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

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

doi:https://doi.org/10.5194/hess-26-3151-2022

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.

https://doi.org/10.5194/hess-26-3151-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 12

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

Supplement

https://doi.org/10.5194/hess-26-3151-2022-supplement

Data sets

Basic World Cities Database (1.6), Pareto Software SimpleMaps https://simplemaps.com/data/world-cities

Tropical and Subtropical Wetlands Distribution (2.0) T. Gumbricht, R. M. Román-Cuesta, L. V. Verchot, M. Herold, F. Wittmann, E. Householder, N. Herold, and D. Murdiyarso https://doi.org/10.17528/CIFOR/DATA.00058

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.