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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 #335275

Research Project: Spatial Modeling of Agricultural Watersheds: Water and Nutrient Management and Targeted Conservation Effects at Field to Watershed Scales

Location: Water Management and Systems Research

Title: Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

Author
item COWLEY, GARRET - Colorado State University
item NIEMANN, JEFFREY - Colorado State University
item Green, Timothy
item Seyfried, Mark
item JONES, ANDREW - Colorado State University
item GRAZAITIS, PETER - Us Army Research

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/21/2017
Publication Date: 1/27/2017
Citation: Cowley, G.S., Niemann, J.D., Green, T.R., Seyfried, M.S., Jones, A.S., Grazaitis, P.J. 2017. Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief. Water Resources Research. 53(2):1553-1574. doi:10.1002/2016WR019907.

Interpretive Summary: Mapping of soil moisture is important for many applications such as flood forecasting, soil protection, and crop management. The Equilibrium Moisture from Topography, plus Vegetation and Soil (EMT+VS) model was developed previously to produce fine-resolution maps of soil moisture from coarse-resolution satellite data combined with fine-resolution topographic, vegetation, and soil data. The EMT+VS model has not been applied to regions with large ranges of elevation that produce substantial variations in precipitation and potential evapo-transpiration (PET). In this research, precipitation and PET relationships with topography are developed. The effects of spatial variations in these variables are then included in EMT+VS to estimate surface soil moisture. The methods were tested against ground truth data measured at the Reynolds Creek Watershed in southern Idaho. The main features of the spatial patterns of both precipitation and PET were captured, which improved fine-resolution soil moisture estimates. Estimated PET patterns provided a larger improvement in the soil moisture patterns than precipitation. This result is likely due to the PET pattern being more persistent through time, and thus more predictable, than the precipitation pattern.

Technical Abstract: Mapping of soil moisture is important for many applications such as flood forecasting, soil protection, and crop management. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (=124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple precipitation and PET downscaling methods are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek Watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables, and the fine-resolution soil moisture estimates improve when these downscaling methods are used. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.