Impact of Best Management Practices on Water Quality of Two Small Watersheds in Indiana: Role of Spatial Scale.

摘要

Transport and fate of sediments and nutrients within watersheds have important implications for water quality and water resources. Water quality issues often arise because sediments serve as carriers for various pollutants such as nutrients, pathogens, and toxic substances. The Clean Water Act provision (CWA) (Section 303(d)) requires all states to develop and implement a Total Maximum Daily Load (TMDL) for their impaired water bodies, and water bodies that are likely to join this list. Implementation of Best Management Practices (BMPs) is a conventional approach for controlling nonpoint sources of sediments and nutrients. However, implementation of BMPs has rarely been followed by a good long-term data monitoring program in place to study how effective they have been in meeting their original goals. Long-term data on flow and water quality within watersheds, before and after placement of BMPs, is not generally available. Utility of mathematical models provides an effective and powerful tool for evaluation of long-term performance of BMPs (especially new ones that have had little or no history of use). In this study, a process-based modeling framework is developed to evaluate the effectiveness of parallel terraces, field borders, grassed waterways, and grade stabilization structures in reducing sediment and nutrient yields in two small agricultural watersheds (<10 km2) in Indiana, with Soil and Water Assessment Tool (SWAT) serving as the watershed model. Based on the functionality of each BMP, appropriate model parameters are selected and altered to represent the effect of the BMP on hydrologic and water quality processes. A sensitivity analysis is performed to evaluate the sensitivity of model computations to selected parameters. Results indicated that parallel terraces and field borders were effective at a field scale, while grassed waterways and grade stabilization structures were the more effective BMPs at a watershed scale.