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Decision support for slowing the flow: A multi-metric matrix focuses efforts to reduce peak flows
Citation:
Hollenhorst, T., M. Wheeler, M. Wick, T. Bernthal, A. Robertson, AND C. Smith. Decision support for slowing the flow: A multi-metric matrix focuses efforts to reduce peak flows. State of Lake Superior Conference, Houghton, MI, October 09 - 12, 2018.
Impact/Purpose:
With increased efforts focused on slowing the flow to reduce flooding and sediment inputs to Lake Superior it’s important to prioritize these efforts and measure their success. This too will help quantify and rank multiple factors that might impact stream hydrology and water quality to help maximize the effectiveness of these efforts.
Description:
Northern Wisconsin has experienced three significant floods in the last six years that have caused millions of dollars in damage. It’s clear the region is receiving more frequent and intense rainfalls. Recently local, state and federal partners have been trying to find ways to slow the flow of storm water running off the landscape as well as reducing the amount of sediment washing into Lake Superior. Conservation efforts have focused on slowing the flow with watershed-scale landscape restoration projects designed to increase roughness, and promote infiltration, and watershed storage. Efforts have included wetland restoration, agricultural best management practices, and reforestation projects. As these high-intensity storms increase in frequency, we are compelled to effectively prioritize these flow efforts and measure their success. To help prioritize these efforts we developed a multi-metric decision support matrix to quantify and rank, across 1500+ sub watersheds, multiple factors that might impact stream hydrology and water quality to maximize the positive impact of various slow the flow efforts. For each sub watershed we calculated peak discharge, and the amount of existing and potential watershed storage, using newly available and updated Wisconsin Wetland Inventory, Wetland Functional Assessment and Potentially Restorable wetland data layers. We also evaluated the amount of open lands and forest within each sub watershed. All of this was compiled in the matrix to score and rank subwatersheds for restoration priorities.