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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]
  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) (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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Works referenced in this record:

Estimation of Intrinsic Length Scales of Flow in Unsaturated Porous Media: LENGTH SCALES OF UNSATURATED MEDIA
journal, November 2017

  • Assouline, Shmuel; Ciriello, Valentina; Tartakovsky, Daniel M.
  • Water Resources Research, Vol. 53, Issue 11
  • DOI: 10.1002/2017WR021629

Global sensitivity analysis using polynomial chaos expansions
journal, July 2008


Comparative analysis of formulations for conservative transport in porous media through sensitivity-based parameter calibration: SENSITIVITY-BASED INTERPRETATION OF TRANSPORT EXPERIMENTS
journal, September 2013

  • Ciriello, Valentina; Guadagnini, Alberto; Di Federico, Vittorio
  • Water Resources Research, Vol. 49, Issue 9
  • DOI: 10.1002/wrcr.20395

Multimodel framework for characterization of transport in porous media: MULTIMODEL FRAMEWORK FOR TRANSPORT IN POROUS MEDIA
journal, May 2015

  • Ciriello, Valentina; Edery, Yaniv; Guadagnini, Alberto
  • Water Resources Research, Vol. 51, Issue 5
  • DOI: 10.1002/2015WR017047

Sensitivity Anaysis as an Ingredient of Modeling
journal, November 2000

  • Campolongo, F.; Tarantola, S.; Saltelli, A.
  • Statistical Science, Vol. 15, Issue 4
  • DOI: 10.1214/ss/1009213004

Impact of uncertainty in soil texture parameters on estimation of soil moisture through radio waves transmission
journal, December 2018


Causality and Bayesian Network PDEs for multiscale representations of porous media
journal, October 2019

  • Um, Kimoon; Hall, Eric J.; Katsoulakis, Markos A.
  • Journal of Computational Physics, Vol. 394
  • DOI: 10.1016/j.jcp.2019.06.007

A new uncertainty importance measure
journal, June 2007


Probabilistic analysis of groundwater-related risks at subsurface excavation sites
journal, January 2012


Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations
journal, January 2015


Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos
journal, November 2013

  • Ashraf, Meisam; Oladyshkin, Sergey; Nowak, Wolfgang
  • International Journal of Greenhouse Gas Control, Vol. 19
  • DOI: 10.1016/j.ijggc.2013.03.023

Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box: ANALYSES TO OPEN THE MODEL BLACK-BOX
journal, September 2017

  • Borgonovo, E.; Lu, X.; Plischke, E.
  • Water Resources Research, Vol. 53, Issue 9
  • DOI: 10.1002/2017WR020767

Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer
journal, August 2012

  • Ciriello, Valentina; Di Federico, Vittorio; Riva, Monica
  • Stochastic Environmental Research and Risk Assessment, Vol. 27, Issue 4
  • DOI: 10.1007/s00477-012-0616-7

A new Measure of rank Correlation
journal, June 1938


Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: The case of ANDRA Meuse/Haute-Marne site
journal, February 2015


Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators
journal, July 2014

  • Marrel, Amandine; Perot, Nadia; Mottet, Clémentine
  • Stochastic Environmental Research and Risk Assessment, Vol. 29, Issue 3
  • DOI: 10.1007/s00477-014-0927-y

The Homogeneous Chaos
journal, October 1938

  • Wiener, Norbert
  • American Journal of Mathematics, Vol. 60, Issue 4
  • DOI: 10.2307/2371268

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
journal, January 2002


Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
journal, January 2017

  • Guo, Danlu; Westra, Seth; Maier, Holger R.
  • Hydrology and Earth System Sciences, Vol. 21, Issue 4
  • DOI: 10.5194/hess-21-2107-2017

Assessment and management of risk in subsurface hydrology: A review and perspective
journal, January 2013


Trends in sensitivity analysis practice in the last decade
journal, October 2016