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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]

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]

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]

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]

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]

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]