Use of Digital Soil Maps in a Rainfall-Runoff Model

Computer programs, collectively named the Soil-Water Balance Modeling System (SWBMS), are developed to evaluate alternative methods for using digital soil maps in rainfall-runoff modeling. The two soils databases of interest are the State Soil Geographic Database (STATSGO ? 1:250,000 scale) and the Soil Survey Geographic Database (SSURGO ? 1:24,000 scale), both maintained by the U.S. Department of Agriculture. In SWBMS, simple conceptual models are used to describe infiltration, percolation, and evaporation processes. Use of simplified process models is justified because there are large uncertainties involved with the specification of soil hydraulic properties and in quantifying rainfall. Representative soil hydraulic properties are assigned based on USDA texture class. Distributed soil properties are used in conjunction with distributed NEXRAD Stage III rainfall data to make to make rainfall excess calculations. SWBMS is applied using hydrologic data from the Little Washita watershed in southwestern Oklahoma. The performance of SWBMS in predicting runoff is evaluated using different levels of spatial complexity to describe soil properties. Spatial complexity is varied in three different ways. (1) Differences between a one and a two-layer conceptual soil model are considered. (2) Simulations in which a single surface soil texture is assigned to the entire watershed, a single surface soil texture is assigned to each NEXRAD rainfall cell, and multiple surface soil textures are considered within each NEXRAD rainfall cell are compared. (3) Soil texture properties are assigned using soil maps created from sources at two different scales (1:250,000 and 1:24,000). Calibration and validation of the SWBMS model has yielded interesting insights. In the two-layer conceptual soil model, the top layer controls direct runoff, but the bottom layer serves as an important control on how much water evaporates and how much water percolates to the groundwater reservoir. When the spatial variability of surface texture is reduced through resampling of soil properties, model performance decreases. Although the 1:250,000 scale soil map contains considerably less spatial detail than the 1:24,000 scale map, use of each data set as input in model validation runs produced similar results. Further simulations should be made to verify these conclusions.