Stochastic analysis and probabilistic downscaling of soil moisture - Deshon, Jordan P., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Cooley, Daniel S., committee member (Colorado State University. Libraries, 2018)
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 downscaling models that reproduce the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments are used for the geostatistical analysis and model development, and two other catchments are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model is used to downscale 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 with the mean 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. It can also reproduce most observed properties if stochastic variations are inserted directly in its soil moisture outputs. These analyses and downscaling models provide insight into the nature of stochastic variations in soil moisture and can be further tested by application to other catchments and larger regions.
Keywords: geostatistical analysis; soil moisture; variability; semivariograms; downscaling; spatial statistics