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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]

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]

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]

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]

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]

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]

• 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]

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]

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]