Data Assimilation for Management of Industrial Groundwater Contamination at a Regional Scale
AuthorsEl Gharamti, Mohamad
ProgramEarth Sciences and Engineering
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
MetadataShow full item record
AbstractGroundwater 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 a ect 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 ow 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 threedimensional coupled ow and reactive transport model. We propose a number of 5 novel assimilation techniques that address di erent 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 e cient numerical solution to the Bayesian ltering problem. The dissertation rst investigates in depth the popular joint and dual ltering formulations of the state-parameters estimation problem. New methodologies, algorithmically similar, but more e cient numerically, are then proposed based on a more consistent derivation with the Bayesian ltering approach. To reduce computational cost, I further extend the formulation of the hybrid EnKF-variational approach to the stateparameter estimation problem and propose an adaptive scheme for the speci cation of the weights of the ow-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 e cient solution is proposed that combines concepts from both information theory and data assimilation.