Global Inundation Extent from Multi-Satellites (GIEMS) dataset at high spatial resolution.

GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds (approximately 500 meter at the equator). The map was generated by downscaling inundated area estimates from the Global Inundation Extent from Multi-Satellites (GIEMS) (1, 2, 3) bias-adjusted with wetland extents from the Global Lakes and Wetlands Database (GLWD) (4). This initiative is part of a general project to downscale the full dynamics of GIEMS (GIEMS-D) (5, 6).

Methods and results of creating GIEMS-D15 are fully described in Fluet-Chouinard et al. (2015; see below for full citation). The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged decision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database (7) and trained on the wetland extent of the GLC2000 global land cover map (8).

GIEMS-D15 represents three states of global inundation extent: mean annual minimum (MAMin, total global area: 6.5×106 km2), mean annual maximum (MAMax, 12.1×106 km2), and long-term maximum (LTMax, 17. 3×106 km2). GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.

  • MAMin and MAMax: These estimates, respectively, represent the average yearly minimum and maximum inundation extent derived from a 12-year record of monthly GIEMS data. Together, these two estimates can be interpreted as typical seasonal low and high inundation situations.

  • LTMax: This inundation extent represents a less frequent state of extensive flooding. It was derived by combining the average 3-year maximum of the GIEMS record with wetland extents depicted in GLWD.

  • Data Request, User Agreement & Format

    GIEMS-D15 is available upon request made to
    Users of GIEMS-D15 must agree to the terms and limitations of a User Agreement that is signed before data distribution.

    GIEMS-D15 is available in three formats:

  • ESRI Geodatabase: 75.4 MB (63.9 MB zipped)
  • GeoTiff: 92.3 MB (60.6 MB zipped)
  • NetCDF: 2.70 GB (43.0 MB zipped)

  • Citation and further reference:

    For further details on map generation and validation, as well as for citation, please refer to:

  • Fluet-Chouinard E., Lehner B., Rebelo L.M., Papa F., Hamilton S.K. (2015): Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment 158: 348-361.
  • Prigent, C., Papa, F., Aires, F., Rossow, W. B. & Matthews, E. (2007). Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research, 112(D12107), 1-1

  • References:

  • (1) Prigent, C., Matthews, E., Aires, F., & Rossow, W. B. (2001). Remote sensing of global wetland dynamics with multiple satellite data sets. Geophysical Research Letters, 28(24), 4631-4634.
  • (2) Prigent, C., Papa, F., Aires, F., Rossow, W. B. & Matthews, E. (2007). Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research, 112(D12107), 1-1
  • (3) Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B., & Matthews, E. (2010). Interannual variability of surface water extent at the global scale, 1993–2004. Journal of Geophysical Research, 115(D12111), 1-17.
  • (4) Lehner, B., & Döll, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, vol. 296(1-4), 1-22.
  • (5) Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013.
  • (6) Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeoroloy, 27, 1958-1979, doi: http://dx/, 2014.
  • (7) Lehner, B., Verdin, K., & Jarvis, A., 2008. New Global Hydrography Derived From Spaceborne Elevation Data. Eos, 89(10), 93-94.
  • (8) Bartholomé, E., & Belward, A. S. (2005), GLC2000: A new approach to global land cover mapping from earth observation data. International Journal of Remote Sensing, 26(9), 1959-1977.