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ABSTRACT This paper demonstrates that the multivariate monitoring methods are capable to underpin the systematic investigation of the hysteretic behaviour occurring during gradually‐varied flows. For this purpose, we present simultaneous measurements of stage, index velocity and free‐surface slope acquired continuously with high‐frequency sampling instruments deployed at several river gaging sites exposed to different storm magnitudes. The experimental evidence reveals intrinsic features of unsteady open‐channel flow mechanics that are hinted by pertinent governing equations but rarely substantiated with in situ measurements. The illustrations are intentionally made for fluvial waves propagating in lowland rivers where the relationships among flow variables are most likely displaying hysteretic phasing in the progression of the hydraulic variables and loops in their relationships. The presented measurements highlight that: (a) the hysteretic behaviour is apparent in both time‐independent and time‐dependent graphical representations of any two of the hydraulic variables; (b) the severity of the hysteresis is commensurate with the geomorphic, hydraulic and hydrological characteristics of the measurement site; and (c) there are flow monitoring paradigms that can more accurately track changes of the flow variables during gradually‐varied flows than those currently used in practice. Also discussed are research needs for advancing the understanding of the mechanisms underlying the movement and storage of water in the lowland river environments as well as for increasing the accuracy of streamflow monitoring, modelling and forecasting. 

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.