Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

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.

Automated summary

This study reviews over 700 research studies in the past 40 years on ensemble forecasting in hydrology, focusing on model-based approaches and categorizing them into statistics-based, climatology-based, and numerical weather prediction-based systems. The review highlights the performance and uncertainty of ensemble forecasting systems, and identifies future research directions, particularly in hybrid dynamical - statistical learning approaches.