Martin Durocher, Donald H. Burn, Shabnam Mostofi Zadeh

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.