Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model
MetadataShow full item record
© 2017 Elsevier Ltd 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%.
Showing items related by title, author, creator and subject.
Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localizationKhaki, M.; Schumacher, M.; Forootan, E.; Kuhn, Michael; Awange, Joseph; van Dijk, A. (2017)© 2017 Elsevier Ltd Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. ...
A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraintKhaki, M.; Ait-El-Fquih, B.; Hoteit, I.; Forootan, E.; Awange, Joseph; Kuhn, Michael (2017)Â© 2017 Elsevier B.V. Assimilating Gravity Recovery And Climate Experiment (GRACE) data into land hydrological models provides a valuable opportunity to improve the modelsâ€™ forecasts and increases our knowledge of ...
Validation of GRACE based groundwater storage anomaly using in-situ groundwater level measurements in IndiaBhanja, S.; Mukherjee, Abhijit; Saha, D.; Velicogna, I.; Famiglietti, J. (2016)In this study, we tried to validate groundwater storage (GWS) anomaly obtained from a combination of GRACE and land-surface model based estimates, for the first time, with GWS anomaly obtained from a dense network of ...