Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses. (May 2021)

• Record Type: Journal Article
• Authors: White, JeremyT.; Hemmings, Brioch; Fienen, MichealN.; Knowling, MatthewJ.
• Is Part Of: Environmental modelling & software. Volume 139(2021)
• Publication Date: 2021-05
• Subjects: Uncertainty analysis -- Decision support -- Automating workflows -- Cognitive load -- Model criticism -- Prior-data conflict; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105022
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses. Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow. Highlights: Python tools are presented that reduce the effort to implement PEST and PEST++. Applying PEST and PEST++ earlier in the workflow allows for improved model insights. Prior-based Monte Carlo can aid in the efficient detection of model deficiencies.