EGU26-19168, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-19168
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Wednesday, 06 May, 08:30–10:15 (CEST), Display time Wednesday, 06 May, 08:30–12:30
 
Hall X1, X1.14
Wildfire Hot Spot Mapping in the Alps - Austria Fire Futures
Andrey Krasovskiy1, Hyun-Woo Jo1, Harald Vacik2, Mariana Silva Andrade2, Herbert Formayer2, Johannes Laimighofer2, Arne Arnberger2, Tobias Schadauer3, Mortimer Müller2, Eunbeen Park1, Johanna San-Pedro1, and Florian Kraxner1
Andrey Krasovskiy et al.
  • 1International Institute for Applied Systems Analysis (IIASA), Biodiversity and Natural Resources, Laxenburg, Austria (krasov@iiasa.ac.at)
  • 2BOKU University, Vienna
  • 3Bundesforschungszentrum fuer Wald, Vienna, Austria

The main objective of the Austria Fire Futures study is to develop a unique and innovative framework for fire risk assessment by producing high-resolution fire risk hotspot maps under multiple climate change scenarios. These maps integrate novel insights on local fuel types into forest and wildfire risk models, including mountain-specific variables such as topography, morphology, and recreational activities.

To generate fire risk information at the local scale, advanced fire hazard modeling is required to identify vulnerable forest types in combination with topographic effects. Recent wildfire events in the Austrian Alps have demonstrated that social factors—particularly hiking tourism—are currently underrepresented in fire risk assessments. In response, this study aims to advance fire risk hotspot mapping as a foundational element for forest and wildfire prevention. Such mapping is essential for integrated fire management, encompassing prevention, suppression, and post-fire measures, while contributing to climate change mitigation and minimizing impacts on ecosystems, ecosystem services, and human well-being.

We present modeling results from the Wildfire Climate Impacts and Adaptation Model (FLAM), a process-based fire risk model operating at a daily time step. FLAM employs machine learning techniques to calibrate extended suppression efficiency based on spatial segmentation of landscapes. Historical ground data on burned areas in Austria were used for model calibration and validation. The results include historical simulations (2001–2020) and future projections (2021–2100) of burned area across Austria at 1 km spatial resolution, based on an ensemble of downscaled climate change scenarios. In addition, FLAM was applied to Lower Austria at 250 m resolution, using the most recent high-resolution datasets on fuels, forest cover, human ignition probability, and response times.

The results improve our understanding of fire-vulnerable forest areas in the Alpine region and how these vulnerabilities may shift over time and space under changing climate and fuel conditions. This knowledge enables experts, practitioners, and the broader public to explore plausible future fire regimes and to derive robust short-, medium-, and long-term recommendations for fire-resilient and sustainable forest management, as well as for wildfire preparedness and emergency planning.

How to cite: Krasovskiy, A., Jo, H.-W., Vacik, H., Silva Andrade, M., Formayer, H., Laimighofer, J., Arnberger, A., Schadauer, T., Müller, M., Park, E., San-Pedro, J., and Kraxner, F.: Wildfire Hot Spot Mapping in the Alps - Austria Fire Futures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19168, https://doi.org/10.5194/egusphere-egu26-19168, 2026.