Yu. I. Kuzmenko, T. D. Lev, O. G. Tishchenko, V. N. Piskun, L. V. Gavlovska
Institute for Safety Problems of Nuclear Power Plants, NAS of Ukraine, 12, Lysogirska st., Kyiv, 03028, Ukraine
The problem of forest fires in the Chornobyl exclusion zone (ChEZ) is urgent, since vegetation in radioactively contaminated areas is annually destroyed and damaged, causing a rise and transfer of radioactive aerosol to significant distances. This affects the ecological state of the environment, including human settlements. Foreign and domestic experience shows the successful use of specialized software (software) for predicting and propagating lower forest fires constructed using the semi-empirical model of Rothermel based on the spatial distribution of complexes of plant combustible materials and local natural and geographical conditions. The results of using software based on the open-source geographic information system GRASS GIS 7.6 and an algorithm for calculating the fire propagation rate based on the Andrews code implemented in the BEHAVE PLUS system are presented in the article. To implement the software in the test area of the ChEZ, specialized geoinformation support was created, including up-to-date maps of the ChEZ vegetation cover identified in the types of Fuel Models, maps of the soil radioactive contamination, morphometric characteristics of the terrain and meteorological conditions. High-precision terrain elevation maps were used. They were obtained using the global set of digital terrain models of the Japan Aerospace Exploration Agency (JAXA) with a grid spacing of 30 m. All input data were interpolated into a regular network with a grid spacing of 30 m and reduced to a single metric coordinate system. The application of the Rothermel model, implemented in the open-source software GRASS GIS and adapted to the ChEZ conditions for modeling fires at the test site, allows one to determine fire propagation parameters under dry conditions. It is shown that, depending on the location of the source of ignition, differences in the propagation velocity of the burn-up area can vary up to 3 times. The largest burn-up areas in this case occur in areas with a predominance of grassy fuel models. The contours of fires can also vary significantly depending on the terrain, the presence of barriers and the configuration of fuel models at a particular location. The contours of the fire spread and the contamination map of the 137Cs territory make it possible to calculate the reserve of radionuclides in the fire source and their changes in time with increasing fire spread. Based on test calculations, output fire distribution maps for various time intervals and maps of total 137Cs reserves along the fire perimeter were obtained, which are the initial information for further modeling of atmospheric emissions and transport of radioactive aerosol during a fire.
Keywords: fuel models, geographic information support, fire spread, modeling.
1. Rothermel R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, 40 p.
2. Andrews P. L. (1986). BEHAVE: fire behavior prediction and fuel modeling system. BURN subsystem, Part 1. General Technical Report INT-194. Ogden, Utah: USDA Forest Service, Intermountain Research Station, 130 p.
3. Scott J. H., Burgan R. E. (2005). Standard fire behavior fuel models: a comprehensive set for use with Rothermels surface fire spread model. General Technical Report RMRS-GTR-153. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station, 80 p.
4. Sullivan A. L. (2009). Wildland surface fire spread modelling, 1990-2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire, vol. 18, no. 4, pp. 349-368.
5. Sullivan A. L. (2009). Wildland surface fire spread modelling, 1990-2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire, vol. 18, no. 4, pp. 369-386.
6. Sullivan A. L. (2009). Wildland surface fire spread modelling, 1990-2007. 3: Simulation and mathematical analogue models. International Journal of Wildland Fire, vol. 18, no. 4, pp. 387-403.
7. Albini F. A. (1976). Estimating wildfire behavior and effects. General Technical Report INT-30. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, 92 p.
8. Anderson H. E. (1982). Aids to determining fuel models for estimating fire behavior. General Technical Report INT-122. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, 28 p.
9. Rothermel R. C. (1983). How to predict the spread and intensity of forest and range fires. General Technical Report INT-143. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, 168 p.
10. Ascoli D., Vacchiano G., Motta R., Bovio G. (2014). Building Rothermel fire behaviour fuel models by genetic algorithm optimization. International Journal of Wildland Fire, vol. 24, no. 3, pp. 317-328. doi: 10.1071/WF14097.
11. Deeming J. E., Lancaster J. W., Fosberg M., Furman W., Schroeder M. J. (1972). The national fire-danger rating system. Research Paper RM-84. N.-Y.; London; Toronto: USDA Forest Service 165 p.
12. Barovik D. V., Taranchuk V. B. (2011). [Rothermel model adaptation for implementation in forest firesforecast software]. Tekhnologii tekhnosfernoy bezopasnosti [Techno-sphere Security Technologies], vol. 40, no. 6, 8 p. Available at: http://elib.bsu.by/bitstream/123456789/9212/1/TVB_ ipb.pdf. (in Russ.)
13. Ministry of Emergency Situations of Ukraine, Intelligence Systems GEO (2011). Atlas. Ukraine. Radioactive contamination. Kyiv: VAITE, 2011, 52 p. (in Ukr.)
14. Kashparov V., Levchuk S., Zhurba M., Protsak V., Khomu-tinin Y., Nicholas A., Beresford J., Chaplow S. (2018). Spatial datasets of radionuclide contaminationin in the Ukrainian Chernobyl Exclusion Zone. Earth Syst. Sci. Data, vol. 10, pp. 339-353.
15. Forestry organization project of the Chornobyl Pushcha State Specialized Complex Enterprise. Kyiv: Lisproekt, 2006. (in Ukr.)
16. Taxation description of forest land plots as of 01.01.2017. SSE “Pivnichna Puscha”, 2017. (in Ukr.)
17. Evangeliou N., Zibtsev S., Myroniuk V., Zhurba M., Hamburger T., Stohl A., Balkanski Y., Paugam R.,. Mousseau T. A., Moller A. P., Kireev S. I. (2016). Resuspension and atmospheric transport of radionuclides due to wildfires near the Chernobyl Nuclear Power Plant in 2015: An impact assessment. Scientific Reports, vol. 6, art. 26062. doi: 10.1038/srep26062.
18. Dosimetric certification of settlements of Ukraine exposed to radioactive contamination after the Chornobyl accident. Volume 5. Kyiv: 1995, 311 p. (in Russ.)
19. Xu J. (1994). Simulating the spread of wildfires using a geographic information system and remote sensing (Phd dissertation). New Brunswick, NJ: Rutgers University.
20. Anderson D. H., Catchpole E. A., De Mestre N. J., Parkes T. (1982). Modelling the spread of grass fires. The Journal of the Australian Mathematical Society. Series B. Applied Mathematics, vol. 23, pp. 451-466.
21. Nelson R. M., Jr. (2000). Prediction of diurnal change in 10-h fuel stick moisture content. Can. J. For. Res., vol. 30, pp. 1071-1087.
22. Fosberg M. A., Rothermel R. C., Andrews P. L. (1981). Moisture content calculations for 1000-hour time lag fuels. Forest Sci., vol. 27, no. 1, pp. 19-26.
23. Volokitina A. V., Sofronova T. M. (2014). Kartografirovanie rastitelnyh goryuchih materialov [Mapping of plant fuels]. Sibirskii lesnoj zhurnal [Siberian Forest Journal], no. 6, pp. 8-28. (in Russ.)
24. Geospatial Modeling and Analysis. GIS582. North Carolina State University. Available at: https://www.koofers.com/north-carolina-state-university-ncsu/gis/582-geospatial-modeling-and-analysis/.
25. Data from satellite imagery of the Earth’s surface of the Landsat satellite. Available at: http://goto.arcgisonline.com/map/World_Imagery.
26. ALOS Global Digital Surface Model “ALOS World 3D — 30m (AW3D30)”. Available at: https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm.
27. Bradstock R. A., Gill A. M., Williams R. J. (eds) (2012). Flammable Australia: fire regimes, biodiversity and ecosystems in a changing world. Collingwood, Vic: CSIRO Publishing, 330 p. Available at: https://www.publish.csiro.au/book/6836.
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