Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) is challenging due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to the coalition of soil and atmospheric drought. Despite a soaring popularity in refining gcs formulation in the LSMs by introducing a link between soil-plant hydraulics and gcs, the utility of gcs has been surprisingly overlooked in SEB models due to the overriding emphasis on eliminating ga uncertainties and the lack of coordination between these two different modeling communities. Therefore, a persistent challenge is to understand the reasons for divergent evaporation estimates from different models during strong soil-atmospheric drought. Here we present a virtual reality experiment over two contrasting European forest sites to understand the apparent sensitivity of the two critical conductances and evaporative fluxes to a water-stress factor (b-factor) in conjunction with land surface temperature (soil drought proxy) and vapor pressure deficit (atmospheric drought proxy) by using a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC1.2) and a prognostic model (Community Land Model, CLM5.0). Results revealed the b-factor and different functional forms of the two conductances to be a significant predictor of divergent response of the conductances to soil and atmospheric drought, which subsequently propagated in the evaporative flux estimates between STIC1.2 and CLM5.0. This analysis reaffirms the need for consensus on theory and models that capture the sensitivity of the biophysical conductances to the complex coalition of soil and atmospheric drought for better evaporation prediction.

Recent European heatwaves have significantly impacted forest ecosystems, leading to increased plant water stress. Advances in land surface models aim to improve the representation of vegetation drought responses by incorporating plant hydraulics into the plant functional type (PFT) classification system. However, reliance on PFTs may inadequately capture the diverse plant hydraulic traits (PHTs), potentially biasing transpiration and vegetation water stress representations. The detection of vegetation drought stress is further complicated by the mixing of different tree species and forest patches. This study uses the Community Land Model version 5.0 to simulate an experimental mixed-forest catchment with configurations representing standalone, patched mixed, and fully mixed forests. Biome-generic, PFT-specific, or species-specific PHTs are employed. Results emphasize the crucial role of accurately representing mixed forests in reproducing observed vegetation water stress and transpiration fluxes for both broadleaf and needleleaf tree species. The dominant vegetation fraction is a key determinant, influencing aggregated vegetation response patterns. Segregation level in PHT parameterizations shapes differences between observed and simulated transpiration fluxes. Simulated root water potential emerges as a potential metric for detecting vegetation stress periods. However, the model’s plant hydraulic system has limitations in reproducing the long-term effects of extreme weather events on needleleaf tree species. These findings highlight the complexity of modeling mixed forests and underscore the need for improved representation of plant diversity in land surface models to enhance the understanding of vegetation water stress under changing climate conditions.

A document by César Dionisio Jimenez-Rodriguez. Click on the document to view its contents.