Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/77003
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Wang, S | en_US |
dc.creator | Ancell, BC | en_US |
dc.creator | Huang, GH | en_US |
dc.creator | Baetz, BW | en_US |
dc.date.accessioned | 2018-07-19T04:45:31Z | - |
dc.date.available | 2018-07-19T04:45:31Z | - |
dc.identifier.issn | 0043-1397 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/77003 | - |
dc.language.iso | en | en_US |
dc.publisher | Wiley-Blackwell | en_US |
dc.rights | © 2018. American Geophysical Union. All Rights Reserved. | en_US |
dc.subject | Data assimilation | en_US |
dc.subject | Hydrologic prediction | en_US |
dc.subject | Interaction | en_US |
dc.subject | Polynomial chaos expansion | en_US |
dc.subject | Pre- and post-processing | en_US |
dc.subject | Uncertainty | en_US |
dc.title | Improving robustness of hydrologic ensemble predictions through probabilistic pre- and post-processing in sequential data assimilation | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2129 | en_US |
dc.identifier.epage | 2151 | en_US |
dc.identifier.volume | 54 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1002/2018WR022546 | en_US |
dcterms.abstract | Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Water resources research, 2018, v. 54, no. 3, p. 2129-2151 | en_US |
dcterms.isPartOf | Water resources research | en_US |
dcterms.issued | 2018 | - |
dc.identifier.scopus | 2-s2.0-85044312863 | - |
dc.identifier.ros | 2017002983 | - |
dc.identifier.eissn | 1944-7973 | en_US |
dc.identifier.rosgroupid | 2017002884 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201807 bcwh | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a0231-n01 | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wang_Improving_Robustness_Hydrologic.pdf | 3.29 MB | Adobe PDF | View/Open |
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