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dc.contributor.authorZiliani, Matteo G.
dc.contributor.authorGhostine, Rabih
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorMcCabe, Matthew
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2019-07-25T13:38:11Z
dc.date.available2019-07-25T13:38:11Z
dc.date.issued2019-07-12
dc.identifier.citationZiliani, M. G., Ghostine, R., Ait-El-Fquih, B., McCabe, M. F., & Hoteit, I. (2019). Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation. Journal of Hydrology, 577, 123924. doi:10.1016/j.jhydrol.2019.123924
dc.identifier.doi10.1016/j.jhydrol.2019.123924
dc.identifier.urihttp://hdl.handle.net/10754/656186
dc.description.abstractAccurate water level forecasts during flood events are crucial to mitigate the loss of human lives and economic damages. However, the accuracy of flood models can be affected by various factors, including the complexity of the terrain geometry and bathymetry, imperfect physics as well as uncertainties in the inflows and parameters. This paper describes a practical implementation of an ensemble Kalman filter (EnKF) based data assimilation system that is aimed towards enhancing the forecasting skill of flood models. The system was implemented and tested with a real world dam break flood, based on the experimentally scaled Toce River valley flood that occurred on July 8th, 1996. Water depth data are available for assimilation from a network of 21 sensors distributed across the domain. Our results demonstrate that assimilating data into the flood model significantly improves the model prediction by up to 90% after assimilation and 60% during forecasting. Assimilating the data more frequently significantly enhances the system performances. Estimating the two-dimensional Manning coefficient together with the model’s dynamical variables (water depth and velocities) further improves the model prediction skill. Overall, our results suggest that assimilating data into the flood model, while jointly inferring the state and (poorly known) parameters, using an EnKF may provide an efficient framework for developing an operational flood forecasting system.
dc.description.sponsorshipResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0022169419306444
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, [[Volume], [Issue], (2019-07-12)] DOI: 10.1016/j.jhydrol.2019.123924 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFlood modeling
dc.subjectShallow water equations
dc.subjectData assimilation
dc.subjectEnsemble Kalman filter
dc.subjectState-parameter estimation
dc.titleEnhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentDivision of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEarth System Observation and Modelling
dc.contributor.departmentEnvironmental Science and Engineering
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentWater Desalination & Reuse Center
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalJournal of Hydrology
dc.rights.embargodate2021-07-12
dc.eprint.versionPost-print
kaust.personZiliani, Matteo G.
kaust.personGhostine, Rabih
kaust.personAit-El-Fquih, Boujemaa
kaust.personMcCabe, Matthew
kaust.personMcCabe, Matthew
kaust.personHoteit, Ibrahim
kaust.personHoteit, Ibrahim
dc.date.published-online2019-07-12
dc.date.published-print2019-10


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