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Incorporating green infrastructure into watershed management plans using multi-objective optimization
Citation:
Piscopo, A., N. Detenbeck, AND T. Stagnitta. Incorporating green infrastructure into watershed management plans using multi-objective optimization. 9th International Congress on Environmental Modelling and Software (iEMSs), Fort Collins, Colorado, June 24 - 28, 2018.
Impact/Purpose:
Decision support tools (DSTs) help stakeholders make informed decisions about managing the water quality and quantity issues in watersheds. In this study, the DST known as the Watershed Management Optimization Support Tool (WMOST) is used to develop water management plans that implement different types and quantities of green infrastructure to address water quality and quantity goals.
Description:
Sustainable management of water resources is challenged by numerous conflicting interests and objectives. Decision support tools (DSTs) are evolving to incorporate multiple objectives (e.g. economic, social, and environmental) into the development of water management plans, rather than focus only on minimizing cost. A recent version of the DST known as WMOST (Watershed Management Optimization Support Tool) uses a multi-objective evolutionary algorithm to generate management plan options that specify the location, quantity, and types of green infrastructure (GI) to implement in a watershed. In this study, we applied WMOST to a watershed in southern Massachusetts to develop management plan options that minimize cost, nutrient loads, and runoff. The resultant set of options are indicative of tradeoffs between these objectives, which were visualized multi-dimensionally to help inform the decision-making processes of stakeholders. Preliminary takeaways include: (1) implementing smaller capacity GI on higher permeability soils leads to stronger performance in the objectives than implementing larger capacity GI on lower permeability soils and (2) implementing more GI on the categories of land use with low nutrient loads is more cost-effective than implementing less GI on the categories of land use with high nutrient loads.