Stochastic analysis and probabilistic downscaling of soil moisture in small catchments

Abstract

Many applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil moisture based on its dependence on topography, vegetation, and soil characteristics. However, observed soil-moisture patterns also contain stochastic variations around such estimates. The objectives of this research are to perform a geostatistical analysis of the stochastic variations in soil moisture and to develop a downscaling model that reproduces the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments (8.0 and 10.5 ha) are used for the geostatistical analysis and model development, and two other catchments (6.0 and 60 ha) are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model is used to estimate soil moisture, and the difference between the point measurements and the EMT + VS estimates are considered to be the stochastic variations. The stochastic variations contain a temporally stable pattern along with temporally unstable patterns. All of these patterns include spatially correlated and uncorrelated variations. Moreover, the spatial variance of the stochastic patterns increases in absolute terms with the spatial-average moisture content. The EMT + VS model can reproduce the observed statistical features if it is generalized to include stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors.