新疆农作物生长期雹灾的时空分布及危害性评估
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摘要
冰雹是新疆农作物生长期频繁出现的严重气象灾害。该文利用1951—2017年雹灾出现次数、受灾面积、经济损失3大灾情要素,分析了新疆雹灾的时空分布规律。利用熵权重计算方法构建了评价雹灾危害性的灾损指数,依据伽玛分布对危害性等级进行了划分,并利用气候因子和农作物播种面积解释了雹灾时空变化的原因。新疆雹灾多发于天山两侧及邻近地区,3大灾情要素呈线性增加趋势。雹灾划分为一般、较重、严重、特重4个等级,塔城博州、奎玛流域、昭苏县、阿克苏地区、伽师县是特重灾区。动力抬升、水汽条件、大气层结不稳定是新疆雹灾时空分布的主要影响因子,而雹灾出现次数、农作物播种面积对受灾面积线性增长趋势的贡献率分别为93%和7%。利用水汽压、气团指数、播种面积作为因子,建立了雹灾出现次数偏多、偏少的年景预测模型,模型预测正确率达73%。研究结果可为新疆人工防雹避灾提供依据。
Hail is a serious meteorological disaster that occurs frequently in the growing period of crops in Xinjiang. Based on 2 629 records of 3 major disaster elements: the occurrence times, disaster area and economic loss of hail disaster in 1951-2017, the temporal and spatial distribution of hail disaster in Xinjiang was analyzed. Based on the entropy weight method(EWM), the disaster loss index which can comprehensively express the 3 disaster information was constructed. According to the gamma distribution function of the disaster loss index, the classification criteria of the damage grade of hail disaster were given objectively and quantitatively. Then the harm of hail disaster was classified and evaluated. The reasons for the regional, seasonal and interannual variation of hail disasters in Xinjiang were explained with water vapor pressure and air mass index. Air mass forecast model of hail disasters was established based on the logical regression. The results showed that: 1) The annual number of occurrences, the area and the economic losses of hail disasters in Xinjiang were 39 times, 75 485 hm~2, and 109.96 million yuan. The hail disasters concentrated from May to August, i.e. in the crop growth period, and mostly in June; Bozhou, Kuitun-Manas Valley, Zhaosu County, Aksu Prefecture and Kashgar Prefecture on both sides of the Tianshan Mountains and adjacent areas had the most occurrences of hail disasters, large area and heavy economic losses; the largest areas affected by the single hail were Manas River Basin and Aksu Prefecture. The area that suffered the most economic losses by the single hail was the northern part of Bazhou and Kashgar Prefecture. The 3 major disaster factors all showed a linear increase trend. The propensity rates were 1.2 times/year, 2 589 hm~2/year, and 3.36 million yuan/year. All of them showed abrupt changes in 1983, 1981, and 1979, respectively. The mean values were significantly different before and after the abrupt changes, and the mean value after the abrupt changes was 3.9 times, 5.7 times and 3.6 times of that before the abrupt changes, respectively. 2) Using the disaster loss index, the hail disaster was classified into 4 hazard levels: general, heavier, serious and extra serious. The extra serious disaster area was located in Tacheng, Bozhou, Kuitun-Manas Valley, Zhaosu County, Aksu Prefeccture, and Jashi. Aksu Prefecture ranked first, followed by Bozhou. In recent years, the hail disasters in the extra serious disaster-hit areas have been on the rise, requiring major defense. Multi defense lines should be added to the moving path of hail cloud, and the density of anti-aircraft guns and rocket launchers should be increased to carry out large-scale anti-hail catalytic operation for the continuous moving and developing hail clouds in order to minimize the harm of hail disaster to the maximum extent. 3) Vapor pressure and air mass index were good indications of the water vapor conditions and atmospheric stability when the hail disaster happened. In the areas, seasons and years where or when water vapor pressure and air mass index were high, more hail disasters occurred, and vice versa. Vapor pressure and air mass index were the main reasons for the impact of disasters. The number of occurrences of disaster and the area planted with crops both had an impact on the affected area. The contribution rates of the former 2 to the linear growth trend of the latter were 93% and 7%, respectively. The water vapor pressure, air mass index and planting area were used as the factors, and an annual forecasting model forecasting high or low frequency of the occurrence times of hail disaster was established. The fitting accuracy of the model was 73% and the fitting effect was good.
Hail is a serious meteorological disaster that occurs frequently in the growing period of crops in Xinjiang. Based on 2 629 records of 3 major disaster elements: the occurrence times, disaster area and economic loss of hail disaster in 1951-2017, the temporal and spatial distribution of hail disaster in Xinjiang was analyzed. Based on the entropy weight method(EWM), the disaster loss index which can comprehensively express the 3 disaster information was constructed. According to the gamma distribution function of the disaster loss index, the classification criteria of the damage grade of hail disaster were given objectively and quantitatively. Then the harm of hail disaster was classified and evaluated. The reasons for the regional, seasonal and interannual variation of hail disasters in Xinjiang were explained with water vapor pressure and air mass index. Air mass forecast model of hail disasters was established based on the logical regression. The results showed that: 1) The annual number of occurrences, the area and the economic losses of hail disasters in Xinjiang were 39 times, 75 485 hm~2, and 109.96 million yuan. The hail disasters concentrated from May to August, i.e. in the crop growth period, and mostly in June; Bozhou, Kuitun-Manas Valley, Zhaosu County, Aksu Prefecture and Kashgar Prefecture on both sides of the Tianshan Mountains and adjacent areas had the most occurrences of hail disasters, large area and heavy economic losses; the largest areas affected by the single hail were Manas River Basin and Aksu Prefecture. The area that suffered the most economic losses by the single hail was the northern part of Bazhou and Kashgar Prefecture. The 3 major disaster factors all showed a linear increase trend. The propensity rates were 1.2 times/year, 2 589 hm~2/year, and 3.36 million yuan/year. All of them showed abrupt changes in 1983, 1981, and 1979, respectively. The mean values were significantly different before and after the abrupt changes, and the mean value after the abrupt changes was 3.9 times, 5.7 times and 3.6 times of that before the abrupt changes, respectively. 2) Using the disaster loss index, the hail disaster was classified into 4 hazard levels: general, heavier, serious and extra serious. The extra serious disaster area was located in Tacheng, Bozhou, Kuitun-Manas Valley, Zhaosu County, Aksu Prefeccture, and Jashi. Aksu Prefecture ranked first, followed by Bozhou. In recent years, the hail disasters in the extra serious disaster-hit areas have been on the rise, requiring major defense. Multi defense lines should be added to the moving path of hail cloud, and the density of anti-aircraft guns and rocket launchers should be increased to carry out large-scale anti-hail catalytic operation for the continuous moving and developing hail clouds in order to minimize the harm of hail disaster to the maximum extent. 3) Vapor pressure and air mass index were good indications of the water vapor conditions and atmospheric stability when the hail disaster happened. In the areas, seasons and years where or when water vapor pressure and air mass index were high, more hail disasters occurred, and vice versa. Vapor pressure and air mass index were the main reasons for the impact of disasters. The number of occurrences of disaster and the area planted with crops both had an impact on the affected area. The contribution rates of the former 2 to the linear growth trend of the latter were 93% and 7%, respectively. The water vapor pressure, air mass index and planting area were used as the factors, and an annual forecasting model forecasting high or low frequency of the occurrence times of hail disaster was established. The fitting accuracy of the model was 73% and the fitting effect was good.
引文
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[2]Stanley A C.Temporal and spatial distributions of damaging hail in the Continental United States[J].Physical Geography,2008,29(4):341-350.
[3]张芳华,高辉.中国冰雹日数的时空分布特征[J].南京气象学院学报,2008,31(5):687-693.Zhang Fanghua,Gao Hui.Temproal and spatial features of hail days in China[J].Journal of Nanjing Institute of Meteorology,2008,31(5):687-693.(in Chinese with English abstract)
[4]王晓明,倪惠,周淑香.吉林省冰雹灾害时空分布规律及特征分析[J].灾害学,1999,14(3):50-54.Wang Xiaoming,Ni Hui,Zhou Shuxiang.Analysis on distribution law and character in time and space of hail disasters in Jilin Province[J].Journal of Catastrophology,1999,14(3):50-54.(in Chinese with English abstract)
[5]杜继稳,王小宁,雷向杰,等.陕北气象灾害与生态环境治理[J].灾害学,2001,16(1):71-77.Du Jiwen,Wang Xiaoning,Lei Xiangjie,et al.Meteorological disasters and ecological management in the North Shanxi[J].Journal of Catastrophology,2001,16(1):71-77.(in Chinese with English abstract)
[6]纪晓玲,马筛艳,丁永红,等.宁夏40年灾害性冰雹天气分析[J].自然灾害学报,2007,16(3):24-28.Ji Xiaoling,Ma Shaiyan,Ding Yonghong,et al.Analysis of disastrous hail weather in Ningxia in recent 40 years[J].Journal of Natural Disasters,2007,16(3):24-28.(in Chinese with English abstract)
[7]包云轩,覃文娜,高苹,等.江苏省近30年冰雹灾害的时空变化规律[J].自然灾害学报,2012,21(5):197-206.Bao Yunxuan,Qin Wenna,Gao Ping,et al.Spatiotemporal change patterns of hail disaster in Jiangsu Province during recent 30 years[J].Journal of Natural Disasters,2012,21(5):197-206.(in Chinese with English abstract)
[8]段玮,胡娟,赵宁坤,等.云南冰雹灾害气候特征及其变化[J].灾害学,2017,32(2):90-96.Duan Wei,Hu Juan,Zhao Ningkun,et al.Climatic characteristics and changes of hail disasters in Yunnan[J].Journal of Catastrophology,2017,32(2):90-96.(in Chinese with English abstract)
[9]张国庆,刘蓓.青海省冰雹灾害分布特征[J].气象科技,2006,34(5):558-562.Zhang Guoqing,Liu Bei.Distributional characteristics of hail disasters in recent 40 years over Qinghai Province[J].Meteorological Science and Technology,2006,34(5):558-562.(in Chinese with English abstract)
[10]龙余良,金勇根,刘志萍,等.江西省冰雹气候特征及冰雹灾害研究[J].自然灾害学报,2009,18(1):53-57.Long Yuliang,Jin Yonggeng,Liu Zhiping,et al.Climatic character and disaster of hail in Jiangxi Province[J].Journal of Natural Disasters,2009,18(1):53-57.(in Chinese with English abstract)
[11]向明燕,范丽红,海米提·依米提,等.新疆近45年气象灾害及其防御措施[J].干旱区研究,2007,24(5):712-716.Xiang Mingyan,Fan Lihong,Haimit Yimiti,et al.Study on the change of meteorological disasters and the prevention measures in Xinjiang since recent 45 years[J].Arid Zone Research,2007,24(5):712-716.(in Chinese with English abstract)
[12]陈彦清,杨建宇,苏伟,等.县级尺度下雪灾风险评价方法[J].农业工程学报,2010,26(增刊2):307-311.Chen Yanqing,Yang Jianyu,Su Wei,et al.Risk assessment of snow disaster on county scale[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(Supp.2):307-311.(in Chinese with English abstract)
[13]秦越,徐翔宇,许凯,等.农业干旱灾害风险模糊评价体系及其应用[J].农业工程学报,2013,29(10):83-91.Qin Yue,Xu Xiangyu,Xu Kai,et al.Fuzzy evaluation system of agriculture drought disaster risk and its application[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(10):83-91.(in Chinese with English abstract)
[14]梁旭,丁建军,曹宁.宁夏自然灾害综合分区及多尺度特征分析[J].干旱区资源与环境,2011,25(4):62-68.Liang Xu,Ding Jianjun,Cao Ning.Comprehensive division and multi-scale character analysis on the natural disasters in Ningxia[J].Journal of Arid Land Resources and Environment,2011,25(4):62-68.(in Chinese with English abstract)
[15]董婷,任东,孟令奎,等.基于阈值优化模糊投票法的农业旱情等级遥感评估[J].农业工程学报,2018,34(12):137-145.Dong Ting,Ren Dong,Meng Lingkui,et al.Remote sensing evaluation of drought degree based on threshold-optimized fuzzy majority voting model[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(12):137-145.(in Chinese with English abstract)
[16]翟志宏,姜会飞,叶彩华,等.基于概率分布模型的北京地区冰雹灾害风险区划[J].中国农业大学学报,2008,13(6):49-53.Zhai Zhihong,Jiang Huifei,Ye Caihua,et al.Hail risk distribution based on probability density model in Beijing region[J].Journal of China Agricultural University,2008,13(6):49-53.(in Chinese with English abstract)
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