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
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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Total article views: 4,396 (including HTML, PDF, and XML)
Thereof 4,219 with geography defined
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Total article views: 2,988 (including HTML, PDF, and XML)
Thereof 2,875 with geography defined
and 113 with unknown origin.
Total article views: 1,408 (including HTML, PDF, and XML)
Thereof 1,344 with geography defined
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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...
