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Title: 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.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]
+ Show Author Affiliations
  1. Univ. di Bologna, Bologna (Italy)
  2. 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):
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.
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Ciriello, Valentina, Lauriola, Ilaria, & Tartakovsky, Daniel M. Distribution–Based Global Sensitivity Analysis in Hydrology. United States. doi:10.1029/2019WR025844.
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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.
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@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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