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Wildfires are among the most destructive natural hazards and lead to substantial economic impacts. Therefore, assessing and quantifying their economic risk is of great interest to governmental authorities and (re-)insurance companies. To estimate the risk, the probabilistic wildfire module in the open-source software CLIMADA (CLIMate ADAptation) simulates wildfire seasons based on a cellular automaton. In this thesis, we extend the wildfire module by incorporating physical constraints. First, we combine land cover and population data to improve the fire spread simulation with the cellular automaton. Second, we add fire ignition probabilities based on population and historical data, defining particularly wildfire-prone regions. Third, we create the possibility to add a climate change signal as input to assess future economic impacts. The extended wildfire module is applied in ten different geographical regions to evaluate its accuracy using the same default parameters. In nine out of ten investigated regions, reasonable economic impact and burned area estimations are found for present-day climate conditions. To gain further insights into the performance of the wildfire module, we conduct two in-depth case studies for Portugal and central Chile. The case studies show that the probabilistically estimated economic impacts and burned area are in the same order of magnitude as derived from historical seasons. Additionally, including an end of the century climate change signal to the wildfire module leads to a substantial increase in economic impacts and burned area in Portugal. In contrast, the approach is not feasible to simulate the end of century climate conditions in central Chile since the interactions between the climatic conditions and wildfires are complex, and humans strongly influence wildfire activity. Therefore, implementing a climate change signal is only applicable in regions where the climatic conditions during the fire season directly determine the wildfire activity. We conclude that the wildfire module provides accurate estimations of the economic impacts and burned area in numerous regions across the world. The consistency in the parameters and the data-efficient approach enables probabilistic risk assessments without great effort, even in data-sparse geographical regions. |
