黄河流域农作物受灾时空演变特征及致灾因子

doi:10.12118/j.issn.1000-6060.2025.685

Abstract

Abstract:

The Yellow River Basin, a region in China acutely vulnerable to ecological damage and prone to agricultural disasters, faces significant challenges from crop failures that threaten both food security and economic development. This study analyzes crop disaster rate data from nine provinces (autonomous regions) in the Yellow River Basin from 2003 to 2023. The study utilizes a comprehensive methodology, including a center-of-gravity migration-standard deviational ellipse, kernel density estimation, the Dagum Gini coefficient, and the geographically and temporally weighted regression model to investigate the spatiotemporal evolution and underlying causes of these disasters. The results revealed the following: (1) The Yellow River Basin experienced an overall decline in its crop disaster rate. (2) The crop disaster rate consistently remained highest in the upper and middle reaches, indicating that these areas bear the brunt of crop disasters. Kernel density estimation further revealed intensifying disparities within the upper reaches, significant internal disparities in the middle reaches, and a polarization phenomenon in the lower reaches. The Dagum Gini coefficient indicated significant overall disparities in the crop disaster rate, primarily driven by differences between regions. (3) The main factors influencing the crop disaster rate in the Yellow River Basin are annual average precipitation, forest coverage, and slope. Slope generally has a positive effect, whereas annual average precipitation and forest coverage have negative effects. These findings offer a scientific basis for developing precise disaster prevention and mitigation strategies, optimizing agricultural planning, and ensuring food security in the Yellow River Basin. This will contribute to achieving ecological protection and high-quality development goals in the region.

Key words: crop disaster rate, spatiotemporal evolution, disaster-inducing factors, GTWR model, Yellow River Basin

LI Xiaopeng, JIA Fugui, LEI Shuang, LI Kang. Spatiotemporal evolution characteristics and disaster-inducing factors of crop disasters in the Yellow River Basin[J].Arid Land Geography, 2026, 49(9): 1815-1825.

Figures/Tables 10

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References 32

[1]	李晓鹏, 贾富贵, 李康, 等. 甘肃省农作物受灾率的时空演变特征及其影响因素[J]. 中国农业气象, 2026, 47(4): 603-615.
	[Li Xiaopeng, Jia Fugui, Li Kang, et al. Spatiotemporal evolution characteristics and influencing factors of crop disaster rates by meteorological disaster in Gansu Province[J]. Chinese Journal of Agrometeorology, 2026, 47(4): 603-615.] doi: 10.3969/j.issn.1000-6362.2026.04.011
[2]	李贤, 王浩阳, 牛文浩, 等. 黄河流域耕地利用转型时空演变及其对粮食生产的影响[J]. 中国农业大学学报, 2024, 29(10): 85-96.
	[Li Xian, Wang Haoyang, Niu Wenhao, et al. Spatial-temporal evolution of cultivated land use transition and its impact on grain production in the Yellow River Basin[J]. Journal of China Agricultural University, 2024, 29(10): 85-96.]
[3]	资添添, 刘静, 宣柯炀. 基于SWAT模型的黄河流域作物水足迹及虚拟水流动研究[J]. 灌溉排水学报, 2025, 44(2): 19-26.
	[Zi Tiantian, Liu Jing, Xuan Keyang. Modelling crop water footprint and virtual water flow in the Yellow River Basin using the SWAT model[J]. Journal of Irrigation and Drainage, 2025, 44(2): 19-26.]
[4]	薛选登, 范晓婕, 谢清华. 农业水资源生态韧性时空演化及多尺度障碍因子分析——以黄河流域9省区为例[J]. 中国农业资源与区划, 2025, 46(2): 91-102.
	[Xue Xuandeng, Fan Xiaojie, Xie Qinghua. Spatio-temporal evolution and multi-scale barrier factor analysis of ecological resilience of agricultural water resources: A case study of nine provinces in the Yellow River Basin[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2025, 46(2): 91-102.]
[5]	占纪文, 郑思宁, 徐学荣. 农作物巨灾风险度量与分层测算[J]. 统计与决策, 2020, 36(3): 37-41.
	[Zhan Jiwen, Zheng Sining, Xu Xuerong. Crop catastrophe risk measurement and its stratified calculation[J]. Statistics & Decision, 2020, 36(3): 37-41.]
[6]	秦鹏程, 周月华, 刘火胜, 等. 基于水动力模型的农作物暴雨洪涝灾害损失评估: 以江汉平原为例[J]. 中国农业气象, 2025, 46(3): 420-431.
	[Qin Pengcheng, Zhou Yuehua, Liu Huosheng, et al. Flood loss assessment for crops based on hydrodynamic modeling: A case study in the Jianghan Plain[J]. Chinese Journal of Agrometeorology, 2025, 46(3): 420-431.] doi: 10.3969/j.issn.1000-6362.2025.03.013
[7]	金欣, 闫格, 李军乔, 等. 青海省春播期气候特征及主要气象灾害影响预估[J]. 干旱地区农业研究, 2025, 43(3): 270-277, 298.
	[Jin Xin, Yan Ge, Li Junqiao, et al. Climatic characteristics and impact assessment of major meteorological disasters during the spring sowing period in Qinghai Province[J]. Agricultural Research in the Arid Areas, 2025, 43(3): 270-277, 298.]
[8]	张波, 罗阳欢, 李浪, 等. 贵州茶树春季低温灾害气候风险评估与区划[J]. 中国农业科技导报(中英文), 2025, 27(4): 230-238.
	[Zhang Bo, Luo Yanghuan, Li Lang, et al. Climate risk assessment and zoning of spring low temperature disasters of tea trees in Guizhou[J]. Journal of Agricultural Science and Technology, 2025, 27(4): 230-238.]
[9]	伍红雨, 邹燕, 郭尧. 损失率评估模型在广东气象灾害损失评估中的应用[J]. 气象, 2023, 49(6): 757-764.
	[Wu Hongyu, Zou Yan, Guo Xiao. Application of loss rate evaluation model in meteorological disaster loss evaluation in Guangdong[J]. Meteorological Monthly, 2023, 49(6): 757-764.]
[10]	李治国. 近40 a河南省农业气象灾害对粮食生产的影响研究[J]. 干旱区资源与环境, 2013, 27(5): 126-130.
	[Li Zhiguo. Influence of agricultural meteorological disasters on grain production in Henan Province[J]. Journal of Arid Land Resources and Environment, 2013, 27(5): 126-130.]
[11]	于小兵, 陈虹, 吉中会, 等. 山东省农业气象灾害与主要农作物产量的灰色关联分析[J]. 灾害学, 2019, 34(3): 103-108.
	[Yu Xiaobing, Chen Hong, Ji Zhonghui, et al. Gray correlation analysis of agricultural meteorological disasters and main crop yields in Shandong Province[J]. Journal of Catastrophology, 2019, 34(3): 103-108.]
[12]	高姣姣, 徐刚. 重庆市气象灾害与农作物单产的灰色关联度分析[J]. 西南大学学报(自然科学版), 2016, 38(2): 32-36.
	[Gao Jiaojiao, Xu Gang. Gray correlation analysis of meteorological disasters and crop yield in Chongqing[J]. Journal of Southwest University (Natural Science Edition), 2016, 38(2): 32-36.]
[13]	李忠辉, 袁福香, 刘实, 等. 基于“Z指数”的吉林省雨涝灾害对农作物的影响评估[J]. 安全与环境学报, 2016, 16(1): 183-186.
	[Li Zhonghui, Yuan Fuxiang, Liu Shi, et al. On the crop-harvest assessment with regard to the waterlog disasters in Jilin with “Z-index” method[J]. Journal of Safety and Environment, 2016, 16(1): 183-186.]
[14]	楚纯洁, 张小磊, 周金风. 1960-2018年河南省短周期旱涝急转事件演变特征[J]. 水土保持研究, 2023, 30(4): 275-285.
	[Chu Chunjie, Zhang Xiaolei, Zhou Jinfeng. Evolution characteristics of short-period drought-flood abrupt alternation events in Henan Province from 1960 to 2018[J]. Research of Soil and Water Conservation, 2023, 30(4): 275-285.]
[15]	曹永强, 杨雪婷, 周姝含. 河北省夏玉米水足迹时空演变特征及影响因子分析[J]. 长江科学院院报, 2023, 40(2): 67-74, 80. doi: 10.11988/ckyyb.20210993
	[Cao Yongqiang, Yang Xueting, Zhou Shuhan. Spatial and temporal evolution and influencing factors of water footprint of summer maize in Hebei Province[J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(2): 67-74, 80.]
[16]	何延波, 李祎君, 张艳红, 等. 2025年夏季气象条件对农业生产的影响[J]. 中国农业气象, 2025, 46(12): 1836-1838.
	[He Yanbo, Li Yijun, Zhang Yanhong, et al. Reports on weather impacts to agricultural production in summer 2025[J]. Chinese Journal of Agrometeorology, 2025, 46(12): 1836-1838.] doi: 10.3969/j.issn.1000-6362.2025.12.014
[17]	于水, 张晓龙, 王妍, 等. 气候变化对中国东北三省主要粮食作物影响研究综述[J]. 中国生态农业学报(中英文), 2024, 32(6): 970-985.
	[Yu Shui, Zhang Xiaolong, Wang Yan, et al. Review of research on the impacts of climate change on staple grain crops in the three provinces of northeast China[J]. Chinese Journal of Eco-Agriculture, 2024, 32(6): 970-985.]
[18]	孔冬艳, 陈会广. 近40年来中国农作物与耕地受灾时空特征及影响因素分析[J]. 长江流域资源与环境, 2020, 29(5): 1236-1246.
	[Kong Dongyan, Chen Huiguang. An analysis of spatial-temporal characteristics and influencing factors for China’s crop and cropland suffered natural disaster in recent 40 years[J]. Resources and Environment in the Yangtze Basin, 2020, 29(5): 1236-1246.]
[19]	程鹏, 彭海洋, 侯丁榕, 等. 黄河流域农业灰水足迹强度空间格局及演进趋势[J]. 干旱区地理, 2025, 48(7): 1185-1197. doi: 10.12118/j.issn.1000-6060.2024.473
	[Cheng Peng, Peng Haiyang, Hou Dingrong, et al. Spatial pattern and evolution trend of agricultural grey water footprint intensity in the Yellow River Basin[J]. Arid Land Geography, 2025, 48(7): 1185-1197.] doi: 10.12118/j.issn.1000-6060.2024.473
[20]	李梦冉, 徐小任, 王梁, 等. 黄河流域农业碳排放时空变化特征及影响因素分析[J]. 干旱区地理, 2025, 48(5): 854-865. doi: 10.12118/j.issn.1000-6060.2024.401
	[Li Mengran, Xu Xiaoren, Wang Liang, et al. Spatial-temporal characteristics and influencing factors of agricultural carbon emissions in the Yellow River Basin[J]. Arid Land Geography, 2025, 48(5): 854-865.] doi: 10.12118/j.issn.1000-6060.2024.401
[21]	马志超. 黄河流域农业碳排放与经济增长时空耦合关系研究[J]. 中国农业资源与区划, 2024, 45(12): 15-26.
	[Ma Zhichao. Spatio-temporal coupling relationship between agricultural carbon emission and economic growth in the Yellow River Basin[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2024, 45(12): 15-26.]
[22]	杨宗辉, 李金锴, 韩晨雪, 等. 我国粮食生产重心变迁及其影响因素研究[J]. 农业现代化研究, 2019, 40(1): 36-43.
	[Yang Zonghui, Li Jinkai, Han Chenxue, et al. The evolution path of China’s grain production base and the influencing factors[J]. Research of Agricultural Modernization, 2019, 40(1): 36-43.]
[23]	刘宝涛. 京津冀城市群建设用地转型的时空演化与驱动因素[J]. 世界地理研究, 2025, 34(7): 98-111. doi: 10.3969/j.issn.1004-9479.2025.07.20230744
	[Liu Baotao. Spatial-temporal evolution and driving factors of construction land transformation in the Beijing-Tianjin-Hebei urban agglomeration[J]. World Regional Studies, 2025, 34(7): 98-111.] doi: 10.3969/j.issn.1004-9479.2025.07.20230744
[24]	Kumar S, Russell R. Technological change, technological catch-up, and capital deepening: Relative contributions to growth and convergence[J]. The American Economic Review, 2002, 92(3): 527-548. doi: 10.1257/00028280260136381
[25]	向云, 李芷萱, 陆倩. 中国农业经济高质量发展的空间非均衡及收敛性[J]. 中国农业大学学报, 2022, 27(11): 305-316.
	[Xiang Yun, Li Zhixuan, Lu Qian. Spatial disequilibrium and convergence of high quality development of agricultural economy in China[J]. Journal of China Agricultural University, 2022, 27(11): 305-316.]
[26]	张军涛, 韩祁祺. 黄河流域农业碳排放时空演变特征及影响因素研究[J]. 环境科学研究, 2025, 38(4): 754-766.
	[Zhang Juntao, Han Qiqi. Spatiotemporal evolution characteristics and influencing factors of agricultural carbon emissions in the Yellow River Basin[J]. Research of Environmental Sciences, 2025, 38(4): 754-766.]
[27]	刘艺欣, 裴婷婷, 陈英, 等. 基于县域尺度的甘肃省乡村振兴发展水平测度及时空分异[J]. 干旱区地理, 2025, 48(9): 1683-1693. doi: 10.12118/j.issn.1000-6060.2024.545
	[Liu Yixin, Pei Tingting, Chen Ying, et al. Measurement and spatial-temporal differentiation of rural revitalization development level in Gansu Province based on county scale[J]. Arid Land Geography, 2025, 48(9): 1683-1693.] doi: 10.12118/j.issn.1000-6060.2024.545
[28]	Huang B, Wu B, Barry M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J]. International Journal of Geographical Information Science, 2010, 24(3): 383-401. doi: 10.1080/13658810802672469
[29]	姚寿福. 我国农业资金投入对抗灾能力的效应分析[J]. 农业现代化研究, 2012, 33(2): 161-165.
	[Yao Shoufu. An analysis of effects of agricultural investments on anti-disaster ability in China[J]. Research of Agricultural Modernization, 2012, 33(2): 161-165.]
[30]	杨建利, 邹雨潼, 宋大威. 黄河流域农业高质量发展水平测度、地区差异及动态演进[J]. 中国农业资源与区划, 2024, 45(10): 212-227.
	[Yang Jianli, Zou Yutong, Song Dawei. The measurement and regional difference and dynamic evolution of high-quality development of agriculture in the Yellow River Basin[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2024, 45(10): 212-227.]
[31]	周帅, 王义民, 畅建霞, 等. 黄河流域干旱时空演变的空间格局研究[J]. 水利学报, 2019, 50(10): 1231-1241.
	[Zhou Shuai, Wang Yimin, Chang Jianxia, et al. Research on spatio-temporal evolution of drought patterns in the Yellow River Basin[J]. Journal of Hydraulic Engineering, 2019, 50(10): 1231-1241.]
[32]	王培培, 柴洪. 黄河流域数字普惠金融对农业绿色发展的影响研究[J]. 人民黄河, 2025, 47(4): 22-27.
	[Wang Peipei, Chai Hong. Research on impact of digital inclusive finance in the Yellow River Basin on the green development of agriculture[J]. Yellow River, 2025, 47(4): 22-27.]

年份	面积/km²	重心经度坐标/E	重心纬度坐标/N	长轴标准差/km	短轴标准差/km	方位角/(°)
2003	2487364.82	108°12′39″	37°00′10″	1139.95	694.60	63.53
2006	2187929.48	105°57′14″	37°15′57″	1156.14	602.44	65.59
2009	2342105.75	108°20′20″	37°58′54″	1136.42	656.07	63.54
2012	2359789.13	107°25′28″	37°32′34″	1196.27	627.96	71.06
2015	2248888.71	106°46′15″	37°57′41″	1250.20	572.64	68.08
2018	2241325.94	108°51′27″	38°18′14″	1099.77	648.76	62.92
2021	1403005.99	108°22′06″	37°20′54″	845.88	528.00	69.18
2023	2101323.99	109°06′47″	38°14′37″	1035.23	646.15	51.73

指标	共线性诊断
指标	显著性	方差膨胀因子（VIF）
年均降水量	0.003	5.661
森林覆盖率	0.001	4.890
海拔	0.001	5.920
坡度	0.001	7.063
水库数量	0.003	3.107
水土流失治理面积	0.734	1.957

模型	OLS	GWR	GTWR
AICc	-242.290	-281.412	-310.473
R²	0.355	0.551	0.726
调整后R²	-	0.539	0.718

指标	2003年	2006年	2009年	2012年	2015年	2018年	2021年	2023年
年均降水量	-0.00028	-0.00053	-0.00060	-0.00026	-0.00030	-0.00020	-0.00006	-0.00003
森林覆盖率	0.00344	-0.00082	-0.00379	-0.00423	-0.00124	-0.00048	-0.00089	0.00090
海拔	0.00001	-0.00005	-0.00009	-0.00003	-0.00001	0.00003	-0.00004	-0.00002
坡度	-0.00180	0.03088	0.04796	0.02457	0.01229	0.01310	0.01950	0.00723
水库数量	-0.00002	-0.00001	-0.00001	-0.00001	-0.00001	-0.00002	-0.00002	-0.00002

Spatiotemporal evolution characteristics and disaster-inducing factors of crop disasters in the Yellow River Basin

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