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Distribution–Based Global Sensitivity Analysis in Hydrology
Abstract
Global sensitivity analysis (GSA) is routinely used in academic setting to quantify the influence of input variability and uncertainty on predictions of a quantity of interest. Practical applications of GSA are hampered by its high computational cost, which arises from the need to run large (e.g., groundwater) models multiple times, and by its reliance on the analysis of variance, which formally requires input parameters to be uncorrelated. The former difficulty can be alleviated by replacing expensive models with inexpensive (e.g., polynomial) surrogates, while adoption of distribution-based (rather than variance-based) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distribution-based GSA feasible for regional-scale models with a large number of degrees of freedom, we supplement it with a surrogate model built with polynomial chaos expansions with analytically updated coefficients. Here, we demonstrate the computational efficiency of our algorithm on a case study dealing with evaluation of the effects of temperature variability on annual evapotranspiration at the regional scale.
- Publication Date:
- Research Org.:
- Stanford Univ., CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
- OSTI Identifier:
- 1574862
- Alternate Identifier(s):
- OSTI ID: 1573835
- Report Number(s):
- DOE-STANFORD-0019130-3
Journal ID: ISSN 0043-1397
- Grant/Contract Number:
- SC0019130
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 55; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING
Citation Formats
Ciriello, Valentina, Lauriola, Ilaria, and Tartakovsky, Daniel M. Distribution–Based Global Sensitivity Analysis in Hydrology. United States: N. p., 2019.
Web. doi:10.1029/2019WR025844.
Ciriello, Valentina, Lauriola, Ilaria, & Tartakovsky, Daniel M. Distribution–Based Global Sensitivity Analysis in Hydrology. United States. doi:10.1029/2019WR025844.
Ciriello, Valentina, Lauriola, Ilaria, and Tartakovsky, Daniel M. Mon .
"Distribution–Based Global Sensitivity Analysis in Hydrology". United States. doi:10.1029/2019WR025844.
@article{osti_1574862,
title = {Distribution–Based Global Sensitivity Analysis in Hydrology},
author = {Ciriello, Valentina and Lauriola, Ilaria and Tartakovsky, Daniel M.},
abstractNote = {Global sensitivity analysis (GSA) is routinely used in academic setting to quantify the influence of input variability and uncertainty on predictions of a quantity of interest. Practical applications of GSA are hampered by its high computational cost, which arises from the need to run large (e.g., groundwater) models multiple times, and by its reliance on the analysis of variance, which formally requires input parameters to be uncorrelated. The former difficulty can be alleviated by replacing expensive models with inexpensive (e.g., polynomial) surrogates, while adoption of distribution-based (rather than variance-based) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distribution-based GSA feasible for regional-scale models with a large number of degrees of freedom, we supplement it with a surrogate model built with polynomial chaos expansions with analytically updated coefficients. Here, we demonstrate the computational efficiency of our algorithm on a case study dealing with evaluation of the effects of temperature variability on annual evapotranspiration at the regional scale.},
doi = {10.1029/2019WR025844},
journal = {Water Resources Research},
number = ,
volume = 55,
place = {United States},
year = {2019},
month = {9}
}
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DOI: 10.1029/2019WR025844
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