Abstract
Climatic time series data are often nonlinear and non-stationary and hence the use of traditional techniques may not be suitable for their analysis. This research is focused on the monthly series of air temperature and precipitation data of 25 years from 1988 to 2012 at Langtang Meteorological Station (LMS), Kyangjing in Langtang River basin, Nepal to extract multi-scale cycles and trends. To address the non-linearity and non-stationary of these time series, we used Empirical Mode Decomposition (EMD) method. EMD decomposed LMS temperature and precipitation series into different oscillatory modes called Intrinsic Mode Functions (IMFs) and residue called trend. The extracted IMFs are subjected to Fast Fourier Transform (FFT) to determine their average period along with their power density. There exist oscillations of 1 year, 3.13 years, 6.25 years, 8.33 years and 12.5 years in temperature data. Among these cycles, only 1 year cycle is distinguished from Gaussian white noise at 95% confidence level. The air temperature at LMS, Kyangjing reflects monotonic positive trend till 2006 but remains as nearly steady state around 3°C from the end of 2006. Similarly, the precipitation data is embedded with cycles of 6 months, 1 year, 2.08 years, 2.27 years and 8.33 years of which only the first three are statistically significant at 95% confidence level. The precipitation shows a mixed trend with decreasing pattern till mid 1990s, increasing pattern till mid 2000s and again decreasing pattern till 2012. One year cycle is dominant in both the time series data. The above results reflect that temperature and precipitation fluctuates on various time scales. The effect of the changes in temperature and precipitation has already been manifested in the form of melting glaciers in this region. The causes for these oscillations might be related to phenomenon like Quasi-biennial Oscillation (QBO), solar activity, El Nino, monsoon climate dynamics and other local characteristics of the basin.
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