Costs of Meeting Water Quality Goals under Climate Change in Urbanizing Watersheds: The Case of Difficult Run, Virginia

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Virginia Tech


Urban environments have been identified as a non-point source contributor of nutrient loadings into watersheds. Interannual surges of nutrient loadings into local water systems are more damaging than mean interannual nutrient loadings. Virginia has outlined the need to reduce urban nutrient loadings. Mean interannual nutrient loadings and interannual nutrient loadings variability are expected to increase under climate change (CC). However, there are few studies that provide a predictive framework for abating nutrient loadings under CC. Thus, there is a lack of information regarding how effective water quality policy will be in the future. Using the Difficult Run watershed in Fairfax County, VA, as a site of study, we used mathematical programming to compare how the costs of abating nutrient loads differed under differing climates in the Mid-Atlantic. We first compared the costs of abating mean interannual nutrient loadings in the watershed based on historical climate conditions to those predicted for CC. We then evaluated how changes in the interannual variability of nutrient loadings for CC affect the costs of meeting watershed goals. We found that abating mean interannual nutrient loadings was substantially costlier for CC relative to meeting the same goals under historical climate conditions. Further, we found that the costs of abating interannual nutrient loadings variability increased under CC relative to meeting the same goals under historical climate. One implication of this study suggests that policy makers seeking to meet water quality goals over time must front-load supplemental BMPs today in order to offset the changes predicted for CC.



Best Management Practices, linear programming, stormwater, Climate Change, nutrient loadings, Chesapeake Bay, cost minimization, non-point source pollution, built environment, math programming