Generating Coherent Ensemble Forecasts After Hydrological Postprocessing: Adaptations of ECC‐Based Methods

Hydrological ensemble forecasts are frequently miscalibrated, and need to be statistically postprocessed in order to account for the total predictive uncertainty. Very often, this step relies on parametric, univariate techniques that ignore the between‐basins and between‐lead times dependencies. This calls for a procedure referred to as sampling‐reordering, which generates a coherent multivariate ensemble from the marginal postprocessed distributions. The ensemble copula coupling (ECC) approach, which is already popular in the field of meteorological postprocessing, is attractive for hydrological forecasts as it preserves the dependence structure of the raw ensemble assumed as spatially and temporally coherent. However, the existing implementations of ECC have strong limitations when applied to hourly streamflow, due to raw ensembles being frequently nondispersive and to streamflow data being strongly autocorrelated. Based on this diagnosis, this paper investigates several variants of ECC, in particular the addition of a perturbation to the raw ensemble to handle the nondispersive cases, and the smoothing of the temporal trajectories to make them more realistic. The evaluation is conducted on a case study of hydrological forecasting over a set of French basins. The results show that the new variants improve upon the existing ECC implementations, while they remain simple and computationally inexpensive. Key Points Existing implementations of ECC show strong limitations when applied to hourly streamflow forecasts Adding a perturbation to the raw ensemble helps handle nondispersive cases Smoothing the temporal trajectories makes them more realistic, and does not impact forecast skill