Melanie Loveridge

Investigation into probabilistic losses for design flood estimation: A case study for the Orara River catchment, NSW

Australian Rainfall and Runoff (Pilgrim, 1987) recommends the design event approach (DEA) as the preferred method for estimating design flood hydrographs, in which a single design event is adopted. More recently, Monte Carlo simulation has been used to allow for the probabilistic nature of input variables in flood modelling. This paper adopts a Monte Carlo framework to evaluate the impact of probabilistic losses on design flood estimates for the Orara River catchment in northeastern NSW. A RORB runoff routing model was used to derive loss values for both the initial loss-continuing loss (IL-CL) and initial loss-proportional loss (IL-PL) models. It has been found that the initial, continuing and proportional losses can be approximated by the Gamma, Weibull and Beta distributions, respectively. When these distributions were compared with non-parametric distributions, differences in the flood estimates were found to be minimal. Another finding was that peak floods estimated using the DEA were more biased for the IL-CL model, than for the IL-PL model. In comparison to the at-site flood frequency curve the IL-CL model produced an overall better fit of the shape of the curve, however, the IL-PL model provided a better fit to the observed flood peaks for mid-range events.

A Monte Carlo framework for assessment of how mitigation options affect flood hydrograph characteristics

Abstract The evolution that is occurring in flood estimation is providing new tools that allow us to better understand the variability of real floods and how to robustly plan large-scale evacuations. The Monte Carlo approach allows design flood estimation inputs to be characterised probabilistically or using an ensemble instead of a single input. While these changes are being used to better estimate design flood levels, they have significant benefit in understanding real flood behaviour by producing thousands of plausible synthetic events. The spatial and temporal variability in rainfall and the timing difference between the key tributaries is modelled. This approach allows impact of management measures to be assessed for all the variability seen in observed events and to properly understand what a mitigation strategy does to average and individual events. This paper discusses the use of Monte Carlo modelling in assessing complex options that are very sensitive to flood characteristics other than the peak flow and compares the results to observed variability of events that cause flood damages. A case study of the Hawkesbury Nepean is discussed.

Monte Carlo simulation for design flood estimation: a review of Australian practice

ABSTRACT Rainfall-based design flood estimation methods in Australia traditionally follow the design event approach. However, the basic assumption of a probability neutral transformation in the design event approach has been widely criticised. For this reason, joint probability approaches (like Monte Carlo simulation) were proposed in the 1970s to account for the probabilistic nature of key inputs in rainfall–runoff modelling. However, these techniques were not seriously tested until the 1990s, when a simple Monte Carlo simulation technique was developed that used existing design data and models, for Australian hydrologic practice. This paper summarises the evolution of Monte Carlo simulation techniques for design flood estimation with a particular emphasis on Australian practice. It has been found that significant advancements have been made in the development and testing of Monte Carlo simulation in Australia; but, there is still a lack of commercial software hindering the routine application of holistic Monte Carlo simulation approaches.