Logaritmik Ölçekte Yenilikçi Yönelim Çözümleme Yöntemi

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.

INNOVATIVE TREND ANALYSIS METHODOLOGY IN LOGARITHMIC AXIS

Future uncertainties of climate change cause people to worry, and therefore, in order toreduce 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 dataincluding evaporation, drought, precipitation, runoff, and flood. Along these lines, the most commonlyused 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 doesnot require initial assumptions, normality, and independence in a data structure. The ITA methodpresents a great visual ability for trend identification in graphical forms in addition to qualitative andquantitative interpretations. In the original form of the ITA approach, scatter points are presented in thearithmetic scale, where changes of scatter points in small values may not be clearly distinguishable likebig values for wide data ranges. In this study, the ITA method is used on arithmetic and logarithmicscales to calculate such differences in two sub-series. The suggested logarithmic scale methodology isreferred to as proportional Şen innovative trend analysis (ITA_P). This method is used to determinepercent trends for the annual, autumn, winter, spring and summer season rains in England. ITA_P issuccessful in determining trends in minimum values compared to the classical ITA.

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