Spatiotemporal Variability of Surface Wind Speed during 1961-2017 in the Jing-Jin-Ji Region,China

Wind speed variations are influenced by both natural climate and human activities.It is important to understand the spatial and temporal distributions of wind speed and to analyze the cause of its changes.In this study,data from 26 meteorological stations in the Jing-Jin-Ji region of North China from 1961 to 2017 are analyzed by using the Mann-Kendall(MK)test.Over the study period,wind speed first decreased by−0.028 m s^-1 yr^-1(p<0.01)in 1961^-1991,and then increased by 0.002 m s^-1 yr^-1(p<0.05)in 1992-2017.Wind speed was the highest in spring(2.98 m s^-1),followed by winter,summer,and autumn.The largest wind speed changes for 1961-1991 and 1992-2017 occurred in winter(−0.0392 and 0.0065 m s^-1 yr^-1,respectively);these values represented 36%and 58%of the annual wind speed changes.More than 90.4%of the wind speed was concentrated in the range of 1-5 m s^-1,according to the variation in the number of days with wind speed of different grades.Specifically,the decrease in wind speed in 1961^-1991 was due to the decrease in days with wind speed of 3-5 m s^-1,while the increase in wind speed in 1992-2017 was mainly due to the increase in days with wind speed of 2-4 m s^-1.In terms of driving factors,variations in wind speed were closely correlated with temperature and atmospheric pressure,whereas elevation and underlying surface also influenced these changes.

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index（NDVI） time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive： regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.