Physics-based urban fire spread simulation coupled with stochastic occurrence of spot fires

  • Tomoaki NishinoEmail author
Original Paper


This paper presents the development and validation of physics-based urban fire spread simulation coupled with stochastic occurrence of spot fires. When a fire occurs in densely-built wooden residential area, the fire easily spreads to adjacent buildings because of the narrow distance between buildings. Although the fire in weak winds is usually extinguished by firefighters before developing into large-scale fires, the fire in strong winds sometimes overwhelm the firefighting capability because of the occurrence of spot fires. Numerous firebrands that are released from burning buildings travel a long distance by strong winds and cause new fire ignitions far from the burning buildings, which are referred to as the spot fires. Actually, firefighters have suffered from the spot fires in the past great urban fires in Japan, such as the Itoigawa fire in 2016. From such an experience, there have been calls for the use of urban fire spread simulation in planning of firefighting operation in strong winds. Therefore, we have been developing a physics-based urban fire spread model including the stochastic occurrence of spot fires. Here, in order to validate the model, we numerically simulated the fire spread in wooden residential area in Itoigawa City using Monte Carlo method and compared the calculated results with the fire damage in 2016. From the calculated results, we concluded that the proposed model could reasonably explain the number of spot fires that occurred in the Itoigawa fire, and could simulate the urban fire spread conservatively in terms of the fire spread rate.


Urban fire Firebrand Spot fire Stochastic model Fire spread simulation 



  1. Albini F (1979) Spot fire distances from burning trees—a predictive model, USDA Forest Service General Technical Report INT-56, Missoula, MTGoogle Scholar
  2. Anthenian R, Tse SD, Fernandez-Pello AC (2006) On the trajectories of embers initially elevated or lofted by small scale ground fire plumes in high winds. Fire Saf J 41:349–363CrossRefGoogle Scholar
  3. Beyler CL (1986) Fire plume and ceiling jet. Fire Saf J 11:53–76CrossRefGoogle Scholar
  4. Cetegen BM, Zukoski EE, Kubota T (1982) Entrainment and flame geometry of fire plumes, California Institute of Technology, NBS Grant No. G8-9014Google Scholar
  5. Elia M, Giannico V, Lafortezza R, Sanesi G (2018) Modeling fire ignition patterns in Mediterranean urban interfaces. Stoch Environ Res Risk Assess.
  6. Finney MA, McHugh CW, Grenfell IC, Riley KL, Short KC (2011) A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25:973–1000CrossRefGoogle Scholar
  7. Fukutani Y, Suppasri A, Imamura F (2015) Stochastic analysis and uncertainty assessment of tsunami wave height using a random source parameter model that targets a Tohoku-type earthquake fault. Stoch Environ Res Risk Assess 29:1763–1779CrossRefGoogle Scholar
  8. Goda K, Petrone C, Risi RD, Rossetto T (2017) Stochastic coupled simulation of strong ground motion and tsunami for the 2011 Tohoku, Japan earthquake. Stoch Environ Res Risk Assess 31:2337–2355CrossRefGoogle Scholar
  9. Hamada M (1951) On fire spreading velocity in disasters. Sagami Shobo, Tokyo (in Japanese) Google Scholar
  10. Himoto K, Tanaka T (2005) Transport of disk-shaped firebrands in a turbulent boundary layer. Fire Saf Sci 8:433–444CrossRefGoogle Scholar
  11. Himoto K, Tanaka T (2008) Development and validation of a physics-based urban fire spread model. Fire Saf J 43:477–494CrossRefGoogle Scholar
  12. Himoto K, Tsuchihashi T, Tanaka Y, Tanaka T (2009) Modeling trajectory of window flame with regard to the flow attachment to the adjacent wall. Fire Saf J 44:250–258CrossRefGoogle Scholar
  13. Investigative Commission on the Firefighting in the Future Based on the Itoigawa City Fire (2017) Report on the firefighting in the future based on the Itoigawa City Fire (in Japanese)Google Scholar
  14. Juan P, Mateu J, Saez M (2012) Pinpointing spatio-temporal interactions in wildfire patterns. Stoch Environ Res Risk Assess 26:1131–1150CrossRefGoogle Scholar
  15. Lee SW, Davidson RA (2010) Physics-based simulation model of post-earthquake fire spread. J Earthq Eng 14:670–687CrossRefGoogle Scholar
  16. Li S, Davidson RA (2013) Application of an urban fire simulation model. Earthq Spectra 29:1369–1389CrossRefGoogle Scholar
  17. Manzello SL, Suzuki S, Hayashi Y (2012) Exposing siding treatments, walls fitted with eaves, and glazing assemblies to firebrand showers. Fire Saf J 50:25–34CrossRefGoogle Scholar
  18. Muraszew A, Fedele JF (1976) Statistical model for spot fire spread. The Aerospace Corporation Report No. ATR-77758801, Los Angeles, CAGoogle Scholar
  19. National Research Institute of Fire and Disaster (1977) Report on the spread of the Sakata City Fire, Technical Report of National Research Institute of Fire and Disaster, 11 (in Japanese)Google Scholar
  20. Nishino T, Tsuburaya S, Himoto K, Tanaka T (2010a) Development of a simplified model for urban fire spread by using a quasi-steady calculation method. J Environ Eng (Trans AIJ) 75:9–18 (in Japanese) CrossRefGoogle Scholar
  21. Nishino T, Himoto K, Tanaka T (2010b) Development of a probabilistic model of spotting fires by firebrands considering resident firefighting. Bull Jpn Assoc Fire Sci Eng 60:11–20 (in Japanese) Google Scholar
  22. Ohmiya Y, Tanaka T, Wakamatsu T (1996) Burning rate of fuels and generation limit of the external flames in compartment fire. Fire Sci Technol 16:1–12CrossRefGoogle Scholar
  23. Serra L, Saez M, Juan P, Varga D, Mateu J (2014) A spatio-temporal Poisson hurdle point process to model wildfires. Stoch Environ Res Risk Assess 28:1671–1684CrossRefGoogle Scholar
  24. Suzuki S, Manzello SL, Lage M, Laing G (2012) Firebrand generation data obtained from a full scale structure burn. Int J Wildland Fire 21:961–968CrossRefGoogle Scholar
  25. Suzuki S, Brown A, Manzello SL, Suzuki J, Hayashi Y (2014) Firebrands generated from a full-scale structure burning under well-controlled laboratory conditions. Fire Saf J 63:43–51CrossRefGoogle Scholar
  26. Suzuki S, Nii D, Manzello SL (2017) The performance of wood and tile roofing assemblies exposed to continuous firebrands assault. Fire Mater 41:84–96CrossRefGoogle Scholar
  27. Tarifa CS, Del Notario PP, Moreno FG (1965) On the flight paths and lifetimes burning particles of wood. Proc Combust Inst 10:1021–1037CrossRefGoogle Scholar
  28. Tohidi A, Kaye NB (2017) Comprehensive wind tunnel experiments of lofting and downwind transport of non-combusting rod-like model firebrands during firebrand shower scenarios. Fire Saf J 90:95–111CrossRefGoogle Scholar
  29. Tsuchihashi T, Tasaka S, Yoshida M, Tanaka T (2000) Estimation of temperature rise when exposed to plumes of multiple heat sources. Proc Annu Meet Archit Inst Jpn A2:13–14 (in Japanese) Google Scholar
  30. Vodvarka FJ (1969) Firebrand field studies—final report. IIT Research Institute, Chicago, ILGoogle Scholar
  31. Vodvarka FJ (1970) Urban burns full-scale field studies—final report. IIT Research Institute, Chicago, ILGoogle Scholar
  32. Wang HH (2011) Analysis of downwind distribution of firebrands sourced from a wildland fire. Fire Technol 47:321–340CrossRefGoogle Scholar
  33. Yokoi S (1970) Temperature distribution in the downwind of a line fire. Disaster Res 7:151–159 (in Japanese) Google Scholar
  34. Zhang Q, Cui L, Zhang J, Liu X, Tong Z (2015) Grid based dynamic risk assessment for grassland fire disaster in Hulunbuir. Stoch Environ Res Risk Assess 29:589–598CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Disaster Prevention Research InstituteKyoto UniversityUjiJapan

Personalised recommendations