Innovative Trend Analysis of Air Temperature and Precipitation in the Jinsha River Basin, China

Abstract

Trend detection based on hydroclimatological time series is crucial for understanding climate change. In this study, the innovative trend analysis (ITA) method was applied to investigate trends in air temperature and precipitation over the Jinsha River Basin (JRB), China, from 1961 to 2016 based on 40 meteorological stations. Climatic factors series were divided into three categories according to percentile, and the hidden trends were evaluated separately. The ITA results show that annual and seasonal temperatures have significantly increased whereas the variation range of annual temperature tended to narrow. Spatial pattern analysis of the temperature indicates that high elevation areas show more increasing trends than flat areas. Furthermore, according to ITA, significant increase trends are observed in annual precipitation and “high” category of spring precipitation. The sub-basins results show a significant decreasing trend in elevation zones of ≤2000 m and an increasing trend where elevation is >2000 m. Moreover, linkage between temperature and precipitation was analyzed and the potential impact of the combined changes was demonstrated. The results of this study provide a reference for future water resources planning in the JRB and will help advance the understanding of climate change in similar areas.

Detailed Trend Analysis of Extreme Climate Indices in the Upper Geum River Basin

“…The trends in datasets can either be monotonic, where a variable consistently increases or decreases through time, or a step trend, where abrupt changes in data may occur at a specific time. Various studies on trend analysis of climate parameters [11][12][13][15][16][17][18][19][21][22][23], used the two non-parametric tests known as the Mann-Kendall trend test, and Theil-Sen slope test, to detect significant trends, and to quantify the magnitude of trends, respectively.…”

“…A set of standard measurements of the extreme climate indices based daily precipitation, and daily (minimum and maximum) temperatures were provided by the Expert Team on Climate Change Detection and Indices (ETCCDI) [9,10]. For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales.…”

“…For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales. While, majority of the studies focused on annual scales, and lesser on monthly scales, the former is insufficient in providing detailed information in a watershed, such as detecting shifting of precipitation patterns.…”

Spatio‐temporal analysis of trends in annual maximum rainfall in the North‐West of Algeria: Comparative analysis of recent and old non‐parametric methods

“…Moreover, the ITA method complies perfectly with the other applied methods but with higher percentages than the others. This discrepancy can be explained by the ability of this method to unmask hidden increasing (decreasing) trends in the time series in Turkey (Esit, 2022; Güçlü, 2020), in Southern Italy (Caloiero, 2019) and in China (Dong et al, 2020; Li et al, 2018).…”

Trend Analysis of Annual Surface Air Temperature for Some Stations over Iraq

“…Spatial pattern analysis of the temperature indicates that high elevation areas show more increasing trends than flat areas. The results suitable fora future water resources planning and this would help advance the understanding of climate change in many areas in this world [10]. This study investigates the dominant model of SAT variation and associated circulation change over Baghdad.…”