Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3151-2022
https://doi.org/10.5194/hess-26-3151-2022
Research article
 | 
21 Jun 2022
Research article |  | 21 Jun 2022

Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations

Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez

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Cited articles

Aires, F., Prigent, C., Fluet-Chouinard, E., Yamazaki, D., Papa, F., and Lehner, B.: Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution, Remote Sens. Environ., 216, 427–441, 2018. 
Alsdorf, D., Beighley, E., Laraque, A., Lee, H., Tshimanga, R., O'Loughlin, F., Mahe, G., Dinga, B., Moukandi, G., and Spencer, R. G. M.: Opportunities for hydrologic research in the Congo Basin, Rev. Geophys., 54, 378–409, https://doi.org/10.1002/2016RG000517, 2016. 
Andersen, I., Dione, O., Jarosewich-Holder, M., and Olivry, J.-C.: The Niger River Basin: A Vision for Sustainable Management, The International Bank for Reconstruction and Development/The World Bank, Washington, DC, https://doi.org/10.1596/978-0-8213-6203-7, 2005.  
Arduini, G., Fink, G., and Martínez-de la Torre, A.: End-user-focused improvements and descriptions of the advances introduced between the WRR tier1 and WRRtier2, http://earth2observe.eu/files/Public Deliverables/D5.3 - End-user-focused improvement report with the advances (last access: 20 June 2022), 2017. 
Balek, J.: Hydrology and water resources in tropical Africa, Elsevier, Amsterdam, the Netherlands, ISBN 9780080869995, 1977. 
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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.