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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #349940

Research Project: Response of Ecosystem Services in Agricultural Watersheds to Changes in Water Availability, Land Use, Management, and Climate

Location: Water Management and Systems Research

Title: A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models

Author
item Tasdighi, Ali - Colorado State University
item Arabi, Mazdak - Colorado State University
item Harmel, Daren
item Line, Daniel - North Carolina State University

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/3/2018
Publication Date: 8/3/2018
Citation: Tasdighi, A., Arabi, M., Harmel, R.D., Line, D. 2018. A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models. Environmental Modelling & Software. 108:240-252 https://doi.org/10.1016/j.envsoft.2018.08.006.
DOI: https://doi.org/10.1016/j.envsoft.2018.08.006

Interpretive Summary: A mathematical uncertainty analysis framework is presented to assess simulated estimates of the effectiveness of watershed management practices in reducing nonpoint source 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 quantified based on probability during the post-BMP period. The framework was used to assess the uncertainties in effectiveness of two BMPs in reducing daily total nitrogen 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 total nitrogen 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.

Technical 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 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 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 total nitrogen 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.