摘要
【目的】潜水蒸发是造成干旱区地下水水量损失和土壤盐渍化的重要因素,准确计算潜水蒸发量对水资源评价和生态耗水量至关重要。【方法】综合采用相关性分析、灰色关联度分析、主成分分析和逐步回归分析法,确定日尺度和小时尺度下潜水蒸发量与气象因子间的相关性,查明影响潜水蒸发量的最显著气象因子,建立预测潜水蒸发量的多元线性回归模型。【结果】0.5 m埋深下,潜水日蒸发量与0 cm处地温、露点温度和10 cm处土壤湿度的相关关系最显著;潜水逐时蒸发量与气温、风速和日照时间的相关关系最显著。【结论】潜水日蒸发量受大气蒸发能力和土壤供水能力的共同影响,潜水逐时蒸发量主要受大气蒸发能力的影响。
【Objective】Phreatic water evaporation is an important factor causing groundwater loss and soil salinization in arid areas.Accurate calculation of phreatic water evaporation is crucial to water resources evaluation and ecological water consumption.【Method】The correlation analysis,grey correlation analysis,principal component analysis and stepwise regression analysis were comprehensively adopted to determine the correlation between phreatic water evaporation and meteorological factors on daily and hourly scales,the most significant meteorological factors affecting phreatic water evaporation were found out,and a multiple linear regression model for predicting phreatic water evaporation was established.【Result】The daily evaporation of phreatic water under 0.5 m depth has the most significant correlation with ground temperature at 0 cm depth,dew point temperature and soil humidity at 10 cm.The hourly evaporation of phreatic water has the most significant correlation with air temperature,wind speed and sunshine hours.【Conclusion】The daily evaporation of phreatic water is affected by both atmospheric evaporation and soil water supply,the hourly evaporation of phreatic water is mainly affected by atmospheric evaporation.
作者
孙英
周金龙
齐子萱
孙振海
季彦桢
SUN Ying;ZHOU Jinlong;QI Zixuan;SUN Zhenhai;JI Yanzhen(College of Water Conservancy and Civil Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Hydrology and Water Resources Engineering Research Center,Urumqi 830052,China;Geological Environment Monitoring Station of Changji Prefecture,Changji 831100,China)
出处
《灌溉排水学报》
CSCD
北大核心
2020年第S02期14-19,共6页
Journal of Irrigation and Drainage
基金
国家自然科学基金项目(51709232)
关键词
潜水蒸发
不同时间尺度
灰色关联度
主成分分析
逐步回归
phreatic water evaporation
different time scales
grey correlation
principal component analysis
stepwise regression
