A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support. (April 2020)

• Record Type: Journal Article
• Authors: White, Jeremy T.; Knowling, Matthew J.; Fienen, Micheal N.; Feinstein, Daniel T.; McDonald, Garry W.; Moore, Catherine R.
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: Emulation under uncertainty -- Groundwater modeling -- Nitrate -- Decision support -- Optimization under uncertainty; 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.2020.104657
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the "stochastic impulse-response emulator" (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context. Highlights: A computationally efficient approach to emulation and optimization under uncertainty is presented. The linear relation between nitrate loading and resulting concentrations is exploited. The emulation approach is shown to facilitate spatially-explicit and risk-based land-used scenario evaluation in near real time.