Abstract
Author(s): Alexander, Clint A. D.; Poulsen, Frank; Robinson, Donald C. E.; Ma, Brian O.; Luster, Ryan A. | Abstract: Management of the Sacramento River and Sacramento–San Joaquin Delta (SRD) is one of California’s greatest challenges, requiring trade-offs between valued components that serve a multiplicity of conflicting purposes. Trade-offs do not signal a failure to create clever enough models, or scenarios that find a single optimal solution. Rather, an optimal solution that meets multiple objectives does not exist. We demonstrate an improved method for multiple-objective allocation of water: “turn-taking” optimization (TTO) within a multi-model cloud computing framework. We apply TTO to an array of physical hydrologic models that are linked with the Ecological Flows Tool (EFT): a multi-species decision support framework to evaluate how specific components of the flow regime promote and balance favorable habitat conditions for 15 representative species and 31 indicators within the SRD. Applying the TTO approach incorporates the existing modelled representation of socio-economic water management criteria, priorities, and constraints — and optimizes water-release patterns each water year using a dynamically shifting set of EFT indicators. Rather than attempting to optimize conditions for all ecological indicators every year, TTO creates flexibility and opportunities for different indicators to be successful in different years, informed by the frequency with which each species’ ecological needs should be met. As an individual EFT indicator is successful in a particular year, its priority in one or more subsequent years is reduced (and vice versa). Comparing TTO to a Reference Case scenario based on current management practices, 12 EFT indicators are improved, 14 show no change, and 5 show a reduction in suitability. When grouped into nine species and life-history groups, performance improved in four (late-fall-run Chinook, winter-run Chinook, spring-run Chinook, and Fremont cottonwood), did not change in four (fall-run Chinook Salmon, Delta Smelt, Splittail, and Longfin Smelt), and was worse in one group (Steelhead).
Highlights
Developing greater awareness of the value of flexibility to manage ecosystem trade-offs among multiple objectives — and providing approaches that enable this flexibility in a real-world setting — is urgently needed
The optimization system we developed is based on the pre-existing Ecological Flows Tool (EFT) linked to the hydrologic simulation models that are in common use in the study area — all embedded within the Multiple-Objective Particle Swarm Optimization (MOPSO) algorithm described above
Cumulative performance measures for each indicator across all study years indicate that the turntaking” optimization (TTO) solutions tended to outperform the Reference Case scenario solutions
Summary
Developing greater awareness of the value of flexibility to manage ecosystem trade-offs among multiple objectives — and providing approaches that enable this flexibility in a real-world setting — is urgently needed. Current management relies largely on deterministic models in which priorities and objectives are decided in advance — an approach which, at best, yields a narrow set of solutions with limited emphasis on — or insight into — what the potential trade-offs might be (Martin et al 2016; Poff et al 2016). Failure to reconcile these trade-offs is not the result of a failure to create a clever enough model that will find the optimal solution. A single optimal “all-years and all-values” solution does not exist (Alexander et al 2014)
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