"Efficient modeling of environmental systems in the face of complexity and uncertainty"Strong industrial development of the last century has led to a significant increase in public demand for different types of energy and, as a consequence, to an enormous increase in demand for natural resources. Naturally, all types of nature resources form a part of our surrounding environment. In order to extract natural resources a wide variety of technologies has been developed. This has led to a strong rise in interventions in the environment continuing up to the present days. At the same time, environmental systems form one of the largest and most important classes of complex dynamic systems. For this reason, society needs a better understanding of the environment in order to have an efficient and safe interaction for the sake of maximized welfare and sustainability in resources management. In particular, the ability to predict how the environment changes over time or how it will react to planned interventions is indispensable. However our surroundings behave non-tr
ivially in various time and spatial scales. Moreover, many environmental systems are heterogeneous, non-linear and dominated by real-time influences of external driving forces. Unfortunately, a complete picture of environmental systems is not available, because many of these systems cannot be observed directly and only can be derived using sparse measurements. Moreover, environmental data is hardly available and expensive to acquire. Overall, this leads to limited observability, and an inherent uncertainty in all modeling endeavors. Still, research over several decades has showed that modeling plays a very important role in reconstructing (as far as possible) the complete and complex picture of the environment systems and offers a unique way to predict behavior of such multifaceted systems. The current thesis contains research in the field of environmental modeling in the face of complexity and uncertainty. The presented thesis is divided into three parts and refers to diverse appli
cations such as underground petroleum reservoirs, groundwater flow, radioactive waste deposits and storage of energy relevant gases. Part 1 focuses on physical concepts and offers several possibilities to accelerate the modeling process. Part 2 deals with efficient model reduction methodologies for uncertainty quantification. Part 3 demonstrates application to the storage of energy relevant gases in geological formations and discusses related challenges.