Preprints
https://doi.org/10.5194/hess-2021-109
https://doi.org/10.5194/hess-2021-109

  23 Apr 2021

23 Apr 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

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

Toby Richard Marthews1, Simon J. Dadson1,2, Douglas B. Clark1, Eleanor M. Blyth1, Garry Hayman1, Dai Yamazaki3, Olivia R. E. Becher2, Alberto Martínez-de la Torre1,4, Catherine Prigent5, and Carlos Jiménez6 Toby Richard Marthews et al.
  • 1UK Centre for Ecology and Hydrology (UKCEH), Maclean Building, Wallingford OX10 8BB, U.K.
  • 2School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, U.K.
  • 3Institute of Industrial Science, University of Tokyo, 4 Chome-6-1 Komaba, Meguro City, Tokyo 153-8505, Japan
  • 4Meteorological Surveillance and Forecasting Group, DT Catalonia, Agencia Estatal de Meteorología (AEMET), Barcelona, Spain
  • 5CNRS, 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
  • 6Estellus, 93 Boulevard de Sébastopol, 75002 Paris, France

Abstract. Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

Toby Richard Marthews et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-109', Anonymous Referee #1, 26 May 2021
    • AC1: 'Reply on RC1', Toby Marthews, 29 Jul 2021
  • RC2: 'Comment on hess-2021-109', Anonymous Referee #2, 27 May 2021
    • AC2: 'Reply on RC2', Toby Marthews, 29 Jul 2021

Toby Richard Marthews et al.

Toby Richard Marthews et al.

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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 nonrandom 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.