9 citations as recorded by crossref.

1. A semi‐empirical wind set‐up forecasting model for Lake Champlain G. Loiselle et al. https://doi.org/10.1002/hyp.14240
2. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions G. Seiller et al. https://doi.org/10.5194/hess-16-1171-2012
3. Hydrological ensemble forecasting using a multi-model framework P. Dion et al. https://doi.org/10.1016/j.jhydrol.2021.126537
4. On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. https://doi.org/10.1080/02626667.2015.1085650
5. Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning K. Doycheva et al. https://doi.org/10.1016/j.aei.2016.11.001
6. Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles G. Seiller et al. https://doi.org/10.1016/j.jhydrol.2017.07.002
7. Analysis of groundwater-level fluctuation and linear regression modeling for prediction of initial groundwater level during irrigation of rice paddies in the Nasunogahara alluvial fan, central Japan T. Tsuchihara et al. https://doi.org/10.1007/s12665-023-11174-w
8. Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods M. Mazzoleni et al. https://doi.org/10.1080/02626667.2018.1430898
9. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. https://doi.org/10.1029/2020WR028392