyrealm.pmodel.quantum_yield` provides -the abstract base class :class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC`, which is -used to support different implementations of the calculation of the intrinsic quantum -yield efficiency of photosynthesis (:math:`\phi_0`, unitless). Note that :math:`\phi_0` -is sometimes used to refer to the quantum yield of electron transfer, which is exactly -four times larger, so check definitions here. +r"""The module :mod:`~pyrealm.pmodel.quantum_yield` provides the abstract base class +:class:`~pyrealm.pmodel.quantum_yield.QuantumYieldABC`, which is used to support +different implementations of the calculation of the intrinsic quantum yield efficiency +of photosynthesis (:math:`\phi_0`, unitless). The module then provides subclasses of the +ABC implementing different approaches. + +Note that :math:`\phi_0` is sometimes used to refer to the quantum yield of electron +transfer, which is exactly four times larger, so check definitions here. """ # noqa D210, D415 from __future__ import annotations @@ -34,7 +36,7 @@ .. code:: python - bianchi_phi_0 = QUANTUM_YIELD_CLASS_REGISTRY['bianchi'] + temperature_phio = QUANTUM_YIELD_CLASS_REGISTRY['temperature'] """ @@ -51,12 +53,46 @@ class QuantumYieldABC(ABC): ``kphio`` and is automatically called by the ``__init__`` method when a subclass instance is created. + Subclasses must define several class attributes when created: + + .. code:: python + + class QuantumYieldFixed( + QuantumYieldABC, + method="method_name", + requires=["an_environment_variable"], + default_reference_kphio=0.049977, + array_reference_kphio_ok=True, + ): + + * The ``method`` argument sets the name of the method, which can then be used to + select the implemented class from the + :data:`~pyrealm.pmodel.quantum_yield.QUANTUM_YIELD_CLASS_REGISTRY`. + * The `requires` argument sets a list of variables that must be present in the + :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` to use this + approach. The core ``tc``, ``vpd``, ``patm`` and ``co2`` variables do not need to + be included in this list. + * The ``default_reference_kphio`` argument sets the default value for :math:`\phi_0` + that will be used by the implementation. The ``__init__`` method will then check + whether array values are accepted and that the shape of an array is congruent with + the other data. + * The ``array_reference_kphio_ok`` argument sets whether the method can accept an + array of :math:`\phi_0` values or whether a single global reference value should + be used. + + The definition of the ``_calculate_kphio`` method for subclasses can also provide C3 + and C4 implementations for calculate :math:`\phi_0` - or possibly raise an error for + one pathway - using the ``use_c4`` attribute. + Args: env: An instance of :class:`~pyrealm.pmodel.pmodel_environment.PModelEnvironment` providing the photosynthetic environment for the model. - pmodel_const: An instance of - :class:`~pyrealm.constants.pmodel_const.PModelConst`. + reference_kphio: An optional value to be used instead of the default reference + kphio for the subclass. This is typically a single float but some approaches + may support an array of values here. + use_c4: Should the calculation use parameterisation for C4 photosynthesis rather + than C3 photosynthesis. Returns: Instances of the abstract base class should not be created - use instances of @@ -184,10 +220,18 @@ class QuantumYieldFixed( default_reference_kphio=0.049977, array_reference_kphio_ok=True, ): - """Constant kphio.""" + r"""Apply a fixed value for :math:`\phi_0`. + + This implementation applies a fixed value for the quantum yield without any + environmental variation. The default value used is :math:`\phi_0 = 0.049977`, + following the ORG settings parameterisation in Table 1. of {cite:t}`Stocker:2020dh`. + + This implementation will accept an array of values to allow externally estimated + values to be passed to a P model. + """ def _calculate_kphio(self) -> None: - """Constant kphio.""" + """Set fixed kphio.""" self.kphio = self.reference_kphio @@ -199,9 +243,23 @@ class QuantumYieldTemperature( default_reference_kphio=0.081785, array_reference_kphio_ok=False, ): - """Calculate temperature modulated kphio. + r"""Calculate temperature dependent of quantum yield efficiency. + + This implementation calculates temperature dependent quantum yield efficiency, as a + quadratic function of temperature (:math:`T`). - This method follows for C3 plants. + .. math:: + + \phi(T) = a + b T - c T^2 + + The values of :math:`a, b, c` are dependent on whether :math:`\phi_0` is being + estimated for C3 or C4 photosynthesis. For C3 photosynthesis, the default values use + the temperature dependence of the maximum quantum yield of photosystem II in + light-adapted tobacco leaves determined by :cite:t:`Bernacchi:2003dc`. For C4 + photosynthesis, the default values are taken from :cite:t:`cai:2020a`. + + The default reference value for this approach is :math:`\phi_0 = 0.081785` following + the BRC parameterisation in Table 1. of {cite:t}`Stocker:2020dh`. """ def _calculate_kphio( @@ -229,10 +287,17 @@ class QuantumYieldSandoval( default_reference_kphio=1.0 / 9.0, array_reference_kphio_ok=False, ): - """Calculate kphio following Sandoval. + r"""Calculate aridity and mean growth temperature effects on quantum yield. + + This experimental approach implements the method of :cite:t:`sandoval:in_prep`. This + approach modifies the maximum possible :math:`\phi_0` as a function of the + climatological aridity index. It then also adjusts the temperature at which the + highest :math:`\phi_0` can be attained as a function of the mean growth temperature + for an observation. It then calculates the expected :math:`\phi_0` as a function of + temperature via a modified Arrhenius relationship. - Reference kphio is the theoretical maximum quantum yield, defaulting to the ratio of - 1/9 in the absence of a Q cycle (Long, 1993). + The reference kphio for this approach is the theoretical maximum quantum yield, + defaulting to the ratio of 1/9 in the absence of a Q cycle :cite:`long:1993a`. """ def peak_quantum_yield(self, aridity: NDArray) -> NDArray: From 67b1e04784f24acbf1731159bacc39a2ed934126 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 8 Aug 2024 10:38:36 +0100 Subject: [PATCH 24/28] Replacing and removing the calc_ftemp_kphio and calc_ftemp_inst_vcmax functions --- pyrealm/constants/pmodel_const.py | 40 +++++----- pyrealm/pmodel/__init__.py | 2 - pyrealm/pmodel/functions.py | 101 ++++-------------------- pyrealm/pmodel/pmodel.py | 26 +++--- tests/regression/pmodel/test_pmodel.py | 65 +++++++++++---- tests/unit/pmodel/test_functions_new.py | 22 ------ 6 files changed, 100 insertions(+), 156 deletions(-) diff --git a/pyrealm/constants/pmodel_const.py b/pyrealm/constants/pmodel_const.py index 817636e0..ea95ca16 100644 --- a/pyrealm/constants/pmodel_const.py +++ b/pyrealm/constants/pmodel_const.py @@ -22,12 +22,8 @@ class PModelConst(ConstantsClass): (:attr:`~pyrealm.constants.pmodel_const.PModelConst.heskel_b`, :attr:`~pyrealm.constants.pmodel_const.PModelConst.heskel_c`) - * **Temperature and entropy of VCMax**. Values taken from Table 3 of - :cite:t:`Kattge:2007db` - (:attr:`~pyrealm.constants.pmodel_const.PModelConst.kattge_knorr_a_ent`, - :attr:`~pyrealm.constants.pmodel_const.PModelConst.kattge_knorr_b_ent`, - :attr:`~pyrealm.constants.pmodel_const.PModelConst.kattge_knorr_Ha`, - :attr:`~pyrealm.constants.pmodel_const.PModelConst.kattge_knorr_Hd`) + * **Enzyme kinetics for VCMax**. Values taken from Table 3 of + :cite:t:`Kattge:2007db`. * **Scaling of Kphio with temperature**. The parameters of quadratic functions for the temperature dependence of Kphio are: @@ -99,11 +95,11 @@ class PModelConst(ConstantsClass): 294.804, 75000.0, ) - """Enzyme kinetics parameters for estimation of kphion from mean growth temperature - in the Sandoval method for estimation of quantum yield efficiency. Values are: the - intercept and slope of activation entropy as a function of the mean growth - temperature (J/mol/K), the deactivation energy constant (J/mol) and the activation - energy (J/mol). """ + """Enzyme kinetics parameters for estimation of kphio from mean growth temperature + in the Sandoval method :cite:t:`Sandoval:in_prep` for estimation of quantum yield + efficiency. Values are: the intercept and slope of activation entropy as a function + of the mean growth temperature (J/mol/K), the deactivation energy constant (J/mol) + and the activation energy (J/mol). """ plant_T_ref: float = 25.0 """Standard baseline reference temperature of photosynthetic processes (Prentice, @@ -115,17 +111,17 @@ class PModelConst(ConstantsClass): heskel_c: float = 0.0005 """Quadratic coefficient of scaling of dark respiration (:math:`c`, 0.0005)""" - # KattgeKnorr - kattge_knorr_a_ent: float = 668.39 - """Offset of entropy vs. temperature relationship (:math:`a_{ent}`, 668.39, - J/mol/K)""" - kattge_knorr_b_ent: float = -1.07 - """Slope of entropy vs. temperature relationship (:math:`b_{ent}`, -1.07, - J/mol/K^2)""" - kattge_knorr_Ha: float = 71513 - """Activation energy (:math:`H_a`, 71513, J/mol)""" - kattge_knorr_Hd: float = 200000 - """Deactivation energy (:math:`H_d`, 200000, J/mol)""" + # KattgeKnorr vcmax + kattge_knorr_kinetics: tuple[float, float, float, float] = ( + 668.39, + -1.07, + 71513, + 200000, + ) + """Enzyme kinetics parameters for estimation of V_cmax25 from Table 3 + of :cite:t:`Kattge:2007db`. Values are: the intercept and slope of activation + entropy as a function of the mean growth temperature (J/mol/K), the deactivation + energy constant (J/mol) and the activation energy (J/mol). """ # Subdaily activatation energy subdaily_vcmax25_ha: float = 65330 diff --git a/pyrealm/pmodel/__init__.py b/pyrealm/pmodel/__init__.py index 96264b43..6ebdfe50 100644 --- a/pyrealm/pmodel/__init__.py +++ b/pyrealm/pmodel/__init__.py @@ -37,8 +37,6 @@ calc_co2_to_ca, calc_ftemp_arrh, calc_ftemp_inst_rd, - calc_ftemp_inst_vcmax, - calc_ftemp_kphio, calc_gammastar, calc_kmm, calc_ns_star, diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 84692690..64fdb566 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -46,9 +46,9 @@ def calc_ftemp_arrh( \] Args: - tk: Temperature (in Kelvin) + tk: Temperature (K) ha: Activation energy (in :math:`J \text{mol}^{-1}`) - tk_ref: The reference temperature for the reaction. + tk_ref: The reference temperature for the reaction (K). pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. core_const: Instance of :class:`~pyrealm.constants.core_const.CoreConst`. @@ -92,7 +92,7 @@ def calc_ftemp_inst_rd( fr = \exp( b (T_o - T) - c ( T_o^2 - T^2 )) Args: - tc: Temperature (degrees Celsius) + tc: Temperature (°C) pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. PModel Parameters: @@ -119,84 +119,6 @@ def calc_ftemp_inst_rd( ) -def calc_ftemp_inst_vcmax( - tc: NDArray, - pmodel_const: PModelConst = PModelConst(), - core_const: CoreConst = CoreConst(), -) -> NDArray: - r"""Calculate temperature scaling of :math:`V_{cmax}`. - - This function calculates the temperature-scaling factor :math:`f` of the - instantaneous temperature response of :math:`V_{cmax}`, given the temperature - (:math:`T`) relative to the standard reference temperature (:math:`T_0`), following - modified Arrhenius kinetics. - - .. math:: - - V = f V_{ref} - - The value of :math:`f` is given by :cite:t:`Kattge:2007db` (Eqn 1) as: - - .. math:: - - f = g(T, H_a) \cdot - \frac{1 + \exp( (T_0 \Delta S - H_d) / (T_0 R))} - {1 + \exp( (T \Delta S - H_d) / (T R))} - - where :math:`g(T, H_a)` is a regular Arrhenius-type temperature response function - (see :func:`~pyrealm.pmodel.functions.calc_ftemp_arrh`). The term :math:`\Delta S` - is the entropy factor, calculated as a linear function of :math:`T` in °C following - :cite:t:`Kattge:2007db` (Table 3, Eqn 4): - - .. math:: - - \Delta S = a + b T - - Args: - tc: temperature, or in general the temperature relevant for - photosynthesis (°C) - pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. - core_const: Instance of :class:`~pyrealm.constants.core_const.CoreConst`. - - PModel Parameters: - Ha: activation energy (:math:`H_a`, ``kattge_knorr_Ha``) - Hd: deactivation energy (:math:`H_d`, ``kattge_knorr_Hd``) - To: standard reference temperature expressed - in Kelvin (`T_0`, ``k_To``) - R: the universal gas constant (:math:`R`, ``k_R``) - a: intercept of the entropy factor (:math:`a`, ``kattge_knorr_a_ent``) - b: slope of the entropy factor (:math:`b`, ``kattge_knorr_b_ent``) - - Returns: - Values for :math:`f` - - Examples: - >>> # Relative change in Vcmax going (instantaneously, i.e. not - >>> # not acclimatedly) from 10 to 25 degrees (percent change): - >>> val = ((calc_ftemp_inst_vcmax(25)/calc_ftemp_inst_vcmax(10)-1) * 100) - >>> np.round(val, 4) - array([283.1775]) - """ - - # Convert temperatures to Kelvin - tkref = pmodel_const.plant_T_ref + core_const.k_CtoK - tk = tc + core_const.k_CtoK - - # Calculate entropy following Kattge & Knorr (2007): slope and intercept - # are defined using temperature in °C, not K!!! 'tcgrowth' corresponds - # to 'tmean' in Nicks, 'tc25' is 'to' in Nick's - dent = pmodel_const.kattge_knorr_a_ent + pmodel_const.kattge_knorr_b_ent * tc - fva = calc_ftemp_arrh(tk, pmodel_const.kattge_knorr_Ha) - fvb = ( - 1 - + np.exp( - (tkref * dent - pmodel_const.kattge_knorr_Hd) / (core_const.k_R * tkref) - ) - ) / (1 + np.exp((tk * dent - pmodel_const.kattge_knorr_Hd) / (core_const.k_R * tk))) - - return fva * fvb - - def calc_modified_arrhenius_factor( tk: NDArray, Ha: float | NDArray, @@ -236,11 +158,20 @@ def calc_modified_arrhenius_factor( Values for :math:`f` Examples: - >>> # Relative change in Vcmax going (instantaneously, i.e. not - >>> # not acclimatedly) from 10 to 25 degrees (percent change): - >>> val = ((calc_ftemp_inst_vcmax(25)/calc_ftemp_inst_vcmax(10)-1) * 100) + >>> # Calculate the factor for the relative rate of V_cmax at 10 degrees + >>> # compared to the rate at the reference temperature of 25°C. + >>> from pyrealm.constants import PModelConst + >>> pmodel_const = PModelConst() + >>> # Get enzyme kinetics parameters + >>> a, b, ha, hd = pmodel_const.kattge_knorr_kinetics + >>> # Calculate entropy as a function of temperature _in °C_ + >>> deltaS = a + b * 10 + >>> # Calculate the arrhenius factor + >>> val = calc_modified_arrhenius_factor( + ... tk= 283.15, Ha=ha, Hd=hd, deltaS=deltaS + ... ) >>> np.round(val, 4) - array([283.1775]) + array([0.261]) """ if mode not in ["M2002", "J1942"]: diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index 354a4e9f..ccfbd20a 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -12,10 +12,7 @@ from pyrealm.constants import CoreConst, PModelConst from pyrealm.core.utilities import check_input_shapes, summarize_attrs -from pyrealm.pmodel.functions import ( - calc_ftemp_inst_rd, - calc_ftemp_inst_vcmax, -) +from pyrealm.pmodel.functions import calc_ftemp_inst_rd, calc_modified_arrhenius_factor from pyrealm.pmodel.jmax_limitation import JmaxLimitation from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY, OptimalChiABC from pyrealm.pmodel.pmodel_environment import PModelEnvironment @@ -84,8 +81,9 @@ class PModel: * The maximum carboxylation capacity (mol C m-2) normalised to the standard temperature as: :math:`V_{cmax25} = V_{cmax} / fv(t)`, where :math:`fv(t)` is the - instantaneous temperature response of :math:`V_{cmax}` implemented in - :func:`~pyrealm.pmodel.functions.calc_ftemp_inst_vcmax` + instantaneous temperature response of :math:`V_{cmax}` calculated using a modified + Arrhenius equation + :func:`~pyrealm.pmodel.functions.calc_modified_arrhenius_factor`. * Dark respiration, calculated as: @@ -375,9 +373,19 @@ def estimate_productivity( # V_cmax self._vcmax = self.kphio.kphio * iabs * self.optchi.mjoc * self.jmaxlim.f_v - # V_cmax25 (vcmax normalized to const.k_To) - ftemp25_inst_vcmax = calc_ftemp_inst_vcmax( - self.env.tc, core_const=self.core_const, pmodel_const=self.pmodel_const + # Calculate the modified arrhenius factor to normalise V_cmax to V_cmax25 + # - Get parameters + kk_a, kk_b, kk_ha, kk_hd = self.pmodel_const.kattge_knorr_kinetics + # Calculate entropy as a function of temperature _in °C_ + kk_deltaS = kk_a + kk_b * self.env.tc + # Calculate the arrhenius factor + ftemp25_inst_vcmax = calc_modified_arrhenius_factor( + tk=self.env.tc + self.core_const.k_CtoK, + Ha=kk_ha, + Hd=kk_hd, + deltaS=kk_deltaS, + pmodel_const=self.pmodel_const, + core_const=self.core_const, ) self._vcmax25 = self._vcmax / ftemp25_inst_vcmax diff --git a/tests/regression/pmodel/test_pmodel.py b/tests/regression/pmodel/test_pmodel.py index ea1e7c10..282eb5ad 100644 --- a/tests/regression/pmodel/test_pmodel.py +++ b/tests/regression/pmodel/test_pmodel.py @@ -124,15 +124,37 @@ def test_calc_ftemp_arrh(values, tk, expvars): ], ) def test_calc_ftemp_inst_vcmax(values, tc, expvars): - """Test the calc_ftemp_inst_vcmax function.""" - from pyrealm.pmodel import calc_ftemp_inst_vcmax + """Test the calculation of values returned by calc_ftemp_inst_vcmax in rpmodel. + + This specific function was retired in favour of a more general modified arrhenius + function, but check the predictions match to the rpmodel outputs for this component. + """ + from pyrealm.constants import CoreConst, PModelConst + from pyrealm.pmodel.functions import calc_modified_arrhenius_factor + + pmodel_const = PModelConst() + core_const = CoreConst() + + kk_a, kk_b, kk_ha, kk_hd = pmodel_const.kattge_knorr_kinetics + + # Calculate entropy as a function of temperature _in °C_ + kk_deltaS = kk_a + kk_b * values[tc] + + # Calculate the arrhenius factor + ret = calc_modified_arrhenius_factor( + tk=values[tc] + core_const.k_CtoK, + Ha=kk_ha, + Hd=kk_hd, + deltaS=kk_deltaS, + pmodel_const=pmodel_const, + core_const=core_const, + ) - ret = calc_ftemp_inst_vcmax(values[tc]) assert np.allclose(ret, values[expvars]) # ------------------------------------------ -# Testing calc_ftemp_inst_vcmax - temp only +# Testing calc_ftemp_kphio - temp only # ------------------------------------------ # TODO - submit pull request to rpmodel with fix for this @@ -148,11 +170,22 @@ def test_calc_ftemp_inst_vcmax(values, tc, expvars): ], ) def test_calc_ftemp_kphio(values, tc, c4, expvars): - """Test the calc_ftemp_kphio function.""" - from pyrealm.pmodel import calc_ftemp_kphio + """Test the calc_ftemp_kphio values. - ret = calc_ftemp_kphio(tc=values[tc], c4=c4) - assert np.allclose(ret, values[expvars]) + This function in rpmodel has been replaced by the wider QuantumYield ABC framework + but make sure the outputs still align. + """ + from pyrealm.pmodel.pmodel_environment import PModelEnvironment + from pyrealm.pmodel.quantum_yield import QuantumYieldTemperature + + # Only tc is used from this environment + env = PModelEnvironment(tc=values[tc], patm=101325, vpd=820, co2=400) + + ret = QuantumYieldTemperature(env=env, use_c4=c4) + + # The QuantumYield class returns the actual kphio, not the correction factor, so + # scale back to the correction factor + assert np.allclose(ret.kphio / ret.reference_kphio, values[expvars]) # ------------------------------------------ @@ -445,23 +478,23 @@ def test_jmax_limitation( # - these have all been synchronised so that anything with type 'mx' or 'ar' # used the tc_ar input - from pyrealm.pmodel import JmaxLimitation, PModelEnvironment, calc_ftemp_kphio + from pyrealm.pmodel import JmaxLimitation, PModelEnvironment from pyrealm.pmodel.optimal_chi import OPTIMAL_CHI_CLASS_REGISTRY + from pyrealm.pmodel.quantum_yield import QuantumYieldTemperature oc_method = "c4" if c4 else "prentice14" - if not ftemp_kphio: - ftemp_kphio = 1.0 - elif tc == "tc_sc": - ftemp_kphio = calc_ftemp_kphio(tc=values[tc], c4=c4) - else: - ftemp_kphio = calc_ftemp_kphio(tc=values[tc], c4=c4) - # Optimal Chi env = PModelEnvironment( tc=values[tc], patm=values[patm], vpd=values[vpd], co2=values[co2] ) + if not ftemp_kphio: + ftemp_kphio = 1.0 + else: + kphio = QuantumYieldTemperature(env=env, use_c4=c4) + ftemp_kphio = kphio.kphio / kphio.reference_kphio + OptChiClass = OPTIMAL_CHI_CLASS_REGISTRY[oc_method] optchi = OptChiClass(env) diff --git a/tests/unit/pmodel/test_functions_new.py b/tests/unit/pmodel/test_functions_new.py index e38dbb4e..59f59a09 100644 --- a/tests/unit/pmodel/test_functions_new.py +++ b/tests/unit/pmodel/test_functions_new.py @@ -5,7 +5,6 @@ """ # D210, D415 import numpy as np -import pytest def test_calc_ftemp_arrh(tk=np.array([300]), ha=40000): @@ -22,27 +21,6 @@ def test_calc_ftemp_inst_rd(tc=np.array([30.0])): assert np.allclose(calc_ftemp_inst_rd(tc), 1.4455646406287255) -def test_calc_ftemp_inst_vcmax(tc=np.array([30.0])): - """Test calc_ftemp_inst_rd.""" - from pyrealm.pmodel.functions import calc_ftemp_inst_vcmax - - assert np.allclose(calc_ftemp_inst_vcmax(tc), 1.5427221126407435) - - -@pytest.mark.parametrize( - argnames="tc,c4,expected", - argvalues=[ - (np.array([30]), False, 0.706), - (np.array([30]), True, 0.4183999999999998), - ], -) -def test_calc_ftemp_kphio(tc, c4, expected): - """Test calc_ftemp_inst_rd.""" - from pyrealm.pmodel.functions import calc_ftemp_kphio - - assert np.allclose(calc_ftemp_kphio(tc, c4), expected) - - def test_calc_gammastar(tc=np.array([30.0]), patm=np.array([123456])): """Test calc_ftemp_inst_rd.""" from pyrealm.pmodel.functions import calc_gammastar From 2467eb362b0669d0da4295abe07c3d850f4d0ecb Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 8 Aug 2024 11:02:36 +0100 Subject: [PATCH 25/28] Working out how to pass around arrhenius mode cleanly --- pyrealm/constants/pmodel_const.py | 6 ++++++ pyrealm/pmodel/functions.py | 14 +++++--------- pyrealm/pmodel/pmodel.py | 3 ++- pyrealm/pmodel/quantum_yield.py | 3 ++- tests/regression/pmodel/test_pmodel.py | 5 +++-- .../pmodel/test_sandoval_quantum_yield.py | 4 ++++ 6 files changed, 22 insertions(+), 13 deletions(-) diff --git a/pyrealm/constants/pmodel_const.py b/pyrealm/constants/pmodel_const.py index ea95ca16..932c97b1 100644 --- a/pyrealm/constants/pmodel_const.py +++ b/pyrealm/constants/pmodel_const.py @@ -101,6 +101,12 @@ class PModelConst(ConstantsClass): of the mean growth temperature (J/mol/K), the deactivation energy constant (J/mol) and the activation energy (J/mol). """ + modified_arrhenius_mode: str = "M2002" + """The calculation mode to use with the modified Arrhenius equation in + :func:`~pyrealm.pmodel.functions.calc_modified_arrhenius_factor`. This mode should + be consistently within an analysis, so forms part of the model constants. The two + options are ``M2002`` and ``J1942``, see :cite:t:`murphy:2021a` for details.""" + plant_T_ref: float = 25.0 """Standard baseline reference temperature of photosynthetic processes (Prentice, unpublished) (:math:`T_o` , 25.0, °C)""" diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 64fdb566..24b88390 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -14,7 +14,7 @@ def calc_ftemp_arrh( tk: NDArray, ha: float | NDArray, - tk_ref: NDArray | None = None, + tk_ref: float | NDArray | None = None, pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ) -> NDArray: @@ -124,9 +124,8 @@ def calc_modified_arrhenius_factor( Ha: float | NDArray, Hd: float | NDArray, deltaS: float | NDArray, + tk_ref: float | NDArray, mode: str = "M2002", - tk_ref: NDArray | None = None, - pmodel_const: PModelConst = PModelConst(), core_const: CoreConst = CoreConst(), ) -> NDArray: r"""Calculate the modified Arrhenius factor with temperature for an enzyme. @@ -136,9 +135,10 @@ def calc_modified_arrhenius_factor( used in the calculation of :math:`V_{cmax}` but also other temperature dependent enzymatic processes. - The function can operate in one of two modes ("M2002" or "J1942") using the two + The function can operate in one of two modes (``M2002`` or ``J1942``) using alternative derivations of the modified Arrhenius relationship presented in - :cite:`murphy:2021a`. + :cite:t:`murphy:2021a`. The ``J1942`` includes an additional factor (tk/tk_ref) that + is ommitted from the simpler ``M2002`` derivation. Args: tk: The temperature at which to calculate the factor (K) @@ -179,10 +179,6 @@ def calc_modified_arrhenius_factor( f"Unknown mode option for calc_modified_arrhenius_factor: {mode}" ) - # Convert temperatures to Kelvin - if tk_ref is None: - tk_ref = np.array([pmodel_const.plant_T_ref + core_const.k_CtoK]) - # Calculate Arrhenius components fva = calc_ftemp_arrh(tk=tk, ha=Ha, tk_ref=tk_ref) diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index ccfbd20a..aa62d631 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -383,8 +383,9 @@ def estimate_productivity( tk=self.env.tc + self.core_const.k_CtoK, Ha=kk_ha, Hd=kk_hd, + tk_ref=self.pmodel_const.plant_T_ref + self.core_const.k_CtoK, + mode=self.pmodel_const.modified_arrhenius_mode, deltaS=kk_deltaS, - pmodel_const=self.pmodel_const, core_const=self.core_const, ) self._vcmax25 = self._vcmax / ftemp25_inst_vcmax diff --git a/pyrealm/pmodel/quantum_yield.py b/pyrealm/pmodel/quantum_yield.py index 2ed797f6..63ece4be 100644 --- a/pyrealm/pmodel/quantum_yield.py +++ b/pyrealm/pmodel/quantum_yield.py @@ -342,8 +342,9 @@ def _calculate_kphio(self) -> None: Ha=Ha, Hd=Hd, deltaS=deltaS, - mode="J1942", tk_ref=Topt, + mode=self.env.pmodel_const.modified_arrhenius_mode, + core_const=self.env.core_const, ) # Apply the factor and store it. diff --git a/tests/regression/pmodel/test_pmodel.py b/tests/regression/pmodel/test_pmodel.py index 282eb5ad..0045424c 100644 --- a/tests/regression/pmodel/test_pmodel.py +++ b/tests/regression/pmodel/test_pmodel.py @@ -132,7 +132,7 @@ def test_calc_ftemp_inst_vcmax(values, tc, expvars): from pyrealm.constants import CoreConst, PModelConst from pyrealm.pmodel.functions import calc_modified_arrhenius_factor - pmodel_const = PModelConst() + pmodel_const = PModelConst(modified_arrhenius_mode="M2002") core_const = CoreConst() kk_a, kk_b, kk_ha, kk_hd = pmodel_const.kattge_knorr_kinetics @@ -145,8 +145,9 @@ def test_calc_ftemp_inst_vcmax(values, tc, expvars): tk=values[tc] + core_const.k_CtoK, Ha=kk_ha, Hd=kk_hd, + tk_ref=pmodel_const.plant_T_ref + core_const.k_CtoK, + mode=pmodel_const.modified_arrhenius_mode, deltaS=kk_deltaS, - pmodel_const=pmodel_const, core_const=core_const, ) diff --git a/tests/regression/pmodel/test_sandoval_quantum_yield.py b/tests/regression/pmodel/test_sandoval_quantum_yield.py index 231567b2..981c3f22 100644 --- a/tests/regression/pmodel/test_sandoval_quantum_yield.py +++ b/tests/regression/pmodel/test_sandoval_quantum_yield.py @@ -23,9 +23,12 @@ def values(): def test_QuantumYieldSandoval(values): """Check implementation against values from original R code.""" + from pyrealm.constants import PModelConst from pyrealm.pmodel import PModelEnvironment from pyrealm.pmodel.quantum_yield import QuantumYieldSandoval + # The reference implementation uses the J1942 derivation for the modified arrhenius + # equation. env = PModelEnvironment( tc=values["temp"].to_numpy(), patm=101325, @@ -33,6 +36,7 @@ def test_QuantumYieldSandoval(values): co2=400, mean_growth_temperature=values["mean_gdd_temp"].to_numpy(), aridity_index=values["aridity_index"].to_numpy(), + pmodel_const=PModelConst(modified_arrhenius_mode="J1942"), ) # Calculate kphio for that environment From 22daf340faa996d70630d592fb82106c30f72200 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 8 Aug 2024 11:59:25 +0100 Subject: [PATCH 26/28] Fixes from @j-emberton and then test and doctest snagging --- docs/source/refs.bib | 1 + .../pmodel/pmodel_details/extreme_values.md | 48 +++++++++++-------- .../subdaily_details/subdaily_calculations.md | 31 +++++++++++- pyrealm/constants/pmodel_const.py | 2 +- pyrealm/pmodel/functions.py | 11 +++-- pyrealm/pmodel/pmodel.py | 2 +- tests/unit/pmodel/test_quantum_yield.py | 6 +++ 7 files changed, 73 insertions(+), 28 deletions(-) diff --git a/docs/source/refs.bib b/docs/source/refs.bib index f7dc458b..700ff609 100644 --- a/docs/source/refs.bib +++ b/docs/source/refs.bib @@ -709,6 +709,7 @@ @article{sandoval:in_prep title = {Aridity and Growth Temperature Effects on Phio Manuscript}, author = {Sandoval, David}, year = {in\_prep}, + journal = {Placeholder}, volume = {?}, pages = {??-??} } diff --git a/docs/source/users/pmodel/pmodel_details/extreme_values.md b/docs/source/users/pmodel/pmodel_details/extreme_values.md index de08db01..7119ac7f 100644 --- a/docs/source/users/pmodel/pmodel_details/extreme_values.md +++ b/docs/source/users/pmodel/pmodel_details/extreme_values.md @@ -5,9 +5,8 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -54,26 +53,29 @@ Note that the default values for C3 photosynthesis give **non-zero values below ```{code-cell} :tags: [hide-input] +:trusted: true from matplotlib import pyplot import numpy as np -from pyrealm.pmodel import calc_ftemp_kphio, calc_gammastar, calc_kmm from pyrealm.core.water import calc_density_h2o +from pyrealm.pmodel import calc_gammastar, calc_kmm, PModelEnvironment +from pyrealm.pmodel.quantum_yield import QuantumYieldTemperature %matplotlib inline # Set the resolution of examples n_pts = 101 -# Create a range of representative values for temperature -tc_1d = np.linspace(-80, 100, n_pts) + +# Create environment containing a range of representative values for temperature +env = PModelEnvironment(tc=np.linspace(-25, 100, n_pts), patm=101325, vpd=820, co2=400) # Calculate temperature dependence of quantum yield efficiency -fkphio_c3 = calc_ftemp_kphio(tc_1d, c4=False) -fkphio_c4 = calc_ftemp_kphio(tc_1d, c4=True) +fkphio_c3 = QuantumYieldTemperature(env, use_c4=False) +fkphio_c4 = QuantumYieldTemperature(env, use_c4=True) # Create a line plot of ftemp kphio -pyplot.plot(tc_1d, fkphio_c3, label="C3") -pyplot.plot(tc_1d, fkphio_c4, label="C4") +pyplot.plot(env.tc, fkphio_c3.kphio, label="C3") +pyplot.plot(env.tc, fkphio_c4.kphio, label="C4") pyplot.title("Temperature dependence of quantum yield efficiency") pyplot.xlabel("Temperature °C") @@ -91,16 +93,22 @@ that again, $\Gamma^_$ has non-zero values for sub-zero temperatures. ```{code-cell} :tags: [hide-input] +:trusted: true # Calculate gammastar at different pressures +tc_1d = np.linspace(-80, 100, n_pts) + +# Create a contour plot of gamma +fig, ax = pyplot.subplots(1, 1) + for patm in [3, 7, 9, 11, 13]: pyplot.plot(tc_1d, calc_gammastar(tc_1d, patm * 1000), label=f"{patm} kPa") -# Create a contour plot of gamma -pyplot.title("Temperature and pressure dependence of $\Gamma^*$") -pyplot.xlabel("Temperature °C") -pyplot.ylabel("$\Gamma^*$") -pyplot.legend() +ax.set_title("Temperature and pressure dependence of $\Gamma^*$") +ax.set_xlabel("Temperature °C") +ax.set_ylabel("$\Gamma^*$") +ax.set_yscale("log") +ax.legend(frameon=False) pyplot.show() ``` @@ -111,9 +119,9 @@ temperature and atmospheric pressure and again behaves smoothly with extreme val ```{code-cell} :tags: [hide-input] +:trusted: true -fig = pyplot.figure() -ax = pyplot.gca() +fig, ax = pyplot.subplots(1, 1) # Calculate K_mm for patm in [3, 7, 9, 11, 13]: @@ -136,9 +144,9 @@ issue with low temperatures arising from the equations for the density of water. ```{code-cell} :tags: [hide-input] +:trusted: true -fig = pyplot.figure() -ax = pyplot.gca() +fig, ax = pyplot.subplots(1, 1) # Calculate rho for patm in [3, 7, 9, 11, 13]: @@ -161,9 +169,9 @@ predictions below about -30 °C. ```{code-cell} :tags: [hide-input] +:trusted: true -fig = pyplot.figure() -ax = pyplot.gca() +fig, ax = pyplot.subplots(1, 1) tc_1d = np.linspace(-40, 20, n_pts) diff --git a/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md b/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md index 21664b8b..1a134c84 100644 --- a/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md +++ b/docs/source/users/pmodel/subdaily_details/subdaily_calculations.md @@ -6,7 +6,7 @@ jupytext: format_name: myst format_version: 0.13 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -21,6 +21,7 @@ are handled internally by the model fitting in `pyrealm`. ```{code-cell} :tags: [hide-input] +:trusted: true from importlib import resources @@ -47,6 +48,8 @@ site](https://fluxnet.org/doi/FLUXNET2015/BE-Vie), which was also used as a demonstration in {cite:t}`mengoli:2022a`. ```{code-cell} +:trusted: true + data_path = resources.files("pyrealm_build_data.subdaily") / "subdaily_BE_Vie_2014.csv" data = pandas.read_csv(str(data_path)) @@ -68,6 +71,8 @@ subdaily timescale. The code below also estimates GPP under the standard P Model slow responses for comparison. ```{code-cell} +:trusted: true + # Calculate the photosynthetic environment subdaily_env = PModelEnvironment( tc=temp_subdaily, @@ -94,6 +99,8 @@ conditions at the observation closest to noon, or the mean environmental conditi window around noon. ```{code-cell} +:trusted: true + # Create the fast slow scaler fsscaler = SubdailyScaler(datetime_subdaily) @@ -115,6 +122,7 @@ pmodel_subdaily = SubdailyPModel( ```{code-cell} :tags: [hide-input] +:trusted: true idx = np.arange(48 * 120, 48 * 130) plt.figure(figsize=(10, 4)) @@ -138,6 +146,8 @@ inputs to the standard P Model to calculate the optimal behaviour of plants unde conditions. ```{code-cell} +:trusted: true + # Get the daily acclimation conditions for the forcing variables temp_acclim = fsscaler.get_daily_means(temp_subdaily) co2_acclim = fsscaler.get_daily_means(co2_subdaily) @@ -151,7 +161,7 @@ daily_acclim_env = PModelEnvironment( tc=temp_acclim, vpd=vpd_acclim, co2=co2_acclim, patm=patm_acclim ) -pmodel_acclim = PModel(daily_acclim_env, kphio=1 / 8) +pmodel_acclim = PModel(daily_acclim_env, reference_kphio=1 / 8) pmodel_acclim.estimate_productivity(fapar=fapar_acclim, ppfd=ppfd_acclim) ``` @@ -169,6 +179,8 @@ at 25°C. This is acheived by multiplying by the reciprocal of the exponential p the Arrhenius equation ($h^{-1}$ in {cite}`mengoli:2022a`). ```{code-cell} +:trusted: true + # Are these any of the existing values in the constants? ha_vcmax25 = 65330 ha_jmax25 = 43900 @@ -184,6 +196,8 @@ The memory effect can now be applied to the three parameters with slow responses to calculate realised values, here using the default 15 day window. ```{code-cell} +:trusted: true + # Calculation of memory effect in xi, vcmax25 and jmax25 xi_real = memory_effect(pmodel_acclim.optchi.xi, alpha=1 / 15) vcmax25_real = memory_effect(vcmax25_acclim, alpha=1 / 15, allow_holdover=True) @@ -196,6 +210,7 @@ application of the memory effect. ```{code-cell} :tags: [hide-input] +:trusted: true fig, axes = plt.subplots(1, 3, figsize=(16, 5)) @@ -229,6 +244,8 @@ temperature at fast scales: responses of $J_{max}$ and $V_{cmax}$. ```{code-cell} +:trusted: true + tk_subdaily = subdaily_env.tc + pmodel_subdaily.env.core_const.k_CtoK # Fill the realised jmax and vcmax from subdaily to daily @@ -249,6 +266,8 @@ optimal $\chi$, rather than calculating the instantaneously optimal values of $\ as is the case in the standard P Model. ```{code-cell} +:trusted: true + # Interpolate xi to subdaily scale xi_subdaily = fsscaler.fill_daily_to_subdaily(xi_real) @@ -268,6 +287,8 @@ include the slow responses of $V_{cmax25}$ and $J_{max25}$ and fast responses to temperature. ```{code-cell} +:trusted: true + # Calculate Ac Ac_subdaily = ( vcmax_subdaily @@ -296,3 +317,9 @@ GPP_subdaily = ( diff = GPP_subdaily - pmodel_subdaily.gpp print(np.nanmin(diff), np.nanmax(diff)) ``` + +```{code-cell} +:trusted: true + + +``` diff --git a/pyrealm/constants/pmodel_const.py b/pyrealm/constants/pmodel_const.py index 932c97b1..5e3746a6 100644 --- a/pyrealm/constants/pmodel_const.py +++ b/pyrealm/constants/pmodel_const.py @@ -96,7 +96,7 @@ class PModelConst(ConstantsClass): 75000.0, ) """Enzyme kinetics parameters for estimation of kphio from mean growth temperature - in the Sandoval method :cite:t:`Sandoval:in_prep` for estimation of quantum yield + in the Sandoval method :cite:t:`sandoval:in_prep` for estimation of quantum yield efficiency. Values are: the intercept and slope of activation entropy as a function of the mean growth temperature (J/mol/K), the deactivation energy constant (J/mol) and the activation energy (J/mol). """ diff --git a/pyrealm/pmodel/functions.py b/pyrealm/pmodel/functions.py index 24b88390..69f4c98e 100644 --- a/pyrealm/pmodel/functions.py +++ b/pyrealm/pmodel/functions.py @@ -48,7 +48,10 @@ def calc_ftemp_arrh( Args: tk: Temperature (K) ha: Activation energy (in :math:`J \text{mol}^{-1}`) - tk_ref: The reference temperature for the reaction (K). + tk_ref: The reference temperature for the reaction (K). Optional, defaulting to + the value of + :attr:`PModelConst.plant_T_ref` + expressed in Kelvin. pmodel_const: Instance of :class:`~pyrealm.constants.pmodel_const.PModelConst`. core_const: Instance of :class:`~pyrealm.constants.core_const.CoreConst`. @@ -109,7 +112,7 @@ def calc_ftemp_inst_rd( Examples: >>> # Relative percentage instantaneous change in Rd going from 10 to 25 degrees >>> val = (calc_ftemp_inst_rd(25) / calc_ftemp_inst_rd(10) - 1) * 100 - >>> round(val, 4) + >>> np.round(val, 4) np.float64(250.9593) """ @@ -168,10 +171,10 @@ def calc_modified_arrhenius_factor( >>> deltaS = a + b * 10 >>> # Calculate the arrhenius factor >>> val = calc_modified_arrhenius_factor( - ... tk= 283.15, Ha=ha, Hd=hd, deltaS=deltaS + ... tk= 10 + 273.15, Ha=ha, Hd=hd, deltaS=deltaS, tk_ref=25 +273.15 ... ) >>> np.round(val, 4) - array([0.261]) + np.float64(0.261) """ if mode not in ["M2002", "J1942"]: diff --git a/pyrealm/pmodel/pmodel.py b/pyrealm/pmodel/pmodel.py index aa62d631..c3ce20ca 100644 --- a/pyrealm/pmodel/pmodel.py +++ b/pyrealm/pmodel/pmodel.py @@ -184,7 +184,7 @@ def __init__( """The CoreConst instance used to create the model environment.""" # ----------------------------------------------------------------------- - # Optimal ci + # Optimal chi # The heart of the P-model: calculate ci:ca ratio (chi) and additional terms # ----------------------------------------------------------------------- diff --git a/tests/unit/pmodel/test_quantum_yield.py b/tests/unit/pmodel/test_quantum_yield.py index a375a0ca..08dd8577 100644 --- a/tests/unit/pmodel/test_quantum_yield.py +++ b/tests/unit/pmodel/test_quantum_yield.py @@ -7,8 +7,13 @@ @pytest.fixture def quantum_yield_env(): """Simple fixture for providing the quantum yield inputs.""" + from pyrealm.constants import PModelConst from pyrealm.pmodel import PModelEnvironment + # The referene implementation uses the J1942 derivation for the arrhenius + # calculation + pmodel_const = PModelConst(modified_arrhenius_mode="J1942") + return PModelEnvironment( tc=np.array([5, 10, 15, 20, 25, 30]), patm=101325, @@ -16,6 +21,7 @@ def quantum_yield_env(): co2=400, mean_growth_temperature=np.array([10, 20, 10, 20, 10, 20]), aridity_index=np.array([0.9, 0.9, 2, 2, 5, 5]), + pmodel_const=pmodel_const, ) From 1ee9a5be85033a3dffcbc37deb3689584528605a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Aug 2024 11:00:30 +0000 Subject: [PATCH 27/28] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/source/api/constants_api.md | 1 - docs/source/api/core_api.md | 1 - docs/source/api/splash_api.md | 1 - docs/source/api/tmodel_api.md | 1 - docs/source/conf.py | 19 +++++++++++++++++++ docs/source/development/code_qa_and_typing.md | 1 - docs/source/development/code_testing.md | 1 - docs/source/development/documentation.md | 1 - docs/source/development/github_actions.md | 1 - docs/source/development/overview.md | 1 - .../development/profiling_and_benchmarking.md | 1 - docs/source/development/pyrealm_build_data.md | 1 - docs/source/development/release_process.md | 1 - docs/source/index.md | 1 - docs/source/users/constants.md | 1 - docs/source/users/hygro.md | 1 - docs/source/users/pmodel/c3c4model.md | 1 - .../users/pmodel/isotopic_discrimination.md | 1 - .../pmodel_details/envt_variation_outputs.md | 1 - .../pmodel/pmodel_details/optimal_chi.md | 1 - .../photosynthetic_environment.md | 1 - .../users/pmodel/pmodel_details/rpmodel.md | 1 - .../pmodel/pmodel_details/soil_moisture.md | 1 - .../pmodel/subdaily_details/acclimation.md | 1 - .../subdaily_model_and_missing_data.md | 1 - .../subdaily_details/subdaily_overview.md | 1 - docs/source/users/splash.md | 1 - docs/source/users/tmodel/tmodel.md | 1 - 28 files changed, 19 insertions(+), 27 deletions(-) diff --git a/docs/source/api/constants_api.md b/docs/source/api/constants_api.md index 80bd4ba2..1b992418 100644 --- a/docs/source/api/constants_api.md +++ b/docs/source/api/constants_api.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/api/core_api.md b/docs/source/api/core_api.md index 89843589..1324720e 100644 --- a/docs/source/api/core_api.md +++ b/docs/source/api/core_api.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/api/splash_api.md b/docs/source/api/splash_api.md index e8177ab3..fe55c923 100644 --- a/docs/source/api/splash_api.md +++ b/docs/source/api/splash_api.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/api/tmodel_api.md b/docs/source/api/tmodel_api.md index d74b7989..b7fa9312 100644 --- a/docs/source/api/tmodel_api.md +++ b/docs/source/api/tmodel_api.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/conf.py b/docs/source/conf.py index 73f896ad..f0f487c5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,3 +1,14 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: all +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version,settings,mystnb +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# --- + """Configure the Sphinx documentation builder. -- Path setup -------------------------------------------------------------- @@ -18,7 +29,9 @@ from pyrealm import __version__ as pyrealm_version +# + sys.path.insert(0, os.path.abspath("../")) +# - # -- Project information ----------------------------------------------------- @@ -55,7 +68,9 @@ external_toc_path = "_toc.yml" # optional, default: _toc.yml external_toc_exclude_missing = False # optional, default: False +# + [markdown] # Citation styling +# - def bracket_style() -> BracketStyle: @@ -142,6 +157,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle): ), ] +# + intersphinx_mapping = { "pytest": ("https://docs.pytest.org/en/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), @@ -149,6 +165,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle): "xarray": ("https://docs.xarray.dev/en/stable/", None), "shapely": ("https://shapely.readthedocs.io/en/stable/", None), } +# - autodoc_default_flags = ["members"] @@ -195,6 +212,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle): # html_theme = 'sphinx_material' html_theme = "sphinx_rtd_theme" +# + html_theme_options = { "logo_only": False, "display_version": True, @@ -208,6 +226,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle): "includehidden": True, "titles_only": False, } +# - # Add any paths that contain custom static files (such as style sheets) here, diff --git a/docs/source/development/code_qa_and_typing.md b/docs/source/development/code_qa_and_typing.md index b951cd8a..848f7a5c 100644 --- a/docs/source/development/code_qa_and_typing.md +++ b/docs/source/development/code_qa_and_typing.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/code_testing.md b/docs/source/development/code_testing.md index 9d126f5a..43829394 100644 --- a/docs/source/development/code_testing.md +++ b/docs/source/development/code_testing.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/documentation.md b/docs/source/development/documentation.md index cbe8cc3f..0ce6b2f8 100644 --- a/docs/source/development/documentation.md +++ b/docs/source/development/documentation.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/github_actions.md b/docs/source/development/github_actions.md index fda8ad3d..d26a7452 100644 --- a/docs/source/development/github_actions.md +++ b/docs/source/development/github_actions.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/overview.md b/docs/source/development/overview.md index 72207d53..837b1efe 100644 --- a/docs/source/development/overview.md +++ b/docs/source/development/overview.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/profiling_and_benchmarking.md b/docs/source/development/profiling_and_benchmarking.md index ccafd4f3..d705d43f 100644 --- a/docs/source/development/profiling_and_benchmarking.md +++ b/docs/source/development/profiling_and_benchmarking.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/pyrealm_build_data.md b/docs/source/development/pyrealm_build_data.md index 0f9dd7c1..b59ec2b2 100644 --- a/docs/source/development/pyrealm_build_data.md +++ b/docs/source/development/pyrealm_build_data.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/development/release_process.md b/docs/source/development/release_process.md index 9396484c..e9ff6b00 100644 --- a/docs/source/development/release_process.md +++ b/docs/source/development/release_process.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/index.md b/docs/source/index.md index 10234635..43aef59b 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/constants.md b/docs/source/users/constants.md index 7997e3a9..899076b3 100644 --- a/docs/source/users/constants.md +++ b/docs/source/users/constants.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/hygro.md b/docs/source/users/hygro.md index 39322beb..a235875e 100644 --- a/docs/source/users/hygro.md +++ b/docs/source/users/hygro.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/c3c4model.md b/docs/source/users/pmodel/c3c4model.md index 4a400e50..ac4ba204 100644 --- a/docs/source/users/pmodel/c3c4model.md +++ b/docs/source/users/pmodel/c3c4model.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/isotopic_discrimination.md b/docs/source/users/pmodel/isotopic_discrimination.md index 5020de17..574b2f67 100644 --- a/docs/source/users/pmodel/isotopic_discrimination.md +++ b/docs/source/users/pmodel/isotopic_discrimination.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/pmodel_details/envt_variation_outputs.md b/docs/source/users/pmodel/pmodel_details/envt_variation_outputs.md index eae62e95..e7364837 100644 --- a/docs/source/users/pmodel/pmodel_details/envt_variation_outputs.md +++ b/docs/source/users/pmodel/pmodel_details/envt_variation_outputs.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/pmodel_details/optimal_chi.md b/docs/source/users/pmodel/pmodel_details/optimal_chi.md index 4c794fe0..fb22b49e 100644 --- a/docs/source/users/pmodel/pmodel_details/optimal_chi.md +++ b/docs/source/users/pmodel/pmodel_details/optimal_chi.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/pmodel_details/photosynthetic_environment.md b/docs/source/users/pmodel/pmodel_details/photosynthetic_environment.md index adbbca1a..cedd27fe 100644 --- a/docs/source/users/pmodel/pmodel_details/photosynthetic_environment.md +++ b/docs/source/users/pmodel/pmodel_details/photosynthetic_environment.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/pmodel_details/rpmodel.md b/docs/source/users/pmodel/pmodel_details/rpmodel.md index f2b4eb93..7e691cc2 100644 --- a/docs/source/users/pmodel/pmodel_details/rpmodel.md +++ b/docs/source/users/pmodel/pmodel_details/rpmodel.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/pmodel_details/soil_moisture.md b/docs/source/users/pmodel/pmodel_details/soil_moisture.md index afe55613..2b4b8a53 100644 --- a/docs/source/users/pmodel/pmodel_details/soil_moisture.md +++ b/docs/source/users/pmodel/pmodel_details/soil_moisture.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/subdaily_details/acclimation.md b/docs/source/users/pmodel/subdaily_details/acclimation.md index 09512e49..193b38f9 100644 --- a/docs/source/users/pmodel/subdaily_details/acclimation.md +++ b/docs/source/users/pmodel/subdaily_details/acclimation.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/subdaily_details/subdaily_model_and_missing_data.md b/docs/source/users/pmodel/subdaily_details/subdaily_model_and_missing_data.md index 5f1e995b..881f71b8 100644 --- a/docs/source/users/pmodel/subdaily_details/subdaily_model_and_missing_data.md +++ b/docs/source/users/pmodel/subdaily_details/subdaily_model_and_missing_data.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/pmodel/subdaily_details/subdaily_overview.md b/docs/source/users/pmodel/subdaily_details/subdaily_overview.md index 31234000..3278b056 100644 --- a/docs/source/users/pmodel/subdaily_details/subdaily_overview.md +++ b/docs/source/users/pmodel/subdaily_details/subdaily_overview.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/splash.md b/docs/source/users/splash.md index e852f49c..e5c4d283 100644 --- a/docs/source/users/splash.md +++ b/docs/source/users/splash.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python diff --git a/docs/source/users/tmodel/tmodel.md b/docs/source/users/tmodel/tmodel.md index ca6c5eec..fda39de0 100644 --- a/docs/source/users/tmodel/tmodel.md +++ b/docs/source/users/tmodel/tmodel.md @@ -5,7 +5,6 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 kernelspec: display_name: Python 3 language: python From d00331e40d677e8f8a1aa9f8b3ecd48b0caa1ae3 Mon Sep 17 00:00:00 2001 From: David Orme Date: Thu, 8 Aug 2024 12:19:04 +0100 Subject: [PATCH 28/28] Do _not_ jupytext the Sphinx conf.py --- .pre-commit-config.yaml | 3 ++- docs/source/conf.py | 11 ----------- 2 files changed, 2 insertions(+), 12 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 278a608b..977a7f45 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,8 @@ repos: hooks: - id: jupytext args: [--pipe, black] - files: docs/source + files: docs/source + exclude: docs/source/conf.py additional_dependencies: - black==24.4.2 # Matches hook \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index f0f487c5..3921eba7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,14 +1,3 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: all -# notebook_metadata_filter: -jupytext.text_representation.jupytext_version,settings,mystnb -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# --- - """Configure the Sphinx documentation builder. -- Path setup --------------------------------------------------------------