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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 #308425
Title: A method to downscale soil moisture to fine-resolutions using topographic, vegetation, and soil data

Author
item RANNEY, KAYLA - Colorad0 State University
item NIEMANN, JEFFREY - Colorad0 State University
item LEHMAN, BRANDON - Colorad0 State University
item Green, Timothy
item JONES, ANDREW - Colorad0 State University

Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2015
Publication Date: 1/10/2015
Citation: Ranney, K.J., Niemann, J.D., Lehman, B.M., Green, T.R., Jones, A.S. 2015. A method to downscale soil moisture to fine-resolutions using topographic, vegetation, and soil data. Advances in Water Resources. 78:81-96. DOI 10.1016/j.advwatres.2014.12.003.

Interpretive Summary: Soil moisture can be estimated over large regions, but many applications require finer resolutions. Several methods to scale down soil moisture use topographic data, but vegetation and soil patterns can also be important. In this paper, a process-based downscaling model that uses fine-resolution topographic, vegetation, and soil data is presented. The method is tested at the Cache la Poudre catchment in Colorado, USA, where detailed vegetation and soil data were collected. Additional testing is performed at the Tarrawarra and Nerrigundah catchments in Australia, where limited soil data are available. Downscaled soil moisture patterns at Cache la Poudre improve when vegetation and soil data are used, and model performance is similar to previous methods. The model performance decreases at Tarrawarra and Nerrigundah using interpolated soil data, suggesting that soil data needs finer spatial detail to be useful for downscaling.

Technical Abstract: Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells). Several methods use topographic data to downscale, but vegetation and soil patterns can also be important. In this paper, a downscaling model that uses fine-resolution topographic, vegetation, and soil data is presented. The method is tested at the Cache la Poudre catchment where detailed vegetation and soil data were collected. Additional testing is performed at the Tarrawarra and Nerrigundah catchments where limited soil data are available. Downscaled soil moisture patterns at Cache la Poudre improve when vegetation and soil data are used, and model performance is similar to an EOF method. Using interpolated soil data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than an EOF method, suggesting that soil data needs greater spatial detail to be useful for downscaling