山洪视角下社区韧性影响因素及其定量分析

doi:10.18306/dlkxjz.2022.03.006

地理科学进展 ›› 2022, Vol. 41 ›› Issue (3): 423-436.doi: 10.18306/dlkxjz.2022.03.006

山洪视角下社区韧性影响因素及其定量分析

钟鸣¹^,²(), 肖璐¹, 张倩³, 温家洪⁴

1. 中山大学地理科学与规划学院,广州 510275
2. 南方海洋科学与工程广东省实验室(珠海),广东珠海 519080
3. 广东省水利水电科学研究院,广州 510275
4. 上海师范大学环境与地理科学学院,上海 200234;

收稿日期:2021-06-08 修回日期:2021-08-22 出版日期:2022-03-28 发布日期:2022-05-28
作者简介:钟鸣(1987— ),女,江西兴国人,博士,副教授,主要从事自然灾害风险与韧性研究。E-mail: zhongm37@mail.sysu.edu.cn
基金资助:
国家重点研发计划项目(2021YFC3001000);南方海洋科学与工程广东省实验室(311021018);国家自然科学基金项目(51709286)

Influencing factors and quantitative analysis of community resilience to flash floods

ZHONG Ming¹^,²(), XIAO Lu¹, ZHANG Qian³, WEN Jiahong⁴

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, Guangdong, China
3. Guangdong Research Institute of Water Resources and Hydropower, Guangzhou 510275, China
4. School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China;

Received:2021-06-08 Revised:2021-08-22 Online:2022-03-28 Published:2022-05-28
Supported by:
National Key Research and Development Program of China, No(2021YFC3001000);Project Supported by Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), No(311021018);National Natural Science Foundation of China, No(51709286)

摘要/Abstract

摘要：

山洪灾害具有广泛性、突发性、破坏性等特征,开展山洪易发区的社区韧性评估,从而提高灾害应对能力是当前防灾减灾的前沿热点和难点。论文提出了一套多学科的综合方法：① 利用中介效应明晰了山洪视角下社区韧性评价体系各指标间的定量化传递关系;② 构建了基于决策实验室分析和解释结构模型的耦合数学模型,确定社区韧性影响因素的多级递阶解释结构模型,分析社区韧性的差异化影响因素;③ 采用信息扩散方法,定量分析山洪灾害社区韧性的变化趋势并排序。以粤北山洪易发区为例,从城镇、村落、城乡结合部3种类型社区进行灾害韧性分析。结果表明：山洪视角下的社区韧性指标体系是一个多维度多层次的复杂网络系统,包括环境、社会、心理、制度和信息沟通5个方面;不同类型社区灾害韧性的直接影响因素呈差异化特征,而供排水设施建设和洪灾应急演练为社区韧性的根本影响因素,对增强山洪视角下社区韧性发挥本质性作用;由于调研村落多位于山洪频发区,居民防范灾害、减轻灾害影响的意识较强,村落较城镇和城乡结合社区呈现出更高的韧性。研究可为提升粤北山洪易发区社区韧性及社区防灾减灾能力提供科学参考,该综合分析方法亦可为其他类型灾害的精细化防灾减灾提供支持。

关键词: 山洪灾害, 社区韧性, 解释结构模型法, 中介效应, 信息扩散

Abstract:

Based on the characteristics of flash floods such as extensiveness, abruptness, and destructiveness, the current focus of disaster prevention and mitigation is to assess the resilience of communities in regions with frequent flash flood disasters, so as to enhance the disaster response level. This study proposed a set of multidisciplinary integrated methods: 1) The quantitative transmission relationship among the indicators of the community resilience evaluation system under the impact of flash floods was clarified by examining the mediating effect. 2) The coupling decision mathematical model of the decision laboratory model and the interpretive structure model was established, the multilevel interpretive structure model of the influencing factors of community resilience was constructed, and then the differential influencing factors of community resilience were analyzed. 3) Information diffusion method was used to quantitatively analyze and rank the community resilience of flash flood disasters. Taking the flood-prone areas in northern Guangdong Province as the research area, this study analyzed three types of communities—that is, towns, villages, and peri-urban areas, and the results show that the community resilience indicator system from the perspective of flash flood impact is a multi-dimensional and multi-layered complex network system, including five aspects: environmental, social, psychological, institutional, and communication. The direct influencing factors of community resilience to different types of disasters are different. The construction of water supply and drainage facilities and flood emergency drills, as the fundamental influencing factors of community resilience, play an essential role in enhancing community resilience to flash floods. Since most of the surveyed villages are located in areas with frequent flash floods, residents have a high awareness of disaster prevention and mitigation and the villages show higher community resilience than the sample towns and peri-urban areas. The study can provide an important reference for improving the resilience of communities in the flood-prone areas in northern Guangdong and their disaster prevention and mitigation capabilities. This comprehensive analysis method can also provide strategic support to the refinement of disaster prevention and mitigation of other types of disasters.

Key words: flash floods, community resilience, interpretative structural model, mediating effect, information diffusion method

钟鸣, 肖璐, 张倩, 温家洪. 山洪视角下社区韧性影响因素及其定量分析[J]. 地理科学进展, 2022, 41(3): 423-436.

ZHONG Ming, XIAO Lu, ZHANG Qian, WEN Jiahong. Influencing factors and quantitative analysis of community resilience to flash floods[J]. PROGRESS IN GEOGRAPHY, 2022, 41(3): 423-436.

图/表 10

图1

图2

表1

表2

表3

图3

图4

表4

图5

表5

参考文献 33

[1]	Rozalis S, Morin E, Yair Y, et al. Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions[J]. Journal of Hydrology, 2010,394:245-255.
[2]	郭良, 张晓蕾, 刘荣华, 等. 全国山洪灾害调查评价成果及规律初探[J]. 地球信息科学学报, 2017,19(12):1548-1556.
	[ Guo Liang, Zhang Xiaolei, Liu Ronghua, et al. Achievements and preliminary analysis on China national flash flood disasters investigation and evaluation. Journal of Geo-Information Science, 2017,19(12):1548-1556. ]
[3]	国家防汛抗旱总指挥部办公室. 全国山洪灾害防治项目实施方案(2017—2020年) [S/OL]. 2018- 01- 03 [2021-04-09]. http://www.qgshzh.com/show/3467cea2-6931-4941-b16a-1b3aed106b68.
	[Office of State Flood Headquarters. Implementation plan of the national flash flood disaster prevention and control project (2017-2020). 2018-01-03 [2021-04-09]. http://www.qgshzh.com/show/3467cea2-6931-4941-b16a-1b3aed106b68. ]
[4]	中华人民共和国水利部. 中国水旱灾害公报(2018) [M]. 北京: 中国水利水电出版社, 2019: 15.
	[ Ministry of Water Resources of the People's Republic of China. Bulletin of flood and drought disasters in China, 2018. Beijing, China: China Water Power Press, 2019: 15. ]
[5]	崔鹏, 邹强. 山洪泥石流风险评估与风险管理理论与方法[J]. 地理科学进展, 2016,35(2):137-147.
	[ Cui Peng, Zou Qiang. Theory and method of risk assessment and risk management of debris flows and flash floods. Progress in Geography, 2016,35(2):137-147. ]
[6]	孙鸿鹄, 甄峰. 居民活动视角的城市雾霾灾害韧性评估: 以南京市主城区为例[J]. 地理科学, 2019,39(5):788-796.
	[ Sun Honghu, Zhen Feng. Evaluation of urban haze disaster resilience from the perspective of residents' activity: A case study of the main urban area of Nanjing City. Scientia Geographica Sinica, 2019,39(5):788-796. ]
[7]	彭翀, 郭祖源, 彭仲仁. 国外社区韧性的理论与实践进展[J]. 国际城市规划, 2017,32(4):60-66.
	[ Peng Chong, Guo Zuyuan, Peng Zhongren. Research progress on the theory and practice of foreign community resilience. Urban Planning International, 2017,32(4):60-66. ]
[8]	杨丽娇, 蒋新宇, 张继权. 自然灾害情景下社区韧性研究评述[J]. 灾害学, 2019,34(4):159-164.
	[ Yang Lijiao, Jiang Xinyu, Zhang Jiquan. A review on community resilience to natural disaster. Journal of Catastrophology, 2019,34(4):159-164. ]
[9]	李亚, 翟国方. 我国城市灾害韧性评估及其提升策略研究[J]. 规划师, 2017,33(8):5-11.
	[ Li Ya, Zhai Guofang. China's urban disaster resilience evaluation and promotion. Planners, 2017,33(8):5-11. ]
[10]	Cutter S L, Ash K D, Emrich C T. Urban-rural differences in disaster resilience[J]. Annals of the American Association of Geographers, 2016,106(6):1236-1252.
[11]	Pfefferbaum R L, Neas B R, Pfefferbaum B, et al. The Communities Advancing Resilience Toolkit (CART): Development of a survey instrument to assess community resilience[J]. International Journal of Emergency Mental Health, 2013,15(1):15-29.
[12]	费璇, 温家洪, 杜士强, 等. 自然灾害恢复力研究进展[J]. 自然灾害学报, 2014,23(6):19-31.
	[ Fei Xuan, Wen Jiahong, Du Shiqiang, et al. Progress in research on natural disaster resilience. Journal of Natural Disasters, 2014,23(6):19-31. ]
[13]	翟国方, 崔功豪, 谢映霞, 等. 风险社会与弹性城市[J]. 城市规划, 2015,39(12):107-112.
	[ Zhai Guofang, Cui Gonghao, Xie Yingxia, et al. Risk society and resilient city. City Planning Review, 2015,39(12):107-112. ]
[14]	Li Y, Hu Y, Zhang X G, et al. An evidential DEMATEL method to identify critical success factors in emergency management[J]. Applied Soft Computing, 2014,22:504-510.
[15]	Li F, Wang W H, Dubljevic S, et al. Analysis on accident-causing factors of urban buried gas pipeline network by combining DEMATEL, ISM and BN methods[J]. Journal of Loss Prevention in the Process Industries, 2019,61:49-57.
[16]	Wang D L, Wang Y D, Huang Z Y, et al. Understanding the resilience of coal industry ecosystem to economic shocks: Influencing factors, dynamic evolution and policy suggestions[J]. Resources Policy, 2020,67:101682. doi: 10.1016/j.resourpol.2020.101682.
[17]	罗周全, 程鹏毅. 基于DEMATEL-ISM的地下金属矿山人机系统事故影响因素分析[J]. 中国安全生产科学技术, 2017,13(12):145-151.
	[ Luo Zhouquan, Cheng Pengyi. Analysis on factors affecting accidents of man-machine system in underground metal mine based on DEMATEL-ISM. Journal of Safety Science and Technology, 2017,13(12):145-151. ]
[18]	Pan X J, Huang C F, Pu C Y. Comprehensive disposal plan design & polymerization disaster risk assessment based on information diffusion technology in Fujiang River basin in Sichuan[J]. Environmental Research, 2020,188:109744. doi: 10.1016/j.envres.2020.109744.
[19]	Liu Y Q, You M, Zhu J L, et al. Integrated risk assessment for agricultural drought and flood disasters based on entropy information diffusion theory in the middle and lower reaches of the Yangtze River, China[J]. International Journal of Disaster Risk Reduction, 2019,38:101194. doi: 10.1016/j.ijdrr.2019.101194.
[20]	温泉沛, 周月华, 霍治国, 等. 气候变暖背景下东南地区暴雨洪涝灾害风险变化[J]. 生态学杂志, 2017,36(2):483-490.
	[ Wen Quanpei, Zhou Yuehua, Huo Zhiguo, et al. Risk changes of storm flood disasters in southeast China under climatic warming. Chinese Journal of Ecology, 2017,36(2):483-490. ]
[21]	Yamane T. Statistics: An introductory analysis[M]. New York, USA: Harper and Row, 1967.
[22]	Xu W P, Zhong M, Hong Y, et al. Enhancing community resilience to urban floods with a network structuring model[J]. Safety Science, 2020,127:104699. doi: 10.1016/j.ssci.2020.104699.
[23]	清远市统计局. 清远统计年鉴(2018) [M/OL]. 2018- 12- 25 [2021-04-09]. http://www.gdqy.gov.cn/xxgk/zzjg/zfjg/qystjj/tjxx/tjnj/index.html.
	[Qingyuan Statistics Bureau. Qingyuan Statistical Yearbook, 2018. 2018-12-25 [2021-04-09]. http://www.gdqy.gov.cn/xxgk/zzjg/zfjg/qystjj/tjxx/tjnj/index.html. ]
[24]	温忠麟, 叶宝娟. 中介效应分析: 方法和模型发展[J]. 心理科学进展, 2014,22(5):731-745.
	[ Wen Zhonglin, Ye Baojuan. Analyses of mediating effects: The development of methods and models. Advances in Psychological Science, 2014,22(5):731-745. ]
[25]	杨姝, 李俊龙. 基于DEMATEL-ISM法的民航飞行员综合安全能力结构模型研究[J]. 安全与环境工程, 2018,25(4):169-174.
	[ Yang Shu, Li Junlong. Comprehensive safety capability structure model of civil aviation pilots based on DEMATEL-ISM method. Safety and Environmental Engineering, 2018,25(4):169-174. ]
[26]	张丽娟, 陈红, 高玉宏, 等. 黑龙江省大风分布特征及风险区划研究[J]. 地理科学进展, 2011,30(7):899-905.
	[ Zhang Lijuan, Chen Hong, Gao Yuhong, et al. Study on the distribution and risk zoning of strong winds in Heilongjiang Province. Progress in Geography, 2011,30(7):899-905. ]
[27]	Yu X B, Yu X R, Li C L, et al. Information diffusion-based risk assessment of natural disasters along the Silk Road Economic Belt in China[J]. Journal of Cleaner Production, 2020,244:118744. doi: 10.1016/j.jclepro.2019.118744.
[28]	Rapaport C, Hornik-Lurie T, Cohen O, et al. The relationship between community type and community resilience[J]. International Journal of Disaster Risk Reduction, 2018,31:470-477.
[29]	Cutter S L, Barnes L, Berry M, et al. A place-based model for understanding community resilience to natural disasters[J]. Global Environmental Change, 2008,18(4):598-606.
[30]	Norris F H, Stevens S P, Pfefferbaum B, et al. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness[J]. American Journal of Community Psychology, 2008,41(1/2):127-150.
[31]	黄海燕. 基于参考临界的社区抗逆力水平研究[D]. 南京: 南京大学, 2013.
	[ Huang Haiyan. Community resilience level based on the reference value. Nanjing, China: Nanjing University, 2013. ]
[32]	朱华桂. 论风险社会中的社区抗逆力问题[J]. 南京大学学报(哲学·人文科学·社会科学版), 2012,49(5):47-53, 159.
	[ Zhu Huagui. A Discussion of community resistibility in a risky society. Journal of Nanjing University (Philosophy, Humanities and Social Sciences), 2012,49(5):47-53, 159. ]
[33]	赵刚, 庞博, 徐宗学, 等. 中国山洪灾害危险性评价[J]. 水利学报, 2016,47(9):1133-1142, 1152.
	[ Zhao Gang, Pang Bo, Xu Zongxue, et al. Assessment on the hazard of flash flood disasters in China. Journal of Hydraulic Engineering, 2016,47(9):1133-1142, 1152. ]

社会人口特征	城镇	村落	城乡结合部
年龄>50岁人口比例/%	29.82	56.85	45.45
男性人口比例/%	35.09	53.42	42.86
汉族人口比例/%	71.63	77.40	42.86
在清远生活的时间>15 a人口比例/%	75.44	90.41	87.01
以粤语为主要语言的人口比例/%	68.42	65.75	61.04
自有住房率/%	85.96	93.84	90.91
税前月收入(多数水平)/元	1500~2000	<500	500~1000
受教育程度(本科或大专及以上)人口比例/%	17.54	8.90	14.29

	代码	指标	所属维度
山洪灾害社区韧性评价指标	S₁	产流能力	环境
	S₂	供排水设施建设	社会
	S₃	本社区居民之间相互帮助	心理
	S₄	洪灾基础知识普及率	制度
	S₅	洪灾宣传教育	制度
	S₆	洪灾应急演练	制度
	S₇	洪灾预警	制度
	S₈	专业人员(如消防员、医护人员)队伍建设	制度
	S₉	社区内机构间协作和协调效率	制度
	S₁₀	面向公众的信息传播渠道	信息沟通
	S₁₁	信息源获取渠道	信息沟通
	S₁₂	对信息源的信任程度	信息沟通
	S₁₃	社区会提供如何应对灾难的信息	信息沟通
	S₁₄	居民从社区获得过有帮助的信息	信息沟通

	S₁	S₂	S₃	S₄	S₅	S₆	S₇	S₈	S₉	S₁₀	S₁₁	S₁₂	S₁₃	S₁₄
S₁	0.03	0.08	0.11	0.08	0.08	0.06	0.08	0.09	0.09	0.09	0.07	0.11	0.06	0.09
S₂	0.11	0.05	0.15	0.11	0.10	0.11	0.11	0.13	0.13	0.13	0.08	0.12	0.13	0.13
S₃	0	0	0.08	0	0	0	0	0	0	0	0	0	0	0
S₄	0.04	0.05	0.12	0.02	0.07	0.02	0.04	0.10	0.08	0.08	0.04	0.07	0.08	0.08
S₅	0.08	0.09	0.14	0.08	0.06	0.11	0.08	0.12	0.09	0.09	0.05	0.09	0.09	0.12
S₆	0.11	0.12	0.16	0.12	0.12	0.05	0.12	0.14	0.13	0.13	0.11	0.13	0.13	0.13
S₇	0.01	0.03	0.10	0.01	0.03	0.01	0.01	0.04	0.04	0.04	0.01	0.04	0.04	0.04
S₈	0	0.03	0.04	0	0	0.01	0	0.03	0.03	0.03	0	0.01	0.01	0.01
S₉	0.01	0.04	0.05	0.01	0.04	0.04	0.01	0.01	0.04	0.04	0.01	0.04	0.04	0.04
S₁₀	0.01	0.04	0.02	0.01	0.01	0.06	0.01	0.01	0.01	0.01	0.01	0.04	0.01	0.04
S₁₁	0.08	0.06	0.13	0.08	0.08	0.08	0.08	0.09	0.09	0.09	0.02	0.09	0.09	0.09
S₁₂	0.11	0.09	0.15	0.11	0.10	0.11	0.09	0.13	0.10	0.10	0.11	0.06	0.12	0.10
S₁₃	0.01	0.03	0.01	0.01	0.03	0.01	0.01	0.01	0.01	0.01	0	0.01	0.03	0.01
S₁₄	0	0	0	0	0	0	0	0	0	0	0	0	0	0

代码	乡镇	社区名称	社区类型	样本数量	Kendall's W	显著性p	排名
H	瑶安瑶族乡	芙蓉村	村落社区	17	0.560	<0.01	1
D	小三江镇	洛阳村	城镇社区	16	0.519	<0.01	2
E	上帅镇	菜园坝	城乡结合社区	8	0.512	<0.01	3
I	丰阳镇	连南县城	城镇社区	21	0.456	<0.01	4
G	连州镇	旱坑村	村落社区	19	0.382	<0.01	5
C	寨岗镇	石坑崀村	村落社区	22	0.379	<0.01	6
A	杜步镇	连官村	城乡结合社区	23	0.379	<0.01	7
F	三江镇	登阳村	村落社区	9	0.319	<0.01	8
B	七拱镇	柯木湾村	村落社区	27	0.313	<0.01	9

乡镇	社区名称	社区类型	社区韧性cr^*	排名
连州镇	旱坑村	村落社区	1.65	1
寨岗镇	石坑崀村	村落社区	1.27	2
瑶安瑶族乡	芙蓉村	村落社区	1.26	3
丰阳镇	连南县城	城镇社区	1.13	4
杜步镇	连官村	城乡结合社区	1.03	5
小三江镇	洛阳村	城镇社区	0.88	6
上帅镇	菜园坝	城乡结合社区	0.53	7

山洪视角下社区韧性影响因素及其定量分析

Influencing factors and quantitative analysis of community resilience to flash floods

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 33

相关文章 3

Metrics

本文评价

推荐阅读 0

[1]	刘晔, 肖童, 刘于琪, 邱婴芝, 刘义, 李志刚. 城市建成环境对广州市居民幸福感的影响——基于15 min步行可达范围的分析[J]. 地理科学进展, 2020, 39(8): 1270-1282.
[2]	张丽娟, 陈红, 高玉宏, 马艳敏, 马骏. 黑龙江省大风分布特征及风险区划研究[J]. 地理科学进展, 2011, 30(7): 899-905.
[3]	唐川,师玉娥. 城市山洪灾害多目标评估方法探讨[J]. 地理科学进展, 2006, 25(4): 13-21.