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Abstract Streamflow forecasting generally relies on coupled rainfall-runoff-routing models calibrated and executed with data estimated by monitoring protocols that do not fully capture the dynamics of unsteady flows. This limits the ability to accurately forecast flood crests and issue hazard warnings. Here we utilize directly measured datasets acquired for streamflow estimation to develop a data-driven forecasting algorithm that does not require conventional physically-based modeling. We test the potential of our algorithm using measurements acquired at an index-velocity gaging station on the Illinois River, USA, between 2014 and 2019. We find that the forecasting protocol is able to deliver short-term predictions of flood crest magnitude and arrival time. The algorithm produces better agreement with larger events and is more reliable for single-peak storms possibly due to the prominence of hysteretic behavior in such events. We conclude that flood hazard can be forecast using directly measured index-velocity and stage alone. 

Recent advances in instruments are transforming our capabilities to better understand, monitor, and model river systems. The present paper illustrates such capabilities by providing new insights into unsteady flows captured with a Horizontal Acoustic Current Profiler (HADCP) integrated at an operational index-velocity gaging station. The illustrations demonstrate that the high-resolution stage and velocity measurements directly acquired during flood wave propagation reveal the intricate interplay among flow variables that are essential for better supporting judicious decision making for river management, flooding, sediment transport, and stream ecology. The paper confirms that the index-velocity method better captures the unsteady flow dynamics in comparison with the stage-discharge monitoring approach. At a time when the intensity and frequency of floods is continuously increasing, a better understanding of the critical features of flood waves during extreme events and the possibility of capturing more accurately their dynamics in real time is of special socio-economic significance.