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.Plain Language Summary Vegetation regulates a large fraction of the terrestrial water and carbon cycles as it adapts and responds to changing environmental conditions such as soil moisture availability, yet our ability to characterize diversity in vegetation soil water-use behavior at large scales is limited. In this study, we analyze satellite observations to estimate thresholds that are commonly used to approximate when vegetation extracts water from the soil. We show that the newly found values are more consistent with global patterns of soil moisture compared to constants found in the literature. Spatially variable plant water uptake thresholds reflect land cover and climate characteristics and can be connected to water-use strategies in biomes not dominated by trees.

Keywords

ecohydrology; satellite soil moisture; wilting point; soil water stress; plant water use strategies; Bayesian inference

Published in

Geophysical Research Letters
2020, Volume: 47, number: 7, article number: e2020GL087077
Publisher: American Geophysical Union ({AGU})

SLU Authors

• Bassiouni, Maoya

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    
  • Oregon State University

UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.1029/2020GL087077

Permanent link to this page (URI)

https://res.slu.se/id/publ/105935