Global Dataset of Ecohydrological Parameters Inferred from Satellite Observations

This dataset contains global maps of

(1) ecohydrological parameters for a theoretical model of the probability distribution of soil saturation;
(2) convergence, uncertainty and goodness-of-fit diagnostics; and
(3) soil water stress and uptake indexes,

associated with analysis in: Bassiouni, M., S.P. Good, C.J. Still, and C.W. Higgins (2020), Plant water uptake thresholds inferred from satellite soil moisture. Geophysical Research Letters. https://doi.org/10.1029/2020GL087077

All variable descriptions and units are included in the .nc metadata.

Code associated with this dataset are publicly available:
Probabilistic Inference of Ecohydrological Parameters (PIEP): http://doi.org/10.5281/zenodo.1257718.
Data Management for Global PIEP: http://doi.org/10.5281/zenodo.3235820

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 maximum rate and at which uptake is zero, in biomes with < 60% woody vegetation at 36-km grid resolution. We estimate thresholds through Bayesian inference using a stochastic water balance framework to construct theoretical soil moisture probability distributions consistent with satellite surface soil moisture. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions derived from satellite soil moisture observations and best-fit theoretical distributions using inferred parameters is 0.8. Spatially variable thresholds capture location-specific vegetation and climate characteristics and can be connected to biome-level water uptake strategies.