Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3151-2022
© 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-26-3151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jun 2022
Research article |
| 21 Jun 2022
| 21 Jun 2022
Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
Simon J. Dadson
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Douglas B. Clark
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
Eleanor M. Blyth
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
Garry D. Hayman
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
Dai Yamazaki
Institute of Industrial Science, University of Tokyo, 4 Chome-6-1
Komaba, Meguro City, Tokyo 153-8505, Japan
Olivia R. E. Becher
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Alberto Martínez-de la Torre
UK Centre for Ecology and Hydrology (UKCEH), Maclean Building,
Wallingford OX10 8BB, UK
Meteorological Surveillance and Forecasting Group, DT Catalonia,
Agencia Estatal de Meteorología (AEMET), Barcelona, Spain
Catherine Prigent
CNRS, Laboratoire d'Etude du Rayonnement et de la Matière en
Astrophysique et Atmosphères (LERMA), Observatoire de Paris, 61 avenue de l'Observatoire, 75014 Paris, France
Carlos Jiménez
Estellus, 93 Boulevard de Sébastopol, 75002 Paris, France
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- Technical note: Surface fields for global environmental modelling M. Choulga et al. 10.5194/hess-28-2991-2024
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8 citations as recorded by crossref.
- Assessing carbon stock and sequestration potential under land use and land cover dynamics in the Upper Blue Nile River Basin, Ethiopia B. Tikuye et al. 10.1080/17583004.2025.2479516
- Quantifying human impacts on surface water dynamics via a convolutional LSTM approach D. Lu et al. 10.1016/j.jhydrol.2025.133656
- Technical note: Surface fields for global environmental modelling M. Choulga et al. 10.5194/hess-28-2991-2024
- Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations R. Parker et al. 10.5194/bg-19-5779-2022
- Effects of climate changes on net primary productivity variation in the marsh area of the Sanjiang Plain F. Yan 10.3389/fevo.2022.1002397
- Modelling the influence of soil moisture on the Turkana jet J. Talib et al. 10.1002/qj.4972
- Climate change and effectiveness of dams in flood mitigation in India U. Vegad & V. Mishra 10.1038/s44304-025-00117-z
- Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands A. Schrapffer et al. 10.5194/gmd-16-5755-2023
1 citations as recorded by crossref.
Latest update: 08 Aug 2025
Short summary
Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Reliable data on global inundated areas remain uncertain. By matching a leading global data...
