SavedAdd your journal

Journals / Türkiye Ormancılık Dergisi / 2026 / Cilt: 27

Long-term temporal trends in lightning-caused forest fire activity in Türkiye

Türkiye’de yıldırım kaynaklı orman yangını aktivitesindeki uzun dönemli değişimin zamansal eğilimi

Merih Göltaş(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, ORMAN FAKÜLTESİ)0000-0002-6052-5373
Pages
295–300
Publication Date
2026-10-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of the study offer crucial insights for understanding changes in the lightning-induced fire activity under climate change conditions and for developing risk management plans for lightning-caused fires.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The vast majority of forest fires in our country result directly or indirectly from human activities.
📊
Key Findings & Data:Although approximately 12% of forest fires in Türkiye during the 1997–2024 period were caused by natural factors, these fires are of considerable importance for understanding natural fire activity.
💡
Conclusion & Impact:The results of the study offer crucial insights for understanding changes in the lightning-induced fire activity under climate change conditions and for developing risk management plans for lightning-caused fires.
🔬Design:Araştırma Makalesi
📍Scope:2024 Verileri
⚙️Variables:Doğal kaynaklı yangınlar · İklim değişikliği · Orman yangını

Abstract

The vast majority of forest fires in our country result directly or indirectly from human activities. Although approximately 12% of forest fires in Türkiye during the 1997–2024 period were caused by natural factors, these fires are of considerable importance for understanding natural fire activity. The fact that lightning, one of the primary ignition sources of natural fires, is affected by atmospheric conditions changing due to climate change necessitates the investigation of trends in lightning-caused forest fires. This study aims to reveal the long-term temporal variation of lightning-caused forest fires occurring in Türkiye during the 1997–2024 period. For this purpose, trends in the number of lightning-caused fires, total burned area, and average fire size were evaluated using the Modified Mann–Kendall test and Sen’s slope estimator. The results revealed a statistically significant increase in the number of lightning-caused forest fires, while showing no significant change in the total burned area and average fire size. These findings demonstrate that lightning-caused fires occur more frequently in Türkiye, but the size of the fires has not increased proportionally. This suggests that technological advancements in wildfire management and increased firefighting capacity may have contributed to more effective fire control. The results of the study offer crucial insights for understanding changes in the lightning-induced fire activity under climate change conditions and for developing risk management plans for lightning-caused fires.

Özet

Ülkemizdeki orman yangınlarının büyük çoğunluğu doğrudan ya da dolaylı insan faaliyetleri sonucu çıkmaktadır. Türkiye’de 1997–2024 döneminde orman yangınların yaklaşık %12’si doğal nedenlerle meydana gelmiş olsa da, bu yangınlar doğal yangın aktivitesinin anlaşılması açısından büyük önem taşımaktadır. Doğal yangınların temel tutuşturucu kaynağı yıldırımın, iklim değişikliğine bağlı olarak değişen atmosferik koşullardan etkilenmesi, yıldırım kaynaklı orman yangınlarının zamansal değişiminin araştırılmasını gerekli kılmaktadır. Bu çalışma, Türkiye’de 1997–2024 döneminde meydana gelen yıldırım kaynaklı orman yangınlarının uzun dönemli zamansal değişimini ortaya koymayı amaçlamaktadır. Bu amaçla, yıldırım kaynaklı yangın sayısı, toplam yanan alan miktarı ve ortalama yangın büyüklüğündeki eğilimler Uyarlanmış Mann–Kendall testi ve Sen’in eğim (Sen's slope) analizi kullanılarak değerlendirilmiştir. Elde edilen sonuçlar, yıldırım kaynaklı orman yangını sayısında istatistiksel olarak anlamlı bir artış olduğunu ortaya koyarken, toplam yanan alan miktarı ve ortalama yangın büyüklüğünde anlamlı bir değişim olmadığını göstermiştir. Bu bulgular, Türkiye’de yıldırım kaynaklı yangınların daha sık meydana geldiğini ancak yangınların büyüklüğünün aynı oranda artmadığını ortaya koymaktadır. Bu durum, yangınla mücadelede teknolojik gelişmeler ve müdahale kapasitesindeki artışın yangınların kontrol altına alınmasında etkili olabileceğine işaret etmektedir. Çalışmanın sonuçları, iklim değişikliği koşullarında yıldırım kaynaklı yangın aktivesindeki değişimlerin anlaşılması ve yıldırım kaynaklı yangınlara yönelik risk yönetim planlarının geliştirilmesi açısından önemli bilgiler sunmaktadır.

🔖

Cite This Article

Göltaş, M. (2026). Long-term temporal trends in lightning-caused forest fire activity in Türkiye. Türkiye Ormancılık Dergisi, Cilt: 27, 295–300. https://doi.org/10.18182/tjf.1973155

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (32)

  1. Avcı, M., Korkmaz, M., & Alkan, H. (2009). Türkiye’de orman yangınlarının nedenleri üzerine bir değerlendirme. In I.Orman Yangınları ile Mücadele Sempozyumu (07-10 Ocak, Antalya, s. 33-45).
  2. Avcı, M., & Korkmaz, M. (2021). Türkiye’de orman yangını sorunu: Güncel bazı konular üzerine değerlendirmeler. Turkish Journal of Forestry, 22(3), 229-240. https://doi.org/10.18182/tjf.942706
  3. Bilgili, E., Kucuk, O., Saglam, B., & Coskuner, K. A. (2021). Mega Forest Fires: Causes, Organization and Management. In T. Kavzaoglu (Ed.), Forest Fires: Causes, Effects, Monitoring, Precautions and Rehabilitation Activities (pp. 1-23). Ankara:Turkish Academy of Sciences. https://doi.org/10.53478/TUBA.2021.039
  4. Canadell, J. G., Meyer, C. P., Cook, G. D., Dowdy, A., Briggs, P. R., Knauer, J., Pepler, A., & Haverd, V. (2021). Multi-decadal increase of forest burned area in Australia is linked to climate change. Nature Communications, 12(1), 6921. https://doi.org/10.1038/s41467-021-27225-4
  5. Chen, Y., Romps, D. M., Seeley, J. T., Veraverbeke, S., Riley, W. J., Mekonnen, Z. A., & Randerson, J. T. (2021). Future increases in Arctic lightning and fire risk for permafrost carbon. Nature Climate Change, 11(5), 404-410. https://doi.org/10.1038/s41558-021-01011-y
  6. Coskuner, K. A., & Bilgili, E. (2020). Orman yangın yönetiminde etkili bir karar destek sisteminin kavramsal çerçevesi. Doğal Afetler ve Çevre Dergisi, 6(2), 288-303. https://doi.org/10.21324/dacd.645701
  7. Daşdemir, İ., Aydın, F., & Ertuğrul, M. (2021). Factors affecting the behavior of large forest fires in Turkey. Environmental management, 67(1), 162-175. https://doi.org/10.1007/s00267-020-01389-z
  8. Fernandes, P. M., Santos, J. A., Castedo-dorado, F., & Almeida, R. (2021). Fire from the Sky in the Anthropocene. Fire, 4, 13. https://doi.org/10.3390/fire4010013
  9. Göltaş, M. (2025, Ekim 16-17). Orman yangınlarının nedenlerine göre maliyetinin değerlendirilmesi. [Conference presentation abstract]. 5th International Conference on Trends in Advanced Research, Konya, Türkiye, p162. https://as-proceeding.com/index.php/ictar/5thictar2025
  10. Göltaş, M., Ayberk, H., & Küçük, O. (2024). Assessment of the monthly forest fire danger potential using GIS-based analytic hierarchy process in Southwest Türkiye. Šumarski List, 148(1-2). https://ojs.srce.hr/sumlist/article/view/29001
  11. Göltaş, M. (2026). Predicting forest fire probability under current conditions and future climate change scenarios in Türkiye. Forest Ecosystems,17, 100525. https://doi.org/10.1016/j.fecs.2026.100525
  12. Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1-4), 182-196. https://10.1016/S0022-1694(97)00125-X
  13. He, J., Loboda, T. V., Chen, D., & French, N. H. (2022). Cloud‐to‐ground lightning and near‐surface fire weather control wildfire occurrence in Arctic Tundra. Geophysical Research Letters, 49(2), e2021GL096814. https://doi.org/10.1029/2021GL096814
  14. Huang, X., & Qiu, L. (2022). Analysis of runoff variation and driving mechanism in Huangfuchuan River Basin in the middle reaches of the Yellow River, China. Applied Water Science, 12, 234. https://doi.org/10.1007/s13201-022-01753-w
  15. Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J., Williamson, G. J., & Bowman, D. M. (2015). Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications, 6(1), 7537. https://doi.org/10.1038/ncomms8537
  16. Kendall, M. (1975). Rank Correlation Methods. Charles Griffin, London.
  17. Küçük, Ö., & Sarı, A. (2026). Analysis of large-scale wildfires (≥ 500 ha) in Türkiye during the 2020 fire season. Turkish Journal of Forestry, 27, 49-60. https://doi.org/10.18182/tjf.1798070
  18. Li, Y., Mickley, L. J., Liu, P., & Kaplan, J. O. (2020). Trends and spatial shifts in lightning fires and smoke concentrations in response to 21st century climate over the national forests and parks of the western United States. Atmospheric Chemistry and Physics, 20(14), 8827-8838. https://doi.org/10.5194/acp-20-8827-2020
  19. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259.
  20. Müller, M. M., Vacik, H., Diendorfer, G., Arpaci, A., Formayer, H., & Gossow, H. (2013). Analysis of lightning-induced forest fires in Austria. Theoretical and Applied Climatology, 111, 183–193. https://doi.org/10.1007/s00704-012-0653-7
  21. Narvaez, J. P., Yanoviak, S. P., Bitzer, P. M., Burchfield, J. C., & Gora, E. M. (2024). Effects of urbanization on cloud-to-ground lightning strike frequency: a global perspective. Journal of the Royal Society, Interface, 21(218), 20240257. https://doi.org/10.1098/rsif.2024.0257
  22. Ogawa, T. (1995). Lightning currents. In H. Volland (Ed.), Handbook of Atmospheric Electrodynamics, Volume I (pp. 93-136). Boca Raton, FL: CRC Press. https://doi.org/10.1201/9780203719503
  23. OGM. (2026). Orman Genel Müdürlüğü. Resmi istatistikler. Ankara, Türkiye. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler
  24. Öztürk, M. G., Bekar, İ., & Tavşanoğlu, Ç. (2024). Rethinking lightning-induced fires: Spatial variability and implications for management policies. Forest Ecology and Management, 572, 122262. https://doi.org/10.1016/j.foreco.2024.122262
  25. Romps, D. M., Seeley, J. T., Vollaro, D., & Molinari, J. (2014). Projected increase in lightning strikes in the United States due to global warming. Science, 346 (6211), 851-854. https://doi/10.1126/science.1259100
  26. Sari, F. (2023). Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. Journal of Forestry Research, 34(2), 355-371. https://doi.org/10.1007/s11676-022-01502-4
  27. Sen, P.K. (1968), Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63, 1379–1389.
  28. Sevinç, V., Kucuk, O., & Goltas, M. (2020). A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management, 457, 117723. https://doi.org/10.1016/j.foreco.2019.117723
  29. Shi, T., Yang, Y., Liu, L., Tian, Y., Zheng, Z., Huang, Y., Xiao, Z., Wang, Y., Wang, Y., & Lu, G. (2023). Spatiotemporal patterns of long series of cloud-to-ground lightning in Beijing and its cause. Urban Climate, 49, 101480. https://doi.org/10.1016/j.uclim.2023.101480
  30. Song, Y., Xu, C.; Li, X., & Oppong, F. (2024). Lightning-induced wildfires: An overview. Fire, 7(3),79. https://doi.org/10.3390/fire7030079
  31. Touseef, M., Chen, L., Yang, K., & Chen, Y. (2020). Long‐term rainfall trends and future projections over Xijiang river basin, China. Advances in Meteorology, (1), 6852148. https://doi.org/10.1155/2020/6852148
  32. Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, 18(3), 201-218. https://doi.org/10.1023/B:WARM.0000043140.61082.60