An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic database

This paper presents an automated approach for deriving gridded a pri1 ori parameters for the National Weather Service (NWS) Sacramento Soil 2 Moisture Accounting (SAC-SMA) model from the Soil Survey Geographic 3 (SSURGO) Database and National Land Cover Database (NLCD). Our ap4 proach considerably extends methods previously used in the NWS and of5 fers automated and geographically invariant ways of extracting soil informa6 tion, interpreting soil texture, and aggregating SAC-SMA parameters. The 7 methodology is comprised of four components, all of which are implemented 8 in open-source software, notably R (a statistical package) and the Geographic 9 Resources Analysis Support System (GRASS; a free Geographic Information 10 System). The first and second components are SSURGO and land cover 11 preprocessors, which are written in R and GRASS, respectively. The third 12 component is the parameter generator based on both R and GRASS; it pro13 duces 11 SAC-SMA parameters for each soil survey area on an approximately 14 30-m resolution grid. The last component is a C++ based postprocessor that 15 creates parameters on the Hydrologic Rainfall Analysis Project (HRAP) grid 16 and for the area of interest. We describe the scientific basis and technical 17 features of the four components, and demonstrate their efficacy through the 18 creation of mosaicked parameter grids covering the geographic domains of 19 six NWS River Forecast Centers (NWSRFCs). 20