A Framework for Downscaling Intermediate-Resolution Soil Moisture to Fine Resolutions using Topographic, Vegetation, and Soil Information

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Monday, 3 February 2014: 2:00 PM
Room C209 (The Georgia World Congress Center )
Jeffrey D. Niemann, Colorado State University, Fort Collins, CO; and K. J. Ranney, A. S. Jones, T. R. Green, T. Giles, and M. Woodbury

Soil moisture (water content in the near surface, e.g., top 5 to 30 cm) is a key state variable for many hydrologic, agricultural, and land-management applications. To be useful to decision makers in these fields, soil moisture estimates must have a very fine spatial resolution (i.e. grid cells with a linear dimension of about 30 m) and extend over relatively large areas (regions with a linear dimension of about 50 km). Directly measuring soil moisture patterns with these characteristics is impractical. However, multiple methods have been proposed to estimate soil moisture at an intermediate (1 km) resolution. For example, passive and active microwave measurements could be assimilated into a weather forecasting model to produce intermediate resolution soil moisture patterns. However, a method is needed to downscale soil moisture from a 1 km resolution to a 30 m resolution. To add fine-resolution detail, supplemental data sources are required that are predictive of soil moisture variations at this scale and are widely available. Topographic attributes, vegetation cover, and soil characteristics have been shown to influence soil moisture variations at the relevant scales. Topographic data are readily available at fine resolutions, and corresponding vegetation and soil data may be available. The objective of this research is to develop a downscaling method that uses variations in topographic, vegetation, and soil characteristics to estimate variations in soil moisture. The proposed method downscales soil moisture by evaluating the water balance for the surface soil layer at equilibrium conditions and inferring the roles of the various hydrologic processes from the supplemental data. The method is tested by applying it to several land areas where soil moisture observations have been collected at hundreds of locations on multiple dates. Each of these areas roughly corresponds to a single 1 km grid cell, so the downscaling method aims to reproduce the observed spatial patterns of soil moisture on multiple dates from the areal average soil moisture values. In all cases, the downscaling algorithm is able to reproduce a substantial fraction of the observed spatial variation and most of the total space-time variation of soil moisture.