## 4 Results and Discussion
### Demonstration Case of 15-17 September 2020. Test of the Integration of the Analyzed Variables
Plot generated with Ocean Data View software ([[https://odv.awi.de](https://odv.awi.de)]([https://odv.awi.de](https://odv.awi.de)), 2021 accessed on 12 July 2022). The time series of turbidity along the water column of the TRP and SN channels obtained from the backscatter conversion of the two bottom-mounted ADCPs are shown in Figure 15. Values ranged from 2 to 25 FNU with a gradient along the water column. Higher turbidity was typically recorded in the bottom layers especially in the ebb-tidal phase when the water flow was stronger, even in the absence of meteo-marine forcings. Turbidity reached maximum values at TRP, where the resuspension can extend to the upper water layers. It is also quite evident that the higher values were associated with the main tide excursions following the trend of diurnal inequality of the tidal signal on the flow. For both stations, the surficial layer was characterized by the same interval of variation recorded by the turbidimeters of the fixed stations. The time series in Figure 15 shows that the turbidity in the ebb-tide phase was higher at TRP than SN, similarly to the trend of fixed turbidimeters reported in Figure 10, panel d. This may be a result of the peculiar morphology of the northern basin of the lagoon, drained by the Treporti Channel [51], where the larger extension of salmarshes, and tidal and subtidal flats are more susceptible to erosion and can potentially supply more suspended particles [25]. Moreover, the northern lagoon basin also has larger perrigenous inputs that are vehiculated in the lagoon by the two main tributaries of the drainage basin [39]. The few observed pulses in the concentration of turbidity at the surface of SN station (Figure 15a) were likely related to the plume of sediment from ongoing dredging activities in the area, as visible on satellite images (Figure 11). The uncertainties of the adopted methodology were evaluated on the basis of long-term datasets for both remote sensing and in situ acoustic techniques. The accuracy of satellite-derived products was assessed by comparing the in situ and estimated data in the validation procedure, as discussed above (Figure 3). To assess the quality of data derived from acoustic backscatter, the errors of the calibration procedure for each bottom-mounted ADCP were considered (Figure 16). The scatter plots of measured turbidity versus  
Figure 15: Time series of turbidity derived from acoustic backscatter of the two bottom-mounted ADCPs along the water column for the three-day test-period: (**a**) SN and (**b**) TRP. The y-axis scale is set according to the different depths of the two channels. Plot generated with Ocean Data View software ([[https://odv.awi.de](https://odv.awi.de)]([https://odv.awi.de](https://odv.awi.de)), 2021 accessed on 12 July 2022). turbidity estimated from the conversion of backscatter from bottom-mounted ADCPs (left panels) show a good correlation (R\({}^{2}\) equal to 0.841 for TRP and 0.845 for SN). The plots of the measured turbidity versus error ratio, calculated as the ratio between estimated and measured turbidity (right panels), show that the calibration is acceptable, with error ratio generally close to 1, except for the lowest concentration range, where uncertainty is larger due to the variable organic/inorganic particulate content. Indeed, the choice of a three-day relatively calm period restricts the values of turbidity to the lowest range where data dispersion is higher. However, it has the advantage of showing the potential and the limits of the methodology in the most common conditions when tidal currents are the dominant factor driving suspended sediment transport. At higher concentrations, typical of storm conditions, the correlations between estimated and measured values generally improve because of the predominant presence of the inorganic particulate that results in a stronger signal-to-noise ratio [25].