第一次全国自然灾害综合风险普查工作的开展，为区域风险性评估实施奠定基础，对全社会灾害风险意识的提升具有重要意义。近年来，我国多地发生自然灾害，造成人员与财产损失严重，其中洪涝灾害以破坏程度高、波及范围广的特点位居自然灾害前列。河南省位于我国华中地区，地势平坦，中东部区域处于平原地区，受季风型气候影响，降雨较多，发生洪涝灾害的风险较大，因此对河南省的洪涝灾害风险评估迫在眉睫。

本文以河南省为研究区域，基于GIS和AHP-熵权法，从降水数据、社会经济数据和地形地貌数据中，选取影响洪涝灾害致灾因子危险性、孕灾环境敏感性、承灾体易损失性和防灾减灾能力4个方面的14个评估因子，构建综合风险评估模型对其进行评估，并分析其在空间上的相关性，采用2000-2019年的除涝面积和其他学者研究结果对评估结果进行验证。基于ARIMA模型预测2022-2024年降水距平百分率，结合预测结果和评估结果，对风险应对及管理提供建议。本文结论如下：

（1）通过参考大量文献以及结合河南省洪涝灾害现状，选取强洪涝频率、一般洪涝频率、年平均降水量作为致灾因子危险性的评估因子，选取河网密度、植被覆盖度、高程及坡度作为孕灾环境敏感性的评估因子，选取经济密度、人均播种面积、畜牧业占经济比重、人口密度作为承灾体易损失性的评估因子，选取人均可支配收入、医护人员密度以及地均粮食产量作为防灾减灾能力的评估因子，并说明各因子选取原因及其特征。

（2）基于AHP-熵权法，对河南省洪涝灾害风险4个影响指标进行评估时发现：对于致灾因子危险性，高等危险性区域主要分布在登封市、巩义市、陕州区、湖滨区，占总面积的2.79%，中高等危险性区域主要分布在河南省西北部的洛阳市和三门峡市，面积为42966km²；对于孕灾环境敏感性，由于郑州至洛阳一线，沿黄河两岸，孕灾敏感性高，因此高等敏感性区域主要分布在河南省北部地区，占总面积的6.33%，中高等敏感性区域面积为54931km²，占总面积33.43%。对于承灾体易损失性，河南省沿京广铁路一线，东部区域易损失性大。高等易损失性区域主要分布在周口市、驻马店市、许昌市，面积为49362km²，中高等易损失性区域分布在商丘市和新乡市大部分区域，占总面积29.78%；对于防灾减灾能力，呈现从南到北，逐渐增强的趋势。低等防灾减灾能力区域主要分布在西部地区，占总面积16.24%，中低等防灾减灾能力区域主要分布在南部和东部，面积为73625km²。

（3）综合4个影响指标，进行洪涝灾害综合风险性评估，得到如下结论：河南省高风险区主要集中于东部的黄淮平原和西北部伊洛河平原，面积为17420km²，中高等风险区域集中于商丘市、驻马店市和颍河上游两岸县区，占总面积17.55%；中风险区集中于东北部和南部的信阳市，面积为42231km²，低等风险区和中低等风险区主要分布在西部的伏牛山和南部的大别山地区，分别占总面积的13.60%和17.55%。

（4）通过莫兰指数分析洪涝灾害综合风险的空间自相关性，灾害风险在空间上呈聚集性，高等综合风险指数主要聚集在驻马店市、周口市、平顶山东南部等地；采用河南省2000-2019年除涝面积指标，并结合相关研究进行验证，评估结果和实际情况相吻合且与其他学者研究结果相符合；基于ARIMA模型对2022-2024年降水距平百分率预测，结果表明未来重涝可能出现的区域主要分布在南阳市卧龙区和宛城区、信阳市部分区域、郑州市中心城区等地。结合综合风险评估结果和预测结果，针对高风险区域、中高风险区域、预测区域提出工程性建议，对全省提出非工程性建议。

The first national comprehensive natural disaster risk survey has laid the foundation for the implementation of regional risk assessments and is of great significance to the promotion of disaster risk awareness in society as a whole. In recent years, natural disasters have occurred in many parts of China, causing serious damage to people and property, with floods being at the forefront of natural disasters with a high degree of damage and a wide range of impacts. Henan Province is located in central China, with a flat topography and a plain area in the central-eastern part of the country, which is subject to a monsoonal climate with high rainfall and a high risk of flooding.

This paper takes Henan Province as the study area, and based on GIS and the AHP-entropy weight method, 14 assessment factors affecting four aspects of flood hazard causation factors, sensitivity of the breeding environment, vulnerability to loss of disaster-bearing bodies and disaster prevention and mitigation capacity are selected from precipitation data, socio-economic data and topographical data, and a comprehensive risk assessment model is constructed to assess them and analyse their spatial correlation, using The assessment results are validated by the flood removal area from 2000-2019 and the results of other scholars' studies. Based on the ARIMA model to predict the percentage of precipitation distance from 2022 to 2024, the prediction results and assessment results are combined to provide suggestions for risk response and management. The conclusions of this paper are as follows.

(1) By referring to a large amount of literature and combining the current situation of flooding in Henan Province, strong flood frequency, general flood frequency and average annual precipitation are selected as the assessment factors for the risk of disaster-causing factors, river network density, vegetation cover, elevation and slope are selected as the assessment factors for the sensitivity of disaster-prone environment, economic density, per capita sown area, livestock share in the economy and population density are selected as the assessment factors for the vulnerability of disaster-bearing bodies to loss The factors of disaster prevention and mitigation capacity were selected as the assessment factors of disposable income per capita, density of medical and nursing personnel, and per capita food production, and the reasons for the selection of each factor and its characteristics were explained.

(2) Based on the AHP-entropy weighting method, the four impact indicators of flood risk in Henan Province were assessed as follows: for the risk of disaster-causing factors, the high-risk areas were mainly located in Dengfeng City, Gongyi City, Shaanxi District and Hubin District, accounting for 2.79% of the total area, while the medium-high risk areas were mainly located in Luoyang City and Sanmenxia City in the northwestern part of Henan Province, with an area of 42,966km²; for the The high sensitivity area is mainly located in the northern part of Henan Province, accounting for 6.33% of the total area, while the medium to high sensitivity area covers 54,931km², accounting for 33.43% of the total area. For the vulnerability to loss of disaster-bearing bodies, the eastern part of Henan Province, along the Beijing-Guangzhou Railway line, has a high vulnerability to loss. Areas of high susceptibility to loss are mainly located in Zhoukou, Zhumadian and Xuchang, with an area of 49,362km², while areas of medium to high susceptibility to loss are located in Shangqiu and most of Xinxiang, accounting for 29.78% of the total area; for disaster prevention and mitigation capacity, there is a trend of gradual increase from south to north. Areas with low disaster prevention and mitigation capacity are mainly located in the western region, accounting for 16.24% of the total area, while areas with medium to low disaster prevention and mitigation capacity are mainly located in the south and east, covering an area of 73,625km².

(3) A comprehensive risk assessment of flooding was carried out by combining the four impact indicators, and the following conclusions were obtained: the high-risk areas in Henan Province are mainly concentrated in the Yellow-Huai Plain in the east and the Yiluo River Plain in the northwest, with an area of 17,420km²; the medium and high-risk areas are concentrated in Shangqiu City, Zhumadian City and the counties on both sides of the upper reaches of the Ying River, accounting for 17.55% of the total area; the medium-risk areas are concentrated in Xinyang City in the northeast and south, with an area of 42,231km². The low risk area and medium-low risk area are mainly distributed in the Fuyiu Mountains in the west and the Dabie Mountains in the south, accounting for 13.60% and 17.55% of the total area respectively.

(4) Analyzing the spatial autocorrelation of comprehensive flood risk through the Moran index, disaster risk is spatially clustered, and the high comprehensive risk index is mainly clustered in Zhumadian City, Zhoukou City and southeastern Pingdingshan Mountain; using the flood removal area index of Henan Province from 2000 to 2019 and combining with relevant studies for verification, the assessment results match the actual situation and are consistent with the results of other scholars' studies The results of the ARIMA model-based prediction of the percentage of precipitation spacing from 2022 to 2024 show that the areas where heavy flooding is likely to occur in the future are mainly located in Wolong District and Wancheng District of Nanyang City, some areas of Xinyang City and the central city of Zhengzhou City. Combining the results of the comprehensive risk assessment and the forecast results, engineering recommendations are made for the high-risk areas, medium-high risk areas and forecast areas, and non-engineering recommendations are made for the whole province.

[1] 张艳明. 甘肃省干旱灾害风险评估及区划研究[D]. 西安: 西安科技大学, 2021.

[2] 尹占娥. 城市自然灾害风险评估与实证研究[D]. 上海: 华东师范大学, 2009.

[3] 沈子琦, 葛鹏. 河南省洪涝灾害的经济社会易损性评价[J]. 人民黄河, 2014, 36(3): 13-16.

[4] 吴泽宁, 申言霞, 王慧亮. 基于能值理论的洪涝灾害脆弱性评估[J]. 南水北调与水利科技,2018,16(6): 9-14, 32.

[5] 刘梦贞. 城市暴雨洪涝灾害脆弱性模糊综合评价研究[D]. 开封: 河南大学, 2016.

[6] 吴雷. 基于RS&GIS的暴雨洪涝灾害风险评估[D]. 武汉: 华中师范大学, 2015.

[7] Kundzewicz Z W , Huang J , Pinskwar I , et al. Climate variability and floods in China - A review[J]. Earth-Science Reviews, 2020, 211(5): 103434.

[8] 冯平, 崔广涛, 钟昀. 城市洪涝灾害直接经济损失的评估与预测[J]. 水利学报, 2001(8): 64-68.

[9] 张情. 河流洪水灾害风险评价及对策研究[D]. 大连: 大连理工大学, 2014.

[10] 张志彤. 实施水旱灾害风险管理 大力推进“两个转变”——中国水利学会2005年学术年会水旱灾害风险管理分会场主旨报告[C]//中国水利学会2005学术年会论文集——水旱灾害风险管理. 北京: 中国水利水电出版社, 2005: 9-13.

[11] 李超超, 程晓陶, 申若竹, 等. 城市化背景下洪涝灾害新特点及其形成机理[J]. 灾害学, 2019, 34(2): 57-62.

[12] Shakeel M, Atta-ur R, Rajib S. Spatial Appraisal of Flood Risk Assessment and Evaluation Using Integrated Hydro-probabilistic Approach in Panjkora River Basin, Pakistan.[J]. Environmental Monitoring and Assessment, 2019, 191(9): 573.

[13] 孙章丽, 朱秀芳, 潘耀忠,等. 洪水灾害风险分析进展与展望[J]. 灾害学, 2017, 32(3): 125-130+136.

[14] 王栋, 潘少明, 吴吉春,等. 洪水风险分析的研究进展与展望[J]. 自然灾害学报, 2006(1): 103-109.

[15] 刘许光. 基于GIS的县域尺度暴雨洪涝灾害风险分析[D]. 呼和浩特: 内蒙古师范大学, 2016.

[16] 徐晨光, 冯新峰, 申子通. 河南省降雨时空分布特征分析[J]. 安徽农业科学, 2014, 42(25): 8655-8659.

[17] 李军玲, 刘忠阳, 邹春辉. 基于GIS 的河南省洪涝灾害风险评估与区划研究[J]. 气象, 2010, 36(2): 87-92.

[19] 景艳芳, 梁轶, 张鹏飞. 河南省洪涝灾害时间序列的分形特征与R/S分析[J]. 灾害学, 2007, 22(4): 34-37.

[19] 张震宇, 王文楷. 河南省自然灾害特征及演变趋势[J]. 中国减灾, 1993, 003(1): 43-46.

[20] 张震宇. 河南省自然灾害动态演变趋势及防灾减灾对策[J]. 地域研究与开发, 1991, 10(3): 45-48+64.

[21] 温彦. 河南自然灾害[M]. 郑州: 河南教育出版社, 1994: 9.

[22] 张建云, 王银堂, 贺瑞敏, 等. 中国城市洪涝问题及成因分析[J]. 水科学进展, 2016, 27(4): 485-491.

[23] 卢舒婷, 吴洁, 周思宇, 等. 基于AHP-模糊数学法洪涝灾害风险评估[J]. 工业安全与环保, 2022, 48(1): 50-54.

[24] Whitfield PH. Floods in Future Climates: a Review[J]. Journal of Flood Risk Management, 2012, 5(4): 336-365.

[25] Alfieri L, Burek P, Feyen L, et al. Global Warming Increases Flood Frequency in Europe Global Warming Increases the Frequency of River Floods in Europe Global Warming Increases Flood Frequency in Europe[J], 2019, 5(4): 36-38.

[26] 许有鹏. 流域城市化与洪涝风险[M]. 南京: 东南大学出版社, 2012: 79.

[27] 翟盘茂. 气候变化与气象灾害[J]. 气候变化与生态环境研讨会, 2003(7): 11-14.

[28] 秦大河, 罗勇, 陈振林, 等. 气候变化科学的最新进展: IPCC第四次评估综合报告解析[J]. 气候变化研究进展, 2007, 3(6): 311-314.

[29] Ferrier N, Haque C. Hazards Risk Assessment Methodology for Emergency Managers: a Standardized Framework for Application[J]. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2003, 28(2): 271-290.

[30] Aalst M . The impacts of climate change on the risk of natural disasters[J]. Disasters, 2010, 30(1): 5-18.

[31] Ramli M W A, Alias N E, Mohd Yusof H, et al. Development of a Local, Integrated Disaster Risk Assessment Framework for Malaysia[J]. Sustainability, 2021, 13(19): 10792-10798.

[32] Dano U , Balogun A L , Matori A N , et al. Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia[J]. Water, 2019, 11(3): 615-618.

[33] Wang Y, Li Z,Tang Z, et al. A Gis-based Spatial Multi-criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China[J]. Water Resources Management, 2011, 25(13): 3465-3484.

[34] Merz B, Blschl G, Vorogushyn S, et al. Causes, Impacts and Patterns of Disastrous River Floods[J]. Nature Reviews Earth & Environment.2021,2: 592-609.

[35] Aerts J C J H, Botzen W J, Clarke K C, et al. Integrating human behaviour dynamics into flood disaster risk assessment[J]. Nature Climate Change, 2018, 8(3): 193-199.

[36] Ntajal J, Lamptey BL, Mahamadou IB, et al. Flood Disaster Risk Mapping in the Lower Mono River Basin in Togo, West Africa[J]. International Journal of Disaster Risk Reduction, 2017,23(2): 93-103.

[37] Rana IA, Routray JK. Integrated Methodology for Flood Risk Assessment and Application in Urban Communities of Pakistan[J]. Natural Hazards, 2018,(91): 239-266.

[38] Khan N. Flood Prediction and Disaster Risk Analysis Using Gis Based Wireless Sensor Networks,a Review[J]. Journel of Basic and Applied Scientific Resaerch, 2013,3(34): 476-492.

[39] Waghwala RK, Agnihotri PG. Flood Risk Assessment and Resilience Strategies for Flood Risk Management: a Case Study of Surat City[J]. International Journal of Disaster Risk Reduction, 2019,7(35): 368-390.

[40] 史培军. 再论灾害研究的理论与实践[J]. 自然灾害学报, 1996(4): 6-17.

[41] 张继权, 李宁. 主要气象灾害风险评价与管理的数量化方法及其应用[M]. 北京: 北京师范大学出版社, 2007: 185.

[42] 游珍, 蒋庆丰, 娄彩荣. 基于土地利用及其格局的自然灾害风险评价模型[J]. 自然灾害学报, 2013, 22(5): 23-30.

[43] 魏一鸣, 张林鹏, 范英. 基于Swarm的洪水灾害演化模拟研究[J]. 管理科学学报, 2002(6): 39-46.

[44] 史培军. 中国自然灾害系统地图集[M]. 北京: 科学出版社, 2003: 45.

[45] 刘家福, 李京, 刘荆, 等. 基于GIS/AHP集成的洪水灾害综合风险评价——以淮河流域为例[J]. 自然灾害学报, 2008, 17(6): 110-114.

[46] 刘小艳, 孙娴, 杜继稳, 等. 气象灾害风险评估研究进展[J]. 江西农业学报, 2009, 21(8): 123-125.

[47] 刘晓冉, 康俊, 王颖, 等. 基于GIS和FloodArea水动力模型的重庆市山洪灾害风险区划[J]. 水土保持通报, 2019, 39(2): 206-211+2+325.

[48] 佟金萍, 张涵玥, 刘辉, 等. 基于XGBoost的长三角核心城市内涝风险评估及影响因素分析[J]. 水利水电技术(中英文), 2021, 52(10): 1-11.

[49] 周存旭, 金世海. 河南省山洪灾害的危害、成因及防治对策[J]. 自然灾害学报, 2008, 17(3): 148-151.

[50] 武洪涛, 张震宇, 邬恺夫, 等. 河南省自然灾害监测与评估信息系统研究[J]. 地域研究与开发, 2005, 24(6): 125-128.

[51] 杜丽萍. 河南省洪涝灾害风险管理研究[D]. 焦作: 河南理工大学, 2016.

[52] 张芳. 层次分析法和加权秩和比法在医院绩效评价中的应用[J]. 价值工程, 2018, 37(25): 74-75.

[53] Yashon O, Ryutaro T. Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-parametric Ahp and Gis: Methodological Overview and Case Study Assessment[J]. Water, 2014, 6(6): 1515-1545.

[54] 曾涛, 吕婧,史 佳良, 等. 基于多层AHP-FCE评价模型的土地整治重大工程效益评价研究[J]. 江西农业大学学报, 2017, 39(6): 1234-1243.

[55] Hammami S, Zouhri L, Souissi , et al. Application of the Gis Based Multi-criteria Decision Analysis and Analytical Hierarchy Process (ahp) in the Flood Susceptibility Mapping (tunisia)[J]. Arabian Journal of Geosciences, 2019, 12(21): 653-657.

[56] Ljubomir G, Dragan P, Zoran B, et al. Application of Gis-interval Rough Ahp Methodology for Flood Hazard Mapping in Urban Areas[J]. Water, 2017, 9(6): 1-26.

[57] 嵇雷, 单文丽. 基于层次分析法的高校思想政治理论课教学方法评价指标权重研究[J]. 湖北经济学院学报：人文社会科学版, 2011, 8(12): 184-185.

[58] 王锦. 基于AHP的城市变电站选址研究[J]. 科技广场, 2015(5): 84-87.

[59] Lawal DU, Matori AN, Hashim AM, et al. Detecting Flood Susceptible Areas Using Gis-based Analytic Hierarchy Process[C]//Proceedings of 2012 International Conference on Future Environment and Energy. Hong Kong: Iacsit Press, 2012: 6-10.

[60] Chakraborty S, Mukhopadhyay S. Assessing Flood Risk Using Analytical Hierarchy Process (ahp) and Geographical Information System (gis): Application in Coochbehar District of West Bengal, India[J]. Natural Hazards, 2019, 99(1): 247-274.

[61] Souissi D, Zouhri L, Hammami S, et al. Gis-based Mcdm – Ahp Modeling for Flood Susceptibility Mapping of Arid Areas, Southeastern Tunisia[J]. Geocarto International, 2020,35(9): 991-1017.

[62] 刘媛媛, 王绍强, 王小博, 等. 基于AHP_熵权法的孟印缅地区洪水灾害风险评估[J]. 地理研究, 2020, 39(8): 1892-1906.

[63] 杨易诚. 基于GIS的山丹县气象灾害风险区划研究[J]. 农业灾害研究, 2019, 9(1): 32-36.

[64] 彭波. 基于GIS的广西中小流域洪涝监测研究[D]. 桂林: 广西师范学院, 2012.

[65] 张超. 基于GIS干旱风险评估与区划方法研究[D]. 石家庄: 河北师范大学, 2010.

[66] 白世彪, 陈晔, 等. 等值线绘图软件SURFER7.0中九种插值法介绍[J]. 物探化探计算技术, 2002(2): 157-162.

[67] 中国气象局. 气象标准汇编. 2011[M]. 北京: 气象出版社, 2012: 174.

[68] 张宇. 顾及空间分布特征的城镇暴雨洪涝灾害风险区划研究[D]. 南京: 南京师范大学,2021.

[69] Li Q, Guo F, Guan Y. A GIS-Based Evaluation of Environmental Sensitivity for an UrbanExpressway in Shen-zhen, China[J]. Engineering, 2018, 4(2): 230-234.

[70] 张玉娟, 曲建光, 叶猛猛. 松花江流域哈尔滨段景观生态风险评价[J]. 福州大学学报(自然科学版),2020,48(3): 361-367.

[71] 郭亚帆, 毛志勇, 米国芳. 应用时间序列分析实验教程[M]. 北京: 经济管理出版社,2017: 85.

[72] 徐雅. 基于GIS技术的主要气象灾害风险评估技术研究[D]. 桂林: 广西师范学院, 2014.

[73] 刘小艳. 陕西省干旱灾害风险评估及区划[D]. 西安: 陕西师范大学, 2010.

[74] 姚玉璧, 张强, 李耀辉, 等. 干旱灾害风险评估技术及其科学问题与展望[J]. 资源科学, 2013, 35(9): 305-323.

[75] 邹敏. 基于GIS技术的黄水河流域山洪灾害风险区划研究[D]. 济南: 山东师范大学, 2007.

[76] 毛巧梅. 经济密度时空格局研究方法综述[J]. 经济视角：下, 2013(6): 78-79.

[77] 杨丰政. 基于GIS的徐水县气象灾害风险评估研究[D]. 南京: 南京信息工程大学, 2012.

[78] 徐雅. 基于3S技术的柳州市雨雪冰冻灾害风险评估[J]. 科技广场, 2017(7): 22-25.

[79] 姜蓝齐, 马艳敏, 张丽娟, 等. 基于GIS的黑龙江省洪涝灾害风险评估与区划[J]. 自然灾害学报, 2013, 22(5): 238-246.