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基于情景模拟的天津市滨海新区2020年暴雨内涝风险评估
引用本文:胡蓓蓓,周俊,王军,许世远,孟伟庆.基于情景模拟的天津市滨海新区2020年暴雨内涝风险评估[J].地理科学,2012(7):846-852.
作者姓名:胡蓓蓓  周俊  王军  许世远  孟伟庆
作者单位:天津师范大学城市与环境科学学院;天津市控制地面沉降工作办公室;华东师范大学地理信息科学教育部重点实验室
基金项目:国家自然科学基金重点项目(40730526);天津市高等学校人文社会科学研究一般项目(20092117);天津师范大学博士基金(52X09019);天津市自然科学基金重点项目(11JCZDJC24500)资助
摘    要:基于多灾种复合动态风险评估理论,依据滨海新区2020年人口规划、土地利用规划以及社会经济发展计划,根据地面沉降和海平面上升预测结果设计了最不利、适中和最理想化三种情景;在此基础上,自行开发了基于GIS的洪水淹没区计算模块,模拟计算不同重现期暴雨内涝的淹没范围、淹没深度及淹没损失。结果表明:2020年,发生1 000 a一遇、200 a一遇和50 a一遇暴雨时,在最不利的情景一下:天津市滨海新区分别将有32.73%,29.34%和26.01%的土地不同程度受淹,受淹人口分别为338万、305万和264万,淹没损失分别达220.89亿元、181.39亿元和139.12亿元。

关 键 词:天津市滨海新区  暴雨内涝  情景模拟  风险评估

Risk Assessment on Rainstorm Waterlogging of Tianjin Binhai New Area Based on Scenario Simulation
HU Bei-bei,ZHOU Jun,WANG Jun,XU Shi-yuan,MENG Wei-qing.Risk Assessment on Rainstorm Waterlogging of Tianjin Binhai New Area Based on Scenario Simulation[J].Scientia Geographica Sinica,2012(7):846-852.
Authors:HU Bei-bei  ZHOU Jun  WANG Jun  XU Shi-yuan  MENG Wei-qing
Institution:1(1.College of Urban and Environment Science,Tianjin Normal University,Tianjin 300387,China;2.Tianjin Land Subsidence Control Office,Tianjin 300061,China;3.Key Laboratory of Geographic Information Science of Ministry of Education,East China Normal University,Shanghai,200062,China)
Abstract:Natural disaster is the hotspot question in international society and academy.Coastal cities are important areas and strategic focus of people convergence,national economy and society development.However,natural disasters occur easily and frequently in these areas.Tianjin Binhai New Area(TBNA) lies in the center of the Bohai Rim Region.Following the Shenzhen Special Economic Zone and Pudong New Area,TBNA becomes a new polarization of economic development which drives regional growth.Affected by physical-geographical conditions and human activities,however,TBNA is one of the areas where the loss from natural disasters is huge and fatal in the coastal areas in China.In view of the main causing factor,the numerical model of land subsidence was established.With three groundwater extraction scenarios,the computer program was compiled to predict the land subsidence in the process of dynamic changes of groundwater level.Based on the complex dynamic multi-hazard risk assessment theory,in accordance with population planning,land use planning and socio-economic development plan of TBNA,and according to prediction of land subsidence and sea level rise,three scenarios were designed,that was the most unfavorable,moderate,and the most ideal scenarios.Pearson-Ⅲ distribution method was used to calculate intensity of the different frequency’s maximum rainstorms in 24 hours by rainstorm data of the past 40 years at 14 hydrological stations in TBNA and its nearby regions.Inverse distance weight and Disjunctive Kriging method were respectively employed to interpolate the different frequency’s maximum rainstorms in 24 hours.The calculation model of flood submerged area was therefore established by GIS.The submerged area and depth were calculated by the module of"non-source flood".On the basis of the loss rate of different submerged depth,the submerged loss was gained.The results showed that,by 2020,under Scenario 1,the submerged area is 32.73%,29.34% and 26.01%;inundated population accounts for 338×104,305×104and 264×104;and the loss of submerged is 220.89×108yuan,181.39×108yuan and 139.12×108yuan respectively induced by torrential rain with return period of 1 000,200,and 50 years.Under Scenario 2,the submerged area is 30.70%,27.47% and 24.23%;inundated population accounts for 318× 104,289×104and 248×104;and the loss of submerged is 199.68×108yuan,150.31×108yuan and 126.03×108yuan respectively induced by torrential rain with return period of 1000,200,and 50 years.Under Scenario 3,the submerged area is 29.06%,25.83% and 22.58%;inundated population accounts for 300×104,268×104 and 224× 104;and the loss of submerged is 174.48×108yuan,135.29×108yuan and 111.53×108yuan respectively induced by torrential rain with return period of 1 000,200,and 50 years.
Keywords:Tianjin Binhai New Area  rainstorm waterlogging  scenario simulation  risk assessment
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