Remote sensing-based assessment of tsunami vulnerability and risk in Alexandria, Egypt

被引:48
|
作者
Eckert, Sandra [1 ]
Jelinek, Robert [1 ]
Zeug, Gunter [1 ]
Krausrnann, Elisabeth [1 ]
机构
[1] European Commiss, Joint Res Ctr, Inst Protect & Secur Citizen, I-21021 Ispra, VA, Italy
关键词
Tsunami; Risk; Vulnerability; Hazard; Remote sensing; Geographical information systems; HAZARD; BUILDINGS;
D O I
10.1016/j.apgeog.2011.08.003
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Tsunamis can cause catastrophic loss of life, destruction of property, engineered structures and coastal infrastructure, and they can lead to major economic losses. Even though tsunamis are relatively rare in the Mediterranean Sea, their potential danger to cities along the Mediterranean coast cannot be neglected. In order to create awareness among the potentially affected people it is important to know the risk and vulnerability of the population and infrastructure related to a possible tsunami impact. In this work a hazard, vulnerability and risk analysis for buildings in two districts of Alexandria was carried out. Relevant input parameters were derived mainly from remote sensing and field data and were analyzed with a geographical information system (GIS). Based on historical records of past tsunamis, two inundation scenarios of 5 m and 9 m were defined and modeled applying a bath-type model. The resulting tsunami building risk zone maps showed that 12% of the buildings in El Gomrok district are at high or very high risk for the 5 m scenario, while the risk for El Montazah area is low. For the 9 m scenario, on the other hand, the majority of the buildings in both districts, 56% of El Gomrok, and 60% of El Montazah, are in the high or very high risk zone. An analysis of the building use indicated that the majority of these buildings are residential and commercial types, highlighting that the potential consequences of a tsunami could be severe. Due to the scarcity of historical data no frequency could be associated with the two selected scenarios. While both are credible we consider the 5 m scenario as possible but unlikely and the 9 m scenario as unlikely. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:714 / 723
页数:10
相关论文
共 50 条
  • [21] Remote sensing-based assessment of Varkala cliff retreat in Kerala, India
    Reshma, K. N.
    John, Jeffy Soly
    Veeravalli, Sai Ganesh
    Mridula, G. M.
    Raju, Satya Kiran A.
    Ramanathan, V
    Murthy, Ramana M., V
    [J]. EIGHTH GEOINFORMATION SCIENCE SYMPOSIUM 2023: GEOINFORMATION SCIENCE FOR SUSTAINABLE PLANET, 2024, 12977
  • [22] Remote sensing-based hazard assessment of glacial lakes in Sikkim Himalaya
    Raj, K. Babu Govindha
    Remya, S. N.
    Kumar, K. Vinod
    [J]. CURRENT SCIENCE, 2013, 104 (03): : 359 - 364
  • [23] Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT
    Gao, Yanchun
    Long, Di
    [J]. HYDROLOGICAL PROCESSES, 2008, 22 (25) : 4850 - 4869
  • [24] Remote sensing-based assessment of impact of Phailin cyclone on rice in Odisha, India
    Dipanwita Haldar
    Rahul Nigam
    C. Patnaik
    Sujay Dutta
    Bimal Bhattacharya
    [J]. Paddy and Water Environment, 2016, 14 : 451 - 461
  • [25] Remote sensing-based assessment of impact of Phailin cyclone on rice in Odisha, India
    Haldar, Dipanwita
    Nigam, Rahul
    Patnaik, C.
    Dutta, Sujay
    Bhattacharya, Bimal
    [J]. PADDY AND WATER ENVIRONMENT, 2016, 14 (04) : 451 - 461
  • [26] Partitioning of evapotranspiration in remote sensing-based models
    Talsma, Carl J.
    Good, Stephen P.
    Jimenez, Carlos
    Martens, Brecht
    Fisher, Joshua B.
    Miralles, Diego G.
    McCabe, Matthew F.
    Purdy, Adam J.
    [J]. AGRICULTURAL AND FOREST METEOROLOGY, 2018, 260 : 131 - 143
  • [27] Remote Sensing-Based Assessment of Fire Danger Conditions Over Boreal Forest
    Akther, M. Shammi
    Hassan, Quazi K.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2011, 4 (04) : 992 - 999
  • [28] Comparative Analysis of Regression Models for Remote Sensing-based Water Quality Assessment
    Mohandas, K. A.
    Brema, J.
    [J]. JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2023, 82 (04): : 466 - 474
  • [29] Remote Sensing-Based Assessment of Soil and Water Pollution in Deep Excavation Scenario
    Qiao, Binbin
    Leng, Zhenghua
    Mao, Shixiang
    Wang, Qiang
    Liu, Hang
    [J]. JOURNAL OF BIOBASED MATERIALS AND BIOENERGY, 2023, 17 (04) : 460 - 468
  • [30] Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
    Ghaffarian, Saman
    Kerle, Norman
    Filatova, Tatiana
    [J]. REMOTE SENSING, 2018, 10 (11)