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Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study

Author

Listed:
  • Zhouyayan Li

    (University of Iowa
    University of Iowa)

  • Jerry Mount

    (University of Iowa)

  • Ibrahim Demir

    (University of Iowa
    University of Iowa)

Abstract

This study examines the uncertainties associated with a rapid flood inundation mapping approach based on height above the nearest drainage (HAND) model's parameters, inputs, reference, and computing procedure. The effects of changing the drainage threshold, water depth, and horizontal resolution were studied in two Iowan regions of around 100 km2. To assess the uncertainty from rasterized inundation extent maps used as the reference, a regional and community-level evaluation with three distinct water depth estimates was performed for a five-county region in Iowa. An inner zone approach was utilized to demonstrate the uncertainties around the computational boundary of regions. The results reveal that the HAND model's performance depends on the research area's morphological characteristics, even though they are not directly input into the calculation. A good parameter set for one site does not ensure good performance for another. Moreover, the findings suggest that the DEM's resolution should be carefully chosen, as overgeneralization reduces the reliability of forecasts. Finally, the HAND model relies on drainage accumulation to build the simulation network; therefore, the calculation around the region border can introduce uncertainty. The findings of this study provide guidelines for effective model setup, adjustments, and performance improvement for non-rating-curve-based HAND.

Suggested Citation

  • Zhouyayan Li & Jerry Mount & Ibrahim Demir, 2022. "Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study," 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. 112(1), pages 977-1004, May.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:1:d:10.1007_s11069-022-05215-z
    DOI: 10.1007/s11069-022-05215-z
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    References listed on IDEAS

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    1. 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.
    2. M. Karamouz & F. Fooladi Mahani, 2021. "DEM Uncertainty Based Coastal Flood Inundation Modeling Considering Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3083-3103, August.
    3. J. Teng & J. Vaze & D. Dutta & S. Marvanek, 2015. "Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2619-2636, June.
    4. Priyamitra Munoth & Rohit Goyal, 2019. "Effects of DEM Source, Spatial Resolution and Drainage Area Threshold Values on Hydrological Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3303-3319, July.
    5. Enes Yildirim & Ibrahim Demir, 2019. "An integrated web framework for HAZUS-MH flood loss estimation 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. 99(1), pages 275-286, October.
    6. Rebecca M Diehl & Jesse D Gourevitch & Stephanie Drago & Beverley C Wemple, 2021. "Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
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    Cited by:

    1. Caisu Meng & Hailiang Jin, 2023. "A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method," Sustainability, MDPI, vol. 15(20), pages 1-22, October.

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