Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2021
Research article |
| 16 Sep 2021
| 16 Sep 2021
Sequential data assimilation for real-time probabilistic flood inundation mapping
Keighobad Jafarzadegan
CORRESPONDING AUTHOR
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Peyman Abbaszadeh
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Hamid Moradkhani
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
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34 citations as recorded by crossref.
- Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations N. Fraehr et al. 10.1029/2022WR033836
- An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation P. Deb et al. 10.1016/j.agrformet.2022.108982
- Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation S. Radfar et al. 10.1016/j.scitotenv.2024.173529
- Joint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water model G. García-Alén et al. 10.1016/j.jhydrol.2023.129667
- Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm O. Mikaeili & M. Shourian 10.1007/s11269-024-03854-4
- Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis T. Huang & V. Merwade 10.1111/jfr3.12982
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. 10.5194/hess-28-2531-2024
- Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. 10.3390/w16091286
- Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. 10.3390/rs16020350
- Iber-PEST: Automatic calibration in fully distributed hydrological models based on the 2D shallow water equations G. García-Alén et al. 10.1016/j.envsoft.2024.106047
- Time-varying parameters of the hydrological simulation model under a changing environment R. Liu et al. 10.1016/j.jhydrol.2024.131943
- Editorial: Identifying hotspots of hydro-hazards under global change M. Pregnolato et al. 10.3389/frwa.2022.1087690
- Toward improved river boundary conditioning for simulation of extreme floods K. Jafarzadegan et al. 10.1016/j.advwatres.2021.104059
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. 10.3390/w15223944
- Comparison of data assimilation based approach for daily streamflow simulation under multiple scenarios in Ganjiang River Basin W. Weiguang et al. 10.18307/2023.0323
- Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
- Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine E. Hamidi et al. 10.1109/TGRS.2023.3240097
- Ensemble Kalman Inversion for upstream parameter estimation and indirect streamflow correction: A simulation study A. Pensoneault et al. 10.1016/j.advwatres.2023.104545
- Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling T. Huang & V. Merwade 10.1029/2023WR034947
- Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers K. Jafarzadegan et al. 10.5194/nhess-22-1419-2022
- Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. 10.1029/2022WR033155
- Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
- Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM A. Tanim et al. 10.1016/j.envsoft.2024.106122
- Towards flood risk mapping based on multi-tiered decision making in a densely urbanized metropolitan city of Istanbul Ö. Ekmekcioğlu et al. 10.1016/j.scs.2022.103759
- A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale J. Schubert et al. 10.1029/2021WR031279
- River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies S. Munier & B. Decharme 10.5194/essd-14-2239-2022
- LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations M. Sharifian et al. 10.5194/gmd-16-2391-2023
- SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT M. Bayat et al. 10.1029/2022WR032397
- Improving flood inundation modeling skill: interconnection between model parameters and boundary conditions N. Oruc Baci et al. 10.1007/s40808-023-01768-5
- Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products F. Gomez et al. 10.5194/nhess-24-2647-2024
- A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. 10.1016/j.eswa.2024.126004
- Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
Latest update: 13 Dec 2024
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
In this study, daily observations are assimilated into a hydrodynamic model to update the...
