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Extension of the Geomorphic Flood Index classifier to predict flood inundation maps for uncalibrated rainfall depths in arid regions

Author

Listed:
  • Mohamed A. Hamouda

    (Dar Al-Handasah Consulting Firm, Shair and Partners
    Fayoum University)

  • Ayman G. Awadallah

    (Fayoum University)

  • Ramadan H. Abdel-Maguid

    (Fayoum University)

Abstract

Flash floods are a rapid hydrological response that occurs within a short time with rapidly rising water levels and could lead to massive structural, social and economic damages. Therefore, generating flood inundation maps becomes necessary to distinguish areas exposed to floods. Hydrodynamic models are commonly used to generate inundation maps; however, they require high computational power and time, depending on the complexity of the model. For that, researchers developed effective, fast and simplified models. Among the simplified models, the Geomorphic Flood Index (GFI) is one of the most useful classifiers to generate inundation maps. Three main objectives are addressed in this study: (1) extend the GFI classifier to predict flood extent maps for uncalibrated rainfall depths, which will enhance early warning models for better risk assessments of extreme events; (2) enhance the accuracy of the simulated inundation maps using different calibration methods; and (3) investigate the performance of the GFI in various terrains with different resolutions. Three case studies in arid regions in Saudi Arabia were examined with different topographies, using terrains of high resolutions of 1 m and resampled low resolutions, as well as various rainfall depths corresponding to 5–100-yr return periods. The HEC-RAS 2D model was used to generate reference flood inundation maps. The obtained flood extent maps show high similarity compared to the reference maps with accuracy above 80%. Strong relationships between rainfall depths and the threshold GFI parameter were developed which allow producing inundation maps for any rainfall event.

Suggested Citation

  • Mohamed A. Hamouda & Ayman G. Awadallah & Ramadan H. Abdel-Maguid, 2024. "Extension of the Geomorphic Flood Index classifier to predict flood inundation maps for uncalibrated rainfall depths in arid regions," 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(5), pages 4633-4655, March.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:5:d:10.1007_s11069-023-06393-0
    DOI: 10.1007/s11069-023-06393-0
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    References listed on IDEAS

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    1. Mohamed M. Fathi & Ayman G. Awadallah & Nabil A. Awadallah, 2021. "Estimation of Regional Sub-Daily Rainfall Ratios Using SKATER Algorithm and Logistic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 555-571, January.
    2. Heather McGrath & Jean-François Bourgon & Jean-Samuel Proulx-Bourque & Miroslav Nastev & Ahmad Abo El Ezz, 2018. "A comparison of simplified conceptual models for rapid web-based flood inundation 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. 93(2), pages 905-920, September.
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