Assimilation of radar altimetry to a routing model of the Brahmaputra River
Article first published online: 7 AUG 2013
©2013. American Geophysical Union. All Rights Reserved.
Water Resources Research
Volume 49, Issue 8, pages 4807–4816, August 2013
How to Cite
2013), Assimilation of radar altimetry to a routing model of the Brahmaputra River, Water Resour. Res., 49, 4807–4816, doi:10.1002/wrcr.20345., , and (
- Issue published online: 23 SEP 2013
- Article first published online: 7 AUG 2013
- Accepted manuscript online: 7 JUN 2013 03:05AM EST
- Manuscript Accepted: 3 JUN 2013
- Manuscript Revised: 27 MAY 2013
- Manuscript Received: 16 DEC 2012
- Danish Ministry of Foreign Affairs. Grant Number: 09-043DTU
- radar altimetry;
 While satellite-based remote sensing has provided hydrologists with valuable new data sets, integration of such data sets in operational modeling systems is usually not straightforward due to spatial or temporal resolution issues or because remote sensing does not directly measure the hydrological quantities of interest. This is the case for satellite-based radar altimetry. River-level variations can be tracked using radar altimetry at a temporal resolution between 10 and 35 days, depending on the satellite, but hydrologists are typically interested in river flows rather than levels and require predictions at daily or even subdaily temporal resolutions. One way to exploit satellite radar altimetry is therefore to combine the data with hydrological models in a data assimilation framework. In this study, radar altimetry data from six ENVISAT virtual stations were assimilated to a routing model of the main reach of the Brahmaputra River driven by the outputs of a calibrated rainfall runoff model. The extended Kalman filter was used to update the routed water volumes for the years 2008–2010. Model performance was improved with the Nash-Sutcliffe model efficiency for daily discharge increasing from 0.78 to 0.84. The method uses very little in situ data and is easily implemented as an add-on to hydrological models, and it therefore has the potential for large-scale application to improve hydrological predictions in many river basins.