2006 Philadelphia Annual Meeting (22–25 October 2006)

Paper No. 8
Presentation Time: 10:15 AM

EFFECTS OF SOIL DATA RESOLUTION ON HYDROLOGY AND NON-POINT SOURCE POLLUTION MODEL PREDICTIONS


GEZA, Mengistu and MCCRAY, John E., Environmental Science and Engineering, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, jmccray@mines.edu

Soil physicochemical information is one of the crucial inputs needed to assess the impacts of existing and alternative agricultural management practices on water quality. Because soil data sets may yield different results in water-quality predictions, it is important to consider this important issue when developing Total Maximum Daily Loads (TMDLs) for impaired watersheds where conflicting interests of stakeholders may opt for different soil data sets to estimate point and non-point source loadings such as sediment and nutrients.

The objective of this study was to assess the effects of different soil data resolutions on stream flow, sediment and nutrient predictions when used as input for Soil and Water Assessment Tool (SWAT). The two U.S. Department of Agriculture soil databases, the State Soil Geographic database (STATSGO) and the Soil Survey Geographic database (SSURGO), are the best alternatives available for modeling purposes. These two soil data sets have different resolutions. SWAT model predictions were compared for the two soil data sets before and after calibration.

A large proportion of the area, which was lumped into hydrologic soil group C when the coarse resolution STATSGO was used, was grouped into hydrologic group D when the fine resolution SSURGO was used as a result of discretization. Therefore, before model calibration, stream flow predicted was higher when SSURGO data was used compared to STATSGO. As a result a lower soil erodibility factor in SSURGO compared to STATSGO, SSURGO predicted less stream loading than STASGO in terms of sediment and sediment-attached nutrients components, and vice versa for dissolved nutrients carried by the flow. When compared to observed flow, STATSGO performed better relative to SSURGO before calibration. SSURGO provided better results after calibration as evaluated by R2 value (0.74 compared to 0.61 for STASGO) and the NSE values (0.70 and 0.61 for SSURGO and STATSGO, respectively) although both are in the same satisfactory range.