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Type: | Journal article |
Title: | A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support |
Author: | White, J.T. Knowling, M.J. Fienen, M.N. Feinstein, D.T. McDonald, G.W. Moore, C.R. |
Citation: | Environmental Modelling and Software, 2020; 126:104657-1-104657-11 |
Publisher: | Elsevier |
Issue Date: | 2020 |
ISSN: | 1364-8152 1873-6726 |
Statement of Responsibility: | Jeremy T.White, Matthew J.Knowling, Micheal N.Fienen, Daniel T.Feinstein, Garry W.McDonald, Catherine R.Moore |
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. |
Keywords: | Emulation under uncertainty; groundwater modeling; nitrate; decision support; optimization under uncertainty |
Rights: | © Elsevier |
DOI: | 10.1016/j.envsoft.2020.104657 |
Published version: | http://dx.doi.org/10.1016/j.envsoft.2020.104657 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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