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dc.contributor.authorChen, Kuan‐Hung
dc.contributor.authorHwang, Cheinway
dc.contributor.authorChang, Liang‐Cheng
dc.contributor.authorTsai, Jui‐Pin
dc.contributor.authorYeh, Tian‐Chyi Jim
dc.contributor.authorCheng, Ching‐Chung
dc.contributor.authorKe, Chien‐Chung
dc.contributor.authorFeng, Wei
dc.date.accessioned2021-01-13T01:48:27Z
dc.date.available2021-01-13T01:48:27Z
dc.date.issued2020-06-08
dc.identifier.citationChen, K. H., Hwang, C., Chang, L. C., Tsai, J. P., Yeh, T. C. J., Cheng, C. C., ... & Feng, W. (2020). Measuring aquifer specific yields with absolute gravimetry: result in the Choushui River Alluvial Fan and Mingchu Basin, central Taiwan. Water Resources Research, 56(9), e2020WR027261.en_US
dc.identifier.issn0043-1397
dc.identifier.doi10.1029/2020wr027261
dc.identifier.urihttp://hdl.handle.net/10150/650737
dc.description.abstractDue to seasonal or interannual variability, the relevance of hydrological processes and of the associated model parameters can vary significantly throughout the simulation period. To achieve accurately identified model parameters, temporal variations in parameter dominance should be taken into account. This is not achieved if performance criteria are applied to the entire model output time series. Even when using complementary performance criteria, it is often only possible to identify some of the model parameters precisely. We present an innovative approach to improve parameter identifiability that exploits the information available regarding temporal variations in parameter dominance. Using daily parameter sensitivity time series, we construct a set of sensitivity-weighted performance criteria, one for each parameter, whereby periods of higher dominance of a model parameter and its corresponding process are assigned higher weights in the calculation of the associated performance criterion. These criteria are used to impose constraints on parameter values. We demonstrate this approach by constraining 12 model parameters for three catchments and examine ensemble hydrological simulations generated using these constrained parameter sets. The sensitivity-weighted approach improves in particular the identifiability for parameters whose corresponding processes are dominant only for short periods of time or have strong seasonal patterns. This results overall in slight improvement of model performance for a set of 10 contrasting performance criteria. We conclude that the sensitivity-weighted approach improves the extraction of hydrologically relevant information from data, thereby resulting in improved parameter identifiability and better representation of model parameters.en_US
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_US
dc.language.isoenen_US
dc.publisherAMER GEOPHYSICAL UNIONen_US
dc.rights© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectparameter identifiabilityen_US
dc.subjectparameter constraintsen_US
dc.subjecttemporal diagnostic analysisen_US
dc.subjectsensitivity analysisen_US
dc.subjectperformance criteriaen_US
dc.titleMeasuring Aquifer Specific Yields With Absolute Gravimetry: Result in the Choushui River Alluvial Fan and Mingchu Basin, Central Taiwanen_US
dc.typeArticleen_US
dc.identifier.eissn1944-7973
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien_US
dc.identifier.journalWATER RESOURCES RESEARCHen_US
dc.description.noteOpen access articleen_US
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_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleWater Resources Research
dc.source.volume56
dc.source.issue9
refterms.dateFOA2021-01-13T01:48:41Z


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© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.