Regional frequency analysis (RFA) is widely used in the design of hydraulic structures at locations where streamflow records are not available. RFA estimates depend on the precise delineation of homogenous regions for accurate information transfer. This study proposes new physiographical variables based on river network features and tests their potential to improve the accuracy of hydrological feature estimates. Information about river network types is used both in the definition of homogenous regions and in the estimation process. Data from 105 river basins in arid and semiarid regions of the United States were used in our analysis. Artificial neural network ensemble models and canonical correlation analysis were used to produce flood quantile estimates, which were validated through tenfold cross and jackknife validations. We conducted analysis for model performance based on statistical indices, such as the Nash–Sutcliffe efficiency, root-mean-square error, relative root-mean-square error, mean absolute error, and relative mean bias. Among various combinations of variables, a model with 10 variables produced the best performance. Further, 49, 36, and 20 river networks in the 105 basins were classified as dendritic, pinnate, and trellis networks, respectively. The model with river network classification for the homogenous regions appeared to provide a superior performance compared with a model without such classification. The results indicated that including our proposed combination of variables could improve the accuracy of RFA flood estimates with the classification of the network types. This finding has considerable implications for hydraulic structure design.
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