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We present the most comprehensive dataset of bedload transport in ephemeral channels compiled to date. These nine ephemeral channels cover a range of dryland climates and channel types. First, we evaluate these channels and how they compare with each other. Next, we contrast this database with a previously compiled bedload dataset encompassing 92 perennial rivers. While previous studies have identified differences between measured bedload flux in perennial and ephemeral systems, we quantify those differences across a wide range of channel types and shear stress conditions. We find that the ephemeral dataset is statistically distinct, showing greater average transport across flow conditions in normalized shear vs. bedload flux space. Prior researchers have variously attributed these high transport rates to a combination of factors that commonly define ephemeral channels: lack of armoring, mixed sand and gravel, flashy hydrographs, erodible banks, and lack of vegetation. We tested the influence of armoring by comparing transport differences at different transport stages, finding that bed armor contributes to the observed differences, but is not the sole reason. In addition to these previously proposed mechanisms, we add that the abundance of very coarse sand and fine gravels in ephemeral channels provides easily-mobilized but difficult-to-suspend particles. 

Abstract We calibrated an acoustic pipe microphone system to monitor bedload flux in a sandy, gravel‐bed ephemeral channel. Ours is a first attempt to test the limit of an acoustic surrogate bedload system in a channel with a high content of sand. Calibrations varied in quality; significant data subsetting was required to achieve R²values >0.75. Several data quality issues had to be addressed: (1) apparent pulses, which occur when a sensor records an impulse from sediment impacting the surrounding substrate rather than directly impacting the sensor, were frequent, especially at higher signal amplifications. (2) The impact sensors were frequently covered by gravel sheets. This prompted the development of a cover detection protocol that rejected part of the impact sensor record when at least one sensor was partially or fully covered. (3) Because of the lack of sensor sensitivity to impacts of sand‐sized particles, which was anticipated, and the considerable sand component of bedload in this channel, a grain size‐limited bedload flux was estimated. This was accomplished by sampling the bedload captured by slot samplers and evaluating the variation of grain size with increasing flow strength. This considerably improved the results when compared to attempts at estimating the flux of the entire distribution of grain sizes. This calibration is a successful first attempt, though the impact sensors required several site‐specific calibration steps. A universal set of equations using impact sensors to estimate bedload transport of fine‐gravel with a large content of sand remains elusive. Notwithstanding, our study demonstrates the utility of impact sensor data, producing relatively low root mean square errors that are independent of measurements of flow strength (i.e. discharge). These tools will be particularly useful in settings that would benefit from new methodologies for estimating bedload transport in sand‐rich gravel‐bed rivers, such as the American desert Southwest. 

Abstract Recent theoretical models and field observations suggest that fluvial bedload flux can be estimated from seismic energy measured within appropriate frequency bands. We present an application of the Tsai et al. (2012,https://doi.org/10.1029/2011gl050255) bedload seismic model to an ephemeral channel located in the semi‐arid southwestern US and incorporate modifications to better estimate bedload flux in this environment. To test the model, we collected streambank seismic signals and directly measured bedload flux during four flash‐floods. Bedload predictions calculated by inversion from the Tsai model underestimated bedload flux observations by one‐to‐two orders of magnitude at low stages. However, model predictions were better for moderate flow depths (>50 cm), where saltation is expected to dominate bedload transport. We explored three differences between the model assumptions and our field conditions: (a) rolling and sliding particles have different impact frequencies than saltating particles; (b) the velocity and angle of impact of rolling particles onto the riverbed differ; and (c) the fine‐grained alluvial character of this and similar riverbeds leads to inelastic impacts, as opposed to the originally conceptualized elastic impacts onto rigid bedrock. We modified the original model to assume inelastic bed impacts and to incorporate rolling and sliding by adjusting the statistical distributions of bedload impact frequency, velocity, and angle. Our modified “multiple‐transport‐mode bedload seismic model” decreased error relative to observations to less than one order of magnitude across all measured flow conditions. Further investigations in other environmental settings are required to demonstrate the robustness and general applicability of the model.