Location: National Soil Erosion ResearchTitle: Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method Author
|Liu, Yaoze - Purdue University|
|Engel, Bernard - Purdue University|
|Gitau, Margaret - Purdue University|
|Mcmillan, Sara - Purdue University|
|Chaubey, Indrajeet - Purdue University|
|Singh, Shweta - Purdue University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/19/2018
Publication Date: 3/22/2018
Citation: Liu, Y., Engel, B.A., Flanagan, D.C., Gitau, M.W., McMillan, S.K., Chaubey, I., Singh, S. 2018. Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method. Journal of Hydrology. 560:530-545.
Interpretive Summary: Preservation and/or improvement of water quality is critical to sustainable systems in both agricultural and urban areas. Water quality can be negatively affected by soil erosion and surface runoff containing sediment, nutrients (N-Nitrogen, P-Phosphorus), pesticides, and other chemicals. Best management practices (BMPs) are typically recommended to control or reduce runoff and pollutant losses, however it can be difficult to determine how effective the BMPs are once installed and over their useful lifetime. Computer simulation models are typically applied to estimate how well BMPs perform, but very commonly the logic and equations used to approximate BMP effectiveness are very (too) simple, and do not consider decreases in effectiveness over time, seasonal impacts, nor the effects of periodic maintenance. Thus the effectiveness of the BMPs may be overestimated, giving unrealistic simulation results of improvements in water quality. In this study a new framework was developed for use in computer simulation models to better determine and describe BMP effectiveness. Inclusion of this framework and method in water quality models should help to improve their predictive capability when simulating land management practices to control runoff, sediment or chemical losses. This research impacts scientist, modelers, students, conservation agency personnel, and others involved in evaluating various land management practices and recommending BMPs to improve water quality.
Technical Abstract: Best management practices (BMPs) are popular approaches used to improve hydrology and water quality. Uncertainties in BMP effectiveness over time may result in overestimating long-term efficiency in watershed planning strategies. To represent varying long-term BMP effectiveness in hydrologic/water quality models, a modeling framework was developed. The components in the framework, including establishment period efficiency, starting efficiency, efficiency for each storm event, efficiency between maintenance, and efficiency over the life cycle, would be combined to represent long-term efficiency for a specific type of practice and specific environmental concern (runoff/pollutant). An approach for possible implementation of the framework is discussed. The long-term impacts of grass buffer strips (agricultural BMP) and bioretention systems (urban BMP) in reducing total phosphorus were simulated to demonstrate the framework. Data gaps were captured in estimating the long-term performance of the BMPs. A Bayesian method was used to match the simulated distribution of long-term BMP efficiencies with the observed distribution with the assumption that the observed data represented long-term BMP efficiencies. The simulated distribution matched the observed distribution well with only small total predictive uncertainties. With additional data, the same method could be used to further improve the simulation results. The modeling framework and results of this study, which can be adopted in hydrologic/water quality models to better represent long-term BMP effectiveness, can help improve decision support systems for creating long-term stormwater management strategies for watershed management projects.