Stomatal conductance
This page is a work in progress. If you have any questions or notice any errors, please raise an issue.
Overview
Stomata regulate gas exchange between the leaf and atmosphere, directly controlling the trade-off between carbon uptake during photosynthesis and water loss through transpiration. Similar to photosynthesis, stomatal conductance, $g_w$, depends on multiple environmental factors, including light availability, CO₂ concentration, and temperature.
Terrarium.AbstractStomatalConductance — Type
abstract type AbstractStomatalConductance{NF} <: Terrarium.AbstractProcess{NF}Base type for stomatal conductance schemes.
subtypes(Terrarium.AbstractStomatalConductance)1-element Vector{Any}:
MedlynStomatalConductanceThe Medlyn stomatal conductance model
Terrarium.MedlynStomatalConductance — Type
struct MedlynStomatalConductance{NF} <: Terrarium.AbstractStomatalConductance{NF}Stomatal conductance implementation from PALADYN (Willeit 2016) following the optimal stomatal conductance model (Medlyn et al. 2011).
Authors: Maha Badri and Matteo Willeit
Properties:
g₁::Any: Parameter in optimal stomatal conductance formulation representing the quasi-linear relationship between conductance and net assimilation, Lin et al. 2015 [-], PFT specificg_min::Any: Minimum stomatal condutance parameter [mm s⁻¹]
variables(MedlynStomatalConductance(Float32))Variables
├─ Prognostic:
├─ Auxiliary:
├── canopy_water_conductance [m s^-1] on XY{Center, Center}
├── leaf_to_air_co2_ratio [-] on XY{Center, Center}
├─ Inputs:
├─ Namespaces:
This implementation uses the optimal stomatal conductance model of Medlyn (2011) [5], adapted from PALADYN [4], which derives stomatal conductance from water-use efficiency optimization as follows
\[\begin{equation} g_w = g_0 + 1.6 \frac{A_n}{c_a} \left(1 + \frac{g_1}{\sqrt{\text{VPD}}}\right) \end{equation}\]
where $g_0$ is the minimum stomatal conductance, $g_1$ is a PFT-specific slope parameter, VPD is the vapor pressure deficit, $A_n$ is the net photosynthesis and $c_a$ is the atmospheric CO₂ concentration.
\[g_w\]
and $A_n$ are also related by the diffusion equation
\[\begin{equation} g_w = g_0 + 1.6 \frac{A_n}{c_a - c_i} \end{equation}\]
where $c_i$ is the intercellular CO2 concentration.
The ratio of intercellular to atmosphere CO₂ concentration $\lambda_c$ can then be derived as
\[\begin{equation} \lambda_c = 1 - \frac{1}{1 + \frac{g_1}{\sqrt{\text{VPD}}}} \end{equation}\]
Process interface
Terrarium.compute_auxiliary! — Method
compute_auxiliary!(
state,
grid,
stomcond::MedlynStomatalConductance,
photo::LUEPhotosynthesis,
atmos::Terrarium.AbstractAtmosphere,
constants::PhysicalConstants,
args...
)
Methods
Terrarium.compute_gw_can — Function
compute_gw_can(
stomcond::MedlynStomatalConductance{NF},
photo::LUEPhotosynthesis{NF},
vpd,
An,
co2,
LAI,
β
) -> Any
Compute canopy-level water conductance [m/s] from the Medlyn et al. (2011) optimal stomatal conductance model. Includes minimum conductance and light extinction effects based on LAI, scaled by soil moisture factor β.
Terrarium.compute_λc — Function
compute_λc(stomcond, vpd)
Computes the ratio of leaf-internal and air CO2 concentration λc, derived from the optimal stomatal conductance model (Medlyn et al. 2011), Eq. 71, PALADYN (Willeit 2016).
Kernel functions
Terrarium.compute_stomatal_conductance! — Function
compute_stomatal_conductance!(
out,
i,
j,
grid,
fields,
stomcond::MedlynStomatalConductance{NF},
photo::LUEPhotosynthesis{NF},
atmos::Terrarium.AbstractAtmosphere,
constants::PhysicalConstants,
args...
) -> Any
Calls compute_stomatal_conductance and stores the result in out.
Terrarium.compute_stomatal_conductance — Function
compute_stomatal_conductance(
i, j, grid, fields,
stomcond::AbstractStomatalConductance,
photo::AbstractPhotosynthesis,
atmos::AbstractAtmosphere,
constants::PhysicalConstants,
args...
)Cell-level stomatal conductance computation. Returns stomatal/canopy conductance and internal CO₂ ratio for the specified cell.
References
- [4]
- M. Willeit and A. Ganopolski. PALADYN v1.0, a Comprehensive Land Surface–Vegetation–Carbon Cycle Model of Intermediate Complexity. Geoscientific Model Development 9, 3817–3857 (2016).
- [5]
- B. E. Medlyn, R. A. Duursma, D. Eamus, D. S. Ellsworth, I. C. Prentice, C. V. Barton, K. Y. Crous, P. De Angelis, M. Freeman and L. Wingate. Reconciling the Optimal and Empirical Approaches to Modelling Stomatal Conductance. Global Change Biology 17, 2134–2144 (2011).