## 5 Discussion  
The presence of a sampling device on the river bottom alters the pattern of the flow and sediment transport in its vicinity [44]. The Helley-Smith sampler was developed to perform collections in environments carrying coarse sediments; however, when used in sand-bed rivers (d < 0.5 mm), the samples can be overestimated [45]. For example, samplings with HS at the East Fork River near Pinedale, Wyoming, USA, where particle sizes varied between 0.25 mm and 0.5 mm, were overestimated by 150% [46]. Studies have shown that the effectiveness of HS for bedload sampling is influenced by mesh size [47; 48], nozzle wall thickness [49], flow turbulence and bedform dynamics [44]. The position of the device on the river bottom (above or below the bedload layer; and aligned or not with the flow) can cause oversampling or undersampling of bedload rates [50; 51]. In  
Figure 16: (a) Total bedload estimates at cross-section P1; (b) discrepancy ratios of total bedload estimates. rivers with bedforms, the spatial variability of bedload transport is a source of uncertainty for the physical samples, since the bedload rate at a specific cross-section tends to decrease temporally from a maximum near the crest to a minimum in the trough as dunes migrate downstream [52]. Even for pressure-difference samplers with hydraulic efficiency near 100%, the sampling efficiency is affected by errors and uncertainties as mentioned above [53]. Therefore, caution is needed when considering the difficulties of obtaining accurate measurements with mechanical traps, especially when recognizing the drawbacks of the HS sampler when used in sand-bed rivers. Although it was not possible to identify the specific factors that caused the lower rates of the HS estimates in the Taquari River, the magnitude of the computed total transport was useful for comparison with the other methods. Considering the surrogate method ADCP-BT, it is interesting to point out that [PERSON] et al. [8] have also estimated bedload transport with ADCP (bottom-tracking), testing the [PERSON] [17] and [PERSON] [27] approaches. After a linear functional analysis comparing bedload estimates from the ADCP-BT method, the Helley-Smith sampler and the Van Rijn formulae [27], they concluded that there was no improvement in either relation, when modeled values (based on Van Rijn [27]) of bedload layer thickness (\(\delta_{b}\)) and bedload concentration (\(c_{b}\)) were used. A possible reason for the high bedload rates estimated with ADCP-BTE is related to \(c_{b}\) determination using Equation (3) with \(p=0.4\), which is equivalent to adopting the maximum possible concentration in the bedload layer. Actually, the expected porosity should be higher than 0.4, even with coarser bed particles. The ADCP-BTvr, in turn, considers a bed particle's saltation height (\(\delta_{b}\)) that, although does exist in sand-bed streams subjected to bed shear velocities, is very small, in the order of a few diameters. [PERSON] et al. [1] assessed bedload rates in a large sand-bed river (Parana river, in Argentina) using ADCP-BT, combining theoretical parameters and apparent velocity of bed-particles from ADCP. Comparing ADCP-BTE and ADCP-BTvr, they concluded that there is indeed a compensation of differences between the methodologies, in such a way that the ADCP-BTE approach yields better results, even though it lacks a clear physical meaning. From [PERSON] et al. [8] and [PERSON] et al. [1] findings, the adoption of constant values of \(\delta_{b}\) and \(c_{b}\) (based on Einstein [17]) seem to be quite appropriate to explore the ADCP-BT capability. The results from the present work at Taquari River endorse this statement, despite the lack of a physically based foundation in the definition of variables. Regarding the complementary use of the ADCP sensor (exploiting its 5 beams) for collecting bathymetric data should be recognized for its lower spatial resolution in comparison to a multibeam echosounder survey.