## 1 Introduction
Airborne LiDAR sensors (one of the possible sources of LiDAR data) are usually not capable of penetrating water bodies (turbid, turbulent or deep streams and rivers), but they constitute the most common LiDAR data due to their cost-effectiveness for wide areas relative to other LiDAR sources. In fact, as is discussed by [PERSON] et al. [31], the available bathymetric LiDAR techniques are usually not designed for shallow waters and are not optimized for providing the spatial resolution necessary for mapping small-to-medium-sized rivers. However, an accurate representation of river bathymetry (bed topography) plays a critical role in multiple hydrologic and hydraulic applications, including but not limited to flood modelling [23]. To solve these limitations, the acquisition of topographic data through ground surveys and subsequent combination with subaerial LiDAR data may be an efficient solution [32; 33; 34]. However, this approach has only been used for short river reaches. When the river reach length increases, so do the logistical and cost considerations. Therefore, under the scenario of long river reach study areas, it is common to use alternative methods or models for estimating bathymetry for use in hydraulic or hydrologic analyses. These alternative models assumed a general simplification of river bathymetric geometry, both from the use of simple geometric shapes (triangular, trapezoidal or parabola for a river cross-section) or from other geomorphological and hydraulic methods. The former approach is more frequent [23; 35; 36; 37; 38]. From a hydraulic perspective, the horizontally divided channel method (HDCM) was previously used [39; 40] to solve the absence of bathymetric data. The HDCM separately considered the flow above and below the bank top and is a better option [39] for dividing the flow than the vertically divided channel method (VDCM) when the floodplain roughness is significantly greater than the channel roughness. When the flow is horizontally divided, the lower part of the flow fits with the bankfull flow (taken from the dominant discharge concept), which has a return period of 1-2 years according to the field observations of [PERSON] and [PERSON] [41]. However, this bankfull flow return period is dependent on local meteorological characteristics, thus it ranges from about 2 years in the north of Spain to 5 or more years in the southern and southeastern parts of the country. Once the bankfull flow is defined, it is detracted from the inflow hydrograph. In the same way, [PERSON] et al. [40] used LiDAR topography to subtract the flow rate at the LiDAR date from the inflow hydrograph as a solution for the absence of river bathymetric data. Whatever the proposed methodology, model calibration (when possible) is one of the main tasks that will ensure the quality of the results obtained. To carry out this calibration process, the availability of field data related to the event to be reproduced is essential. When this information is available, model calibration by varying the value of the Manning coefficient is one of the most frequent approaches, which is described by [PERSON] and [PERSON] [42]. However, valid information is not always available for the calibration of hydraulic models. This situation is more frequent when we do not try to model a specific event, but rather intend to model a design flow event that has a low frequency of occurrence (high return period). In these cases, and in many others, the lack of available information to calibrate the hydraulic models is compensated by the consideration of a benchmark model against which the results obtained in the rest of the models will be compared [43]. In general, this control model is defined by the availability of more or better data for it. Several studies have already shown that incorporating bathymetry provides more accurate hydraulic simulations and flooding area estimations, which improve flood hazard analysis at the same time as flood risk assessments.