Novel mass‐aggregation‐based calibration of an acoustic method of monitoring bedload flux by infrequent desert flash floods

Abstract

The monitoring of bedload flux under flash flood conditions has been successfully achieved since 1992 using slot samplers in the semiarid Nahal Eshtemoa. In the present study, a surrogate bedload monitoring techniquethe Japanese plate microphonehas been deployed and calibrated against data from the slot samplers. Since a slot sampler has a sensitivity threshold that becomes especially important when transport rates are low, different averaging periods should be considered for high and low fluxes. In order to overcome the deficiencies of time-based aggregation used hitherto, we have developed a new method involving mass aggregation and commensurably variable intervals, thereby enabling a more accurate analysis and optimizing the bedload sampler's capabilities. The data derived with this new method has then been utilized to calibrate the Japanese plate microphone. The Eshtemoa is an ephemeral gravel bed channel with a high proportion of fine gravel (< 0.02m); for these conditions, acoustic sensors have not been calibrated as yet. Two multiple linear regression models incorporating the effect of median bedload grain size on pulse rate have been established to predict bedload flux and cumulative transported bedload mass. The coefficients in these models are statistically significant. Good predictions are obtained for bedload flux (adj. r 2 = 0.83) and for cumulative bedload mass (adj. r 2 = 0.98) during flood recession. Overall, the multiple linear regression models, used in conjunction with the mass aggregation method of estimating bedload flux, suggest that field calibration of acoustic devices is feasible under these conditions for ca. 90% of the duration of bedload transport.

Grain‐Size Distribution and Propagation Effects on Seismic Signals Generated by Bedload Transport

“…Finally, in the third phase (Phase 3 in Figure 2a), all of the samplers were full, and q bd was determined using the calibrated plate microphone located immediately upstream of the left sampler (R 2 = 0.76). The calibration was done using a mass aggregation method (Halfi et al, 2020), with the constant mass interval of 4 kg. This mass corresponds to the sensitivity of the sensor and allows to discriminate noise from the real bedload flux measurement.…”

Toward a general calibration of the Swiss plate geophone system for fractional bedload transport

“…The drawback of these monitoring technologies is that in order to provide quantitative measurements, they require intensive calibration through direct bedload sampling with retention basins (Rickenmann and McArdell, 2008), slot samplers (e.g. Habersack et al, 2017;Halfi et al, 2020) or mobile bag samplers (e.g. Bunte et al, 2004;Dell'Agnese et al, 2014;Hilldale et al, 2015;Mao et al, 2016;Kreisler et al, 2017;Nicollier et al, 2021a).…”

Good vibrations: Big data impact bedload research

“…For a given discharge, measured bedload transport rates in both Turkey Brook and Arroyo de los Pinos vary over two orders of magnitude. This variability has been termed 'noise', which mass aggregation is designed to eliminate (Halfi et al, 2020). However, as Figure 2 shows, the novel estimates obtained through mass aggregation provide a radically different perspective on the transport rate-flow relation to those obtained by other methods that seek to correlate the magnitude of bedload transport with an independent attribute.…”