INNOVATIVE TREND ANALYSIS METHODOLOGY IN LOGARITHMIC AXIS

Abstract

Future uncertainties of climate change cause people to worry, and therefore, in order to
reduce the associated risks, scientific research methodologies are improved continuously. For instance,
temperature raises as a result of carbon content increase cause variations in hydro-meteorological data
including evaporation, drought, precipitation, runoff, and flood. Along these lines, the most commonly
used trend analysis methods are linear regression analysis, Mann-Kendall, sequential Mann-Kendall,
Spearman’s Rho, and recently a new method referred to as innovative trend analysis (ITA), which does
not require initial assumptions, normality, and independence in a data structure. The ITA method
presents a great visual ability for trend identification in graphical forms in addition to qualitative and
quantitative interpretations. In the original form of the ITA approach, scatter points are presented in the
arithmetic scale, where changes of scatter points in small values may not be clearly distinguishable like
big values for wide data ranges. In this study, the ITA method is used on arithmetic and logarithmic
scales to calculate such differences in two sub-series. The suggested logarithmic scale methodology is
referred to as proportional Şen innovative trend analysis (ITA_P). This method is used to determine
percent trends for the annual, autumn, winter, spring and summer season rains in England. ITA_P is
successful in determining trends in minimum values compared to the classical ITA.

Keywords

• Innovative trend analysis
• hydro-meteorological data
• proportional innovative trend analysis
• climate change

Logaritmik Ölçekte Yenilikçi Yönelim Çözümleme Yöntemi

Öz

Gelecekle ilgili belirsizlikler insanoğlunu endişelendirmekte ve olası riskleri azaltmak için bilimsel
araştırma yöntemleri sürekli geliştirilmektedir. Örneğin karbon salınımının artışıyla birlikte artan
sıcaklıklar buharlaşma, yağış gibi iklim olaylarının değişimini artırarak akışlar üzerinde kuraklık ve
taşkınlara neden olabilmektedir. Bu olaylar üzerindeki eğilimi belirlemek üzere Mann-Kendall, sıralı
Mann-Kendall, Spearman rho ve son zamanlarda ortaya atılan Şen’in yenilikçi yönelim çözümleme
(Şen_ITA) yöntemleri literatürde sıklıkla kullanılmaktadır. Bu yöntemlerden Şen_ITA yöntemi
normallik ve bağımsız seri gibi başlangıç kabulleri gerektirmemektedir. Ayrıca niteliksel ve nicel
yorumlamaları yanında grafiksel olarak görsel kabiliyeti yüksek bir yöntemdir. Şen_ITA yöntemi esasen
aritmetik ölçekte kullanılır ve bu durum değişim katsayısı yüksek bir seri üzerinde minimum değerler
üzerindeki eğilimin maksimum değerlerin yanında gözden kaçabilmesine neden olabilmektedir. Bu
çalışmada, Şen_ITA yöntemi aritmetik ve logaritmik ölçekte kıyaslanmıştır. Önerilen yaklaşım, oransal
Şen yenilikçi yönelim çözümleme yöntemi olarak adlandırılmıştır (ITA_P). Bu yaklaşım İngiltere’nin
mevsimsel ve yıllık yağışları üzerindeki oransal eğilimleri belirlemek için kullanılmıştır. ITA_P
yaklaşımının klasik Şen_ITA yöntemine göre bir seri üzerinde minimum değerlerdeki eğilimleri
belirlemede daha başarılı olduğu belirlenmiştir.

Anahtar Kelimeler

• Yenilikçi yönelim çözümlemesi
• hidro-meteorolojik veri
• oransal yenilikçi yönelim çözümlemesi
• iklim değişikliği

References

1. Ahmad, I., Zhang, F., Tayyab, M., Anjum, M. N., Zaman, M., Liu, J., Farid, H. U., and Saddique, Q. (2018). “Spatiotemporal analysis of precipitation variability in annual, seasonal and extreme values over upper Indus River basin.” Atmospheric Research, 213, 346–360.
2. Alashan, S. (2018). “An improved version of innovative trend analyses.” Arabian Journal of Geosciences, 11(3), 50.
3. Büyükyıldız, M., and Berktay, A. (2004). “Parametrik Olmayan Testler Kullanilarak Sakarya Havzasi Yağişlarinin Trend Analizi.” Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 19(2), 23– 38.
4. Cui, L., Wang, L., Lai, Z., Tian, Q., Liu, W., and Li, J. (2017). “Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015.” Journal of Atmospheric and Solar-Terrestrial Physics.
5. Dabanlı, İ., Şen, Z., Yeleğen, M. Ö., Şişman, E., Selek, B., and Güçlü, Y. S. (2016). “Trend Assessment by the Innovative-Şen Method.” Water Resources Management.
6. Deng, S., Li, M., Sun, H., Chen, Y., Qu, L., and Zhang, X. (2017). “Exploring temporal and spatial variability of precipitation of Weizhou Island, South China Sea.” Journal of Hydrology: Regional Studies, 9, 183–198.
7. Elouissi, A., Şen, Z., and Habi, M. (2016). “Algerian rainfall innovative trend analysis and its implications to Macta watershed.” Arabian Journal of Geosciences, 9(4).
8. Fowler, H. J., and Kilsby, C. G. (2003). “Implications of changes in seasonal and annual extreme rainfall.” Geophysical Research Letters.

9. Güçlü, Y. S. (2018a). “Alternative Trend Analysis: Half Time Series Methodology.” Water Resources Management.
10. Güçlü, Y. S. (2018b). “Multiple Şen-innovative trend analyses and partial Mann-Kendall test.” Journal of Hydrology, Elsevier, 566, 685–704.
11. Kambezidis, H. D. (2018). “The solar radiation climate of Athens: Variations and tendencies in the period 1992–2017, the brightening era.” Solar Energy.
12. Kendall, M. G. (1975). “Rank Correlation Methods, Charles Griffin, London (1975).” Google Scholar.de
13. Leeuw, J., Methven, J., and Blackburn, M. (2016). “Variability and trends in England and Wales precipitation.” International Journal of Climatology.
14. Lin, X., Zhang, Y., Yao, Z., Gong, T., Wang, H., Chu, D., Liu, L., and Zhang, F. (2008). “The trend on runoff variations in the Lhasa River Basin.” Journal of Geographical Sciences, 18(1), 95–106.
15. Mann, H. B. (1945). “Nonparametric Tests Against Trend.” Econometrica, 13(3), 245.
16. Mohorji, A. M., Şen, Z., and Almazroui, M. (2017). “Trend Analyses Revision and Global Monthly Temperature Innovative Multi-Duration Analysis.” Earth Systems and Environment, 1(1), 9.
17. Nisansala, W. D. S., Abeysingha, N. S., Islam, A., and Bandara, A. M. K. R. (2019). “Recent rainfall trend over Sri Lanka (1987–2017).” International Journal of Climatology.
18. Nourani, V., Danandeh Mehr, A., and Azad, N. (2018). “Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann--Kendall and Şen tests.” Environmental Earth Sciences, 77(5), 207.
19. Osborn, T. J., and Hulme, M. (2002). “Evidence for trends in heavy rainfall events over the UK.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
20. Öztopal, A., and Şen, Z. (2017). “Innovative Trend Methodology Applications to Precipitation Records in Turkey.” Water Resources Management, 31(3), 727–737.
21. Shahid, S. (2011). “Trends in extreme rainfall events of Bangladesh.” Theoretical and Applied Climatology, 104(3–4), 489–499.
22. Sonali, P., and Nagesh Kumar, D. (2013). “Review of trend detection methods and their application to detect temperature changes in India.” Journal of Hydrology, 476, 212–227.
23. Swed, F. S., and Eisenhart, C. (1943). “Tables for Testing Randomness of Grouping in a Sequence of Alternatives.” The Annals of Mathematical Statistics.
24. Şen, Z. (2012). “Innovative Trend Analysis Methodology.” Journal of Hydrologic Engineering, 17(9), 1042– 1046.
25. Şen, Z. (2014). “Trend Identification Simulation and Application.” Journal of Hydrologic Engineering, American Society of Civil Engineers, 19(3), 635–642.
26. Şen, Z. (2017). “Innovative trend significance test and applications.” Theoretical and Applied Climatology, 127(3–4), 939–947.
27. Vinet, L., and Zhedanov, A. (2010). “A ‘missing’ family of classical orthogonal polynomials.” Journal of the American Statistical Association, 63(324), 1379–1389.
28. Wu, H., Li, X., and Qian, H. (2017). “Detection of anomalies and changes of rainfall in the Yellow River Basin, China, through two graphical methods.” Water (Switzerland).
29. Wu, H., and Qian, H. (2017). “Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s.” International Journal of Climatology, 37(5), 2582–2592.