|dc.description.abstract||Impairment in the quality of water due to nutrients and sediments originating from watersheds is a serious problem in the USA and the world. Identification of critical source areas (CSAs), which contribute most of the pollutants, is important for cost-effective implementation of best management practices. Watershed models are widely used for this purpose. In this work, we looked into two key issues related to CSA identification. First is whether model choice and model complexity effects CSAs. Second is whether an uncalibrated model can identify CSAs correctly.
A complex model- Soil and Water Assessment Tool (SWAT), and a simple model- Generalized Watershed Loading Function (GWLF) was used in this study to identify the CSAs in the Saugahatchee Creek watershed in east central Alabama. The objective of this study was to explore the effect of model choice and model complexity in location of CSAs. Models were calibrated and validated for flow, sediment, TN and TP on a monthly time scale. Performance of SWAT model was slightly better for predicting sediment, TP and TN. CSAs were identified at sub-watershed scale for sediment, TP and TN. It was found that although a simple model (GWLF) is certainly useful in watershed modeling and identification of CSAs, it may not capture all the CSAs. In the study watershed, SWAT and GWLF identified mostly the same areas as CSAs. However, GWLF failed to capture some CSAs.
We also studied the effects of model calibration on location of CSAs. SWAT was applied to two watersheds with differing characteristics. CSAs were identified at HRU level (Hydrologic Response Unit) based on loadings per unit area. Results revealed that identified CSAs and their location for sediment, TP and TN were similar with calibrated and uncalibrated models in both watersheds. The study thus concluded that calibration of model based on data at the watershed outlet has little effect on location of CSAs. SWAT can thus be used with no calibration for identifying the CSAs in watersheds lacking sufficient data for model calibration.||en