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Quantile trends of subhourly extreme rainfall: Marmara Region, Turkey

  • Research Article - Hydrology
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Abstract

Global climate change will probably cause intensification of the hydrologic cycle, which can lead to alterations in extreme precipitation properties. In this study, we investigated the trend of 5-, 10-, 15-, and 30-min annual maximum rainfall series at 12 stations in the Marmara Region, Turkey, using quantile regression. The data ranges were from 46 to 71 years long. Five quantiles were used to examine the extreme rainfall series, and their quantile regression parameters were calculated. The results show that quantile regression is a powerful tool to compute trends with a more inferential context, which was validated with the notable differences between the trends at chosen quantiles and the classical ordinary least squares method. Concerning the problem of the analysis of climate trends, the quantile regression method seems to provide a perspective from a more detailed understanding of processes in the climate system in terms of characteristics of climate variability and extremity.

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References

  • Abbasnia M, Toros H (2018) 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. https://doi.org/10.1007/s00024-018-1888-8

    Article  Google Scholar 

  • Abbasnia M, Toros H (2020) Trend analysis of weather extremes across the coastal and non-coastal areas (case study: Turkey). J Earth Syst Sci 129:1–13. https://doi.org/10.1007/s12040-020-1359-3

    Article  Google Scholar 

  • Amini M, Ghadami M, Fathian F, Modarres R (2020) Teleconnections between oceanic: atmospheric indices and drought over Iran using quantile regressions. Hydrol Sci J 65:2286–2295

    Article  Google Scholar 

  • Aziz R, Yucel I (2021) Assessing nonstationarity impacts for historical and projected extreme precipitation in Turkey. Theor Appl Climatol. https://doi.org/10.1007/s00704-020-03503-x

    Article  Google Scholar 

  • Baltaci H (2019) Spatiotemporal variability of climate extremes in the Marmara Region (NW Turkey). Int J Glob Warm 18(3/4):239–252

    Article  Google Scholar 

  • Barbosa SM (2008) Quantile trends in Baltic Sea level. Geophys Res Lett 35:L22704. https://doi.org/10.1029/2008GL035182

    Article  Google Scholar 

  • Dabanlı İ, Şen Z, Yeleğen MÖ, Şişman E, Selek B, Güçlü YS (2016) Trend assessment by the innovative-Şen method. Water Resour Manag 30(14):5193–5203

    Article  Google Scholar 

  • Demircan M, Gürkan H, Eskioğlu O, Arabacı H, Coşkun M (2017) Climate change projections for Turkey: three models and two scenarios. Turk J Water Sci Manag 1(1):22–43. https://doi.org/10.31807/tjwsm.297183

    Article  Google Scholar 

  • Donat M, Lowry A, Alexander L et al (2016) More extreme precipitation in the world’s dry and wet regions. Nat Clim Change 6:508–513. https://doi.org/10.1038/nclimate2941

    Article  Google Scholar 

  • Gandini A, Garmendia L, Prieto I, Álvarez I, San-José J (2020) A holistic and multi-stakeholder methodology for vulnerability assessment of cities to flooding and extreme precipitation events. Sustain Cities Soc 63:102437

    Article  Google Scholar 

  • Gao N, Ma Y, Zhao M, Zhang L, Zhan H, Cai S, He Q (2020) Quantile analysis of long-term trends of near-surface chlorophyll: a in the pearl river plume. Water 12(6):1662. https://doi.org/10.3390/w12061662

    Article  Google Scholar 

  • Hosseinzadehtalaei P, Tabari H, Willems P (2020) Climate change impact on short-duration extreme precipitation and intensity–duration–frequency curves over Europe. J Hydrol 590:125249

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London

    Google Scholar 

  • Koenker RW (2005) Quantile regression. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Koenker R (2021) Quantreg: Quantile Regression. R package version 5.86. https://CRAN.R-project.org/package=quantreg

  • Koenker RW, Bassett G Jr (1978) Regression quantiles. Econometrica 46:33–50. https://doi.org/10.2307/1913643

    Article  Google Scholar 

  • Koenker R, Hallock KF (2001) Quantile regression. J Econ Perspect 15:143–156

    Article  Google Scholar 

  • Li-jun F (2014) Quantile trends in temperature extremes in China. Atmos Ocean Sci Lett 7:304–308

    Article  Google Scholar 

  • Lolis CJ, Türkeş M (2016) Atmospheric circulation characteristics favouring extreme precipitation in Turkey. Clim Res 71:139–153. https://doi.org/10.3354/cr01433

    Article  Google Scholar 

  • Malik N, Bookhagen B, Mucha PJ (2016) Spatiotemporal patterns and trends of Indian monsoonal rainfall extremes. Geophys Res Lett 43:1710–1717. https://doi.org/10.1002/2016GL067841

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171

    Article  Google Scholar 

  • Mohsenipour M, Shahid S, Ziarh GF et al (2020) Changes in monsoon rainfall distribution of Bangladesh using quantile regression model. Theor Appl Climatol 142:1329–1342. https://doi.org/10.1007/s00704-020-03387-x

    Article  Google Scholar 

  • Myhre G, Alterskjær K, Stjern CW, Hodnebrog Ø, Marelle L, Samset BH, Sillmann J, Schaller N, Fischer E, Schulz M, Stohl A (2019) Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 9:16063. https://doi.org/10.1038/s41598-019-52277-4

    Article  Google Scholar 

  • Oruc S (2021) Visual and statistical inference of hourly and sub-hourly extreme rainfall trends Central Anatolia, Turkey case. Acta Geophys. https://doi.org/10.1007/s11600-020-00512-2

    Article  Google Scholar 

  • Pumo D, Noto L (2021) Exploring the linkage between dew point temperature and precipitation extremes: a multi-time-scale analysis on a semi-arid Mediterranean region. Atmos Res 254:105508

    Article  Google Scholar 

  • Sarış F (2020) The spatial pattern of selected extreme precipitation indices for Turkey (1975–2012). Bull Geogr Phys Geogr Ser 19(1):19–30. https://doi.org/10.2478/bgeo-2020-0007

    Article  Google Scholar 

  • Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17:1042–1046

    Article  Google Scholar 

  • Şenocak S, Emek M (2019) Evaluation of monthly precipitation amount in Eastern Anatolia region by using trend analysis methods. Avrupa Bilim Ve Teknoloji Dergisi 17:807–822. https://doi.org/10.31590/ejosat.646266

    Article  Google Scholar 

  • 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. In: 6th atmospheric science symposium, 24–26 Apr 2013. ITU, Istanbu

  • Serencam U (2019) Innovative trend analysis of total annual rainfall and temperature variability case study: Yesilirmak region, Turkey. Arab J Geosci 12(23):1–9

    Article  Google Scholar 

  • Shiau J, Huang W (2015) Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression. J Hydro-Environ Res 9:368–380

    Article  Google Scholar 

  • Şişman E, Kizilöz B (2021) The application of piecewise ITA method in Oxford, 1870–2019. Theor Appl Climatol 145:1451–1465. https://doi.org/10.1007/s00704-021-03703-z

    Article  Google Scholar 

  • Srivastava A, Grotjahn R, Ullrich P (2020) Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather Clim Extremes 29:100268

    Article  Google Scholar 

  • Sterin AM, Lavrov AS (2020) On long period trend estimates of upper-air extreme and sub-extreme temperatures by use of quantile regression. IOP Conf Ser Earth Environ Sci 611:012043

    Article  Google Scholar 

  • Tabari H, Madani K, Willems P (2020) The contribution of anthropogenic influence to more anomalous extreme precipitation in Europe. Environ Res Lett 15:104077

    Article  Google Scholar 

  • Taylan D, Aydın T (2018) The trend analysis of lakes region precipitation data in Turkey. Cumhur Sci J 39(1):258–273. https://doi.org/10.17776/csj.406271

    Article  Google Scholar 

  • Tharu B, Dhakal N (2020) On the use of Bayesian quantile regression method to explore the historical trends in extreme precipitation and their connections with large-scale climate patterns over the contiguous USA. Theoret Appl Climatol 139:1277–1290

    Article  Google Scholar 

  • Toros H, Abbasnia M (2017) Trend analysis of extreme temperature indices for Marmara region of Turkey. In: The 8th atmospheric sciences symposium—ATMOS 2017, Istanbul, Turkey

  • Tosunoğlu F (2017) Trend analysis of daily maximum rainfall series in Çoruh Basin, Turkey. J Inst Sci Technol 7(1):195–205

    Article  Google Scholar 

  • Treppiedi D, Cipolla G, Francipane A, Noto LV (2021) Detecting precipitation trend using a multiscale approach based on quantile regression over a Mediterranean area. Int J Climatol. https://doi.org/10.1002/joc.7161

    Article  Google Scholar 

  • TSMS (Turkish State Meteorological Service) (2021a) State of the climate in Turkey in 2020. Turkish State Meteorological Service

  • TSMS (Turkish State Meteorological Service) (2021b) 2020 Yili Yağiş Değerlendirmesi (in Turkish). Turkish State Meteorological Service

  • Türkeş M, Koç T, Sariş F (2009) Spatiotemporal variability of precipitation total series over Turkey. Int J Climatol 29:1056–1074. https://doi.org/10.1002/joc.1768

    Article  Google Scholar 

  • Uranchimeg S, Kwon H, Kim B, Kim T (2020) Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach. Hydrol Res 51:699–719

    Article  Google Scholar 

  • Vaheddoost B (2020) A comparison of several methods in tracking short-term trends associated with the precipitation time series. Uludağ Univ J Fac Eng 25(1):153–168. https://doi.org/10.17482/uumfd.656175

    Article  Google Scholar 

  • Wasko C, Sharma A (2014) Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour Res 50:3608–3614. https://doi.org/10.1002/2013WR015194

    Article  Google Scholar 

  • Yurtseven I, Serengil Y (2017) Changes and trends of seasonal total rainfall in the province of Istanbul, Turkey. J Fac For Istanb Univ 67(1):1–12. https://doi.org/10.17099/jffiu.30673

    Article  Google Scholar 

  • Zhang S, Gan TY, Bush A (2020) Variability of arctic sea ice based on quantile regression and the teleconnection with large-scale climate patterns. J Clim 33:4009–4025

    Article  Google Scholar 

Download references

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This research received no external funding.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering and Architecture, Kırşehir Ahi Evran University, 40100, Kırşehir, Turkey

    Sertac Oruc

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  1. Sertac Oruc
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Correspondence to Sertac Oruc.

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Communicated by Prof. Renata Romanowicz (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Oruc, S. Quantile trends of subhourly extreme rainfall: Marmara Region, Turkey. Acta Geophys. 69, 2453–2473 (2021). https://doi.org/10.1007/s11600-021-00692-5

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  • DOI: https://doi.org/10.1007/s11600-021-00692-5

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