Soil and Atmospheric Drought Explain the Biophysical Conductance Responses in Diagnostic and Prognostic Evaporation Models Over Two Contrasting European Forest Sites
Authors
Mallick K., Sulis M., Jiménez-Rodríguez C.D., Hu T., Jia A., Drewry D.T.
Reference
Journal of Geophysical Research: Biogeosciences, vol. 129, n° 6, art. no. e2023JG007784, 2024
Description
Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) faces challenges due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to concurrent soil and atmospheric drought. Despite a soaring popularity to refine gcs formulation in LSMs by integrating soil-plant hydraulics, SEB models often overlook the utility of gcs. This oversight is attributed to the overriding emphasis on reducing ga uncertainties and the lack of coordination between these two modeling communities. This disengagement between modeling communities poses a persistent challenge in understanding divergent evaporation estimates during intense soil-atmospheric drought. Here we conducted a theoretical experiment over two contrasting European forest sites to examine the sensitivity of conductances and evaporative fluxes to a water-stress factor (β-factor), coupled with land surface temperature (LST) and vapor pressure deficit (representing soil and atmospheric drought proxy). Utilizing a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC) and a prognostic model (Community Land Model, CLM5.0), the analysis revealed that the β-factor, alongside different functional forms of conductances and the loose coupling of CLM5.0 conductances to LST, significantly influenced the response of the two models to soil and atmospheric drought. These discrepancies propagated in the estimates of evaporative fluxes between STIC and CLM5.0. The analysis reaffirms the need for a consensus on theory and models capturing the sensitivity of biophysical conductances to soil-atmospheric drought interplay. It emphasizes the need for fostering collaboration between modeling communities to enhance the prediction of evaporation in complex environmental conditions.
