Bias-aware data assimilation in integrated hydrological modelling

Marc-Etienne Ridler, Donghua Zhang, Henrik Madsen, Jacob Kidmose, Jens C. Refsgaard, Karsten H. Jensen

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


One of the major challenges in hydrological data assimilation applications is the presence of bias in both models and observations. The present study uses the ensemble transform Kalman filtering (ETKF) method and an observational bias estimation technique to estimate groundwater hydraulic heads. The study was carried out in a relatively complex, groundwater dominated, catchment in Denmark using the MIKE SHE model code. The method is implemented and evaluated using synthetic data and subsequently tested against real observations. The results from the synthetic experiments show that the bias-aware filter outperforms the standard filter, with improved state estimate and correct bias estimate. The assimilation using real observations further demonstrates the robustness of bias-aware ETKF, and the potential improvements using integrated hydrological modelling. Furthermore, the experiments with assimilating over different depths show that the state estimates depend on correlation across layers.

Original languageEnglish
Pages (from-to)989-1004
Number of pages16
JournalHydrology Research
Issue number4
Publication statusPublished - Aug 2018


  • Bias-aware filter
  • Data assimilation
  • Ensemble transform Kalman filter
  • Integrated hydrological modelling

Programme Area

  • Programme Area 2: Water Resources


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