Theoretical and Applied Climatology
Six-decade temporal change and seasonal decomposition of climate variables in Lake Dianchi Watershed (China): Stable trend or abrupt shift?
Jing Zhou, Zhongyao Liang, Yong Liu, Huaicheng Guo , Dan He, Lei Zhao
Abstract: Meteorological trend analysis is a useful tool for understanding climate change and can provide useful information on the possibility of future change. Lake Dianchi is the sixth largest freshwater body in China with serious eutrophication. Algal blooms outbreak was proven to be closely associated with some climatic factors in Lake Dianchi. It is therefore essential to explore the trends of climatic time series to understand the mechanism of climate change on lake eutrophication. We proposed an integrated method of Mann–Kendall (MK) test, Seasonal-Trend Decomposition using LOESS (STL) and Regime Shift Index (RSI) to decompose the trend analysis and identify the stable and abrupt changes of some climate variables from 1951 to 2009. The variables include mean air temperature (Tm), maximum air temperatures (Tmax), minimum air temperatures (Tmin), precipitation (Prec), average relative humidity (Hum) and average wind speed (Wind). The results showed that (a) annual Tm, Tmax and Tmin have a significant increasing trend with the increasing rates of 0.26℃, 0.15℃and 0.43℃ per decade respectively; (b) annual precipitation has an insignificant decreasing trend with the decreasing rate of 3.17 mm per decade; (c) annual Hum has a significant decreasing trend in all seasons; and (d) there are two turning points for temperature rise around 1980 and 1995 and two abrupt change periods for precipitation with the extreme points appearing in 1963 and 1976. Temperature rise, precipitation decline in summer and autumn as well as wind speed decrease after 1990s may be an important reason for algal blooms outbreak in Lake Dianchi. This study was expected to provide foundation and reference for regional water resources management.
Keywords: Trends analysis; Mann–Kendall test; Seasonal-Trend Decomposition using LOESS (STL); Climate variability; Lake Dianchi watershed