Tytuł artykułu
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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
Abstrakty
Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to dependent series and cannot detect non-monotonic trends. Şen-innovative trend analysis method is launched into literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial independence and normal distribution and examines a given time series as equally divided into two sub-series. The Şen multiple innovative trend analysis methodology is improved to detect partial trends on different sub-series, again with equal lengths. Climate change strongly affects hydro-meteorological parameters today compared to the last twenty or thirty years and gives asymmetrical trend change points in hydro-meteorological time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, the Şen innovative trend analysis method is revised to satisfy these requirements (ITA_DL). The new approach compared with the traditional Mann-Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. The ITA_DL gives four monotonic trends on May, July, September, and October rainfall series of Oxford although the MK gives three mono tonic trends in the May, July, and December and cannot detect trends on the September and October. In the ITA_DL visual inspection, the December rainfall series does not show an overall or partial trend. The ITA_DL trend results are consistent with the Şen_ITA except for the September rainfall series, although it has different trend slope amounts.
Wydawca
Czasopismo
Rocznik
Tom
Strony
373--383
Opis fizyczny
Bibliogr. 44 poz.
Twórcy
autor
- Civil Engineering Department, Bingöl University, Bingöl, Turkey
Bibliografia
- 1. Alashan S (2020a) Can innovative trend analysis identify trend change points? Brill Eng. https://doi.org/10.36937/ben.2020.003.02
- 2. Alashan S (2020b) Testing and improving type 1 error performance of Şen’s innovative trend analysis method. Theor Appl Climatol. https://doi.org/10.1007/s00704-020-03363-5
- 3. Alifujiang Y, Abuduwaili J, Maihemuti B, Emin B, Groll M (2020) Innovative trend analysis of precipitation in the Lake Issyk-Kul Basin Kyrgyzstan. Atmosphere (basel). https://doi.org/10.3390/atmos11040332
- 4. Arab Amiri M, Gocić M (2021) Innovative trend analysis of annual precipitation in Serbia during 1946–2019. Environ Earth Sci 80:777. https://doi.org/10.1007/s12665-021-10095-w
- 5. Bayazit M, Önöz B (2007) To prewhiten or not to prewhiten in trend analysis? Hydrol Sci J 52:611–624. https://doi.org/10.1623/hysj.52.4.611
- 6. Cox DR, Stuart A (1955) Some quick sign tests for trend in location and dispersion. Biometrika. https://doi.org/10.2307/2333424
- 7. Douglas EM, Vogel RM, Kroll CN (2000) Trends in floods and low flows in the United States: impact of spatial correlation. J Hydrol 240:90–105. https://doi.org/10.1016/S0022-1694(00)00336-X
- 8. Fanta SS (2022) Analysis of spatiotemporal rainfall variability and trend in Gilgel Gibe Watershed, Southwest Ethiopia: 1985–2017. Arab J Geosci 15:778. https://doi.org/10.1007/s12517-022-10053-1
- 9. Güçlü YS (2018b) Multiple Şen-innovative trend analyses and partial Mann-Kendall test. J Hydrol 566:685–704. https://doi.org/10.1016/J.JHYDROL.2018.09.034
- 10. Güçlü YS (2020) Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.124674
- 11. Güçlü YS, Şişman E, Dabanlı İ (2020) Innovative triangular trend analysis. Arab J Geosci. https://doi.org/10.1007/s12517-019-5048-y
- 12. Güçlü YS (2018a) Alternative trend analysis: half time series methodology. Water Resour Manag. https://doi.org/10.1007/s11269-018-1942-4
- 13. Hamed KH, Ramachandra Rao A (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204:182–196. https://doi.org/10.1016/S0022-1694(97)00125-X
- 14. He Y, Lu Z, Wang W, Zhang D, Zhang Y, Qin B, Shi K, Yang X (2022) Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images. Water Res 215:118241. https://doi.org/10.1016/j.watres.2022.118241
- 15. IPCC (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Summary for policymakers., in: climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change.
- 16. Kendall MG, (1975) Rank correlation methods, Charles Griffin, London, Google Sch.
- 17. Kotrike T, Pratap D, Keesara VR (2021) Validation and trend analysis of satellite-based AOD data over southern India. Aerosol Sci Eng 5:32–43. https://doi.org/10.1007/s41810-020-00082-2
- 18. Kulkarni A, Von Storch H (1995) Monte Carlo experiments on the effect of serial correlation on the Mann-Kendall test of trend. Meteorol Zeitschrift 4:82
- 19. Ma D, Wang T, Gao C, Pan S, Sun Z, Xu Y-P (2018) Potential evapotranspiration changes in Lancang River Basin and Yarlung Zangbo River Basin, southwest China. Hydrol Sci J 63:1653–1668. https://doi.org/10.1080/02626667.2018.1524147
- 20. Mallakpour I, Villarini G (2016) A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrol Sci J. https://doi.org/10.1080/02626667.2015.1008482
- 21. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245. https://doi.org/10.2307/1907187
- 22. Marak JDK, Sarma AK, Bhattacharjya RK (2020) Innovative trend analysis of spatial and temporal rainfall variations in Umiam and Umtru watersheds in Meghalaya India. Theor Appl Climatol 142:1397–1412. https://doi.org/10.1007/s00704-020-03383-1
- 23. Mohorji AM, Şen Z, Almazroui M (2017) Trend analyses revision and global monthly temperature innovative multi-duration analysis. Earth Syst Environ 1:9. https://doi.org/10.1007/s41748-017-0014-x
- 24. Oruc S (2021) Visual and statistical inference of hourly and sub-hourly extreme rainfall trends Central Anatolia Turkey Case. Acta Geophys 69:199–216. https://doi.org/10.1007/s11600-020-00512-2
- 25. Pandey BK, Khare D, Tiwari H, Mishra PK (2021) Analysis and visualization of meteorological extremes in humid subtropical regions. Nat Hazards. https://doi.org/10.1007/s11069-021-04700-1
- 26. Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat. https://doi.org/10.2307/2346729
- 27. Phuong DND, Hai LM, Dung HM, Loi NK (2021) Temporal trend possibilities of annual rainfall and standardized precipitation index in the central, highlands Vietnam. Earth Syst Environ. https://doi.org/10.1007/s41748-021-00211-y
- 28. Şan M, Akçay F, Linh NTT, Kankal M, Pham QB (2021) Innovative and polygonal trend analyses applications for rainfall data in Vietnam. Theor Appl Climatol 144:809–822. https://doi.org/10.1007/s00704-021-03574-4
- 29. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
- 30. Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17:1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556
- 31. Şen Z (2017) Hydrological trend analysis with innovative and over-whitening procedures. Hydrol Sci J 62:294–305. https://doi.org/10.1080/02626667.2016.1222533
- 32. Serinaldi F, Chebana F, Kilsby CG (2020) Dissecting innovative trend analysis. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-020-01797-x
- 33. Şişman E (2021) Power law characteristics of trend analysis in Turkey. Theor Appl Climatol 143:1529–1541. https://doi.org/10.1007/s00704-020-03408-9
- 34. 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
- 35. Sonali P, Nagesh Kumar D (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227. https://doi.org/10.1016/j.jhydrol.2012.10.034
- 36. Spearman C (1987) The proof and measurement of association between two things. By C. Spearman, 1904. Am J Psychol 100:441–471. https://doi.org/10.2307/1422689
- 37. Von Storch H (1995) Misuses of statistical analysis in climate. In: analysis of climate variability: applications of statistical techniques. https://doi.org/10.1007/978-3-662-03744-7
- 38. Wang W, Chen Y, Becker S, Liu B (2015) Variance correction prewhitening method for trend detection in autocorrelated data. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001234
- 39. Wang W, Zhu Y, Liu B, Chen Y, Zhao X (2019) Innovative variance corrected Sen’s trend test on persistent hydrometeorological data. Water (switzerland). https://doi.org/10.3390/w11102119
- 40. Wang Y, Xu Y, Tabari H, Wang J, Wang Q, Song S, Hu Z (2020) Innovative trend analysis of annual and seasonal rainfall in the Yangtze river delta, eastern China. Atmos Res. https://doi.org/10.1016/j.atmosres.2019.104673
- 41. Yilmaz M, Tosunoglu F (2019) Trend assessment of annual instantaneous maximum flows in Turkey. Hydrol Sci J 64:820–834. https://doi.org/10.1080/02626667.2019.1608996
- 42. Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process. https://doi.org/10.1002/hyp.1095
- 43. Yue S, Pilon P, Phinney B (2003) Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol Sci J 48:51–64. https://doi.org/10.1623/hysj.48.1.51.43478
- 44. Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag. https://doi.org/10.1023/B:WARM.0000043140.61082.60
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-25303f5e-22f7-4365-a601-71d7a5c97f77