Zain et al. (2026) A Comparison of Rainfall Runoff Models for Australia

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Huda Zain, Ataur Rahman, M. A. Alim, David J. Kemp
• DOI: 10.1007/978-3-032-18708-6_27

Research Groups

• Western Sydney University, Sydney, New South Wales, Australia (Huda Zain, Ataur Rahman, Mohammad Abdul Alim)
• University of South Australia, Mawson Lakes, Australia (David Kemp)

Short Summary

This paper presents a comparative review of four widely used event-based conceptual rainfall-runoff models (RORB, WBNM, URBS, RAFTS) for design flood estimation in Australia, highlighting their characteristics, recent enhancements, and persistent challenges in uncertainty quantification and application to ungauged catchments. It advocates for standardized benchmarking datasets and advanced tools to improve model reliability and future-readiness.

Objective

• To conduct a comparative review of four widely used event-based conceptual rainfall-runoff models (RORB, WBNM, URBS, RAFTS) for design flood estimation in Australia, detailing their shared structures, key differences, simulation capabilities, and recent enhancements, particularly in the context of Australian Rainfall and Runoff (ARR) 2019 guidelines.

Study Configuration

• Spatial Scale: Australia (national flood modelling framework)
• Temporal Scale: Event-based (design storm conditions, flood events)

Methodology and Data

• Models used: RORB, WBNM, URBS, RAFTS (event-based conceptual rainfall-runoff models). The review also discusses the integration of stochastic design approaches, specifically Monte Carlo simulation, with models like URBS and RORB.
• Data sources: The paper is a comparative review and does not use primary data. It discusses the need for "standardized benchmarking datasets" and the utility of the "Regional Flood Frequency Estimation (RFFE) system" for data-scarce regions. It references the "Australian Rainfall and Runoff (ARR) 2019 guidelines."

Main Results

• The review compares RORB, WBNM, URBS, and RAFTS, noting their common reliance on nonlinear loss and routing structures, while identifying differences in routing techniques and simulation capabilities.
• Recent advancements, guided by ARR 2019, include the integration of stochastic design approaches, such as Monte Carlo simulation, into models like URBS and RORB to enhance uncertainty quantification.
• Significant challenges persist, particularly in ungauged catchments, due to the absence of calibration data, equifinality, and conceptual ambiguity, which hinder parameter regionalization and model reliability.
• The study emphasizes the critical need for standardized benchmarking datasets and coordinated intermodal performance assessments to facilitate objective model selection.
• It advocates for the use of tools like the Regional Flood Frequency Estimation (RFFE) system to improve predictions in data-scarce regions.
• Future directions for Australia's flood modelling framework include incorporating climate change impacts, advancing uncertainty analysis, and applying artificial intelligence to achieve a more robust, consistent, and future-ready system.

Contributions

• Provides a comprehensive comparative review of four prominent event-based rainfall-runoff models (RORB, WBNM, URBS, RAFTS) specifically tailored to the Australian context and design flood estimation practices.
• Highlights the current state of model integration with stochastic methods for uncertainty quantification, aligning with the Australian Rainfall and Runoff (ARR) 2019 guidelines.
• Identifies and elaborates on critical challenges in model application, particularly for ungauged catchments, including issues of data scarcity, equifinality, and conceptual ambiguity.
• Proposes concrete recommendations for improving model selection and reliability through standardized benchmarking datasets, coordinated intermodal performance assessments, and the utilization of the RFFE system.
• Outlines a forward-looking roadmap for the evolution of Australia's flood modelling framework, emphasizing the importance of climate change integration, advanced uncertainty analysis, and artificial intelligence applications.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Zain2026Comparison,
  author = {Zain, Huda and Rahman, Ataur and Alim, M. A. and Kemp, David J.},
  title = {A Comparison of Rainfall Runoff Models for Australia},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-3-032-18708-6_27},
  url = {https://doi.org/10.1007/978-3-032-18708-6_27}
}