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.

中文翻译：

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用于季节性趋势测试的创新趋势分析方法


虽然整体趋势识别是必不可少的，但它没有考虑识别季节性趋势行为所需的周期性趋势特征，例如每月趋势特征。此外，确定季节性趋势有助于管理或调节水资源系统以及灌溉和农业经营。在本研究中，创新趋势显着性检验 （ITST）、修订的 ITST 和 Wilcoxon 符号秩检验是非参数方法，通过将它们与创新的多边形趋势分析 （IPTA） 相结合，提出了替代季节性趋势检验，后者提供季节性行为的视觉和语言检查。来自 Chilgrove House（英国）的地下水位数据、来自多瑙河流域（罗马尼亚）的流量数据、来自土耳其的降水和温度数据用于将所提出的方法与季节性 Mann-Kendall （SMK） 方法进行比较。对于温度和地下水位数据，所有方法在不同置信区间均呈现上升趋势，而对于其他数据，根据方法的特点出现趋势。因此，在趋势检测方面，一些提出的方法比 SMK 更严格，而另一些则更敏感。除了应用于季节性趋势行为的优势外，所提出的显著性检验还与 IPTA 方法和与 SMK 类似的替代方法相结合。因此，与SMK方法相反，在所提出的组合方法中可以直观地看到明显增加或减少的季节。因此，季节性趋势行为和季节间转换都可以以图形方式进行检查，并且可以应用季节性显著性检验。 根据这些组合方法的特点，它们不仅可以像 SMK 方法一样用于季节性趋势测试，还可以用于使用站点数据而不是季节的区域趋势测试。