Articles | Volume 22, issue 4
Hydrol. Earth Syst. Sci., 22, 2135–2162, 2018
Hydrol. Earth Syst. Sci., 22, 2135–2162, 2018

Research article 06 Apr 2018

Research article | 06 Apr 2018

Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product

Charlotte Marie Emery1,a, Adrien Paris1,2,3,b, Sylvain Biancamaria1, Aaron Boone4, Stéphane Calmant1, Pierre-André Garambois5, and Joecila Santos da Silva6 Charlotte Marie Emery et al.
  • 1LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
  • 2GET, Université de Toulouse, UPS, CNRS, IRD, Toulouse, France
  • 3LMI OCE IRD/UNB, Campus Darcy Ribeiro, Brasilia, Brazil
  • 4Meteo France CNRS, CNRM UMR 3589, Toulouse, France
  • 5ICUBE – UMR 7357, Fluid Mechanics Team, INSA, Strasbourg, France
  • 6CESTU, Universidade do Estado do Amazonas, Manaus, Brazil
  • anow at: JPL, Pasadena, CA, USA
  • bnow at: CLS, Ramonville-Saint-Agne, France

Abstract. Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21 % and at Óbidos, near the outlet, RMSE is reduced by up to 52 % compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries.

Short summary
This study uses remotely sensed river discharge data to correct river storage and discharge in a large-scale hydrological model. The method is based on an ensemble Kalman filter and also introduces an additional technique that allows for better constraint of the correction (called localization). The approach is applied over the entire Amazon basin. Results show that the method is able to improve river discharge and localization to produce better results along main tributaries.