scholarly journals On the use of an innovative trend analysis methodology for temporal trend identification in extreme rainfall indices over the Central Highlands, Vietnam

2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Temporal Trend ◽  
Extreme Rainfall ◽  
Analysis Methodology ◽  
Innovative Trend Analysis ◽  
Central Highlands ◽  
Trend Identification

scholarly journals On the Use of an Innovative Trend Analysis Methodology for Temporal Trend Identification in Extreme Rainfall Indices Over the Central Highlands, Vietnam

2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Heavy Rainfall ◽  
Precipitation Amount ◽  
Extreme Rainfall ◽  
Rainfall Events ◽  
Monotonic Trend ◽  
Innovative Trend Analysis ◽  
Central Highlands ◽  
Warming World ◽  
Heavy Rainfall Events

Abstract Understanding past changes in the characteristics of climate extremes (such as frequency, intensity, and duration) forms an essential part of viable countermeasures to cope with climate-induced risks under a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands of Vietnam by employing the Şen’s innovative trend analysis (ITA) method in conjunction with the well-defined extreme rainfall indices developed by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The outcomes show that the overall trends in most extreme rainfall indices exhibited significant increases at several stations. Moreover, the high-value subgroups of most analyzed indices (such as maximum 5-day precipitation amount (Rx5day), simple daily intensity index (SDII), very wet days (R95p), extremely wet days (R99p), number of extremely heavy precipitation days (R50mm), and consecutive dry days (CDD)) were characterized mainly by significant increasing trends, thereby implying that heavy rainfall events have become more frequent and intense over recent decades. Some stations also exposed significant increasing trend behaviors in a given extreme index within all low-, medium-, and high-value subgroups. In general, it is expected that these findings yield more insightful knowledge on rainfall extremes to local decision-makers and other stakeholders.

scholarly journals An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 727
Author(s):  
Bilel Zerouali ◽  
Nadhir Al-Ansari ◽  
Mohamed Chettih ◽  
Mesbah Mohamed ◽  
Zaki Abda ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Rainfall Variability ◽  
Extreme Rainfall ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Extreme Rainfall Event ◽  
Good Efficiency ◽  
High Rainfall ◽  
Trend Identification ◽  
Annual Scale

The world is currently witnessing high rainfall variability at the spatiotemporal level. In this paper, data from three representative rain gauges in northern Algeria, from 1920 to 2011, at an annual scale, were used to assess a relatively new hybrid method, which combines the innovative triangular trend analysis (ITTA) with the orthogonal discrete wavelet transform (DWT) for partial trend identification. The analysis revealed that the period from 1950 to 1975 transported the wettest periods, followed by a long-term dry period beginning in 1973. The analysis also revealed a rainfall increase during the latter decade. The combined method (ITTA–DWT) showed a good efficiency for extreme rainfall event detection. In addition, the analysis indicated the inter- to multiannual phenomena that explained the short to medium processes that dominated the high rainfall variability, masking the partial trend components existing in the rainfall time series and making the identification of such trends a challenging task. The results indicate that the approaches—combining ITTA and selected input combination models resulting from the DWT—are auspicious compared to those found using the original rainfall observations. This analysis revealed that the ITTA–DWT method outperformed the ITTA method for partial trend identification, which proved DWT’s efficiency as a coupling method.

scholarly journals Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Yilinuer Alifujiang ◽  
Jilili Abuduwaili ◽  
Yongxiao Ge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
River Runoff ◽  
Temporal Trends ◽  
Temporal Distribution ◽  
Trend Test ◽  
Data Series ◽  
Mk Test ◽  
Comparison Results ◽  
Innovative Trend Analysis

This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the “peak” values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for “low” values, and 40 time series (44.94%) showed increasing patterns for “medium” values. According to the “low”, “medium”, and “peak” values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating “low”, “medium”, and “peak” values.

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