Rumble on the River: Analyzing Model Performance in a Year-Long, Live Streamflow Forecasting Competition

Abstract

<p>Accurate streamflow forecasts equip water managers to adapt to changing flow regimes and constraints, increase water supply reliability, reduce flood risk, and maximize revenue. Over the 2021 water year, the Upstream Tech team took part in a live, 1-10 day ahead streamflow forecasting competition using our flow forecast system, HydroForecast. The competition was a chance to objectively compare operational forecasts using a range of modeling approaches from national agencies, hydropower utilities’ in-house teams, private forecasters and individual modelers at 19 sites in North America. HydroForecast outperformed both statistical and conceptual models and won the competition. We evaluate HydroForecast’s performance relative to other models to identify its strengths and areas for further research by region, season, and forecast horizon. We also share what our theory-guided machine learning approach to hydrologic modeling means in practice for HydroForecast, focusing on the key facets of our approach which contribute most to our accuracy. Finally, we describe the largest opportunities for further forecast accuracy gains we identified in this competition and some of the research efforts we are working on to meet those opportunities.</p>