Research on wind disaster risk assessment for facility agriculture in Shandong Province
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摘要: 本研究利用气象观测、高分卫星遥感、灾情直报等数据, 基于自然灾害风险评估理论, 探索了设施农业风灾风险评估方法; 以山东省为例, 从设施农业风灾危险性、暴露度和脆弱性以及防灾减灾能力4个方面, 选择因子构建设施农业风灾风险评估指数, 并对设施农业风灾风险进行了分析和验证。结果显示, 风灾危险性受地形影响, 高值区主要分布在鲁中山区和山东半岛; 暴露度与设施种植建设规范基本一致, 高值区主要分布在鲁中的东部、鲁西北和鲁西南; 脆弱性与基本风压分布较为吻合, 中高值区主要分布在鲁南和鲁中的东部地区; 防灾减灾能力较弱地区主要分布在气象灾害预警能力相对较弱、经济欠发达的鲁西北、鲁西南和鲁中的西部; 设施农业风灾风险高低由危险性、暴露度、脆弱性和防灾减灾能力共同决定, 高风险区主要分布在鲁西北、鲁西南以及鲁中。经检验, 设施农业风灾风险分布与风灾灾情发生情况空间上具有显著一致性, 基于自然灾害风险理论构建的设施农业风灾害风险评估指数合理、方法可行, 结果可为设施农业风灾风险管理以及防灾减损提供参考。Abstract: Wind disaster is one of the meteorological disasters that have a great impact on China’s facility agriculture production, and it is of great theoretical and practical significance to conduct research on wind disaster risk assessment for effective disaster prevention and mitigation in facility agriculture. This paper explored the wind risk assessment method for facility agriculture based on the natural disaster risk theory and its application in Shandong Province. Using the extreme wind speed observation data of Shandong Province from 1991 to 2020, fuzzy information distribution theory was used to calculate the probability of wind disaster occurrence for each level, and the hazard index of wind disaster was constructed by combining with wind intensity. Based on the GF-6 satellite remote sensing image data in 2020, the exposure index was constructed by extracting the area of agriculture facilities in each district and county. Based on the wind resistance design standard of agricultural facilities, the vulnerability index was constructed by using the maximum wind speed data. Based on the wind damage information and wind speed observation data from 2010 to 2020, the wind damage disaster index was constructed to assess the disaster prevention and mitigation capability. The above four risk assessment elements were integrated to construct a wind damage risk assessment index for facility agriculture. The analysis results showed that the wind hazard was influenced by topography and geomorphology, with the highest hazard in the central mountainous area and Shandong Peninsula. The distribution of facility agriculture was more consistent with the norms of facility planting construction, and the high exposure value were mainly distributed in the eastern part of central Shandong, northwestern Shandong and part of southwestern Shandong. The distribution of vulnerability was more consistent with the basic wind pressure, and the medium-high value areas were mainly distributed in southern Shandong and eastern parts of central Shandong, where plastic greenhouses and medium-sized arches were more concentrated. The areas with weaker disaster prevention and mitigation capacity were mainly distributed in northwestern Shandong, southwestern Shandong and west-central Shandong, where the meteorological disaster warning capacity was relatively weak and the economy was less developed. The risk of wind disaster in facility agriculture was determined by integrating four factors: hazard, exposure, vulnerability, disaster prevention and mitigation capacity. The high-risk areas for wind disaster in facility agriculture were mainly in northwestern Shandong, where exposure was high, southwestern Shandong, where vulnerability was high and disaster prevention and mitigation capacity was weak; and central Shandong, where exposure and hazard were high and disaster prevention and mitigation capacity was also weak. The results of wind disaster risk assessment in Shandong Province showed that the wind disaster risk index and the occurrence of wind disaster in each district and county were significantly consistent in space, and the wind disaster risk assessment index for facility agriculture constructed based on natural disaster risk theory was reasonable and feasible, which could provide reference for scientific management of wind disaster risk and effective disaster prevention and mitigation in facility agriculture.
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Key words:
- Wind disaster /
- Risk assessment /
- Solar greenhouse /
- Plastic greenhouse /
- Shandong Province
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图 1 山东省气象站分布
Figure 1. Distribution of meteorological observation sites in Shandong Province
图 2 GF-1号2 m卫星影像图(左)和设施农业遥感提取结果影像图(右)
绿色框线内是验证区域, 红色区域为提取结果。The area inside the green line is the validation area, and the red area is the extraction result.
Figure 2. GF-1 2 m satellite image map (left) and image map of remote sensing extraction result of facility agriculture (right)
图 3 山东省设施农业风灾危险性指数空间分布
Figure 3. Spatial distribution of hazard index of wind disaster for facility agriculture in Shandong Province
图 4 山东省设施农业风灾暴露度指数空间分布
Figure 4. Spatial distribution of exposure index of wind disaster for facility agriculture in Shandong Province
图 5 山东省设施农业风灾脆弱性指数空间分布
Figure 5. Spatial distribution of vulnerability index of wind disaster for facility agriculture in Shandong Province
图 6 山东省设施农业成灾指数空间分布
Figure 6. Spatial distribution of disaster index of wind disaster for facility agriculture in Shandong Province
图 7 山东省设施农业风灾风险空间分布
Figure 7. Spatial distribution of wind disaster risk of wind disaster for facility agriculture in Shandong Province
表 1 山东省设施农业风灾气象等级
Table 1. Meteorological rating of wind damage in facility agriculture in Shandong Province
等级
Grade极大风速
Extremely high wind speed (m∙s−1)影响程度
Degree of impact1 [12, 16) 轻度 Mild 2 [16, 20) 中度 Moderate 3 ≥20 重度 Severe 
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表 2 设施农业面积提取样本信息及准确率
Table 2. Information and accuracy of extracted samples of facility agricultural area
区域类型
Area type样本序号
Sample
number调查面积
Survey area
(hm2)提取面积
Extracted area
(hm2)准确率
Accuracy
(%)主要种植区域
Main planting areas1 37.8 40 94.2 2 26.2 26.9 97.2 3 22 25 86.0 混合种植区域
Mixed planting area1 5.8 5.1 88.8 2 7.6 7.3 96.3 3 7.6 7.2 94.6
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[1] 王春乙, 张继权, 霍治国, 等. 农业气象灾害风险评估研究进展与展望[J]. 气象学报, 2015, 73(1): 1−19WANG C Y, ZHANG J Q, HUO Z G, et al. Prospects and progresses in the research of risk assessment of agro-meteorological disasters[J]. Acta Meteorologica Sinica, 2015, 73(1): 1−19 [2] 高晓容, 王春乙, 张继权, 等. 东北地区玉米主要气象灾害风险评价模型研究[J]. 中国农业科学, 2014, 47(21): 4257−4268GAO X R, WANG C Y, ZHANG J Q, et al. A risk assessment system of the main meteorological disasters for maize in northeast China[J]. Scientia Agricultura Sinica, 2014, 47(21): 4257−4268 [3] 陈怀亮, 李树岩. 气候变暖背景下河南省夏玉米花期高温灾害风险预估[J]. 中国生态农业学报(中英文), 2020, 28(3): 337−348CHEN H L, LI S Y. Prediction of high temperature disaster risks during summer maize flowering under future climate warming background in Henan Province[J]. Chinese Journal of Eco-Agriculture, 2020, 28(3): 337−348 [4] HU Z H, WU Z R, ZHANG Y X, et al. Risk assessment of drought disaster in summer maize cultivated areas of the Huang-Huai-Hai plain, eastern China[J]. Environmental Monitoring and Assessment, 2021, 193(7): 441 doi: 10.1007/s10661-021-09224-6 [5] 刘锦銮, 杜尧东, 毛慧琴. 华南地区荔枝寒害风险分析与区划[J]. 自然灾害学报, 2003, 12(3): 126−130LIU J L, DU Y D, MAO H Q. Risk analysis and zonation of cold damage to litchi in South China[J]. Journal of Natural Disasters, 2003, 12(3): 126−130 [6] 蔡大鑫, 王春乙, 张京红, 等. 基于产量的海南省香蕉寒害风险分析与区划[J]. 生态学杂志, 2013, 32(7): 1896−1902CAI D X, WANG C Y, ZHANG J H, et al. Cold-damage risk analysis and division of banana production in Hainan Province of South China based on yield[J]. Chinese Journal of Ecology, 2013, 32(7): 1896−1902 [7] 毛红丹, 霍治国, 张蕾, 等. 环渤海葡萄涝渍指标构建及风险评估[J]. 应用气象学报, 2022, 33(1): 92−103MAO H D, HUO Z G, ZHANG L, et al. Indicator construction and risk assessment of grape waterlogging in the Bohai rim[J]. Journal of Applied Meteorological Science, 2022, 33(1): 92−103 [8] 刘洪, 郭文利, 李慧君. 北京地区日光温室光环境模拟及分析[J]. 应用气象学报, 2008, 19(3): 350−355LIU H, GUO W L, LI H J. Simulation and analysis on light environment of greenhouse in Beijing area[J]. Journal of Applied Meteorological Science, 2008, 19(3): 350−355 [9] 孟力力, 杨其长, Gerard P A Bot, 等. 日光温室热环境模拟模型的构建[J]. 农业工程学报, 2009, 25(1): 164−170MENG L L, YANG Q C, GERARD. P. A. BOT, et al. Visual simulation model for thermal environment in Chinese solar greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(1): 164−170 [10] 杨世琼, 杨再强, 蔡霞, 等. 高温高湿胁迫下设施番茄光响应曲线的拟合[J]. 生态学杂志, 2018, 37(7): 2003−2012YANG S Q, YANG Z Q, CAI X, et al. Simulation of light response of photosynthesis for greenhouse tomato leaves under high temperature and high humidity stress[J]. Chinese Journal of Ecology, 2018, 37(7): 2003−2012 [11] 程陈, 冯利平, 薛庆禹, 等. 日光温室黄瓜生长发育模拟模型[J]. 应用生态学报, 2019, 30(10): 3491−3500CHENG C, FENG L P, XUE Q Y, et al. Simulation model for cucumber growth and development in sunlight greenhouse[J]. Chinese Journal of Applied Ecology, 2019, 30(10): 3491−3500 [12] 魏瑞江, 王鑫, 朱慧钦. 日光温室黄瓜小气候适宜度定量评价模型[J]. 气象, 2015, 41(5): 630−638WEI R J, WANG X, ZHU H Q. Quantitative evaluation model for cucumber microclimate suitability-degree of solar greenhouse[J]. Meteorological Monthly, 2015, 41(5): 630−638 [13] 谭方颖, 何亮, 赵晓凤, 等. 日光温室和塑料大棚风灾指标构建[J]. 气象, 2022, 48(9): 1186−1194TAN F Y, HE L, ZHAO X F, et al. Construction of wind disaster indexes for solar greenhouses and plastic tunnels[J]. Meteorological Monthly, 2022, 48(9): 1186−1194 [14] 黎贞发, 王铁, 宫志宏, 等. 基于物联网的日光温室低温灾害监测预警技术及应用[J]. 农业工程学报, 2013, 29(4): 229−236LI Z F, WANG T, GONG Z H, et al. Forewarning technology and application for monitoring low temperature disaster in solar greenhouses based on Internet of Things[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(4): 229−236 [15] 杜子璇, 刘忠阳, 刘静, 等. 河南设施农业黄瓜寡照灾害时空分布及风险评价[J]. 干旱气象, 2015, 33(4): 694−701DU Z X, LIU Z Y, LIU J, et al. Temporal and spatial distribution and risk assessment of few sunshine disasters for cucumber in He’nan facility agriculture[J]. Journal of Arid Meteorology, 2015, 33(4): 694−701 [16] 郑艳姣, 杨再强, 王琳, 等. 中国南方设施番茄高温热害风险区划[J]. 应用气象学报, 2021, 32(4): 432−442ZHENG Y J, YANG Z Q, WANG L, et al. Refined risk zoning of high temperature and heat damage to greenhouse tomato in southern China[J]. Journal of Applied Meteorological Science, 2021, 32(4): 432−442 [17] Carter D A. The Scaled Risk Integral- A Simple Numerical Representation of Case Societal Risk for Land Use Planning in the Vicinity of Major Accident Hazards[C]. 8th International Symposium on Loss Prevention and Safety Promotion in the Process Industries, Amsterdam: Elsevier, 1995 [18] MIKULSEN M, DIDUCK A P. Towards an integrated approach to disaster management and food safety governance[J]. International Journal of Disaster Risk Reduction, 2016, 15: 116−124 doi: 10.1016/j.ijdrr.2016.01.003 [19] LI Y, HUANG G H, ZHANG L, et al. Phthalate esters (PAEs) in soil and vegetables in solar greenhouses irrigated with reclaimed water[J]. Environmental Science and Pollution Research International, 2020, 27(18): 22658−22669 doi: 10.1007/s11356-020-08882-2 [20] 黄川容, 杨再强, 刘洪, 等. 北京日光温室风灾风险分析及区划[J]. 自然灾害学报, 2012, 21(3): 43−49HUANG C R, YANG Z Q, LIU H, et al. Risk analysis and regionalization of wind hazard to sunlight greenhouse in Beijing[J]. Journal of Natural Disasters, 2012, 21(3): 43−49 [21] 杨再强, 张婷华, 黄海静, 等. 北方地区日光温室气象灾害风险评价[J]. 中国农业气象, 2013, 34(3): 342−349YANG Z Q, ZHANG T H, HUANG H J, et al. Meteorological disaster risk evaluation of solar greenhouse in North China[J]. Chinese Journal of Agrometeorology, 2013, 34(3): 342−349 [22] 李楠, 薛晓萍, 李鸿怡. 基于信息扩散的山东省日光温室风灾风险评估[J]. 气象与环境学报, 2018, 34(5): 149−155LI N, XUE X P, LI H Y. The risk assessment of wind disaster of greenhouse in Shandong Province based on information dissemination theory[J]. Journal of Meteorology and Environment, 2018, 34(5): 149−155 [23] 姚益平, 苏高利, 罗卫红, 等. 基于光热资源的中国温室气候区划与能耗估算系统建立[J]. 中国农业科学, 2011, 44(5): 898−908YAO Y P, SU G L, LUO W H, et al. A photo-thermal resources based system for greenhouse climate zonation and energy consumption estimation in China[J]. Scientia Agricultura Sinica, 2011, 44(5): 898−908 [24] 闫俊月, 周磊, 周长吉, 等. 塑料大棚设计中基本风压取值方法[J]. 农业工程学报, 2014, 30(12): 171−176YAN J Y, ZHOU L, ZHOU C J, et al. Method for calculating basic wind pressure of plastic greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(12): 171−176 [25] 谭方颖, 张艳红, 何亮, 等. 设施农业风灾气象等级 日光温室和塑料大棚 T/CMSA0023—2021[S]. 北京: 中国气象服务协会团体标准, 2021TAN F Y, ZHANG Y H, HE L, et al. (T/CMSA0023-2021. Meteorological grades of wind disaster for facility agriculture — Sunlight greenhouse and plastic tunnel T/CMSA0023—2021 [S]. Beijing: China Meteorological Services Association, 2021 [26] 黄崇福. 自然灾害风险评价: 理论与实践[M]. 北京: 科学出版社, 2005HUANG C F. Risk Assessment of Natural Disasters: Theory and Practice[M]. Beijing: Science Press, 2005 [27] ZHANG L, HUO Z G, ZHANG L Z, et al. Integrated risk assessment of major meteorological disasters with paprika pepper in Hainan Province[J]. Journal of Tropical Meteorology, 2017, 23(3): 334−344 [28] 王春乙, 姚蓬娟, 张继权, 等. 长江中下游地区双季早稻冷害、热害综合风险评价[J]. 中国农业科学, 2016, 49(13): 2469−2483 doi: 10.3864/j.issn.0578-1752.2016.13.003WANG C Y, YAO P J, ZHANG J Q, et al. Risk assessment of cold and hot damages for double-cropping early rice(DCER) in lower-middle reaches of the Yangtze River Basin[J]. Scientia Agricultura Sinica, 2016, 49(13): 2469−2483 doi: 10.3864/j.issn.0578-1752.2016.13.003 [29] 刘丽雅. 基于国产GF-1的高寒山区土地利用/覆盖分类研究[D]. 杭州: 浙江大学, 2016LIU L Y. Based on China-Made GF-1 for Land Use/Cover Classification of High Cold Mountain Areas[D]. Hangzhou: Zhejiang University, 2016 [30] 中华人民共和国住房和城乡建设部. 农业温室结构荷载规范: GB/T 51183—2016[S]. 北京: 中国计划出版社, 2017Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Structural load specification for agricultural greenhouse: GB/T 51183—2016 [S]. Beijing: Chinese Planning Press, 2017 [31] 中华人民共和国住房和城乡建设部. 种植塑料大棚工程技术规范: GB/T 51057—2015[S]. 北京: 中国计划出版社, 2016Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Technical specification for plastic greenhouses: GB/T 51057—2015[S]. Beijing: Chinese Planning Press, 2015 -
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