Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands

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...

Ensemble-Based History Matching for Channelized Petroleum Reservoirs

The oil industry is at the backbone of global economy and, as natural resources are becoming scarce, there is a pressing need for efficient extraction strategies. This has led to the development of reservoir models and simulators, able to predict future field behaviour, when paired with accurate geological information. However, the data obtained...

Improving the Algal Bloom Prediction in the North Sea by Ensemble Kalman Filtering in the GEM/BLOOM Model

The ecological state of the North Sea surface water can be indicated by ocean variables such as the Chlorophyll-a (Chlfa) concentration. Chlfa is the principal photosynthetic pigment and is common to all phytoplankton and can therefore be used as a measure of phytoplankton biomass. The GEM/BLOOM model developed at Deltares is a generic...