Assimilating GRACE Terrestrial Water Storage observations into a hydrological model

Assimilating GRACE Terrestrial Water Storage observations into a hydrological model

Tsompanopoulos, E.

Van de Giesen, N. (mentor)

Civil Engineering and Geosciences

Water Resources Department

Msc Geomatics

2010-12-10

Based on satellite observations of Earths time variable gravity field from the Gravity Recovery and Climate Experiment (GRACE), it is possible to derive variations in terrestrial water storage. Tiny variations of gravity from monthly into a decade time scales are mainly due to redistributions of water mass inside the surface fluid envelops of our planet (Ramillien et al., 2008). This allows us to derive variations in TWS (Terrestrial Water Storage) from satellite observations of the gravity field. Firstly, the Ensemble Kalman filter (EnKF) and the Ensemble Kalman smoother (EnKS) have been applied to assimilate the GRACE TWS variation data into the HBV-96 model, a conceptual rainfall-runoff model over the Rhine river basin. Based on prior work on this field, in this thesis it was intended from the very beginning the improvement of methods used, starting from the study period, which was extended from February 1st 2003 to December 26st 2006. Afterwards, newer versions of TWS variation estimates were inferred from three sets of GRACE solutions, one from DEOS TU Delft, and another from CSR - University of Texas. A third dataset named as RBF, based on radial basis function was also included. All of the solutions use different filtering methods which yield different estimates. The following step was to change the state vector and how it is updated. In this way a more realistic method for the individual stores of the Terrestrial Water Stores was used. Finally the Ensemble Moving Batch Smoother have been applied to assimilate the GRACE data into the HBV-96 model. This new assimilation smoother uses observations beyond the estimation time, which are also useful in the estimation.

GRACE
Data Assimilation
Ensemble Kalman Filter
Ensemble Kalman Smoother
Ensemble Moving Batch Smoother

http://resolver.tudelft.nl/uuid:5c2d31a2-5f13-40fc-9aab-608aaa937180

Student theses

master thesis

(c) 2010 Tsompanopoulos, E.