摘要
研究稀疏植被下垫面水热通量特征对于提高农业水管理和高效利用农业气候资源极为重要,利用2018年葡萄园波文比系统数据和气象资料,分析了半湿润地区葡萄园下垫面水热通量在不同生育阶段和典型天气条件下的分配特征及潜热通量对相关气象因素的响应,并运用BP神经网络对葡萄园潜热通量进行预测,根据预测模型,分析比较潜热通量对环境因子变化的敏感性。结果表明,水热通量的日变化呈单峰二次曲线,从7∶00左右开始增长,12∶30左右达到峰值后开始减小,夜间基本不变。感热通量在全部生育期都占了地表能量的绝大部分,潜热通量较小,所占比例随生育期时间逐渐增大,在果实膨大期达到最大后开始减小。在雨天,水热通量各分量都比晴天小,潜热通量略大于感热通量。而阴天潜热通量的变化幅度明显比其他天气条件下都大。BP神经网络根据气象因子模拟出的潜热通量与波文比法的计算值相关性较高,同时,潜热通量对净辐射和土壤热通量的变化最为敏感,而对风速变化的敏感性较低。
Studying the characteristics of water and heat flux on the undersurface of sparse vegetation is of great importance for improving agricultural water management and use efficiency of agricultural climate resources.The distribution characteristics of water and heat flux on the underlying surface of the vineyard at different growth stages and under typical weather conditions and the response of latent heat flux to relevant meteorological factors were analyzed by using the data of vineyard Bowen ratio system and meteorological data in 2018.The BP neural network was used to predict the latent heat flux of vineyard.According to the prediction model,the sensitivity of latent heat flux to environmental factors was analyzed and compared.The results showed that the diurnal variation of water and heat flux presented a single-peak conic curve,which started increasing from about 7∶00 a.m.to about 12∶30 p.m.Sensible heat flux accounted for most of the surface energy in the whole growth period.The latent heat flux was small,and the proportion increased with the growth period,and then decreased after the fruit reached the maximum expansion stage.In rainy days,each component of water-heat flux was smaller than that of sunny days,and latent heat flux was slightly larger than sensible heat flux.The variation range of latent heat flux in overcast days was obviously larger than that in other weather conditions.The latent heat flux simulated by BP neural network according to meteorological factors was highly correlated with the calculated value of Bowen ratio method.Meanwhile,the latent heat flux was most sensitive to the change of net radiation and soil heat flux,but less sensitive to the change of wind speed.
作者
余昭君
胡笑涛
冉辉
王雪梦
王文娥
何雪霞
YU Zhao-jun;HU Xiao-tao;RAN Hui;WANG Xue-meng;WANG Wen-e;HE Xue-xia(Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Xianyang 712100, Shaanxi Province, China)
出处
《节水灌溉》
北大核心
2020年第2期96-101,108,共7页
Water Saving Irrigation
基金
国家重点研发计划课题(2017YFD0201508)
陕西省科技统筹创新工程项目(2016KTZDNY-01-05)。
关键词
半湿润地区
稀疏植被
水热通量
BP神经网络
semi-humid region
sparse vegetation
water and heat fluxes
BP neural network
