PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Innovative trend pivot analysis method (ITPAM): a case study for precipitation data of Susurluk Basin in Turkey

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Throughout the geological history of the earth, there have been many climate changes due to natural and external factors. In the past, the changes in climate were caused by natural causes, and today it is primarily caused by human activities. Besides being diferent climate types, Turkey is among countries that will be afected by climate change induced by global warming. Climate changes in the regions will be afected diferently and degrees due to the country’s surroundings by seas, fragmented topography and orographic features. Trend analysis methods are used in many areas such as on various engi neering, agriculture, environmental and water resources, especially in climate change impact studies resulting from global warming. When data are analyzed with classical trend analysis methods, forward-looking predictions are generally made as low, medium, high, decreasing and increasing. However, risk classes showing changes between available data sets are not known. Innovative Trend Pivot Analysis Method (ITPAM) determines risk classes by establishing a relationship between data. Furthermore, in this method, increasing and decreasing trend regions are separated into fve classes more clearly than classical/traditional trend methods. In this study, Susurluk Basin’s total monthly precipitation data (2006–2017) were analyzed by using ITPAM which the newest trend method. When arithmetic mean analysis results are examined, a signifcant change is observed between frst data set and second data set at two stations (Bandirma and Uludag). When examined at other stations, it is observed that at least one month of almost every station is in 1st degree risk group. When standard deviation analysis results of each station are examined, a signifcant change is observed between frst data set and second data set at many stations. Because while trend class of a point in developed IPTA graph is the medium degree, this point is in 1st risk class in the risk graph.
Czasopismo
Rocznik
Strony
1465--1480
Opis fizyczny
Bibliogr. 41 poz.
Twórcy
  • Department of Civil Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey
  • Department of Civil Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey
autor
  • Key Laboratory of Mountain Surface Process and Ecological Regulations, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China
  • University of Chinese Academy of Sciences (UCAS), Beijing, China
Bibliografia
  • 1. Albayrak S, Caglar S, Mulayim A, Kurt-Sahin G, Balkis H, Cinar NF, Atabay H, Tutak B, Bahceci H (2019) A case study: ecological quality status of Susurluk river basin (Marmara Sea). Fresenius Environ Bull 28:769–776
  • 2. Bocheva L, Marinova T, Simeonov P, Gospodinov I (2009) Variability and trends of extreme precipitation events over Bulgaria (1961–2005). Atmos Res 93(1–3):490–497
  • 3. Bulut H, Saler S (2018) Seasonal variations in zooplankton community of an aquatic ecosystem at Susurluk basin (Balikesir-Turkey). Fresenius Environ Bull 27:2530–2535
  • 4. Ceribasi G (2018a) Analysis of meteorological and hydrological data of Iznik lake basin by using innovative sen method. J Env Prot Eco 19(1):15–24
  • 5. Ceribasi G (2018b) Analysis of rainfall datas of the west black sea basin by innovative sen method. Acad Platf J Eng Science 6(3):168–173
  • 6. Ceribasi G (2019) Analyzing rainfall datas’ of eastern black sea basin by using sen method and trend methods. J Inst Sci Technol 9(1):254–264
  • 7. Ceribasi G, Aytulun U (2020) Investigation of the effect of climate change on precipitation and temperature data of Susurluk basin and Van Lake closed basin. Int J Glob Warm 22(1):54–71
  • 8. Ceribasi G, Ceyhunlu AI (2020) Analysis of total monthly precipitation of Susurluk basin in Turkey using innovative polygon trend analysis method. J Water Clim Change. https://doi.org/10.2166/wcc.2020.253
  • 9. Ceribasi G, Ceyhunlu AI (2020) Estimation of energy to be produced in hydroelectric power plants by using artificial neural networks and innovative sen method. J Fundam Ren Energy App 10(5):1–5
  • 10. Dabanli I, Sen Z, Yelegen MO, Sisman E, Selek B, Guclu YS (2016) Trend assessment by the innovative-Sen method. Water Resour Manag 30(14):5193–5203
  • 11. Demir V (2018) Trend analysis of precipitation in Blacksea region. Ondokuz Mayis University Institute of Science. Department of Topographical Engineering. Samsun. Turkey
  • 12. Feidas H, Noulopoulou CH, Makrogiannis T, Bora-Senta E (2007) Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001. Theor Appl Climatol 87:155–177
  • 13. Guclu YS (2018) Multiple Şen-innovative trend analyses and partial Mann-Kendall test. J Hydrol 566:685–704
  • 14. Guclu YS, Sisman E, Dabanli I (2020) Innovative triangular trend analysis. Arabian J of Geosci 13(1):1–8
  • 15. Gulen L (2019) Trend analysis of hydro-meteorologic data in upper Euphrates basin. Ataturk University. Graduate School of Natural and Applied Sciences. Department of Civil Engineering. Erzurum. Turkey
  • 16. Han J, Singh VP (2020) Forecasting of droughts and tree mortality under global warming: a review of causative mechanisms and modeling methods. J Water Clim Change 11(3):600–632
  • 17. Jones JR, Schwartz JS, Ellis KN, Hathaway JM, Jawdy CM (2015) Temporal variability of precipitation in the Upper Tennessee Valley. J Hydrol Reg Stud 3:125–138
  • 18. Karabulut M (2015) Drought analysis in Antakya-Kahramanmaraş Graben, Turkey. J Arid Land 7:741–754
  • 19. Karmeshu N (2012) Trend detection in annual temperature & precipitation using the Mann Kendall test – a case study to assess climate change on select states in the northeastern United States. University of Pennsylvania. Department of Earth and Environmental Science. Pennsylvania. USA
  • 20. Kendall MG (1975) Rank correlation methods. Charles Griffin (p. 202)
  • 21. Korhonen J, Kuusisto E (2010) Long-term changes in the discharge regime in Finland. Hydrol Res 41(3–4):253
  • 22. Kyselý J (2009) Trends in heavy precipitation in the Czech Republic over 1961–2005. Int J Climatol 29(12):1745–1758
  • 23. Li T, Zhou Z, Fu Q, Liu D, Li M, Hou R, Pei W, Li L (2020) Analysis of precipitation changes and its possible reasons in Songhua River Basin of China. J Water Clim Change 11(3):839–864
  • 24. Mann H (1945) Mann Nonparametric test against trend. Econometrica 13:245–259
  • 25. Nikakhtar M, Rahmati SH, Bavani ARM (2020) Impact of climate change on the future quality of surface waters: case study of the Ardak River, northeast of Iran. J Water Clim Change 11(3):685–702
  • 26. Reihan A, Kriauciuniene J, Meilutyte-Barauskiene D, Kolcova T (2012) Temporal variation of spring flood in rivers of the Baltic States. Hydrol Res 43(4):301–314
  • 27. Sanikhani H, Kisi O, Gajbhiye MR, Meshram S (2018) Trend analysis of rainfall pattern over the Central India during 1901–2010. Arab J Geosci 11(437):1–14
  • 28. Sattari MT, Sureh FS, Kahya E (2020) Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models. Nat Hazards J Int Soc Pre Mit Nat Hazards 102:1077–1094
  • 29. Sattari MT, Mirabbasi R, Jarhan S, Sureh FS, Ahmad S (2020) Trend and abrupt change analysis in water quality of Urmia Lake in comparison with changes in lake water level. Environ Monit Assess 192(623):1–16
  • 30. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63(324):1379–1389
  • 31. Sen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046
  • 32. Sen Z, Sisman E, Dabanli I (2019) Innovative polygon trend analysis (IPTA) and applications. J Hydrol 575:202–210
  • 33. Tabari H, Taye MT, Onyutha C, Willems P (2017) Decadal analysis of river flow extremes using quantile-based approaches. Water Resour Manag 31(11):3371–3387
  • 34. Tokgoz S, Partal T (2020) Trend analysis with innovative sen and mann-kendall methods of annual precipitation and temperature data in the black sea region. J Inst Sci Technol 10(2):1107–1118
  • 35. Topuz M, Feidas H, Karabulut M (2021) Trend analysis of precipitation data in Turkey and relations to atmospheric circulation: (1955–2013). Italian J Agr 2:91–107
  • 36. Turkes M, Koc T, Saris F (2009) Spatiotemporal variability of precipitation total series over Turkey. Int J Clim 29(8):1056–1074
  • 37. Wilson D, Hisdal H, Lawrence D (2010) Has streamflow changed in the Nordic countries?—Recent trends and comparisons to hydrological projections. J Hydrol 394(3–4):334–346
  • 38. Yang XL, Xu LR, Liu KK, Li CH, Hu J, Xia XH (2012) Trends in temperature and precipitation in the Zhangweinan river basin during the last 53 years. Pro Env Sci 13:1966–1974
  • 39. Zamani R, Mirabbasi R, Abdollahi S, Jhajharia D (2017) Streamflow trend analysis by considering autocorrelation structure, long-term persistence, and Hurst coefficient in a semi-arid region of Iran. Theo App Clim 129(1–2):33–45
  • 40. Zeybekoglu U, Partal T (2018) Evaluated of monthly total annual mean and rainfall intensity series of standard duration of Sinop by different trend analysis methods. Clim Change Env 3:1–8
  • 41. Zhang A, Zheng C, Wang S, Yao Y (2015) Analysis of streamflow variations in the Heihe River Basin, northwest China: trends, abrupt changes, driving factors and ecological influences. J Hydrol Reg Stud 3:106–124
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b14d9a7f-6889-4da9-aa2f-c20b5d5199cb
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.