Research Article
BibTex RIS Cite

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

Year 2022, Volume: 33 Issue: 2, 11749 - 11778, 01.03.2022
https://doi.org/10.18400/tekderg.714980

Abstract

This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend.

References

  • [1] 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.
  • [2] IPCC, (2014a). Climate change 2014. Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • [3] IPCC, (2014b). Climate change 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • [4] 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.
  • [5] 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
  • [6] 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
  • [7] 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.
  • [8] 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
  • [9] 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
  • [10] Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., & Kossin, J. (2012). Changes in climate extremes and their impacts on the natural physical environment. Cambridge University Press, Cambridge, UK, and New York, NY.
  • [11] Ozturk, T., Turp, M. T., Türkeş, M., & Kurnaz, M. L. (2018). Future projections of temperature and precipitation climatology for CORDEX-MENA domain using RegCM4.4. Atmospheric Research, 206, 87-107. doi:10.1016/j.atmosres.2018.02.00
  • [12] Kusunoki, S., (2017). Future changes in global precipitation projected by the atmospheric model MRI-AGCM3.2H with a 60-km size. Atmosphere, 8, 93
  • [13] Buttstadt, M., & Schneider, C. (2014). Climate change signal of future climate projections for Aachen, Germany, in terms of temperature and precipitation. Mareike, 68(2), 71-83.
  • [14] Meld, (2013). Climate change adaptation in Norway Meld. St. 33 (2012–2013) Report to the Storting (white paper) Recommendation of 7. May 2013 from the Ministry of the Environment, approved in the Council of State the same day. (White paper from the Stoltenberg II Government).
  • [15] IPCC, (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change.
  • [16] Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R. G., Robert, B., Wolfgang, K., Gerardo, B., Yasushi, H., Kiyoshi, T., & Boris, S. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1-28. doi: 10.1080/02626667.2013.857411
  • [17] Li, J., Johnson, F., Evans, J., & Sharma, A. (2017). A comparison of methods to estimate future sub-daily design rainfall. Advances in Water Resources, 110. 10.1016/j.advwatres.2017.10.020.
  • [18] Hettiarachchi, S., Wasko, C., & Sharma, A. (2018). Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns, Hydrol. Earth Syst. 22, 2041-2056.
  • [19] 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
  • [20] Sarhadi, A., & Soulis, E. D. (2017). Time-varying extreme rainfallintensity-duration-frequency curvesin a changing climate, Geophys. Res.Lett., 44. doi:10.1002/2016GL072201
  • [21] 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
  • [22] 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
  • [23] Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Gregersen, I.B., Madsen, H., Nguyen, V.T.V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, London, UK.
  • [24] Yuan, X.-C., Wei, Y.-M., Wang, B., and Mi, Z. (2017). Risk management of extreme events under climate change, J. Clean. Prod., 166,1169–1174, https://doi.org/10.1016/j.jclepro.2017.07.209, 2017
  • [25] Yoon, J. H., Wang, S. Y., Gillies, R. R., Kravitz, B., Hipps, L., & Rasch, P. J. (2015). Increasing water cycle extremes in California and relation to ENSO cycle under global warming. Nat. Commun, 6, 8657 doi: 10.1038/ncomms9657
  • [26] Simonovic, S. P. (2012). Floods in a changing climate: Risk management, 194. Cambridge: Cambridge University Pres
  • [27] Huntington, T. G. (2006). Evidence for ıntensification of the global water cycle: review and synthesis. Journal of Hydrology, 319, 83-95. http://dx.doi.org/10.1016/j.jhydrol.2005.07.003
  • [28] Abdellatif, M., Atherton, W., & Alkhaddar, R. (2013). Application of the stochastic model for temporal rainfall disaggregation for hydrological studies in North Western England. Journal of Hydroinformatics, 15(2), 555-567.
  • [29] Harisaweni, Z., & Fadhilah, Y. (2016). The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia. Sains Malaysiana, 45 (1). 87-97.
  • [30] Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016b). HyetosMinute, A package for temporal stochastic simulation of rainfall at fine time scales, Version 2.0.
  • [31] SMS, (2020). Republic of Turkey, the ministry of forestry and water affairs, state meteorological service, State of the Climate in Turkey in 2019, January 2020
  • [32] Sensoy, S., Türkoğlu, N., Akçakaya, A., Ulupınar, Y., Ekici, M., Demircan, M., Atay, H., Tüvan, A., & Demirbaş, H. (2013). Trends in Turkey climate indices from 1960 to 2010, 6th Atmospheric Science Symposium, 24 - 26 April 2013, ITU, Istanbul, Turkey.
  • [33] Danandeh Mehr, A. and Kahya, E. (2016). Climate change impacts on catchment-scale extreme rainfall variability: Case Study of Rize Province, Turkey. Journal of Hydrologic Engineering, 10.1061/(ASCE)HE.1943-5584.0001477, 05016037.
  • [34] 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.
  • [35] Yilmaz, A. G. (2015). The effects of climate change on historical and future extreme rainfall in Antalya, Turkey. Hydrological Sciences Journal, 60(12), 2148-2162. doi: 10.1080/02626667.2014.945455
  • [36] Tayanç, M., İm, U., Doğruel, M., & Karaca, M. (2009). Climate change in Turkey for the last half century. Climatic Change, 94, 483-502.
  • [37] Turunçoğlu, U. U., Türkeş, M., Bozkurt, D., Önol, B., Şen, Ö. L., & Dalfes, H. N. (2018). The Soils of Turkey. World Soils Book Series. Springer, Cham.
  • [38] WRP, (2016). Republic of Turkey, the ministry of forestry and water affairs, general directorate of water management. Climate Change Impacts On Water Resources Project.
  • [39] Aziz, R. (2018). Impacts Of Climate Nonstationarities On Hydroclimatological Variables In Turkey, PhD. Dissertation. Middle East Technical University
  • [40] Aziz, R.,Yucel, I., Yozgatligil, C. (2020). Nonstationarity impacts on frequency analysis of yearly and seasonal extreme temperature in Turkey. Atmospheric Research, 238, doi.org/10.1016/j.atmosres.2020.104875
  • [41] Maraun, D. (2016). Bias correcting climate change simulations—a critical review. Curr Clim Change Rep 2(4):211–220. doi: 10.1007/s40641-016-0050-x
  • [42] Willkofer, F.; Schmid, F.J.; Komischke, H.; Korck, J.; Braun, M.; Ludwig, R. The impact of bias correcting regional climate model results on hydrological indicators for Bavarian catchments. J. Hydrol. Reg. Stud. 2018, 19, 25–41
  • [43] Demi̇rcan, M , Gürkan, H , Eski̇oğlu, O , Arabacı, H , Coşkun, M . (2017). Climate Change Projections for Turkey: Three Models and Two Scenarios . Turkish Journal of Water Science and Management , 1 (1) , 22-43 . DOI: 10.31807/tjwsm.297183
  • [44] Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016a). A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980-992.
  • [45] Kaczmarska, J. M., Isham, V. S., & Northrop, P. (2015). Local generalised method of moments: an application to point process‐based rainfall models. Environmetrics, 26, 312–325. doi: 10.1002/env.2338
  • [46] Ritschel, C., Ulbrich, U., Névir, P., & Rust, H. W. (2017). Precipitation extremes on multiple timescales – Bartlett–Lewis rectangular pulse model and intensity–duration–frequency curves. Hydrology and Earth System Sciences, 21(12), 6501.
  • [47] Yılmaz, E. (2013). Ankara Şehrinde Isı Adası Oluşumu. (Doktora Tezi), Ankara Üniversitesi, Sosyal Bilimler Enstitüsü, Ankara.
  • [48] Governorate of Ankara, (2018). Geography and demographics. [online] Available at: http://eng.ankara.gov.tr/geography-and-demographics. [Accessed 01 June 2018].
  • [49] Batuman, B. (2013). City profile: Ankara. Cities, 31. 578–590. 10.1016/j.cities.2012.05.016
  • [50] Sensoy, S., Turkoglu, N., Cicek I., Demircan, M., Arabacı, H., Bölük, E., 2014, Urbanization Effect on Trends of Extreme Temperature Indices in Ankara, 7th Atmospheric Science Symposium, 28-30 April 2015, İstanbul
  • [51] Çiçek, I., & Turkoglu, N.. (2005). Urban effects on precipitation in Ankara. Atmósfera, 18(3), 173-187.
  • [52] Kossieris, P., Koutsoyiannis, D., Onof, C., Tyralis, H., & Efstratiadis, A. (2012). HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales. European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union.
  • [53] Rodriguez-Iturbe, I., Cox, D. R., & Isham, V. (1987a). Some models for rainfall based on stochastic point processes. Proceedings of the Royal Society, 410, 269–288.
  • [54] Rodriguez-Iturbe, I., Febres de Power, B., & Valdes, J. B. (1987b). Rectangular pulses point process models for rainfall: analysis of empirical data. Journal of Geophysical Research, 92, 9645–9656.
  • [55] Villani, V., Di Serafino, D., Guido, R., & Mercogliano, P. (2016). Stochastic models for the disaggregation of precipitation time series on sub-daily scale: identification of parameters by global optimization. CMCC Research Paper No. RP0256. http://dx.doi.org/10.2139/ssrn.2602889
  • [56] Lu, Y., & Qin, X. S. (2012). Comparison of stochastic point process models of rainfall in Singapore. Proceedings of 2012 International Conference of World Academy on Science, Engineering and Technology (WASET), 68, 1234-1238.
  • [57] Rozos, E., Efstratiadis, A., Nalbantis, I., & Koutsoyiannis, D. (2004).Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49(5), 819-842.
  • [58] Efstratiadis, A., and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, (2002). Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423-1428, International Water Association, (http: //itia.ntua.gr/el/docinfo/524/)
  • [59] Yilmaz, A. G., & Perera, B. J. C. (2014). Extreme rainfall non-stationarity investigation and intensity-frequency-duration relationship. J. Hydrol. Eng. 19, 1160-1172. doi: 10.1061/(ASCE)HE.1943-5584.0000878
  • [60] Mann, H. B. (1945). Non-parametric tests against trend, Econometrica, 13,163-171.
  • [61] Kendall, M. G. (1975). Rank correlation methods, 4th edition, Charles Griffin, London.
  • [62] Gilbert, R. O. (1987). Statistical Methods for Environmental Pollution Monitoring. Wiley, NY.
  • [63] Onyutha, C., Tabari, H., Taye, M. T., Nyandwaro, G. N., & Willems, P. (2015). Analyses of rainfall trends in the Nile River basin. J Hydro Environ Res, 13, 36–51.
  • [64] Yucel, I., Güventürk, A. and Sen, O. L. (2014), Climate change impacts on snowmelt runoff for mountainous transboundary basins in eastern Turkey. Int. J. Climatol., 35: 215-228. doi:10.1002/joc.3974
  • [65] 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.
  • [66] 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.
  • [67] Coles, S. G., & Sparks, R. S. J. (2006). Extreme value methods for modelling historical series of large volcanic magnitudes. Chapter 5, Statistics in Volcanology.
  • [68] 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.
  • [69] 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
  • [70] Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London.
  • [71] 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
  • [72] Bayazit, M. (2015). Nonstationarity of hydrological records and recent trends in trend analysis: A State-of-the-art Review. Environmental Processes, 2, 527-542.
  • [73] Pohlert T. (2016). Non-Parametric Trend Tests and Change-Point Detection. R package Version 0.2.0.
  • [74] Gül, G., Aşıkoğlu, Ö., Gül, A., Gülçem, Y. F., & Benzeden, E. (2014). Nonstationarity in flood time series. Journal of Hydrologic Engineering, 19, 1349-1360
  • [75] Šraj, M., Viglione, A., Parajka, J., & Blöschl, G. (2016). The influence of non-stationarity in extreme hydrological events on flood frequency estimation. Journal of Hydrology and Hydromechanics, 64, 426-437
  • [76] Cheng, L. (2014). Frameworks for univariate and multivariate non-stationary analysis of climatic extremes, PhD. Dissertation, UC Irvine.
  • [77] Wang, Y., & Liu, Q. (2006). Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships. Fish. Res. 77, 220-225.
  • [78] Sienz, F., Schneidereit, A., Blender, R., Fraedrich, K., & Lunkeit, F. (2010). Extreme value statistics for North Atlantic cyclones. Tellus A, 62(4), 347–360.
  • [79] Alam, S. (2014). Construction of the intensity-duration-frequency (idf) curves under climate change. Master of Science, University of Saskatchewan.
  • [80] DeGaetano, A. T., & Castellano, C. M. (2017). Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Climate Services, 5, 23-35.
  • [81] Fadhel, S., Rico-Ramirez, M. A., & Han, D. (2017). Uncertainty of intensity duration–frequency (IDF) curves due to varied climate baseline periods. Journal of Hydrology, 547, 600-612.
  • [82] Kara, F. (2014). Effects of climate change on water resources in Omerlı basin. PhD. Dissertation. Middle East Technical University
  • [83] Simonovic, S. P., Schardong, A., & Sandink, D. (2017). Mapping extreme rainfall statistics for canada under climate change using updated intensity-duration-frequency curves. ASCE Journal of Water Resources Planning and Management, 143(3), 04016078-1 -04016078-12.
  • [84] Arnbjerg-Nielsen, K., Willems, P., Olsson, J., Beecham, S., Pathirana, A., Gregersen, I. B., & Nguyen, V-T. V. (2013). Impacts of climate change on rainfall extremes and urban drainage systems: A review. Water Science and Technology, 68(1), 16-28. Doi: 10.2166/wst.2013.251.
  • [85] Almazroui, M., Şen, Z., Mohorji, A.M. et al. Impacts of Climate Change on Water Engineering Structures in Arid Regions: Case Studies in Turkey and Saudi Arabia. Earth Syst Environ 3, 43–57 (2019). https://doi.org/10.1007/s41748-018-0082-6
  • [86] Şen, K , Aksu, H . (2021). İstanbul İçin Standart Süreli Gözlenen En Büyük Yağışların Eğilimleri. Teknik Dergi , 32 (1) , 1-2 . DOI: 10.18400/tekderg.647558

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

Year 2022, Volume: 33 Issue: 2, 11749 - 11778, 01.03.2022
https://doi.org/10.18400/tekderg.714980

Abstract

This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend.

References

  • [1] 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.
  • [2] IPCC, (2014a). Climate change 2014. Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • [3] IPCC, (2014b). Climate change 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • [4] 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.
  • [5] 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
  • [6] 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
  • [7] 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.
  • [8] 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
  • [9] 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
  • [10] Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., & Kossin, J. (2012). Changes in climate extremes and their impacts on the natural physical environment. Cambridge University Press, Cambridge, UK, and New York, NY.
  • [11] Ozturk, T., Turp, M. T., Türkeş, M., & Kurnaz, M. L. (2018). Future projections of temperature and precipitation climatology for CORDEX-MENA domain using RegCM4.4. Atmospheric Research, 206, 87-107. doi:10.1016/j.atmosres.2018.02.00
  • [12] Kusunoki, S., (2017). Future changes in global precipitation projected by the atmospheric model MRI-AGCM3.2H with a 60-km size. Atmosphere, 8, 93
  • [13] Buttstadt, M., & Schneider, C. (2014). Climate change signal of future climate projections for Aachen, Germany, in terms of temperature and precipitation. Mareike, 68(2), 71-83.
  • [14] Meld, (2013). Climate change adaptation in Norway Meld. St. 33 (2012–2013) Report to the Storting (white paper) Recommendation of 7. May 2013 from the Ministry of the Environment, approved in the Council of State the same day. (White paper from the Stoltenberg II Government).
  • [15] IPCC, (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change.
  • [16] Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R. G., Robert, B., Wolfgang, K., Gerardo, B., Yasushi, H., Kiyoshi, T., & Boris, S. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1-28. doi: 10.1080/02626667.2013.857411
  • [17] Li, J., Johnson, F., Evans, J., & Sharma, A. (2017). A comparison of methods to estimate future sub-daily design rainfall. Advances in Water Resources, 110. 10.1016/j.advwatres.2017.10.020.
  • [18] Hettiarachchi, S., Wasko, C., & Sharma, A. (2018). Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns, Hydrol. Earth Syst. 22, 2041-2056.
  • [19] 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
  • [20] Sarhadi, A., & Soulis, E. D. (2017). Time-varying extreme rainfallintensity-duration-frequency curvesin a changing climate, Geophys. Res.Lett., 44. doi:10.1002/2016GL072201
  • [21] 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
  • [22] 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
  • [23] Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Gregersen, I.B., Madsen, H., Nguyen, V.T.V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, London, UK.
  • [24] Yuan, X.-C., Wei, Y.-M., Wang, B., and Mi, Z. (2017). Risk management of extreme events under climate change, J. Clean. Prod., 166,1169–1174, https://doi.org/10.1016/j.jclepro.2017.07.209, 2017
  • [25] Yoon, J. H., Wang, S. Y., Gillies, R. R., Kravitz, B., Hipps, L., & Rasch, P. J. (2015). Increasing water cycle extremes in California and relation to ENSO cycle under global warming. Nat. Commun, 6, 8657 doi: 10.1038/ncomms9657
  • [26] Simonovic, S. P. (2012). Floods in a changing climate: Risk management, 194. Cambridge: Cambridge University Pres
  • [27] Huntington, T. G. (2006). Evidence for ıntensification of the global water cycle: review and synthesis. Journal of Hydrology, 319, 83-95. http://dx.doi.org/10.1016/j.jhydrol.2005.07.003
  • [28] Abdellatif, M., Atherton, W., & Alkhaddar, R. (2013). Application of the stochastic model for temporal rainfall disaggregation for hydrological studies in North Western England. Journal of Hydroinformatics, 15(2), 555-567.
  • [29] Harisaweni, Z., & Fadhilah, Y. (2016). The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia. Sains Malaysiana, 45 (1). 87-97.
  • [30] Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016b). HyetosMinute, A package for temporal stochastic simulation of rainfall at fine time scales, Version 2.0.
  • [31] SMS, (2020). Republic of Turkey, the ministry of forestry and water affairs, state meteorological service, State of the Climate in Turkey in 2019, January 2020
  • [32] Sensoy, S., Türkoğlu, N., Akçakaya, A., Ulupınar, Y., Ekici, M., Demircan, M., Atay, H., Tüvan, A., & Demirbaş, H. (2013). Trends in Turkey climate indices from 1960 to 2010, 6th Atmospheric Science Symposium, 24 - 26 April 2013, ITU, Istanbul, Turkey.
  • [33] Danandeh Mehr, A. and Kahya, E. (2016). Climate change impacts on catchment-scale extreme rainfall variability: Case Study of Rize Province, Turkey. Journal of Hydrologic Engineering, 10.1061/(ASCE)HE.1943-5584.0001477, 05016037.
  • [34] 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.
  • [35] Yilmaz, A. G. (2015). The effects of climate change on historical and future extreme rainfall in Antalya, Turkey. Hydrological Sciences Journal, 60(12), 2148-2162. doi: 10.1080/02626667.2014.945455
  • [36] Tayanç, M., İm, U., Doğruel, M., & Karaca, M. (2009). Climate change in Turkey for the last half century. Climatic Change, 94, 483-502.
  • [37] Turunçoğlu, U. U., Türkeş, M., Bozkurt, D., Önol, B., Şen, Ö. L., & Dalfes, H. N. (2018). The Soils of Turkey. World Soils Book Series. Springer, Cham.
  • [38] WRP, (2016). Republic of Turkey, the ministry of forestry and water affairs, general directorate of water management. Climate Change Impacts On Water Resources Project.
  • [39] Aziz, R. (2018). Impacts Of Climate Nonstationarities On Hydroclimatological Variables In Turkey, PhD. Dissertation. Middle East Technical University
  • [40] Aziz, R.,Yucel, I., Yozgatligil, C. (2020). Nonstationarity impacts on frequency analysis of yearly and seasonal extreme temperature in Turkey. Atmospheric Research, 238, doi.org/10.1016/j.atmosres.2020.104875
  • [41] Maraun, D. (2016). Bias correcting climate change simulations—a critical review. Curr Clim Change Rep 2(4):211–220. doi: 10.1007/s40641-016-0050-x
  • [42] Willkofer, F.; Schmid, F.J.; Komischke, H.; Korck, J.; Braun, M.; Ludwig, R. The impact of bias correcting regional climate model results on hydrological indicators for Bavarian catchments. J. Hydrol. Reg. Stud. 2018, 19, 25–41
  • [43] Demi̇rcan, M , Gürkan, H , Eski̇oğlu, O , Arabacı, H , Coşkun, M . (2017). Climate Change Projections for Turkey: Three Models and Two Scenarios . Turkish Journal of Water Science and Management , 1 (1) , 22-43 . DOI: 10.31807/tjwsm.297183
  • [44] Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016a). A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980-992.
  • [45] Kaczmarska, J. M., Isham, V. S., & Northrop, P. (2015). Local generalised method of moments: an application to point process‐based rainfall models. Environmetrics, 26, 312–325. doi: 10.1002/env.2338
  • [46] Ritschel, C., Ulbrich, U., Névir, P., & Rust, H. W. (2017). Precipitation extremes on multiple timescales – Bartlett–Lewis rectangular pulse model and intensity–duration–frequency curves. Hydrology and Earth System Sciences, 21(12), 6501.
  • [47] Yılmaz, E. (2013). Ankara Şehrinde Isı Adası Oluşumu. (Doktora Tezi), Ankara Üniversitesi, Sosyal Bilimler Enstitüsü, Ankara.
  • [48] Governorate of Ankara, (2018). Geography and demographics. [online] Available at: http://eng.ankara.gov.tr/geography-and-demographics. [Accessed 01 June 2018].
  • [49] Batuman, B. (2013). City profile: Ankara. Cities, 31. 578–590. 10.1016/j.cities.2012.05.016
  • [50] Sensoy, S., Turkoglu, N., Cicek I., Demircan, M., Arabacı, H., Bölük, E., 2014, Urbanization Effect on Trends of Extreme Temperature Indices in Ankara, 7th Atmospheric Science Symposium, 28-30 April 2015, İstanbul
  • [51] Çiçek, I., & Turkoglu, N.. (2005). Urban effects on precipitation in Ankara. Atmósfera, 18(3), 173-187.
  • [52] Kossieris, P., Koutsoyiannis, D., Onof, C., Tyralis, H., & Efstratiadis, A. (2012). HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales. European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union.
  • [53] Rodriguez-Iturbe, I., Cox, D. R., & Isham, V. (1987a). Some models for rainfall based on stochastic point processes. Proceedings of the Royal Society, 410, 269–288.
  • [54] Rodriguez-Iturbe, I., Febres de Power, B., & Valdes, J. B. (1987b). Rectangular pulses point process models for rainfall: analysis of empirical data. Journal of Geophysical Research, 92, 9645–9656.
  • [55] Villani, V., Di Serafino, D., Guido, R., & Mercogliano, P. (2016). Stochastic models for the disaggregation of precipitation time series on sub-daily scale: identification of parameters by global optimization. CMCC Research Paper No. RP0256. http://dx.doi.org/10.2139/ssrn.2602889
  • [56] Lu, Y., & Qin, X. S. (2012). Comparison of stochastic point process models of rainfall in Singapore. Proceedings of 2012 International Conference of World Academy on Science, Engineering and Technology (WASET), 68, 1234-1238.
  • [57] Rozos, E., Efstratiadis, A., Nalbantis, I., & Koutsoyiannis, D. (2004).Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49(5), 819-842.
  • [58] Efstratiadis, A., and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, (2002). Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423-1428, International Water Association, (http: //itia.ntua.gr/el/docinfo/524/)
  • [59] Yilmaz, A. G., & Perera, B. J. C. (2014). Extreme rainfall non-stationarity investigation and intensity-frequency-duration relationship. J. Hydrol. Eng. 19, 1160-1172. doi: 10.1061/(ASCE)HE.1943-5584.0000878
  • [60] Mann, H. B. (1945). Non-parametric tests against trend, Econometrica, 13,163-171.
  • [61] Kendall, M. G. (1975). Rank correlation methods, 4th edition, Charles Griffin, London.
  • [62] Gilbert, R. O. (1987). Statistical Methods for Environmental Pollution Monitoring. Wiley, NY.
  • [63] Onyutha, C., Tabari, H., Taye, M. T., Nyandwaro, G. N., & Willems, P. (2015). Analyses of rainfall trends in the Nile River basin. J Hydro Environ Res, 13, 36–51.
  • [64] Yucel, I., Güventürk, A. and Sen, O. L. (2014), Climate change impacts on snowmelt runoff for mountainous transboundary basins in eastern Turkey. Int. J. Climatol., 35: 215-228. doi:10.1002/joc.3974
  • [65] 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.
  • [66] 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.
  • [67] Coles, S. G., & Sparks, R. S. J. (2006). Extreme value methods for modelling historical series of large volcanic magnitudes. Chapter 5, Statistics in Volcanology.
  • [68] 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.
  • [69] 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
  • [70] Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London.
  • [71] 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
  • [72] Bayazit, M. (2015). Nonstationarity of hydrological records and recent trends in trend analysis: A State-of-the-art Review. Environmental Processes, 2, 527-542.
  • [73] Pohlert T. (2016). Non-Parametric Trend Tests and Change-Point Detection. R package Version 0.2.0.
  • [74] Gül, G., Aşıkoğlu, Ö., Gül, A., Gülçem, Y. F., & Benzeden, E. (2014). Nonstationarity in flood time series. Journal of Hydrologic Engineering, 19, 1349-1360
  • [75] Šraj, M., Viglione, A., Parajka, J., & Blöschl, G. (2016). The influence of non-stationarity in extreme hydrological events on flood frequency estimation. Journal of Hydrology and Hydromechanics, 64, 426-437
  • [76] Cheng, L. (2014). Frameworks for univariate and multivariate non-stationary analysis of climatic extremes, PhD. Dissertation, UC Irvine.
  • [77] Wang, Y., & Liu, Q. (2006). Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships. Fish. Res. 77, 220-225.
  • [78] Sienz, F., Schneidereit, A., Blender, R., Fraedrich, K., & Lunkeit, F. (2010). Extreme value statistics for North Atlantic cyclones. Tellus A, 62(4), 347–360.
  • [79] Alam, S. (2014). Construction of the intensity-duration-frequency (idf) curves under climate change. Master of Science, University of Saskatchewan.
  • [80] DeGaetano, A. T., & Castellano, C. M. (2017). Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Climate Services, 5, 23-35.
  • [81] Fadhel, S., Rico-Ramirez, M. A., & Han, D. (2017). Uncertainty of intensity duration–frequency (IDF) curves due to varied climate baseline periods. Journal of Hydrology, 547, 600-612.
  • [82] Kara, F. (2014). Effects of climate change on water resources in Omerlı basin. PhD. Dissertation. Middle East Technical University
  • [83] Simonovic, S. P., Schardong, A., & Sandink, D. (2017). Mapping extreme rainfall statistics for canada under climate change using updated intensity-duration-frequency curves. ASCE Journal of Water Resources Planning and Management, 143(3), 04016078-1 -04016078-12.
  • [84] Arnbjerg-Nielsen, K., Willems, P., Olsson, J., Beecham, S., Pathirana, A., Gregersen, I. B., & Nguyen, V-T. V. (2013). Impacts of climate change on rainfall extremes and urban drainage systems: A review. Water Science and Technology, 68(1), 16-28. Doi: 10.2166/wst.2013.251.
  • [85] Almazroui, M., Şen, Z., Mohorji, A.M. et al. Impacts of Climate Change on Water Engineering Structures in Arid Regions: Case Studies in Turkey and Saudi Arabia. Earth Syst Environ 3, 43–57 (2019). https://doi.org/10.1007/s41748-018-0082-6
  • [86] Şen, K , Aksu, H . (2021). İstanbul İçin Standart Süreli Gözlenen En Büyük Yağışların Eğilimleri. Teknik Dergi , 32 (1) , 1-2 . DOI: 10.18400/tekderg.647558
There are 86 citations in total.

Details

Primary Language English
Subjects Civil Engineering
Journal Section Articles
Authors

Sertac Oruc 0000-0003-2906-0771

İsmail Yücel 0000-0001-9073-9324

Ayşen Yılmaz 0000-0001-9341-4832

Publication Date March 1, 2022
Submission Date April 6, 2020
Published in Issue Year 2022 Volume: 33 Issue: 2

Cite

APA Oruc, S., Yücel, İ., & Yılmaz, A. (2022). Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case. Teknik Dergi, 33(2), 11749-11778. https://doi.org/10.18400/tekderg.714980
AMA Oruc S, Yücel İ, Yılmaz A. Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case. Teknik Dergi. March 2022;33(2):11749-11778. doi:10.18400/tekderg.714980
Chicago Oruc, Sertac, İsmail Yücel, and Ayşen Yılmaz. “Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case”. Teknik Dergi 33, no. 2 (March 2022): 11749-78. https://doi.org/10.18400/tekderg.714980.
EndNote Oruc S, Yücel İ, Yılmaz A (March 1, 2022) Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case. Teknik Dergi 33 2 11749–11778.
IEEE S. Oruc, İ. Yücel, and A. Yılmaz, “Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case”, Teknik Dergi, vol. 33, no. 2, pp. 11749–11778, 2022, doi: 10.18400/tekderg.714980.
ISNAD Oruc, Sertac et al. “Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case”. Teknik Dergi 33/2 (March 2022), 11749-11778. https://doi.org/10.18400/tekderg.714980.
JAMA Oruc S, Yücel İ, Yılmaz A. Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case. Teknik Dergi. 2022;33:11749–11778.
MLA Oruc, Sertac et al. “Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case”. Teknik Dergi, vol. 33, no. 2, 2022, pp. 11749-78, doi:10.18400/tekderg.714980.
Vancouver Oruc S, Yücel İ, Yılmaz A. Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case. Teknik Dergi. 2022;33(2):11749-78.