On the Value of River Network Information in Regional Frequency Analysis

Abstract

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 semi-arid regions of the USA 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.

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

“…These criteria can assess the accuracy and bias of the model's results. In this study, we used the RMSE for the model accuracy instead of the MAE, because the RMSE were applied for the evaluation of model performance in several studies as one of the general methods in evaluation criteria [20,32,37,38]. The indices are calculated using the following equations:…”

“…The PGA is adopted in this study because it can easily express how much the seismic wave shakes the ground. Detailed procedures for the estimation of the wave feature are presented in the analysis by Papazafeiropoulos and Plevris [37].…”

“…The PGA is adopted in this study because it can easily express how much the seismic wave shakes the ground. Detailed procedures for the estimation of the wave feature are presented in the analysis by Papazafeiropoulos and Plevris [37]. Based on the aforementioned 13 wave properties obtained for this study, a correlation analysis among the features was performed to determine important variables for structuring the seismic model.…”