Improving hydrogeological characterization using groundwater numerical models and multiple lines of evidence

Groundwater is Earth’s largest liquid freshwater resource. It is a significant component of the hydrological cycle and a buffer that sustainsrivers and freshwater-dependent ecosystems during droughts. Approximately half of the world’s population depends on groundwater fordrinking water, food, and hygiene. It is used extensively in agricultural irrigation, food production and for industrial processes. However,pollution and over-exploitation pose serious risks to its sustainability, representing a global problem manifested on a local scale. Therefore, theresponsible management of groundwater is critical to ensure its quality and availability for future generations.Informed decision-making on groundwater management requires the underground, i.e. the material in which groundwater is stored and throughwhich it flows, to be characterized. This thesis focuses on how this characterization can be improved by using groundwater numerical modelsas a framework for assimilating diverse types of data, including direct and indirect measurements of groundwater and underground properties,as well as expert knowledge. The scope of this thesis is twofold. Firstly, it investigates the extent and manner in which groundwater numericalmodels are currently applied within the industry to solve groundwater-related problems, as analyzed through the current state of the art indecision-support modelling. For practical reasons, this investigation focuses on applications in Sweden, but highlights insights applicable in aninternational context. Secondly, it explores methods for improving hydrogeological characterization through the assimilation of conventionaland unconventional data types, with a focus on contaminated sites. These data types are then evaluated in terms of their contribution towardsreducing the uncertainty of model predictions, providing insights on the value of information.The findings highlights a significant gap between important academic advances in groundwater modelling and practical application within theindustry, tracing this discrepancy to a lack of inclusion of concepts such as data assimilation and uncertainty quantification in groundwatereducation. Suggestions for improvement are presented, which include the formulation of flexible guideline recommendations for practitionersand the inclusion of aforementioned concepts in groundwater education. Additional findings highlights the high value of unconvential data,demonstrating that, depending on the model prediction, they can be as valuable as conventional measurements of hydraulic head or more.This likely challenges the prevailing line of thinking within the industry, but also presents an opportunity for improved modelling workflowsamong practitioners willing to embrace new concepts. This thesis presents tangible examples for how this can be achieved in order to improvehydrogeological site characterization, demonstrated using transparent and reproducible model workflows of two contaminated sites in Sweden.