Skip to main content

Advertisement

SpringerLink
Log in

Power law characteristics of trend analysis in Turkey

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Trend identification analyses in any hydrometeorological data are necessary and critical for predictions and planning in many disciplines such as atmospheric, environmental, and oceanographic sciences; water engineering; global warming; and climate change applications. In the literature, many researches have employed trend analyses by Mann-Kendall (MK) and innovative trend analysis (ITA) methods. Especially in recent years, the ITA method is preferred in trend identification methodological applications, because it can present trend graphs with visual inspection, verbal inferences, and objective quantitative calculations. In this paper, the trends of sea surface temperature (SST) data are identified by the MK and ITA methods on double logarithmic graphs, which provide fractal geometrical appearances with power law features. The SST data trends are evaluated at 22 coastal area stations of Turkey with monthly records from 1969 to 2014. According to the MK trend test, only 6 of the 22 stations had a significant upward trend at 5% significant level and 4 of these stations are in the Mediterranean Sea coastal area. At 10% significance level increasing trend numbers become 14 stations. According to the MK test results, the data records have been grouped into two parts as warming and cooling periods considering the physical conditions of the SST data. On the other hand, the results of ITA application provide average behavioral form according to the mathematical power law equations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abungba JA, Khare D, Pingale SM, Adjei KA, Gyamfi C, Odai SN (2020) Assessment of hydro-climatic trends and variability over the Black Volta Basin in Ghana. Earth Syst Environ, 1-17

  • Aksoy AO (2017) A Investigation of sea level trends and the effect of the north atlantic oscillation (NAO) on the black sea and the eastern Mediterranean sea. Theor Appl Climatol 129(1-2):129–137. https://doi.org/10.1007/s00704-016-1759-0

    Article  Google Scholar 

  • Alashan S (2018a) An improved version of innovative trend analyses. Arab J Geosci 11(3):50

    Article  Google Scholar 

  • Alashan S (2018b) Yenilikçi Yönelim Analiz Yönteminin Logaritmik Eksende Değerlendirilmesi (In Turkish). İklim Değişikliği ve Çevre 3(1):16–21

    Google Scholar 

  • Almazroui M, Şen Z, Mohorji AM, Islam MN (2019) Impacts of climate change on water engineering structures in arid regions: case studies in Turkey and Saudi Arabia. Earth Syst Environ 3(1):43–57

    Article  Google Scholar 

  • Amirataee B, Montaseri M, Sanikhani H (2016) The analysis of trend variations of reference evapotranspiration via eliminating the significance effect of all autocorrelation coefficients. Theor Appl Climatol 126(1-2):131–139

    Article  Google Scholar 

  • Bhunia P, Das P, Maiti R (2020) Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst Environ 4(1):43–55

    Article  Google Scholar 

  • Caloiero T (2020) Evaluation of rainfall trends in the South Island of New Zealand through the innovative trend analysis (ITA). Theor Appl Climatol 139(1-2):493–504

    Article  Google Scholar 

  • Caloiero T, Coscarelli R, Ferrari E (2018) Application of the innovative trend analysis method for the trend analysis of rainfall anomalies in southern Italy. Water Resour Manag 32(15):4971–4983

    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 

  • Gajbhiye S, Meshram C, Mirabbasi R, Sharma SK (2016) Trend analysis of rainfall time series for Sindh river basin in India. Theor Appl Climatol 125(3-4):593–608

    Article  Google Scholar 

  • Gedefaw M, Yan D, Wang H, Qin T, Girma A, Abiyu A, Batsuren D (2018) Innovative trend analysis of annual and seasonal rainfall variability in Amhara Regional State, Ethiopia. Atmosphere 9(9):326

    Article  Google Scholar 

  • Güçlü YS (2020) Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA. J Hydrol 584:124674

    Article  Google Scholar 

  • Güçlü YS, Dabanlı İ, Şişman E, Şen Z (2019) Air quality (AQ) identification by innovative trend diagram and AQ index combinations in Istanbul megacity. Atmos Pollut Res 10(1):88–96

    Article  Google Scholar 

  • Güçlü YS, Şişman E, Dabanlı İ (2020) Innovative triangular trend analysis. Arab J Geosci 13(2):1–8

    Google Scholar 

  • Haan CT (1977) Statistical methods in hydrology, The Iowa State University Press, Ames, Iowa, 378ss

  • Houghton Filho JT, LGM CB, Harris N, Kattenberg A, Maskell K (1996) Climate change 1995: the science of climate change. Cambridge University Press, Cambridge, UK, 1996572

  • 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. https://doi.org/10.1016/j.ejrh.2014.10.006

    Article  Google Scholar 

  • Kazmin AS, Zatsepin AG (2007) Long term variability of surface temperature in the Black Sea, and its connection with the large-scale atmospheric forcing. J Mar Syst 68(1-2):293–301

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation method, 4 th Edition, Charless Griffin, London, 202ss

  • Kumar S, Merwade V, Kam J, Thurner K (2009) Streamflow trends in Indiana: effects of long-term persistence, precipitation and subsurface drains. J Hydrol 374(1-2):171–183

    Article  Google Scholar 

  • Mann HB (1945) Nonparametric test against trend. Econometrica 13(3):245–259. https://doi.org/10.2307/1907187

    Article  Google Scholar 

  • Mohorji AM, Şen Z, Almazroui M (2017) Trend analyses revision and global monthly temperature innovative multi-duration analysis. Earth Syst Environ 1(1):9

    Article  Google Scholar 

  • Nalley D, Adamowski J, Khalil B, Ozga-Zielinski B (2013) Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967-2006 using the discrete wavelet transform. Atmos Res 132–133:375–398. https://doi.org/10.1016/j.atmosres.2013.06.011

    Article  Google Scholar 

  • Öztopal A, Şen Z (2017) Innovative trend methodology applications to precipitation records in Turkey. Water Resour Manag 31(3):727–737. https://doi.org/10.1007/s11269-016-1343-5

    Article  Google Scholar 

  • Park KA, Lee EY, Chang E, Hong S (2015) Spatial and temporal variability of sea surface temperature and warming trends in the yellow sea. J Mar Syst 143:24–38

    Article  Google Scholar 

  • Robertson R, Visbeck M, Gordon AL, Fahrbach E (2002) Long-term temperature trends in the deep waters of the Weddell Sea. Deep-Sea Res II Top Stud Oceanogr 49(21):4791–4806. https://doi.org/10.1016/S0967-0645(02)00159-5

    Article  Google Scholar 

  • Sanikhani H, Kisi O, Mirabbasi R, Meshram SG (2018) Trend analysis of rainfall pattern over the Central India during 1901–2010. Arab J Geosci 11(15):437

    Article  Google Scholar 

  • Saplioglu K, Kilit M, Yavuz BK (2014) Trend analysis of streams in the Western Mediterranean basin of Turkey. Fresenius Environ Bull 23(1A):313–327

    Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389

    Article  Google Scholar 

  • Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556

    Article  Google Scholar 

  • Şen Z (2014) Trend identification simulation and application. J Hydrol Eng 19(3):635–642. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000811

    Article  Google Scholar 

  • Şen Z (2017a) Innovative trend significance test and applications. Theor Appl Climatol 127(3–4):939–947. https://doi.org/10.1007/s00704-015-1681-x

    Article  Google Scholar 

  • Şen Z (2017b) Innovative trend methodologies in science and engineering. Springer International Publishing, New York, pp 1–349. https://doi.org/10.1007/978-3-319-52338-5

    Book  Google Scholar 

  • Şen Z, Şişman E, Dabanli I (2019) Innovative polygon trend analysis (IPTA) and applications. J Hydrol 575:202–210

    Article  Google Scholar 

  • Şişman E (2019a) Ege ve Akdeniz Kıyılarında Seçilen İstasyonlarda Deniz Suyu Sıcaklıkları İçin Soğuma Dönemi Trend Analizleri (In Turkish). Doğal Afetler ve Çevre Dergisi 5(2):291–304

    Google Scholar 

  • Şişman E (2019b) Türkiye’de seçilen hava kalitesi izleme istasyonları için eğilim (trend) değerlendirmeleri (In Turkish). Doğal Afetler ve Çevre Dergisi 5(1):134–152

    Google Scholar 

  • Şişman E (2019c) Piecewise wet and dry spell duration-number relationship and possible climate change impact identification in Turkey. Arab J Geosci 12(24):787

    Article  Google Scholar 

  • Skliris N, Sofianos S, Gkanasos A, Mantziafou A, Vervatis V, Axaopoulos P, Lascaratos A (2012) Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability. Ocean Dyn 62(1):13–30. https://doi.org/10.1007/s10236-011-0493-5

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s11269-017-1673-y

    Article  Google Scholar 

  • Yan T, Bai ZS (2017) Spatial and temporal changes in temperature, precipitation, and streamflow in the Miyun Reservoir Basin of China. Water 9(2):78

    Article  Google Scholar 

  • 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. Theor Appl Climatol 129(1-2):33–45

    Article  Google Scholar 

  • Zveryaev II (2015) Seasonal differences in intraseasonal and interannual variability of Mediterranean Sea surface temperature. J Geophys Res C: Oceans 120(4):2813–2825

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Natural Sciences, Civil Engineering Department, Istanbul Medipol University, Kavacık, 34181, Istanbul, Turkey

    Eyüp Şişman

  2. Climate Change Researches Application and Research Center, (IKLIMER), Istanbul Medipol University, Kavacık, 34181, Istanbul, Turkey

    Eyüp Şişman

Authors
  1. Eyüp Şişman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eyüp Şişman.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Şişman, E. Power law characteristics of trend analysis in Turkey. Theor Appl Climatol 143, 1529–1541 (2021). https://doi.org/10.1007/s00704-020-03408-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-020-03408-9

Search

Navigation