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dc.contributor.authorDell'Oca, Aronne
dc.contributor.authorRiva, Monica
dc.contributor.authorGuadagnini, Alberto
dc.date.accessioned2018-01-30T23:48:19Z
dc.date.available2018-01-30T23:48:19Z
dc.date.issued2017-12-08
dc.identifier.citationMoment-based metrics for global sensitivity analysis of hydrological systems 2017, 21 (12):6219 Hydrology and Earth System Sciencesen
dc.identifier.issn1607-7938
dc.identifier.doi10.5194/hess-21-6219-2017
dc.identifier.urihttp://hdl.handle.net/10150/626437
dc.description.abstractWe propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher-order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized polynomial chaos expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. The approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment.
dc.description.sponsorshipEuropean Union's Horizon Research and Innovation program (project: Furthering the knowledge Base for Reducing the Environmental Footprint of Shale Gas Development - FRACRISK) [636811]; Italian Ministry of Education, University and Research; Water JPI; WaterWorks, project: WE-NEED (WatEr NEEDs, availability, quality and sustainability)en
dc.language.isoenen
dc.publisherCOPERNICUS GESELLSCHAFT MBHen
dc.relation.urlhttps://www.hydrol-earth-syst-sci.net/21/6219/2017/en
dc.rights© Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License.en
dc.titleMoment-based metrics for global sensitivity analysis of hydrological systemsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalHydrology and Earth System Sciencesen
dc.description.noteOpen access journal.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-12T01:07:23Z
html.description.abstractWe propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher-order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized polynomial chaos expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. The approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment.


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