Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

Oruc, Sertac

doi:10.1007/s11600-020-00518-w

Artykuł - szczegóły

Tytuł artykułu

Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

Autorzy

Oruc Sertac

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-020-00518-w

Warianty tytułu

Języki publikacji

Abstrakty

The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. Nonstationary extreme value modelling is one of the ways to incorporate changing conditions into analyses. Although the defnition of nonstationary is still debated, the existence of nonstationarity is determined by the presence of signifcant monotonic upward or downward trends and/or shifts in the mean or variance. On the other hand, trend tests may not be a sign of nonstationarity and a lack of signifcant trend cannot be accepted as time series being stationary. Thus, this study investigated the relation between trend and nonstationarity for 5, 10, 15, and 30 min and 1, 3, 6, and 24 h annual maximum rainfall series at 13 stations in Central Anatolia, Turkey. Trend tests such as Mann– Kendall (MK), Cox–Stuart (CS), and Pettitt’s (P) tests were applied and nonstationary generalized extreme value models were generated. MK test and CS test results showed that 33% and 27% of 104 time series indicate a signifcant trend (with p<0.01–p<0.05–p<0.1 signifcance level), respectively. Moreover, 43% of time series have outperformed nonstationary (NST) models that used time as covariate. Among fve diferent time-variant nonstationary models, the model with a location parameter as a linear function of time and the model with a location and scale parameter as a linear function of time performed better. Considering the rainfall series with a signifcant trend, increasing trend power may increase how well fitted nonstationary models are. However, it is not necessary to have a signifcant trend to obtain outperforming nonstationary models. This study supported that it is not necessarily time series to have a trend to perform better nonstationary models and acceptance of nonstationarity solely depending on the presence of trend may be misleading.

Słowa kluczowe

nonstationarity Mann–Kendall test Cox–Stuart test Pettitt test trend generalized extreme value

niestacjonarność test Manna-Kendalla test Coxa-Stuarta test Pettitta tendencja uogólniona wartość ekstremalna

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2021

Tom

Vol. 69, no. 1

Strony

243--255

Opis fizyczny

Bibliogr. 75 poz.

Twórcy

autor

Oruc Sertac

sertac.oruc@ahievran.edu.tr

Kırşehir Ahi Evran University, Faculty of Engineering and Architecture, Department of Civil Engineering, 40100 Kır

https://orcid.org/0000-0003-2906-0771

Bibliografia

1. 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
2. Agilan V, Umamahesh NV (2017) What are the best covariates for developing nonstationary rainfall intensity-duration-frequency relationship? Adv Water Resour 101:11–22
3. Apaydin H, Anli AS, Ozturk F (2011) Evaluation of topographical and geographical effects on some climatic parameters in the Central Anatolia Region of Turkey. Int J Climatol 31:1264–1279. https://doi.org/10.1002/joc.2154
4. Aziz R (2018) Impacts of climate nonstationarıties on hydroclimatological variables in Turkey. (2018). PhD Dissertation, Middle East Technical University. Ankara
5. Bayazit M (2015) Nonstationarity of hydrological records and recent trends in trend analysis: a state-of-the-art review. Environ Process 2:527–542. https://doi.org/10.1007/s40710-015-0081-7
6. Burić D, Doderović M (2020) Changes in temperature and precipitation in the instrumental period (1951–2018) and projections up to 2100 in Podgorica (Montenegro). Int J Climatol. https://doi.org/10.1002/joc.6671
7. Cai Y, Hames D (2010) Minimum sample size determination for generalized extreme value distribution. Commun Stat Simul Comput 40:87–98. https://doi.org/10.1080/03610918.2010.530368
8. Chen X, Huang G (2020) Applicability and hydrologic substitutability of TMPA satellite precipitation product in the feilaixia catchment, China. Water 2020:12
9. Cheng L, AghaKouchak A (2014) Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci Rep 4:7093. https://doi.org/10.1038/srep07093
10. Cheng L, Phillips TJ, AghaKouchak A (2015) Non-stationary return levels of CMIP5 multi-model temperature extremes. Clim Dyn 44:2947–2963. https://doi.org/10.1007/s00382-015-2625-y
11. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London
12. Coles SG, Sparks RSJ (2006) Extreme value methods for modelling historical series of large volcanic magnitudes. Statistics in Volcanology, H. M. Mader, S. G. Coles, C. B. Connor, L. J. Connor
13. Collet L, Beevers L, Prudhomme C (2017) Assessing the impact of climate change and extreme value uncertainty to extreme flows across Great Britain. Water 9:103. https://doi.org/10.3390/w9020103
14. Conover WJ (1999) Practical nonparametric statistics, 3rd edn. Wiley, Hoboken, p 166
15. Cox DR, Stuart A (1955) Some quick sign tests for trend in location and dispersion. Biometrika 42:80–95
16. 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 1:22–43. https://doi.org/10.31807/tjwsm.297183
17. Fatichi S, Barbosa S, Caporali E, Silva ME (2009) Deterministic versus stochastic trends: detection and challenges. J Geophys Res Space Phys. https://doi.org/10.1029/2009jd011960
18. Faulkner D, Warren S, Spencer P, Sharkey P (2019) Can we still predict the future from the past? Implementing non-stationary flood frequency analysis in the UK. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12582
19. Fernández CG, Peek D (2020) Smart and sustainable? Positioning adaptation to climate change in the European smart city. Smart Cities 3:511–526. https://doi.org/10.3390/smartcities3020027
20. Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Wiley, New York
21. Gilleland E (2020) ExtRemes: extreme value analysis; R package Version 2.0-12
22. Gilleland E, Katz R (2016) extRemes 2.0: an extreme value analysis package in R. J Stat Softw 72:1–39. https://doi.org/10.18637/jss.v072.i08
23. Giorgi F (2006) Climate change hot-spots. Geophys Res Lett 33:L08707. https://doi.org/10.1029/2006gl025734
24. Haktanir T, Citakoglu H (2014) Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. J Hydrol Eng 19:05014009. https://doi.org/10.1061/(asce)he.1943-5584.0000973
25. Hooten MB, Hobbs NT (2015) A guide to Bayesian model selection for ecologists. Ecol Monogr 85:3–28. https://doi.org/10.1890/14-0661.1
26. IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA
27. Keggenhoff I, Elizbarashvili M, Amiri-Farahani A, King L (2014) Trends in daily temperature and precipitation extremes over Georgia, 1971–2010. Weather Clim Extremes 4:75–85. https://doi.org/10.1016/j.wace.2014.05.001
28. Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London
29. Lange MA (2019) Impacts of climate change on the eastern mediterranean and the middle east and north Africa region and the water-energy nexus. Atmosphere 10:455. https://doi.org/10.3390/atmos10080455
30. Ledvinka O (2014) Is the Cox-Stuart test for trend really insensitive to autocorrelation? In: van Lanen HAJ, Demuth S, van der Heijden A (eds) Proceedings of the 7th Global FRIEND-water conference poster proceedings-hydrology in a changing world: environmental and human dimensions. Wageningen University: Wageningen, The Netherland; UNESCO: Paris, France, pp 68–69
31. Liew SC, Raghavan SV, Liong S-Y (2014) How to construct future IDF curves, under changing climate, for sites with scarce rainfall records? Hydrol Process 28:3276–3287. https://doi.org/10.1002/hyp.9839
32. Mahmoodian M (2018) Chapter 4: time-dependent reliability analysis. Reliability and Maintainability of In-service Pipelines. 103–117 https://doi.org/10.1016/c2016-0-05259-2
33. Makkonen L, Tikanmäki M (2019) An improved method of extreme value analysis. J Hydrol X 2:100012. https://doi.org/10.1016/j.hydroa.2018.100012
34. Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171
35. McGarigal K (2017) ECO602: analysis of environmental data, Week 8 Notes, Retrieved from: http://www.umass.edu/landeco/teaching/ecodata/schedule/likelihood.pdf
36. Militino AF, Moradi M, Ugarte MD (2020) On the performances of trend and change-point detection methods for remote sensing data. Remote Sens 12:1008. https://doi.org/10.3390/rs12061008
37. Mostafa A, Wheida A, Nazer ME, Adel M, Leithy LE, Siour G, Coman A, Borbon A, Magdy AW, Omar M, Saad-Hussein A, Alfaro S (2019) Past (1950–2017) and future (−2100) temperature and precipitation trends in Egypt. Weather Clim Extremes 26:100225
38. Nigussie TA, Altunkaynak A (2018) Impacts of climate change on the trends of extreme rainfall indices and values of maximum precipitation at Olimpiyat Station, Istanbul, Turkey. Theor Appl Clim 135:1501–1515. https://doi.org/10.1007/s00704-018-2449-x
39. Onyutha C, Tabari H, Taye MT, Nyandwaro GN, Willems P (2016) Analyses of rainfall trends in the Nile River Basin. Hydro Res 13:36–51. https://doi.org/10.1016/j.jher.2015.09.002
40. Öztürk T, Turp MT, Türkeş M, Kurnaz ML (2017) Projected Changes in Temperature and Precipitation Climatology of Central Asia CORDEX Region 8 by using RegCM.4.3.5. Atmos Res 183:296–307
41. Panthou G, Lebel T, Blanchet J, Quantin G, Vischel T, Ali A (2012) Extreme rainfall in West Africa: a regional modeling. Water Resour Res 48:W08501. https://doi.org/10.1029/2012wr012052
42. Patakamuri SK, Muthiah K, Sridhar V (2020) Long-term homogeneity, trend, and change-point analysis of rainfall in the arid district of Ananthapuramu, Andhra Pradesh State, India. Water 12:211
43. Pettitt AN (1979) A non-parametric approach to the change-point problem. J R Stat Soc Ser C Appl Stat 28:126. https://doi.org/10.2307/2346729
44. Pohl B, Macron C, Monerie P-A (2017) Fewer rainy days and more extreme rainfall by the end of the century in Southern Africa. Sci Rep 7:46466. https://doi.org/10.1038/srep46466
45. Pohlert T (2020) Trend: non-parametric trend tests and change-point detection; R Package Version 1.1.2
46. Putnam A, Broecker WS (2017) Human-induced changes in the distribution of rainfall. Sci Adv 3:e1600871. https://doi.org/10.1126/sciadv.1600871
47. Razavi S, Elshorbagy A, Wheater H, Sauchyn D (2015) Toward understanding nonstationarity in climate and hydrology through tree ring proxy records. Water Resour Res 51:1813–1830. https://doi.org/10.1002/2014wr015696
48. Ren H, Hou Z, Wigmosta MS, Liu Y, Leung LR (2019) Impacts of spatial heterogeneity and temporal non-stationarity on intensity-duration-frequency estimates—a case study in a mountainous california-nevada watershed. Water 11:1296. https://doi.org/10.3390/w11061296
49. Roslan RA, Na CS, Gabda D (2020) Parameter estimations of the generalized extreme value distributions for small sample size. Math Stat 8(2A):47–51. https://doi.org/10.13189/ms.2020.081308
50. Rutkowska A (2015) Properties of the cox-stuart test for trend in application to hydrological series: the simulation study. Commun Stat Simul Comput 44:565–579. https://doi.org/10.1080/03610918.2013.784988
51. Sarhadi A, Soulis ED (2017) Time-varying extreme rainfall intensity-duration-frequency curves in a changing climate. Geophys Res Lett 44:2454–2463. https://doi.org/10.1002/2016gl072201
52. Scotto M, Barbosa S, Alonso AM (2011) Extreme value and cluster analysis of European daily temperature series. J Appl Stat 38:2793–2804. https://doi.org/10.1080/02664763.2011.570317
53. Sen AK, Niedzielski T (2010) Statistical characteristics of riverflow variability in the Odra River Basin, Southwestern Poland. Pol J Environ Stud 19:387–397
54. Sen B, Topcu S, Türkeş M, Warner J (2012) Projecting climate change, drought conditions and crop productivity in Turkey. Clim Res 52:175–191. https://doi.org/10.3354/cr01074
55. 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: Proceedings of the 6th atmospheric science symposium, ITU, Istanbul, Turkey, 24–26
56. Serinaldi F, Kilsby CG, Lombardo F (2018) Untenable nonstationarity: an assessment of the fitness for purpose of trend tests in hydrology. Adv Water Resour 111:132–155. https://doi.org/10.1016/j.advwatres.2017.10.015
57. Šraj M, Viglione A, Parajka J, Blöschl G (2016) The influence of non-stationarity in extreme hydrological events on flood frequency estimation. J Hydrol Hydromech 64:426–437. https://doi.org/10.1515/johh-2016-0032
58. Steinke VA, Martins Palhares de Melo LA, Luiz Melo M, Rodrigues da Franca R, Luna Lucena R, TorresSteinke E (2020) Trend analysis of air temperature in the federal district of Brazil: 1980–2010. Climate 8(8):89. https://doi.org/10.3390/cli8080089
59. Tan X, Gan TY (2015) Nonstationary analysis of annual maximum streamflow of Canada. J Clim 28:1788–1805. https://doi.org/10.1175/JCLI-D-14-00538.1
60. Tian Q, Li Z, Sun X (2020) Frequency analysis of precipitation extremes under a changing climate: a case study in Heihe River basin, China. J Water Clim Chang. https://doi.org/10.2166/wcc.2020.170
61. Totaro V, Gioia A, Iacobellis V (2019) Power of parametric and non-parametric tests for trend detection in annual maximum series. Hydrol Earth Syst Sci Discussions, 1–28
62. Turkes M, Turp MT, An N, Ozturk T, Kurnaz ML (2020) Impacts of climate change on precipitation climatology and variability in Turkey. In: Harmancioglu N, Altinbilek D (eds) Water resources of Turkey. World water resources, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-11729-0_14
63. Umbricht A, Fukutome S, Liniger MA, Frei C, Appenzeller C (2013) Seasonal variation of daily extreme precipitation in Switzerland. Sci Rep MeteoSwiss 97:122
64. Vahedifard F, Tehran FS, Galavi V, Ragno E, AghaKouchak A (2017) Resilience of MSE walls with marginal backfill under a changing climate: quantitative assessment for extreme precipitation events. J Geotech Geoenviron Eng 143:04017056. https://doi.org/10.1061/(asce)gt.1943-5606.0001743
65. 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
66. 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. Math Probl Eng 2016:1–9. https://doi.org/10.1155/2016/6372197
67. Wang Y, Liu G, Guo E (2019) Spatial distribution and temporal variation of drought in Inner Mongolia during 1901–2014 using standardized precipitation evapotranspiration index. Sci Total Environ 654:850–862
68. Wang F, Shao W, Yu H, Kan G, He X, Zhang D, Ren M, Wang G (2020) Re-evaluation of the power of the mann-kendall test for detecting monotonic trends in hydrometeorological time series. Front Earth Sci 8:14. https://doi.org/10.3389/feart.2020.00014
69. Wi S, Valdés J, Steinschneider S, Kim T-W (2016) Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima. Stoch Environ Res Risk Assess 30:583–606. https://doi.org/10.1007/s00477-015-1180-8
70. Willems P, Olsson J, Arnbjerg-Nielsen K, Beecham S, Pathirana A, Gregersen IB, Madsen H, Nguyen VTV (2012) Impacts of climate change on rainfall extremes and urban drainage systems. IWA Publishing
71. Yang L, Li J, Sun H, Guo Y, Engel BA (2019) Calculation of nonstationary flood return period considering historical extraordinary flood events. J Flood Risk Manag 12:e12463. https://doi.org/10.1111/jfr3.12463
72. Yilmaz AG (2015) The effects of climate change on historical and future extreme rainfall in Antalya, Turkey. Hydrol Sci J 60(12):2148–2162. https://doi.org/10.1080/02626667.2014.945455
73. Yilmaz AG (2017) Climate change effects and extreme rainfall non-stationarity. Proc Inst Civ Eng Water Manag 170:57–65. https://doi.org/10.1680/jwama.15.00049.56
74. Yilmaz AG, Perera C (2014) Extreme rainfall nonstationarity investigation and intensity–frequency–duration relationship. J Hydrol Eng 19:1160–1172. https://doi.org/10.1061/(asce)he.1943-5584.0000878
75. Yucel I, Güventürk A, ŞenÖmer L (2014) Climate change impacts on snowmelt runoff for mountainous transboundary basins in eastern Turkey. Int J Clim 35:215–228. https://doi.org/10.1002/joc.3974

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-d8692e04-afc2-4263-8ea7-a0a1ec3e8cfc