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

• Published in: Hydrology and earth system sciences Vol. 22; no. 4; pp. 2135 - 2162
• Main Authors: Emery, Charlotte Marie, Paris, Adrien, Biancamaria, Sylvain, Boone, Aaron, Calmant, Stéphane, Garambois, Pierre-André, Santos da Silva, Joecila
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 06-04-2018; Copernicus Publications
• Subjects: Altimeters; Climate models; Data assimilation; Data collection; Environmental aspects; Flow velocity; Groundwater; Hydrologic cycle; Hydrologic models; Hydrologic networks; Hydrological cycle; Hydrology; Kalman filters; Lakes; Land surface models; Localization; Methods; Model accuracy; Outlets; Parameter estimation; Parameter uncertainty; Remote sensing; River basins; River discharge; River flow; River networks; River water; Rivers; Root-mean-square errors; Satellite altimetry; Satellites; Scale (ratio); Soil; Soil moisture; State variable; Studies; Surface water; Topography; Tributaries; Water cycle; Water discharge; Water purification; Water storage; Amazon River
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-22-2135-2018

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.