A tool for efficient, model-independent management optimization under uncertainty. (February 2018) - White, Jeremy T.; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.
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. Highlights: PESTPP-OPT is a model-independent tool for management optimization. PESTPP-OPT uses sequential linear programming for high-dimensional problems. PESTPP-OPT implements parallel run management to reduce computational burden. PESTPP-OPT implements optimization under uncertainty with on-the-fly chance-constrained programming.