Ensemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting.

A warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies.

Spatial discretization in hydrological models has a strong impact on computation times. This study investigates its effect on the performance of the Soil and Water Assessment Tool (SWAT) applied to a French Mediterranean watershed. It quantifies how spatial discretization (the number of sub-basins and hydrological response units (HRUs)) affects the SWAT model’s performance in simulating daily streamflow and whether this effect depends on the choice of soil and land use input datasets. Sixty-eight SWAT model configurations were created using various soil and land use datasets and 17 discretization setups, evaluated from 2001 to 2021 with the Kling–Gupta efficiency (KGE) metric. The key findings include (1) while the number of sub-basins does not impact model performance, increasing HRUs significantly degrades it (KGE loss of 0.13 to 0.26) regardless of the number of sub-basins or input datasets. (2) SWAT is found to be more sensitive to variations in soil datasets than in land use datasets, but the observed performance decline with more HRUs is attributed to the calibration process and the increased heterogeneity in soil types rather than input dataset spatial resolution. (3) Minimizing the number of HRUs may improve both the accuracy of streamflow simulations and the computational efficiency of the SWAT model.