Enhanced joint probability approach for flood modelling

This thesis develops a regionalised Enhanced Monte Carlo Simulation Technique (EMCST) for eastern NSW as this part of NSW has adequate pluviograph and stream gauging stations of acceptable quantity and quality to develop and test a regional EMCST. This thesis uses data from 86 pluviograph stations and 12 catchments to derive regional distributions of various stochastic model inputs and parameters that are needed to apply a runoff routing model, i.e. rainfall complete storm duration (DCS), rainfall inter-event duration (IED), rainfall depth (intensity-frequency-duration, IFD), rainfall temporal pattern (TP), initial loss (IL), continuing loss (CL) and runoff routing model’s storage delay parameter (k). Two different probability distributions (exponential and gamma) are tested to fit the observed data of DCS, IED, IL, CL and k by applying three goodness-of-fit tests (Chi-Squared, Kolmogorov-Smirnov and Anderson-Darling) at 5% level of significance. A spatial proximity method has been adopted to regionalise the model inputs and parameters. An Inverse Distance Weighted Averaging (IDWA) method has been used to regionalise the DCS, IED and IFD data. These regionalised stochastic inputs/parameters are then used with the EMCST to obtain derived flood frequency curve (DFFC) at a number of selected catchments in NSW. A sensitivity analysis has been undertaken to assess the impacts of possible uncertainty in these inputs/parameter values on the DFFCs. Model validation is carried out by comparing the results of the EMCST with those of the DEA, Australian Rainfall and Runoff Regional Flood Frequency Estimates (ARR-RFFE) 2012 model (test version) and ARR 1987-PRM. Based on the three goodness-of-fit tests, it has been found that the regional distributions of the DCS, IED, IL and k data can be approximated by two-parameter gamma distribution and the CL data by one-parameter exponential distribution. Based on IDWA method adopted, the DCS, IED and IFD data can be regionalised by using DCS, IED and IFD data from three to five nearby pluviograph stations (whenever it is available, otherwise one pluviograph station is found to be adequate) within 30 km radius from the approximate centre of the catchment of interest. The TP data from 15 nearby pluviograph stations can be pooled to form regional TP data for application at any arbitrary location in eastern NSW. The sensitivities of the input variables and storage delay parameter have been found to be in the following order (the most sensitive to the least sensitive one): k (-30% to 95%), IED (-29% to 60%), DCS (-30% to 50%), IL (-40% to 40%), IFD (10% to 24%), TP (9% to 15%) and CL (-10% to 14%). In addition, it has been shown that up to about 10% variations in the stochastic model inputs/parameters do not make any notable effects on the DFFCs. The independent testing to six catchments shows that the EMCST generally out-performs the DEA, ARR-RFFE 2012 model (test version) and the ARR-PRM. The developed EMCST can be applied at any arbitrary location in eastern NSW. Although the method and design data developed here are primarily applicable to eastern NSW, the method can be adapted to other Australian states and other countries. The developed EMCST will assist in making a shift from the application of the DEA to MCST in Australia as per the recommendations of the upcoming new edition of ARR