Interannual Variations of River Water Storage in the Rio Negro River basin from a Multiple Satellite Approach Frédéric Frappart (1) , Fabrice Papa (2) , James S. Famiglietti (1) , Catherine Prigent (3) , William B. Rossow (3) , Frédérique Seyler (4) (1) Department of Earth System Science, University of California, Irvine, USA. frederic.frappart@cesbio.cnes.fr , jfamigli@uci.edu (2) NOAA-Cooperative Remote Sensing Science and Technology Center, City College of New York, New York, USA. fpapa@giss.nasa.gov , wrossow@giss.nasa.gov (3) CNRS, Laboratoire d’Etudes du Rayonnement et de la Matiere en Astrophysique, Observatoire de Paris, France. catherine.prigent@obspm.fr (4) IRD, Brasilia, Brazil. frederique.seyler@ird.fr INTRODUCTION Spatio-temporal variations of water volume over inundated areas located in large river basin have been determined using combined observations from a multi-satellite inundation dataset, the Topex/Poseidon (T/P) altimetry satellite, and in-situ hydrographic stations for the water levels over rivers and floodplains. We computed maps of monthly surface water volume change over eight successive years (1993-2000), the period of common availability of T/P and the multisatellite data. The basin of the Negro River (which area is around 700,000 km 2 ), the tributary which carries the largest discharge volume to the Amazon River, was selected as a test site. A strong seasonal signal is observed with minima in October and maxima in June. A strong interannual component is also present, particularly important during ENSO years. The results are consistent with previous water volume change obtained for two months over the same area using the JERS mosaic data. The surface water volume change is then compared to the total (i.e., surface plus underground) water volume change inferred from the GRACE satellite for an average annual cycle. The water volume changes are also evaluated using in-situ discharge measurements and the rain GPCP product. It clearly shows high potentials for the new technique to bring valuable information to improve our understanding of large river basin hydrologic processes and modeling and will be extended soon to other large watersheds.