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

Maurizio Mazzoleni, Seong Jin Noh, Haksu Lee, Yuqiong Liu, Dong Jun Seo, Alessandro Amaranto, Leonardo Alfonso, Dimitri P. Solomatine

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)386-407
Number of pages22
JournalHydrological Sciences Journal
Volume63
Issue number3
DOIs
Publication statusPublished - 17 Feb 2018

Keywords

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

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