## 4 Results and Discussion
### Flow Depth Models Analysis and Optimal Model Selection
When the results were analyzed as a function of the distance to the riverbanks (Figure 6), less deviation was also observed for the LiDAR scenario models with [PERSON]'s \(n\) values ranging from 0.011 to 0.015 (the model with an \(n\) value of 0.016 began to show a slight deviation towards flow depth overestimation). From the best-calibrated models, those linked to a [PERSON]'s \(n\) value equal to 0.013 or 0.014 probably showed the lowest dispersion against the zero error line. If the analysis of flow depth values was carried out in intervals of distance from the riverbanks (Figure 7), the scenarios that best adjusted the intervals of maximum-minimum deviation were also those ranging from [PERSON]'s \(n\) value equal to 0.010 to 0.014. The \"LiDAR scenario ([PERSON]'s \(n\) value = 0.010)\" showed the best results for shorter distances from the riverbank, with a very good fit for distances from 0 to 150 m. The \"LiDAR scenario  
Figure 6: Scatter plot graphical representation of the relationship between the variable flow depth and distance to the riverbank for different [PERSON]’s \(n\) value calibrated models. The red line shows an error equal to zero and the black line shows a logarithmic fitting to the point values. ([PERSON]'s \(n\) value = 0.012)\" gave the best results for distances from 150-300 m off the riverbank. Moreover, the \"LiDAR scenario ([PERSON]'s \(n\) value = 0.013)\" showed a good fit for all distances up to 300 m. However, for distances over 300 m, the \"LiDAR scenario ([PERSON]'s \(n\) value = 0.014)\" probably showed the best fit from the range of different [PERSON]'s \(n\) values calibrated. For this model, most of the error in the flow depth value lay within an interval of \(\pm\) 10-12 cm, although this error increased to 15-20 cm when we got close to the riverbank. All these models substantially improved the results obtained in the model in which the natural value of [PERSON]'s parameter n was maintained. When we analyzed the results that were associated with the spatially distributed Manning's parameter n model and those associated with the HDCM model, we observed that in both cases, the results were better than those associated with the model that preserved the natural value of Manning's n parameter. However, in neither case did the results approach the best of the models with a constant and calibrated Manning's \(n\) value. Thus,  
Figure 7: Box plot graphics showing the relationship between the flow depth errors and the distance to the riverbank (within 50 m intervals). Positive errors were related to the underestimation of the flow depth value by the calibrated model, and vice versa. the HDCM model was one of the worst performers in this study. The bad performance of the HDCM model may have been due to the large difference between flow rates on the date of the LiDAR data and the 500-year return period peak flow, as well as the likely significant differences in flow velocity in each case, where the higher flow velocities would require less of a channel cross-sectional area (Figure 3). On the other hand, the model with a spatially distributed Manning's \(n\) value provided a very good fit with the control model (\"real scenario\") of up to about 500 m distance from the channel; however, at further distances, it underestimated the flow depth more than the models with a constant Manning's n parameter and values between 0.013 and 0.015. Therefore, if the risk is to be assessed at a short distance because this is where the exposed and vulnerable elements are located (farms, transport infrastructure, etc. ), the scenario \"LiDAR scenario (Manning's \(n\) value = 0.011)\" or the spatial distributed Manning's \(n\) value model are of interest, while if risk analysis is to be carried out for elements distant from the riverbed (homes and towns far from the river but within a flood zone), the scenario \"LiDAR scenario (Manning's \(n\) value = 0.012 to 0.015)\" can be used.