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

Environmental Modelling and Software

By:
ORCID iD , ORCID iD , ORCID iD , and
https://doi.org/10.1016/j.envsoft.2017.11.019

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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.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A tool for efficient, model-independent management optimization under uncertainty
Series title:
Environmental Modelling and Software
DOI:
10.1016/j.envsoft.2017.11.019
Volume:
100
Year Published:
2018
Language:
English
Publisher:
Elsevier
Contributing office(s):
Wisconsin Water Science Center
Description:
9 p.
First page:
213
Last page:
221