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    Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

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    HYDROL 123924.pdf
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    Description:
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    Type
    Article
    Authors
    Ziliani, Matteo G.
    Ghostine, Rabih
    Ait-El-Fquih, Boujemaa
    McCabe, Matthew cc
    Hoteit, Ibrahim cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering
    Earth Science and Engineering Program
    Earth System Observation and Modelling
    Environmental Science and Engineering
    Environmental Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Water Desalination & Reuse Center
    Water Desalination and Reuse Research Center (WDRC)
    Date
    2019-07-12
    Online Publication Date
    2019-07-12
    Print Publication Date
    2019-10
    Embargo End Date
    2021-07-12
    Permanent link to this record
    http://hdl.handle.net/10754/656186
    
    Metadata
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    Abstract
    Accurate 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.
    Citation
    Ziliani, 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
    Sponsors
    Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Elsevier BV
    Journal
    Journal of Hydrology
    DOI
    10.1016/j.jhydrol.2019.123924
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0022169419306444
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jhydrol.2019.123924
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Environmental Science and Engineering Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)

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