Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye

Yıl 2024, Cilt: 10 Sayı: 2, 365 – 381, 18.07.2024

Cenk Sezen

https://doi.org/10.21324/dacd.1422683

Öz

Hidrolojik döngü bileşenlerindeki değişimlerin analiz edilmesi su kaynaklarının planlanması ve yönetilmesi açısından önem arz etmektedir. Bu çalışmada, Türkiye’nin Doğu Karadeniz Havzası’nda yer alan beş farklı akım gözlem istasyonuna ait akım verileri ile Arktik Salınım (AO), Doğu Atlantik-Batı Rusya Paterni (EAWR), Kuzey Atlantik Salınımı (NAO) ve Kuzey Denizi Hazar Paterni (NCP) arasındaki ilişki araştırılmıştır. Bu amaçla, Spearman’s korelasyon testi, toplu ampirik mod ayrıştırma metodu (EEMD) ve nispi önem analizi kullanılmıştır. Buna göre, Spearman’s korelasyon katsayıları hem ham akım verileri hem de EEMD ile bileşenlerine ayrılmış akım değerleri ile atmosferik tele bağlantılar arasında hesaplanmıştır. Daha sonra, atmosferik tele bağlantıların akım verileri üzerindeki etkisini belirlemek amacıyla nispi önem analizi uygulanmıştır. Elde edilen bulgular, ham akım verileri ile atmosferik tele bağlantılar arasındaki ilişkinin genel olarak kış ve ilkbahar aylarında daha önemli ve negatif olduğunu göstermiştir. Bununla birlikte, bileşenlerine ayrılmış akım verileri ile atmosferik tele bağlantılar arasındaki ilişkinin her bir bileşen için farklılık gösterebildiği gözlenmiştir. Atmosferik tele bağlantılar ile ham akım verileri arasında bazı aylar için herhangi bir ilişki bulunmamasına rağmen, bileşenlerine ayrılmış akım verileri ile atmosferik tele bağlantılar arasında önemli korelasyonlar tespit edilmiştir. Bu durum, atmosferik tele bağlantılar ile akım verileri arasındaki ilişkinin farklı periyotlarda incelenmesinin önemini ortaya koymaktadır. Nispi önem analizi atmosferik tele bağlantıların akım verileri üzerindeki etkisinin istasyondan istasyona ve bileşenden bileşene gösterdiğini ortaya koymuştur. Bu çalışma, atmosferik tele bağlantıların akım verileri üzerindeki etkisinin farklı bileşenler ve periyotlar için araştırılmasının önemini göstermiştir.

Anahtar Kelimeler

Akım, Atmosferik Tele Bağlantılar, EEMD, Korelasyon, Nispi Önem, Doğu Karadeniz

Kaynakça

  • Abdelkader, M., & Yerdelen, C. (2022). Hydrological drought variability and its teleconnections with climate indices. Journal of Hydrology, 605, Article 127290. https://doi.org/10.1016/j.jhydrol.2021.127290
  • Akbas, A., & Ozdemir, H. (2023). Influence of atmospheric circulation on the variability of hydroclimatic parameters in the Marmara Sea river basins. Hydrological Sciences Journal, 68(9), 1229-1240. https://doi.org/10.1080/02626667.2023.2206970
  • Baltaci, H., Akkoyunlu, B. O., & Tayanc, M. (2018). Relationships between teleconnection patterns and Turkish climatic extremes. Theoretical and Applied Climatology, 134, 1365-1386. https://doi.org/10.1007/s00704-017-2350-z
  • Barnston, A. G., & Livezey, R. E. (1987). Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review, 115(6), 1083-1126. https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2
  • Best, D. J., & Roberts, D. E. (1975). Algorithm AS 89: the upper tail probabilities of Spearman's rho. Journal of the Royal Statistical Society. Series C (Applied Statistics), 24(3), 377-379. https://doi.org/10.2307/2347111
  • Chevan, A., & Sutherland, M. (1991). Hierarchical partitioning. The American Statistician, 45(2), 90-96. https://doi.org/10.1080/00031305.1991.10475776
  • Citakoglu, H., & Minarecioglu, N. (2021). Trend analysis and change point determination for hydro-meteorological and groundwater data of Kizilirmak basin. Theoretical and Applied Climatology, 145(3), 1275-1292. https://doi.org/10.1007/s00704-021-03696-9
  • Citakoglu, H., & Coskun, O. (2022). Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environmental Science and Pollution Research, 29(50), 75487-75511. https://doi.org/10.1007/s11356-022-21083-3
  • Coskun, O., & Citakoglu, H. (2023). Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye. Physics and Chemistry of the Earth, Parts A/B/C, 131, Article 103418. https://doi.org/10.1016/j.pce.2023.103418
  • Demir, H. B. (2019). Güneyli salınımın İç Anadolu Bölgesi yıllık yağış verileri üzerine etkisi [Yüksek Lisans tezi, Konya Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Duzenli, E., Tabari, H., Willems, P., & Yilmaz, M.T. (2018). Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns. Hydrological Processes, 32(23), 3513-3528. https://doi.org/10.1002/hyp.13275
  • Kebapcioglu, E., & Partal, T. (2021). Yeşilırmak ve Kızılırmak Havzaları Akımları Üzerinde Kuzey Atlantik Salınımı ve Arktik Salınımının Etkilerinin Belirlenmesi. DSI Technical Bulletin, 138, 27-35.
  • Forootan, E., Khaki, M., Schumacher, M., Wulfmeyer, V., Mehrnegar, N., van Dijk, A. I., Brocca, L., Farzaneh, S., Akinluyi, F., Ramillien, G., Shum, C.K., Awange, J., & Mostafaie, A. (2019). Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections. Science of the Total Environment, 650, 2587-2604. https://doi.org/10.1016/j.scitotenv.2018.09.231
  • Gan, R., Li, D., Chen, C., Yang, F., Zhang, X., & Guo, X. (2023). Spatiotemporal characteristics of extreme hydrometeorological events and its potential influencing factors in the Huaihe River Basin, China. Stochastic Environmental Research and Risk Assessment, 37, 2693–2712. https://doi.org/10.1007/s00477-023-02413-4
  • General Directorate of Water Management. (2020). Flood Management Plans. Retrieved July 17, 2023, from https://www.tarimorman.gov.tr/SYGM/Sayfalar/Detay.aspx?SayfaId=53
  • Gromping, U. (2006). Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software, 17, 1-27. https://doi.org/ 10.18637/jss.v017.i01
  • Guan, B.T. (2014). Ensemble empirical mode decomposition for analyzing phenological responses to warming. Agricultural and Forest Meteorology, 194, 1-7. https://doi.org/10.1016/j.agrformet.2014.03.010
  • Hurrell, J. W., & Deser, C. (2010). North Atlantic climate variability: the role of the North Atlantic Oscillation. Journal of Marine Systems, 79(3-4), 231-244. https://doi.org/10.1016/j.jmarsys.2009.11.002
  • Jiang, R., Wang, Y., Xie, J., Zhao, Y., Li, F., & Wang, X. (2019). Multiscale characteristics of Jing-Jin-Ji’s seasonal precipitation and their teleconnection with large-scale climate indices. Theoretical and Applied Climatology, 137, 1495-1513. https://doi.org/10.1007/s00704-018-2682-3
  • Karabork, M. Ç., Kahya, E., & Karaca, M. (2005). The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey. Hydrological Processes, 19(6), 1185-1211. https://doi.org/10.1002/hyp.5560
  • Krichak, S. O., & Alpert, P. (2005). Decadal trends in the east Atlantic–west Russia pattern and Mediterranean precipitation. International Journal of Climatology, 25(2), 183-192. https://doi.org/10.1002/joc.1124
  • Kutiel, H., & Benaroch, Y. (2002). North Sea-Caspian Pattern (NCP)–an upper level atmospheric teleconnection affecting the Eastern Mediterranean: Identification and definition. Theoretical and Applied Climatology, 71, 17-28. https://doi.org/10.1007/s704-002-8205-x
  • Kutiel, H., Maheras, P., Turkes, M., & Paz, S. (2002). North Sea–Caspian Pattern (NCP)–an upper level atmospheric teleconnection affecting the eastern Mediterranean–implications on the regional climate. Theoretical and Applied Climatology, 72, 173-192. https://doi.org/10.1007/s00704-002-0674-8
  • Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Scott, Foresman, Glenview, IL.
  • Microsoft Corporation. (2023). Microsoft Excel. https://office.microsoft.com/excel
  • Oertel, M., Meza, F.J., & Gironas, J. (2020). Observed trends and relationships between ENSO and standardized hydrometeorological drought indices in central Chile. Hydrological Processes, 34(2), 159-174. https://doi.org/10.1002/hyp.13596
  • Prasad, R., Deo, R.C., Li, Y., & Maraseni, T. (2018). Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition. Geoderma, 330, 136-161. https://doi.org/10.1016/j.geoderma.2018.05.035
  • R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  • Rathinasamy, M., Agarwal, A., Sivakumar, B., Marwan, N., & Kurths, J. (2019). Wavelet analysis of precipitation extremes over India and teleconnections to climate indices. Stochastic Environmental Research and Risk Assessment, 33, 2053-2069. https://doi.org/10.1007/s00477-019-01738-3
  • Sezen, C., & Partal, T. (2019). The impacts of Arctic oscillation and the North Sea Caspian pattern on the temperature and precipitation regime in Turkey. Meteorology and Atmospheric Physics, 131, 1677-1696. https://doi.org/10.1007/s00703-019-00665-w
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  • Sharma, P. J., Patel, P. L., & Jothiprakash, V. (2020). Hydroclimatic teleconnections of large-scale oceanic-atmospheric circulations on hydrometeorological extremes of Tapi Basin, India. Atmospheric Research, 235, Article 104791. https://doi.org/10.1016/j.atmosres.2019.104791
  • Shi, X., Huang, Q., & Li, K. (2021). Decomposition-based teleconnection between monthly streamflow and global climatic oscillation. Journal of Hydrology, 602, Article 126651. https://doi.org/10.1016/j.jhydrol.2021.126651
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  • Tosunoglu, F., Can, I., & Kahya, E. (2018). Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey. International Journal of Climatology, 38(12), 4579-4596. https://doi.org/10.1002/joc.5698
  • Turkes, M., & Erlat, E. (2003). Precipitation changes and variability in Turkey linked to the North Atlantic Oscillation during the period 1930–2000. International Journal of Climatology, 23(14), 1771-1796. https://doi.org/10.1002/joc.962
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Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye

Yıl 2024, Cilt: 10 Sayı: 2, 365 – 381, 18.07.2024

Cenk Sezen

https://doi.org/10.21324/dacd.1422683

Öz

Analysing the variations in hydrological cycle components is essential for water resources planning and management. In this study, the relationship between the streamflow data belonging to five discharge gauging stations in the Eastern Black Sea Basin in Türkiye and the Arctic Oscillation (AO), East Atlantic-Western Russia (EAWR), North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP) was investigated. For this purpose, Spearman’s correlation test, ensemble empirical mode decomposition (EEMD) and relative importance analysis were used. Accordingly, Spearman’s correlation coefficients were calculated between raw streamflow data, decomposed streamflow data via EEMD and atmospheric teleconnections. Then, the relative importance analysis was applied to determine the atmospheric teleconnections’ influences on streamflow data. The findings showed that the relationship between raw streamflow data and atmospheric teleconnections is generally more significant and negative in the winter and spring. Furthermore, it was observed that the linkage between the decomposed streamflow data and atmospheric teleconnections could differentiate. Although no significant correlation between atmospheric teleconnections and raw streamflow data was detected in some months, significant correlations were detected between atmospheric teleconnections and decomposed streamflow data. This reveals the importance of examining the relationship between atmospheric teleconnections and streamflow data for different periods. The relative importance analysis revealed that the influence of atmospheric teleconnections on streamflow data could change from station to station and from component to component. This study showed that investigating the effects of atmospheric teleconnections on streamflow data for different components and periods is important.

Anahtar Kelimeler

Streamflow, Atmospheric Teleconnections, EEMD, Correlation, Relative Importance, Eastern Black Sea

Kaynakça

  • Abdelkader, M., & Yerdelen, C. (2022). Hydrological drought variability and its teleconnections with climate indices. Journal of Hydrology, 605, Article 127290. https://doi.org/10.1016/j.jhydrol.2021.127290
  • Akbas, A., & Ozdemir, H. (2023). Influence of atmospheric circulation on the variability of hydroclimatic parameters in the Marmara Sea river basins. Hydrological Sciences Journal, 68(9), 1229-1240. https://doi.org/10.1080/02626667.2023.2206970
  • Baltaci, H., Akkoyunlu, B. O., & Tayanc, M. (2018). Relationships between teleconnection patterns and Turkish climatic extremes. Theoretical and Applied Climatology, 134, 1365-1386. https://doi.org/10.1007/s00704-017-2350-z
  • Barnston, A. G., & Livezey, R. E. (1987). Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review, 115(6), 1083-1126. https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2
  • Best, D. J., & Roberts, D. E. (1975). Algorithm AS 89: the upper tail probabilities of Spearman's rho. Journal of the Royal Statistical Society. Series C (Applied Statistics), 24(3), 377-379. https://doi.org/10.2307/2347111
  • Chevan, A., & Sutherland, M. (1991). Hierarchical partitioning. The American Statistician, 45(2), 90-96. https://doi.org/10.1080/00031305.1991.10475776
  • Citakoglu, H., & Minarecioglu, N. (2021). Trend analysis and change point determination for hydro-meteorological and groundwater data of Kizilirmak basin. Theoretical and Applied Climatology, 145(3), 1275-1292. https://doi.org/10.1007/s00704-021-03696-9
  • Citakoglu, H., & Coskun, O. (2022). Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environmental Science and Pollution Research, 29(50), 75487-75511. https://doi.org/10.1007/s11356-022-21083-3
  • Coskun, O., & Citakoglu, H. (2023). Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye. Physics and Chemistry of the Earth, Parts A/B/C, 131, Article 103418. https://doi.org/10.1016/j.pce.2023.103418
  • Demir, H. B. (2019). Güneyli salınımın İç Anadolu Bölgesi yıllık yağış verileri üzerine etkisi [Yüksek Lisans tezi, Konya Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Duzenli, E., Tabari, H., Willems, P., & Yilmaz, M.T. (2018). Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns. Hydrological Processes, 32(23), 3513-3528. https://doi.org/10.1002/hyp.13275
  • Kebapcioglu, E., & Partal, T. (2021). Yeşilırmak ve Kızılırmak Havzaları Akımları Üzerinde Kuzey Atlantik Salınımı ve Arktik Salınımının Etkilerinin Belirlenmesi. DSI Technical Bulletin, 138, 27-35.
  • Forootan, E., Khaki, M., Schumacher, M., Wulfmeyer, V., Mehrnegar, N., van Dijk, A. I., Brocca, L., Farzaneh, S., Akinluyi, F., Ramillien, G., Shum, C.K., Awange, J., & Mostafaie, A. (2019). Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections. Science of the Total Environment, 650, 2587-2604. https://doi.org/10.1016/j.scitotenv.2018.09.231
  • Gan, R., Li, D., Chen, C., Yang, F., Zhang, X., & Guo, X. (2023). Spatiotemporal characteristics of extreme hydrometeorological events and its potential influencing factors in the Huaihe River Basin, China. Stochastic Environmental Research and Risk Assessment, 37, 2693–2712. https://doi.org/10.1007/s00477-023-02413-4
  • General Directorate of Water Management. (2020). Flood Management Plans. Retrieved July 17, 2023, from https://www.tarimorman.gov.tr/SYGM/Sayfalar/Detay.aspx?SayfaId=53
  • Gromping, U. (2006). Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software, 17, 1-27. https://doi.org/ 10.18637/jss.v017.i01
  • Guan, B.T. (2014). Ensemble empirical mode decomposition for analyzing phenological responses to warming. Agricultural and Forest Meteorology, 194, 1-7. https://doi.org/10.1016/j.agrformet.2014.03.010
  • Hurrell, J. W., & Deser, C. (2010). North Atlantic climate variability: the role of the North Atlantic Oscillation. Journal of Marine Systems, 79(3-4), 231-244. https://doi.org/10.1016/j.jmarsys.2009.11.002
  • Jiang, R., Wang, Y., Xie, J., Zhao, Y., Li, F., & Wang, X. (2019). Multiscale characteristics of Jing-Jin-Ji’s seasonal precipitation and their teleconnection with large-scale climate indices. Theoretical and Applied Climatology, 137, 1495-1513. https://doi.org/10.1007/s00704-018-2682-3
  • Karabork, M. Ç., Kahya, E., & Karaca, M. (2005). The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey. Hydrological Processes, 19(6), 1185-1211. https://doi.org/10.1002/hyp.5560
  • Krichak, S. O., & Alpert, P. (2005). Decadal trends in the east Atlantic–west Russia pattern and Mediterranean precipitation. International Journal of Climatology, 25(2), 183-192. https://doi.org/10.1002/joc.1124
  • Kutiel, H., & Benaroch, Y. (2002). North Sea-Caspian Pattern (NCP)–an upper level atmospheric teleconnection affecting the Eastern Mediterranean: Identification and definition. Theoretical and Applied Climatology, 71, 17-28. https://doi.org/10.1007/s704-002-8205-x
  • Kutiel, H., Maheras, P., Turkes, M., & Paz, S. (2002). North Sea–Caspian Pattern (NCP)–an upper level atmospheric teleconnection affecting the eastern Mediterranean–implications on the regional climate. Theoretical and Applied Climatology, 72, 173-192. https://doi.org/10.1007/s00704-002-0674-8
  • Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Scott, Foresman, Glenview, IL.
  • Microsoft Corporation. (2023). Microsoft Excel. https://office.microsoft.com/excel
  • Oertel, M., Meza, F.J., & Gironas, J. (2020). Observed trends and relationships between ENSO and standardized hydrometeorological drought indices in central Chile. Hydrological Processes, 34(2), 159-174. https://doi.org/10.1002/hyp.13596
  • Prasad, R., Deo, R.C., Li, Y., & Maraseni, T. (2018). Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition. Geoderma, 330, 136-161. https://doi.org/10.1016/j.geoderma.2018.05.035
  • R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  • Rathinasamy, M., Agarwal, A., Sivakumar, B., Marwan, N., & Kurths, J. (2019). Wavelet analysis of precipitation extremes over India and teleconnections to climate indices. Stochastic Environmental Research and Risk Assessment, 33, 2053-2069. https://doi.org/10.1007/s00477-019-01738-3
  • Sezen, C., & Partal, T. (2019). The impacts of Arctic oscillation and the North Sea Caspian pattern on the temperature and precipitation regime in Turkey. Meteorology and Atmospheric Physics, 131, 1677-1696. https://doi.org/10.1007/s00703-019-00665-w
  • Sezen, C. (2023). A new wavelet combined innovative polygon trend analysis (W-IPTA) approach for investigating the trends in the streamflow regime in the Konya Closed Basin, Turkey. Theoretical and Applied Climatology, 151(3-4), 1523-1565. https://doi.org/10.1007/s00704-022-04328-6
  • Sharma, P. J., Patel, P. L., & Jothiprakash, V. (2020). Hydroclimatic teleconnections of large-scale oceanic-atmospheric circulations on hydrometeorological extremes of Tapi Basin, India. Atmospheric Research, 235, Article 104791. https://doi.org/10.1016/j.atmosres.2019.104791
  • Shi, X., Huang, Q., & Li, K. (2021). Decomposition-based teleconnection between monthly streamflow and global climatic oscillation. Journal of Hydrology, 602, Article 126651. https://doi.org/10.1016/j.jhydrol.2021.126651
  • The MathWorks Inc. (2023). Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com
  • Thompson, D. W., & Wallace, J. M. (1998). The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophysical Research Letters, 25(9), 1297-1300. https://doi.org/10.1029/98GL00950
  • Tosunoglu, F., Can, I., & Kahya, E. (2018). Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey. International Journal of Climatology, 38(12), 4579-4596. https://doi.org/10.1002/joc.5698
  • Turkes, M., & Erlat, E. (2003). Precipitation changes and variability in Turkey linked to the North Atlantic Oscillation during the period 1930–2000. International Journal of Climatology, 23(14), 1771-1796. https://doi.org/10.1002/joc.962
  • Turkes, M., & Erlat, E. (2008). Influence of the Arctic Oscillation on the variability of winter mean temperatures in Turkey. Theoretical and Applied Climatology, 92, 75-85. https://doi.org/10.1007/s00704-007-0310-8
  • Unal, Y. S., Deniz, A., Toros, H., & Incecik, S. (2012). Temporal and spatial patterns of precipitation variability for annual, wet, and dry seasons in Turkey. International Journal of Climatology, 32(3), 392-405. https://doi.org/10.1002/joc.2274
  • Vazifehkhah, S., & Kahya, E. (2018). Hydrological drought associations with extreme phases of the North Atlantic and Arctic Oscillations over Turkey and northern Iran. International Journal of Climatology, 38(12), 4459-4475. https://doi.org/10.1002/joc.5680
  • Wang, J., Wang, X., hui Lei, X., Wang, H., hua Zhang, X., jun You, J., feng Tan, Q., & lia Liu, X. (2020). Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition. Journal of Hydrology, 582, Article 124411. https://doi.org/10.1016/j.jhydrol.2019.124411
  • Wang, T., Song, C., & Chen, X. (2023). Clarifying the relationship between annual maximum daily precipitation and climate variables by wavelet analysis. Atmospheric Research, 295, Article 106981. https://doi.org/10.1016/j.atmosres.2023.106981
  • Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1-41. https://doi.org/10.1142/S1793536909000047
  • Yarbasi, G. E. (2019). Güneyli salınımın Karadeniz Bölgesi yıllık yağış verileri üzerine etkisi [Yüksek Lisans tezi, Konya Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Yilmaz, C. B., Demir, V., & Sevimli, M. F. (2020). Karadeniz yağışlarının Kuzey Atlantik salınımı ile ilişkisi. Gazi Mühendislik Bilimleri Dergisi, 6(3), 248-254. https://dergipark.org.tr/en/pub/gmbd/issue/58697/772005
  • Zhang, H., Wu, C., Yeh, P. J. F., & Hu, B. X. (2020a). Global pattern of short‐term concurrent hot and dry extremes and its relationship to large‐scale climate indices. International Journal of Climatology, 40(14), 5906-5924. https://doi.org/10.1002/joc.6555
  • Zhang, R., Xu, Z., Zuo, D., & Ban, C. (2020b). Hydro-meteorological trends in the Yarlung Zangbo River Basin and possible associations with large-scale circulation. Water, 12(1), Article 144. https://doi.org/10.3390/w12010144

Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği (Diğer)
BölümAraştırma Makalesi
Yazarlar

Cenk Sezen ONDOKUZ MAYIS ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ 0000-0003-1088-9360 Türkiye

Yayımlanma Tarihi18 Temmuz 2024
Gönderilme Tarihi19 Ocak 2024
Kabul Tarihi30 Mart 2024
Yayımlandığı Sayı Yıl 2024Cilt: 10 Sayı: 2

Kaynak Göster

APASezen, C. (2024). Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye. Doğal Afetler Ve Çevre Dergisi, 10(2), 365-381. https://doi.org/10.21324/dacd.1422683
AMASezen C. Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye. Doğ Afet Çev Derg. Temmuz 2024;10(2):365-381. doi:10.21324/dacd.1422683
ChicagoSezen, Cenk. “Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye”. Doğal Afetler Ve Çevre Dergisi 10, sy. 2 (Temmuz 2024): 365-81. https://doi.org/10.21324/dacd.1422683.
EndNoteSezen C (01 Temmuz 2024) Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye. Doğal Afetler ve Çevre Dergisi 10 2 365–381.
IEEEC. Sezen, “Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye”, Doğ Afet Çev Derg, c. 10, sy. 2, ss. 365–381, 2024, doi: 10.21324/dacd.1422683.
ISNADSezen, Cenk. “Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye”. Doğal Afetler ve Çevre Dergisi 10/2 (Temmuz 2024), 365-381. https://doi.org/10.21324/dacd.1422683.
JAMASezen C. Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye. Doğ Afet Çev Derg. 2024;10:365–381.
MLASezen, Cenk. “Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye”. Doğal Afetler Ve Çevre Dergisi, c. 10, sy. 2, 2024, ss. 365-81, doi:10.21324/dacd.1422683.
VancouverSezen C. Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye. Doğ Afet Çev Derg. 2024;10(2):365-81.

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