A nationwide regional flood frequency analysis at ungauged sites using ROI/GLS with copulas and super regions

Abstract

Region of influence is a common approach to estimate runoff information at ungauged locations. To estimate flood quantiles from annual maximum discharges, the Generalized Least Squares (GLS) framework has been recommended to account for unequal sampling variance and intersite correlation, which requires a proper evaluation of the sampling covariance structure. Since some jurisdictions do not have clear guidelines to perform this evaluation, a general procedure using copulas and a nonparametric intersite correlation model is investigated to estimate sampling covariance structure in situations where no common at-site distribution is imposed or when some paired sites do not have common periods of record.The investigated methodology is applied on 771 sites in Canada. The Normal copula is verified to be an adequate model that better fit paired observations than other types of extreme copulas. A sensitivity analysis is carried out to evaluate the impact of either ignoring, or considering a simpler form of, intersite correlation. Additionally, super regions are defined based on drainage area and mean annual precipitation to improve the calibration of pooling groups across large territories and a wide range of climate conditions. Performance criteria based on cross-validation revealed that using super regions and a combination of geographic distance and similarity between catchment descriptors improves the calibration of the pooling groups by providing more accurate estimates.

Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia

“…Clusters 4 and 5 show a large negative BIAS and cluster 5 shows a very large negative RBIAS. The results here are notably higher than those reported in Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [47], and Shu and Ouarda [69]. In Rahman et al [6], independent component regression was adopted to develop flood prediction equations using the same data set as of this study, where error values are similar to this study.…”

“…These studies were carried out using ordinary kriging in PCA-space, generalized additive model and single artificial neural network. Studies carried out by Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [20] and Shu and Ouarda [69] show RBIAS values ranging from −5% to −20% for 10% AEP flood and −7% to −27% for 1% AEP flood. A study carried out by Rahman et al [6] found RBIAS values ranging from 22% to 69% for the six AEP floods.…”

“…From Table 4, it is clear that KNN10 performs the poorest with largest MSE and R r , which means it is preferable to select more than ten sites to form a region to use PCR. Durocher et al [67] carried out a study in Southern Quebec (Canada) and their results show a RMSE in the range of 38 m 3 /s and 45 m 3 /s in case of 10% and 1% AEP floods using spatial copula method. For the same dataset in Québec a number of studies [47,68,69] were carried out.…”

A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis

“…The hydrological neighborhood‐based method is gaining popularity due to its improved performance and flexibility in selecting sites (Ouarda, ; Ouarda et al, ). CCA (Ouarda et al, ) and the region of influence (ROI) (Burn, ; Burn, ; Durocher et al, ; Haddad & Rahman, ) are among others commonly utilized and reliable approaches in the delineation of homogeneous regions. Table summarizes a few commonly utilized methods of delineating homogeneous regions.…”

Investigation of Attributes for Identifying Homogeneous Flood Regions for Regional Flood Frequency Analysis in Canada

“…Within this network, Sandink et al and Zahmatkesh at al. [23,24] examined FFA using a quantile regression model that considered ungauged catchments across Canada. Zhang et al [39] demonstrated the generalized extreme value (GEV) distribution fits Canadian annual maximum flow data considerably better than other well-known distributions, including generalized logistic, Pearson type III, and log Pearson type III distributions.…”