Monthly Precipitation Trend Analysis by Applying Nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) Tests In Dongting Lake, China: 1961-2012

Muhammad Tayyab (Huazhong University of Science and Technology)
Jianzhong Zhou (Huazhong University of Science and Technology)
Rana Adnan (Huazhong University of Science and Technology)
Aqeela Zahra (South China Normal University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 5, No 1: January 2017

This research highlights the precipitation trends and presents the results of the study in temporal and spatial scales. Precise predictions of precipitation trends can play imperative part in economic growth of a state. This study examined precipitation inconsistency for 23 stations at the Dongting Lake, China, over a 52-years study phase (1961–2012). Statistical, nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) tests were applied to identify trends within monthly, seasonal, and annual precipitation. The trend-free prewhitening method used to exclude sequential correlation in the precipitation time series. The performance of the Mann- Kendall (MK) and Spearman’s rho (SR) tests was steady at the tested significance level. The results showed fusion of increasing (positive) and decreasing (negative) trends at different stations within monthly and seasonal time scale. In case of whole Dongting basin on monthly time scale, significant positive trend is found, while at Yuanjiang River and Xianjiag River both positive and negative significant trends are identified.