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

Omid Memarian Sorkhabi () and Yahya Djamour ()
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Omid Memarian Sorkhabi: CeADAR-Ireland’s Centre for Applied AI, University College Dublin
Yahya Djamour: Shahid Beheshti University (SBU)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 1, No 9, 195 pages

Abstract: 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.

Keywords: Flood; GPS; PWV; Tomography; 4D modeling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-023-06185-6

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