Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context
Embargo Lift Date
MetadataShow full item record
With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.
Showing items related by title, author, creator and subject.
Khaki, M.; Hamilton, F.; Forootan, E.; Hoteit, I.; Awange, Joseph; Kuhn, Michael (2018)Data assimilation, which relies on explicit knowledge of dynamical models, is a well-known approach that addresses models' limitations due to various reasons, such as errors in input and forcing data sets. This approach, ...
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 ...