## 4 Results
### Bedform Characteristics and mISSDOTv2  
After correcting and interpolating the bathymetric data on the curvilinear grid, longitudinal bed profiles were extracted from the underwater DEM. Figure 13 shows a pair of longitudinal bed profiles (at grid position N = 26) of 1 February 2018 (blue) and 2 February 2018 (red). This pair of profiles met the selection criteria for use in the mISSDOTv2 method, i.e., presented erosion-deposition ratios within the range [0.8-1.2]. Inconsistencies observed in some profiles might be related to bathymetric data interpolations on the grid, which is a constraint of the modified ISSDOTv2 in comparison to the original methodology based on multibeam surveys. Despite the feasibility of identifying bedforms along the illustrated profiles, it is challenging to determine by visual inspection the height, wavelength and celerity of the dunes. Intending to decrease subjectiveness of personal interpretation while determining geometries and dimensions, the methodology developed by [PERSON] et al. [16; 39] was applied. Figure 11: Unit bedload transport rates estimated with Helley-Smith sampler versus depth-averaged water velocities. Figure 12: Unit bedload transport rates estimated with ADCP-BT, following [PERSON] [17] and [PERSON] [27] formulations to compute the parameters of Equation (1). The first wavelet analysis was performed on the original signal (longitudinal bed elevation profile) and generated a global wavelet spectrum indicating the most frequent bedform wavelengths. Figure 14 presents the first step results for the profile N = 26 (1 February 2018). The wavelet power spectrum contours show the spatial distribution of wavelengths (\(\lambda\)), with a 95% confidence interval, and the cone of influence (dashed blue line) discriminates reliable from spurious local power spectrum results. The global wavelet power spectrum indicates the main wavelength frequencies found at the analyzed profile (peaks located to the right of the dotted red line correspond to wavelengths at 95% of the confidence level). Characteristic wavelengths for the presented profile are: 14 m, 32 m, 100 m, 168 m and 387 m.  
Figure 14: Wavelet analysis for profile N = 26 on 1 February 2018. Flow occurs from left to right. (**a**) Longitudinal bed elevation; (**b**) spatial distribution of wavelengths (\(\lambda\)), through wavelet power spectrum contours using the Morlet function; (**c**) global wavelet power spectrum, indicating the main wavelength frequencies. Figure 13: Pair of longitudinal bed profiles (at N = 26) measured at Taquari River. (**a**) Elevation view; (**b**) plan view (grid). On the second step, longitudinal bedforms were decomposed and hierarchized based on the wavelengths previously found. Three hierarchies were defined: the first referring to small dunes; the second, associated with medium or large dunes; and the third hierarchy, comprising mega-dunes or bars. Figure 15 illustrates the decomposition of the longitudinal profile N = 26 (1 February 2018). The results from the wavelet transforms provided information to analytically quantify bedform dimensions so that the mISSDOTv2 method could be applied with the correction term (Equation (11)). Nevertheless, it was necessary to visually assess the dune celerity for each selected longitudinal profile pair, computing the ratio of the distance between the dune's crests to the time interval of 24 h. As there were many dunes with varying sizes and speeds over a single profile path, it was necessary to average the individual estimates to establish a unique dune celerity value for use in Equation (11). Thus, it was not possible to use the same objectivity as in the wavelet-spline methodology. The second hierarchy (h\({}_{2,3}\)) resulting from the wavelet-spline discrimination was adopted as the bedform reference to evaluate the systemic loss of area and correct the dune transport rate using Equation (11).