A tool for efficient, model-independent management optimization under uncertainty

Jeremy T. White; Michael N. Fienen; Paul M. Barlow; Dave E. Welter

doi:10.1016/j.envsoft.2017.11.019

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A tool for efficient, model-independent management optimization under uncertainty

Jeremy T. White·Michael N. Fienen·Paul M. Barlow·Dave E. Welter

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Abstract

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

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Jeremy T. White, Michael N. Fienen, Paul M. Barlow, Dave E. Welter. 2018. A tool for efficient, model-independent management optimization under uncertainty. https://doi.org/10.1016/j.envsoft.2017.11.019

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