Stomatal optimization modelling in JSBACH: an in-depth case study on a boreal forest measurement site

Abstract

<p>The stomata on the leaves of terrestrial plants are a crucial pathway both in the soil-plant-atmosphere hydrological continuum and in the global carbon cycle. Stomatal optimization approaches have proven to be relevant in modelling the trade-off between carbon assimilation and water stress avoidance. In this in-depth case study, we use new optimization-based stomatal models in modelling vegetation gas exchange with the land surface model JSBACH.</p><p>The theoretical framework presented in Dewar et al. (2018) combines different optimization hypotheses and photosynthesis models to provide analytical solutions for various leaf-level state variables such as stomatal conductance and photosynthesis rate. The most successful combinations assume that plants regulate stomata as if to maximize photosynthesis at all times, and that photosynthesis is restricted by non-stomatal limitations related to water stress. In this study, we further develop the framework, which yields several promising stomatal conductance models.</p><p>We implement these stomatal models in the land surface model JSBACH, which we run for a single boreal forest site, the SMEAR II measurement station in southern Finland. The model runs are constrained with meteorological and soil moisture data and parametrized with plant properties previously measured at the site, such as xylem hydraulic conductance and photosynthetic parameters. Gross primary production and transpiration rates predicted by JSBACH under different stomatal and photosynthesis models are compared to eddy covariance measurements from SMEAR II, covering the years 2006 through 2012. The model results are also compared to each other and to those obtained using the Unified Stomatal Optimization model by Medlyn et al. (2011). The comparison is restricted to dry daytime hours in the growing season.</p><p> </p><p>References:<br>Dewar et al. 2018, <em>New Phytol. </em>217: 571–581<br>Medlyn et al. 2011, <em>Glob. Change Biol.</em> 17: 2134–2144</p>