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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 5, issue 4
Geosci. Model Dev., 5, 941-962, 2012
https://doi.org/10.5194/gmd-5-941-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 5, 941-962, 2012
https://doi.org/10.5194/gmd-5-941-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 17 Jul 2012

Model description paper | 17 Jul 2012

Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data

B. Ringeval1,*, B. Decharme2, S. L. Piao3,10, P. Ciais1, F. Papa4, N. de Noblet-Ducoudré1, C. Prigent5, P. Friedlingstein6, I. Gouttevin7, C. Koven8, and A. Ducharne9 B. Ringeval et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement (LSCE), UMR8212, Unité mixte CEA-CNRS-UVSQ, Orme des Merisiers Bât. 712, Point Courrier 132, 91191 Gif-sur-Yvette cédex, France
  • 2Météo-France, CNRM/GMGEC/UDC, 31000 Toulouse, France
  • 3Department of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
  • 4IRD LEGOS, 14 Avenue E. Belin, 31400 Toulouse, France
  • 5Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique, Observatoire de Paris, CNRS, Paris, France
  • 6University of Exeter, Exeter, UK
  • 7Laboratoire de Glaciologie et Géophysique de l'Environnement, 38402 Saint Martin d'Hères, France
  • 8Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., MS 90-1116, Berkeley, CA 94720, USA
  • 9UMR Sisyphe, UPMC/CNRS, 4 place Jussieu, 75252 Paris Cedex 05, France
  • 10Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
  • *current address: Institute for Marine and Atmospheric Research Utrecht, Utrecht, The Netherlands

Abstract. The quality of the global hydrological simulations performed by land surface models (LSMs) strongly depends on processes that occur at unresolved spatial scales. Approaches such as TOPMODEL have been developed, which allow soil moisture redistribution within each grid-cell, based upon sub-grid scale topography. Moreover, the coupling between TOPMODEL and a LSM appears as a potential way to simulate wetland extent dynamic and its sensitivity to climate, a recently identified research problem for biogeochemical modelling, including methane emissions. Global evaluation of the coupling between TOPMODEL and an LSM is difficult, and prior attempts have been indirect, based on the evaluation of the simulated river flow. This study presents a new way to evaluate this coupling, within the ORCHIDEE LSM, using remote sensing data of inundated areas. Because of differences in nature between the satellite derived information – inundation extent – and the variable diagnosed by TOPMODEL/ORCHIDEE – area at maximum soil water content, the evaluation focuses on the spatial distribution of these two quantities as well as on their temporal variation. Despite some difficulties in exactly matching observed localized inundated events, we obtain a rather good agreement in the distribution of these two quantities at a global scale. Floodplains are not accounted for in the model, and this is a major limitation. The difficulty of reproducing the year-to-year variability of the observed inundated area (for instance, the decreasing trend by the end of 90s) is also underlined. Classical indirect evaluation based on comparison between simulated and observed river flow is also performed and underlines difficulties to simulate river flow after coupling with TOPMODEL. The relationship between inundation and river flow at the basin scale in the model is analyzed, using both methods (evaluation against remote sensing data and river flow). Finally, we discuss the potential of the TOPMODEL/LSM coupling to simulate wetland areas. A major limitation of the coupling for this purpose is linked to its ability to simulate a global wetland coverage consistent with the commonly used datasets. However, it seems to be a good opportunity to account for the wetland areas sensitivity to the climate and thus to simulate its temporal variability.

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