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- MakaleAçık Erişim2025Scopus SJRQ1Combined innovative trend analysis methods for seasonal trend testing
Şan, Murat
While holistic trend identification is essential, it does not consider the periodic, e.g., monthly, trend characteristics needed to identify seasonal trend behavior. Furthermore, identifying seasonal trends can help manage or regulate water resources systems and irrigation and agricultural operations. In this study, the innovative trend significance test (ITST), the revised ITST, and the Wilcoxon signed-rank test, which are non-parametric methods, are proposed as alternative seasonal trend tests by combining them with the innovative polygon trend analysis (IPTA), which provides visual and linguistic examinations of seasonal behaviors. Groundwater level data from the Chilgrove House (United Kingdom), flow data from the Danube River Basin (Romania), precipitation and temperature data from Türkiye were used to compare the proposed methods with the seasonal Mann-Kendall (SMK) method. For temperature and groundwater level data, all methods showed an increasing trend at different confidence intervals, but for other data, trends emerged according to the characteristics of the methods. So, some of the proposed methods are more rigid than the SMK in trend detection, while others are more sensitive. In addition to the advantage of its applications to seasonal trend behaviors, the proposed significance tests are combined with the IPTA method and comparable alternative methods to the SMK. So, seasons with a clear increase or decrease can be seen visually in the proposed combined methods contrary to the SMK method. Thus, both seasonal trend behaviors and inter-seasonal transitions can be examined graphically, and seasonal significance testing can be applied. According to the characteristics of these combined methods, they can be used not only for seasonal trend tests, just like the SMK method, but also for regional trend tests using stations data instead of seasons.
AtıfScopus28WoS25OpenAlex30
İncele - MakaleYalnız künye2021Scopus SJRQ2Innovative and polygonal trend analyses applications for rainfall data in Vietnam
Şan, Murat; Akçay, Fatma; Linh, Nguyen Thi Thuy; Kankal, Murat; Pham, Quoc Bao
It is a known fact that the size, frequency, and spatial variability of hydrometeorological variables will irregularly increase under the impact of climate change. Among the hydrometeorological variables, rainfall is one of the most important. Trend analysis is one of the most effective methods of observing the effects of climate change on rainfall. Recently, new graphical methods have been proposed as an alternative to classical trend analysis methods. Innovative Polygon Trend Analysis (IPTA), which evolved from Innovative Trend Analysis (ITA), is currently one of the proposed methods and it does not contain any assumptions. The aim of this study is to compare IPTA, ITA with the Significance Test and Mann-Kendall (MK) methods. To achieve this, the monthly total rainfall trends of 15 stations in the Vu Gia-Thu Bon River Basin (VGTBRB) of Vietnam have been examined for the period 1979–2016. The analyses show that rainfall tends to increase (decrease) in March (June) at nearly all stations. IPTA and ITA with the Significance Test are more sensitive than MK in determining the trends. While trends were detected in approximately 90% of all months in IPTA and ITA with the Significance Test, this rate was only 23% in the MK test. Although the arithmetic mean graphs in the 1-year hydrometeorological cycle are considerably regular at almost all stations, their standard deviations are relatively irregular. The most critical month for trend transitions between consecutive months for all the stations is October, which has an average trend slope of −1.35 and a trend slope ranging from −3.98 to −0.21, which shows a decreasing trend.
AtıfScopus81WoS71OpenAlex87İncele