06 Jul 2022
06 Jul 2022

Assimilation of Transformed Water Surface Elevation to Improve River Discharge Estimation in a Continental-Scale River

Menaka Revel1, Xudong Zhou1, Dai Yamazaki1, and Shinjiro Kanae2 Menaka Revel et al.
  • 1Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
  • 2Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan

Abstract. Quantifying continental-scale river discharge is essential to understanding the terrestrial water cycle but is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation [WSE], water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically-based empirical localization method applied to the Amazon Basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with Nash-Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon Basin, normalized assimilation (median NSE = 0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE = 0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.

Menaka Revel et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-412', Anonymous Referee #1, 20 Jul 2022
    • AC1: 'Reply on RC1', Menaka Revel, 18 Aug 2022
      • RC3: 'Reply on AC1', Anonymous Referee #1, 30 Aug 2022
        • AC3: 'Reply on RC3', Menaka Revel, 13 Sep 2022
  • RC2: 'Comment on egusphere-2022-412', Anonymous Referee #2, 30 Jul 2022
    • AC2: 'Reply on RC2', Menaka Revel, 18 Aug 2022
  • RC4: 'Comment on egusphere-2022-412', Anonymous Referee #3, 31 Aug 2022
    • AC3: 'Reply on RC3', Menaka Revel, 13 Sep 2022
  • AC4: 'Comment on egusphere-2022-412', Menaka Revel, 13 Sep 2022

Menaka Revel et al.

Data sets

HydroDA v1.0 Revel, M., X. Zhou, S. Kanae, D. Yamazaki

Model code and software

HydroDA 1.0 Revel, M.

Menaka Revel et al.


Total article views: 619 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
430 166 23 619 47 5 3
  • HTML: 430
  • PDF: 166
  • XML: 23
  • Total: 619
  • Supplement: 47
  • BibTeX: 5
  • EndNote: 3
Views and downloads (calculated since 06 Jul 2022)
Cumulative views and downloads (calculated since 06 Jul 2022)

Viewed (geographical distribution)

Total article views: 568 (including HTML, PDF, and XML) Thereof 568 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 29 Nov 2022
Short summary
The capacity to discern surface water improved as satellites became more available. Because remote sensing data is discontinuous, integrating models with satellite observations will improve knowledge of water resources. However, given the current limitations (e.g., parameter errors) of water resource modeling, merging satellite data with simulations is problematic. Integrating observations and models with the unique approaches given here can lead to a better estimation of surface water dynamics.