Assimilation of soil moisture with the ensemble Kalman filter for intermediate scale soil moisture predictions in the Netherlands
by D. de Koning
This thesis discusses the applicability of assimilation of artificial SMAP data into a quasi steady state hydrological model to improve soil moisture estimates. The model used for this research was SIMGRO and since no real SMAP data were available at the time of the research, artificial SMAP data were used. The ensemble Kalman filter was used to assimilate the data. To test the method a case study was performed in the North-East of the Netherlands. It was found that this is a feasible method for improving soil moisture estimates. Even if a quasi steady state model is used. However, for practical application more research is necessary and it is very important to use a correctly validated and calibrated model. The systematic errors of the model should be as small as possible.
A fully dynamic model could improve the results. Furthermore it was found that due to the difference in scale between the model (1 x 1 km) and the SMAP data (3 x 3 km) the effect of the Kalman filter is not as large on the finer grid as on the coarser observation grid. This effect might increase when a even finer grid is used.
Due to the generic nature of the method it can be applied to more locations in the Netherlands, where it can potentially help improving soil moisture estimates in real time forecasting systems.
Student:
Dave de Koning
Committee
Supervisor: Dr. S. C. Steele-Dunne, TU Delft
Thesis committee:
Prof. dr. ir. N. C. van de Giesen, TU Delft
Dr. J. F. Yeh, NUS
Dr. P. G. Ditmar, TU Delft
Ir.M. A. A. Alderlieste, HydroLogic
Ir.M. Spijker, HydroLogic
Ir. J. Dong, TU Delft
Examiner NUS: Dr. S. Liong NUS