Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2541-2019
© Author(s) 2019. 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-23-2541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2019
Research article |
| 05 Jun 2019
| 05 Jun 2019
Observation operators for assimilation of satellite observations in fluvial inundation forecasting
Department of Meteorology, University of Reading, Reading, UK
Sarah L. Dance
Department of Meteorology, University of Reading, Reading, UK
Department of Mathematics and Statistics, University of Reading, Reading, UK
Javier García-Pintado
MARUM Center for Marine Environmental Sciences, Department of Geosciences, University of Bremen, Bremen, Germany
Nancy K. Nichols
Department of Meteorology, University of Reading, Reading, UK
Department of Mathematics and Statistics, University of Reading, Reading, UK
Polly J. Smith
Department of Meteorology, University of Reading, Reading, UK
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Cited
19 citations as recorded by crossref.
- 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
- A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation A. Dasgupta et al. 10.1029/2020WR027859
- Spatial scale evaluation of forecast flood inundation maps H. Hooker et al. 10.1016/j.jhydrol.2022.128170
- Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps D. Mason et al. 10.3390/w13111577
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping J. Zhao et al. 10.1016/j.rse.2021.112668
- 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
- Improved urban flood detection in deeper floods using synthetic aperture radar double-scattering intensity and interferometric coherence D. Mason & S. Dance 10.1117/1.JRS.19.021007
- Enable high-resolution, real-time ensemble simulation and data assimilation of flood inundation using distributed GPU parallelization J. Wei et al. 10.1016/j.jhydrol.2023.129277
- Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations T. Nguyen et al. 10.1109/TGRS.2023.3338296
- On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance A. Dasgupta et al. 10.1029/2020WR028238
- Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
- Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. 10.1007/s10712-022-09751-y
- Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept C. Di Mauro et al. 10.5194/hess-25-4081-2021
- Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. 10.1029/2022WR033155
- 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
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Impact of GPS radio occultation assimilation on the 18–21 July 2021 heavy rainfall event in Henan S. Chen et al. 10.1016/j.atmosres.2023.106661
- A multi-system comparison of forecast flooding extent using a scale-selective approach H. Hooker et al. 10.2166/nh.2023.025
19 citations as recorded by crossref.
- 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
- A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation A. Dasgupta et al. 10.1029/2020WR027859
- Spatial scale evaluation of forecast flood inundation maps H. Hooker et al. 10.1016/j.jhydrol.2022.128170
- Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps D. Mason et al. 10.3390/w13111577
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping J. Zhao et al. 10.1016/j.rse.2021.112668
- 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
- Improved urban flood detection in deeper floods using synthetic aperture radar double-scattering intensity and interferometric coherence D. Mason & S. Dance 10.1117/1.JRS.19.021007
- Enable high-resolution, real-time ensemble simulation and data assimilation of flood inundation using distributed GPU parallelization J. Wei et al. 10.1016/j.jhydrol.2023.129277
- Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations T. Nguyen et al. 10.1109/TGRS.2023.3338296
- On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance A. Dasgupta et al. 10.1029/2020WR028238
- Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
- Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. 10.1007/s10712-022-09751-y
- Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept C. Di Mauro et al. 10.5194/hess-25-4081-2021
- Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. 10.1029/2022WR033155
- 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
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Impact of GPS radio occultation assimilation on the 18–21 July 2021 heavy rainfall event in Henan S. Chen et al. 10.1016/j.atmosres.2023.106661
- A multi-system comparison of forecast flooding extent using a scale-selective approach H. Hooker et al. 10.2166/nh.2023.025
Discussed (preprint)
Latest update: 14 Dec 2024
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to...
