Abstract
This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/group formed with smaller sized catchments performs better (with a median relative error values ranging from 22% to 37%) than other clusters/groups. No linkage is found between the degree of heterogeneity in the clusters/groups and precision of flood quantile prediction by the multiple linear regression technique.
Highlights
Design flood estimation in poorly gauged or ungauged catchments is often a common problem in flood frequency analysis (FFA)
Correlation between the predictors, i.e., catchment area, rainfall intensity, shape factor, stream density, mean annual rainfall, mean annual evapo-transpiration, slope and fraction forest (‘A’, ‘I62 ’, ‘Shape factor (SF)’, ‘sden’, ‘Mean annual rainfall (MAR)’, ‘mean annual evapotranspiration (MAE)’, ‘S1085’, and ‘forest’, respectively) are calculated using the method described in methods section
Rainfall intensity has a positive correlation with the variables shape factor, stream density, fraction forest, mean annual rainfall and mean annual evapo-transpiration, where the maximum positive correlation is between mean annual rainfall and rainfall intensity being 0.83 (p-value ≈ 0) and mean annual evapo-transpiration and rainfall intensity being 0.67 (p-value ≈ 0)
Summary
Design flood estimation in poorly gauged or ungauged catchments is often a common problem in flood frequency analysis (FFA). Some researchers have adopted climatic and catchment characteristics to form homogeneous regions by using multivariate statistical techniques such as cluster analysis [8,17,18,19,20,21,22,23,24,25]. For regression-based RFFA models, a linear relationship is generally assumed between the dependent variable (flood quantiles) and predictor variables (catchment and climatic characteristics). To fill this research gap, this study examines the formation of homogeneous regions using PCA and cluster analysis This investigates the use of PCR method in RFFA with ROI and fixed region approaches. A leave-one-out validation technique is adopted to assess the performance of the developed models
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