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Urban flood hazard mapping using a hydraulic–GIS combined model

Author

Listed:
  • Boyu Feng

    (The University of Western Ontario)

  • Jinfei Wang

    (The University of Western Ontario)

  • Ying Zhang

    (Natural Resources Canada)

  • Brent Hall

    (Esri Canada)

  • Chuiqing Zeng

    (The University of Western Ontario)

Abstract

Urban flooding is a reoccurring disaster, and its frequency and intensity are likely to increase in the future due to the increasing frequency of storm events. Up-to-date monitoring on the distribution of flood hazards in cities is necessary and valuable for urban planning. This research combines two common urban flooding approaches, namely hydraulic and GIS models, in a case study of London, Ontario, Canada. The hydraulic–GIS combined model employs the hydraulic concept in a simplified GIS frame, hence avoiding heavy computation in the hydraulic model and arbitrary coefficients in a GIS model. We used a binary logistic regression model to integrate the hydraulic concept in a GIS model. The multi-criteria GIS model built by binary logistic regression was able to simulate the results from the hydraulic model with good consistency. Such a strategy serves as a promising prototype for addressing similar geographical modelling issues, where the time-consuming physical model can be potentially replaced by a simplified GIS model. Furthermore, the impervious surface percentage is an important input in the hydraulic model. This research experimented different impervious surface percentages as input to the hydraulic model and found that a spatially variable impervious surface percentage achieves better agreement with hydraulic modelling than that of uniform (25% and 42%) impervious surface percentages.

Suggested Citation

  • Boyu Feng & Jinfei Wang & Ying Zhang & Brent Hall & Chuiqing Zeng, 2020. "Urban flood hazard mapping using a hydraulic–GIS combined model," 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(3), pages 1089-1104, February.
    • Handle: RePEc:spr:nathaz:v:100:y:2020:i:3:d:10.1007_s11069-019-03850-7
    DOI: 10.1007/s11069-019-03850-7
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    References listed on IDEAS

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    1. N. Nirupama & Slobodan Simonovic, 2007. "Increase of Flood Risk due to Urbanisation: A Canadian Example," 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. 40(1), pages 25-41, January.
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    Cited by:

    1. Jiun-Huei Jang & Petr Vohnicky & Yen-Lien Kuo, 2021. "Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2215-2230, May.
    2. Minh Pham Quang & Krti Tallam, 2022. "Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    3. Bikram Manandhar & Shenghui Cui & Lihong Wang & Sabita Shrestha, 2023. "Urban Flood Hazard Assessment and Management Practices in South Asia: A Review," Land, MDPI, vol. 12(3), pages 1-29, March.
    4. Lloyd Ling & Zulkifli Yusop & Joan Lucille Ling, 2021. "Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia," Mathematics, MDPI, vol. 9(8), pages 1-24, April.

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