## 4 Results and Discussion
### Flow Depth Models Analysis and Optimal Model Selection
This gives rise to an interesting discussion on the need to use different roughness indices depending on the flow rate and its return period, as some authors have already pointed out (but in the opposite direction to these results [55]). This variation in the parameters and indices to be used in hydrological and hydraulic models depending on the magnitude of the event has already been described extensively in the scientific-technical literature for other parameters, such as initial abstractions (curve number) as a function of precipitation intensity. The coefficient of water bottom friction was investigated extensively and is known to depend on the particle sizes of materials on the river bed. There have been many studies on friction parameter estimation, especially on a relationship between estimated Manning's coefficients and river bed conditions. These range from the classical tables and lists [57,58], to present-day estimations using fractals and connectivity [59,60] from remote sensing information [61], as well as including visual guides [45] and technical determination procedures [62,63]; all of these methods can be grouped in two kinds of approaches: (i) grain size-roughness relationships for different river bottom patches or polygons and (ii) micro-topographical analyses of bathymetrical data. The first group is used in technical reports and studies of large river reaches for hydrodynamic modelling and civil engineering; the second group is usual in scientific detailed studies of small river channels for geomorphological and ecological analysis. Both approaches are necessary and complementary because they depend on the scale and objectives of roughness estimation. However, it is very important to quantify predictive uncertainty in the hydrodynamic modelling of shallow water flow in response to uncertainty in friction parameterization [64]. However, in any case, a fully spatially distributed Manning's coefficient based upon the physical characteristics of terrain (mainly the riverbed characteristics) was not achieved due to the complex distribution and the high degree of spatial variability in the physical characteristics of the terrain (grain-size and micro-topography distributions) and vegetation. This objective is already feasible for floodplains [61] by using UAVs, but not for the submerged areas of river channels. Based on the whole scope of the results of our analysis, the use of a spatially distributed Manning's \(n\) value became necessary. Manning's \(n\) value is usually calibrated within a river reach using a uniform value for all reaches, although the use of non-uniform values was pointed out by previous studies [65] that used a different Manning's \(n\) value for each river reach at the Lower Tapi River (India) to get the best-fit calibrated HEC-RAS model. In this sense, [PERSON] and [PERSON] [66] showed a methodological framework for the automatic river segmentation into different river reaches that were fitted with a non-uniform Manning's \(n\) value. Both approaches were utilized prior to the use of a non-uniform value for the roughness coefficient along a sequential river reach segmentation, but they did not use a real spatially distributed Manning's \(n\) value. Although a more complete study of spatially distributed values of Manning's n parameter would be necessary and convenient, the approximation to its use that was carried out in the present study did not show significantly better results than those of the other models considered. In our methodological approach, we used the 500-year return period peak flow to develop the methodological framework, while the 100-year return period peak flow was used as the test model. The statistical results of the test model (Table S1 in Supplementary Materials) showed a slight difference from the 500-year return period peak flow model. A similar dependence on the statistic used was observed relative to the hydraulic model, which offered better results compared to the control model.