Hewa et al. (2026) Unlocking the Future of Flood Risk Reduction Through Integrated Research and Practice

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Guna Hewa, David J. Kemp
• DOI: 10.1007/978-3-032-18708-6_25

Research Groups

• School of Civil Engineering and Construction Management (CECM), Adelaide University, Mawson Lakes, SA, Australia

Short Summary

This paper presents insights from ongoing research into the core methodologies of flood estimation, highlighting challenges in applying the Australian Rainfall and Runoff (ARR2016/2019) Monte Carlo framework due to uncertainties in rating curve extrapolation, variable runoff routing model performance, and inadequate constant hydrological loss assumptions, particularly in South Australia. The study emphasizes the need to revisit foundational assumptions to enhance design flood estimate accuracy and improve flood risk management.

Objective

• To investigate the core methodologies of flood estimation, specifically addressing challenges in applying the Monte Carlo framework introduced by ARR2016/2019, with the aim of enhancing the accuracy of design flood estimates and mitigating associated flood risks in South Australia.

Study Configuration

• Spatial Scale: South Australia, with specific reference to Mount Lofty Ranges catchments.
• Temporal Scale: Focus on current and recent flood estimation practices (ARR2016/2019 guidelines) and the implications of seasonal rainfall patterns (summer and winter events) for hydrological losses.

Methodology and Data

• Models used: Industry-standard runoff routing models such as RORB, URBS, and WBNM. The study also refers to the Monte Carlo framework for integrating rainfall intensities, temporal patterns, storm durations, and losses.
• Data sources: Implied from the discussion, including observed streamflow data used for rating curves, and meteorological/hydrological observations for rainfall intensities, temporal patterns, storm durations, and hydrological losses.

Main Results

• Streamflow estimates frequently rely on rating curves that extrapolate significantly beyond observed data, introducing substantial uncertainty into flood quantification.
• The performance of industry-standard runoff routing models (RORB, URBS, WBNM) for rainfall-based flood estimations varies depending on model structure and temporal resolution, with time step selection directly impacting both storage parameters and loss estimates.
• The assumption of constant hydrological losses, as adopted in ARR2019, is inadequate, particularly in South Australia where distinct seasonal rainfall patterns necessitate separate loss regimes for summer and winter events.
• Revisiting foundational assumptions in flood estimation is critical for improving the accuracy of design flood estimates, thereby enhancing flood risk mitigation and community resilience.

Contributions

• Identifies critical limitations and sources of uncertainty within the recently updated Australian flood estimation guidelines (ARR2016/2019), specifically concerning rating curve extrapolation, runoff routing model performance variability, and the inadequacy of constant hydrological loss assumptions in a regional context (South Australia).
• Provides key insights from ongoing research that challenge existing practices and propose avenues for improving the accuracy of design flood estimates.
• Underscores the importance of integrating research findings into practical flood risk management strategies to foster greater resilience in flood-prone communities.

Funding

• Australian Government through the Disaster Ready Fund (Round 1) for the project titled ‘Improving Flood Estimation for Risk Mitigation in South Australia.’

Citation

@article{Hewa2026Unlocking,
  author = {Hewa, Guna and Kemp, David J.},
  title = {Unlocking the Future of Flood Risk Reduction Through Integrated Research and Practice},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-3-032-18708-6_25},
  url = {https://doi.org/10.1007/978-3-032-18708-6_25}
}