UBC Theses and Dissertations
Development of methods for regional flood estimates in the province of British Columbia, Canada Wang, Yuzhang
Flood estimation in the province of British Columbia is often based on either single-site frequency analysis or graphical peak-flow regionalization procedures. These methods involve large uncertainties, especially at short-term record stations and ungauged sites, because of the vague selection of frequency distributions and delineation of homogeneous regions. To reduce these uncertainties and overcome the drawbacks of the current methods, an innovative regional frequency analysis was proposed in this study. L-moments were used for the three stages, namely delineating and testing homogeneous regions, identifying and fitting regional distributions, and developing regional functions for the transfer of information from gauged to ungauged watersheds. Based on the most recently available flood database, the province of British Columbia was divided into 19 homogeneous regions of which 14 are non-mixture regions and five are mixture regions. A mixture means in some years the annual floods are generated by one mechanism, while in other years they are generated by other physically different mechanisms. It was found that either the generalized logistic (GLOG) or the generalized extreme value (GEV) may be considered as the regional parent distribution for any of the non-mixture regions, whereas the non-parametric distribution can be used for the mixture regions. In the non-mixture regions, hierarchical approaches and regression models were developed for gauged and ungauged watersheds. For the hierarchical approaches, the first two parameters of the GLOG or GEV distribution were estimated from at-site data while the third parameter was from the region. For the regression models, the parameters of the GLOG or GEV distribution were regressed on the catchment size. In the mixture regions, a non-parametric method was combined with the regression method for the development of regional models. Monte Carlo simulation studies showed that the developed hierarchical approaches were substantially more accurate than the single-site methods, especially for long-term flood quantiles. In particular, it was shown that about three times more data were required for the single-site models to be as accurate as the developed hierarchical approaches. The proposed regression models were validated through split-sampling experiments. Statistical tests showed that the quantiles from the regression models were in good agreement with those from actual observations.
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