李德萍, 张凯静, 张璐, 董海鹰, 郭丽娜, 刘学刚. 青岛地区倒春寒时空特征及气象指标研究[J]. 中国生态农业学报(中英文), 2020, 28(11): 1673-1681. DOI: 10.13930/j.cnki.cjea.200560
引用本文: 李德萍, 张凯静, 张璐, 董海鹰, 郭丽娜, 刘学刚. 青岛地区倒春寒时空特征及气象指标研究[J]. 中国生态农业学报(中英文), 2020, 28(11): 1673-1681. DOI: 10.13930/j.cnki.cjea.200560
LI Deping, ZHANG Kaijing, ZHANG Lu, DONG Haiying, GUO Lina, LIU Xuegang. Spatial and temporal characteristics and meteorological indexes of late spring coldness in Qingdao[J]. Chinese Journal of Eco-Agriculture, 2020, 28(11): 1673-1681. DOI: 10.13930/j.cnki.cjea.200560
Citation: LI Deping, ZHANG Kaijing, ZHANG Lu, DONG Haiying, GUO Lina, LIU Xuegang. Spatial and temporal characteristics and meteorological indexes of late spring coldness in Qingdao[J]. Chinese Journal of Eco-Agriculture, 2020, 28(11): 1673-1681. DOI: 10.13930/j.cnki.cjea.200560

青岛地区倒春寒时空特征及气象指标研究

Spatial and temporal characteristics and meteorological indexes of late spring coldness in Qingdao

  • 摘要: 本文基于青岛地区7个国家气象站1961—2015年3—5月逐日平均气温、最低气温资料及农作物霜冻、低温冷害或冻害资料,依据GB/T 34816—2017《倒春寒气象指标》,统计研究出倒春寒气象指标及时空特征,为提高作物防御能力提供参考。结果表明,青岛地区倒春寒分轻度和中度两级,分别占67.3%和32.7%,无重度。倒春寒年均发生1.1站次,主要在4月份。自20世纪90年代以来,年际或年代际变化主要呈减少趋势。空间分布从西北部地区到东南沿海逐渐减少,即墨中度出现概率最大。进一步分析得知,倒春寒致灾概率为26.3%。通过灾情发生时气象条件分析得出致灾性倒春寒气象指标:轻度型最低气温降至0~5℃、过程平均气温偏低2~4℃,持续时间3~5 d;中度型最低气温降至0℃以下、过程平均气温偏低4℃以上,持续时间6 d以上。从影响范围、发生时间和致灾性分析,青岛地区中度倒春寒范围大,为区域性发生,出现在3月下旬—4月中旬,可造成农作物冻害;轻度倒春寒影响范围较小,一般2站以下,出现时间略晚,4月中下旬概率较大,易造成霜冻或低温冷害。总之,春季冷空气入侵引发青岛地区倒春寒天气现象,其中26.3%为致灾性倒春寒,造成农业生产经济损失。值得重视的是轻度倒春寒,因其发生频率高于中度,且发生时段与本地主要农作物生长关键期重叠,如小麦拔节—孕穗期、大部分果树开花期,需加以防范。

     

    Abstract: Meteorological services play a role in agriculture and improve defense capability. This study used data from seven national meteorological stations in Qingdao, China, including the daily mean and minimum temperatures during the spring (3-5 months) of 1961-2015 and the crop frost or low-temperature damage from disasters, as categorized by the national standard of GB/T 34816-2017 "meteorological indicators of late spring coldness."The meteorological index and temporal and spatial characteristics of the late spring coldness were statistically analyzed. The results showed that there were two grades of mild and moderate cold periods (no severe) in late spring (67.3% and 32.7%, respectively). The average annual occurrence of late spring coldness was 1.1 stations, mainly in April. Since the 1990s, the interannual or intergenerational changes had decreased, and the spatial distribution gradually decreased from the northwest to the southeast coast. Moderate Jimo had the greatest probability of occurrence, and the probability of causing late spring coldness was 26.3%. Based on the meteorological conditions of the disaster, we can derive the meteorological index of disaster-induced late spring coldness. The mild minimum temperature dropped to 0-5℃, the average temperature anomaly was -4 to -2℃, and the duration was 3-5 days. The moderate minimum temperature dropped below 0℃, and the average temperature anomaly was < -4℃ for more than 6 days. From the influence scope, occurrence time, and disaster-causing analysis, the moderate spring coldness impact range was regional and occurred from late March to mid-April, which may cause crop freezing damage. The mild influence range was small, generally < 2 stations, the time was late, and the probability was greater in the middle and late April, which may cause frost or chilling damage. The cold spring weather caused late spring coldness; 26.3% of the coldness resulted in agricultural production-associated economic losses. Interestingly, the frequency of mild late spring coldness was higher than that of moderate late spring coldness, and the occurrence time overlapped with the critical growth period of the main crops (i.e., the wheat jointing stage to booting stage, the flowering stage of most fruit trees). This needs to be prevented. Frost damage, late frost injury, or low-temperature damage in the Qingdao area were all caused by late spring coldness. The interannual variability of the late spring coldness in Qingdao was not obvious, even interdecadal variability had even increased, which was not consistent with the overall distribution in Qingdao. This was related to the regulatory effects of the ocean. There are temperature differences from the south-eastern coast to the north-western inland region of Qingdao, and the marine climate effects also gradually weaken.

     

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