Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada

Abstract

Hydrological forecasts contain biases that need to be addressed for their effective use in operational decision-making in water resources management. Performing post-processing allows reducing the overall systematic bias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping (QM) post-processing method was applied on weather forecasts following three temporal configurations (monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in southern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather forecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for eight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 days and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous Ranked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast sharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application of QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of the catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the three QM configurations in most catchments. Interestingly, there are watersheds where the implementation of QM exhibit either poorer or no change in performance and sharpness compared to raw forecasts.