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Incorporating green infrastructure into water resources management plans to address water quality impairments
Citation:
Piscopo, A. AND N. Detenbeck. Incorporating green infrastructure into water resources management plans to address water quality impairments. American Geophysical Union Fall Meeting, New Orleans, Louisiana, December 11 - 15, 2017.
Impact/Purpose:
Many urban watersheds have water quality problems related to excessive nutrient levels. Green infrastructure (GI) has the potential to alleviate water quality problems when incorporated into municipal water resources management plans. However, for GI to be implemented effectively, municipalities must have guidance on the type of GI to implement and its placement within the watershed. Additionally, information on the cost and co-benefits (such as reducing runoff) of management plan options is valuable to municipalities for decision-making purposes. This research determines GI options that minimize cost and maximize co-benefits to improve water quality in urban watersheds.
Description:
Managers of urban watersheds with excessive nutrient loads are more frequently turning to green infrastructure (GI) to manage their water quality impairments. The effectiveness of GI is dependent on a number of factors, including (1) the type and placement of GI within the watershed, (2) the specific nutrients to be treated, and (3) the uncertainty in future climates. Although many studies have investigated the effectiveness of individual GI units for different types of nutrients, relatively few have considered the effectiveness of GI on a watershed scale, the scale most relevant to management plans. At the watershed scale, endless combinations of GI type and location are possible, each with different effectiveness in reducing nutrient loads, minimizing costs, and maximizing co-benefits such as reducing runoff. To efficiently generate management plan options that balance the tradeoffs among these objectives, we simulate candidate options using EPA’s Stormwater Management Model for multiple future climates and determine the Pareto optimal set of solution options using a multi-objective evolutionary algorithm. Our approach is demonstrated for an urban watershed in Rockville, Maryland.