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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 #347186
Research Project: Improving the Sustainability of Irrigated Farming Systems in Semi-Arid Regions

Location: Water Management and Systems Research

Title: Hydrologic downscaling of soil moisture using global data without site-specific calibration

Author
item GRIECO, NICHOLAS - Colorado State University
item NIEMANN, JEFFREY - Colorado State University
item Green, Timothy
item JONES, ANDREW - Colorado State University
item GRAZAITIS, PETER - Us Army Research

Submitted to: Journal Hydrologic Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2018
Publication Date: 9/14/2018
Citation: Grieco, N.R., Niemann, J.D., Green, T.R., Jones, A.S., Grazaitis, P.J. 2018. Hydrologic downscaling of soil moisture using global data without site-specific calibration. Journal Hydrologic Engineering. Vol. 23, Issue 11. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001702.
DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001702

Interpretive Summary: Numerous applications require fine-resolution soil moisture patterns, but most satellite remote sensing methods provide very coarse-resolution (tens of kilometers) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data. In previous applications, the model parameters were calibrated using detailed ground-based soil moisture data, but very few regions have such data. The objective of this study is to estimate soil moisture patterns from global datasets. The global model is applied to six study sites with detailed soil moisture observations. The use of EMT+VS with global datasets provides more reliable estimates than simply using the coarse-resolution soil moisture.

Technical Abstract: Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data, but it requires specification of sixteen parameters. In previous applications, the parameters have been calibrated using detailed in-situ soil moisture data, but very few regions have such data. The objective of this study is to evaluate EMT+VS model performance when the parameters are estimated from global datasets instead of site-specific calibration. Methods are developed to identify key model parameters that can be estimated from the global datasets to provide downscaling skill. The global model (without site-specific calibration) is applied to six study sites, and its results are compared to the results of locally-calibrated models and to in-situ soil moisture observations. The use of global datasets decreases EMT+VS downscaling performance and reduces the spatial variability in the fine-resolution soil moisture patterns. Yet, overall, the global model provides more reliable soil moisture estimates than simply using the coarse-resolution moisture.