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Türkiye’deki Orman Yangın Sayıları ile Güneş Leke Sayılarının Periyodik Analizi

Yıl 2022, , 49 - 56, 31.07.2022

Taner Mustafa Cengiz

https://doi.org/10.55581/ejeas.1137100

Öz

Bu çalışmada Türkiye orman yangın sayıları ile güneş leke sayılarının periyodik yapılarının incelenmesi amaçlanmıştır. Çalışmanın temel hedefi sürekli dalgacık dönüşümleri ve global spektrumlar yöntemini kullanarak Türkiye orman yangın sayıları ile güneş lekesi sayılarının değişkenliğini araştırmaktır. Türkiye son yıllarda orman yangın sayılarının artması üzerinde odaklanılan sorunlardan birisidir. Güneş faaliyetini göstermek için genellikle güneş lekesi sayısı kullanılmaktadır. Dalgacık dönüşümü Fourier dönüşümünün gelişmiş ve iyi planlanmış bir sürümü olup sinyallerin frekans bileşenlerini ortaya çıkarmaktadır. Dalgacık dönüşüm teknikleri yaklaşımı Türkiye’deki orman yangınlarının sayıları ve güneş lekesi sayılarının periyodik analizi için uygulanmıştır. Bu kapsamda Orman Genel Müdürlüğünün (OMG) 1937 yılından 2020 yılı sonuna kadarki güncel yangın sayıları kayıtları ile güneş lekesi sayılarının kayıtları değerlendirilmiştir. Mevcut olan 84 yıllık toplam orman yangın sayıları verileri ve aynı dönemde olan güneş lekeleri sayıları verilerinin periyodik yapısı Sürekli Dalgacık dönüşümü (SDD) ve Global Dalgacık Spektrumu (GDS) tekniğinden yararlanılarak analiz edilmiştir. Orman yangın sayıları için hâkim periyodik bileşen 6.60 yıllık bulunurken güneş leke sayılarında ise 10.67 yıllık periyodik bileşen belirlenmiştir. Her iki değişkenin uzun yıllık diğer periyodik bileşenleri de tespit edilmiştir. Türkiye’deki orman yangın sayıları ile güneş leke sayılarının periyodik yapılarının birbirlerinden çok farklı yapılarda olduğu bu çalışma sonucunda saptanmıştır.

Anahtar Kelimeler

Orman Yangın Sayısı Güneş Leke Sayısı Dalgacık Dönüşümü Periyodiklik.

Kaynakça

  • Bilgili, E., (2020). Türkiye’de orman yangınlarına genel bir bakış. Yeşil Dünya, Orman Mühendisleri Odası, 57(1-2-3), 58-67.
  • Sevinc, 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.
  • Barenklau K. E., (2001). Agricultural Safety. Florida, ABD Lewis Publisher, 120-130.
  • Küçük, Ö., & Sağlam, B. (2004). Orman yangınları ve hava halleri. Kastamonu Orman Fakültesi Dergisi, 4(2), 220-231.
  • Cardil, A., Molina, D. M., Ramirez, J., & Vega-García, C. (2013). Trends in adverse weather patterns and large wildland fires in Aragón (NE Spain) from 1978 to 2010. Natural Hazards and Earth System Sciences, 13(5),1393-1399.
  • OGM (2020). 2010-2019 Yılları Arasında Meydana Gelen Orman Yangınlarının Çıkış Sebeplerine Dağılımı. Orman Yangınları Değerlendirme Raporu, Orman Yangınlarıyla Mücadele Daire Başkanlığı, 48
  • OGM (2017). Orman Yangınları Değerlendirme Raporu, S 26.
  • Tatli, H. & Türkeş, M., (2014). Climatological evaluation of Haines forest fire weather index over the Mediterranean Basin. Meteorological Applications, 21(3), 545-552.
  • Küçük, Ö., Bilgili, E., Durmaz, B. D., Sağlam, B., & Baysal, İ. (2009). Örtü yangınının tepe yangınına geçişinde etkili olan faktörler. Kastamonu University Journal of Forestry Faculty, 9(2), 80-85.
  • Yavuz, M., Sağlam, B., Küçük, Ö., & Tüfekçioğlu, A. (2018). Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty, 18(2), 171-188.
  • Trouet, V., Taylor, A. H., Carleton, A. M., & Skinner, C. N. (2009). Interannual variations in fire weather, fire extent, and synoptic-scale circulation patterns in northern California and Oregon. Theoretical and Applied Climatology, 95(3-4), 349- 360.
  • McCaw, L., Marchetti, P., Elliott, G., & Reader, G. (2007). Bushfire weather climatology of the Haines Index in southwestern Australia. Australian Meteorological Magazine, 56 (2).
  • Pausas, J. G. (2004). Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Climatic change, 63(3), 337-350.
  • Sivrikaya, F., Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region, Ecological Informatics, 68, 101537,
  • Urbieta IR, Zavala G, Bedia J, Gutie´rrez JM, San Miguel-Ayanz J, Camia A, Keeley JE, & Moreno JM. (2015). Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA. Environmental Research Letters, 10(11), 114013.
  • Gomes J.F. & Radovanovic M., (2008). Solar activity as a possible cause of large forest fires-a case study: analysis of the Portuguese forest fires. Science of the Total Environment, 394(1),197-205.
  • Parker, D. E., Jones, P. D., Folland, C. K., & Bevan, A. (1994). Interdecadal changes of surface temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres, 99(D7), 14373-14399.
  • Jones, P. D., New, M., Parker, D. E., Martin, S., & Rigor, I. G.: (1999). Surface Air Temperature and its Changes over the Past 150 Years, Reviews of Geophysics 37, 173–199.
  • Barnett, E. & Halverson, J., (2001). Local increases in coronary heart disease mortality among blacks & whites in the United States, 1985–1995. American Journal of Public Health: 91(9), 1499-1506.
  • Eddy, J. A. (1977). Climate and the changing sun. Climatic Change, 1(2), 173-190.
  • Haigh, J.D. (2007). The Sun and the Earth's Climate, Living Reviews in Solar Physics, 4, 1-64.
  • Scafetta, N. & West, B. J. (2008). Is climate sensitive to solar variability? Physics Today, 61(3), 50.
  • Scafetta, N., (2009). Empirical analysis of the contribution to global mean air surface temperature change. Journal of Atmospheric and Solar-Terrestrial Physics 71,1918–1923.
  • Echer, M. S., Echer, E., Rigozo, N. R., Brum, C. G. M., Nordemann, D. J. R., & Gonzalez, W. D., (2012). On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity. Journal of atmospheric and solar-terrestrial physics, 74, 87-93.
  • Currie, R.G., (1974). Solar cycle signal in surface air temperature. Journal of Geophysical Research, 79, 5657–5660.
  • Herman, J.R. & Goldberg, R., (1978). A ‘Sun, Weather, and Climate’. NASA, Washington, DC.
  • Haigh, J.D. (2007). The Sun and the Earth's Climate, Living Reviews in Solar Physics, 4, 1-64.
  • Echer, E., Gonzalez, W. D., Guarnieri, F. L., Dal Lago, A., & Vieira, L. E. A. (2005). Introduction to space weather. Advances in Space Research, 35(5), 855-865.
  • Echer, M. S., Echer, E., Rigozo, N. R., Brum, C. G. M., Nordemann, D. J. R., & Gonzalez, W. D., (2012). On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity. Journal of atmospheric and solar-terrestrial physics, 74, 87-93.
  • Syphard, A. D., Radeloff, V. C., Keeley, J. E., Hawbaker, T. J., Clayton, M. K., Stewart, S. I., & Hammer, R. B. (2007). Human influence on California fire regimes. Ecological Applications, 17(5), 1388-1402.
  • Ager, A. A., Preisler, H. K., Arca, B., Spano, D., & Salis, M. (2014). Wildfire risk estimation in the Mediterranean area. Environmetrics, 25(6), 384-396.
  • Zhang G., (1999). Predictions for maximum-value sunspot number-time and the end time of cycle 23. Progress in Geophysics, 14(S1), 99-103.
  • Frick, P., Galyagin, D., Hoyt, D. V., Nesme-Ribes, E., Schatten, K. H., Sokoloff, D., & Zakharov, V., (1997). Wavelet analysis of solar activity recorded by sunspot groups. Astronomy and Astrophysics, 328, 670-681.
  • Le, G. M. & Wang, J. L. (2003). Wavelet analysis of several important periodic properties in the relative sunspot numbers. Chinese Journal of Astronomy and Astrophysics, 3(5), 391.
  • Lagerquist, R., Flannigan, M. D., Wang, X., & Marshall, G. A. (2017). Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada. Canadian Journal of Forest Research, 47(9), 1175-1183.
  • Mhawej, M., Faour, G., Abdallah, C., & Adjizian-Gerard, J. (2016). Towards an establishment of a wildfire risk system in a Mediterranean country. Ecological informatics, 32, 167- 184.
  • Flannigan, M.D. & Wotton, B.M., (2001). Climate, weather and area burned in Forest fires, Aca demic Press, 351-373.
  • Baltacı, U. ve Yıldırım F. (2017). Orman yangınları açısından riskli yılların güneş leke döngüsüne bağlı olarak önceden tahmin edilebilmesi. Ormancılık Araştırma Dergisi, 4(2), 133-142.
  • Mwanzia, D. (2021). A study of solar variability and its effects on Earth’s Climate. Thesis, University of Nairobi.
  • Marov, M. Y. (2020). Radiation and space flights safety: an insight. Acta Astronautica, 176, 580-590.
  • Shuyang, W., & Guoyu, S. (1994). Study on relations between heavy-disaster-period of forest fire activity and sunspot activity, SSTA. Journal of Northeast Forestry University, 5(4), 27-32.
  • Polyansky, O. L., Zobov, N. F., Viti, S., Tennyson, J., Bernath, P. F., & Wallace, L. (1997). Water on the sun: line assignments based on variational calculations. Science, 277(5324), 346-348.
  • Wright, J. G. (1940). Sun spots and forest fires in New Brunswick. The Forestry Chronicle, 16(4), 233-238.
  • Herrera V.G., (2016). Mexican forest fires and their decadal variations. Advances in Space Research, V. 58 (1), 2104-2115.
  • Baltacı, U. ve Yıldırım F. (2017). Orman yangınları açısından riskli yılların güneş leke döngüsüne bağlı olarak önceden tahmin edilebilmesi. Ormancılık Araştırma Dergisi, 4(2), 133-142.
  • OGM, 2021. Orman Yangınlarıyla Mücadele Değerlendirme Raporları. Ankara
  • Anonim, (2021). https://www.bis.sidc.be/silso/datafiles. Erişim Tarihi: 13.04.2021
  • Daubechies, I., (1990). The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Transactions on Information Theory, 36 (5), 961-1005.
  • Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis, 15(4), 723-736.
  • Torrence, C., & Compo, G. P., (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.org/10.1016/j.foreco.2019.117723.
  • Küçük, M. & Ağiralioğlu, N., (2006). Wavelet regression technique for streamflow prediction. Journal of applied statistics, 33(9), 943-960.
  • Cohen, M.X. (2019). A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage, 199, 81–86.
  • Arı, N, Özen S. ve Çolak Ö. H. (2008). Dalgacık Teorisi, Palme Yayıncılık, Ankara.
  • Partal, T. (2012). Wavelet analysis and multi-scale characteristics of the runoff and precipitation series of the Aegean region (Turkey). International Journal of Climatology, 32,108-120
  • Öner, İ.V., Yeşilyurt, M.K. ve Yılmaz, E.Ç. (2017). Wavelet analiz tekniği ve uygulama alanları. Ordu Üniversitesi. Bilim Teknik Dergisi, 7(1), 42-56.
  • Gray, L. J., Beer, J., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., ... & White, W. (2010). Solar influences on climate. Reviews of Geophysics, 48(4).
  • Cameron, R. H., & Schuessler, M. (2019). Solar activity: periodicities beyond 11 years are consistent with random forcing. Astronomy & Astrophysics, 625, A2

Periodic analysis of forest fire numbers and sunspot numbers in Türkiye

Yıl 2022, , 49 - 56, 31.07.2022

Taner Mustafa Cengiz

https://doi.org/10.55581/ejeas.1137100

Öz

The aim of this study is to examine the periodic structures of forest fire numbers in Türkiye and sunspot numbers. The main purpose of this study is to investigate the variability of forest fire numbers in Turkey and sunspot numbers using continuous wavelet transforms and global spectra method. The change of sunspots observed in the photosphere layer of the sun is one of its important activities affecting the world. The sunspot number is often used to show solar activity. Wavelet analysis reveals the frequency components of signals such as the Fourier transform yet wavelet transform is actually a advanced and well planned version of Fourier transform. Wavelet transform techniques approach has been applied to the periodic analysis of the number of forest fires and sunspot numbers in Turkey. In this context, in the period from 1937 to the end of 2020, the records of the current fire numbers of the General Directorate of Forestry (OMG) and the sunspots numbers for the same period have been evaluated. The periodic structure of the existing 84-year forest fire numbers data and the sunspot numbers data in the same period are analyzed using Continuous Wavelet transform (SDD) and Global Wavelet Spectrum (GDS) techniques. While the dominant periodic component for forest fire numbers was 6.60 years, 10.67 years periodic component is determined for sunspot numbers. Other long-year periodic components of both variables have also been determined. As a result of this study, it has been determined that the periodic structures of the number of forest fires and the number of sunspots in Türkiye are very different from each other.

Anahtar Kelimeler

Number of Forest Fires Number of Sunspot Wavelet Transform Periodicity.

Kaynakça

  • Bilgili, E., (2020). Türkiye’de orman yangınlarına genel bir bakış. Yeşil Dünya, Orman Mühendisleri Odası, 57(1-2-3), 58-67.
  • Sevinc, 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.
  • Barenklau K. E., (2001). Agricultural Safety. Florida, ABD Lewis Publisher, 120-130.
  • Küçük, Ö., & Sağlam, B. (2004). Orman yangınları ve hava halleri. Kastamonu Orman Fakültesi Dergisi, 4(2), 220-231.
  • Cardil, A., Molina, D. M., Ramirez, J., & Vega-García, C. (2013). Trends in adverse weather patterns and large wildland fires in Aragón (NE Spain) from 1978 to 2010. Natural Hazards and Earth System Sciences, 13(5),1393-1399.
  • OGM (2020). 2010-2019 Yılları Arasında Meydana Gelen Orman Yangınlarının Çıkış Sebeplerine Dağılımı. Orman Yangınları Değerlendirme Raporu, Orman Yangınlarıyla Mücadele Daire Başkanlığı, 48
  • OGM (2017). Orman Yangınları Değerlendirme Raporu, S 26.
  • Tatli, H. & Türkeş, M., (2014). Climatological evaluation of Haines forest fire weather index over the Mediterranean Basin. Meteorological Applications, 21(3), 545-552.
  • Küçük, Ö., Bilgili, E., Durmaz, B. D., Sağlam, B., & Baysal, İ. (2009). Örtü yangınının tepe yangınına geçişinde etkili olan faktörler. Kastamonu University Journal of Forestry Faculty, 9(2), 80-85.
  • Yavuz, M., Sağlam, B., Küçük, Ö., & Tüfekçioğlu, A. (2018). Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty, 18(2), 171-188.
  • Trouet, V., Taylor, A. H., Carleton, A. M., & Skinner, C. N. (2009). Interannual variations in fire weather, fire extent, and synoptic-scale circulation patterns in northern California and Oregon. Theoretical and Applied Climatology, 95(3-4), 349- 360.
  • McCaw, L., Marchetti, P., Elliott, G., & Reader, G. (2007). Bushfire weather climatology of the Haines Index in southwestern Australia. Australian Meteorological Magazine, 56 (2).
  • Pausas, J. G. (2004). Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Climatic change, 63(3), 337-350.
  • Sivrikaya, F., Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region, Ecological Informatics, 68, 101537,
  • Urbieta IR, Zavala G, Bedia J, Gutie´rrez JM, San Miguel-Ayanz J, Camia A, Keeley JE, & Moreno JM. (2015). Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA. Environmental Research Letters, 10(11), 114013.
  • Gomes J.F. & Radovanovic M., (2008). Solar activity as a possible cause of large forest fires-a case study: analysis of the Portuguese forest fires. Science of the Total Environment, 394(1),197-205.
  • Parker, D. E., Jones, P. D., Folland, C. K., & Bevan, A. (1994). Interdecadal changes of surface temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres, 99(D7), 14373-14399.
  • Jones, P. D., New, M., Parker, D. E., Martin, S., & Rigor, I. G.: (1999). Surface Air Temperature and its Changes over the Past 150 Years, Reviews of Geophysics 37, 173–199.
  • Barnett, E. & Halverson, J., (2001). Local increases in coronary heart disease mortality among blacks & whites in the United States, 1985–1995. American Journal of Public Health: 91(9), 1499-1506.
  • Eddy, J. A. (1977). Climate and the changing sun. Climatic Change, 1(2), 173-190.
  • Haigh, J.D. (2007). The Sun and the Earth's Climate, Living Reviews in Solar Physics, 4, 1-64.
  • Scafetta, N. & West, B. J. (2008). Is climate sensitive to solar variability? Physics Today, 61(3), 50.
  • Scafetta, N., (2009). Empirical analysis of the contribution to global mean air surface temperature change. Journal of Atmospheric and Solar-Terrestrial Physics 71,1918–1923.
  • Echer, M. S., Echer, E., Rigozo, N. R., Brum, C. G. M., Nordemann, D. J. R., & Gonzalez, W. D., (2012). On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity. Journal of atmospheric and solar-terrestrial physics, 74, 87-93.
  • Currie, R.G., (1974). Solar cycle signal in surface air temperature. Journal of Geophysical Research, 79, 5657–5660.
  • Herman, J.R. & Goldberg, R., (1978). A ‘Sun, Weather, and Climate’. NASA, Washington, DC.
  • Haigh, J.D. (2007). The Sun and the Earth's Climate, Living Reviews in Solar Physics, 4, 1-64.
  • Echer, E., Gonzalez, W. D., Guarnieri, F. L., Dal Lago, A., & Vieira, L. E. A. (2005). Introduction to space weather. Advances in Space Research, 35(5), 855-865.
  • Echer, M. S., Echer, E., Rigozo, N. R., Brum, C. G. M., Nordemann, D. J. R., & Gonzalez, W. D., (2012). On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity. Journal of atmospheric and solar-terrestrial physics, 74, 87-93.
  • Syphard, A. D., Radeloff, V. C., Keeley, J. E., Hawbaker, T. J., Clayton, M. K., Stewart, S. I., & Hammer, R. B. (2007). Human influence on California fire regimes. Ecological Applications, 17(5), 1388-1402.
  • Ager, A. A., Preisler, H. K., Arca, B., Spano, D., & Salis, M. (2014). Wildfire risk estimation in the Mediterranean area. Environmetrics, 25(6), 384-396.
  • Zhang G., (1999). Predictions for maximum-value sunspot number-time and the end time of cycle 23. Progress in Geophysics, 14(S1), 99-103.
  • Frick, P., Galyagin, D., Hoyt, D. V., Nesme-Ribes, E., Schatten, K. H., Sokoloff, D., & Zakharov, V., (1997). Wavelet analysis of solar activity recorded by sunspot groups. Astronomy and Astrophysics, 328, 670-681.
  • Le, G. M. & Wang, J. L. (2003). Wavelet analysis of several important periodic properties in the relative sunspot numbers. Chinese Journal of Astronomy and Astrophysics, 3(5), 391.
  • Lagerquist, R., Flannigan, M. D., Wang, X., & Marshall, G. A. (2017). Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada. Canadian Journal of Forest Research, 47(9), 1175-1183.
  • Mhawej, M., Faour, G., Abdallah, C., & Adjizian-Gerard, J. (2016). Towards an establishment of a wildfire risk system in a Mediterranean country. Ecological informatics, 32, 167- 184.
  • Flannigan, M.D. & Wotton, B.M., (2001). Climate, weather and area burned in Forest fires, Aca demic Press, 351-373.
  • Baltacı, U. ve Yıldırım F. (2017). Orman yangınları açısından riskli yılların güneş leke döngüsüne bağlı olarak önceden tahmin edilebilmesi. Ormancılık Araştırma Dergisi, 4(2), 133-142.
  • Mwanzia, D. (2021). A study of solar variability and its effects on Earth’s Climate. Thesis, University of Nairobi.
  • Marov, M. Y. (2020). Radiation and space flights safety: an insight. Acta Astronautica, 176, 580-590.
  • Shuyang, W., & Guoyu, S. (1994). Study on relations between heavy-disaster-period of forest fire activity and sunspot activity, SSTA. Journal of Northeast Forestry University, 5(4), 27-32.
  • Polyansky, O. L., Zobov, N. F., Viti, S., Tennyson, J., Bernath, P. F., & Wallace, L. (1997). Water on the sun: line assignments based on variational calculations. Science, 277(5324), 346-348.
  • Wright, J. G. (1940). Sun spots and forest fires in New Brunswick. The Forestry Chronicle, 16(4), 233-238.
  • Herrera V.G., (2016). Mexican forest fires and their decadal variations. Advances in Space Research, V. 58 (1), 2104-2115.
  • Baltacı, U. ve Yıldırım F. (2017). Orman yangınları açısından riskli yılların güneş leke döngüsüne bağlı olarak önceden tahmin edilebilmesi. Ormancılık Araştırma Dergisi, 4(2), 133-142.
  • OGM, 2021. Orman Yangınlarıyla Mücadele Değerlendirme Raporları. Ankara
  • Anonim, (2021). https://www.bis.sidc.be/silso/datafiles. Erişim Tarihi: 13.04.2021
  • Daubechies, I., (1990). The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Transactions on Information Theory, 36 (5), 961-1005.
  • Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis, 15(4), 723-736.
  • Torrence, C., & Compo, G. P., (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.org/10.1016/j.foreco.2019.117723.
  • Küçük, M. & Ağiralioğlu, N., (2006). Wavelet regression technique for streamflow prediction. Journal of applied statistics, 33(9), 943-960.
  • Cohen, M.X. (2019). A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage, 199, 81–86.
  • Arı, N, Özen S. ve Çolak Ö. H. (2008). Dalgacık Teorisi, Palme Yayıncılık, Ankara.
  • Partal, T. (2012). Wavelet analysis and multi-scale characteristics of the runoff and precipitation series of the Aegean region (Turkey). International Journal of Climatology, 32,108-120
  • Öner, İ.V., Yeşilyurt, M.K. ve Yılmaz, E.Ç. (2017). Wavelet analiz tekniği ve uygulama alanları. Ordu Üniversitesi. Bilim Teknik Dergisi, 7(1), 42-56.
  • Gray, L. J., Beer, J., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., ... & White, W. (2010). Solar influences on climate. Reviews of Geophysics, 48(4).
  • Cameron, R. H., & Schuessler, M. (2019). Solar activity: periodicities beyond 11 years are consistent with random forcing. Astronomy & Astrophysics, 625, A2
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Taner Mustafa Cengiz 0000-0003-1752-8875

Yayımlanma Tarihi 31 Temmuz 2022
Gönderilme Tarihi 28 Haziran 2022
Yayımlandığı Sayı Yıl 2022