Regional flood frequency analysis in the range of small to large floods : development and testing of Bayesian regression-based approaches

Abstract

Design flood estimation in the range of frequent to medium (2 - 100 years) and large to rare (greater than 100 and up to 2000 years) average recurrence intervals (ARI) is frequently required in the design of many engineering works such as design of culverts, bridges, farm dams, spill ways, land use planning and flood insurance studies. These sorts of infrastructure works and investigations are of notable economic significance. Design flood estimation is ideally made adopting a flood frequency analysis technique; however, this needs a relatively longer period of recorded streamflow data. In many cases, recorded streamflow data is quite short or completely absent (i.e. ungauged catchment situation). In such cases, regional flood frequency analysis (RFFA) techniques are usually adopted, which attempts to utilise spatial data to compensate for temporal data on the assumption of regional homogeneity. This thesis focuses on RFFA techniques, in particular how the RFFA techniques can be enhanced by adopting an ensemble of advanced statistical techniques as well as by minimising the error and noise often found in the flood data. This thesis uses data from 682 catchments from the continent of Australia to (i) develop prediction equations involving readily obtainable catchment characteristics data for floods in the frequent to medium range ARIs (2 - 100 years) (ii) investigate the validation of the developed prediction equations using the most commonly used leave-one-out validation (LOO) and to compare it with the more recent Monte Carlo cross validation (MCCV) technique and (iii) to develop a large flood regionalisation model (LFRM) that corrects for spatial dependence in the annual maximum flood series data (AMFS) for flood estimation in the large to rare flood range (100 - 2000 years ARI). Overall, the experimental results of the analysis show that, in general, spatial dependence decreases with larger network size and that some Australian states exhibit more spatial dependence than others. While there are some limitations with this analysis, a reasonable indication of the behaviour of Ne has been established. The derived generalised spatial dependence model has then been used with the LFRM to correct for the spatial dependence by adjusting the plotting position points of the LFRM frequency distribution curve. An independent validation has showed that the developed LFRM is able to estimate design floods for 100 to 1000 years ARIs with reasonable confidence as compared to at-site flood frequency analysis results, other regional flood models and the world model. Overall, the newly developed LFRM that corrects for spatially correlated data and coupled with BGLSR - ROI approach offers a powerful yet simple method of regional flood estimation for floods in the large to rare ARI range.

Date of Award	2013
Original language	English