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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 27 | Copyright
Adv. Geosci., 27, 45-50, 2010
https://doi.org/10.5194/adgeo-27-45-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  25 Aug 2010

25 Aug 2010

Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling

L. Adam1, P. Döll1, C. Prigent2, and F. Papa3 L. Adam et al.
  • 1Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, Germany
  • 2Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique, Observatoire de Paris, Centre National de la Recherche Scientifique, Paris, France
  • 3Columbia University, NASA Goddard Institute for Space Studies, New York, USA

Abstract. Floodplains play an important role in the terrestrial water cycle and are very important for biodiversity. Therefore, an improved representation of the dynamics of floodplain water flows and storage in global hydrological and land surface models is required. To support model validation, we combined monthly time series of satellite-derived inundation areas (Papa et al., 2010) with data on irrigated rice areas (Portmann et al., 2010). In this way, we obtained global-scale time series of naturally inundated areas (NIA), with monthly values of inundation extent during 1993–2004 and a spatial resolution of 0.5°. For most grid cells (0.5°×0.5°), the mean annual maximum of NIA agrees well with the static open water extent of the Global Lakes and Wetlands database (GLWD) (Lehner and Döll, 2004), but in 16% of the cells NIA is larger than GLWD. In some regions, like Northwestern Europe, NIA clearly overestimates inundated areas, probably because of confounding very wet soils with inundated areas. In other areas, such as South Asia, it is likely that NIA can help to enhance GLWD. NIA data will be very useful for developing and validating a floodplain modeling algorithm for the global hydrological model WGHM. For example, we found that monthly NIAs correlate with observed river discharges.

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