Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system

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Abstract

Water resource managers are faced with difficult decisions on how to satisfy human water needs while maintaining or restoring riverine ecosystems. Decision sciences have developed approaches and tools that can be used to break down difficult water management decisions into their component parts. An essential aspect of these approaches is the use of quantitative models to evaluate alternative management strategies. Here, we describe four integrated decision support models for evaluating the effect of flows on two life history stages of Chinook salmon (Oncorhynchus tshawytscha) and Steelhead (O. mykiss). We then use constrained nonlinear optimization to identify optimal flow regimes for the water year type with the least available water. These flow regimes were then used by managers to develop candidate minimum flow strategies that were evaluated using forward simulation and sensitivity analyses. We found that optimal flow regimes differed markedly from existing regulations and varied among species and life history stages. However, evaluation of tradeoffs among the four competing objectives indicated relatively minimal losses for most objectives when the optimal flows were based on equally weighting the objectives. Sensitivity analysis indicated that water temperature was the primary driver of estimated outcomes and suggested that managers consider alternative means of managing temperatures. Decision sciences have created multiple analytical tools and approaches that simplify complex problems, such as water resource management, and we believe that water resource management would benefit from their increased use.

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Peterson, J. T., Pease, J. E., Whitman, L., White, J., Stratton-Garvin, L., Rounds, S., & Wallick, R. (2022). Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system. River Research and Applications, 38(2), 293–308. https://doi.org/10.1002/rra.3903

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