黄土高原露日数变化趋势分析

露日数是预防和控制植物病害的重要因子,探讨气候变化条件下露日数,可为区域植物病害预测及防治提供事实依据。本文基于52个气象站点1961—2010年逐日监测气象数据,计算了黄土高原不同时空尺度的露日数,利用去趋势预置白处理(trend-free pre-whitening,TFPW)的Mann-Kendall法和Sen趋势度估计法(Sen’slope)分析了露日数变化趋势,并借助相关分析法探讨了露日数的成因。结果表明,在月尺度上,黄土高原露水发生在3—11月,全区域月平均值为7 d,9月露日数最长,其中南部、东南部和西北部露日数达8~12 d。5.77%~25.00%气象站点露日数在6月和8—11月以0.02~0.15 d×a~(-1)显著增加,17.31%和7.68%气象站点露日数在4月和7月以-0.09 d×a~(-1)和-0.02 d×a~(-1)显著降低。在季尺度上,黄土高原露水发生在春、夏和秋季,全区域季平均值为15 d,秋季露日数最长,其中南部、东南部和西北部露日数达14~26 d。仅3.85%和5.77%的气象站露日数在夏季、秋季分别以0.25~0.09 d×a~(-1)和0.15~0.09 d×a~(-1)显著增加,5.77%的气象站露日数在春季以-0.34~-0.07 d×a~(-1)显著降低。相对湿度和温度是影响上述露日数时空变化的最关键因子。

Spatiotemporal analysis of dew days in China's Loess Plateau

Global warming due to greenhouse effect has altered meteorological variables such as temperature, relative humidity, rainfall and sunshine hours. The resulting change of these variables could have strong effects that threaten population, agriculture, environment, economy and industry. It could even affect global food security and supply/demand of water resources in the world. The Loess Plateau in North China is a semiarid and sub-humid climate region and is well-known for severe soil erosion, fragile ecological environment and sensitivity to climate change. Climate change will have a major impact on the ecological environment and agricultural ecosystems. Given the above, temporal and spatial distribution of meteorological elements for the Loess Plateau region has been analyzed. However, there was little information on dew days on the plateau. Dew Dew day was a key parameter of hydrologic cycle and plant disease prevention. Analysis of the spatial distribution and long-term temporal trends of dew days and the relatedness with climatic variables may provide the basis for plant disease prediction and prevention in local areas. In this study, dew day data from 52 meteorological stations for the period 1961-2010 were calculated using a model. The spatial distribution of seasonal and monthly dew days was interpolated by Kriging and the temporal trends of the days examined using trend-free pre-whitening (TFPW) and Sen''s slope estimator. Correlation analysis explained the dew-day formation. The results showed that at monthly scale, dew days started in March and ended in November, with a monthly mean of 7 dew days. The maximum dew days were in the south, southeast and northwest of the Loess Plateau in September, with a range of 8-12 days. Analysis of dew days indicated significant positive trends for 5.77%-25.00% of the stations, with a variation of 0.02-0.15 d·a^-1 during the periods from August through November and June. Dew days with significant negative trends were found too, with the decrease in July and April by 0.02-0.09 d·a^-1 and for 7.68%-17.31% of the stations. At seasonal scale, dew days occurred in spring, summer and autumn, with a seasonal mean of 15 dew days. The maximum dew days were in autumn, with 14-26 dew days in the south, southeast and northwest of the plateau. Dew days with significant positive trends were observed in summer and autumn, which varied respectively by 0.09-0.25 d·a^-1 and 0.09-0.15 d·a^-1 for 3.85% and 5.77% of the stations. Dew days with significant negative trends were evident in spring, which varied by -0.34 to -0.07 d·a^-1 for 5.77% of the stations. Relative humidity and temperature had clear and dominant effects on the spatiotemporal trend of dew days. The study provided a quantitative basis for understanding dew day distribution and trend in the Loess Plateau under global climate change. It also provided a vital reference for future plant disease forecast, prevention and risk assessment.