Groundwater is one of the main sources for drinking water and agricultural activities. Various activities of both humans and nature may lead to groundwater pollution. Very often, pollution, or contamination, of groundwater goes undetected for long periods of time until it begins to affect human health and/or the environment. Cleanup technologies used to remediate pollution can be costly and remediation processes are often protracted. A more practical and feasible way to manage groundwater contamination is to monitor and predict contamination and act as soon as there is risk to the population and the environment. Predicting groundwater contamination requires advanced numerical models of groundwater flow and solute transport. Such numerical modeling is increasingly becoming a reference criterion for water resources assessment and environmental protection. Subsurface numerical models are, however, subject to many sources of uncertainties from unknown parameters and approximate dynamics. This dissertation considers the sequential data assimilation approach and tackles the groundwater contamination problem at the port of Rotterdam in the Netherlands. Industrial concentration data are used to monitor and predict the fate of organic contaminants using a three dimensional coupled flow and reactive transport model. We propose a number of 5 novel assimilation techniques that address different challenges, including prohibitive computational burden, the nonlinearity and coupling of the subsurface dynamics, and the structural and parametric uncertainties. We also investigate the problem of optimal observational designs to optimize the location and the number of wells. The proposed new methods are based on the ensemble Kalman Filter (EnKF), which provides an efficient numerical solution to the Bayesian filtering problem. The dissertation first investigates in depth the popular joint and dual filtering formulations of the state-parameters estimation problem. New methodologies, algorithmically similar, but more efficient numerically, are then proposed based on a more consistent derivation with the Bayesian filtering approach. To reduce computational cost, I further extend the formulation of the hybrid EnKF-variational approach to the state parameter estimation problem and propose an adaptive scheme for the specification of the weights of the flow-dependent and static background covariance matrices. The new adaptive hybrid scheme is shown to provide much better results than the EnKF while using a fraction of the ensemble size. The new methods are implemented and successfully tested with a realistic coupled subsurface and transport-reaction model of the port of Rotterdam by assimilating industrial data on biodegradable chlorinated hydrocarbons. The observational design problem for placing hydrologic wells is subsequently considered and a new efficient solution is proposed that combines concepts from both information theory and data assimilation