Moment-based metrics for global sensitivity analysis of hydrological systems
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherCOPERNICUS GESELLSCHAFT MBH
CitationMoment-based metrics for global sensitivity analysis of hydrological systems 2017, 21 (12):6219 Hydrology and Earth System Sciences
Rights© Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License.
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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.
NoteOpen access journal.
VersionFinal published version
SponsorsEuropean Union's Horizon Research and Innovation program (project: Furthering the knowledge Base for Reducing the Environmental Footprint of Shale Gas Development - FRACRISK) ; Italian Ministry of Education, University and Research; Water JPI; WaterWorks, project: WE-NEED (WatEr NEEDs, availability, quality and sustainability)
Except where otherwise noted, this item's license is described as © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License.