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Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

Research output: Contribution to journalArticle (Academic Journal)

  • M. Khaki
  • I. Hoteit
  • M. Kuhn
  • J. Awange
  • E. Forootan
  • A. I.J.M. van Dijk
  • M. Schumacher
  • C. Pattiaratchi
Original languageEnglish
Pages (from-to)301-316
Number of pages16
JournalAdvances in Water Resources
Early online date6 Jul 2017
DateAccepted/In press - 3 Jul 2017
DateE-pub ahead of print - 6 Jul 2017
DatePublished (current) - 1 Sep 2017


The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively, improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

    Research areas

  • Data assimilation, GRACE, Hydrological modeling, Kalman filtering, Particle filtering

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via ELSEVIER at! . Please refer to any applicable terms of use of the publisher

    Accepted author manuscript, 5.91 MB, PDF document

    Licence: CC BY-NC-ND


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