StomtaModels API

Stomatal model schemes

The StomataModels module relies mainly on Photosynthesis and PlantHydraulics modules to predict stomatal behavior from plant physiology. This module has both empirical and optimal stomatal models. These stomatal models are abstractized to an abstract AbstractStomatalModel, which further has subtypes EmpiricalStomatalModel and OptimizationStomatalModel.

Land.StomataModels.AbstractStomatalModel — Type

type AbstractStomatalModel

Hierarchy of the AbstractStomatalModel:

EmpiricalStomatalModel
OptimizationStomatalModel

Land.StomataModels.EmpiricalStomatalModel — Type

type EmpiricalStomatalModel

Hierarchy of the EmpiricalStomatalModel:

ESMBallBerry
ESMGentine
ESMLeuning
ESMMedlyn

Land.StomataModels.OptimizationStomatalModel — Type

type OptimizationStomatalModel

Hierarchy of the OptimizationStomatalModel:

OSMEller
OSMSperry
OSMWang
OSMWAP
OSMWAPMod

Currently, the StomataModels module has four empirical model schemes, and they are

Land.StomataModels.ESMBallBerry — Type

struct ESMBallBerry{FT}

An empirical model parameter set type for Ball-Berry type model. The equation used for Ball-Berry type model is

\[gs = g0 + g1 ⋅ RH ⋅ \dfrac{A}{Cs}\]

Fields

g0::Any

: minimal stomatal conductance g0 [mol m⁻² s⁻¹]

g1::Any

: slope of conductance-photosynthesis correlation [unitless]

Land.StomataModels.ESMGentine — Type

struct ESMGentine{FT}

An empirical model parameter set type for Gentine type model. The equation used for Gentine type model is

\[gs = g0 + g1 ⋅ \dfrac{k_{leaf}}{k_{max}} ⋅ \dfrac{A}{Ca}.\]

Note it that the Gentine model does not require for a β function to tune the soil drought response, but the use of k_leaf also does not permit post-drought stomatal response unless k_leaf can be recovered.

Fields

g0::Any

: minimal stomatal conductance g0 [mol m⁻² s⁻¹]

g1::Any

: slope of conductance-photosynthesis correlation [unitless]

Land.StomataModels.ESMLeuning — Type

struct ESMLeuning{FT}

An empirical model parameter set type for Leuning type model. The equation used for Leuning type model is

\[gs = g0 + g1 ⋅ \dfrac{A}{Cs - Γ^{*}} ⋅ \dfrac{1}{1 + \dfrac{VPD}{d0}}\]

Fields

g0::Any

: minimal stomatal conductance g0 [mol m⁻² s⁻¹]

g1::Any

: slope of conductance-photosynthesis correlation [unitless]

d0::Any

: fitting parameter of d/d0 below the fraction, same unit as vpd [Pa]

Land.StomataModels.ESMMedlyn — Type

struct ESMMedlyn{FT}

An empirical model parameter set type for Medlyn type model. The equation used in Medlyn type model is

\[gs = g0 + 1.6 ⋅ \left( 1 + \dfrac{g1}{\sqrt{VPD}} \right) ⋅ \dfrac{A}{Ca}\]

Fields

g0::Any

: minimal stomatal conductance g0 [mol m⁻² s⁻¹]

g1::Any

: slope of conductance-photosynthesis correlation [Pa⁽⁵⁾]

All the empirical models rely on beta functions to make corrections over stomatal conductance to account for the stomatal closure with drier soil. We have the following prescribed beta function types, and they are:

Land.StomataModels.AbstractBetaFunction — Type

abstract type AbstractBetaFunction{FT}

Hierachy of AbstractBetaFunction:

AbstractBetaG
AbstractBetaV

Some beta functions make correction over the g1 parameter as in the empitical models, and they are:

Land.StomataModels.AbstractBetaG — Type

abstract type AbstractBetaG{FT}

Hierachy of AbstractBetaG:

BetaGLinearKleaf
BetaGLinearPleaf
BetaGLinearPsoil
BetaGLinearSWC

Land.StomataModels.BetaGLinearKleaf — Type

struct BetaGLinearKleaf{FT}

Linear β function for g1 based on leaf hydraulic conductance.

Fields

Land.StomataModels.BetaGLinearKsoil — Type

struct BetaGLinearKsoil{FT}

Linear β function for g1 based on soil hydraulic conductance.

Fields

Land.StomataModels.BetaGLinearPleaf — Type

mutable struct BetaGLinearPleaf{FT}

Linear β function for g1 based on soil water potential.

Fields

p_max::Any

: Upper bound of Pleaf [MPa]

p_min::Any

: Lower bound of Pleaf [MPa]

Land.StomataModels.BetaGLinearPsoil — Type

mutable struct BetaGLinearPsoil{FT}

Linear β function for g1 based on soil water potential.

Fields

p_max::Any

: Upper bound of Psoil [MPa]

p_min::Any

: Lower bound of Psoil [MPa]

Land.StomataModels.BetaGLinearSWC — Type

mutable struct BetaGLinearSWC{FT}

Linear β function for g1 based on soil water content.

Fields

swc_max::Any

: Upper bound of SWC

swc_min::Any

: Lower bound of SWC

Some beta functions make correction over the photosynthetic capacity as in the Photosynthesis module, and they are:

Land.StomataModels.AbstractBetaV — Type

abstract type AbstractBetaV{FT}

Hierachy of AbstractBetaV:

BetaVLinearKleaf
BetaVLinearPleaf
BetaVLinearPsoil
BetaVLinearSWC

Land.StomataModels.BetaVLinearKleaf — Type

struct BetaVLinearKleaf{FT}

Linear β function for Vcmax based on leaf hydraulic conductance.

Fields

Land.StomataModels.BetaVLinearPleaf — Type

mutable struct BetaVLinearPleaf{FT}

Linear β function for Vcmax based on soil water potential.

Fields

p_max::Any

: Upper bound of Pleaf [MPa]

p_min::Any

: Lower bound of Pleaf [MPa]

Land.StomataModels.BetaVLinearPsoil — Type

mutable struct BetaVLinearPsoil{FT}

Linear β function for Vcmax based on soil water potential.

Fields

p_max::Any

: Upper bound of Psoil [MPa]

p_min::Any

: Lower bound of Psoil [MPa]

Land.StomataModels.BetaVLinearSWC — Type

mutable struct BetaVLinearSWC{FT}

Linear β function for Vcmax based on soil water content.

Fields

swc_max::Any

: Upper bound of SWC

swc_min::Any

: Lower bound of SWC

The beta functions are generalized with

Land.StomataModels.β_factor — Function

β_factor(hs::LeafHydraulics{FT},
         svc::AbstractSoilVC{FT},
         bt::AbstractBetaFunction{FT},
         p_leaf::FT,
         p_soil::FT,
         swc::FT
) where {FT<:AbstractFloat}

Calculate the β correction factor, given

hs LeafHydraulics structure
svc Soil vulnerability curve
bt AbstractBetaFunction type struct
p_leaf Leaf water potential [MPa]
p_soil Soil water potential [MPa]
swc Soil water content

The StomataModels module also contains five optimization model schemes:

Land.StomataModels.OSMEller — Type

struct OSMEller

An optimization model parameter set type for Eller model. The equation used for Eller model is

\[\dfrac{∂Θ}{∂E} = -\dfrac{∂K}{∂E} ⋅ \dfrac{A}{K}\]

where K is $\dfrac{∂E}{∂P}$.

Fields

Land.StomataModels.OSMSperry — Type

struct OSMSperry

An optimization model parameter set type for Sperry model. The equation used for Sperry model is

\[\dfrac{∂Θ}{∂E} = -\dfrac{∂K}{∂E} ⋅ \dfrac{A_{max}}{K_{max}}\]

where K is $\dfrac{∂E}{∂P}$.

Fields

Land.StomataModels.OSMWang — Type

struct OSMWang

An optimization model parameter set type for Eller type model. The equation used for Wang model is

\[\dfrac{∂Θ}{∂E} = \dfrac{A}{E_{crit} - E}\]

Fields

Land.StomataModels.OSMWAP — Type

struct OSMWAP{FT}

An optimization model parameter set type for Wolf-Anderegg-Pacala type model. The equation used for Wolf-Anderegg-Pacala model is

\[\dfrac{∂Θ}{∂E} = \dfrac{2aP + b}{K}\]

where K is ∂P/∂E.

Fields

a::Any

: Quadratic equation parameter [μmol m⁻² s⁻¹ MPa⁻²]

b::Any

: Quadratic equation parameter [μmol m⁻² s⁻¹ MPa⁻¹]

Land.StomataModels.OSMWAPMod — Type

struct OSMWAPMod{FT}

An optimization model parameter set type for Wolf-Anderegg-Pacala type model, modified by adding a photosynthesis component while set b and c = 0. The equation used for modified Wolf-Anderegg-Pacala model is

\[\dfrac{∂Θ}{∂E} = \dfrac{aAP}{K}\]

where P is absolute value of leaf xylem pressure.

Fields

a::Any

: Quadratic equation parameter [mol mol⁻¹ MPa⁻¹]

CanopyLayer

The StomataModels module is designed for multi-layer canopies, and each canopy has multiple leaves. The stomatal behaviors are modeled per layer basis, and the layer may contain any number of leaves starting from 1. Photosynthesis-related information is stored in CanopyLayer struct, but be aware that the leaves have uniform photosynthetic parameters and temperature (conductances are different in response to light environment).

Land.StomataModels.CanopyLayer — Type

struct CanopyLayer{FT}

Struct to store leaf information (multi-dimensional).

Fields

ps::Land.Photosynthesis.Leaf

: leaf photosynthesis system

ps_m::Land.Photosynthesis.Leaf

: Memory leaf photosynthesis system

LA::AbstractFloat

: Total leaf area [m²]

LAI::AbstractFloat

: Leaf area index in the layer

tLAI::AbstractFloat

: Total leaf area index in the layer

APAR_m::AbstractFloat

: Memory APAR [μmol m⁻² s⁻¹]

envir_m::Land.Photosynthesis.AirLayer

: Memory environment

n_leaf::Int64
H::Vector{FT} where FT<:AbstractFloat

: Sensible Heat Flux [W m⁻²]

LE::Vector{FT} where FT<:AbstractFloat

: Latent Heat Flux [W m⁻²]

Rn::Vector{FT} where FT<:AbstractFloat

: Net Radiation Balance [W m⁻²]

LV::AbstractFloat

: Latent Heat of evaporation [J mol⁻¹]

T::AbstractFloat

: Temperature [K]

T_old::AbstractFloat

: Old temperature [K]

width::AbstractFloat

: Leaf width [m]

g_bc::Vector{FT} where FT<:AbstractFloat

: Boundary layer conductance to CO₂ [mol m⁻² s⁻¹]

g_bh::Vector{FT} where FT<:AbstractFloat

: Boundary layer conductance to heat [mol m⁻² s⁻¹]

g_bw::Vector{FT} where FT<:AbstractFloat

: Boundary layer conductance to H₂O [mol m⁻² s⁻¹]

g_lc::Vector{FT} where FT<:AbstractFloat

: Leaf diffusive conductance to water CO₂ [mol m⁻² s⁻¹]

g_lw::Vector{FT} where FT<:AbstractFloat

: Leaf diffusive conductance to water H₂O [mol m⁻² s⁻¹]

g_m::Vector{FT} where FT<:AbstractFloat

: Mesophyll conductance for CO₂ [mol m⁻² s⁻¹]

g_sc::Vector{FT} where FT<:AbstractFloat

: Stomatal conductance to water CO₂ [mol m⁻² s⁻¹]

g_sw::Vector{FT} where FT<:AbstractFloat

: Stomatal conductance to water H₂O [mol m⁻² s⁻¹]

g_ias_c::AbstractFloat

: Gias correction constant

g_ias_e::AbstractFloat

: Gias correction exponent

g_max::AbstractFloat

: Maximal leaf diffusive conductance [mol m⁻² s⁻¹]

g_max25::AbstractFloat

: Maximal leaf diffusive conductance at 298.15 K [mol m⁻² s⁻¹]

g_min::AbstractFloat

: Minimal leaf diffusive conductance [mol m⁻² s⁻¹]

g_min25::AbstractFloat

: Minimal leaf diffusive conductance at 298.15 K [mol m⁻² s⁻¹]

p_i::Vector{FT} where FT<:AbstractFloat

: Leaf internal CO₂ partial pressure [Pa]

p_s::Vector{FT} where FT<:AbstractFloat

: Leaf surface CO₂ partial pressure [Pa]

p_sat::AbstractFloat

: Leaf saturation vapor pressure [Pa]

Ac::Vector{FT} where FT<:AbstractFloat

: RubisCO limited photosynthetic rate [μmol m⁻² s⁻¹]

Aj::Vector{FT} where FT<:AbstractFloat

: Light limited photosynthetic rate [μmol m⁻² s⁻¹]

Ag::Vector{FT} where FT<:AbstractFloat

: Gross photosynthetic rate [μmol m⁻² s⁻¹]

An::Vector{FT} where FT<:AbstractFloat

: Net photosynthetic rate [μmol m⁻² s⁻¹]

Ap::Vector{FT} where FT<:AbstractFloat

: Product limited photosynthetic rate [μmol m⁻² s⁻¹]

J::Vector{FT} where FT<:AbstractFloat

: Electron transport [μmol m⁻² s⁻¹]

J_pot::Vector{FT} where FT<:AbstractFloat

: Potential Electron Transport Rate [μmol m⁻² s⁻¹]

e2c::Vector{FT} where FT<:AbstractFloat

: Total efficiency, incl. photorespiration [mol CO₂ mol⁻¹ e-]

Fm′::Vector{FT} where FT<:AbstractFloat

: light adapted yield (Kp=0)

Fo′::Vector{FT} where FT<:AbstractFloat

: light-adapted fluorescence yield in the dark (Kp=max)

Ja::Vector{FT} where FT<:AbstractFloat

: Actual electron transport rate [μmol m⁻² s⁻¹]

NPQ::Vector{FT} where FT<:AbstractFloat

: Non-Photochemical quenching

qQ::Vector{FT} where FT<:AbstractFloat

: Photochemical quenching

qE::Vector{FT} where FT<:AbstractFloat

: energy quenching

φ::Vector{FT} where FT<:AbstractFloat

: PSII yield

φs::Vector{FT} where FT<:AbstractFloat

: Steady-state (light-adapted) yield (aka Fs)

Fm::AbstractFloat

: dark adapted yield (Kp=0)

Fo::AbstractFloat

: dark-adapted fluorescence yield (Kp=max)

APAR::Vector{FT} where FT<:AbstractFloat

: Absorbed photosynthetic active radiation [μmol m⁻² s⁻¹]

LAIx::Vector{FT} where FT<:AbstractFloat

: Leaf area fractions

a_max::Vector{FT} where FT<:AbstractFloat

: Maximal photosynthetic rate [μmol m⁻² s⁻¹]

e::Vector{FT} where FT<:AbstractFloat

: Flow rate [mol m⁻² s⁻¹]

ec::AbstractFloat

: Critical flow rate [mol m⁻² s⁻¹]

kr_max::AbstractFloat

: Maximal hydraulic conductance ratio

p_ups::AbstractFloat

: Base xylem pressre [MPa]

p_old::AbstractFloat

: Base xylem pressre memory [MPa]

ff::AbstractFloat

: Fitness factor for nighttime stomtal conductance

τ_esm::AbstractFloat

: τ for empirical stomatal models [-], Δg/Δt = (g_ss - gsw) / τ

τ_osm::AbstractFloat

: τ for optimal stomatal models [μmol⁻¹], Δg/Δt = (∂A/∂E - ∂Θ/∂E) * τ

τ_noc::AbstractFloat

: τ for nighttime optimal stomatal models [μmol⁻¹]

Stomatal conductance

For empirical stomatal models, the stomatal conductance is computed as the intercept of two functions: an empirical function that describe stomatal responses to the physiological and environmental cues and an function that follows the diffusion nature of H₂O and CO₂. The abstractized function for the empirical correlation is

Land.StomataModels.stomatal_conductance — Function

stomatal_conductance(
            model::EmpiricalStomatalModel{FT},
            leaf::Leaf{FT},
            envir::AirLayer{FT},
            β::FT
) where {FT<:AbstractFloat}
stomatal_conductance(
            model::EmpiricalStomatalModel{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            β::FT
) where {FT<:AbstractFloat}
stomatal_conductance(
            model::EmpiricalStomatalModel{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            β::FT,
            ind::Int
) where {FT<:AbstractFloat}

Steady state gsw from empirical approach given

model EmpiricalStomatalModel type empirical model parameter set
leaf [Leaf] type struct
canopyi CanopyLayer type struct
envir [AirLayer] type struct
β Correction factor over the g1 part of an empirical model
ind Nth leaf in the canopy layer

For optimization stomatal models, the stomatal conductance is computed as the point where the marginal carbon gains equals the marginal carbon risk. The marginal carbon gain and risk are generally numerically computed by marginally increasing transpiration rate.

This module uses ConstrainedRootSolver module to iterate through the two functions to find the solution. The aim is to find the stomatal conductance when the solution_diff! function equals 0. The solution_diff! returns the diference between real and model-predicted conductances for empirical stomatal models, and the difference between marginal carbon gain and risk for optimization stomatal models.

Land.StomataModels.solution_diff! — Function

solution_diff!(x::FT,
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            svc::AbstractSoilVC{FT},
            psoil::FT,
            swc::FT,
            envir::AirLayer{FT},
            sm::OptimizationStomatalModel{FT},
            bt::AbstractBetaFunction{FT},
            mode::GlcDrive,
            ind::Int
) where {FT<:AbstractFloat}
solution_diff!(x::FT,
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            envir::AirLayer{FT},
            sm::AbstractStomatalModel{FT},
            mode::AbstractDrive,
            ind::Int
) where {FT<:AbstractFloat}

Calculate the difference to be minimized for a given

x Assumed leaf diffusive conductance or stomatal conductance, depending on mode
photo_set[C3ParaSet] or [C4ParaSet] type parameter set
canopyiCanopyLayer type struct
hs Leaf hydraulic system
psoil Soil water potential [MPa]
swc Soil water content
envir[AirLayer] type struct
sm EmpiricalStomatalModel or OptimizationStomatalModel
bt AbstractBetaFunction type struct
mode GlcDrive or GswDrive mode
ind Nth leaf in the canopy layer

The former function works for all empirical stomatal models, and the latter works for all optimization based models.

In the solution_diff! function, leaf photosynthetic rates is modeled using gas_exchange!, which calculates the gas exchange rates from a known total leaf diffusive conductance using GlcDrive mode.

Land.StomataModels.AbstractDrive — Type

abstract type AbstractDrive

Hierarchy of AbstractDrive

GlcDrive
GswDrive

Land.StomataModels.GlcDrive — Type

struct GlcDrive

Gas exchange update is driven by changes in total leaf diffusive conductance to CO₂

Land.StomataModels.GswDrive — Type

struct GswDrive

Gas exchange update is driven by changes in stomtal conductance to H₂O

However, these functions do not force stomatal conductance to stay in its ranges. For example, the stomatal conductance solution is set to be zero if light is lower than the compensation point. In this case, the solution_diff! function has to be used along with a control function to guarantee realistic stomatal conductance.

Land.StomataModels.gsw_control! — Function

gsw_control!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            ind::Int
) where {FT<:AbstractFloat}
gsw_control!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT}
) where {FT<:AbstractFloat}

make sure g_sw is in its physiological range limited by diffusion, given

photo_set [C3ParaSet] or [C4ParaSet] type parameter set
canopyi CanopyLayer type struct
envir [AirLayer] type struct
ind Nth leaf

Note that this function is meant to use jointly with gas_exchange! when computing optimal stomtal conductance.

To facilitate the use of the StomataModels module, an abstractized function is provided for conveniently obtaining stomatal conductance from given environmental conditions.

Land.StomataModels.gas_exchange! — Function

gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            psoil::FT,
            swc::FT,
            envir::AirLayer{FT},
            sm::EmpiricalStomatalModel{FT},
            bt::AbstractBetaFunction{FT}
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            psoil::FT,
            swc::FT,
            envir::AirLayer{FT},
            sm::EmpiricalStomatalModel{FT},
            bt::AbstractBetaFunction{FT},
            ind::Int
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            envir::AirLayer{FT},
            sm::AbstractStomatalModel{FT}
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            envir::AirLayer{FT},
            sm::AbstractStomatalModel{FT},
            ind::Int
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::TreeSimple{FT},
            envir::AirLayer{FT},
            sm::OSMWang{FT}
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GlcDrive,
            ind::Int,
            glc::FT
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GlcDrive,
            ind::Int
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GlcDrive
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GswDrive,
            ind::Int,
            gsw::FT
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GswDrive,
            ind::Int
) where {FT<:AbstractFloat}
gas_exchange!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            envir::AirLayer{FT},
            drive::GswDrive
) where {FT<:AbstractFloat}

Calculate steady state gas exchange rates, given

photo_set [C3ParaSet] or [C4ParaSet] type parameter set
canopyi CanopyLayer type struct
hs Leaf hydraulic system or TreeSimple hydraulic organism
envir [AirLayer] type struct
sm EmpiricalStomatalModel or OptimizationStomatalModel
bt AbstractBetaFunction type struct
ind Nth leaf in canopyi
drive GlcDrive or GswDrive drive mode
glc Given leaf diffusive conductance to CO₂
gsw Given stomatal conductance to H₂O

Note 1: When there is no drive mode in the parameter list, the function calculates the steady state stomatal conductance first, and then the gas exchange rates.

Note 2: When using GlcDrive mode, gas exchange rates are computed using the given glc. However, this option does not make the gsw control, so it is not guaranteed that gsw is within the physiological range. Thus, gsw control should be made outside this function. This option is supposed to be used in the optimal stomatl conductance models only, because optimal conductance can be outside the physiological stomatal conductance range. Thus, using this option for other purposes need to be cautious. In this case, it is recommended to use the GswDrive mode.

Note 3: When using GswDrive mode, gas exchange rates are computed using the given gsw. Moreover, gsw control so that gsw is within the physiological range.

To speed up the calculations, leaf physiological parameters are updated only if the environmental conditions changes. For example, PAR (photosyntheis active radiation) is constant when we iterate solution_diff!, and the electron transport is only updated once. Similar to the cases of leaf temperature and soil moisture. This kind of functions used in the present module are

Land.StomataModels.update_leaf_TP! — Function

update_leaf_TP!(
            photo_set::AbstractPhotoModelParaSet{FT},
            canopyi::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            envir::AirLayer{FT}
) where {FT<:AbstractFloat}

Update leaf physiological parameters if temperature or pressure changes in the daytime, given

photo_set [C3ParaSet] or [C4ParaSet] type parameter set
canopyi CanopyLayer type struct
hs Leaf hydraulic system
envir [AirLayer] type struct

Land.StomataModels.update_leaf_AK! — Function

update_leaf_AK!(
        photo_set::AbstractPhotoModelParaSet{FT},
        canopyi::CanopyLayer{FT},
        hs::LeafHydraulics{FT},
        envir::AirLayer{FT}
) where {FT<:AbstractFloat}

Update leaf maximal A and K for Sperry model, given

photo_set [C3ParaSet] or [C4ParaSet] type parameter set
canopyi CanopyLayer type struct
envir [AirLayer] type struct

I'd like to emphasize it here that the gas_exchange! function only applies to the case of constant leaf temperature because leaf energy budget is not calculated, and thus gas_exchange! is only applicable to (1) known leaf temperature, and (2) prognostically modeling the non-steady state stomatal behaviors. As to the steady state case, leaf energy budget has to be considered. For the prognotic stomatal conductance, it is recommended to use gas_exchange! function at GswDrive mode.

Note it here that stomtal conductance is controlled in this function, and thus no additional control like gsw_control! is required if gas_exchange! is used.

Other functions

Land.StomataModels.dRdE — Function

dRdE(photo_set::AbstractPhotoModelParaSet{FT},
     clayer::CanopyLayer{FT},
     envir::AirLayer{FT},
     LAI::FT
) where {FT<:AbstractFloat}

Calculate the marginal decrease of respiration rate, given

photo_set AbstractPhotoModelParaSet type struct
clayer CanopyLayer type of struct
envir AirLayer type struct
LAI Total leaf area index of whole canopy

Land.StomataModels.dTdE — Function

dTdE(clayer::CanopyLayer{FT},
     envir::AirLayer{FT}
) where {FT<:AbstractFloat}

Calculate the margian decrease in leaf temperature, given

clayer CanopyLayer type of struct
envir AirLayer type struct
LAI Total leaf area index of whole canopy

Land.StomataModels.dΘdE — Function

dΘdE(photo_set::AbstractPhotoModelParaSet{FT},
     clayer::CanopyLayer{FT},
     sm::OSMWang{FT}(),
     g_sw::FT
) where {FT<:AbstractFloat}

Calculate the margian carbon cost related to nighttime transpiration, given

photo_set AbstractPhotoModelParaSet type struct
clayer CanopyLayer type of struct
sm OSMWang type stomatal model
g_sw Given leaf level stomatal conductance

Land.StomataModels.nocturnal_diff! — Function

nocturnal_diff!(
            x::FT,
            photo_set::AbstractPhotoModelParaSet{FT},
            clayer::CanopyLayer{FT},
            envir::AirLayer{FT},
            sm::OSMWang{FT}()
) where {FT<:AbstractFloat}

Calculate the difference between marginal gain and risk of nighttime transpiration, given

x Given leaf level stomatal conductance
photo_set AbstractPhotoModelParaSet type struct
clayer CanopyLayer type of struct
envir AirLayer type struct
sm OSMWang type stomatal model

Land.StomataModels.prognostic_gsw! — Function

prognostic_gsw!(
            clayer::CanopyLayer{FT},
            envir::AirLayer{FT},
            sm::EmpiricalStomatalModel{FT},
            β::FT,
            Δt::FT
) where {FT<:AbstractFloat}
prognostic_gsw!(
            photo_set::AbstractPhotoModelParaSet{FT},
            clayer::CanopyLayer{FT},
            hs::LeafHydraulics{FT},
            envir::AirLayer{FT},
            sm::OSMWang{FT},
            Δt::FT
) where {FT<:AbstractFloat}

Update g_sw prognostically, given

clayer A CanopyLayer type struct
envir AirLayer type environmental conditions
sm EmpiricalStomatalModel or OSMWang type stomatal model
β Tune factor to stomatal g1. 1 for AbstractBetaV mode
Δt Time interval for prognostic stomatal conductance
photo_set AbstractPhotoModelParaSet type photosynthesis model, currently supports OSMWang model only
hs Leaf hydraulic system