ZAMAN et al. (2026) Flood Frequency and Trend Analysis for Williams River at Tillegra in Hunter Basin New South Wales Australia

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Arfan ZAMAN, Sumya Rahman, Ataur Rahman
• DOI: 10.1007/978-3-032-18708-6_17

Research Groups

• EnviroWater Sydney, Plumpton, NSW, Australia (Arfan M. Zaman, Amira Rahman)
• School of Health Science, Western Sydney University, Sydney, Australia (Sumya Rahman)
• School of Engineering, Western Sydney University, Kingswood, Penrith, NSW, Australia (Ataur Rahman)

Short Summary

This study conducts a flood frequency and trend analysis for the Williams River at Tillegra, Australia, utilizing 93 years of annual maximum flood data to compare various probability distributions and recommend the most reliable for infrastructure design. The findings indicate that Log-Pearson Type III and Generalized Pareto distributions are most reliable for quantile estimation, and no significant long-term trend in flood magnitudes was detected.

Objective

• To perform a flood frequency analysis for the Williams River at Tillegra (Station 210011) using a 93-year record of annual maximum flood data.
• To compare the performance of five probability distributions (Gumbel, log-normal, log-Pearson Type III, Generalized Extreme Value, and Generalized Pareto) in estimating flood quantiles against Australian Rainfall and Runoff (ARR) RFFE estimates.
• To conduct a trend analysis on the annual maximum flood data to identify any significant long-term changes.
• To recommend the most suitable probability distribution for infrastructure design applications at the study site.

Study Configuration

• Spatial Scale: Williams River at Tillegra (Station 210011), located in the Hunter River basin, New South Wales, Australia.
• Temporal Scale: 93-year record of annual maximum flood (AMF) data, spanning from 1932 to 2024.

Methodology and Data

• Models used: Gumbel, log-normal (LN), log-Pearson Type III (LP3), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) probability distributions were fitted using FLIKE software. Results were compared with Australian Rainfall and Runoff (ARR) RFFE estimates. Trend analysis was performed using regression and non-parametric tests.
• Data sources: A 93-year record (1932–2024) of annual maximum flood (AMF) data for the Williams River at Tillegra, with peak flows ranging from 14 m³/s to 1349 m³/s.

Main Results

• The Log-Pearson Type III (LP3) and Generalized Pareto (GPA) distributions provided the most reliable quantile estimates across various return periods.
• The LP3 estimate for the 100-year flood was 1568 m³/s, which closely aligned with values obtained from the GPA distribution and ARR RFFE.
• The Generalized Extreme Value (GEV) and log-normal (LN) distributions tended to overestimate design floods, while the Gumbel distribution underestimated them.
• Trend analysis, utilizing both regression and non-parametric tests, revealed no significant long-term change in the annual maximum flood data.
• The LP3 distribution is recommended for infrastructure design applications at the Williams River at Tillegra site.

Contributions

• This is the first study conducted for the Williams River at Tillegra that comprehensively compares five different probability distributions (Gumbel, log-normal, log-Pearson Type III, Generalized Extreme Value, and Generalized Pareto) with the ARR RFFE model for flood frequency analysis.
• Provides a specific, data-driven recommendation (Log-Pearson Type III distribution) for flood infrastructure design at this particular river location, addressing a gap in existing literature for the site.

Funding

• No specific funding projects, programs, or reference codes were mentioned in the provided paper text.

Citation

@article{ZAMAN2026Flood,
  author = {ZAMAN, Arfan and Rahman, Sumya and Rahman, Ataur and Rahman, Ataur},
  title = {Flood Frequency and Trend Analysis for Williams River at Tillegra in Hunter Basin New South Wales Australia},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-3-032-18708-6_17},
  url = {https://doi.org/10.1007/978-3-032-18708-6_17}
}