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"River channels"
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Rivers in the landscape
This title offers a comprehensive and accessible overview of the current state of knowledge for river process and form, taking a holistic approach to the subject with coverage of integrated river science and management in practice.
Book
Braided Rivers From SWOT: Water Surface Dynamics Over the Multi‐Channel Brahmaputra River
The Surface Water and Ocean Topography (SWOT) satellite can simultaneously observe river elevation, width, and slope with high spatio‐temporal coverage and resolution (Fu et al., 2024, https://doi.org/10.1029/2023gl107652). The River Single‐Pass (RiverSP) product from SWOT provides geolocated river surface water dynamics with global coverage, but it utilizes simplified river centerlines that do not properly represent braided rivers. We present a method for integrating high‐resolution pixel cloud SWOT data with Sentinel‐2 multispectral imagery for multi‐channel river analysis, with a case study over the Brahmaputra River. We generate dynamic river centerlines from multispectral river masks and apply SWOT observations to the generated centerlines. We find that refined centerline knowledge allows for improved estimates of river surface water dynamics in braided channels when compared to RiverSP. For the Brahmaputra, we find that water surface elevation can vary by 0.5 m and slope can vary by 2 cm/km between parallel channels of the braided river.
Journal Article
A century of stream burial due to urbanization in the Tokyo Metropolitan Area
The alteration of hydrologic systems due to urbanization causes many problems including urban flood inundation, water quality degradation, and stream ecosystem damage related to river channel modifications such as stream burial. The purpose of this study was, therefore, to clarify the factors affecting the process of stream burial by performing a time series investigation of the changes in six river systems flowing through the Tokyo Metropolitan Area from 1909 to 2020 using historical topographic maps. The observed changes revealed that the stream burial process consists of: (1) river improvements such as meander cutoff and integration of flow channels by branch abolishment, (2) stream burial of relatively small tributaries, and (3) stream burial of large tributaries and part of the mainstream. Stream burial was remarkable in the center of the Tokyo Metropolitan Area, where the river channels and condition of stream burial were nearly the same in 2020 as they were in 1945. However, meandering channels partly remained in the tributaries of relatively large stream systems. Straight channels constituted about 10% of all channels in 1909, but constituted greater than 40% in 2020, suggesting that the remaining open channels were considerably influenced by river improvements such as meander cutoff. Furthermore, the degree of stream burial was observed to be strongly related to the proportion of urbanization and the scale of the target river system. Stream burial was found to be advanced not only by changes in land use, but also by social factors including earthquake and war damage reconstruction projects as well as measures to counter flooding and improve water quality. The findings of this study advance research into evaluating measures and goals for urban river restoration projects.
Journal Article
GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
New remote sensing technologies, and the meter‐scale geospatial data they create, now allow for detailed urban landscape characterization, thereby advancing grid‐based hydrodynamic models. However, using a uniform fine grid over urban catchments generally result in dense grids and can lead to prohibitive computational costs. Moreover, an inability to see below the water surface and measure river bathymetry in most terrain remote sensing can severely impact local‐scale river hydraulics calculations given the significant volume of water conveyed by the channel. This paper introduces a super grid channel model which allow river channels with any width above that of the grid resolution to be simulated in 1D manner. As an extension of a previous subgrid model, this integration facilitates a seamless transition between subgrid and super grid channels, accommodating situations where channel width may surpass or fall below the grid resolution. The key contribution is the integration of the novel 1D channel representation with a nonuniform structured 2D floodplain hydrodynamic model and then coding this for application on GPU. Compared with the previous pure 2D nonuniform structured approaches, the new model presents an efficient compromise for riverine urban flooding where we are less concerned about fine‐scale details of in‐channel flow. Three tests reveal that the proposed model maintains accuracy but with significantly reduced computational cost. By leveraging GPU architectures, a ∼10× speedup compared to CPU computations is achieved, and a typical 6‐day urban flooding problem (domain size 1.42 km2) at 1 m resolution can be achieved within 10 hr on a single 8 GB GPU. Key Points A nonuniform structured grid with a super grid river channel model is implemented on GPU for efficient meter‐scale urban flood modeling As an extension of the subgrid model approach, this integration allows a seamless transition between subgrid and super grid channels A ∼10× speedup compared to CPU computations is achieved by leveraging GPU architectures
Journal Article
Developing 3D River Channel Modeling with UAV-Based Point Cloud Data
Accurate characterization of river channel geometry is essential for hydrological and hydraulic analyses, yet the increasing use of unmanned aerial vehicle (UAV) photogrammetry introduces challenges related to uneven point density, shadow-induced data gaps, and spurious outliers. This study proposed a novel approach for reconstructing 3D river channels from UAV-derived point clouds, emphasizing K-nearest neighbor local regression (KLR), and compared it with the LOWESS model. Method performance was examined through controlled simulations of trapezoidal, triangular, and U-shaped synthetic channels, where KLR consistently preserved morphological fidelity and produced lower RMSE than LOWESS, particularly at channel bends and bed undulations, while a neighborhood selection heuristic approach demonstrated robust results across varying data densities. Synthetic channel experiments show that the proposed K-nearest-neighbor local linear regression (KLR) method achieves RMSE values below 0.06 all tested geometries. In contrast, LOWESS produces substantially larger errors, with RMSE values exceeding 0.9 across all channel shapes. Subsequent application to two South Korean field sites reinforced these findings. In the data-scarce Migok-cheon stream, KLR effectively interpolated missing surfaces while maintaining geomorphic realism, whereas LOWESS generated over-smoothed representations. Within the dense Ogsan Bridge dataset, KLR retained small-scale bed features critical for hydraulic simulations and cross-sectional delineation, while LOWESS obscured local variability. Conclusively, the results demonstrate that KLR provides a more reliable and computationally efficient framework for UAV-based 3D river channel reconstruction, with clear implications for hydraulic modeling, flood risk management, and the advancement of digital-twin systems in operational hydrology.
Journal Article
River Channel Management
River Channel Management is the first book to deal comprehensively with recent revolutions in river channel management. It explores the multi-disciplinary nature of river channel management in relation to modern management techniques that bear the background of the entire drainage basin in mind, use channel restoration where appropriate, and are designed to be sustainable.
River Channel Management is divided into five sections:
The Introduction outlines the need for river channel management
Retrospective Review offers an overview of twentieth century engineering methods and the ways that river channel systems operate.
Realisation explains how greater understanding of river channel adjustments, channel hazards and river basin planning created a context for twenty-first century management.
Requirements for Management explains and examines environmental assessment, restoration-based approaches, and methods that work towards 'design with nature'
Final Revision speculates about prospects for twenty-first century river channel management.
River Channel Management is written for higher-level undergraduates and for postgraduates in geography, ecology, engineering, planning, geology and environmental science, for professionals involved in river channel management, and for staff in environmental agencies.
eBook
Numerical Simulation of River Channel Change in the Suspended Sediment-Dominated Downstream Reach of the Sangu River
This study aims to clarify the characteristics of the riverbed deformation, bank erosion, and channel changes in the lower Sangu River basin using a depth-averaged 2-D flow model where suspended sediment transport is dominant and its flow characteristics are influenced by active tides. A 45 km long area including the river mouth is computed using the 2-D model, and the results are compared with the observed channel changes. The computation results show that bed deformation and channel change are mainly caused during the ebb tide period of the spring tide particularly in an area of about 10 km from the river mouth. The detail study domain calculation results show that the flow concentration at the bend area causes the bed erosion there, and the eddy separated from the main stream causes the sediment deposition at the inner bank, while this eddy is not developed at the outer bank, resulting in the bank erosion there. Through these investigations, the characteristics and mechanisms of the morphodynamics in the lower Sangu River reach, particularly the potential of the combination of tides and floods to enhance riverbed deformations and the associated bank shifts, have been clarified.
Journal Article
Numerical investigation of the Baige landslide-induced wave propagation in a narrow river channel
Landslide-induced waves pose significant risks to human life, property, and infrastructure, especially in relatively narrow channels where wave propagation differs from that in reservoirs or coastal areas. This study introduces a drift-flux model, treating the two-phase mixture as a whole to simulate flow-like landslide-induced waves efficiently. The model combines the renormalization group
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turbulence model and volume of fluid method to accurately describe wave formation and propagation. After verification through mesh size convergence tests and a benchmark experiment, the model is applied to the Baige landslide-induced waves in a narrow river channel on October 10, 2018. The results indicate that wave evolution occurs in four stages: run-up, inundation, run-down, and propagation along the valley. The run-up heights and wave decays vary between upstream and downstream locations at the same distance from the landslide center, depending on the extension direction of the river channel. The numerical predicted maximum run-up height of the Baige landslide-induced waves on the opposite hill slope is 112 m, consistent with the actual situation. However, the maximum run-up heights predicted by empirical equations are lower than both the actual and numerical simulated values due to the lack of consideration of multiple wave reflections in a narrow river channel. Utilizing the previous empirical equations to evaluate landslide-induced waves in a narrow river channel may result in underestimating their hazard. This study contributes to the risk assessment of landslide-induced waves in narrow water bodies, and its findings are essential for safety management and siting decisions regarding infrastructure and facilities.
Journal Article
RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
Extracting river channels from remote sensing images is crucial for locating river water bodies and efficiently managing water resources, especially in cold and arid regions. The dynamic nature of river channels in these regions during the flood season necessitates a method that can finely delineate the edges of perennially changing river channels and accurately capture information about variable fine river branches. To address this need, we propose a river channel extraction method designed specifically for detecting fine river branches in remote sensing images within cold and arid regions. The method introduces a novel river attention U-shaped network structure (RAU-Net++), leveraging the rich convolutional features of VGG16 for effective feature extraction. For optimal feature extraction along channel edges and fine river branches, we incorporate a CBAM attention module into the upper sampling area at the end of the encoder. Additionally, a residual attention feature fusion module (RAFF) is embedded at each short jump connection in the dense jump connection. Dense skip connections play a crucial role in extracting detailed texture features from river channel features with varying receptive fields obtained during the downsampling process. The integration of the RAFF module mitigates the loss of river information, optimizing the extraction of lost river detail feature information in the original dense jump connection. This tightens the combination between the detailed texture features of the river and the high-level semantic features. To enhance network performance and reduce pixel-level segmentation errors in medium-resolution remote sensing imagery, we employ a weighted loss function comprising cross-entropy (CE) loss, dice loss, focal loss, and Jaccard loss. The RAU-Net++ demonstrates impressive performance metrics, with precision, IOU, recall, and F1 scores reaching 99.78%, 99.39%, 99.71%, and 99.75%, respectively. Meanwhile, both ED and ED′ of the RAU-Net++ are optimal, with values of 1.411 and 0.003, respectively. Moreover, its effectiveness has been validated on NWPU-RESISC45 datasets. Experimental results conclusively demonstrate the superiority of the proposed network over existing mainstream methods.
Journal Article
DEM Generation Incorporating River Channels in Data-Scarce Contexts: The “Fluvial Domain Method”
This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology.
Journal Article