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

Hydrological forecasts often contain biases or uncertainty that make them less useful to water resources system managers. They can, however, be further improved using post-processing methods. Post-processing has the capability to reduce overall bias and improve the uncertainty quantification (spread), in order to enhance the usefulness of the forecasts in decision-making. In this study, a Quantile Mapping (QM) post-processing method was implemented on meteorological forecasts assessed in three different configurations: A monthly, a seasonal, and an annual quantile mapping schemes. The evaluation was carried out over 22 watersheds with different basin areas in the south of Canada. Post-processing methods were trained on ECMWF operational forecasts from 2015-2019 inclusively, then applied on forecasts from 2020 and fed to 8 assimilated hydrological models on each catchment. The hydrological forecasts for the year 2020 were generated at a lead time of 8 days and a timestep of 6 hours. The methodology and results were evaluated using the Continuous Ranked Probability Score (CRPS) metric. Results show that all three QM combinations improve the performance of the forecasts at the most distant lead times, showing significant improvements from day 4. The annual QM implementation was shown to perform the best, followed by seasonal or monthly, depending on the watershed.