Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling

• Published in: Journal of hydrology (Amsterdam) Vol. 600; p. 126520
• Main Authors: Siqueira, Vinícius Alencar, Weerts, Albrecht, Klein, Bastian, Fan, Fernando Mainardi, Paiva, Rodrigo Cauduro Dias de, Collischonn, Walter
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: ECC; EMOS; Ensemble forecasting; Medium-range; Post-processing; South America; South America; ECC; Ensemble forecasting; Post-processing; EMOS; Medium-range
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2021.126520

•We assess EMOS and ECC to postprocess daily streamflow forecasts in South America.•We used global rainfall data and ECMWF reforecasts as inputs to a continental model.•EMOS substantially improves forecast skill relative to climatology and persistence.•ECC performance is linked to the raw ensemble spread and width of EMOS distribution.•EMOS and ECC postprocessing are promising even with real-time data limitations. Probabilistic hydrological forecasting and ensemble techniques have leveraged streamflow prediction at regional to continental scales up to several weeks in advance. However, ensembles that only account for meteorological forecast uncertainty are typically biased and subject to dispersion errors, thus limiting their use for rational decision-making and optimization systems. Statistical postprocessing is therefore necessary to convert ensemble forecasts into calibrated and sharp predictive distributions, and it should also account for dependencies between lead times to enable realistic forecast trajectories. This work provides a continental-scale assessment of the use of statistical postprocessing on medium-range, ensemble streamflow forecasts over South America (SA). These forecasts were produced through a large-scale hydrologic–hydrodynamic model forced with a global precipitation dataset and ECMWF reforecast data. The Ensemble Model Output Statistics (EMOS) technique was used to generate conditional predictive distributions in 488 locations at each forecast lead time, while the Ensemble Copula Coupling method with the transformation scheme (ECC-T) was applied to derive ensemble traces from EMOS distributions. Postprocessed streamflow forecasts were cross-validated for the period from 1996 to 2014 using a range of verification metrics. Results showed that the skill and reliability of EMOS forecasts substantially improve over the raw ensembles, and that EMOS leads to skillful predictions relative to discharge climatology and persistence forecasts up to 15 days in advance in most locations. Furthermore, EMOS results in predictive distributions that are generally sharper than the climatology. Limitations in depicting autocorrelations of forecast trajectories were observed in rivers for which the raw ensemble spread is very low and EMOS has to largely increase dispersion, especially at short lead times. The study’s findings suggest that combining a continental-scale hydrological model with EMOS and ECC-T methods can lead to skillful predictions and realistic ensemble traces in several locations in SA, even if in situ hydrometeorological observations are not available in real time.