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Updated: Aug 26, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Using Structured Decision Making to Evaluate Wetland Restoration Opportunities in the Chesapeake Bay Watershed
David M Martin1, Amy D Jacobs2, Chase McLean2
1Maryland/DC Chapter, The Nature Conservancy, 425 Barlow Place, Suite 100, Bethesda, MD, 20814, USA. David.Martin@tnc.org.
Abstract:
Wetland restoration is an important water quality and climate resilience strategy. Wetland restoration rarely considers tradeoffs at large spatial and temporal scales, which limits capacity to aid decision makers. High resolution data can reveal hundreds to thousands of possible restoration options across a landscape, but guidance for setting restoration targets at these scales is limited. This study uses structured decision making (SDM) as a process for evaluating the desirability of numerous restoration options, with a case study on the Outer Coastal Plain of the Chesapeake Bay watershed, USA. The Nature Conservancy, in partnership with federal, state, and nonprofit organizations, evaluated a decision to target large-scale wetland restoration based on two fundamental objectives: improve water quality and enhance climate resilience. A total of 964 potentially restorable alternatives were delineated across the study area. The alternatives were evaluated on seven water quality and climate resilience criteria. High-priority alternatives were mapped based on multi-criteria ranking methods and principal component analysis. Sensitivity analysis included varying nutrient load data, implementing multiple ranking methods with different assumptions, and varying criteria weights. The maps revealed seven distinct regions of restoration opportunities. Tradeoffs were evaluated to distinguish between desirable and less desirable regions. Results indicated that three regions were promising choices to initiate landowner engagement and outreach. This study highlights the advantages of SDM to structure large-scale restoration decisions. In doing so, our work offers a roadmap toward further developing SDM in future applied restoration contexts.
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