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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #358547
Research Project: Response of Ecosystem Services in Agricultural Watersheds to Changes in Water Availability, Land Use, Management, and Climate

Location: Water Management and Systems Research

Title: Stochastic analysis and probabilistic downscaling of soil moisture

Author
item DESHON, JORDAN - Colorad0 State University
item NIEMANN, JEFFREY - Colorad0 State University
item Green, Timothy
item JONES, ANDREW - Colorad0 State University
item GRAZAITIS, PETER - Us Army Research

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/17/2020
Publication Date: 2/21/2020
Citation: Deshon, J.P., Niemann, J.D., Green, T.R., Jones, A.S., Grazaitis, P.J. 2020. Stochastic analysis and probabilistic downscaling of soil moisture. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124711.
DOI: https://doi.org/10.1016/j.jhydrol.2020.124711

Interpretive Summary: Many applications require fine-resolution soil-moisture maps with realistic statistical properties. Existing downscaling models can estimate soil moisture based on its dependence on topography, vegetation, and soil characteristics, but real soil-moisture patterns contain additional variations. The objectives of this research are to analyze the spatial statistics of variations in soil moisture and to develop a downscaling model that reproduces the observed statistical features. Extensive soil-moisture observations from two catchments are used for the spatial analysis and model development, and two other catchments are used for model evaluation. The difference between the point measurements and the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model estimates are stochastic variations containing temporally stable and unstable patterns. All of the patterns include spatially correlated and uncorrelated variations, and the spatial variance of the stochastic patterns increases with the spatial-average moisture content. The generalized EMT+VS model with stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors can reproduce observed statistical features.

Technical 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 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 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 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.