Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

Khaki, M., Hoteit, I., Kuhn, M., Forootan, Ehsan ORCID: and Awange, J. 2019. Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context. Science of the Total Environment 647 , pp. 1031-1043. 10.1016/j.scitotenv.2018.08.032

[thumbnail of KhakiForootan_Stoten2019deposite.pdf]
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview


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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Publisher: Elsevier
ISSN: 0048-9697
Date of First Compliant Deposit: 21 August 2018
Date of Acceptance: 3 August 2018
Last Modified: 24 Oct 2022 20:28

Citation Data

Cited 22 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics