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Water & Energy Cycles

Advances in understanding land surface processes requires spatially extensive and intensive descriptions of soil moisture in four dimensions; which suggests roles for in situ, remote sensing, and model products. Remotely sensed soil moisture data are usually available at low spatial resolutions and only for a shallow soil depth. In contrast, ground-based point measurements can extend beyond the rooting zone but are difficult to extrapolate spatially. Reconciling remote sensing and ground-based features, especially in situ networks, is of paramount importance in the development of a robust hydrologic prediction methodology at different spatial scales. Building on previous field campaigns, experience with in situ networks, microwave remote sensing, hydrologic process-based modeling, and data assimilation research, we propose a comprehensive analysis of soil moisture scaling behavior that focuses on the key issues; measurement support size and the precision of in situ and remote sensing platforms and appropriate downscaling schemes to sub-grid/application scales. This research is proposed to be conducted over the Southern Great Plains (SGP). Existing in situ resources in the SGP provide an ideal test bed for soil moisture scaling research. These networks would be integrated with data from remote sensing platforms at different resolutions. Characterizing the point observations (accuracy and reliability) and developing techniques for scaling to these diverse resolutions will be accomplished using a number of statistical and modeling approaches and additional intensive observational studies designed to resolve issues with existing infrastructure. We will adopt a multi-facet approach to this problem where soil moisture will be estimated across different scales by exploiting the correlations with static and/or transient hydrologic controls such as soil texture, topographic attributes, vegetation characteristics, and forcings. Characterizing the scaling behavior of the extensive SGP soil moisture resources and tying these to remote sensing and data assimilation products will also contribute to broader water and energy cycle research.

Project PI: Binayak Mohanty/Texas A&M; University

Texas A&M; University 2117 TAMU 301C Scoates Hall

Phone: (979)458-4421

Fax: (979)845-3932

http://baen.tamu.edu/users/bmohanty/

Email: bmohanty@tamu.edu

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Started: Aug 10, 2010

Last Activity: Dec 16, 2010

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