Abstract. 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 < 1 to 15 %.
The parameter identifiability was not significantly improved in the more
complex seasonal model; however, small differences in parameter values
indicate that the annual model may have absorbed dry season dynamics.
Parameter estimates were most constrained for scales and locations at which
soil water dynamics are more sensitive to the fitted ecohydrological
parameters of interest. In these cases, model inversion converged more slowly
but ultimately provided better goodness of fit and lower uncertainty. Results
were robust using as few as 100 daily observations randomly sampled from the
full records, demonstrating the advantage of analyzing soil saturation pdfs
instead of time series to estimate ecohydrological parameters from sparse
records. Our work combines modeling and empirical approaches in ecohydrology
and provides a simple framework to obtain scale- and site-specific analytical
descriptions of soil moisture dynamics consistent with soil moisture
observations.
Simplified stochastic soil-moisture models: a look at infiltration
