Abstract
Hydrologic response at all resolutions is controlled by physical processes. Accurately capturing the physical process at a high-resolution is essential for down scaling many satellite observations at coarse resolutions. In this paper, a four-dimensional process representative soil moisture downscaling model is developed to downscale the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) 25 km resolution soil moisture product. The model is composed of the calculation of an antecedent precipitation accumulation (APA) index to capture soil moisture spatial and temporal variations at the 500 m resolution, and the application of a Geographic Information System (GIS) to simulate physical processes which can regulate soil moisture changes throughout the watersheds. APA index, as a representation of the provisional value of soil moisture, is calculated by adopting an exponential formulation to synthesize the effects of infiltration, soil evaporative efficiency, and vegetation resistance on soil water content following precipitation. Five days of AMSR-E soil moisture derivatives spanning the start of the monsoon and the duration of the storm are selected for downscaling. The results show that soil moisture spatial variation is primarily controlled by the distribution of precipitation and soil properties. Subsequently relative soil moisture, radiation, and vegetation become significant in controlling landsurface fluxes and thus influence soil moisture variation as time progresses. The downscaled soil moisture data (500 m resolution) are assessed using in-situ soil moisture measurements from the National Oceanic and Atmospheric Administration (NOAA) Hydrometeorology Testbed (HMT) and the U.S. Department of Agriculture (USDA) Southwest Watershed Research Center (SWRC) Walnut Gulch Experimental Watershed (WGEW) observing networks. The root mean square error (RMSE) between the disaggregated and in-situ soil moisture is 0.034 vol./vol. with percent bias (PBIAS) 0.85%. The overall R2 value is 0.788.
Highlights
Many studies have pointed out the importance of soil moisture in controlling the partitioning of rainfall into runoff and percolation as well as the separation of incident solar energy into sensible heat and latent heat across the land-atmosphere interface
The methodology for retrieving high-resolution soil moisture is composed of two tasks: 1) developing a multiple time-scale exponential model to be used for calculating antecedent precipitation accumulation (APA) index and 2) creating a Geographic Information System (GIS) model for downscaling Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture based on the APA index
root mean square error (RMSE) gradually increases after the monsoon season started on July 10, 2008
Summary
Many studies have pointed out the importance of soil moisture in controlling the partitioning of rainfall into runoff and percolation as well as the separation of incident solar energy into sensible heat and latent heat across the land-atmosphere interface. Though LSMs can generate high resolution soil moisture at various depths, the simulated soil moisture at relatively high spatial resolutions are often biased due to the uncertainties of their parameterization, numerical integration, and various physical processes represented [7,8] To resolve these problems, many researches have turned to new strategies, such as the development of simplified soil-moisture models [9,10,11], the application of data assimilation techniques [12,13], and the involvement of remote sensing data [14]. The importance of these strategies is that they offset mathematical simplicity and an authentic representation of the nonlinearity of land, atmospheric, hydrologic, and ecologic dynamics at the watershed scale
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