IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v102y2020i2d10.1007_s11069-019-03571-x.html
   My bibliography  Save this article

Vulnerability assessment for flash floods using GIS spatial modeling and remotely sensed data in El-Arish City, North Sinai, Egypt

Author

Listed:
  • Soha A. Mohamed

    (University of Alexandria)

  • Mohamed E. El-Raey

    (University of Alexandria)

Abstract

Egypt suffers from freshwater crisis, and the shortage is predicted to become severe by 2025. Egypt is exposed to flash floods, especially in Sinai governorate, causing rapid rises of water in a short amount of time and can trigger other catastrophic hazards associated with damage, danger to human life, properties and environment. Flash floods may be considered a source of water that can be explored to meet the water shortage problem. In this study, a composite flash floods vulnerability index based on an integrated hydro-climatic and physical vulnerability component was created. The composite index was based on eight parameters including rainfall distribution, elevation and slope, flow direction, streams, geomorphological features, soil type and land cover. The composite index was ranked into three categories: high, moderate and low. The index can help identify the weak and strong points to support the decision-making process concerned with water management as an essential prerequisite for Egypt sustainable development. The results revealed that the urban, vegetation cover, loamy sand, sand dunes, the low elevation and the flat areas are the most affected by the flash floods in EL-Arish City in Sinai governorate. 42% of Wadi El-Arish had low vulnerability, 45% moderate vulnerability and 13% high vulnerability.

Suggested Citation

  • Soha A. Mohamed & Mohamed E. El-Raey, 2020. "Vulnerability assessment for flash floods using GIS spatial modeling and remotely sensed data in El-Arish City, North Sinai, Egypt," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(2), pages 707-728, June.
  • Handle: RePEc:spr:nathaz:v:102:y:2020:i:2:d:10.1007_s11069-019-03571-x
    DOI: 10.1007/s11069-019-03571-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-019-03571-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-019-03571-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Reza Ghazavi & Abbasali Vali & Saeid Eslamian, 2010. "Impact of Flood Spreading on Infiltration Rate and Soil Properties in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2781-2793, September.
    2. M. Monirul Qader Mirza, 2003. "Three Recent Extreme Floods in Bangladesh: A Hydro-Meteorological Analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 28(1), pages 35-64, January.
    3. Chen Cao & Peihua Xu & Yihong Wang & Jianping Chen & Lianjing Zheng & Cencen Niu, 2016. "Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas," Sustainability, MDPI, vol. 8(9), pages 1-18, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Atiq Ur Rehman Tariq & Cheuk Yin Wai & Nitin Muttil, 2020. "Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process," Future Internet, MDPI, vol. 12(12), pages 1-21, December.
    2. Zaineb Ali & Noura Dahri & Marnik Vanclooster & Ali Mehmandoostkotlar & Adnane Labbaci & Mongi Ben Zaied & Mohamed Ouessar, 2023. "Hybrid Fuzzy AHP and Frequency Ratio Methods for Assessing Flood Susceptibility in Bayech Basin, Southwestern Tunisia," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
    3. Yesen Liu & Yaohuan Huang & Jinhong Wan & Zhenshan Yang & Xiaolei Zhang, 2020. "Analysis of Human Activity Impact on Flash Floods in China from 1950 to 2015," Sustainability, MDPI, vol. 13(1), pages 1-12, December.
    4. Alaa Ahmed & Abdullah Alrajhi & Abdulaziz Alquwaizany & Ali Al Maliki & Guna Hewa, 2022. "Flood Susceptibility Mapping Using Watershed Geomorphic Data in the Onkaparinga Basin, South Australia," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    5. Mustafa El-Rawy & Wael M. Elsadek & Florimond Smedt, 2023. "Flood hazard assessment and mitigation using a multi-criteria approach in the Sinai Peninsula, Egypt," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 215-236, January.
    6. Hamid Rezaei & Elżbieta Macioszek & Parisa Derakhshesh & Hassan Houshyar & Elias Ghabouli & Amir Reza Bakhshi Lomer & Ronak Ghanbari & Abdulsalam Esmailzadeh, 2023. "A Spatial Decision Support System for Modeling Urban Resilience to Natural Hazards," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Showmitra Kumar Sarkar & Saifullah Bin Ansar & Khondaker Mohammed Mohiuddin Ekram & Mehedi Hasan Khan & Swapan Talukdar & Mohd Waseem Naikoo & Abu Reza Towfiqul Islam & Atiqur Rahman & Amir Mosavi, 2022. "Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    2. Vangelis Pitidis & Deodato Tapete & Jon Coaffee & Leon Kapetas & João Porto de Albuquerque, 2018. "Understanding the Implementation Challenges of Urban Resilience Policies: Investigating the Influence of Urban Geological Risk in Thessaloniki, Greece," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
    3. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    4. Martina Linnenluecke & Andrew Griffiths, 2012. "Assessing organizational resilience to climate and weather extremes: complexities and methodological pathways," Climatic Change, Springer, vol. 113(3), pages 933-947, August.
    5. Atta-ur-Rahman & Amir Khan, 2013. "Analysis of 2010-flood causes, nature and magnitude in the Khyber Pakhtunkhwa, Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(2), pages 887-904, March.
    6. Bhagawat Rimal & Lifu Zhang & Hamidreza Keshtkar & Xuejian Sun & Sushila Rijal, 2018. "Quantifying the Spatiotemporal Pattern of Urban Expansion and Hazard and Risk Area Identification in the Kaski District of Nepal," Land, MDPI, vol. 7(1), pages 1-22, March.
    7. Y. Yang & Patrick Ray & Casey Brown & Abedalrazq Khalil & Winston Yu, 2015. "Estimation of flood damage functions for river basin planning: a case study in Bangladesh," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(3), pages 2773-2791, February.
    8. Ahmad Rajabi & Saeid Shabanlou & Fariborz Yosefvand & Afshin Kiani, 2021. "Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 871-901, October.
    9. Lorena Liuzzo & Vincenzo Sammartano & Gabriele Freni, 2019. "Comparison between Different Distributed Methods for Flood Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3155-3173, July.
    10. Hydar Ebrahimi & Reza Ghazavi & Haji Karimi, 2016. "Estimation of Groundwater Recharge from the Rainfall and Irrigation in an Arid Environment Using Inverse Modeling Approach and RS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 1939-1951, April.
    11. Alaa M. Al-Abadi & Noor A. Al-Najar, 2020. "Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(2), pages 461-491, January.
    12. Hossain, Mohammad Khalid & Meng, Qingmin, 2020. "A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood," Land Use Policy, Elsevier, vol. 99(C).
    13. Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
    14. Romulus Costache, 2019. "Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3239-3256, July.
    15. World Bank, 2010. "Economic of Adaptation to Climate Change : Bangladesh, Volume 1. Main Report," World Bank Publications - Reports 12837, The World Bank Group.
    16. Chen Cao & Jianping Chen & Wen Zhang & Peihua Xu & Lianjing Zheng & Chun Zhu, 2019. "Geospatial Analysis of Mass-Wasting Susceptibility of Four Small Catchments in Mountainous Area of Miyun County, Beijing," IJERPH, MDPI, vol. 16(15), pages 1-19, August.
    17. M. M. Yagoub & Aishah A. Alsereidi & Elfadil A. Mohamed & Punitha Periyasamy & Reem Alameri & Salama Aldarmaki & Yaqein Alhashmi, 2020. "Newspapers as a validation proxy for GIS modeling in Fujairah, United Arab Emirates: identifying flood-prone areas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 111-141, October.
    18. Richard Mind’je & Lanhai Li & Jean Baptiste Nsengiyumva & Christophe Mupenzi & Enan Muhire Nyesheja & Patient Mindje Kayumba & Aboubakar Gasirabo & Egide Hakorimana, 2020. "Landslide susceptibility and influencing factors analysis in Rwanda," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 7985-8012, December.
    19. Zaw Myo Khaing & Ke Zhang & Hisaya Sawano & Badri Bhakra Shrestha & Takahiro Sayama & Kazuhiro Nakamura, 2019. "Flood hazard mapping and assessment in data-scarce Nyaungdon area, Myanmar," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-18, November.
    20. Muhammad Aslam Baig & Donghong Xiong & Mahfuzur Rahman & Md. Monirul Islam & Ahmed Elbeltagi & Belayneh Yigez & Dil Kumar Rai & Muhammad Tayab & Ashraf Dewan, 2022. "How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1543-1562, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:102:y:2020:i:2:d:10.1007_s11069-019-03571-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.