Re-examining the assumption of dominant regional wind and fire spread directions
Assaf Shmuel A * and Eyal Heifetz AA Porter school of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
International Journal of Wildland Fire 31(5) 480-491 https://doi.org/10.1071/WF21070
Submitted: 24 May 2021 Accepted: 23 March 2022 Published: 11 May 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
The goal of decreasing wildfire hazard as much as possible, using minimal fuel treatments, has led to increasing scholarly interest in fuel reduction spatial optimisation. Most models in the field rest on the assumption of a known wind direction and a corresponding dominant direction of fire spread, and plan firebreaks in perpendicular directions. This strategy is effective when the wind blows in the hypothesised direction, but is quite ineffective when the wind direction is parallel to the firebreaks. In this article, we re-examine this assumption using a global fire dataset covering more than a decade. We perform a variety of circular statistical analyses including circular variance and principal component analysis (PCA). We find that the direction of fire spread in most regions is not limited to a single direction. We also find that the wind direction during fire weather is characterised by a high variance in a substantial fraction of regions around the globe. We validate this finding with a dataset comprised of over a hundred meteorological stations in Israel. We conclude that forest management should consider regional historical data of wind directions and fire spread directions, but also should plan firebreaks so that they are effective in various fire scenarios.
Keywords: fire spread direction, firebreaks, forest management, fuel reduction, principal component analysis, spatial optimisation, wind direction variability, wind roses.
References
Andela N, Morton DC, Giglio L, et al. (2017) A human-driven decline in global burned area. Science 356, 1356–1362.| A human-driven decline in global burned area.Crossref | GoogleScholarGoogle Scholar | 28663495PubMed |
Andela N, Morton DC, Giglio L, Randerson JT (2019) Global fire atlas with characteristics of individual fires, 2003–2016. ORNL DAAC, Oak Ridge, Tennessee, USA. Available at
| Crossref |
Ascoli D, Russo L, Giannino F, Siettos C, Moreira F (2018) Firebreak and fuelbreak. In ‘Encyclopedia of Wildfires and Wildland–Urban Interface (WUI) Fires’. (Ed. SL Manzello) (Springer: Raleigh, NC, USA)
Batschelet E (1981) ‘Circular Statistics in Biology.’ p. 388. (Academic Press: New York, NY, USA)
Berens P (2009) CircStat: a MATLAB toolbox for circular statistics. Journal of Statistical Software 31, 1–21.
| CircStat: a MATLAB toolbox for circular statistics.Crossref | GoogleScholarGoogle Scholar |
Berger VW, Zhou Y (2014) Kolmogorov–Smirnov test: overview. Wiley StatsRef: Statistics reference online.
| Crossref |
Burgess T, Burgmann JR, Hall S, Holmes D, Turner E (2020) Black summer: Australian newspaper reporting on the nation’s worst bushfire season. Monash Climate Change Communication Research Hub, Monash University, Melbourne, Vic., Australia.
Codling EA (2003) Biased random walks in biology. Doctoral Dissertation, University of Leeds, Leeds, UK.
Codling EA, Hill NA, Pitchford JW, Simpson SD (2004) Random walk models for the movement and recruitment of reef fish larvae. Marine Ecology Progress Series 279, 215–224.
| Random walk models for the movement and recruitment of reef fish larvae.Crossref | GoogleScholarGoogle Scholar |
Cui X, Alam MA, Perry GL, Paterson AM, Wyse SV, Curran TJ (2019) Green firebreaks as a management tool for wildfires: lessons from China. Journal of Environmental Management 233, 329–336.
| Green firebreaks as a management tool for wildfires: lessons from China.Crossref | GoogleScholarGoogle Scholar | 30584964PubMed |
FAO (2002) Guidelines on fire management in temperate and boreal Forests. Forest Protection Working Papers, Working Paper FP/1/E. Forest Resources Development Service, Forest Resources Division. FAO, Rome, Italy.
Finney MA (2001) Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science 47, 219–228.
Finney MA (2008) A computational method for optimising fuel treatment locations. International Journal of Wildland Fire 16, 702–711.
| A computational method for optimising fuel treatment locations.Crossref | GoogleScholarGoogle Scholar |
Fisher NI, Lewis T, Embleton BJ (1993) Statistical Analysis of Spherical Data. Cambridge University Press.
Forest Fire Management Office of State Forestry Administration (2003) ‘The Construction of Fuelbreak in China.’ (China Forestry Publishing House: Beijing, China.)
Grad S (2020) ‘Six of California’s largest fires in history ignited this year. Here’s what we know.’ Los Angeles Times, 11 September, 2020. Available at https://www.latimes.com/california/story/2020-09-11/six-of-californias-largest-fires-in-history-are-burning-right-now
Hamadeh N, Karouni A, Daya B, Chauvet P (2017) Using correlative data analysis to develop weather index that estimates the risk of forest fires in Lebanon and Mediterranean: assessment versus prevalent meteorological indices. Case Studies in Fire Safety 7, 8–22.
| Using correlative data analysis to develop weather index that estimates the risk of forest fires in Lebanon and Mediterranean: assessment versus prevalent meteorological indices.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz SJ, Nicolas J, Peubey C, Radu R, Rozum I, Schepers D, Simmons A, Soci C, Dee D, Thépaut JN (2018) ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
| Crossref |
Huang Y, Wu S, Kaplan JO (2015) Sensitivity of global wildfire occurrences to various factors in the context of global change. Atmospheric Environment 121, 86–92.
| Sensitivity of global wildfire occurrences to various factors in the context of global change.Crossref | GoogleScholarGoogle Scholar |
Hurst AJ, Lawrence MA, Klein RM (2019) How does spatial attention influence the probability and fidelity of colour perception? Vision 3, 31
| How does spatial attention influence the probability and fidelity of colour perception?Crossref | GoogleScholarGoogle Scholar |
Katz T, Ginat H, Eyal G, Steiner Z, Braun Y, Shalev S, Goodman-Tchernov BN (2015) Desert flash floods form hyperpycnal flows in the coral-rich Gulf of Aqaba, Red Sea. Earth and Planetary Science Letters 417, 87–98.
| Desert flash floods form hyperpycnal flows in the coral-rich Gulf of Aqaba, Red Sea.Crossref | GoogleScholarGoogle Scholar |
Kruger FJ, Bigalke RC (1984) Fire in fynbos. In ‘Ecological Effects of Fire in South African Ecosystems’. Ecological Studies. (Eds PV de Booysen, NM Tainton) pp. 67–114. (Springer: Berlin, Germany)
| Crossref |
Krueger ES, Ochsner TE, Engle DM, Carlson JD, Twidwell D, Fuhlendorf SD (2015) Soil moisture affects growing‐season wildfire size in the southern Great Plains. Soil Science Society of America Journal 79, 1567–1576.
| Soil moisture affects growing‐season wildfire size in the southern Great Plains.Crossref | GoogleScholarGoogle Scholar |
Landler L, Ruxton GD, Malkemper EP (2018) Circular data in biology: advice for effectively implementing statistical procedures. Behavioral Ecology and Sociobiology 72, 128
| Circular data in biology: advice for effectively implementing statistical procedures.Crossref | GoogleScholarGoogle Scholar | 30100666PubMed |
León J, Reijnders VM, Hearne JW, Ozlen M, Reinke KJ (2019) A landscape-scale optimisation model to break the hazardous fuel continuum while maintaining habitat quality. Environmental Modeling & Assessment 24, 369–379.
| A landscape-scale optimisation model to break the hazardous fuel continuum while maintaining habitat quality.Crossref | GoogleScholarGoogle Scholar |
Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology 49, 764–766.
| Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median.Crossref | GoogleScholarGoogle Scholar |
Lucas C (2010) On developing a historical fire weather data-set for Australia. Australian Meteorological and Oceanographic Journal 60, 1–14.
| On developing a historical fire weather data-set for Australia.Crossref | GoogleScholarGoogle Scholar |
Luke RH, McArthur AG (1978) Bush fires in Australia. (Australian Government Publishing Service: Canberra, ACT, Australia)
Manasrah R, Uli Lass H, Fennel W (2006) Circulation in the Gulf of Aqaba (Red Sea) during winter–spring. Journal of Oceanography 62, 219–225.
| Circulation in the Gulf of Aqaba (Red Sea) during winter–spring.Crossref | GoogleScholarGoogle Scholar |
Marshall G, Thompson DK, Anderson K, Simpson B, Linn R, Schroeder D (2020) The impact of fuel treatments on wildfire behavior in North American boreal fuels: a simulation study using FIRETEC. Fire 3, 18
| The impact of fuel treatments on wildfire behavior in North American boreal fuels: a simulation study using FIRETEC.Crossref | GoogleScholarGoogle Scholar |
Massey FJ (1951) The Kolmogorov–Smirnov test for goodness of fit. Journal of the American statistical Association 46, 68–78.
| The Kolmogorov–Smirnov test for goodness of fit.Crossref | GoogleScholarGoogle Scholar |
Matsypura D, Prokopyev OA, Zahar A (2018) Wildfire fuel management: network-based models and optimization of prescribed burning. European Journal of Operational Research 264, 774–796.
| Wildfire fuel management: network-based models and optimization of prescribed burning.Crossref | GoogleScholarGoogle Scholar |
Minas JP, Hearne JW, Martell DL (2014) A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts. European Journal of Operational Research 232, 412–422.
| A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts.Crossref | GoogleScholarGoogle Scholar |
Minas J, Hearne J, Martell D (2015) An integrated optimization model for fuel management and fire suppression preparedness planning. Annals of operations Research 232, 201–215.
Pashardes S, Christofides C (1995) Statistical analysis of wind speed and direction in Cyprus. Solar Energy 55, 405–414.
| Statistical analysis of wind speed and direction in Cyprus.Crossref | GoogleScholarGoogle Scholar |
Pereira D (2021) Wind Rose. MATLAB Central File Exchange. Available at https://www.mathworks.com/matlabcentral/fileexchange/47248-wind-rose [Verified 17 March 2021]
PFX – Pacific Fire Exchange (2020) Fuel break design. Available at https://static1.squarespace.com/static/54825edae4b0426dc2c78f10/t/5f16280092f0a40baa2c57b6/1595287553498/Fuel+Break+Design.jpg [Verified 25 December 2020]
Prior KW (1958) The Balmoral forest fire. New Zealand Journal of Forestry 7, 35–50.
Rachmawati R, Ozlen M, Reinke KJ, Hearne JW (2016) An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions. Forest Ecology and Management 368, 94–104.
| An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions.Crossref | GoogleScholarGoogle Scholar |
RED (2015) Infraestructuras de prevencion de incendios forestales. Available at http://www.agroambient.gva.es/documents/162905929/163206728/NT+%C3%81reas+cortafuegos+%2810%2C1Mb%29/c23b3dd0-3381-4e08-bde8-6f1a96f48171 [In Spanish] [Verified 25 December 2020]
Saaroni H, Maza E, Ziv B (2003) Summer sea breeze in the Gulf of Eilat and its effect on the climate of Eilat city. In 'Proceedings of the Fifth International Conference on Urban Climate,' Vol. 2, pp. 293–296.
Schwilch G, Riva MJ, Liniger H, Hessel R, van den Elsen E, Ritsema CJ (2016) Comprehensive guidelines for natural resource managers. CASCADE Report Series, Number 17.
Skittides C, Früh WG (2014) Wind forecasting using principal component analysis. Renewable Energy 69, 365–374.
| Wind forecasting using principal component analysis.Crossref | GoogleScholarGoogle Scholar |
Thakar V (2017) A spatial optimization approach to finding locations for wildfire fuel treatments. Doctoral Dissertation, The University of Texas at Dallas, Texas, United States.
Wang M, Zhou R, Ren Y (2012) Key parameters and methods of forest fire prevention forest belt design in North China. Forest Fire Prevention 4, 54–57. [In Chinese]
Wang H, Tao T, Wu T, Mao J, Li A (2015) Joint distribution of wind speed and direction in the context of field measurement. Wind and Structures 20, 701–718.
| Joint distribution of wind speed and direction in the context of field measurement.Crossref | GoogleScholarGoogle Scholar |
Wei Y (2012) Optimize landscape fuel treatment locations to create control opportunities for future fires. Canadian Journal of Forest Research 42, 1002–1014.
| Optimize landscape fuel treatment locations to create control opportunities for future fires.Crossref | GoogleScholarGoogle Scholar |
Weise DR, Fletcher TH, Cole W, Mahalingam S, Zhou X, Sun L, Li J (2018) Fire behavior in chaparral: evaluating flame models with laboratory data. Combustion and Flame 191, 500–512.
| Fire behavior in chaparral: evaluating flame models with laboratory data.Crossref | GoogleScholarGoogle Scholar |
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics and intelligent laboratory systems 2, 37–52.
| Principal component analysis.Crossref | GoogleScholarGoogle Scholar |
Xanthopoulos G, Caballero D, Galante M, Alexandrian D, Rigolot E, Marzano R (2006) Forest fuels management in Europe. In ‘Fuels Management – How to Measure Success: Conference Proceedings, 28–30 March 2006, Portland, OR. Proceedings RMRS-P-41’. (Eds PL Andrews, W Butler) Vol. 41, pp. 29–46. (US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA)
Zhang L, Li Q, Guo Y, Yang Z, Zhang L (2018) An investigation of wind direction and speed in a featured wind farm using joint probability distribution methods. Sustainability 10, 4338
| An investigation of wind direction and speed in a featured wind farm using joint probability distribution methods.Crossref | GoogleScholarGoogle Scholar |
Zhang YM, Wang H, Wan HP, Mao JX, Xu YC (2020) Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model. Structural Health Monitoring 20, 2936–2952.
| Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model.Crossref | GoogleScholarGoogle Scholar |
Zhang YM, Wang H, Mao JX, Xu ZD, Zhang YF (2021) Probabilistic framework with Bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge. Journal of Structural Engineering 147, 04020297
| Probabilistic framework with Bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge.Crossref | GoogleScholarGoogle Scholar |
Zinck RD, Grimm V (2009) Unifying wildfire models from ecology and statistical physics. The American Naturalist 174, E170–E185.
| Unifying wildfire models from ecology and statistical physics.Crossref | GoogleScholarGoogle Scholar | 19799499PubMed |