A framework for spatial optimization of watershed restoration to improve water quality

Authors: Beverley Wemple*, Gund Institute & Department of Geography, University of Vermont, Nitin Singh, Gund Institute & Rubenstein School of Environment and Natural Resources, University of Vermont, Jesse D. Gourevitch, Gund Institute & Rubenstein School of Environment and Natural Resources, University of Vermont, Keri B. Watson, Earth and Environmental Systems, University of The South, Sewanee, TN , Donna M. Rizzo, Gund Institute & Department of Civil and Engineering, University of Vermont, Taylor Ricketts, Gund Institute & Rubenstein School of Environment and Natural Resources, University of Vermont
Topics: Water Resources and Hydrology, Spatial Analysis & Modeling
Keywords: water quality, optimization, nutrient pollution, restoration
Session Type: Guided Poster
Day: 4/5/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Roosevelt 3.5, Marriott, Exhibition Level
Presentation File: No File Uploaded


Effective watershed restoration strategies require tools to identify optimal locations for investments that meet stakeholder targets. We describe the development of a spatial optimization framework to evaluate wetland restoration schemes that meet joint objectives of maximizing phosphorus (P) reduction while minimizing restoration costs. Our approach uses spatially explicit maps of P sources in the watershed and an ecosystem services (ES) model to quantify the P retention of wetlands. We use a multi-objective genetic optimization algorithm to find optimal sets of wetlands to restore and quantify the importance of key wetlands in optimal solutions using an “irreplaceability” index. We apply the modeling framework within the Vermont (USA) portion of the Lake Champlain basin. Our findings suggest that wetland restoration may reduce P export up to 10% over baseline at a cost of up to $200 million. Using the irreplaceability index, we identify and map a set of key wetlands for targeting restoration projects across a range of investments. We also show that the model projections are highly sensitive to the spatial resolution of the source pollutant map, with modeled estimates of greater P reductions as source map resolution increases. Our approach uses a computationally elegant method for targeting restoration efforts at regional scales, identifies trades offs in that restoration space, and is readily adaptable to the inclusion of additional ecosystem services provided by wetlands or other restoration strategies.

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