Bedload movement is fundamentally probabilistic. Our quantitative understanding of gravel transport is particularly limited when flow conditions just exceed thresholds of motion, in part because of difficulties in measuring transport statistics during floods. We used accelerometer-embedded tracer clasts to precisely measure the timing of grain motions and rests during snowmelt floods in Halfmoon Creek, a gravel-bed mountain stream in Colorado, USA. These new data let us explore how probabilities of tracer movement vary with snowmelt discharge. Bedload hysteresis occurred over both daily and seasonal timescales, and included clockwise, counter-clockwise, and figure-eight patterns. We quantitatively explain these observations in terms of how thresholds of motion progressively evolved over 22 days during a seasonal snowmelt flood. Our results suggest that thresholds of motion are functions of both (a) cumulative shear stress and (b) temporal changes in shear stress during floods.

We quantify how changes in natural flood discharge control bedload rest time distributions and may influence particle diffusion through mountain river networks. We embedded accelerometers and gyroscopes into artificial cobbles deployed in Halfmoon Creek, Colorado, USA, and measured bedload transport during 28 daily snowmelt flood hydrographs in 2015. From the motion sensor data we calculate motion and rest distributions over ~6 orders of temporal magnitude, from ~2 seconds to ~1 month. Motion durations follow a thin-tailed exponential distribution. Rests >12 hours can be well fit by both truncated Pareto distributions and exponentially-tempered Pareto distributions, suggesting ambiguity in whether rests remain heavy-tailed or transition to thin tails at even longer timescales. Rest time scaling varies not only with timescale but also with flow intensity, becoming less heavy-tailed as shear stress increases. A rest time scaling break at ~12 hours may be caused by daily discharge cyclicity.