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

Fire risk assessment for prevention improvement in the Chornobyl exclusion zone

Viktor Myroniuk1,2, Sergiy Zibtsev1,2, Johann G. Goldammer3, Vadim Bogomolov1,4, Olexandr Borsuk5, Olexandr Soshenskii1,2, Vasyl Gumeniuk1,2, and Erin Zibtseva1
Viktor Myroniuk et al.
  • 1Regional Eastern Europe Fire Monitoring Center (REEFMC), Kyiv, Ukraine
  • 2National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
  • 3Global Fire Monitoring Center (GFMC), Freiburg, Germany
  • 4Ukrainian Research Institute of Forestry and Forest Melioration (URIFFM), Kharkiv, Ukraine
  • 5Chornobyl Radioecological Biosphere Reserve, Chornobyl, Ukraine

Large landscape fires in 2015 and 2020 in the Chornobyl Exclusion Zone (CEZ), that burnt in total more than 82 thousand ha of highly radioactive forest lands all over the territory, including Red Forest, located near the Unit 4 Confinement, posed a significant threat for health of fire fighters who participated in the suppression and other personnel of the Zone. Burning of forest fuel contaminated with six radionuclides generated smoke that migrated far beyond borders of the Exclusion Zone with prevailing winds towards populated areas. Future uncertainties caused by climate change require risk assessment for development of fire resilient landscape and risk-based integrated fire management system.            

To improve fire prevention in CEZ we have developed a web-based framework for assessing the potential risk of a wildfire that integrates weather data, ignition likelihood, models burn probability, contamination by radionuclides, and available firefighting resources. We combined available field sampling and forest inventory data to parametrize our fuel models. Landsat time series were used for mapping the seasonal pattern of fuels distribution, which conforms to landscape flammability. Canopy fuels were predicted using machine learning models and remote sensing data. We calibrated surface and canopy fuel metrics so that the perimeters of the largest wildfires matched those simulated using the FARSITE fire modelling system based on hourly weather data (i.e., wind speed, wind direction, precipitation etc.).

For modelling of the current risk of fires according to fire weather parameters, the relations of the area and number of fires (according to the MODIS MCD64A1 product) and the modified for Ukraine PORTU fire weather index were calculated on the basis of historical meteorological data for the period from 2010 to 2020 for CEZ. Python scripts have been developed, in order to automatically download fire weather data several times per day and calculate PORTU index in 16 km grid cells.

The research in CEZ funded by European Union’s Horizon 2020 Program within the project FirEUrisk “Development a holistic, risk-wise strategy for European wildfire management” (GA 101003890).      

How to cite: Myroniuk, V., Zibtsev, S., G. Goldammer, J., Bogomolov, V., Borsuk, O., Soshenskii, O., Gumeniuk, V., and Zibtseva, E.: Fire risk assessment for prevention improvement in the Chornobyl exclusion zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13368,, 2022.


Display file

Comments on the display

to access the discussion