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

February 1, 2018

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.

Publication Year 2018
Title A tool for efficient, model-independent management optimization under uncertainty
DOI 10.1016/j.envsoft.2017.11.019
Authors Jeremy T. White, Michael N. Fienen, Paul M. Barlow, Dave E. Welter
Publication Type Article
Publication Subtype Journal Article
Series Title Environmental Modelling and Software
Index ID 70196067
Record Source USGS Publications Warehouse
USGS Organization Wisconsin Water Science Center