EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Downscaling global wildfire model output to a relevant scale for probabilistic wildfire risk assessment of economic impacts

Carmen B. Steinmann1,2, Samuel Lüthi1,2, Samuel Gübeli1, Benoît P. Guillod3, and David N. Bresch1,2
Carmen B. Steinmann et al.
  • 1Institute for Environmental Decisions, ETH Zurich, Zurich, 8092, Switzerland
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, 8058, Switzerland
  • 3CelsiusPro AG, Seebahnstrasse 85, Zurich, 8003, Switzerland

Accurately estimating wildfire risk is essential for many use cases, such as prioritizing adaptation resources or offering insurance coverage for these devastating events. In collaboration with the Zurich-based InsurTech company CelsiusPro we present a globally consistent, open-source wildfire hazard, based on state-of-the-art fire models and providing high-resolution, probabilistic fire seasons suitable for risk analysis and insurance coverage pricing.

For the probabilistic part, we build upon the existing wildfire hazard model available on the open-source climate risk modelling platform CLIMADA (CLIMate ADAptation). This model creates stochastic wildfire events at 1 km resolution using a random walk generator that assigns a grid-point specific fire ignition and propagation probability based on Fire Information for Resource Management System (FIRMS) satellite data and physical constraints such as population density and land cover. However, this model does not account for key physical drivers, such as wind.

On the other hand, data from state-of-the-art fire models are available through the Fire Model Intercomparison Project (FireMIP), which coordinates the evaluation and comparison of these models. While most available models account for the complexity of fire ignition and propagation including relevant physical drivers, their resolution (ranging from 0.5° to 2.8°) is too coarse for the assessment of economic impacts as needed for insurance coverage pricing. In addition, most models are not fully probabilistic, but provide their outputs for present and future climate conditions.

In this work, we combine the annual fraction of burnt area provided as FireMIP output with CLIMADA’s stochastic model, resulting in a probabilistic, high-resolution wildfire hazard model that is based on state-of-the-art fire modelling. This allows us to compute a globally consistent economic risk of wildfires to physical assets by combining the newly developed hazard with an exposure and vulnerability.

How to cite: Steinmann, C. B., Lüthi, S., Gübeli, S., Guillod, B. P., and Bresch, D. N.: Downscaling global wildfire model output to a relevant scale for probabilistic wildfire risk assessment of economic impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8854,, 2022.