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INVESTIGATION OF MONTHLY PRECIPITATION REGIME CHANGE AND FUTURE RAINFALL-RUNOFF RESPONSE IN COASTAL REGIONS UNDER THE IMPACT OF CLIMATE CHANGE: A STUDY OF RIZE AND ANTALYA

Yıl 2025, Cilt: 28 Sayı: 2, 1020 - 1035, 03.06.2025

Hasan Törehan Babacan

https://doi.org/10.17780/ksujes.1657507

Öz

This study examines changes in monthly precipitation regimes and future precipitation-runoff responses in Rize and Antalya under the impact of climate change. Historical precipitation data from 1970 to 2022 and future projections based on the RCP8.5 emission scenario of CMIP5 were analyzed. Markov Chain transition matrices were created to evaluate precipitation regime shifts, while Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), and Artificial Neural Networks (YSA) models were used for streamflow predictions. In Antalya, the historical average precipitation in January was 148 mm, while projections indicate a decrease to 120 mm according to the GFDL model and an increase to 160 mm according to the HadGEM model. In Rize, the historical annual average precipitation is 2300 mm, with a projected 5% decrease in the GFDL model and a 3% increase in the HadGEM model. For streamflow prediction, MARS performed best in Antalya (R=0.74, RMSE=36.79), while CART was most accurate in Rize (R=0.84, RMSE=1.04). The results indicate that climate change will lead to more abrupt precipitation regime shifts, increasing agricultural risks in Antalya and landslide and flood risks in Rize. This study provides crucial insights for water management and disaster planning in coastal regions.

Anahtar Kelimeler

Climate Change Hydro-Meteorological Data Change of Rainfall Regime CMIP5

Kaynakça

  • Aktürk, G., & Hauser, S. J. (2021). Detection of disaster-prone vernacular heritage sites at district scale: the case of Fındıklı in Rize, Turkey. International Journal of Disaster Risk Reduction, 58, 102238. https://doi.org/10.1016/j.ijdrr.2021.102238
  • Ali, M. H., Biswas, P., Hassan, M. Q., & Islam, M. A. (2025). Clımate Change and Its Impacts on Hydrologıcal Regımes Over the Bengaldelta. Results in Engineering, 103861.
  • Atalay, I., Efe, R., & Ozturk, M. (2014). Effects of Topography and Climate on the Ecology of Taurus Mountains in the Mediterranean Region of Turkey. Procedia - Social and Behavioral Sciences, 120, 142-156. https://doi.org/10.1016/J.SBSPRO.2014.02.091.
  • Babacan, H. T., & Yüksek, Ö. (2024). Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103599. https://doi.org/10.1016/j.pce.2024.103599
  • Babacan, H. T., Yüksek, Ö., & Saka, F. (2022). Yapay zeka ve sezgisel regresyon yöntemlerinin yağış-akış modellemesi için performans değerlendirmesi: Aksu Deresi için bir uygulama. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11(3), 744-751. https://doi.org/10.28948/ngumuh.1079616
  • Bhuyan, M. D. I., Islam, M. M., & Bhuiyan, M. E. K. (2018). A trend analysis of temperature and rainfall to predict climate change for northwestern region of Bangladesh. American Journal of Climate Change, 7(2), 115-134.
  • Bola, G. B., Tshimanga, R. M., Neal, J., Trigg, M. A., Hawker, L., Lukanda, V. M., & Bates, P. (2022). Understanding flood seasonality and flood regime shift in the Congo River Basin. Hydrological Sciences Journal, 67(10), 1496-1515. https://doi.org/10.1080/02626667.2022.2083966
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  • Cheng, T., Xu, Z., Yang, H., Hong, S., & Leitao, J. P. (2020). Analysis of effect of rainfall patterns on urban flood process by coupled hydrological and hydrodynamic modeling. Journal of Hydrologic Engineering, 25(1), 04019061. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001867
  • Dang, C., Shao, Z., Huang, X., Qian, J., Cheng, G., Ding, Q., & Fan, Y. (2022). Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence. Global Change Biology, 28, 2066–2080. https://doi.org/10.1111/gcb.16043
  • Danladi Bello, A.-A., Hashim, N. B., & Mohd Haniffah, M. R. (2017). Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers. Climate, 5(3), 58. https://doi.org/10.3390/cli5030058
  • De Luca, D. L., Ridolfi, E., Russo, F., Moccia, B., & Napolitano, F. (2024). Climate change effects on rainfall extreme value distribution: the role of skewness. Journal of Hydrology, 634, 130958. https://doi.org/10.1016/j.jhydrol.2024.130958
  • Demircan, M., Gürkan, H., Eskioğlu, O., Arabacı, H., vd. (2017). Climate Change Projections for Turkey: Three Models and Two Scenarios. Turkish Journal of Water Science and Management, 1(1), 22-43. https://doi.org/10.31807/tjwsm.297183
  • Demirtaş, M. (2016). The October 2011 devastating flash flood event of Antalya: triggering mechanisms and quantitative precipitation forecasting. Quarterly Journal of the Royal Meteorological Society, 142(699), 2336-2346. https://doi.org/10.1002/qj.2827
  • Dhamodaran, S., & Lakshmi, M. (2020). Comparative analysis of spatial interpolation with climatic changes using inverse distance method. Journal of Ambient Intelligence and Humanized Computing, 12, 6725-6734. https://doi.org/10.1007/s12652-022-04128-w.
  • Du, J., Wu, X., Wang, Z., Li, J., & Chen, X. (2020). Reservoir-Induced Hydrological Alterations Using Ecologically Related Hydrologic Metrics: Case Study in the Beijiang River, China. Water. https://doi.org/10.3390/w12072008.
  • Farzin, S., Anaraki, M. V., Kadkhodazadeh, M., & Morshed-Bozorgdel, A. (2025). Novel methodology for prediction of missing values in River flow based on convolution neural networks: Principles and application in Iran country. Physics and Chemistry of the Earth, Parts A/B/C, 103875. https://doi.org/10.1016/j.pce.2025.103875
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1-67.
  • Grimm, N., Chapin, S., Bierwagen, B., Gonzalez, P., Groffman, P., Luo, Y., Melton, F., Nadelhoffer, K., Pairis, A., Raymond, P., Schimel, J., & Williamson, C. (2013). The impacts of climate change on ecosystem structure and function. Frontiers in Ecology and the Environment, 11, 474-482. https://doi.org/10.1890/120282.
  • Harding, B. L., Wood, A. W., & Prairie, J. R. (2012). The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin. Hydrology and Earth System Sciences, 16(11), 3989-4007. https://doi.org/10.5194/hess-16-3989-2012
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  • Knapp, A. K., Hoover, D. L., Wilcox, K. R., Avolio, M. L., Koerner, S. E., La Pierre, K. J., ... & Smith, M. D. (2015). Characterizing differences in precipitation regimes of extreme wet and dry years: implications for climate change experiments. Global change biology, 21(7), 2624-2633. https://doi.org/10.1111/gcb.12888
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İKLİM DEĞİŞİKLİĞİ ETKİSİ ALTINDA KIYI BÖLGELERİNDE AYLIK YAĞIŞ REJİMİ DEĞİŞİMİ VE GELECEK YAĞIŞ-AKIŞ TEPKİSİNİN ARAŞTIRILMASI: RİZE VE ANTALYA ÇALIŞMASI

Yıl 2025, Cilt: 28 Sayı: 2, 1020 - 1035, 03.06.2025

Hasan Törehan Babacan

https://doi.org/10.17780/ksujes.1657507

Öz

Bu çalışma, Rize ve Antalya illerindeki aylık yağış rejimi değişimlerini ve gelecekteki yağış-akış tepkisini iklim değişikliği etkisi altında incelemektedir. Tarihsel yağış verileri ile CMIP5 kapsamında RCP8.5 emisyon senaryosu kullanılarak gelecek projeksiyonları değerlendirilmiştir. Çalışmada, Markov Zinciri yöntemiyle yağış rejimi geçiş matrisleri oluşturulmuş ve Çok Değişkenli Uyarlanabilir Regresyon Eğrileri (MARS), Karar Ağaçları Yöntemi (CART) ve Yapay Sinir Ağları (YSA) modelleri kullanılarak akış tahminleri yapılmıştır. Antalya’da tarihsel dönemde Ocak ayında ortalama 148 mm yağış gözlenirken, GFDL projeksiyonuna göre bu değerin 120 mm’ye düşmesi, HadGEM projeksiyonuna göre ise 160 mm’ye çıkması beklenmektedir. Rize’de tarihsel yıllık ortalama yağış 2300 mm olup, gelecek dönemde GFDL modeline göre %5 azalış, HadGEM modeline göre ise %3 artış tahmin edilmiştir. Akış tahminlerinde, Antalya için en başarılı model MARS (R=0.74, RMSE=36.79), Rize için ise CART (R=0.84, RMSE=1.04) olarak belirlenmiştir. Sonuçlar, iklim değişikliğiyle birlikte her iki şehirde de yağış rejimi geçişlerinin keskinleşeceğini, bu durumun Antalya’da tarımsal faaliyetleri, Rize’de ise heyelan ve taşkın risklerini artıracağını göstermektedir. Çalışma, su yönetimi ve afet planlaması açısından kritik bulgular sunmaktadır.

Anahtar Kelimeler

İklim Değişikliği Hidrometeorolojik Veri Yağış Rejimi Değişimi CMIP5

Kaynakça

  • Aktürk, G., & Hauser, S. J. (2021). Detection of disaster-prone vernacular heritage sites at district scale: the case of Fındıklı in Rize, Turkey. International Journal of Disaster Risk Reduction, 58, 102238. https://doi.org/10.1016/j.ijdrr.2021.102238
  • Ali, M. H., Biswas, P., Hassan, M. Q., & Islam, M. A. (2025). Clımate Change and Its Impacts on Hydrologıcal Regımes Over the Bengaldelta. Results in Engineering, 103861.
  • Atalay, I., Efe, R., & Ozturk, M. (2014). Effects of Topography and Climate on the Ecology of Taurus Mountains in the Mediterranean Region of Turkey. Procedia - Social and Behavioral Sciences, 120, 142-156. https://doi.org/10.1016/J.SBSPRO.2014.02.091.
  • Babacan, H. T., & Yüksek, Ö. (2024). Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103599. https://doi.org/10.1016/j.pce.2024.103599
  • Babacan, H. T., Yüksek, Ö., & Saka, F. (2022). Yapay zeka ve sezgisel regresyon yöntemlerinin yağış-akış modellemesi için performans değerlendirmesi: Aksu Deresi için bir uygulama. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11(3), 744-751. https://doi.org/10.28948/ngumuh.1079616
  • Bhuyan, M. D. I., Islam, M. M., & Bhuiyan, M. E. K. (2018). A trend analysis of temperature and rainfall to predict climate change for northwestern region of Bangladesh. American Journal of Climate Change, 7(2), 115-134.
  • Bola, G. B., Tshimanga, R. M., Neal, J., Trigg, M. A., Hawker, L., Lukanda, V. M., & Bates, P. (2022). Understanding flood seasonality and flood regime shift in the Congo River Basin. Hydrological Sciences Journal, 67(10), 1496-1515. https://doi.org/10.1080/02626667.2022.2083966
  • Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470
  • Cheng, T., Xu, Z., Yang, H., Hong, S., & Leitao, J. P. (2020). Analysis of effect of rainfall patterns on urban flood process by coupled hydrological and hydrodynamic modeling. Journal of Hydrologic Engineering, 25(1), 04019061. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001867
  • Dang, C., Shao, Z., Huang, X., Qian, J., Cheng, G., Ding, Q., & Fan, Y. (2022). Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence. Global Change Biology, 28, 2066–2080. https://doi.org/10.1111/gcb.16043
  • Danladi Bello, A.-A., Hashim, N. B., & Mohd Haniffah, M. R. (2017). Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers. Climate, 5(3), 58. https://doi.org/10.3390/cli5030058
  • De Luca, D. L., Ridolfi, E., Russo, F., Moccia, B., & Napolitano, F. (2024). Climate change effects on rainfall extreme value distribution: the role of skewness. Journal of Hydrology, 634, 130958. https://doi.org/10.1016/j.jhydrol.2024.130958
  • Demircan, M., Gürkan, H., Eskioğlu, O., Arabacı, H., vd. (2017). Climate Change Projections for Turkey: Three Models and Two Scenarios. Turkish Journal of Water Science and Management, 1(1), 22-43. https://doi.org/10.31807/tjwsm.297183
  • Demirtaş, M. (2016). The October 2011 devastating flash flood event of Antalya: triggering mechanisms and quantitative precipitation forecasting. Quarterly Journal of the Royal Meteorological Society, 142(699), 2336-2346. https://doi.org/10.1002/qj.2827
  • Dhamodaran, S., & Lakshmi, M. (2020). Comparative analysis of spatial interpolation with climatic changes using inverse distance method. Journal of Ambient Intelligence and Humanized Computing, 12, 6725-6734. https://doi.org/10.1007/s12652-022-04128-w.
  • Du, J., Wu, X., Wang, Z., Li, J., & Chen, X. (2020). Reservoir-Induced Hydrological Alterations Using Ecologically Related Hydrologic Metrics: Case Study in the Beijiang River, China. Water. https://doi.org/10.3390/w12072008.
  • Farzin, S., Anaraki, M. V., Kadkhodazadeh, M., & Morshed-Bozorgdel, A. (2025). Novel methodology for prediction of missing values in River flow based on convolution neural networks: Principles and application in Iran country. Physics and Chemistry of the Earth, Parts A/B/C, 103875. https://doi.org/10.1016/j.pce.2025.103875
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1-67.
  • Grimm, N., Chapin, S., Bierwagen, B., Gonzalez, P., Groffman, P., Luo, Y., Melton, F., Nadelhoffer, K., Pairis, A., Raymond, P., Schimel, J., & Williamson, C. (2013). The impacts of climate change on ecosystem structure and function. Frontiers in Ecology and the Environment, 11, 474-482. https://doi.org/10.1890/120282.
  • Harding, B. L., Wood, A. W., & Prairie, J. R. (2012). The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin. Hydrology and Earth System Sciences, 16(11), 3989-4007. https://doi.org/10.5194/hess-16-3989-2012
  • Haywood, J. M., Jones, A., & Jones, G. S. (2014). The impact of volcanic eruptions in the period 2000–2013 on global mean temperature trends evaluated in the HadGEM2‐ES climate model. Atmospheric Science Letters, 15(2), 92-96. https://doi.org/10.1002/asl2.471.
  • Humphrey, G. B., Gibbs, M. S., Dandy, G. C., & Maier, H. R. (2016). A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623-640. https://doi.org/10.1016/j.jhydrol.2016.06.026
  • Knapp, A. K., Hoover, D. L., Wilcox, K. R., Avolio, M. L., Koerner, S. E., La Pierre, K. J., ... & Smith, M. D. (2015). Characterizing differences in precipitation regimes of extreme wet and dry years: implications for climate change experiments. Global change biology, 21(7), 2624-2633. https://doi.org/10.1111/gcb.12888
  • Konrad, C. P., Booth, D. B., & Burges, S. J. (2005). Effects of urban development in the Puget Lowland, Washington, on interannual streamflow patterns: Consequences for channel form and streambed disturbance. Water resources research, 41(7). https://doi.org/10.1029/2005WR004097
  • Koutný, L., Skoupil, J., & Veselý, D. (2014). Physical Characteristics Affecting the Infiltration of High Intensity Rainfall into a Soil Profile. Soil & Water Research, 9(3).
  • Kundu, S., Khare, D., & Mondal, A. (2017). Interrelationship of rainfall, temperature and reference evapotranspiration trends and their net response to the climate change in Central India. Theoretical and Applied Climatology, 130, 879-900.
  • Leventeli, Y. (2011). Potential Human Impact on Coastal Area, Antalya—Turkey. Journal of Coastal Research, (61), 403-407. https://doi.org/10.2112/SI61-001.48
  • Li, X., Tan, L., Li, Y., Qi, J., Feng, P., Li, B., ... & Chen, Y. (2022). Effects of global climate change on the hydrological cycle and crop growth under heavily irrigated management–A comparison between CMIP5 and CMIP6. Computers and Electronics in Agriculture, 202, 107408.
  • Lin, L., Lonla, P. Y., Vijayakumar, J., Khan, M. K., Di Emidio, G., Krekelbergh, N., ... & Cornelis, W. (2025). Soil surface properties and infiltration response to crust forming of a sandy loam and silt loam. Soil and Tillage Research, 248, 106440.
  • Liu, Y., Jia, Z., Ma, X., Wang, Y., Guan, R., Guan, Z., Gu, Y., & Zhao, W. (2022). Analysis of Drought Characteristics Projections for the Tibetan Plateau Based on the GFDL-ESM2M Climate Model. Remote Sensing, 14(20), 5084. https://doi.org/10.3390/rs14205084.
  • Lu, J., Jia, L., Menenti, M., Zheng, C., Hu, G., & Ji, D. (2025). The impacts of drought on water availability: spatial and temporal analysis in the Belt and Road region (2001–2020). International Journal of Digital Earth, 18(1), 2449706.
  • Mendoza, P., Clark, M., Mizukami, N., Gutmann, E., Arnold, J., Brekke, L., & Rajagopalan, B. (2016). How do hydrologic modeling decisions affect the portrayal of climate change impacts?. Hydrological Processes, 30, 1071 - 1095. https://doi.org/10.1002/hyp.10684.
  • Norris, J.R., (1997). Markov Chains. Cambridge: Cambridge University Press (Cambridge Series in Statistical and Probabilistic Mathematics). ISBN 9780511810633, https://doi.org/10.1017/CBO9780511810633
  • Ouarda, T. B., Charron, C., Kumar, K. N., Marpu, P. R., Ghedira, H., Molini, A., & Khayal, I. (2014). Evolution of the rainfall regime in the United Arab Emirates. Journal of Hydrology, 514, 258-270. https://doi.org/10.1016/j.jhydrol.2014.04.032
  • Piacentini, T., Galli, A., Marsala, V., & Miccadei, E. (2018). Analysis of soil erosion induced by heavy rainfall: A case study from the NE Abruzzo Hills Area in Central Italy. Water, 10(10), 1314. https://doi.org/10.3390/w10101314
  • Piguet, E., Pécoud, A., & De Guchteneire, P. (2011). Migration and climate change: An overview. Refugee Survey Quarterly, 30(3), 1-23.
  • Polson, D., Hegerl, G., & Solomon, S. (2016). Precipitation sensitivity to warming estimated from long island records. Environmental Research Letters, 11. https://doi.org/10.1088/1748-9326/11/7/074024.
  • Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), 6188-6202. https://doi.org/10.3390/s8106188
  • Rind, D., Rosenzweig, C., Rosenzweig, C., Goldberg, R., & Goldberg, R. (1992). Modelling the hydrological cycle in assessments of climate change. Nature, 358, 119-122. https://doi.org/10.1038/358119A0.
  • Ross, S., (2014) Introduction to Probability Models (Eleventh Edition), Academic Press, Pages 183-276, ISBN 9780124079489, https://doi.org/10.1016/B978-0-12-407948-9.00004-9.
  • Sayat, A., Lyazzat, M., Elmira, T., Gaukhar, B., & Gulsara, M. (2025). Assessment of the impacts of climate change on drought intensity and frequency using SPI and SPEI in the Southern Pre-Balkash region, Kazakhstan. Watershed Ecology and the Environment, 7, 11-22.
  • Sharma, P., Singh, S., & Sharma, S. D. (2022). Artificial neural network approach for hydrologic river flow time series forecasting. Agricultural Research, 11(3), 465-476. https://doi.org/10.1007/s40003-021-00585-5
  • Skendžić, S., Zovko, M., Živković, I. P., Lešić, V., & Lemić, D. (2021). The Impact of Climate Change on Agricultural Insect Pests. Insects, 12(5), 440. https://doi.org/10.3390/insects12050440
  • Tam, T. H., Abdul Rahman, M. Z., Harun, S., Shahid, S., Try, S., Jamal, M. H., ... & Abdul Wahab, Y. F. (2025). Flood hazard assessment using design rainfall under climate change scenarios in the Kelantan River Basin, Malaysia. International Journal of Disaster Resilience in the Built Environment, 16(1), 1-19.
  • URL-1: https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111 Erişim Tarihi: 01.02.2025
  • Wang, J., Yang, X., Tao, Z., & Shen, F. (2025). Failure characteristics and instability mechanism of the Hengshanbei catastrophic high-locality landslide in Guangdong, China. Natural Hazards, 1-21. https://doi.org/10.1007/s11069-025-07158-7
  • Wang, W., Van Gelder, P. H., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324(1-4), 383-399. https://doi.org/10.1016/j.jhydrol.2005.09.032
  • Wu, C. L., & Chau, K. W. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23(8), 1350-1367. https://doi.org/10.1016/j.engappai.2010.04.003
  • Y. Lu, T. Ye and J. Zheng, (2022). Decision Tree Algorithm in Machine Learning, IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 2022, pp. 1014-1017, doi: 10.1109/AEECA55500.2022.9918857.
  • Yılmaz, E. (2018). Türkiye'de aylık yağış eğilimleri, yağış kaymaları ve yağış Eğilim Rejimleri (1971-2010)(Monthly Precipitation Trends, Precipitation Temporal Shifts and Precipitation Trends Regimes in Turkey (1971–2010)). Journal of Human Sciences, 15(4), 2066-2091. https://doi.org/10.14687/jhs.v15i4.5479
  • Yılmaz, M., & Terzi, F. (2020). Characteristics of spatio-temporal urban growth patterns due to the driving forces of urbanization: The Coastal City of Antalya, Turkey. International Review for Spatial Planning and Sustainable Development, 8(3), 16-33. https://doi.org/10.14246/irspsd.8.3_16
  • Yin, Z., Feng, Q., Wen, X., Deo, R. C., Yang, L., Si, J., & He, Z. (2018). Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment. Stochastic Environmental Research and Risk Assessment, 32, 2457-2476. https://doi.org/10.1007/s00477-018-1585-2
  • Zhang, H., Liu, G., Zhao, C., Zhang, L., Zhang, Q., Fu, H., & Cao, S. (2023). Loess erosion change modeling during heavy rainfall. International Journal of Sediment Research, 38(1), 24-32. https://doi.org/10.1016/j.ijsrc.2022.08.004
  • Zhang, P., Sun, W., Xiao, P., Yao, W., & Liu, G. (2022). Driving Factors of Heavy Rainfall Causing Flash Floods in the Middle Reaches of the Yellow River: A Case Study in the Wuding River Basin, China. Sustainability, 14(13), 8004. https://doi.org/10.3390/su14138004
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İnşaat Mühendisliğinde Sayısal Modelleme, Su Kaynakları Mühendisliği
Bölüm İnşaat Mühendisliği
Yazarlar

Hasan Törehan Babacan 0000-0001-9570-1966

Yayımlanma Tarihi 3 Haziran 2025
Gönderilme Tarihi 13 Mart 2025
Kabul Tarihi 7 Mayıs 2025
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 2

Kaynak Göster

APA Babacan, H. T. (2025). İKLİM DEĞİŞİKLİĞİ ETKİSİ ALTINDA KIYI BÖLGELERİNDE AYLIK YAĞIŞ REJİMİ DEĞİŞİMİ VE GELECEK YAĞIŞ-AKIŞ TEPKİSİNİN ARAŞTIRILMASI: RİZE VE ANTALYA ÇALIŞMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 1020-1035. https://doi.org/10.17780/ksujes.1657507
 Kapak Resmi İndir