黄淮冬麦区气象因子与小麦晚霜冻害关系研究——以商丘市为例

Correlation between Meteorological Factors and Late Frost Damage in Huanghuai Winter Wheat Region——A Case Study of Shangqiu in Henan Province

基于1956-2015年商丘市气象数据和霜冻害资料,研究黄淮冬麦区商丘市气象因子与霜冻害的关系,以及气温、地温、相对湿度、风速与草面温度的偏相关和多元线性回归关系。结果表明:随着全球气温变暖,商丘市冬春季积温以4.35℃·d·a^(-1)的速率逐年增加;晚霜冻害除受冷空气活动影响外,与前期积温和降水量密切相关,冬春季积温偏高或降水量偏少的情况下易发生晚霜冻害;随着小麦幼穗的发育,其对低温的敏感度增加,且可引起霜冻害的最低温度有逐渐升高的趋势;气温、地表温度、平均相对湿度、平均风速与草面温度呈极显著相关(P<0.01),偏相关系数的大小表现为地表温度>气温>平均相对湿度>平均风速,可见,除气温、地表温度外,平均相对湿度、平均风速也对霜冻害的发生及轻重程度起关键作用;各因子与草面温度可用模型表述为Y=0.558ST+0.482AT+0.087RH+1.304WS-12.704,经检验,线性回归方程成立,可通过该模型对草面温度进行监测,并为未安装草面温度传感器的地区提供可靠的冻害评估结果。

Based on the meteorological data and frost damage observation data in Shangqiu during 1956-2015, meteorological conditions for the occurrence of late frosts were analyzed firstly, and then partial correlation and linear regression model of grass surface temperature（GT） and air temperature（AT）, ground temperature（ST）, relative humidity（RH）, wind speed（WS） were studied to explore the relationship between meteorological factors and late frost damage. The results showed that the accumulated temperature（CT） in winter and spring increased year by year at 4.35℃·d·y^-1 with the climate warming. Late frost injury to winter wheat was closely related to the antecedent precipitation and accumulated temperature in addition to the cold air activity. If previous accumulated temperature was too high or the amount of precipitation was less, frost damage was prone to occur. With the development of young ear, its sensitivity to sub-freezing temperature increased, and the minimum temperature causing frost injury had a tendency to rise. Air temperature, ground temperature, average relative humidity and average wind speed were significantly correlated with grass surface temperature（P〈0.01）, with an order of ground temperature〉air temperature〉average relative humidity〉average wind speed. It could be seen that the average relative humidity and average wind speed also played important roles to the occurrence and severity of frost damage, in addition to air temperature and ground temperature. The multiple linear regression model of the four meteorological factors and grass surface temperature was established by following formula： Y=0.558ST＋0.482AT＋0.087RH＋1.304WS-12.704. The model has been tested by 0.01 significant levels and could predict grass surface temperature very well. Furthermore, reliable frost damage assessment results could provide for areas where the grass surface temperature sensor was not installed.