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Flood Hazard Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

journal contribution
posted on 2024-11-02, 21:18 authored by Matthew Kelly, Yuriy KuleshovYuriy Kuleshov
Floods are among the costliest natural hazards, in Australia and globally. In this study, we used an indicator-based method to assess flood hazard risk in Australia’s Hawkesbury-Nepean catchment (HNC). Australian flood risk assessments are typically spatially constrained through the common use of resource-intensive flood modelling. The large spatial scale of this study area is the primary element of novelty in this research. The indicators of maximum 3-day precipitation (M3DP), distance to river—elevation weighted (DREW), and soil moisture (SM) were used to create the final Flood Hazard Index (FHI). The 17–26 March 2021 flood event in the HNC was used as a case study. It was found that almost 85% of the HNC was classified by the FHI at ‘severe’ or ‘extreme’ level, illustrating the extremity of the studied event. The urbanised floodplain area in the central-east of the HNC had the highest FHI values. Conversely, regions along the western border of the catchment had the lowest flood hazard risk. The DREW indicator strongly correlated with the FHI. The M3DP indicator displayed strong trends of extreme rainfall totals increasing towards the eastern catchment border. The SM indicator was highly variable, but featured extreme values in conservation areas of the HNC. This study introduces a method of large-scale proxy flood hazard assessment that is novel in an Australian context. A proof-of-concept methodology of flood hazard assessment developed for the HNC is replicable and could be applied to other flood-prone areas elsewhere.

History

Related Materials

  1. 1.
    DOI - Is published in 10.3390/s22166251
  2. 2.
    ISSN - Is published in 14248220

Journal

Sensors

Volume

22

Number

6251

Issue

16

Start page

1

End page

27

Total pages

27

Publisher

MDPI

Place published

Basel, Switzerland

Language

English

Copyright

© 2022 Kelly & Kuleshov. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution License.

Former Identifier

2006118322

Esploro creation date

2023-03-25

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