What capacities are needed for effective response to compound events? And to what extent do these capacities exist within a selection of Swedish municipalities? Compound events are understood as when multiple crises occur simultaneously or in close succession within a geographic area. Scientists in the natural sciences emphasize that the occurrence of compound events will become more common in the future and request knowledge on how they should be managed. The identified capabilities considered particularly relevant to compound events are; awareness, collaboration, flexibility, planning, exercises, resources, and anchoring. The empirical study found that the capability most prevalent among the studied municipalities was awareness, 15 of 17 municipalities. 13 municipalities demonstrated anchoring, about half of the municipalities demonstrated collaboration, flexibility, planning and exercises, and resources was found in only one of the municipalities. Based on the policy cycle it can be observed that a majority of the municipalities are in the initial stage of the cycle, which is identifying the problem. The steps towards implementing and allocate resources to strengthen the necessary capabilities vary between the municipalities but have not been taken to any significant extent. Therefore, it can be concluded that the municipalities lack relevant capabilities to manage compound events, which becomes problematic due to the extensive disruptions and consequences they cause in the society. The contribution of this study is a theoretical framework containing relevant capabilities for managing compound events, as well as an empirical description of how these capabilities appear within the studied municipalities. The study concludes with suggestions for future research focusing specifically on a deeper understanding of the capabilities within the framework of the policy cycle.

Flooding is of particular concern in low-lying coastal zones that are prone to impacts from multiple flooding drivers, such as coastal (storm surge and waves), fluvial (excessive river discharge), and pluvial (excessive surface runoff). Failure to account for dependence (and its changes over time) between flood drivers, when dependence exists between them, may lead to underestimation of flood risk and under-design of flood defense measures. Characterizing the dependence between compound flooding drivers in space and across seasons (tropical and extra-tropical), and how this dependence changed over time is essential in this context. In this dissertation, compound flooding potential from all relevant flooding drivers is assessed at 35 locations along the contiguous United States (CONUS) coastline. Different dependence measures are derived and analyzed using observations and state-of-the-art re-analysis data sets. In addition, temporal changes in the extremal dependence are assessed, using a sliding time window approach and possible associations with large-scale climate indices are explored. The effects of changes in dependence and marginal distributions over time between coastal and fluvial flooding drivers are investigated in more detail for a selected case study location. To overcome the computational expense of numerical modeling for flood mapping of large sets of events, a framework is introduced based on hybrid statistical modeling and one-dimensional hydraulic modeling combined with a flood inundation tool capable of propagating spatially variable along-river water surface elevations inland. The framework to delineate the flood transition zone is implemented for the Potomac River and different flood scenarios are analyzed to assess how different combinations of coastal water levels and river discharge modulate the flood hazard (specifically flood depth and extent). Overall, the findings provide new insights into characterizing compound flooding potential, its changes in space and time and how incorporating flood driver dependencies affects flood hazard.

This paper presents an innovat ive cons t ruct ion of a probabilistic model for predicting chance situations. It describes the construction of a four phase model, derived from an intense qualitative analysis of the written responses of 94 mathematically talented middle school students to the probabilistic compound event problem: “How many doubles are expected when rolling two dice fifty times?” We found that the students’ comprehension process of compound event situations can be broken down into a four phase model: beliefs, subjective estimations, chance estimations and probabilistic calculations. The paper focuses on the development of the model over the course of the experiment, identifying the process the students underwent as they attempted to answer the question. We explain each phase as it was reflected in the students\' rationalizations. All phases, including their definitions and students’ citations, will be presented in the paper. While not every student necessarily goes through all four phases, an awareness and understanding of them all allows for efficient, effective intervention during the learning process. We found that guidance and learning intervention helped shorten the preliminary phases, leading to more relative time spent on probabilistic calculations.

This paper presents an innovat ive cons t ruct ion of a probabilistic model for predicting chance situations. It describes the construction of a four phase model, derived from an intense qualitative analysis of the written responses of 94 mathematically talented middle school students to the probabilistic compound event problem: “How many doubles are expected when rolling two dice fifty times?” We found that the students’ comprehension process of compound event situations can be broken down into a four phase model: beliefs, subjective estimations, chance estimations and probabilistic calculations. The paper focuses on the development of the model over the course of the experiment, identifying the process the students underwent as they attempted to answer the question. We explain each phase as it was reflected in the students\'' rationalizations. All phases, including their definitions and students’ citations, will be presented in the paper. While not every student necessarily goes through all four phases, an awareness and understanding of them all allows for efficient, effective intervention during the learning process. We found that guidance and learning intervention helped shorten the preliminary phases, leading to more relative time spent on probabilistic calculations.