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Flood Study

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Joint Probability Approach to Design Flood Estimation: Sensitivity of Model Parameters Hitesh Patel
(BE Chemical, Grad Dip Environmental Engineer)
MEng (Honours) Candidate
Principal Supervisor: Dr Ataur Rahman
Co-supervisor: A/Prof Chin Leo
School of Engineering
University of Western Sydney, Penrith Campus (Kingswood)

Background:

Rainfall-based flood estimation method is often adopted when a complete design hydrograph is required and/or in the situations where the recorded streamflow data are not long enough to characterize the underlying at-site flood frequency distribution with sufficient accuracy. The Design Event Approach (DEA) is currently recommended rainfall-based flood estimation method in Australia according to Australian Rainfall and Runoff – the national guide to flood estimation. However, DEA does not account for the probabilistic nature of the key flood producing variables except for the rainfall depth. This arbitrary treatment of key inputs and model parameters in DEA can lead to inconsistencies and significant bias in flood estimates for a given average recurrence interval (ARI). A significant improvement in design flood estimates can be achieved through a Joint Probability Approach (JPA), which is more holistic in nature that uses probability-distributed input variables/model parameters and their correlations to obtain probability-distributed flood output. More recently, there have been notable researches in Australia on Monte Carlo simulation technique (MCST) for flood estimation based on the principles of Joint Probability that can employ many of the commonly adopted flood estimation models and design data in Australia and have demonstrated significant promise with the JPA. However, all previous study has considered rainfall duration, rainfall intensity, rainfall temporal pattern and initial loss as random variable input in the model but the probabilistic nature of key runoff routing model storage delay parameter k has been...