Real-time assimilation of streamflow observations into a hydrological routing model

Effects of model structures and updating methods

Journal article (2018)

Authors

M. Mazzoleni IHE Delft Institute for Water Education
Seong Jin Noh Univ. of Texas at Arlington
Haksu Lee LEN Technologies
Yuqiong Liu LEN Technologies
Dong Jun Seo Univ. of Texas at Arlington
A. Amaranto IHE Delft Institute for Water Education, University of Nebraska–Lincoln
Leonardo Alfonso IHE Delft Institute for Water Education
D.P. Solomatine IHE Delft Institute for Water Education, Water Resources - CEG
Research Group
Water Resources (CEG) (TU Delft)
Data assimilation Distributed routing Hydrological routing Three-parameter Muskingum model
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Published Date

17-02-2018

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Water Management

Research Group

Water Resources

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.