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Comparisons of Global Terrestrial Surface Water Datasets over 15 Years

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  • 1 Laboratoire d’Études du Rayonnement et de la Matière en Astrophysique et Atmosphères, CNRS, Observatoire de Paris, Paris, France, and Space and Aeronautics Department, University of Science and Technology of Hanoi, Hanoi, Vietnam
  • | 2 Laboratoire d’Études du Rayonnement et de la Matière en Astrophysique et Atmosphères, CNRS, Observatoire de Paris, Paris, France
  • | 3 LEGOS, Université de Toulouse, IRD, CNES, CNRS, UPS, Toulouse, France, and Indo-French Cell for Water Sciences, IRD-IISc-NIO-IITM, Indian Institute of Science, Bangalore, India
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Abstract

Continental surface water extents and dynamics are key information to model Earth’s hydrological and biochemical cycles. This study presents global and regional comparisons between two multisatellite surface water extent datasets, the Global Inundation Extent from Multi-Satellites (GIEMS) and the Surface Water Microwave Product Series (SWAMPS), for the 1993–2007 period, along with two widely used static inundation datasets, the Global Lakes and Wetlands Database (GLWD) and the Matthews and Fung wetland estimates. Maximum surface water extents derived from these datasets are largely different: ~13 × 106 km2 from GLWD, ~5.3 × 106 km2 from Matthews and Fung, ~6.2 × 106 km2 from GIEMS, and ~10.3 × 106 km2 from SWAMPS. SWAMPS global maximum surface extent reduces by nearly 51% (to ~5 × 106 km2) when applying a coastal filter, showing a strong contamination in this retrieval over the coastal regions. Anomalous surface waters are also detected with SWAMPS over desert areas. The seasonal amplitude of the GIEMS surface waters is much larger than the SWAMPS estimates, and GIEMS dynamics is more consistent with other hydrological variables such as the river discharge. Over the Amazon basin, GIEMS and SWAMPS show a very high time series correlation (95%), but with SWAMPS maximum extent half the size of that from GIEMS and from previous synthetic aperture radar estimates. Over the Niger basin, SWAMPS seasonal cycle is out of phase with both GIEMS and MODIS-derived water extent estimates, as well as with river discharge data.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0206.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Binh Pham-Duc, pham.binh@obspm.fr

Abstract

Continental surface water extents and dynamics are key information to model Earth’s hydrological and biochemical cycles. This study presents global and regional comparisons between two multisatellite surface water extent datasets, the Global Inundation Extent from Multi-Satellites (GIEMS) and the Surface Water Microwave Product Series (SWAMPS), for the 1993–2007 period, along with two widely used static inundation datasets, the Global Lakes and Wetlands Database (GLWD) and the Matthews and Fung wetland estimates. Maximum surface water extents derived from these datasets are largely different: ~13 × 106 km2 from GLWD, ~5.3 × 106 km2 from Matthews and Fung, ~6.2 × 106 km2 from GIEMS, and ~10.3 × 106 km2 from SWAMPS. SWAMPS global maximum surface extent reduces by nearly 51% (to ~5 × 106 km2) when applying a coastal filter, showing a strong contamination in this retrieval over the coastal regions. Anomalous surface waters are also detected with SWAMPS over desert areas. The seasonal amplitude of the GIEMS surface waters is much larger than the SWAMPS estimates, and GIEMS dynamics is more consistent with other hydrological variables such as the river discharge. Over the Amazon basin, GIEMS and SWAMPS show a very high time series correlation (95%), but with SWAMPS maximum extent half the size of that from GIEMS and from previous synthetic aperture radar estimates. Over the Niger basin, SWAMPS seasonal cycle is out of phase with both GIEMS and MODIS-derived water extent estimates, as well as with river discharge data.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0206.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Binh Pham-Duc, pham.binh@obspm.fr

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