In rainfall–runoff modeling, Design Event Approach is widely adopted in practice, which assumes that the rainfall depth of a given annual exceedance probability (AEP), can be converted to a flood peak of the same AEP by assuming a representative fixed value for the other model inputs/parameters such as temporal pattern, losses and storage-delay parameter of the runoff routing model. This paper presents a case study which applies Monte Carlo simulation technique (MCST) to assess the probabilistic nature of the storage delay parameter (kc) of the RORB model for the Cooper's Creek catchment in New South Wales, Australia. It has been found that the values of kc exhibit a high degree of variability, and different sets of plausible values of kc result in quite different flood peak estimates. It has been shown that a stochastic kc in the MCST provides more accurate design flood estimates than a fixed representative value of kc. The method presented in this study can be adapted to other catchments/countries to derive more accurate design flood estimates, in particular for important flood study projects, which require a sensitivity analysis to investigate the impacts of parameter uncertainty on design flood estimates.
CASE STUDY: APPRAISAL OF FOUR SOIL-BASED METRICS IN THE ESTABLISHMENT OF SUSTAINABLE UPLAND GRASSLAND AT A MINE SITE IN SOUTH WALES, UNITED KINGDOM
