A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models. (October 2018) - Tasdighi, Ali; Arabi, Mazdak; Harmel, Daren; Line, Daniel
Abstract: A Bayesian total uncertainty analysis framework is presented to assess the model estimates of the effectiveness of watershed management practices in reducing nonpoint source (NPS) pollution. The framework entails a two-stage procedure. First, various sources of modeling uncertainties are characterized during the period before implementing Best Management Practices (BMPs). Second, the effectiveness of BMPs are probabilistically quantified during the post-BMP period. The framework was used to assess the uncertainties in effectiveness of two BMPs in reducing daily total nitrogen (TN) loads in a 54 ha agricultural watershed in North Carolina using the SWAT model. The results indicated that the modeling uncertainties in quantifying the effectiveness of selected BMPs were relatively large. Assessment of measured data uncertainty revealed that higher errors were observed in simulating TN loads during high flow events. The results of this study have important implications for decision-making under uncertainty when models are used for water quality simulation. Highlights: A framework was developed for quantifying various uncertainties in modeling BMPs. Uncertainties in modeling the effectiveness BMPs were relatively large. Higher measured data errors were observed in TN loads during high flow events. The framework helps more informed decision-making under uncertainty.