Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4081-2021
© 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-25-4081-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2021
Research article |
| 14 Jul 2021
Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept
Concetta Di Mauro
Luxembourg Institute of Science and Technology, Esch sur Alzette, Luxembourg
Institute of Hydrology and Water Resources Management,
Vienna University of Technology, Vienna, Austria
Renaud Hostache
Luxembourg Institute of Science and Technology, Esch sur Alzette, Luxembourg
Patrick Matgen
Luxembourg Institute of Science and Technology, Esch sur Alzette, Luxembourg
Ramona Pelich
Luxembourg Institute of Science and Technology, Esch sur Alzette, Luxembourg
Marco Chini
Luxembourg Institute of Science and Technology, Esch sur Alzette, Luxembourg
Peter Jan van Leeuwen
Department of Meteorology, University of Reading, Reading, UK
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Nancy K. Nichols
Department of Mathematics and Statistics, University of Reading, Reading, UK
Günter Blöschl
Centre for Water Resource Systems, Vienna University of Technology, Vienna, Austria
Institute of Hydrology and Water Resources Management,
Vienna University of Technology, Vienna, Austria
Viewed
Total article views: 2,844 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Sep 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,176 | 631 | 37 | 2,844 | 40 | 31 |
- HTML: 2,176
- PDF: 631
- XML: 37
- Total: 2,844
- BibTeX: 40
- EndNote: 31
Total article views: 1,838 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Jul 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,462 | 345 | 31 | 1,838 | 31 | 23 |
- HTML: 1,462
- PDF: 345
- XML: 31
- Total: 1,838
- BibTeX: 31
- EndNote: 23
Total article views: 1,006 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Sep 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 714 | 286 | 6 | 1,006 | 9 | 8 |
- HTML: 714
- PDF: 286
- XML: 6
- Total: 1,006
- BibTeX: 9
- EndNote: 8
Viewed (geographical distribution)
Total article views: 2,844 (including HTML, PDF, and XML)
Thereof 2,587 with geography defined
and 257 with unknown origin.
Total article views: 1,838 (including HTML, PDF, and XML)
Thereof 1,702 with geography defined
and 136 with unknown origin.
Total article views: 1,006 (including HTML, PDF, and XML)
Thereof 885 with geography defined
and 121 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. 10.1007/s10712-022-09751-y
- Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping J. Zhao et al. 10.1016/j.rse.2021.112668
- FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series K. Karamvasis & V. Karathanassi 10.3390/w13212943
- Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique) L. Cea et al. 10.1016/j.ejrh.2022.101247
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood S. Sherpa & M. Shirzaei 10.1111/jfr3.12770
- Hydrometeorological Extreme Events in Africa: The Role of Satellite Observations for Monitoring Pluvial and Fluvial Flood Risk M. Gosset et al. 10.1007/s10712-022-09749-6
- Water science must be Open Science E. Schymanski & S. Schymanski 10.1038/s44221-022-00014-z
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features H. Farahmand et al. 10.1038/s41598-023-32548-x
- Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
- 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
- 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
- Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. 10.1029/2022WR033155
- Two-Dimensional Flood Inundation Modeling in the Godavari River Basin, India—Insights on Model Output Uncertainty V. Sharma & S. Regonda 10.3390/w13020191
17 citations as recorded by crossref.
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. 10.1007/s10712-022-09751-y
- Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping J. Zhao et al. 10.1016/j.rse.2021.112668
- FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series K. Karamvasis & V. Karathanassi 10.3390/w13212943
- Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique) L. Cea et al. 10.1016/j.ejrh.2022.101247
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood S. Sherpa & M. Shirzaei 10.1111/jfr3.12770
- Hydrometeorological Extreme Events in Africa: The Role of Satellite Observations for Monitoring Pluvial and Fluvial Flood Risk M. Gosset et al. 10.1007/s10712-022-09749-6
- Water science must be Open Science E. Schymanski & S. Schymanski 10.1038/s44221-022-00014-z
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features H. Farahmand et al. 10.1038/s41598-023-32548-x
- Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
- 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
- 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
- Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. 10.1029/2022WR033155
Latest update: 25 Sep 2023
Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
This study evaluates how the sequential assimilation of flood extent derived from synthetic...
