Articles | Volume 26, issue 4
https://doi.org/10.5194/hess-26-1019-2022
© Author(s) 2022. 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-26-1019-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Feb 2022
Research article |
| 22 Feb 2022
| 22 Feb 2022
Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping
WARREDOC, University for Foreigners of Perugia, Perugia, Italy
DICEA, University of Florence, Florence, Italy
Fernando Nardi
WARREDOC, University for Foreigners of Perugia, Perugia, Italy
Institute of Water and Environment, Florida International University, Miami, USA
Fabio Castelli
DICEA, University of Florence, Florence, Italy
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Cited
15 citations as recorded by crossref.
- An Effective Approach for Automatic River Features Extraction Using High-Resolution UAV Imagery M. La Salandra et al. 10.3390/drones7020070
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- The role of residual risk on flood damage assessment: A continuous hydrologic-hydraulic modelling approach for the historical city of Rome, Italy A. Fiori et al. 10.1016/j.ejrh.2023.101506
- BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation B. Liu et al. 10.1016/j.jhydrol.2024.132139
- Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall‐runoff‐inundation model: A repository‐based dynamic covariance matrix generation approach M. Khaniya et al. 10.1111/jfr3.13017
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
- Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames A. Spasiano et al. 10.3389/frwa.2023.1050378
- Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations L. M. C. Rápalo et al. 10.1016/j.jhydrol.2024.131929
- 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 the particle filter for data assimilation in hydraulic modeling by using a Cauchy likelihood function C. Jiang et al. 10.1016/j.jhydrol.2022.129050
- Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
- Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
- A Tempered Particle Filter to Enhance the Assimilation of SAR‐Derived Flood Extent Maps Into Flood Forecasting Models C. Di Mauro et al. 10.1029/2022WR031940
- Study of multi-source river discharge data assimilation in collaboration with EKF and MIKE11HD Z. Yinqin et al. 10.1080/02626667.2025.2492884
13 citations as recorded by crossref.
- An Effective Approach for Automatic River Features Extraction Using High-Resolution UAV Imagery M. La Salandra et al. 10.3390/drones7020070
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- The role of residual risk on flood damage assessment: A continuous hydrologic-hydraulic modelling approach for the historical city of Rome, Italy A. Fiori et al. 10.1016/j.ejrh.2023.101506
- BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation B. Liu et al. 10.1016/j.jhydrol.2024.132139
- Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall‐runoff‐inundation model: A repository‐based dynamic covariance matrix generation approach M. Khaniya et al. 10.1111/jfr3.13017
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
- Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames A. Spasiano et al. 10.3389/frwa.2023.1050378
- Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations L. M. C. Rápalo et al. 10.1016/j.jhydrol.2024.131929
- 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 the particle filter for data assimilation in hydraulic modeling by using a Cauchy likelihood function C. Jiang et al. 10.1016/j.jhydrol.2022.129050
- Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
- Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
2 citations as recorded by crossref.
Latest update: 23 Apr 2025
Short summary
In this work, we proposed a multi-source data assimilation framework for near-real-time flood mapping. We used a quasi-2D hydraulic model to update model states by injecting both stage gauge observations and satellite-derived flood extents. Results showed improvements in terms of water level prediction and reduction of flood extent uncertainty when assimilating both stage gauges and satellite images with respect to the disjoint assimilation of both observations.
In this work, we proposed a multi-source data assimilation framework for near-real-time flood...
