IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i1d10.1007_s11069-023-06185-6.html
   My bibliography  Save this article

4D modeling of precipitable water vapor to assess flood forecasting by using GPS signals

Author

Listed:
  • Omid Memarian Sorkhabi

    (CeADAR-Ireland’s Centre for Applied AI, University College Dublin)

  • Yahya Djamour

    (Shahid Beheshti University (SBU))

Abstract

Every year the government and people suffer from the cost of damages caused by natural disasters such as hurricanes and floods. Thanks to Global Positioning System (GPS) permanent station data, today it is possible to determine the amount of Water Vapor (WV) very accurately and reliably. GPS signals pass through different layers of the atmosphere, such as the ionosphere and troposphere, and therefore they are delayed. A significant part of the delay is due to the troposphere, which is one of the serious sources of error in GPS. In this research, by using 5 GPS permanent stations located in the Arasbaran region, in northwest of Iran, the Precipitable Water Vapor (PWV) is evaluated in order to show the capacity of this method in forecasting the October 2012 flood over the region. The results showed that the amount of PWV in the atmosphere had increased by 2 mm a few hours before the flood, which could be used as a forecast indicator. The Tikhonov regularization for tropospheric tomography covering the GPS stations region was also performed for latitude and altitude components for 3 consecutive days: the day before the flood, the flood day, and the day after the flood. A high amount of PWV was observed in the troposphere on the day before the flood, and especially on the flood day. During the study period, the results from tomography and the radiosonde were in good agreement, with a significance correlation of around 0.95. Also, the values of PWV obtained from GPS and MODIS-NIR do not have an absolute error more than 4.4 mm over the 8 days from 8 October 2012 to 15 October 2012.

Suggested Citation

  • Omid Memarian Sorkhabi & Yahya Djamour, 2024. "4D modeling of precipitable water vapor to assess flood forecasting by using GPS signals," 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. 120(1), pages 181-195, January.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:1:d:10.1007_s11069-023-06185-6
    DOI: 10.1007/s11069-023-06185-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06185-6
    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/s11069-023-06185-6?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. Francis Yongwa Dtissibe & Ado Adamou Abba Ari & Chafiq Titouna & Ousmane Thiare & Abdelhak Mourad Gueroui, 2020. "Flood forecasting based on an artificial neural network scheme," 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(2), pages 1211-1237, November.
    Full references (including those not matched with items on IDEAS)

    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. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    2. Marzieh Khajehali & Hamid R. Safavi & Mohammad Reza Nikoo & Mahmood Fooladi, 2024. "A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, 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. 120(9), pages 8483-8504, July.
    3. Barry Sheehan & Martin Mullins & Darren Shannon & Orla McCullagh, 2023. "On the benefits of insurance and disaster risk management integration for improved climate-related natural catastrophe resilience," Environment Systems and Decisions, Springer, vol. 43(4), pages 639-648, December.

    More about this item

    Keywords

    Flood; GPS; PWV; Tomography; 4D modeling;
    All these keywords.

    Statistics

    Access and download statistics

    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:120:y:2024:i:1:d:10.1007_s11069-023-06185-6. 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.