Wavelet analysis and nonparametric test for climate change in Tarim River Basin of Xinjiang during 1959–2006

Abstract

Using wavelet analysis, regression analysis and the Mann-Kendall test, this paper analyzed time-series weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tarim River Basin of Xinjiang Uygur Autonomous Region, China. Major findings are as follows: 1) In the 48-year study period, average annual temperature, annual precipitation and average annual relative humidity all presented nonlinear trends. 2) At the 16-year time scale, all three climate indices unanimously showed a rather flat before 1964 and a detectable pickup thereafter. At the 8-year time scale, an S-shaped nonlinear and uprising trend was revealed with slight fluctuations in the entire process for all three indices. Incidentally, they all showed similar pattern of a slight increase before 1980 and a noticeable up-swing afterwards. The 4-year time scale provided a highly fluctuating pattern of periodical oscillations and spiral increases. 3) Average annual relative humidity presented a negative correlation with average annual temperature and a positive correlation with annual precipitation at each time scale, which revealed a close dynamic relationship among them at the confidence level of 0.001. 4) The Mann-Kendall test at the 0.05 confidence level demonstrated that the climate warming trend, as represented by the rising average annual temperature, was remarkable, but the climate wetting trend, as indicated by the rising annual precipitation and average annual relative humidity, was not obvious.

Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)

“…They found that both annual precipitation and streamflow experience inter-annual to decadal variability. Xu et al (2009) studied the impact of climate change in the Tarim River basin in China for the period of 1959-2006, by approximating non-linear trends in annual temperature, precipitation, and relative humidity time series using a wavelet-based decomposition and reconstruction technique. They found that all variables showed non-linear trends and/or fluctuating patterns, especially at the 4-and 8-year scales.…”

An integrated statistical approach to identify the nonlinear trend of runoff in the Hotan River and its relation with climatic factors

“…It is difficult to model the physical hydro-climatic process because of the difficulties to obtain extensive precise data on underlying surface by the grid method, whereas traditional statistical analysis cannot directly deal with the complicated nonlinear process of the hydro-climatic system (Xu et al 2008b(Xu et al , 2009b. To date, these questions have not been answered satisfactorily; therefore, more studies are required to explore the nonlinear characteristics of hydro-climatic processes from different perspectives using different methods.…”

Air Temperature Change in the Southern Tarim River Basin, China, 1964–2011

“…The MK nonparametric trend test is commonly used to assess the significance of monotonic trends in meteorological and hydrologic series all over the world [19, 20]. For a time series X = { x 1 , x 2 … x n }, in which n > 10, the standard normal statistic Z is estimated as $\begin{array}{c}{Z}_c=\{\begin{array}{cc}\frac{S-\mathrm{normal1}}{\sqrt{\text{var}\left(S\right)}},\hfill & S>\mathrm{normal0}\hfill \\ \mathrm{normal0},\hfill & S=\mathrm{normal0}\hfill \\ \frac{S+\mathrm{normal1}}{\sqrt{\text{var}\left(S\right)}},\hfill & S<\mathrm{normal0},\hfill \end{array}\end{array}$ where $\begin{array}{c}S=\underset{i=\mathrm{normal1}}{\overset{n-\mathrm{normal1}}{{\displaystyle false\sum }}}\underset{k=i+\mathrm{normal1}}{\overset{n}{{\displaystyle false\sum }}}\mathrm{sgn}\left(x_k-x_i\right),\\ \mathrm{sgn}\left(\theta \right)=\{\begin{array}{cc}\mathrm{normal1},\hfill & \theta >\mathrm{normal0}\hfill \\ \mathrm{normal0},\hfill & \theta =\mathrm{normal0}\hfill \\ -\mathrm{normal1},\hfill & \theta <\mathrm{normal0},\hfill \end{array}\\ \text{var}\left[S\right]=\frac{\left[n\left(n-\mathrm{1}\right)\left(\mathrm{2}n+\mathrm{5}\right)-{\sum }_t^{}t\left(t-\mathrm{1}\right)\left(\mathrm{2}t+\mathrm{5}\right)\right]}{\mathrm{18}}.\end{array}$ …”