1. Introduction
Sediments transported by rolling, sliding, or saltating on the bottom of an aquatic environment are called bedload. The topmost stratum of the streambed, which is partially mobile, is called the active bedload layer [
1]. Quantifying the mass of bedload that moves within the active layer per unit time defines the bedload transport rate.
Accurate estimation of bedload transport characteristics in a riverine environment is still an open problem in engineering. Measuring the transport rate and using direct conventional methods (i.e., bedload traps or samplers) is notoriously difficult and labor-intensive [
2]. Therefore, bedload measurements are seldom available and sometimes considered statistically unreliable. The uncertainty associated with conventional techniques can be characterized as systematic and stochastic [
3,
4]. The sources of the systematic uncertainty result from the technical design of the sampler, such as disturbance of the riverbed, correct positioning, length of the sampling time, and clogging of the mesh [
5]. The stochastic uncertainty originates from the sporadic and rather inhomogeneous nature of the motion of bedload particles [
4,
6].
To overcome these problems, new surrogate methods have recently been introduced, allowing nonintrusive, continuous, and more accurate measurements. These surrogate techniques involve videography, active and passive acoustic sensing, seismic registering mechanisms, and so forth.
In the last few decades, various videography techniques have been developed to investigate the behavior of bedload particles, mostly on a small scale and in controlled conditions, involving analyzing individual particle tracks and velocities [
6,
7,
8,
9], denoted as particle tracking velocimetry (PTV). Additional image processing techniques, such as optical flow and image differencing, have been successfully applied to calculate mobile bed velocity, the surface concentration of mobile particles, and even the shape of particles in reasonably set-up conditions [
10,
11,
12,
13,
14]. However, image processing techniques are limited to laboratory applications due to light conditions, small sampling areas, large data storage requirements, and relatively difficult installation.
Hydroacoustic techniques are easy to deploy, do not require extensive data storage, and offer continuous measurements even in high-flow situations. Acoustic sensors are typically divided into two categories: active and passive. Passive sensor-based techniques record the acoustic noise of the bedload particle collisions, whereas active sensor-based systems register the effect of the motion of the particles on the reflected acoustic signal from the riverbed. Passive acoustic systems consist of a set of hydrophones and geophones that are relatively easy to install. Data postprocessing can effectively distinguish the noise generated by bedload particles; however, its calibration can be exhaustive and unreliable [
15,
16].
Active acoustic systems can be monostatic, equipped with a transducer that emits and receives the acoustic signal and bistatic when the transmitter and receivers are reasonably separated. Acoustic current Doppler profilers (ADCPs) are the most popular active acoustic system and frequently used for measuring river discharge and flow velocity in conditions with a changing water depth across a river channel. ADCPs’ diverging beam configuration enables taking measurements at variable water depths. Given this advantage, ADCPs were also applied for bathymetric surveying and measuring bedload velocity. Regarding bedload measurements, Rennie [
17] initially reported that the velocity measured by the bottom tracking (BT) feature of an ADCP is well correlated to the bedload transport rate. This technique introduced the BT velocity bias, defined as the difference between the GPS boat velocity and boat velocity measured by the BT feature of the ADCP (
va =
vgps − vBT). This velocity is also denoted as the apparent bedload velocity (
va) [
14,
17]. Studies have reported a good correlation between apparent bedload velocity and bedload transport [
18], attempting to develop site-specific or grain-size-dependent relationships with the bedload transport rate [
19]. In addition,
va was successfully used for mapping bedload transport intensity [
20] and evaluating morphodynamical processes in large rivers [
21]. An analytical relationship between the real bedload particle velocity and apparent bedload velocity, considering the scattering properties of the mobile and immobile bedload particles, was proposed by Gaeuman and Jacobson [
22]. This correlation with the real bedload velocity was experimentally proven using the image processing technique as a comparison [
14]. The same study described the coupling problem in the bedload reflected acoustic signal associated with the simultaneous volume scattering of mobile particles and surface scattering of immobile particles beneath. It was noted that different ADCPs do not deliver the same apparent velocity despite measuring the same condition [
14,
23,
24,
25,
26]. The main sources of these differences are due to instrument-specific configurations and signal processing [
25], instrument-carrying frequency [
14], attenuation occurring in the active bedload layer [
14,
26], and reverberation from the surface scattering of immobile bedload particles. All these factors, together with the sporadic nature of the bedload transport, influence acoustic sampling, resulting in erroneous apparent bedload velocity measurements. Part of these errors are efficiently filtered using the water velocity direction [
14,
25], but some remain nested; thus, each instrument requires specific postprocessing and eventual calibration for the accurate measurement of bedload characteristics [
26]. It was recently shown that backscattering strength registered by ADCPs at the same time as the apparent bedload velocity is strongly correlated with the bedload concentration and particle size distribution of the sediments [
26]. However, it remains unknown to which degree these sources influence the erroneous velocity and backscatter outcome of the ADCPs; therefore, it is impossible to accurately predict the bedload transport rate.
Bistatic sonar systems are currently still limited to laboratory conditions because of the fixed-range distance, which can be a maximum of 40 to 50 cm and depends on the distance between applied transducers [
27]. Bistatic systems, often referred to as acoustic Doppler velocity profilers (ADVP), can deliver detailed velocity profiles close to the bed and estimate the concentration of the material resuspended above the bed [
28]. Another laboratory study demonstrated that ADVPs could depict several cells of bedload velocities inside the active layer [
13]. Consequently, bistatic acoustic systems allow detailed measurements of bedload transport characteristics, clearly distinguishing between the volume scattering of mobile particles and the surface scattering of the immobile sediment bed. On the contrary, the distinction between volume and surface scattering is one of the key issues with monostatic systems.
This study aims to evaluate and investigate the limitations of monostatic acoustic systems in measuring bedload characteristics compared with bistatic systems to eventually improve the use of ADCP monostatic systems in the field. Two experiments were conducted in the hydraulic laboratories of the Norwegian University of Science and Technology (NTNU) and the University of Bologna (UniBo) with fine (NTNU) and medium-sized sand (UniBo) used as a sediment bed. ADCP Stream Pro (2 MHz) by RDI [
29] and Ubertone’s ultrasound velocity profilers (UVPs) [
30] working on three different frequencies (3 MHz, 1.5 MHz, and 0.5 MHz) were deployed as monostatic instruments. Ubertone’s ADVP [
27] was used as a bistatic device. It is worth noting that UVPs were used to simulate one beam of the ADCPs, with the advantage of changing some of the instrument parameters. The instruments were tested in different bedload transport conditions, and the bedload mean transport rate was measured by a bedload trap placed at the end of the flume. Simultaneously, high-speed cameras were installed to record the bed sediments, providing an estimation of both the bedload surface concentration and velocity.
The sampling area of the monostatic systems is analyzed in detail as the acoustic sampling limitation induced by the specific beam geometry of the monostatic systems is typically associated with the blanking zone of the ADCPs.
The constraints and ranges of measurement for both systems are analyzed and discussed, specifically focusing on the different velocity ranges measured by the monostatic instruments and the bedload velocity profiles obtained by the bistatic ADVP. The sensitivity of the backscattering strength towards different bedload concentrations is also tested. Finally, this study demonstrates that some limitations of the monostatic system could be resolved by adjusting the acoustic parameters or instrument geometry. The influence of the acoustic beam geometry parameters (e.g., grazing angle, beam opening) on the backscattering strength and Doppler velocity estimation are discussed.
5. Conclusions
This study showed that using monostatic and bistatic ultrasound systems in laboratory conditions is a promising technique to measure bedload velocity, one of the most important characteristics of bedload transport. However, both systems showed that inhomogeneous bedload motion introduces noise and errors in the instantaneous times series, which had to be despiked and time-averaged to obtain reasonable values.
The apparent bedload velocity measured by the bistatic sonar (ADVP) corresponded to the most realistic representation of the true bedload velocity, delivering the full-profile time-averaged velocity of the active layer. This clearly showed that the acoustic geometry plays a crucial role in acoustic sampling, which determines both the backscattering strength from the flume bottom (i.e., EI) and the corresponding apparent bedload velocity (va).
In general, the bistatic configuration has shown the following advantages:
A finer cell resolution and the possibility of profiling the bedload velocities.
The possibility of detecting the thickness of the active bedload layer.
Easier characterization of the backscattering sources, e.g., the influence of the immobile surface irregularity, isolating the surface from volume scattering.
The ADVP helped to explain some of the assumptions and limitations of the monostatic systems and define the main sources of error in the apparent bedload velocity estimation.
The geometrical components of the monostatic systems, such as the grazing angle, the beam opening, transducer size, together with the internal settings, such as carrying frequency, PRF (velocity range limitation), and pulse length, exhibited a rather complex parameter tuning, which coupled with the nature of bedload mobility and resulted in relatively high deviations in the apparent velocities and echo intensity. For example, the low-carrying frequency transducer (0.5 MHz), in combination with a larger cell size and small transducer resulted, in a five times lower apparent velocity than the true mean bedload velocity and an echo intensity twice less than that measured with higher frequencies transducers (e.g., 1.5 MHz) for the more abundant bedload transport conditions.
This study supported that a careful choice and tuning of monostatic acoustic instrumentation can also lead to more accurate estimation of the bedload velocities and correct backscattering conversion related only to the active bedload layer, which may be useful for field studies where the deployment of ADCP monostatic systems appears to be more feasible than bistatic systems. The following list details the advantages of monostatic acoustic transducers:
More focused beams, i.e., a smaller beam opening angle, φ, should lead to a more superficial sampling of the bedload. This implied that for a given frequency, a larger transducer should return more realistic data in the absence of water bias.
A lower frequency (e.g., 0.5 MHz) should be avoided in laboratory conditions because the measured velocities severely underestimated the real bedload velocity. An exception could be considered in cases of high suspended sediments in a water column or a very deep environment where stronger penetration and longer ranges are required [
22].
The finer the resolution, the better the results. This implies shorter pulses for PC but not for the BB, in which the pulse and cell sizes are partially independent [
39]. However, in field applications, it is impossible to have a cell size in the range of the bedload active layer. Thus, more attention should be paid to more efficient acoustic sampling.
The higher grazing angle, θ, results in a higher underestimation of the bedload velocity and less sensitivity in the echo intensity when the bedload transport conditions change. However, it should not be larger than the critical angle of reflection [
38].
The echo intensity might be used as an indicator for the bedload concentration if the cell resolution and source intensity are known a priori.
Echo intensity variations within the bedload profiling (for the bistatic instrument) and for the changing bedload rate were clearly related to the prevailing effect of surface and volume scattering from loose-steady and moving particles, respectively. These two sources of scattering may also correspond to fixed-bottom and water biases [
17], respectively, which additionally contribute to an incorrect estimation of the bedload velocity. This evidence indicates that the backscatter measurement from the riverbed is a good candidate for assessing the reliability of apparent velocity for bedload transport quantification. Therefore, both the apparent velocity and corresponding backscattering strength may be implemented to repair bedload conditions in the field by using ADCPs. This will also require comprehensive backscatter correction due to sound propagation into the water column.
Although a general conclusion is that the bistatic instruments have better acoustic sampling and offer more detailed results, their application is still limited in laboratory conditions (e.g., a maximum range of 400 mm). Future research should be focused on developing a bistatic instrument for field application. Moreover, the existing bistatic sonar should be tested in laboratory conditions with different bedload materials and larger resolutions (i.e., closer to a feasible setting for field use) to examine the sensitivity in those cases.
Regarding the monostatic configuration, more experiments should be conducted to better define the correlation between the transducer characteristics and the measurements of the apparent bedload velocity. The internal signal processing should be focused on the development of finer resolution (e.g., upgrade of the coded element BB modulation) because the bedload active layer in most of the cases has a negligible thickness when compared to the water depth. Finally, multiparameter optimization should be performed to design the best possible configuration for taking bedload measurements.