What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin

• Using a super-ensemble improves streamflow forecasting regardless of the lead time. • We developed dynamic and static combination approaches to reduce the size of the super-ensemble. • Reducing super-ensemble complexity with these methods does not impair the forecast. • Semi-distribution brings no benefit compared with the lumped approach on average over our test sample. Streamflow forecasting is useful for various purposes, from ensuring the safety of populations during floods to managing hydraulic structures. The aim of this work is to combine two hydrological modelling approaches widely used in streamflow forecasting in order to define their benefits and limits in a probabilistic framework: the multi-model approach (which accounts for structural and parametric model uncertainty) and the semi-distributed approach (which considers explicitly the spatial variability of precipitation and hydrological processes). The study focuses on 12 tributaries of the Rhône River, which were modelled using 39 hydrological model configurations. Tests were carried out at an hourly time step for lead times ranging from 1 h to 120 h, considering ensemble meteorological forecasts. The results show that explicitly considering uncertainty with a probabilistic super-ensemble (meteorological ensemble chained to a multi-model approach) improves the quality of streamflow forecasts. On the other hand, there is no clear benefit from a semi-distributed approach compared with a lumped framework. This paper also explored the structure and size of the super-ensemble, showing that it is possible to reduce its complexity through model selection or combination methods without impairing predictive performance. This study provides valuable insights into the strengths and limitations of a super-ensemble approach and how to limit its complexity, contributing to the ongoing efforts to improve streamflow forecasting for operational purposes.