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Zusammenfassung

This paper addresses the assessment of adaptation options to forest fires in Europe under projected climate change. To our knowledge this is the first quantitative estimation of impacts of reactive and preventive adaptation strategies within one modelling framework at a regional scale. We follow a rather pragmatic approach and based on a state-of-the-art forest fire module linked with the Community Land Model (CLM) develop a standalone fire model (SFM). With SFM, we explore fuel removal through prescribed burnings and improved fire suppression as adaptation options, selected in consultation with stakeholders. We quantify impacts of climate change on forest fires informed by three climate models reflecting the SRES A2 scenario. The quantitative results we obtained show satisfying performance of SFM model in terms of agreement of the modelled burned areas in Europe for selected test countries with observed data coming from two different sources (European Forest Fire Information System and Global Fires Emissions Database). We highlight unequal modelling accuracy across the selected test countries that should be taken into account for further interpretation. The projections of climate change impact (without adaptation) and assessment of prescribed burnings efficiency (under present climate) derived as by-products for comparison purposes are in line with existing literature. Our estimation of the potential increase of burned areas in Europe under “no adaptation” scenario is about 200% by 2090 (compared to 2000-2008). The application of prescribed burnings has the potential to keep that increase below 50%. Improvements in fire suppression might reduce this impact even further, e.g. boosting the probability of putting out a fire within a day by 10% would result in about a 30% decrease in annual burned areas. By taking more adaptation options into consideration, such as utilizing agricultural fields as fire breaks, behavioural changes and long-term options, burned areas can be potentially reduced further than projected in our analysis.