## 2 Study Area and Data Description  
This study focused on comparing the flood area extension over different hydrodynamic models, where Manning's roughness coefficient was changed in a calibration process. The study area comprises the Douro River reach in Zamora city (Spain), where most of the historical urban center is located on the right side of Douro River, but the urban center located on the left side is developed over lower-lying and flood-prone areas. The Douro River reach in Zamora (Figure 1A) is 7 km long with a mean width of 225 m, and is char acterized by a relatively deep, sand-clayey channel with some fully vegetated sandy bars and with several meanders and a flat floodplain. Topographic LiDAR data [44] are publicly available in the form of point cloud files, which are filtered and classified following the American Society for Photogrammetry and Remote Sensing (ASPRS) standard classification. The vertical accuracy of the LiDAR data used in this study is reported to be lower than 20 cm [44]. The terrain and building types of the LiDAR classification were used to derive the different DEMs. Due to the absence of LiDAR data in the main river channel (since water has low reflectance), it was modelled using a second-order polynomial interpolation of multiple LiDAR cross-sections. Each of these sections presented a height equal to the minimum LiDAR point value and had a length that was restricted to channel boundaries. Although a spline and a third-order polynomial interpolation were tried, the second-order one was chosen because it provided a smooth and downstream-constant slope surface. Bathymetry data were obtained in the form of cross-section measurements that were obtained from boat-mounted echo sounders with D-GPS (differential global positioning system) data acquisition. Both sets of topographic data were combined into a 1 m spatial resolution to create a final DEM (Figure 1C), which was considered to be the \"real scenario\" (or benchmark scenario). On the other hand, a LiDAR point cloud was also interpolated in the absence of bathymetric data into a 1 m spatial resolution DEM (Figure 1B), which was considered to be the \"LiDAR scenario\". The peak flow values that were used for the hydrodynamic models were obtained from a streamflow gauge at the upstream location of the Douro River reach. A generalized extreme values (GEV) distribution was fitted to the annual maximum flow time series, and the 500-year return period peak flow value (2274 m\({}^{3}\) s\({}^{-1}\)) was selected for our analysis upon the assumption that this value can be considered as a low-frequency or \"extraordinary\"  
Figure 1: The study area and cultural heritage sites location map **(A)**. The digital elevation model (DEM) from LiDAR data (**B**) not showing bathymetry, which was included from echo sounders with a D-GPS **(C)**. Example of a cross-section flow depth calibration (**D**). -flood event. To test the efficiency and validity of the proposal, a second peak flow value was selected: in this case, the peak flow value associated with a return period of 100 years (1872 m\({}^{3}\) s\({}^{-1}\)). The reason for not selecting a lower peak flow value, therefore one that was more contrasted with the 500-year return period, was the need for the hydraulic model to include the overflow of the main channel in the flood plain. This need was based on the premise that the usefulness of our proposal is only fulfilled in the floodplain, and therefore, in both cases (different peak flows), the floodplain should be affected. Finally, PNOA ortho-photographs from 2017 (0.25 m of spatial resolution) were used in combination with a field survey to define the value of Manning's roughness coefficient (Manning's n); this map was created for the whole study area at a scale of 1:4.000. It contains ten different roughness units, each of which was assigned a unique Manning's n according to previously proposed tables [45; 46]. For the \"real scenario\", a Manning's roughness coefficient value of 0.027 was assigned for the Douro River's main channel.