Dew is an important source of water which significantly influences the physiological status of vegetation and the microclimate environment. For quantifying the characteristics of dew events and analyzing the underlying mechanism of dew formation in different ecosystems, we measured, based on the flux-profile method, the amount, frequency and duration of dew events in two croplands, an arid artificial oasis cropland in Zhangye, Gansu province and a sub-humid cropland in Luancheng, Hebei province in China. The results showed that dew events were observed in a total of 69 days in Zhangye, which accounted for 59% of the growing season（from 28 May to 21 September, 2012）, while 128 days in Luancheng, which accounted for 79% of the growing season（from 5 April to 13 September, 2008）. The frequencies of dew events were 2.8 and 2.4 times of those of precipitation in Zhangye and Luancheng, respectively. In addition, the dew amount reached up to 9.9 and 20.2 mm in Zhangye and Luancheng, which accounted for 9.5% and 4.1% of precipitation, respectively. The average amount of dew was 0.14 and 0.16 mm/night in Zhangye and Luancheng, respectively and the duration of dew events ranged from 0.5 to 12.0 h in the two study sites. Dew amounts were associated with the gradient of atmospheric water vapor concentration and dew duration（P＆lt;0.001） in both the two sites. The result implies that dew events play a more important role in crop growth in arid areas in comparison to sub-humid areas considering the dew occurrence frequency and the amount per night.
贝叶斯最大熵方法(bayesian maximum entropy,简称BME)是现代时空地统计学的重要组成部分。该方法采用统计学中的贝叶斯理论和信息论中熵的概念来认识和处理时空变量,可以将所研究时空要素的软数据和硬数据系统合理地综合到对该要素的空间估计和分析制图过程中。本文首先结构化梳理贝叶斯最大熵方法的原理,对理论较深奥、公式较复杂的贝叶斯最大熵方法及该方法的特点加以概括,同时归纳与总结贝叶斯最大熵方法在地球科学领域内多个方向的应用研究进展,最后对该方法及其应用作总结与展望。经国内外学者多年的研究和实践,贝叶斯最大熵方法已被证明在地球科学领域有着更广阔的应用前景。
