当前位置: X-MOL 学术Theor. Appl. Climatol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Innovative and polygonal trend analyses applications for rainfall data in Vietnam
Theoretical and Applied Climatology ( IF 2.8 ) Pub Date : 2021-03-01 , DOI: 10.1007/s00704-021-03574-4
Murat Şan , Fatma Akçay , Nguyen Thi Thuy Linh , Murat Kankal , Quoc Bao Pham

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.



中文翻译:

创新和多边形趋势分析在越南降雨数据中的应用

众所周知的事实是,在气候变化的影响下,水文气象变量的大小,频率和空间变异性会不规则地增加。在水文气象变量中,降雨量是最重要的变量之一。趋势分析是观察气候变化对降雨影响的最有效方法之一。近来,已经提出了新的图形方法作为经典趋势分析方法的替代。从创新趋势分析(ITA)演变而来的创新多边形趋势分析(IPTA)是目前提出的方法之一,它不包含任何假设。这项研究的目的是将IPTA,ITA与显着性检验和Mann-Kendall(MK)方法进行比较。为了达成这个,考察了越南Vu Gia-Thu Bon流域(VGTBRB)的1979-2016年期间的15个站的月降水总量趋势。分析表明,3月(6月)的几乎所有站点降雨都有增加(减少)的趋势。IPTA和ITA的重要性检验在确定趋势方面比MK更为敏感。尽管在IPTA和ITA中使用显着性测试在大约90%的月份中都发现了趋势,但是在MK测试中,这一比率仅为23%。尽管一年水文气象周期中的算术平均值图在几乎所有站点上都是相当规则的,但它们的标准偏差却相对不规则。所有站点连续月份之间趋势转换的最关键月份是十月,其平均趋势斜率为-1.35,趋势斜率为-3.98至-0.21,

更新日期:2021-03-02
down
wechat
bug