Quantitative risk assessments of pollution and data related to the effectiveness of mitigating best management practices (BMPs) are important aspects of nonpoint source pollution control efforts, particularly those driven by specific water quality objectives and by measurable improvement goals, such as the total maximum daily load (TMDL) requirements. Targeting critical source areas (CSAs) that generate disproportionately high pollutant loads within a watershed is a crucial step in successfully controlling nonpoint source pollution. The importance of watershed simulation models in assisting with the quantitative assessments of CSAs of pollution (relative to their magnitudes and extents) and of the effectiveness of associated BMPs has been well recognized. However, due to the distinct disconnect between the hydrological scale in which these models conduct their evaluation and the farm scale at which feasible BMPs are actually selected and implemented, and due to the difficulty and uncertainty involved in transferring watershed model data to farm fields, there are limited practical applications of these tools in the current nonpoint source pollution control efforts by conservation specialists for delineating CSAs and planning targeting measures. There are also limited approaches developed that can assess impacts of CSA-targeted BMPs on farm productivity and profitability together with the assessment of water quality improvements expected from applying these measures. This study developed a modeling framework that integrates farm economics and environmental aspects (such as identification and mitigation of CSAs) through joint use of watershed- and farm-scale models in a closed feedback loop. The integration of models in a closed feedback loop provides a way for environmental changes to be evaluated with regard to the impact on the practical aspects of farm management and economics, adjusted or reformulated as necessary, and revaluated with respect to effectiveness of environmental mitigation at the farm- and watershed-levels. This paper also outlines steps needed to extract important CSA-related information from a watershed model to help inform targeting decisions at the farm scale. The modeling framework is demonstrated with two unique case studies in the northeastern United States (New York and Vermont), with supporting data from numerous published, location-specific studies at both the watershed and farm scales. Using the integrated modeling framework, it can be possible to compare the costs (in terms of changes required in farm system components or financial compensations for retiring crop lands) and benefits (in terms of measurable water quality improvement goals) of implementing targeted BMPs. This multi-scale modeling approach can be used in the multi-objective task of mitigating CSAs of pollution to meet water quality goals while maintaining farm-level economic viability.
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