首页> 中文期刊> 《河北农业大学学报》 >太行山山前平原近40年潜在蒸发变化及影响因子分析--以藁城区为例

太行山山前平原近40年潜在蒸发变化及影响因子分析--以藁城区为例

         

摘要

为研究太行山山前平原区潜在蒸发量的变化,促进对生态环境治理和水资源的合理利用,利用1972—2012年该区典型站点———石家庄市藁城区蒸发皿蒸发量以及其他气象要素的观测资料为例,采用线性倾向估计、相关系数法和彭曼-蒙蒂斯(Penman‐M onteinl)方程,计算并分析了太行山山前平原区潜在蒸发量的变化特征及其影响因子。结果表明:藁城区潜在蒸发量呈明显减少趋势,但潜在蒸发量减小趋势小于小型蒸发皿实测值。在潜在蒸发构成项中,动力蒸发项与辐射蒸发项的年际变化趋势均与潜在蒸发在0.01水平上呈显著正相关,相关系数分别为0.8561和0.7454。在潜在蒸发的年际变化中,日照时数影响最大,相关系数为0.716(P=0.01)。在潜在蒸发季节变化中,春季、夏季和秋季日照时数对潜在蒸发的影响起到了最为决定性的作用,相关系数分别为0.818、0.875和0.752,其次是风速和相对湿度。在冬季的潜在蒸发变化过程中,温度的影响作用最大,相关系数0.699( P=0.01)。%In order to study the potential evaporation in the Piedmont plain of the Taihang‐Mountains ,and promote the ecological environment management and rational utilization of water resources ,with the data of evaporating pan as well as other meteorological observations in Gaochengdistrict ,Shijiazhuang , Hebei ,China ,a typical station of Piedmont plain of the TaihangMountains as the example ,the authors calculated and analyzed the climatic features of potential evaporation and influence factors from1972 to 2012 in the Piedmont plain of the Taihang mountains by the methods of linear tendency estimation ,correlation coefficient and Penman-Monteinl potential evaporation amount .The results showed that :Potential evapo‐ration in Gaochengdistrictw asdecreasing in all the seasons over the past 40 years ,but Penman-Monteinl potential evaporation amount awsless than the measured value of evaporating dish .In the contributing parts of potential evaporation ,the interannual variation trend of dy‐namic evaporation and radiation evaporation was significantly positive correlated(P= 0 .01) , the correlation coefficientswere0 .8561 and 0 .7454 respectively .The sunshine was found the biggest impact factor of P-M potential evaporation with the correlation coefficient of0 .716(P=0 .01) .In the seasonal variation of P-M potential evaporation ,in spring ,summer and au‐tumn , the sunshine affectedthepotential evaporation greatly , the correlation coeffi‐cientswere0 .818 ,0 .875and 0 .752 respectively .In the winter ,the temperature played the greatestrole ,the correlation coefficient was 0 .699(P=0 .01) .

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