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A review of modelling methodologies for flood source area (FSA) identification

Author

Listed:
  • Amrie Singh

    (University of Leeds)

  • David Dawson

    (University of Leeds)

  • Mark Trigg

    (University of Leeds)

  • Nigel Wright

    (Nottingham Trent University)

Abstract

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises.

Suggested Citation

  • Amrie Singh & David Dawson & Mark Trigg & Nigel Wright, 2021. "A review of modelling methodologies for flood source area (FSA) identification," 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. 107(2), pages 1047-1068, June.
  • Handle: RePEc:spr:nathaz:v:107:y:2021:i:2:d:10.1007_s11069-021-04672-2
    DOI: 10.1007/s11069-021-04672-2
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    References listed on IDEAS

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    Cited by:

    1. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.

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