Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2207-2020
© Author(s) 2020. 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-24-2207-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2020
Research article |
| 06 May 2020
| 06 May 2020
Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters
Charlotte Marie Emery
CORRESPONDING AUTHOR
LEGOS, 16 Avenue Edouard Belin, 31400 Toulouse, France
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
now at: CS-Group, Space Business Unit, 31500 Toulouse, France
Sylvain Biancamaria
LEGOS, 16 Avenue Edouard Belin, 31400 Toulouse, France
Aaron Boone
CNRM-GAME, Meteo-France, 42 Avenue Gaspard Coriolis, 31000 Toulouse, France
Sophie Ricci
CECI, Université de Toulouse, CERFACS, CNRS, 42 Avenue Gaspard Coriolis, 31057 Toulouse CEDEX 1, France
Mélanie C. Rochoux
CECI, Université de Toulouse, CERFACS, CNRS, 42 Avenue Gaspard Coriolis, 31057 Toulouse CEDEX 1, France
Vanessa Pedinotti
Magellium, 1 Rue Ariane, 31520 Ramonville-Saint-Agne, France
Cédric H. David
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Cited
14 citations as recorded by crossref.
- Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker J. Chen et al. 10.1109/JSTARS.2020.3035686
- Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed Y. Ishitsuka et al. 10.1029/2020WR027794
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- 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
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- Streamflow simulation in poorly gauged basins with regionalised assimilation using Kalman filter I. Georgievová et al. 10.1016/j.jhydrol.2023.129373
- Intercomparison of global ERA reanalysis products for streamflow simulations at the high-resolution continental scale R. Bain et al. 10.1016/j.jhydrol.2022.128624
- Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river M. Revel et al. 10.5194/hess-27-647-2023
- Building User‐Readiness for Satellite Earth Observing Missions: The Case of the Surface Water and Ocean Topography (SWOT) Mission F. Hossain et al. 10.1029/2022AV000680
- A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry M. Revel et al. 10.1029/2020WR027876
- RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat R. Riggs et al. 10.1016/j.envsoft.2021.105254
- Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences F. Papa & F. Frappart 10.3390/rs13204162
- Water Level Regime of Arctic Rivers according to Modeling and Satellite Measurements E. Zakharova et al. 10.3103/S1068373923120087
- Hydrologic Model Parameter Estimation in Ungauged Basins Using Simulated SWOT Discharge Observations N. Elmer et al. 10.1029/2021WR029655
14 citations as recorded by crossref.
- Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker J. Chen et al. 10.1109/JSTARS.2020.3035686
- Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed Y. Ishitsuka et al. 10.1029/2020WR027794
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- 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
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- Streamflow simulation in poorly gauged basins with regionalised assimilation using Kalman filter I. Georgievová et al. 10.1016/j.jhydrol.2023.129373
- Intercomparison of global ERA reanalysis products for streamflow simulations at the high-resolution continental scale R. Bain et al. 10.1016/j.jhydrol.2022.128624
- Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river M. Revel et al. 10.5194/hess-27-647-2023
- Building User‐Readiness for Satellite Earth Observing Missions: The Case of the Surface Water and Ocean Topography (SWOT) Mission F. Hossain et al. 10.1029/2022AV000680
- A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry M. Revel et al. 10.1029/2020WR027876
- RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat R. Riggs et al. 10.1016/j.envsoft.2021.105254
- Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences F. Papa & F. Frappart 10.3390/rs13204162
- Water Level Regime of Arctic Rivers according to Modeling and Satellite Measurements E. Zakharova et al. 10.3103/S1068373923120087
- Hydrologic Model Parameter Estimation in Ungauged Basins Using Simulated SWOT Discharge Observations N. Elmer et al. 10.1029/2021WR029655
Latest update: 15 Apr 2024
Short summary
The flow of freshwater in rivers is commonly studied with computer programs known as hydrological models. An important component of those programs lies in the description of the river environment, such as the channel resistance to the flow, that is critical to accurately predict the river flow but is still not well known. Satellite data can be combined with models to enrich our knowledge of these features. Here, we show that the coming SWOT mission can help better know this channel resistance.
The flow of freshwater in rivers is commonly studied with computer programs known as...
