Global Vegetation Stress Drivers based on Hybrid Modelling and Explainable AI

Terrestrial evaporation (E) is a critical component of the water cycle, returning nearly 60% of continental precipitation to the atmosphere and dissipating approximately 50% of surface net radiation. A prevalent approach for estimating E involves computing a theoretical maximum, known as potential evaporation (E_p), and scaling it based on a multiplicative stress factor, often referred to as “evaporative stress” (S) or “transpiration stress” (S_t) when specifically applied to plant transpiration. Like stomatal or surface conductance, S_t is governed by a complex nonlinear interplay of environmental drivers such as soil moisture, air temperature, radiation, and atmospheric vapor pressure deficit. This complexity is not yet fully understood, which further hampers its accurate physical modelling and limits our ability to comprehend transpiration’s sensitivity to the changing environment.

The fourth generation of the Global Land Evaporation Amsterdam Model (GLEAM4) has yielded a global dataset of transpiration by integrating multi-source remote sensing data following a hybrid approach, in which E_p is computed based on a process-based model and S_t is calculated by employing deep neural networks. These neural networks are trained on global eddy covariance and sap flow measurements for both tall and short vegetation, and are informed by a set of environmental controls or biotic factors. These factors include soil moisture, vapor pressure deficit, atmospheric CO₂ concentration, wind velocity, air temperature, downwelling shortwave radiation, LAI, and vegetation optical depth. Beyond the predictive capabilities of these deep neural networks, the relationships between environmental controls and S_t within these neural networks remain under exploration, leaving uncertainty as to whether GLEAM4 accurately represents real-world processes. To explore the relationships, we employ the SHapley Additive exPlanation (SHAP) method, which quantifies the marginal contributions of predictors to model predictions, offering insights into the relative importance of environmental drivers in determining S_t.

Our findings highlight dominant S_t drivers across various climatic regimes and ecosystems, revealing their contributions' temporal evolution. Additionally, we investigate how S_t responds to shifts in environmental conditions, including climate and vegetation changes, water stress, atmospheric aridity, and rising CO₂ levels. Our study enhances global understanding of transpiration dynamics and provides critical insights into the impacts of diverse hydroclimatic drivers, thereby supporting broader applications within the hydrology and climate communities.