Evaluation of gridded climate datasets over Canada using univariate and bivariate approaches: Implications for hydrological modelling

Abstract

This study evaluates the univariate and bivariate characteristics of five gridded products with respect to the Adjusted and Homogenized Canadian Climate Data for 1980–2010. Spatial variations of temperature and precipitation biases including the ones corresponding to the magnitude and frequency of extremes are assessed over Canada. The dependence structure between the two variables is analysed across each dataset using a goodness of fit test based on copulas. The propagation of univariate and bivariate biases into streamflow simulations is then investigated by driving a semi-distributed hydrological model for three watersheds with distinct characteristics in western Canada. The input data include original (unadjusted) and bias-corrected climate data based on the univariate quantile delta mapping and multivariate bias correction approaches. The univariate analyses show that all datasets have relatively significant cold and wet biases to the west of the Rocky Mountains and hot and dry biases over the Prairies. Bivariate evaluations using copulas show that all products fail to capture the dependence structure between temperature and precipitation at the majority of the locations, which can undermine their suitability for compound event assessments. The differences between univariate and multivariate bias correction approaches are highlighted by the significant differences in the interrelationships between precipitation and temperature. Hydrological modelling results show major improvements in the detection of extremes after correcting the bivariate biases of the input datasets.