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The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This study evaluates the performance of three bathymetric LiDAR sensors, CZMIL Supernova, Riegl VQ880-G, and Riegl VQ840-G, used with different acquisition approaches, in mapping Lærdal River bathymetry in Norway. The performance was evaluated based on comparing the sensors against a multibeam echosounder (MBES), a terrestrial laser scanner (TLS), and by an intercomparison between the individual sensors. The comparison was completed by comparing point clouds from the instruments and through the comparison of DEMs created from the point clouds. For the comparison against the MBES, the results show that the median residuals range between 3 to 13 cm, while against the TLS the median residuals range between 0 to 5 cm. The comparison of the CZMIL sensor against the two Riegl sensors shows median residuals of around 12 cm where the CZMIL map is shallower against the VQ880-G and deeper against the VQ840-G sensor. For the two Riegl sensors, the results show a median difference of 2 cm with the VQ880-G map deeper. We do observe that areas with high residuals are linked to river features such as large substrate variability, steep banks, and whitewater/turbulent flow. The study shows that all the LiDAR instruments provide high-quality representations of the river geometry and create a solid foundation for planning, modelling, or other work in rivers where detailed bathymetry is needed. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), affordable precise Global Navigation Satellite System hardware, echo sounders, open-source 3D hydrodynamic modelling software, and freely available satellite data have opened up opportunities for a robust, affordable, physics-based approach to monitor river flows. In short, the hardware can be used to produce the geometry. 3D hydrodynamic modelling offers a framework to establish relationships between river flow and state variables such as width and depth, while satellite images with surface water detection methods or altimetry records can be used to operationally monitor flows through the established rating curve. Uncertainties in the data acquisition may propagate into uncertainties in the relationships found between discharge and state variables. Variations in acquired geometry emanate from the different ground control point (GCP) densities and distributions which are used during photogrammetry-based terrain reconstruction. In this study, we develop a rating curve using affordable data collection methods and basic principles of physics. The specific objectives were to: determine how the rating curve based on a 3D hydraulic model compares with conventional methods; investigate the impact of geometry uncertainty on estimated discharge when applied in a hydraulic model; and investigate how uncertainties in continuous observations of depth and width from satellite platforms propagate into uncertainties in river flow estimates using the rating curves obtained. The study shows comparable results between the 3D and traditional river rating discharge estimations. The rating curve derived on the basis of 3D hydraulic modelling was within a 95 % confidence interval of the traditional gauging based rating curve. The physics- based estimation requires determination of the roughness coefficient within the permanent bed and the floodplain using field observation as both the end of dry and wet season. Furthermore, the study demonstrates that variations in the density of GCPs beyond an optimal number (9) has no significant influence on the resultant rating relationships. Finally, the study observes that it depends on the magnitude of the flow which state variable approximation (water level & river width) is most promising to use. Combining stage appropriate proxies (water level when the floodplain is entirely filled, and width when the floodplain is filling) in data limited environments yields more accurate discharge estimations. The study was able to successfully apply low cost technologies for accurate river monitoring through bhydraulic modelling. In future studies, a larger amount of in-situ gauge readings may be considered so as to optimise the validation process. [ABSTRACT FROM AUTHOR]

Accurate river bathymetry is required for applications including hydrodynamic flow modelling and understanding morphological processes. Bathymetric measurements are typically a set of depths at discrete points that must be reconstructed into a continuous surface. A number of algorithms exist for this reconstruction, including spline-based techniques and kriging methods. A novel and efficient method is introduced to produce a co-ordinate system fitted to the river path suitable for bathymetric reconstructions. The method is based on numerical conformal mapping and can handle topological features such as islands and branches in the river. Bathymetric surfaces generated using interpolation over a conformal map are compared to spline-based and kriging methods on a section of the Balonne River, Australia. The results show that the conformal mapping algorithm produces reconstructions comparable in quality to existing methods, preserves flow-wise features and is relatively insensitive to the number of sample points, enabling faster data collection in the field. • Introduction of a new method for rapidly reconstructing river bathymetry from a set of point depth measurements. • Unlike some existing techniques, the method can handle complex river morphologies such as islands and branches. • The method only requires a set of depth points and a mask of the land and water areas, making it suitable for automation. [ABSTRACT FROM AUTHOR]

The main objective of this study was to quantify the error associated with input data, including various resolutions of elevation datasets and Manning’s roughness for travel time computation and floodplain mapping. This was accomplished on the test bed, the Grand River (Ohio, USA) using the HEC-RAS model. LiDAR data integrated with survey data provided conservative predictions, whereas coarser elevation datasets provided a positive difference in the travel time (11.03-15.01%) and inundation area (32.56-44.52%). The minimum differences in travel time and inundation area were 0.50-4.33% and 3.55-7.16%, respectively, when the result from LiDAR integrated with survey data was compared with a 10-m DEM integrated with survey data. The results suggest that a 10-m DEM in the channel and LiDAR data in the floodplain combined with survey data would be appropriate for a flood warning system. Additionally, Manning’s roughness of the channel section was found to be more sensitive than that of the floodplain. The decrease in inundation area was highest (8.97%) for the lower value of Manning’s roughness. [ABSTRACT FROM AUTHOR]

Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river bed, various data interpolation methods are used, especially when single-beam bathymetric measurements do not cover the entire area and when there are areas which are not measured. Interpolation methods are based on numerical models applied to natural landscapes (e.g., meandering river) by taking into account various morphometric and morphologies and a wide range of scales. Obviously, each interpolation method, used in standard or customised form, yields different results. This study aims at testing four interpolation methods in order to determine the most appropriate method which will give an accurate description of the riverbed, based on single-beam bathymetric measurements. The four interpolation methods selected in the present research are: inverse distance weighting (IDW), radial basis function (RBF) with completely regularized spline (CRS) which uses deterministic interpolation, simple kriging (KRG) which is a geo-statistical method, and Topo to Raster (TopoR), a particular method specifically designed for creating continuous surfaces from various elevation points, contour, or polygon data, suitable for creating surfaces for hydrologic analysis. Digital elevation models (DEM's) were statistically analyzed and precision and errors were evaluated. The single-beam bathymetric measurements were made on the Siret River, between 0 and 35 km. To check and validate the methods, the experiment was repeated for five randomly selected cross-sections in a 1500 m section of the river. The results were then compared with the data extracted from each elevation model generated with each of the four interpolation methods. Our results show that: 1) TopoR is the most accurate technique, and 2) the two deterministic methods give large errors in bank areas, for the entire river channel and for the particular cross-sections. [ABSTRACT FROM AUTHOR]