Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

摘要

Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions?(pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i)?an annual rainfall pattern and (ii)?a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from?0.89 to?0.99. The coefficient of variation of posterior parameter distributions ranged from