京津冀地区县域单元地质灾害风险评估

doi:10.18306/dlkxjz.2017.03.008

Abstract

Abstract:

Taking the Beijing-Tianjin-Hebei region as an example, this article puts forward a method of geological hazard risk assessment. By calculating the level of threat of geological hazards and vulnerability of hazard-affected bodies first, we evaluated the geological hazard risk of counties and districts by qualitative comprehensive assessment. Vulnerability of hazard-affected bodies is composed of population vulnerability and property vulnerability, which are represented by the rate of deaths and rate of direct economic loss caused by geological hazards. Combing with the data of geological disaster situation during 2001-2015 from the Ministry of Land and Resources and taking into account the 1950-2000 disaster data, we proposed the classification standards of population vulnerability and property vulnerability. These standards and calculation were applied in the Beijing-Tianjin-Hebei region. The result shows that the overall geological hazard risk is low. Only six areas—Laiyuan, Laishui, Wuan, Qinglong Manchu Autonomous County, Shijingshan, and Yanqing—show medium risk among the total of 204 assessed county and district units. The rest of the counties and districts are in the low risk area, which is in agreement with the reality. The method proposed here is simple and easy to use, and data can be acquired continuously. The spatial difference assessment result can be used to support the disaster prevention and risk reduction efforts and spatial planning and management.

Key words: county unit, geological hazard, vulnerability, risk assessment, Beijing-Tianjin-Hebei region

Hui MENG, Chunyan LI, Ruolin ZHANG, Yamin LI. Risk assessment of geological hazards for counties and districts of the Beijing-Tianjin-Hebei region[J].PROGRESS IN GEOGRAPHY, 2017, 36(3): 327-334.

Figures/Tables 12

Tab.1

Tab.2

Tab.3

Fig.1

Tab.4

Result of geological hazard assessment of counties and districts in the Beijing-Tianjin-Hebei region"

危险性等级	个数	县 (市、区) 名
高	15	河北：灵寿县、平山县、井陉县、井陉矿区、邢台县、涉县、武安市、涞水县、迁西县、峰峰矿区、行唐县、曲阳县、青龙满族自治县、宽城满族自治县、兴隆县
中	46	北京：延庆区、平谷区、石景山区、门头沟区、房山区、密云区、怀柔区; 河北：磁县、阜平县、内丘县、沙河市、蔚县、涞源县、唐县、鹿泉区、涿鹿县、元氏县、赞皇县、临城县、桥西区、桥东区、卢龙县、抚宁县、复兴区、丛台区、邯山区、邯郸县、顺平县、易县、遵化市、满城县、平泉县、承德县、双桥区、双滦区、鹰手营子矿区、丰宁满族自治县、隆化县、滦平县、宣化县、怀安县、万全县、桥西区、下花园区、崇礼县、赤城县
低	50	北京：东城区、海淀区、西城区、丰台区、昌平区、顺义区、朝阳区; 天津：蓟县; 河北：阳原县、桥西区、任县、隆尧县、柏乡县、高邑县、南和县、迁安市、路北区、路南区、海港区、山海关区、昌黎县、滦南县、平乡县、鸡泽县、曲周县、永年县、临漳县、成安县、定兴县、三河市、大厂回族自治县、丰润区、开平区、涿州市、裕华区、栾城区、古冶区、肥乡县、滦县、丰南区、北戴河区、尚义县、围场满族蒙古族自治县、康保县、沽源县、张北县、桥东区、宣化区、怀来县、广平县
极低	93	北京：通州区、大兴区; 天津：河东区、东丽区、河北区、红桥区、南开区、河西区、武清区、宝坻区、西青区、宁河县、北辰区、津南区、和平区、滨海新区、静海县; 河北：正定县、藁城区、无极县、定州市、安国市、望都县、固安县、新华区、长安区、桥东区、赵县、宁晋县、晋州市、深泽县、博野县、新市区、北市区、安新县、高阳县、肃宁县、新河县、巨鹿县、广阳区、乐亭县、霸州市、桃城区、新华区、景县、威县、邱县、大名县、魏县、广宗县、新乐市、徐水县、辛集市、安平县、饶阳县、冀州市、香河县、永清县、安次区、玉田县、献县、曹妃甸区、运河区、吴桥县、临西县、黄骅市、蠡县、南市区、雄县、深州市、河间市、大城县、武强县、武邑县、海兴县、南皮县、孟村回族自治县、东光县、阜城县、南宫市、枣强县、高碑店市、清苑县、容城县、文安县、任丘市、青县、沧县、泊头市、盐山县、馆陶县、清河县、故城县

Tab.4

Tab.5

Fig.2

Fig.3

Fig.4

Tab.6

Result of property vulnerability assessment of counties and districts in the Beijing-Tianjin-Hebei region"

省(市)	市(区)	县(市、区)	2001-2015年因灾直接经济损失/万元	县(市、区)GDP总量均值/百万元	因灾直接经济损失比/(万元/百万元)	资产易损性等级
河北省	唐山市	迁西县	2037.0	21754.73	0.0062423	极低
河北省	保定市	涞水县	215.0	2876.04	0.0049837	极低
河北省	承德市	兴隆县	341.7	4989.76	0.0045653	极低
河北省	承德市	承德县	155.0	6362.33	0.0016241	极低
河北省	石家庄市	灵寿县	115.0	5012.95	0.0015294	极低
河北省	张家口市	尚义县	33.0	1609.70	0.0013667	极低
河北省	承德市	丰宁满族自治县	50.0	4516.71	0.0007380	极低
河北省	秦皇岛市	抚宁县	70.0	8000.29	0.0005836	极低
河北省	张家口市	桥西区	95.0	12174.96	0.0005202	极低
河北省	张家口市	蔚县	33.0	4777.18	0.0004605	极低
河北省	张家口市	万全县	18.0	2869.99	0.0004181	极低
河北省	秦皇岛市	青龙满族自治县	29.0	5686.12	0.0003400	极低
河北省	唐山市	迁安市	210.0	47741.32	0.0002932	极低
河北省	承德市	隆化县	20.0	5320.59	0.0002506	极低
河北省	承德市	双滦区	10.0	2898.28	0.0002300	极低
河北省	承德市	围场满族蒙古族自治县	13.3	4701.75	0.0001886	极低
河北省	保定市	唐县	10.0	3703.33	0.0001800	极低
河北省	邢台市	邢台县	15.0	8258.44	0.0001211	极低
河北省	保定市	满城县	10.0	5711.45	0.0001167	极低
河北省	张家口市	赤城县	6.0	3501.79	0.0001142	极低
河北省	邢台市	隆尧县	7.2	6440.32	0.0000745	极低
河北省	邢台市	沙河市	11.0	12141.20	0.0000604	极低
河北省	秦皇岛市	昌黎县	5.0	10076.58	0.0000331	极低
河北省	承德市	宽城满族自治县	5.0	10224.54	0.0000326	极低
河北省	石家庄市	平山县	3.5	11474.09	0.0000203	极低
河北省	石家庄市	行唐县	1.0	6709.36	0.0000099	极低
河北省	承德市	双桥区	1.0	9935.11	0.0000067	极低
北京市	市辖区	门头沟区	124.0	25217.97	0.0003278	极低
北京市	市辖区	延庆区	22.5	5732.52	0.0002617	极低
北京市	市辖区	怀柔区	20.0	18856.69	0.0000707	极低
北京市	市辖区	房山区	46.0	62958.37	0.0000487	极低
北京市	市辖区	丰台区	70.0	110760.30	0.0000421	极低
北京市	市辖区	密云区	3.0	11476.43	0.0000174	极低
北京市	市辖区	海淀区	32.0	152591.09	0.0000140	极低
北京市	市辖区	昌平区	15.5	78106.29	0.0000132	极低
北京市	市辖区	朝阳区	2.0	224274.23	0.0000006	极低

Tab.6

Tab.7

Fig.5

References 15

[1]	范强, 巨能攀, 向喜琼, 等. 2014. 证据权法在区域滑坡危险性评价中的应用: 以贵州省为例[J]. 工程地质学报, 22(3): 474-481. doi: 10.13544/j.cnki.jeg.2014.03.017
	[Fan Q, Ju N P, Xiang X Q, et al.2014. Landslides hazards assessment with weights of case study in Guizhou, China[J]. Journal of Engineering Geology, 22(3): 474-481.] doi: 10.13544/j.cnki.jeg.2014.03.017
[2]	刘光旭, 戴尔阜, 吴绍洪, 等. 2012. 泥石流灾害风险评估理论与方法研究[J]. 地理科学进展, 31(3): 383-391. doi: 10.11820/dlkxjz.2012.03.015
	[Liu G X, Dai E F, Wu S H, et al.2012. A study on theory and method in debris flow risk assessment[J]. Progress in Geography, 31(3): 383-391.] doi: 10.11820/dlkxjz.2012.03.015
[3]	刘希林, 莫多闻, 王小丹. 2001. 区域泥石流易损性评价[J]. 中国地质灾害与防治学报, 12(2): 7-12. doi: 10.3969/j.issn.1003-8035.2001.02.002
	[Liu X L, Mo D W, Wang X D.2001. Regional vulnerability assessment of debris flows[J]. The Chinese Journal of Geological Hazard and Control, 12(2): 7-12.] doi: 10.3969/j.issn.1003-8035.2001.02.002
[4]	刘希林, 尚志海. 2014. 自然灾害风险主要分析方法及其适用性述评[J]. 地理科学进展, 33(11): 1486-1497. doi: 10.11820/dlkxjz.2014.11.006
	[Lin X L, Shang Z H.2014. Risk analysis methods of natural disasters and their applicability[J]. Progress in Geography, 33(11): 1486-1497.] doi: 10.11820/dlkxjz.2014.11.006
[5]	罗元华. 1998. 地质灾害风险评估方法[M]. 北京: 地质出版社.
	[Luo Y H.1998. Dizhi zaihai fengxian pinggu fangfa[M]. Beijing, China: Geological Publishing House.]
[6]	尚志海, 刘希林. 2010. 可接受风险与灾害研究[J]. 地理科学进展, 29(1): 23-30. doi: 10.11820/dlkxjz.2010.01.004
	[Shang Z H, Liu X L.2010. Acceptable risk and disaster research[J]. Progress in Geography, 29(1): 23-30.] doi: 10.11820/dlkxjz.2010.01.004
[7]	石菊松, 石玲, 吴树仁. 2007. 滑坡风险评估的难点和进展[J]. 地质论评, 53(6): 797-806.
	[Shi J S, Shi L, Wu S R.2007. Difficulties and problematical aspects of landslide risk assessment: An overview[J]. Geological Review, 53(6): 797-806.]
[8]	吴树仁, 石菊松, 张春山, 等. 2012. 滑坡风险评估理论与技术[M]. 北京: 科学出版社.
	[Wu S R, Shi J S, Zhang C S, et al.2012. Huapo fengxian pinggu lilun yu jishu[M]. Beijing, China: Science Press.]
[9]	向喜琼, 黄润秋. 2000. 基于GIS的人工神经网络模型在地质灾害危险性区划中的应用[J]. 中国地质灾害与防治学报, 11(3): 23-27. doi: 10.3969/j.issn.1003-8035.2000.03.006
	[Xiang X Q, Huang R Q.2000. Application of GIS-based artificial neural networks on assessment of geohazards risk[J]. The Chinese Journal of Geological Hazard and Control, 11(3): 23-27.] doi: 10.3969/j.issn.1003-8035.2000.03.006
[10]	张春山, 李国俊, 张业成, 等. 2006. 黄河上游地区崩塌滑坡泥石流地质灾害风险评价[J]. 地质力学学报, 12(2): 211-218. doi: 10.3969/j.issn.1006-6616.2006.02.014
	[Zhang C S, Li G J, Zhang Y C, et al.2006. Risk evaluation of avalanche, landslide and mudflow hazards in the upper reaches of the Yellow River[J]. Journal of Geomechanics, 12(2): 211-218.] doi: 10.3969/j.issn.1006-6616.2006.02.014
[11]	张春山, 张业成, 马寅生. 2003. 黄河上游地区崩塌、滑坡、泥石流地质灾害区域危险性评价[J]. 地质力学学报, 9(2): 143-153. doi: 10.3969/j.issn.1006-6616.2003.02.007
	[Zhang C S, Zhang Y C, Ma Y S.2003. Regional dangerous on the geological hazards of collapse, landslide and debris flow in the upper reaches of the Yellow River[J]. Journal of Geomechanics, 9(2): 143-153.] doi: 10.3969/j.issn.1006-6616.2003.02.007
[12]	张梁, 殷坤龙, 雷明堂, 等. 2002. 全国地质灾害风险区划研究报告[R]. 河北: 中国国土资源经济研究院, 17(6): 291-298.
	[Zhang L, Yin K L, Lei M T, et al.2002. Quanguo dizhi zaihai fengxian quhua yanjiu baogao[R]. Hebei, China: Chinese Academy of Land & Resource Economic, 17(6): 291-298.]
[13]	张茂省, 唐亚明. 2008. 地质灾害风险调查的方法与实践[J]. 地质通报, 27(8): 1205-1216. doi: 10.3969/j.issn.1671-2552.2008.08.017
	[Zhang M S, Tang Y M.2008. Risk investigation method and practice of geohazards[J]. Geological Bulletin of China, 27(8): 1205-1216.] doi: 10.3969/j.issn.1671-2552.2008.08.017
[14]	张若琳, 孟晖, 连建发, 等. 2010. 基于GIS的概率比率模型的滑坡易发性评价[J]. 地学前缘, 17(6): 291-297.
	[Zhang R L, Meng H, Lian J F, et al.2010. Landslide susceptibility assessment by probability ratio model based on GIS[J]. Earth Science Frontiers, 17(6): 291-297. ]
[15]	朱良峰, 殷坤龙, 张梁, 等. 2002. GIS支持下的地质灾害风险分析[J]. 长江科学院院报, 19(5): 42-45.
	[Zhu L F, Yin K L, Zhang L, et al.2002. Risk analysis system of geo-hazards supported by GIS[J]. Journal of Yangtze River Scientific Research Institute, 19(5): 42-45.]

Risk assessment of geological hazards for counties and districts of the Beijing-Tianjin-Hebei region

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地质灾害危险性	承灾体易损性
地质灾害危险性	高	中	低	极低
高	高	高	中	低
中	高	中	中	低
低	中	中	低	低
极低	低	低	低	低

省(市)	市(区)	县(市、区)	2001-2015年因灾死亡人数/人	县(市、区)人口总数/万人	因灾死亡人口比/(人/万人)	人口安全易损性等级
河北省	保定市	涞源县	10	22.76	0.029295	中
	邯郸市	武安市	8	71.50	0.007459	低
	秦皇岛市	青龙满族自治县	4	43.36	0.006150	低
	保定市	涞水县	1	29.60	0.002252	低
北京市	市辖区	延庆区	3	27.71	0.007217	低
	市辖区	石景山区	2	53.79	0.002479	低
	市辖区	顺义区	2	76.52	0.001743	低
	市辖区	房山区	1	82.48	0.000808	极低

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