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Agent-Based Modeling of Flood Insurance Futures  

Linda Geaves

Agent-based models have facilitated greater understanding of flood insurance futures, and will continue to advance this field as modeling technology develops further. As the pressures of climate-change increase and global populations grow, the insurance industry will be required to adapt to a less predictable operating environment. Complicating the future of flood insurance is the role flood insurance plays within a state, as well as how insurers impact the interests of other stakeholders, such as mortgage providers, property developers, and householders. As such, flood insurance is inextricably linked with the politics, economy, and social welfare of a state, and can be considered as part of a complex system of changing environments and diverse stakeholders. Agent-based models are capable of modeling complex systems, and, as such, have utility for flood insurance systems. These models can be considered as a platform in which the actions of autonomous agents, both individuals and collectives, are simulated. Cellular automata are the lowest level of an agent-based model and are discrete and abstract computational systems. These automata, which operate within a local and/or universal environment, can be programmed with characteristics of stakeholders and can act independently or interact collectively. Due to this, agent-based models can capture the complexities of a multi-stakeholder environment displaying diversity of behavior and, concurrently, can cater for the changing flood environment. Agent-based models of flood insurance futures have primarily been developed for predictive purposes, such as understanding the impact of introductions of policy instruments. However, the ways in which these situations have been approached by researchers have varied; some have focused on recreating consumer behavior and psychology, while others have sought to recreate agent interactions within a flood environment. The opportunities for agent-based models are likely to become more pronounced as online data becomes more readily available and artificial intelligence technology supports model development.


Future Lake Development in Deglaciating Mountain Ranges  

Wilfried Haeberli and Fabian Drenkhan

Continued retreat and disappearance of glaciers cause fundamental changes in cold mountain ranges and new landscapes to develop, and the consequences can reach far beyond the still ice-covered areas. A key element is the formation of numerous new lakes where overdeepened parts of glacier beds become exposed. With the first model results from the Swiss Alps around 2010 of distributed glacier thicknesses over entire mountain regions, the derivation of glacier beds as potential future surface topographies became possible. Since then, climate-, water-, and hazard-related quantitative research about future lakes in deglaciating mountains all over the world rapidly evolved. Currently growing and potential future open water bodies are part of new environments in marked imbalance. The surrounding steep icy slopes and peaks are affected by glacial debuttressing and permafrost degradation, with associated long-term stability reduction. This makes the new lakes potential sources of far-reaching floods or debris flows, and they represent serious multipliers of hazards and risks to down-valley humans and their infrastructure. Such hazard and risk aspects are also of primary importance where the lakes potentially connect with hydropower production, freshwater supply, tourism, cultural values, and landscape protection. Planning for sustainable adaptation strategies optimally starts from the anticipation in space and time of possible lake formation in glacier-covered areas by numerical modeling combined with analyses of ice-morphological indications. In a second step, hazards and risks related to worst-case scenarios of possible impact and flood waves must be assessed. These results then define the range of possibilities for use and management of future lakes. Careful weighing of both potential synergies and conflicts is necessary. In some cases, multipurpose projects may open viable avenues for combining solutions related to technical challenges, safety requirements, funding problems, and societal acceptance. Successful implementation of adaptive projects requires early integration of technical-scientific and local knowledge, including the needs and interests of local users and decision makers, into comprehensive, participatory, and long-term planning. A key question is the handling of risks from extreme events with disastrous damage potential and low but increasing probability of occurrence. As future landscapes and lakes develop rapidly and are of considerable socioeconomic and political interest, they present often difficult and complex situations for which solutions must be found soon. Related transdisciplinary work will need to adequately address the sociocultural, economic, and political aspects.


Remote Sensing and Physical Modeling of Fires, Floods, and Landslides  

Mahesh Prakash, James Hilton, Claire Miller, Vincent Lemiale, Raymond Cohen, and Yunze Wang

Remotely sensed data for the observation and analysis of natural hazards is becoming increasingly commonplace and accessible. Furthermore, the accuracy and coverage of such data is rapidly improving. In parallel with this growth are ongoing developments in computational methods to store, process, and analyze these data for a variety of geospatial needs. One such use of this geospatial data is for input and calibration for the modeling of natural hazards, such as the spread of wildfires, flooding, tidal inundation, and landslides. Computational models for natural hazards show increasing real-world applicability, and it is only recently that the full potential of using remotely sensed data in these models is being understood and investigated. Some examples of geospatial data required for natural hazard modeling include: • elevation models derived from RADAR and Light Detection and Ranging (LIDAR) techniques for flooding, landslide, and wildfire spread models • accurate vertical datum calculations from geodetic measurements for flooding and tidal inundation models • multispectral imaging techniques to provide land cover information for fuel types in wildfire models or roughness maps for flood inundation studies Accurate modeling of such natural hazards allows a qualitative and quantitative estimate of risks associated with such events. With increasing spatial and temporal resolution, there is also an opportunity to investigate further value-added usage of remotely sensed data in the disaster modeling context. Improving spatial data resolution allows greater fidelity in models allowing, for example, the impact of fires or flooding on individual households to be determined. Improving temporal data allows short and long-term trends to be incorporated into models, such as the changing conditions through a fire season or the changing depth and meander of a water channel.