Use of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters

Parameters for conceptual models such as the Sacramento Soil Moisture Accounting model (SAC-SMA, the NWS operational model) can be derived from observed hydrograph analysis, but are not readily derived from physical basin characteristics. While soil property data are available now through the entire country as high resolution gridded files (e.g., STASGO), they are used mostly as a qualitative information. It restricts significantly application of these models (e.g., ungaged basins, semi-distributed versions, etc.).

The basic physics of the SAC-SMA model is a two soil layer structure. Each layer consists of tension and free water storages that interact in generating soil moisture states and five runoff components. Most of the 16 parameters of the model have to be calibrated using historical rainfall/runoff data. Initial model parameters are usually estimated based on hydrograph analysis at a river basin outlet. This study is focused on developing a procedure to derive the SAC-SMA model parameters based on soil texture data. To quantify relationships of model parameters with soil properties, the assumption was made that the SAC-SMA tension water storages relate to an available soil water, and that free water storages relate to gravitational soil water. Porosity, field capacity, and wilting point derived from STASGO dominant soil texture for eleven standard layers were used in estimating available and gravitational water storages. SCS runoff curve numbers and saturated hydraulic conductivity of different soils were also used. Analytical relationships were derived for 11 SAC-SMA model parameters. Preliminary tests on a few basins in different regions suggest that most parameters derived from soil properties agreed reasonably well with calibrated parameters for those basins. Accuracy statistics of hydrographs simulated using calibrated and derived parameters were also close. Although calibrated parameter simulations usually give higher accuracy, the gain is not significant. It means that parameters derived from soils data are very reasonable, and can be improved by using calibration if observed historical data are available.