Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

ABSTRACT

This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.

KEYWORDS:

• hydrological routing
• data assimilation
• three-parameter Muskingum model
• distributed routing

Editor A. Castellarin Associate editor G. Thirel

Editor A. Castellarin Associate editor G. Thirel

Acknowledgements

We would like to thank the West Gulf River Forecast Center for providing river discharge data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded in the framework of the European FP7 Project WeSenseIt: Citizen Observatory of Water [grant agreement no. 308429]. Support for Seongjin Noh and Dong-Jun Seo was provided by the National Science Foundation under Grant CyberSEES-1442735 (Dong-Jun Seo, University of Texas at Arlington, PI), and by NOAA/OAR/OWAQ/JTTI under Grant NA17OAR4590174. This support is gratefully acknowledged; Directorate for Computer and Information Science and Engineering [CyberSEES-1442735]; Seventh Framework Programme [308429].