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The Beas River, located in the Western Himalayan mountainous regions in India, is one of the major tributaries of the Indus River. However, recent changes of precipitation and related climatic teleconnections in this river basin have rarely been investigated yet. In this study, the trend and concentration characteristics of precipitation during1982–2010 are investigated by using Mann–Kendall trend test and two kinds of concentration indices. The climatic teleconnections are explored with the help of cross correlation, wavelet transform and composite analysis, revealing the relationship of precipitation with climatic indices of Indian summer monsoon (ISM), El Nino/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO). The results indicate that: (1) Precipitation of most of the stations increased in the monsoon season while precipitation of all the stations decreased in the non-monsoon seasons. As a result, the annual precipitation of the majority of the stations was on the decrease. (2) A general increase in the precipitation Gini coefficient and precipitation concentration degree (PCD) was detected. Moreover, the precipitation concentration period (PCP) is mainly within the period from May to August, and more PCP occurred in the monsoon months recently. (3) The relationship between monsoon precipitation and ISM is not significant in the Beas River basin. The relationship between precipitation and ENSO in winter is less significant than in the monsoon season, and the relationship of monsoon/winter precipitation with IOD is not as evident as that with ENSO. Besides, ENSO and NAO play important roles in the changes of monsoon and winter precipitation in the Beas River basin. [ABSTRACT FROM AUTHOR]

Identifying the links between variations in large-scale climate patterns and precipitation is of tremendous assistance in characterizing surplus or deficit of precipitation, which is especially important for evaluation of local water resources and ecosystems in semi-humid and semi-arid regions. Restricted by current limited knowledge on underlying mechanisms, statistical correlation methods are often used rather than physical based model to characterize the connections. Nevertheless, available correlation methods are generally unable to reveal the interactions among a wide range of climate oscillations and associated effects on precipitation, especially on extreme precipitation. In this work, a probabilistic analysis approach by means of a state-of-the-art Copula-based joint probability distribution is developed to characterize the aggregated behaviors for large-scale climate patterns and their connections to precipitation. This method is employed to identify the complex connections between climate patterns (Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and seasonal precipitation over a typical semi-humid and semi-arid region, the Haihe River Basin in China. Results show that the interactions among multiple climate oscillations are non-uniform in most seasons and phases. Certain joint extreme phases can significantly trigger extreme precipitation (flood and drought) owing to the amplification effect among climate oscillations. [ABSTRACT FROM AUTHOR]