Wildfires Evolution
Providing a refined view of wildfire risks in a changing climate.
In a nutshell
- In recent years, Europe has been affected by an increasing number of wildfires. The warmer and drier weather conditions caused by climate change are considered as one of the factors that affects wildfire frequency and spread.
- The goal of this use case is to provide tools to assess the present and future wildfire risk and potential solutions thanks to the capabilities of the Climate DT.
- It is expected that the developed tools will help decision-makers to obtain more accurate information for risk estimation and climate adaptation policy.
Technical Overview
Challenge
The occurrence of wildfires, as well as the duration of the fire season, are rapidly escalating throughout the world. The factors that often influence the nature of wildfire include temperature, vegetation cover, soil moisture, humidity. With the increasing temperatures, altered precipitation patterns and extended drought periods fuelled by climate change, the likelihood of extreme wildfires is projected to further grow. This, in turn, exerts stress on the planet’s ecosystem and the communities that populate the areas at risk.
To tackle the challenges exacerbated by climate change and protect the vulnerable areas, there is an urgent need to strengthen emergency preparedness and response capacity. To achieve this, common efforts should be targeted at refining the weather and fire monitoring systems, as well as ensuring the availability of data for decision-makers and civil protection agencies.
DestinE Solution
The Finnish forestry management project HIILIPOLKU and the North Karelian-Rescue Services are the key users of this use case, seeking to provide detailed information about the potential evolution of wildfires for Finland, initially and for the Northern Hemisphere at further stages.
The application will combine the mechanistic fire model Spitfire, of which a Python version was created especially for this use case, with the Prometheus fire growth model, developed by several Canadian agencies and the Canadian forest fire weather index.
The combination of adjustable fuel, landscape pattern and topography information and the data from the Climate DT runs on the LUMI supercomputer will provide enhanced simulations of the potential evolution of wildfires in a changing climate.
Simulation of forest fire spread in Muhos, Finland on 29.6.2020, where the fire burned an 250 hectare area within a 10 hour period. Red curve shows burned area at 22-23 EET and thin coloured lines show fire spread at one hour interval after ignition simulated by Prometheus. Credit: FMI
Impact
This use case aims at providing a refined view of wildfire risks in a changing climate by combining the applications involved with the capabilities of the Climate DT. The project aims at developing tools for assessing the present and future wildfire risk and potential solutions applicable to the Northern Hemisphere, even though the initial experiments will apply only to Finland.
Decision-makers will have improved information for risk estimation and adaptation policies at spatial scales similar to those used operationally by fire forecast and emergency services. High resolution input data from users on landscape patterns and fuel amount to the wildfire application allows to quantify to what extent land use and forestry practices influence fire risk.
By providing a better understanding of wildfire risk and spreading characteristics related to the landscape variability, this application may benefit users such as policymakers and experts through specialised centres such as EFFIS – European Forest Fire Information System, and national and international adaptation bodies involved in adaptation policies, e.g. the EEA (European Environment Agency).
Projected change in fire danger index in Fennoscandia during summer months (JJA) from the period 1981–2010 to 2071–2100 under the RCP4.5 and RCP8.5 climate change scenarios as simulated using the Spitfire model. The model was driven with CORDEX data based on the global driver model CanESM2. Credit: FMI
Contributions
Providers
