This paper presents an automated approach for processing the Soil Survey Geographic (SSURGO) Database and the National Land Cover Database (NLCD), and deriving gridded a priori parameters for the National Weather Service (NWS) Sacramento Soil Moisture Accounting (SAC-SMA) model from these data sets. Our approach considerably extends methods previously used in the NWS and offers automated and geographically invariant ways of extracting soil information, interpreting soil texture, and spatially aggregating SAC-SMA parameters. The methodology is composed of four components. The first and second components are SSURGO and NLCD preprocessors. The third component is a parameter generator producing SAC-SMA parameters for each soil survey area on an approximately 30-m grid mesh. The last component is a postprocessor creating parameters for user-specified areas of interest on the Hydrologic Rainfall Analysis Project (HRAP) grid. Implemented in open-source software, this approach was employed by creating a set of SAC-SMA parameter and related soil property grids spanning 25 states, wherein it was shown to greatly reduce the derivation time and meanwhile yield results comparable to those based on the State Soil Geographic Database (STATSGO). The broad applicability of the methodologies and associated intermediate products to hydrologic modeling is discussed.
['Yu Zhang', 'Ziya Zhang', 'Seann Reed', 'Victor Koren']
An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic database