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In the broad field of sensors and 3D information retrieval, bathymetric reconstruction from side-scan sonar imaging is associated with unique technical hurdles. Neural Networks have recently led to promising new solutions in this field, but the available methods tend to be complex and data-intensive in a way typically making their use in a riverine environment impossible. Throughout our work, we have focused on simplifying the problem-handling and treating compatibility with a riverine environment as priority. In our work, Long Short-Term Memory proved to be effective in a surprisingly simple form. Combined with traditional post-processing techniques in the GIS environment, like median filtered focal statistics, our workflow ultimately results in ∼0.259 m median of error on the evaluation dataset of the Dráva River.

River bathymetry (RB) is a fundamental dataset in the field of river research. However, mountainous regions often lack comprehensive data due to topographical and transportation challenges. Remote sensing technology provides an innovative method for estimating RB. In this study, the theoretical relationship between the water level and the river width is established by generalizing the channel cross-section shape. A novel RB estimation method was proposed, integrating the Hydroweb dataset and Sentinel-1 images. The impacts of exposure, reach-average length, and remote sensing observation errors on estimation accuracy were systematically analyzed. The method was applied to the Upper Yangtze River to evaluate its potential for estimating river discharge. Results reveal that: ① The estimation error of the riverbed elevation ranges from 4.00 m to 4.06 m, with the estimated cross-section representing 73.69% to 80.29% of the actual area, indicating precise RB estimation. ② Exposure rate emerges as a primary factor, significantly enhancing estimation accuracy. An appropriate reach-average length improves the estimation precision and optimal length of 10km is advised for the Upper Yangtze River. Furthermore, the accuracy of RB estimation is more susceptible to water level errors in remote sensing than to river width. ③ The method demonstrates the potential to estimate river discharge achieving a Nash efficiency coefficient of 0.92. The research outcome can provide a novel approach to RB monitoring in data-scarce regions. [ABSTRACT FROM AUTHOR]

Digital Elevation Models (DEMs) act as an essential input to hydraulic models for the simulation of flood hazard maps. Coarser resolution DEMs are subject to higher levels of uncertainty, particularly in capturing river‐bed bathymetry. The principal aim of the present study was to assess the potential use of freely available DEMs and the 1D and 2D HEC‐RAS modeling approaches for flood mapping in four different types of river morphology. The DEM created from direct river survey (Surveyed DEM) was used as a benchmark to assess three remote sensing‐based DEMs (i.e., ALOS, SRTM, and ASTER). The results indicated that ALOS is more accurate. However, the regeneration of main‐channel bathymetry was poor for floodplain‐meandering and braided river reaches. The DEMs were then corrected by optimizing the number and layout of representative sections. A total of 144 tests were carried out. The results indicated that the flood‐mapping index F of ALOS increased from 86% to 91%. The flood mapping with a 1D model was sufficient in straight reaches (F‐index 84%). In floodplain‐meandering rivers, a 2D model had to be used to reach an F‐index up to 81%. The responses of both 1D and 2D models were almost identical in the case of wide‐braided rivers. [ABSTRACT FROM AUTHOR]

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]

Abstract Digital Elevation Models (DEMs) act as an essential input to hydraulic models for the simulation of flood hazard maps. Coarser resolution DEMs are subject to higher levels of uncertainty, particularly in capturing river‐bed bathymetry. The principal aim of the present study was to assess the potential use of freely available DEMs and the 1D and 2D HEC‐RAS modeling approaches for flood mapping in four different types of river morphology. The DEM created from direct river survey (Surveyed DEM) was used as a benchmark to assess three remote sensing‐based DEMs (i.e., ALOS, SRTM, and ASTER). The results indicated that ALOS is more accurate. However, the regeneration of main‐channel bathymetry was poor for floodplain‐meandering and braided river reaches. The DEMs were then corrected by optimizing the number and layout of representative sections. A total of 144 tests were carried out. The results indicated that the flood‐mapping index F of ALOS increased from 86% to 91%. The flood mapping with a 1D model was sufficient in straight reaches (F‐index 84%). In floodplain‐meandering rivers, a 2D model had to be used to reach an F‐index up to 81%. The responses of both 1D and 2D models were almost identical in the case of wide‐braided rivers.

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]

Several studies have focused on the importance of river bathymetry (channel geometry) in hydrodynamic routing along individual reaches. However, its effect on other watershed processes such as infiltration and surface water (SW)‐groundwater (GW) interactions has not been explored across large river networks. Surface and sbsurface processes are interdependent, therefore, errors due to inaccurate representation of one watershed process can cascade across other hydraulic or hydrologic processes. This study hypothesizes that accurate bathymetric representation is not only essential for simulating channel hydrodynamics but also affects subsurface processes by impacting SW‐GW interactions. Moreover, quantifying the effect of bathymetry on surface and subsurface hydrological processes across a river network can facilitate an improved understanding of how bathymetric characteristics affect these processes across large spatial domains. The study tests this hypothesis by developing physically based distributed models capable of bidirectional coupling (SW‐GW) with four configurations with progressively reduced levels of bathymetric representation. A comparison of hydrologic and hydrodynamic outputs shows that changes in channel geometry across the four configurations has a considerable effect on infiltration, lateral seepage, and location of water table across the entire river network. For example, when using bathymetry with inaccurate channel conveyance capacity but accurate channel depth, peak lateral seepage rate exhibited 58% error. The results from this study provide insights into the level of bathymetric detail required for accurately simulating flooding‐related physical processes while also highlighting potential issues with ignoring bathymetry across lower order streams such as spurious backwater flow, inaccurate water table elevations, and incorrect inundation extents. Key Points: The effect of river geometry on subsurface processes, such as infiltration and lateral seepage in large‐scale fluvial modeling is highlightedSubsurface processes in the floodplain are controlled by overall channel characteristics, rather than channel shapeChannel conveyance capacity and longitudinal slope are critical bathymetric controls of subsurface processes in floodplains of river network [ABSTRACT FROM AUTHOR]

River bathymetry is an important parameter for hydrodynamic modeling; however, it is associated with large bias because direct large‐scale measurements are impractical. Recent studies adjusted river bathymetry data based on assessment of the difference between modeled and observed water surface elevation (WSE); however, model uncertainties in river discharge can lead to unintended errors in correcting river bathymetry. In this study, we propose a simple but robust and rational correction method of river bathymetry using the bias between stage–discharge rating curves rather than WSE time series data. The rating curve represents the internal characteristics of the river section, and is not sensitive to the instantaneous simulated discharge errors. Our results showed that the corrected river bathymetry are robust to bias in runoff as they converged among experiments driven by noise‐corrupted or multimodel runoff forcing. Evaluation with the corrected river bathymetry against virtual truth demonstrated that the new method reduced 0.85–1.12 m of the absolute bias than the result from the conventional method. The deviation among the results reduced by more than 70% particularly in river sections with no backwater effects. Evaluation of the corrected river model output also showed the advantage of rating‐curve bias correction, as the simulated WSE is reasonably better only with better runoff and it does not conceal errors in runoff inputs. Given the difficulty of eliminating discharge errors and bias in runoff, a method for correcting river bathymetry that is free from discharge and runoff errors is important for improving hydrodynamic modeling. Plain Language Summary: We propose a simple method for correcting river bathymetry in river models using bias between stage–discharge rating curves rather than direct bias of water surface elevation as used in conventional methods. The rating‐curve bias is insensitive to runoff and discharge and the corrected river bathymetry are robust to biases in runoff or discharge modeling errors. The performance of corrected river model is also more reasonable than that of conventional method as it does not conceal the errors in runoff or discharge. Accurately modeling discharge is challenging due to climate forcing, model complexity, inhomogeneity of model parameters, and the difficulty of representing human interventions. Therefore, a robust method for correcting river bathymetry in hydrodynamic models, which is independent of river discharge bias, is required. Key Points: Empirical river bathymetry approximation equations introduce bias into local water surface elevation (WSE) simulationsWSE bias between the rating curves for simulations and observations reflects only river bathymetry biasRiver bathymetry corrections based on the rating curve are robust, regardless of runoff uncertainties and discharge errors [ABSTRACT FROM AUTHOR]

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.

Reconstruction of complex braided river bathymetry is important for supporting hydrodynamic simulation and understanding river morphological processes. To our knowledge, existing methods generate and interpolate channel-fitted coordinate lines using the measured cross-sectional data to reconstruct river bathymetry. Nevertheless, in these methods, the generation of channel-fitted lines obeys the law of splines or a set of equations without considering the laws of river dynamics. Moreover, it is difficult to interpolate bathymetry using the channel-fitted lines when dealing with complex braided rivers. This paper introduced a novel method to fill the gap. Briefly, in the introduced method, an initial bathymetry is interpolated and a velocity field is calculated using a set of simplified two-dimensional (2D) shallow-water equations. Then, streamlines are generated using the velocity field data, and the elevations of the nodes on the streamlines are interpolated using the measured cross-sectional data. Finally, the bathymetry of the domain is interpolated using the streamlines and the measured elevation points or contour lines at the sandbars and floodplains. For demonstration purpose, the introduced method was applied to a 20-km section in the middle reaches of the Yellow River (China) with many branches and sandbars. The reconstructed bathymetry of the domain was investigated in terms of their geometrical shape and hydrodynamic performance (including inundation area and water level). When compared with the measured water level, the hydrodynamic results of the reconstructed bathymetry showed acceptable accuracy. In addition, because the channel-fitted method is widely applied to interpolate the river bathymetry, we compare the channel-fitted method with the introduced method in discussion section. [ABSTRACT FROM AUTHOR]

The Ohashi River is a narrow water stream that connects two brackish lakes in Japan. Intermittent saline water intrusion often occurs in Lake Shinji from Lake Nakaumi through Ohashi River. In this study, two approaches were discussed to reproduce the hydrodynamic conditions of a morphologically complex river. In the first approach, the river sinuosity was straightened. The straightening of the river resulted in a higher flow velocity and water flux coefficient due to the reduction in the flow path and the resistance, and this approach was found to be appropriate for the reproduction of the flow velocity. However, the river shape was visually quite different from the actual river morphology. In the second approach, the prime focus was given to the shape and bathymetry to quantitively reproduce the flowrate of the saline water intrusion. This approach resulted in an underestimation of the flow velocity, which was compensated by increasing the cross-sectional area of the river. A slower flow velocity causes up to a 3-h time lag for the water mass to pass through the Ohashi River, which in principle should affect the temporal variations of the water temperature and salinity. Fortunately, as the typical time scale for water temperature and salinity fluctuations in the Ohashi River is a few days, a 3-h time lag did not cause any problems.

Topography represented in the form of Digital Elevation Models (DEMs) has profound applications in hydrological modeling. DEMs can be generated from several sources including satellite products, contours, survey data, and LiDAR, each with their own merits and demerits. Where high resolution, accuracy, and spatial extent are concerned, it is often found that a DEM from one source alone is not able to represent the topography of the target area with full accuracy. Upon comparing different DEMs, it was found that most were able to successfully represent mountainous regions but failed to represent flat deltaic regions. Therefore, in this research with Bago River basin, Myanmar as a study area, a new methodology to combine multiple sources of data with different data types is developed. The inputs are: (a) a 10 m DEM, developed using contour data, point elevation data, and UTM topographic maps; (b) a 5 m Digital Surface Model (DSM) acquired by the Advanced Land Observation Satellite (ALOS); and (c) 168 sets of multiple-point elevation data representing a cross-sectional survey along the Bago River and the Bago-Sittaung canal. The output is a 10 m resolution Enhanced DEM (EnDEM) which is able to preserve the merits of all the input data, i.e., upper mountainous region, lower flat deltaic basin, and the river bathymetry. This paper provides a novel approach to DEM integration and burning of the river cross-section onto the DEM. [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]

• River bathymetry affects various flood inundation outputs differently. • Cross-sectional area, and not the channel shape, is critical to hydraulic modeling. • Both channel area and thalweg are important for velocity and shear estimates. • Simplified geometries can be used for reaches with small sinuosity, width or depth. River bathymetry, which is vital for accurate hydraulic modeling, is not readily available at large scales because of the logistical difficulties in field surveys and uncertainties associated with remote sensing techniques. Several studies have explored the potential of conceptual models and interpolation algorithms to estimate bathymetry. These models have certain underlying assumptions that limit their accuracy and widespread implementation. This study aims to provide insights into the choice of bathymetric model for different hydraulic applications by analyzing the effect of different bathymetric models on hydraulic modeling under different geomorphologic and flow settings. This study does not aim to reinforce the importance of bathymetry, rather its objective is to identify the bathymetric properties that are critical to accurate estimation of different hydraulic outputs. The study accomplishes its objectives by implementing three different bathymetric models with varying cost and efficiency at four sites with diverse bathymetric characteristics. Five hydraulic outputs, namely wetted cross-sectional area, water surface elevation, inundation extents, velocity and shear are estimated at three characteristic low and high flows and the results from the four sites are grouped together to perform an unbiased and robust evaluation. The performance of these models is evaluated using the best available bathymetric representation generated from detailed field surveys as a benchmark. The results indicate that 1D flood modeling is somewhat insensitive to channel shape as long as the estimated shape reflects the channel storage accurately. Velocity and shear related applications should incorporate bathymetry that represent both the cross-sectional area and channel thalweg accurately. Reaches with low sinuosity (<1.5), or small width and depth can be modeled reasonably using simple geometric shapes, whereas for larger streams, interpolation of surveyed data along the thalweg or complex bathymetric models that account for river anisotropy, are recommended. [ABSTRACT FROM AUTHOR]

River discharge estimation requires knowledge of bathymetry. However, aside from a few locations where surveys have been conducted, bathymetric data are unavailable, even for major rivers. It has been suggested that water surface elevation and flow width measurements from the upcoming Surface Water and Ocean Topography (SWOT) satellite mission (planned launch 2021) may be used to infer the submerged channel geometry; however, the full potential of these measurements for inferring bathymetry has yet to be explored. We apply four different techniques, with varying assumptions about height‐width relationships, to predict unknown bathymetry. We call these "curve‐fitting methods" the linear, slope break, nonlinear, and nonlinear slope break (NLSB) methods. The linear and slope break methods are based on a linear height‐width relationship, while the nonlinear and NLSB methods are based on a height‐width relationship derived from hydraulic geometry equations. We generate SWOT‐like observations of height and width based on 5‐m gridded Upper Mississippi River data and evaluate the performance of each curve‐fitting method given the SWOT‐like observations. The NLSB method predicts bed elevation and low flow area with the least error, although the nonlinear method may be preferred in low data conditions. Additionally, we show that our method outperforms previously suggested methods, and we propose an NLSB‐based bathymetry prior for Bayesian discharge estimation algorithms. Key Points: We predict unobserved bathymetry using a limited number of error‐corrupted height and width measurementsWe compare four different height‐width models and find that piecewise, nonlinear models predict bathymetry with the least errorWe develop bathymetry priors for discharge estimation algorithms that rely on prior distributions to constrain unknown parameters [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: A reliable representation of river terrains is essential to river research. Field surveys of river channel geometry are time‐consuming, costly, and logistically constrained and thus would encounter difficulties to achieve sufficient spatial coverage, resolution, and frequency of resurvey. This paper aims to demonstrate an efficient approach to building a river terrain model (RTM), the emphasis being on how to combine bathymetry and topography derived from satellite images captured at different flow stages. A method for calculating the difference between high and low stages (DHLS) based on the uniform‐flow theory is proposed. Calculations are carried out for a 13‐km long meandering section of the gravel‐bed Goulais River in Canada, which features pools and riffles, alternating point bars, and midchannel bars. A RTM for this complex section has been successfully produced. It consists of three data components: bathymetry at low stage, topography at high stage, and DHLS. The results capture realistic characteristics, including thalweg shift, steep outer banks, and gradual inner banks. They also show realistic longitudinal and lateral locations of pools and riffles. To illustrate potential applications of RTM, this paper has computed extreme bed shear stresses at bankfull discharge through hydrodynamics simulations of depth‐averaged flow in the river section and further estimated bed‐sediment grain‐size distributions. The estimates compare well with field measurements. The DHLS can vary significantly along a river channel. The proposed method for determining it is not site‐specific, and hence applicable to other rivers. The novelty of the methodology discussed lies in combining remote sensing techniques with physical flow laws. [ABSTRACT FROM AUTHOR]

River bathymetry has a crucial role in numerical modelling of flow hydrodynamics. The aim of the present study is to (1) investigate the capability of three different methods to trace the river bed, with the geodetic data derived from in situ survey of the river bed topography to be used as reference data, and moreover (2) assess the impact of the different river geometries on the performance of a one‐dimensional (1D) model of flood hydraulics. The Hydrologic Engineering Center's River Analysis System (HEC‐RAS) hydraulic model, in particular, calibrated and validated on hydrometric data uses a number of cross‐sections derived from aerial laser scanning (ALS), artificial sinking of riverbed, and acoustic Doppler current profiler (ADCP) methods. Water level, discharges, and flood extent derived from the model for 3 N‐year floods (Q1, Q50, Q100) were evaluated while the extent of the inundation areas were exported into geographic information systems for visualisation and interpretation of the findings. Case study area is a 2.2 km river section of the Otava River (Czech Republic), with a history of flood events. The results showed that ALS data need further processing when dealing with the riverbed zone, while ADCP presented high correlation to the reference model. [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]

Abstract: Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD‐FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high‐resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing‐derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing‐derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations. [ABSTRACT FROM AUTHOR]

• Estimated river bathymetry using the satellites data on river width and water level. • Derived nonlinear z-w relationship from generalized channel cross section. • Investigated the influences of channel exposure on river bathymetry estimation. • Studied the effects of reach-averaging length and remote sensing data on bathymetry. River bathymetry is a key variable in estimating river discharge by remote sensing. However, traditional methods of mapping river bathymetry are expensive and time-consuming, which hinders the estimation of river discharge. In this study, we propose and test a method for estimating river bathymetry by combining remotely sensed observations of water level (z) and river width (w) with a nonlinear z - w relationship derived from a generalized channel cross-sectional shape. The results from five reaches of the upper Yangtze River show that the absolute relative error for the estimate is between 1.25% and 6.18%, indicating the ability of this method to provide accurate estimates. The channel exposure (e) is the main limitation of the proposed method, and the river bathymetry estimates improve significantly with increasing e. For the upper Yangtze River, averaging reaches over an appropriate length can reduce variability in river bathymetry estimate, especially when data are reach-averaged over a length of 10 km. Considering that a priori bathymetric information is not required and its computational process is relatively simple, the proposed method could open up the possibility of bathymetry modeling of global rivers. [ABSTRACT FROM AUTHOR]

An appropriate digital elevation model (DEM) is required for purposes of hydrodynamic modelling of floods. Such a DEM describes a river’s bathymetry (bed topography) as well as its surrounding area. Extensive measurements for creating accurate bathymetry are time-consuming and expensive. Mathematical modelling can provide an alternative way for representing river bathymetry. This study explores new possibilities in mathematical depiction of river bathymetry. A new bathymetric model (Bathy-supp) is proposed, and the model’s ability to represent actual bathymetry is assessed. Three statistical methods for the determination of model parameters were evaluated. The best results were achieved by the random forest (RF) method. A two-dimensional (2D) hydrodynamic model was used to evaluate the influence of the Bathy-supp model on the hydrodynamic modelling results. Also presented is a comparison of the proposed model with another state-of-the-art bathymetric model. The study was carried out on a reach of the Otava River in the Czech Republic. The results show that the proposed model’s ability to represent river bathymetry exceeds that of his current competitor. Use of the bathymetric model may have a significant impact on improving the hydrodynamic model results.

The main purpose of the present research is to develop software for reconstruction of the river bed on the basis of sparse cross-section measurements. The tools prepared should support the process of hydrodynamic model preparation for simulation of river flow. Considering the formats of available data and the requirements of modern modeling techniques, the prepared software is fully integrated with the GIS environment. The scripting language Python 2.7 implemented in ArcGIS 10.5.1 was chosen for this purpose. Two study cases were selected to validate and test the prepared procedures. These are stream reaches in Poland. The first is located on the Warta river, and the second on the Ner river. The data necessary for the whole procedure are: a digital elevation model, measurements of the cross-sections in the form of points, and two polyline layers representing an arbitrary river centerline and river banks. In the presented research the concept of a channel-oriented coordinate system is applied. The elevations are linearly interpolated along the longitudinal and transversal directions. The interpolation along the channel is implemented in three computational schemes linking different tools available in ArcGIS and ArcToolbox. A simplified comparison of memory usage and computational time is presented. The scheme linking longitudinal and spatial interpolation algorithms seems to be the most advantageous.

Remote sensing from satellites and airborne platforms provides valuable data for monitoring and gauging river discharge. One effective approach first estimates river stage from satellite-measured inundation area based on the inundation area-river stage relationship (IARSR), and then the estimated river stage is used to compute river discharge based on the stage-discharge rating (SDR) curve. However, this approach is difficult to implement because of a lack of data for constructing the SDR curves. This study proposes a new method to construct the SDR curves using remotely sensed river cross-sectional inundation areas and river bathymetry. The proposed method was tested over a river reach between two USGS gauging stations, i.e., Kingston Mines (KM) and Copperas Creek (CC) along the Illinois River. First a polygon over each of two cross sections was defined. A complete IARSR curve was constructed inside each polygon using digital elevation model (DEM) and river bathymetric data. The constructed IARSR curves were then used to estimate 47 river water surface elevations at each cross section based on 47 river inundation areas estimated from Landsat TM images collected during 1994–2002. The estimated water surface elevations were substituted into an objective function formed by the Bernoulli equation of gradually varied open channel flow. A nonlinear global optimization scheme was applied to solve the Manning’s coefficient through minimizing the objective function value. Finally the SDR curve was constructed at the KM site using the solved Manning’s coefficient, channel cross sectional geometry and the Manning’s equation, and employed to estimate river discharges. The root mean square error (RMSE) in the estimated river discharges against the USGS measured river discharges is 112.4 m 3 /s. To consider the variation of the Manning’s coefficient in the vertical direction, this study also suggested a power-law function to describe the vertical decline of the Manning’s coefficient with the water level from the channel bed lowest elevation to the bank-full level. The constructed SDR curve with the vertical variation of the Manning’s coefficient reduced the RMSE in the estimated river discharges to 83.9 m 3 /s. These results indicate that the method developed and tested in this study is effective and robust, and has the potential for improving our ability of remote sensing of river discharge and providing data for water resources management, global water cycle study, and flood forecasting and prevention. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) of river channel bathymetries are developed by interpolating elevations between data collected at discrete points or along transects. The accuracy of interpolated bathymetries depends on measurement error, the density and distribution of point data, and the interpolation method. Whereas point measurement errors can be minimized by selecting the most efficient equipment, the effect of data density and interpolation method on river bathymetry is relatively unknown. Thus, this study focuses on transect-based collection methods and investigates the effects of transect location, the spacing between transects, and interpolation methods on the accuracy of interpolated bathymetry. This is accomplished by comparing four control bathymetries generated from accurate and high resolution, sub-meter scale data to bathymetries interpolated from transect data extracted from the control bathymetries using two transect locating methods and four interpolation methods. The transect locating methods are a morphologically-spaced and an equally-spaced model. The four interpolation methods are Ordinary Kriging, Delaunay Triangulation, and Simple Linear, which are applied in curvilinear coordinates (Delaunay Triangulation is also applied in Cartesian coordinates), and Natural Neighbor only in Cartesian Coordinates. The bathymetric data were obtained from morphologically simple and complex reaches of a large (average bankfull width = 90 m) and a small (average bankfull width = 17 m) river. The accuracy of the developed DEMs is assessed using statistical analysis of the differences between the control and interpolated bathymetries and hydraulic parameters assessed from bankfull water surface elevations. Results indicate that DEM accuracy is not influenced by the choice of transect location method (with same averaged cross-section spacing) or a specific interpolation method, but rather by the coordinate system for which the interpolation method is applied and the spacing between transects. They also show negligible differences between the mean depths and surface areas calculated from bathymetries with dense or coarse spacing. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on estimating river bathymetry for retrieving river discharge from the upcoming Surface Water and Ocean Topography (SWOT) satellite mission using a data assimilation algorithm coupled with a hydrodynamic model. The SWOT observations will include water surface elevation (WSE), its spatial and temporal derivatives, and inundated area. We assimilated synthetic SWOT observations into the LISFLOOD-FP hydrodynamic model using a local ensemble batch smoother (LEnBS), simultaneously estimating river bathymetry and flow depth. SWOT observations were obtained by sampling a “true” LISFLOOD-FP simulation based on the SWOT instrument design; the “true” discharge boundary condition was derived from USGS gages. The first-guess discharge boundary conditions were produced by the Variable Infiltration Capacity model, with discharge uncertainty controlled via precipitation uncertainty. First-guess estimates of bathymetry were derived from SWOT observations assuming a uniform spatial depth; bathymetric variability was modeled using an exponential correlation function. Thus, discharge and bathymetry errors were modeled realistically. The LEnBS recovered the bathymetry from SWOT observations with 0.52m reach-average root mean square error (RMSE), which was 67.8% less than the first-guess RMSE. The RMSE of bathymetry estimates decreased sequentially as more SWOT observations were used in the estimate; we illustrate sequential processing of 6months of SWOT observations. The better estimates of bathymetry lead to improved discharge estimates. The normalized RMSE of the river discharge estimates was 10.5%, 71.2% less than the first-guess error. [ABSTRACT FROM AUTHOR]

Abstract: An important element in hydraulic modelling is the topography data of the riverbed and the floodplain area. The latter can be obtained directly from digital terrain models (DTM), but the measurement of the riverbed topography is not straightforward. Since in the aerial survey only the elevation of the water surface is recorded, information about the riverbed topography cannot be obtained from the DTM. Therefore a method for the integration of river topography data in a DTM was developed. While the DTM contains in most cases regularly spaced data points, the resolution of the cross section profiles is generally much higher in the lateral than in the longitudinal direction of the river. An algorithm was developed which combines lateral river profiles with a DTM to produce a grid that can be used for flow modelling. The cross section data are interpolated to the grid points of the DTM that are part of the channel by using a bilinear technique with bounding breaklines, which can be obtained from the river cross section profiles. Additionally the algorithm can be adapted so that only the submerged part of the riverbed is subject to interpolation. It preserves the original DTM in the exposed part of the gravel bars. The algorithm was applied to a restored reach of the river Thur in Switzerland. The corrected grid represents well the complex morphological features of the river such as gravel bars and widenings. The accuracy of the algorithm was tested by comparing the interpolated elevation of the exposed part of the gravel bar to measurements obtained from an aerial survey. The mean height difference is in the order of about 9cm. Further the interpolated grid was used for a two dimensional flow simulation and the resulting water level was compared to the one recorded in the original DTM. [Copyright &y& Elsevier]

Summary: Technological aspects of producing, delivering and updating of flood hazard maps in the US have has gone through a revolutionary change through Federal Emergency Management Agency’s Map Modernization program. In addition, the use of topographic information derived from Light Detection and Ranging (LIDAR) is enabling creation of relatively more accurate flood inundation maps. However, LIDAR is not available for the entire United States. Even for areas, where LIDAR data are available, the effect of other factors such as cross-section configuration in one-dimensional (1D) models, mesh resolution in two-dimensional models (2D), representation of river bathymetry, and modeling approach is not well studied or documented. The objective of this paper is to address some of these issues by comparing newly developed flood inundation maps from LIDAR data to maps that are developed using different topography, geometric description and modeling approach. The methodology involves use of six topographic datasets with different horizontal resolutions, vertical accuracies and bathymetry details. Each topographic dataset is used to create a flood inundation map for twelve different cross-section configurations using 1D HEC-RAS model, and two mesh resolutions using 2D FESWMS model. Comparison of resulting maps for two study areas (Strouds Creek in North Carolina and Brazos River in Texas) show that the flood inundation area reduces with improved horizontal resolution and vertical accuracy in the topographic data. This reduction is further enhanced by incorporating river bathymetry in topography data. Overall, the inundation extent predicted by FESWMS is smaller compared to prediction from HEC-RAS for the study areas, and that the variations in the flood inundation maps arising from different factors are smaller in FESWMS compared to HEC-RAS. [Copyright &y& Elsevier]

By facilitating measurement of river channel morphology, remote sensing techniques could enable significant advances in our understanding of fluvial systems. To realize this potential, researchers must first gain confidence in image-derived river information, as well as an appreciation of its inherent limitations. This paper describes a forward image model (FIM) for examining the capabilities and constraints associated with passive optical remote sensing of river bathymetry. Image data are simulated “from the streambed up” by first using information on depth and bottom reflectance to parameterize models of radiative transfer within the water column and atmosphere and then incorporating sensor technical specifications. This physics-based framework provides a means of assessing the potential for spectrally-based depth retrieval from a particular river of interest, given a sensor configuration. Forward image modeling of both a hypothetical meander bend and an actual gravel-bed river indicated that bathymetric accuracy and precision vary spatially as a function of channel morphology, with less reliable depth estimates in pools. A simpler, more computationally efficient analytical model highlighted additional controls on bathymetric uncertainty: optical depth and the ratio of the smallest detectable change in radiance to the bottom-reflected radiance. Application of the FIM to a complex, natural channel illustrated how the model can be used to quantify the effects of various sensor characteristics. Bathymetric accuracy was determined primarily by spatial resolution, due to mixed pixels along the banks and sub-pixel scale variations in depth, whereas depth retrieval precision depended on the sensor''s ability to resolve subtle changes in radiance. This flexible forward modeling approach thus allows the utility of image-derived river information to be evaluated in the context of specific investigations, leading to more efficient, more informed use of remote sensing methods across a range of fluvial environments. [Copyright &y& Elsevier]

The Ohashi River is a narrow water stream that connects two brackish lakes in Japan. Intermittent saline water intrusion often occurs in Lake Shinji from Lake Nakaumi through Ohashi River. In this study, two approaches were discussed to reproduce the hydrodynamic conditions of a morphologically complex river. In the first approach, the river sinuosity was straightened. The straightening of the river resulted in a higher flow velocity and water flux coefficient due to the reduction in the flow path and the resistance, and this approach was found to be appropriate for the reproduction of the flow velocity. However, the river shape was visually quite different from the actual river morphology. In the second approach, the prime focus was given to the shape and bathymetry to quantitively reproduce the flowrate of the saline water intrusion. This approach resulted in an underestimation of the flow velocity, which was compensated by increasing the cross-sectional area of the river. A slower flow velocity causes up to a 3-h time lag for the water mass to pass through the Ohashi River, which in principle should affect the temporal variations of the water temperature and salinity. Fortunately, as the typical time scale for water temperature and salinity fluctuations in the Ohashi River is a few days, a 3-h time lag did not cause any problems. [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]

Topographic data plays an essential role in hydraulic modelling of floods. A high‐precision digital elevation model (DEM) including river bathymetry (bed topography) is required. DEMs can be derived from such various data sources as ground surveying or remote sensing techniques. This study is focused on (a) the DEM error that results from the inability to scan the morphology of the channel using remote sensing methods, and (b) assessment of its impact on the results of a one‐dimensional (1D) hydraulic model. DEMs produced by remote sensing techniques were tested in combination with ground surveying and by software‐updated remote sensing data. Differences in riverbed representation and thalweg position were evaluated. The 1D hydraulic model HEC−RAS was chosen to determine the impact of various DEM sources on the hydraulic quantities (water surface elevation, inundation area). The study was carried out on a reach of the River Vltava (Czech Republic). The best results were achieved by DEMs that combined remote sensing data with ground mapping. Good results also were obtained using software‐updated remote sensing data. Neglecting of cross‐sectional area in remote sensing data has an important impact on the results of hydrodynamic models. [ABSTRACT FROM AUTHOR]

An appropriate digital elevation model (DEM) is required for purposes of hydrodynamic modelling of floods. Such a DEM describes a river's bathymetry (bed topography) as well as its surrounding area. Extensive measurements for creating accurate bathymetry are time-consuming and expensive. Mathematical modelling can provide an alternative way for representing river bathymetry. This study explores new possibilities in mathematical depiction of river bathymetry. A new bathymetric model (Bathy-supp) is proposed, and the model's ability to represent actual bathymetry is assessed. Three statistical methods for the determination of model parameters were evaluated. The best results were achieved by the random forest (RF) method. A two-dimensional (2D) hydrodynamic model was used to evaluate the influence of the Bathy-supp model on the hydrodynamic modelling results. Also presented is a comparison of the proposed model with another state-of-the-art bathymetric model. The study was carried out on a reach of the Otava River in the Czech Republic. The results show that the proposed model's ability to represent river bathymetry exceeds that of his current competitor. Use of the bathymetric model may have a significant impact on improving the hydrodynamic model results. [ABSTRACT FROM AUTHOR]

The main purpose of the present research is to develop software for reconstruction of the river bed on the basis of sparse cross-section measurements. The tools prepared should support the process of hydrodynamic model preparation for simulation of river flow. Considering the formats of available data and the requirements of modern modeling techniques, the prepared software is fully integrated with the GIS environment. The scripting language Python 2.7 implemented in ArcGIS 10.5.1 was chosen for this purpose. Two study cases were selected to validate and test the prepared procedures. These are stream reaches in Poland. The first is located on the Warta river, and the second on the Ner river. The data necessary for the whole procedure are: a digital elevation model, measurements of the cross-sections in the form of points, and two polyline layers representing an arbitrary river centerline and river banks. In the presented research the concept of a channel-oriented coordinate system is applied. The elevations are linearly interpolated along the longitudinal and transversal directions. The interpolation along the channel is implemented in three computational schemes linking different tools available in ArcGIS and ArcToolbox. A simplified comparison of memory usage and computational time is presented. The scheme linking longitudinal and spatial interpolation algorithms seems to be the most advantageous. [ABSTRACT FROM AUTHOR]