Plant Water Uptake Thresholds Inferred From Satellite Soil Moisture
Maoya Bassiouni, Stephen P. Good, Christopher J. Still, Chad W. Higgins
Abstract
Abstract Empirical functions are widely used in hydrological, agricultural, and Earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its well‐watered rate and at which uptake ceases, in biomes with <60% woody vegetation at 36‐km grid resolution. We estimate thresholds through Bayesian inference using a stochastic soil water balance framework to construct theoretical soil moisture probability distributions consistent with empirical distributions derived from satellite soil moisture observations. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions and theoretical distributions using reference constants, inferred median parameters per biome, and spatially variable inferred parameters are 0.38, 0.59, and 0.8, respectively. Spatially variable thresholds capture location‐specific vegetation and climate characteristics and can be connected to biome‐level water uptake strategies. Results demonstrate that satellite soil moisture probability distributions encode information, valuable to understanding biome‐level ecohydrological adaptation and resistance to climate variability.