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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 #411710
Research Project: Improving Resiliency of Semi-Arid Agroecosystems and Watersheds to Change and Disturbance through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: Assessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado

Author
item FISCHER, SAMANTHA - Colorado State University
item NIEMANN, JEFFREY - Colorado State University
item SCALIA, JOSEPH - Colorado State University
item BULLOCK, MATTHEW - Colorado State University
item PROUXL, HOLLY - Colorado State University
item KIM, BORAN - Colorado State University
item Green, Timothy
item GRAZAITIS, PETER - Us Army Research

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/7/2024
Publication Date: 11/23/2024
Citation: Fischer, S.C., Niemann, J.D., Scalia, J., Bullock, M., Prouxl, H.E., Kim, B., Green, T.R., Grazaitis, P.J. 2024. Assessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado. Journal of Hydrology. 65(April 2025):e132397. https://doi.org/10.1016/j.jhydrol.2024.132397.
DOI: https://doi.org/10.1016/j.jhydrol.2024.132397

Interpretive Summary: Soil moisture can be estimated over large land areas using microwave satellites or land surface models, but these estimates must be downscaled to be useful for off-road vehicle mobility, drought forecasting, and other applications. The objective of this study is to test the Equilibrium Moisture from Topography plus Vegetation and Soil (EMT+VS) downscaling method for how the choice of model inputs affects the accuracy of the resulting soil moisture estimates. The model is applied to a 4,000-ha ranch in northern Colorado that includes diverse topography, vegetation, and soils. The results are compared to soil moisture measurements that span a wide range of conditions. We show that the EMT+VS model can reproduce much of the variability in the ground-based measurements, outperforming two other soil moisture downscaling methods. The choice of satellite data and index used to characterize vegetation impacts the accuracy of the soil moisture results.

Technical Abstract: Soil moisture is an important variable in the hydrologic cycle and a key consideration for off-road vehicle mobility, drought forecasting, and many other applications. Soil moisture can be estimated at coarse resolutions (~10 km grid cells) using microwave satellites or land surface models, but these estimates must be downscaled to ~10 m resolutions to be useful for many applications. The Equilibrium Moisture from Topography Plus Vegetation and Soil (EMT+VS) downscaling model has been tested over several small catchments, but it has not been tested when applied across a large microwave satellite or land surface model grid cell. The objective of this study is to test the EMT+VS soil moisture downscaling method for such conditions, examining how the choice of model inputs affects the accuracy of the resulting soil moisture estimates. The model is applied to a 4,000-ha ranch in Northern Colorado that includes diverse topography, vegetation, and soils. The results are compared to in situ soil moisture observations collected at 86 locations on 10 dates that span a wide range of conditions. The study finds that the EMT+VS model can reproduce much of the space-time variation in the observations and outperforms its coarse resolution input and two other soil moisture estimation methods. The model can produce similar results using a variety of coarse resolution soil moisture inputs and fine resolution (30 m, 10 m, or 3 m) topographic inputs, but the choice of satellite, image date, and index used to characterize vegetation impacts the accuracy of the soil moisture results.