A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Wang, Qian; Guillaume, Joseph H. A.; Jakeman, John Davis; Bennett, Frederick R.; Croke, Barry F. W.; Fu, Baihua; Yang, Tao; Jakeman, Anthony J.

doi:10.1029/2022wr032194

Title: A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Journal Article · 30 July 2023 · Water Resources Research

DOI: https://doi.org/10.1029/2022wr032194 · OSTI ID:2311342

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^[2]; Jakeman, John Davis ^[3];

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Hohai University, Nanjing (China). Key Lab. of Water Disaster Prevention; Australian National Univ., Canberra, ACT (Australia)
Australian National Univ., Canberra, ACT (Australia)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Queensland Government, Brisbane, QLD (Australia)
Hohai University, Nanjing (China). Key Lab. of Water Disaster Prevention

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311342

Report Number(s):: SAND--2023-10584J

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 8 Vol. 59; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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