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 distributionbased (rather than variancebased) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distributionbased GSA feasible for regionalscale 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.
 Authors:

 Univ. di Bologna, Bologna (Italy)
 Stanford Univ., Stanford, CA (United States)
 Publication Date:
 Research Org.:
 Stanford Univ., CA (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Basic Energy Sciences (BES)
 OSTI Identifier:
 1574862
 Alternate Identifier(s):
 OSTI ID: 1573835
 Report Number(s):
 DOESTANFORD00191303
Journal ID: ISSN 00431397
 Grant/Contract Number:
 SC0019130
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Water Resources Research
 Additional Journal Information:
 Journal Volume: 55; Journal ID: ISSN 00431397
 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. https://www.osti.gov/servlets/purl/1574862.
@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 distributionbased (rather than variancebased) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distributionbased GSA feasible for regionalscale 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}
}
Web of Science
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