Research Article
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Year 2020, , 136 - 161, 24.07.2020
https://doi.org/10.31807/tjwsm.746134

Abstract

References

  • 1 Abbasnia, M., Toros, H. Analysis of long-term changes in extreme climatic indices: a case study of the Mediterranean climate, Marmara Region, Turkey. Pure Appl. Geophys. 175, 3861–3873 (2018). https://doi.org/10.1007/s00024-018-1888-8
  • 2 Ali, R.; Kuriqi, A.; Abubaker, S.; Kisi, O. Long-Term Trends and Seasonality Detection of the Observed Flow in Yangtze River Using Mann-Kendall and Sen’s Innovative Trend Method. Water 2019, 11, 1855.
  • 3 Alifujiang, Y.; Abuduwaili, J.; Maihemuti, B.; Emin, B.; Groll, M. Innovative Trend Analysis of Precipitation in the Lake Issyk-Kul Basin, Kyrgyzstan. Atmosphere 2020, 11, 332.
  • 4 Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020
  • 5 Cai, Y., & Hames, D. (2010). Minimum sample size determination for generalized extreme value distribution,communications in statistics. Simulation and Computation, 40(1), 87-98. doi: 10.1080/03610918.2010.530368
  • 6 Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015: Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?. J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1
  • 7 Chen, J., Brissette, F. P., Chaumont, D., and Braun, M. ( 2013), Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49, 4187– 4205, doi:10.1002/wrcr.20331.
  • 8 Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci. Rep. 4, 7093. doi:10.1038/srep07093
  • 9 Cheng, L., Phillips, T.J. & AghaKouchak, A. Non-stationary return levels of CMIP5 multi-model temperature extremes. Clim Dyn 44, 2947–2963 (2015). https://doi.org/10.1007/s00382-015-2625-y
  • 10 Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London.
  • 11 Collet L., Beevers, L., & Prudhomme C. (2017). Assessing the impact of climate change and extreme value uncertainty to extreme flows across Great Britain. Water, 9(2),103.
  • 12 Dai, A., 2006: Precipitation Characteristics in Eighteen Coupled Climate Models. J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1
  • 13 Fatih Kara, Ismail Yucel & Zuhal Akyurek (2016) Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling, Hydrological Sciences Journal, 61:14, 2481-2495, DOI: 10.1080/02626667.2015.1133911
  • 14 Gilleland, E., & Katz, R. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1-39. doi: 10.18637/jss.v072.i08
  • 15 Göran Lindström, Charlotta Pers, Jörgen Rosberg, Johan Strömqvist, Berit Arheimer; Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research 1 June 2010; 41 (3-4): 295–319. doi: https://doi.org/10.2166/nh.2010.007
  • 16 Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
  • 17 Güçlü, Y., Şişman, E. and Yeleğen, M. (2018), Climate change and FID curves. J. Flood Risk Manage, 11: S403-S418. doi:10.1111/jfr3.12229
  • 18 Haktanir, T., & Citakoglu, H. (2014). Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. Journal of Hydrologic Engineering, 19, 9. DOI: 10.1061/(ASCE)HE.1943-5584.0000973.
  • 19 Heo, J.-H.; Ahn, H.; Shin, J.-Y.; Kjeldsen, T.R.; Jeong, C. Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change. Water 2019, 11, 1475.
  • 20 Hosseinzadehtalaei, P., Tabari, H., & Willems, P. (2017). Precipitation intensity– duration–frequency curves for central Belgium with an ensemble of Eurocordex simulations, and associated uncertainties. Atmospheric Research, 200, 1-12. doi:10.1016/j.atmosres.2017.09.015
  • 21 Hundecha, Y., Arheimer, B., Donnelly, C., Pechlivanidis, I. (2016). A regional parameter estimation scheme for a pan-European multi-basin model. Journal of Hydrology: Regional Studies, Volume 6, June 2016, Pages 90-111. doi:10.1016/j.ejrh.2016.04.002
  • 22 IPCC, (2013). Climate change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • 23 Lazoglou, G.; Anagnostopoulou, C. An Overview of Statistical Methods for Studying the Extreme Rainfalls in Mediterranean. Proceedings 2017, 1, 681.
  • 24 Liew, S. C., Raghavan, S. V., & Liong, S.Y. (2014). How to construct future IDF curves, under changing climate, for sites with scarce rainfall records?. Hydrol. Process., 28, 3276–3287. doi:10.1002/hyp.9839
  • 25 Makkonen, L., and M. Tikanmäki. 2019. An improved method of extreme value analysis. Journal of Hydrology X 2:100012. doi:10.1016/j.hydroa.2018.100012. 
  • 26 Mendez, M.; Maathuis, B.; Hein-Griggs, D.; Alvarado-Gamboa, L.-F. Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica. Water 2020, 12, 482
  • 27 Ngai, S.T.; Tangang, F.; Juneng, L. Bias correction of global and regional simulated daily precipitation andsurface mean temperature over Southeast Asia using quantile mapping method. Glob. Planet. Chang.2017,149, 79–90.
  • 28 Osborn, T. J., Gosling, S., Wallace, C., & Dorling, S. (2015). The Water Cycle in a Changing Climate. 7th World Water Forum. Faircount Media Group, London, 14–19.
  • 29 Papagiannaki, K., Lagouvardos, K., Kotroni, V., & Bezes, A. (2015). Flash flood occurrence and relation to the rainfall hazard in a highly urbanized area, Nat. Hazards Earth Syst. Sci., 15, 1859-1871.
  • 30 Peck, A., Prodanovic, P., & Simonovic, S. P. (2012). Rainfall intensity duration frequency curves under climate change: city of London, Ontario, Canada. Can. Water Res. J., 37(3), 177–189. http://dx.doi.org/10.4296/cwrj2011-935
  • 31 Piani, C., O. Haerter, and E. Corpola (2010), Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187– 192.
  • 32 Pohl, B., Macron, C., & Monerie, P-A. (2017). Fewer rainy days and more extreme rainfall by the end of the century in Southern Africa. Scientific Reports, 7, 46466. doi: 10.1038/srep46466
  • 33 Sarhadi, A., & Soulis, E. D. (2017). Time-varying extreme rainfallintensity-duration-frequency curvesin a changing climate, Geophys. Res.Lett., 44. doi:10.1002/2016GL072201
  • 34 Sirdaş, S., Diriker, A.B., & Kumar, V. (2016). Heavy Precipitation Events in Marmara Region and connections with the North Atlantic and Arctic Oscillation Patterns. Environment and Natural Resources Research, 6, 1.
  • 35 Şen, Z. Innovative Trend Analysis Methodology. J. Hydrol. Eng. 2012, 17, 1042–1046.
  • 36 Themeßl, M. J., A. Gobiet, and A. Leuprecht (2010), Empirical statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530– 1544, doi:10.1002.joc.2168.
  • 37 Themeßl, M. J., A. Gobiet, and G. Heinrich (2011), Empirical‐statistical downscaling and error correction of regional climate models and its impact on the climate change signal, Clim. Change, 112(2), 449– 468, doi:10.1007/s10584‐011‐0224‐4.
  • 38 Trinh-Tuan L, Matsumoto J, Tangang FT, Juneng L, Cruz F, Narisma G, Santisirisomboon J, Phan-Van T, Gunawan D, Aldrian E, Ngo-Duc T (2019) Application of quantile mapping bias correction for mid-future precipitation projections over Vietnam. SOLA 15:1–6.
  • 39 Tuğba ÜSTÜN TOPAL,Aslı KORKUT,Pınar GÜLTÜRK.(2016).Kentsel Peyzaj Yapılarında Zemin Geçirimliliği Üzerine Bir Araştırma: Tekirdağ Örneği.Kastamonu Üniversitesi Orman Fakültesi Dergisi
  • 40 Umbricht, A., Fukutome, S., Liniger, M. A., Frei, C., & Appenzeller, C. (2013). Seasonal variation of daily extreme precipitation in Switzerland. Scientific Report. MeteoSwiss, 97, 122.
  • 41 url 1: http://www.tekirdag.bel.tr/cografya
  • 42 Vahedifard, F., Tehrani, F. S., Galavi, V., Ragno, E., & AghaKouchak, A. (2017). Resilience of MSE walls with marginal backfill under a changing climate: Quantitative assessment for extreme precipitation events. Journal of Geotechnical and Geoenvironmental Engineering, 143(9), 04017056‐1–04017056‐14
  • 43 Wang, J., You, S., Wu, Y., Zhang, Y., & Bin, S. (2016). A method of selecting the block size of bmm for estimating extreme loads in engineering vehicles. Mathematical Problems in Engineering. 1-9. 10.1155/2016/6372197.
  • 44 Willems, P. “Revision of Urban Drainage Design Rules after Assessment of Climate Change Impacts on Precipitation Extremes at Uccle, Belgium.” Journal of Hydrology, vol. 496, 2013, pp. 166–177., doi:10.1016/j.jhydrol.2013.05.037.
  • 45 Wuthiwongyothin S., Mili S., Phadungkarnlert N. (2020) A Study of Correcting Climate Model Daily Rainfall Product Using Quantile Mapping in Upper Ping River Basin, Thailand. In: Trung Viet N., Xiping D., Thanh Tung T. (eds) APAC 2019. APAC 2019. Springer, Singapore
  • 46 Zhou, Q., Arnbjerg-Nielsen, K., Mikkelsen, P. S., Nielsen, S. B., & Halsnæs, K. (2012). Urban drainage design and climate change adaptation decision making. Kgs. Lyngby: DTU Environment

Investigation of The Effect of Climate Change on Extreme Precipitation: Tekirdağ Case

Year 2020, , 136 - 161, 24.07.2020
https://doi.org/10.31807/tjwsm.746134

Abstract

This study examines the potential impacts of climate change on extreme precipitation in a specific location, Tekirdağ, Turkey. Trends in rainfall extremes for (1963-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 4, 5, 6, 8, 12, 18, 24 hours are determined by using 1:1 straight line method and Mann-Kendall trend test. Also, daily (24h) future projections for Tekirdağ region are assessed and bias corrected with Quantile Mapping method for the 2015-2050 period. Subsequently, observed and bias corrected daily (24h) time series are used for the Generalized Extreme Value analyses to quantify the potential changes with respect to observation period. Most of the observed time series show increasing trend tendency. Considering the projected data driven analyses results; for shorter return periods results show smaller variations while variability increase with the increasing return period. Depending on the models and Representative Concentration Pathways, there are different results for the future extreme rainfall; yet all results indicate an increasing extreme daily rainfall magnitude at Tekirdağ Province.

References

  • 1 Abbasnia, M., Toros, H. Analysis of long-term changes in extreme climatic indices: a case study of the Mediterranean climate, Marmara Region, Turkey. Pure Appl. Geophys. 175, 3861–3873 (2018). https://doi.org/10.1007/s00024-018-1888-8
  • 2 Ali, R.; Kuriqi, A.; Abubaker, S.; Kisi, O. Long-Term Trends and Seasonality Detection of the Observed Flow in Yangtze River Using Mann-Kendall and Sen’s Innovative Trend Method. Water 2019, 11, 1855.
  • 3 Alifujiang, Y.; Abuduwaili, J.; Maihemuti, B.; Emin, B.; Groll, M. Innovative Trend Analysis of Precipitation in the Lake Issyk-Kul Basin, Kyrgyzstan. Atmosphere 2020, 11, 332.
  • 4 Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020
  • 5 Cai, Y., & Hames, D. (2010). Minimum sample size determination for generalized extreme value distribution,communications in statistics. Simulation and Computation, 40(1), 87-98. doi: 10.1080/03610918.2010.530368
  • 6 Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015: Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?. J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1
  • 7 Chen, J., Brissette, F. P., Chaumont, D., and Braun, M. ( 2013), Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49, 4187– 4205, doi:10.1002/wrcr.20331.
  • 8 Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci. Rep. 4, 7093. doi:10.1038/srep07093
  • 9 Cheng, L., Phillips, T.J. & AghaKouchak, A. Non-stationary return levels of CMIP5 multi-model temperature extremes. Clim Dyn 44, 2947–2963 (2015). https://doi.org/10.1007/s00382-015-2625-y
  • 10 Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London.
  • 11 Collet L., Beevers, L., & Prudhomme C. (2017). Assessing the impact of climate change and extreme value uncertainty to extreme flows across Great Britain. Water, 9(2),103.
  • 12 Dai, A., 2006: Precipitation Characteristics in Eighteen Coupled Climate Models. J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1
  • 13 Fatih Kara, Ismail Yucel & Zuhal Akyurek (2016) Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling, Hydrological Sciences Journal, 61:14, 2481-2495, DOI: 10.1080/02626667.2015.1133911
  • 14 Gilleland, E., & Katz, R. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1-39. doi: 10.18637/jss.v072.i08
  • 15 Göran Lindström, Charlotta Pers, Jörgen Rosberg, Johan Strömqvist, Berit Arheimer; Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research 1 June 2010; 41 (3-4): 295–319. doi: https://doi.org/10.2166/nh.2010.007
  • 16 Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
  • 17 Güçlü, Y., Şişman, E. and Yeleğen, M. (2018), Climate change and FID curves. J. Flood Risk Manage, 11: S403-S418. doi:10.1111/jfr3.12229
  • 18 Haktanir, T., & Citakoglu, H. (2014). Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. Journal of Hydrologic Engineering, 19, 9. DOI: 10.1061/(ASCE)HE.1943-5584.0000973.
  • 19 Heo, J.-H.; Ahn, H.; Shin, J.-Y.; Kjeldsen, T.R.; Jeong, C. Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change. Water 2019, 11, 1475.
  • 20 Hosseinzadehtalaei, P., Tabari, H., & Willems, P. (2017). Precipitation intensity– duration–frequency curves for central Belgium with an ensemble of Eurocordex simulations, and associated uncertainties. Atmospheric Research, 200, 1-12. doi:10.1016/j.atmosres.2017.09.015
  • 21 Hundecha, Y., Arheimer, B., Donnelly, C., Pechlivanidis, I. (2016). A regional parameter estimation scheme for a pan-European multi-basin model. Journal of Hydrology: Regional Studies, Volume 6, June 2016, Pages 90-111. doi:10.1016/j.ejrh.2016.04.002
  • 22 IPCC, (2013). Climate change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • 23 Lazoglou, G.; Anagnostopoulou, C. An Overview of Statistical Methods for Studying the Extreme Rainfalls in Mediterranean. Proceedings 2017, 1, 681.
  • 24 Liew, S. C., Raghavan, S. V., & Liong, S.Y. (2014). How to construct future IDF curves, under changing climate, for sites with scarce rainfall records?. Hydrol. Process., 28, 3276–3287. doi:10.1002/hyp.9839
  • 25 Makkonen, L., and M. Tikanmäki. 2019. An improved method of extreme value analysis. Journal of Hydrology X 2:100012. doi:10.1016/j.hydroa.2018.100012. 
  • 26 Mendez, M.; Maathuis, B.; Hein-Griggs, D.; Alvarado-Gamboa, L.-F. Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica. Water 2020, 12, 482
  • 27 Ngai, S.T.; Tangang, F.; Juneng, L. Bias correction of global and regional simulated daily precipitation andsurface mean temperature over Southeast Asia using quantile mapping method. Glob. Planet. Chang.2017,149, 79–90.
  • 28 Osborn, T. J., Gosling, S., Wallace, C., & Dorling, S. (2015). The Water Cycle in a Changing Climate. 7th World Water Forum. Faircount Media Group, London, 14–19.
  • 29 Papagiannaki, K., Lagouvardos, K., Kotroni, V., & Bezes, A. (2015). Flash flood occurrence and relation to the rainfall hazard in a highly urbanized area, Nat. Hazards Earth Syst. Sci., 15, 1859-1871.
  • 30 Peck, A., Prodanovic, P., & Simonovic, S. P. (2012). Rainfall intensity duration frequency curves under climate change: city of London, Ontario, Canada. Can. Water Res. J., 37(3), 177–189. http://dx.doi.org/10.4296/cwrj2011-935
  • 31 Piani, C., O. Haerter, and E. Corpola (2010), Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187– 192.
  • 32 Pohl, B., Macron, C., & Monerie, P-A. (2017). Fewer rainy days and more extreme rainfall by the end of the century in Southern Africa. Scientific Reports, 7, 46466. doi: 10.1038/srep46466
  • 33 Sarhadi, A., & Soulis, E. D. (2017). Time-varying extreme rainfallintensity-duration-frequency curvesin a changing climate, Geophys. Res.Lett., 44. doi:10.1002/2016GL072201
  • 34 Sirdaş, S., Diriker, A.B., & Kumar, V. (2016). Heavy Precipitation Events in Marmara Region and connections with the North Atlantic and Arctic Oscillation Patterns. Environment and Natural Resources Research, 6, 1.
  • 35 Şen, Z. Innovative Trend Analysis Methodology. J. Hydrol. Eng. 2012, 17, 1042–1046.
  • 36 Themeßl, M. J., A. Gobiet, and A. Leuprecht (2010), Empirical statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530– 1544, doi:10.1002.joc.2168.
  • 37 Themeßl, M. J., A. Gobiet, and G. Heinrich (2011), Empirical‐statistical downscaling and error correction of regional climate models and its impact on the climate change signal, Clim. Change, 112(2), 449– 468, doi:10.1007/s10584‐011‐0224‐4.
  • 38 Trinh-Tuan L, Matsumoto J, Tangang FT, Juneng L, Cruz F, Narisma G, Santisirisomboon J, Phan-Van T, Gunawan D, Aldrian E, Ngo-Duc T (2019) Application of quantile mapping bias correction for mid-future precipitation projections over Vietnam. SOLA 15:1–6.
  • 39 Tuğba ÜSTÜN TOPAL,Aslı KORKUT,Pınar GÜLTÜRK.(2016).Kentsel Peyzaj Yapılarında Zemin Geçirimliliği Üzerine Bir Araştırma: Tekirdağ Örneği.Kastamonu Üniversitesi Orman Fakültesi Dergisi
  • 40 Umbricht, A., Fukutome, S., Liniger, M. A., Frei, C., & Appenzeller, C. (2013). Seasonal variation of daily extreme precipitation in Switzerland. Scientific Report. MeteoSwiss, 97, 122.
  • 41 url 1: http://www.tekirdag.bel.tr/cografya
  • 42 Vahedifard, F., Tehrani, F. S., Galavi, V., Ragno, E., & AghaKouchak, A. (2017). Resilience of MSE walls with marginal backfill under a changing climate: Quantitative assessment for extreme precipitation events. Journal of Geotechnical and Geoenvironmental Engineering, 143(9), 04017056‐1–04017056‐14
  • 43 Wang, J., You, S., Wu, Y., Zhang, Y., & Bin, S. (2016). A method of selecting the block size of bmm for estimating extreme loads in engineering vehicles. Mathematical Problems in Engineering. 1-9. 10.1155/2016/6372197.
  • 44 Willems, P. “Revision of Urban Drainage Design Rules after Assessment of Climate Change Impacts on Precipitation Extremes at Uccle, Belgium.” Journal of Hydrology, vol. 496, 2013, pp. 166–177., doi:10.1016/j.jhydrol.2013.05.037.
  • 45 Wuthiwongyothin S., Mili S., Phadungkarnlert N. (2020) A Study of Correcting Climate Model Daily Rainfall Product Using Quantile Mapping in Upper Ping River Basin, Thailand. In: Trung Viet N., Xiping D., Thanh Tung T. (eds) APAC 2019. APAC 2019. Springer, Singapore
  • 46 Zhou, Q., Arnbjerg-Nielsen, K., Mikkelsen, P. S., Nielsen, S. B., & Halsnæs, K. (2012). Urban drainage design and climate change adaptation decision making. Kgs. Lyngby: DTU Environment
There are 46 citations in total.

Details

Primary Language English
Journal Section TURKISH JOURNAL OF WATER SCIENCES AND MANAGEMENT
Authors

Sertac Oruc

Publication Date July 24, 2020
Published in Issue Year 2020

Cite

APA Oruc, S. (2020). Investigation of The Effect of Climate Change on Extreme Precipitation: Tekirdağ Case. Turkish Journal of Water Science and Management, 4(2), 136-161. https://doi.org/10.31807/tjwsm.746134