River Surface Velocity Research Articles

River Surface Velocity Measurement for Rapid Levee Breach Emergency Response Based on DFP-P-LK Algorithm.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas-Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.

Satellite Video Remote Sensing for Flood Model Validation

AbstractSatellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

Measuring Velocity and Discharge of High Turbidity Rivers Using an Improved Near-Field Remote-Sensing Measurement System

Large-scale particle image velocimetry (LSPIV) is a computer vision-based technique renowned for its precise and efficient measurement of river surface velocity. However, a crucial prerequisite for utilizing LSPIV involves camera calibration. Conventional techniques rely on ground control points, thus restricting their scope of application. This study introduced a near-field remote-sensing measurement system based on LSPIV, capable of accurately measuring river surface velocity sans reliance on ground control points. The system acquires gravity-acceleration data using a triaxial accelerometer and converts this data into a camera pose, thereby facilitating swift camera calibration. This study validates the system through method verification and field measurements. The method verification results indicate that the system’s method for retroactively deriving ground control-point coordinates achieves an accuracy exceeding 90%. Then, field measurements were performed five times to assess the surface velocity of the Datong River. These measured results were analyzed and compared with data collected from the radar wave velocity meter (RWCM) and the LS1206B velocity meter. Finally, a comprehensive sensitivity analysis of each parameter was conducted to identify those significantly impacting the river’s surface velocity. The findings revealed that this system achieved an accuracy exceeding 92% for all river surface velocities measured.

River Discharge Inversion Algorithm Based on the Surface Velocity of Microwave Doppler Radar

Non-contact methods, which are of great significance to the measurement of river discharge, can not only improve the efficiency of measurement but can also ensure the safety of equipment and personnel. However, owing to their inherent drawbacks such as the requirement of riverbed topography measurements and the difficulty in determining hydrological parameters such as equivalent roughness height, velocity index, etc., there are still challenges for measuring river discharge with high levels of efficiency and accuracy using non-contact methods. To overcome the aforementioned challenges, a new river discharge inversion method is proposed in this paper. In this method, vertical velocities are divided into inner and outer region velocities which can be described by the logarithmic law and the parabolic law, respectively. Applying the river surface velocities collected by microwave Doppler radar and the vertical velocity distributions, the water depths are estimated according to the continuity of the vertical velocities and the shear stresses, and then, the river discharges are obtained by the velocity–area method. The proposed method not only has a simple formula but also comprehensively considers the influence of different hydrological conditions, making it suitable for different river widths and water depths. In this paper, surface velocities collected by microwave Doppler radar on the Yangtze River and the San Joaquin River are used to invert the river discharge, and the results show that for wide–shallow, wide–deep, and narrow–shallow river conditions, the mean percent error (MPE) values of the discharges invertedby the proposed method are 3.91%, 3.82%, and 3.6%, respectively; the root mean square error (RMSE) values are 4.53%, 5.19%, and 4.81%, respectively; and the maximum percent error (MaPE) is less than 15%. The results prove that the proposed method can invert the river discharge with high efficiency and high accuracy under different river widths and water depths without measuring water depth in advance, making it is possible to automatically measure the river discharge in real time.

Clutter and Interference Cancellation in River Surface Velocity Measurement with a Coherent S-Band Radar

Using a Doppler radar to measure river surface velocity is a safe and effective technique. However, the measurement would be severely affected by undesired targets that enter the illuminated area of radar. The issue is worsened when measuring the surface velocities of wide rivers because undesired targets such as boats and ships are more likely to be present. The buoy boats fixed on the river surface and cargo ships sailing on the river would generate ground clutter and moving target interference, respectively. The clutter and interference can mask the signal produced by the Bragg scattering and seriously bias the extraction result of river surface velocity. This paper proposes two effective methods to remove ground clutter and moving target interference, respectively. One is an improved phase-based method that eliminates ground clutter after obtaining its boundaries through the phase in the frequency domain, and another is an improved constant false alarm rate (CFAR) detector that combines smallest-of selection logic and a multi-step deletion scheme to detect and remove interference in the time-Doppler spectrum. The experimental data measuring the surface velocity of the Yangtze River with a coherent S-band radar in July 2022 are used to verify the proposed methods. The results show that the proposed methods can effectively remove ground clutter and moving target interference, respectively. After clutter and interference cancellation, a more reasonable result of river surface velocity distribution can be extracted. Therefore, the methods proposed in this paper can be used to remove clutter and interference when extracting the surface velocity of rivers with numerous undesired targets.

River Surface Velocimetry Based on Virtual River Dataset and Modulated-RAFT

Many image-based velocimetry methods, such as large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV), have been proposed to estimate river surface velocity (RSV) efficiently and precisely. Among these methods, OFV-based methods have drawn great attention due to its high field resolution and low requirement of tracers. As a powerful approach for estimate optical flow accurately and efficiently, deep optical flow estimation is utilized in OFV-based methods as well. However, these methods often use irrelevant datasets for training due to the immeasurability of optical flow. Models obtained by such approaches suffer from limited generalization because of domain drift. Besides, high similarity of river surfaces often leads to ambiguous correlation volume extracted by deep optical flow estimation model, which can cause mismatch. This work proposed a method for accurately and robustly RSV estimation under velocity range of 0-6.0 m/s. Specifically, we introduced a method for generating relative optical flow datasets and proposed MRAFT, a deep optical flow estimation model combining with correlation volume modulation. Experiment results demonstrate that the generated virtual river datasets effectively improve the generalization of the model and MRAFT remarkably alleviates the mismatch. Our work facilitates the application of deep optical flow on RSV estimation and provides other related researches with optical flow datasets for fine-tuning.

Uncertainty Analysis for Image-Based Streamflow Measurement: The Influence of Ground Control Points

Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained.

Long-distance laser Doppler water flow velocimetry method based on adaptive Gaussian weighted integration.

Velocity measurement has a high application value in hydrological monitoring and flood disaster warning. The long-distance laser Doppler water flow velocimetry technology has the advantage of strong anti-interference ability and high spatial resolution, and it can realize the high-precision measurement of water flow velocity. Because water flow has low reflectance characteristics, how to extract Doppler frequency from weak non-stationary coherent signals is a crucial problem to be solved to realize long-distance water flow velocity measurement. However, the classical method requires the time domain signal to have high stationarity and is not suitable for processing the coherent signal in the water flow velocity measurement. Aiming at this problem, we proposed a water flow velocimetry method based on adaptive Gaussian weighted integral (AGWI). First, the spectral characteristics of the coherent signal are analyzed in detail, and a statistical model of weak non-stationary signals is established. A second-order Kaiser self-multiplication window (KSMW) is designed to suppress spectral leakage for the asynchronously sampled data. Then, an adaptive homogenization power spectral subtraction (AHPSS) is designed to reduce system noise. Finally, the Doppler spectrum reconstruction and Doppler frequency estimation are performed using the AGWI method to obtain the Doppler frequency, which is further processed to get the water flow velocity. The experimental results show that the method proposed in this paper can achieve accurate and stable measurement of river surface velocity under long-distance conditions.

River Surface Velocity Estimation Using Optical Flow Velocimetry Improved With Attention Mechanism and Position Encoding

River surface velocity (RSV) estimation is crucial in adjusting water allocation, and preventing flood. The intrusive measurement equipment of RSV, which has insufficient real-time performance and a narrow detection range, cannot meet the needs for large-scale system deployment. In contrast, image-based velocimetry methods require no direct contact with river and can acquire almost real-time velocity information within the whole field of view of shooting camera. However, image-based velocimetry methods, such as current optical flow velocimetry (OFV), suffer from poor measurement accuracy in river scenes where massive optical noise from reflection and flickering light exists. To overcome this defect, a neural-network-based optical flow estimation model which adopts a framework composed of feature extraction and optical flow optimization is proposed. Attention mechanism and position encoding are employed in the optical flow optimization process to improve the estimation performance in complex optical conditions. As results, the proposed method achieves an average RSV estimation accuracy of 92.03% in normal scenes and maintains a low average relative error of only 11.61% even in scenes with complex optical noise. We believe that this work delineates the perspective of RSV in deep optical flow estimation method and prompt the application of water-related disaster warning.

Measuring the Water Surface Speed of the Cikidang River as a Supporting Facility for the Development of Ecotourism Areas

Pangandaran Regency is famous for its natural beauty, especially the beauty of its beaches. In this district there are many famous beaches. Besides the beach, there are also rivers that offer beautiful views. However, not many tourists have made the rivers in Pangandaran as tourist destinations, especially the Cikidang River. Cikidang River has the potential to be used as ecotourism. In building an ecotourism area on the banks of a river, it is necessary to carry out an in-depth analysis of the damage that might occur due to erosion. One of the factors that can increase erosion is the speed of water on the surface. Therefore, in this paper, measurement of river surface velocity is carried out in a simple way and analyzed using the velocity formula. From the results of this measurement, a recommendation was made to direct all parties involved in measuring the surface velocity of the Cikidang River as a means of supporting the development of ecotourism areas.

Challenges with Regard to Unmanned Aerial Systems (UASs) Measurement of River Surface Velocity Using Doppler Radar

Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remotely-accessible locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic–morphological conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g., foam, debris) when possible, or with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm s−1 and a coefficient of determination (R2) typically larger than 0.7 (in half of the cases larger than 0.85), when compared with acoustic Doppler current profiler (ADCP) or velocity probe, in all investigated rivers. However, UAS-borne Doppler radar measurements show low reliability because of UAS vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators suggesting that additional research is needed to measure surface velocity from UAS-borne Doppler radar.

A photogrammetry-based variational optimization method for river surface velocity measurement

The ease of access to media resources and computational power has recently generated interest in using vision-based approaches for hydraulic monitoring. A key challenge for non-intrusive, image-based hydrology measurement methods is incorporating different hydraulic variables as prior knowledge with image information. We propose a photogrammetry-based method called L1-Diffusion derived from the convection–diffusion equation commonly used in hydrodynamics with an additional regularization term to estimate the fluid motion field in the image plane, from which the free surface velocity can be further obtained using the photogrammetric projection relationship between the image plane and world coordinates. The inverse problem is used to discuss the relationship between the widely used space–time image velocimetry (STIV) and the proposed L1-Diffusion. To validate the proposed method, unmanned aerial vehicle (UAV) images as well as in-situ acoustic Doppler current profiler (ADCP) experiments were carried out. Based on comparison results with the ADCP measurement and vision-based flow field estimation, the newly proposed L1-Diffusion algorithm can accurately and efficiently estimate the free surface velocity of a river from the image sequences in a variety of scenarios.

Development of Distortion Correction Technique in Tilted Image for River Surface Velocity Measurement

Development of Distortion Correction Technique in Tilted Image for River Surface Velocity Measurement

Towards harmonisation of image velocimetry techniques for river surface velocity observations

Abstract. Since the turn of the 21st century, image-based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed that are equipped with differing image pre-processing and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis (along with reference data) is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate and describe datasets acquired from seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al., 2020, https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

Measurements of Surface River Doppler Velocities With Along-Track InSAR Using a Single Antenna

Nowadays, a worldwide database containing the historical and reliable data concerning the water surface speed of rivers is not available and would be highly desirable. In order to meet this requirement, the present work is aimed at the design of an estimation procedure for water flow velocity by means of synthetic aperture radar (SAR) data. The main technical aspect of the proposed procedure is that an along-track geometry is synthesized using a single antenna and a single image. This is achieved by exploiting a multichromatic analysis in the Doppler domain. The application of this approach allows us to obtain along-track interferometry equivalent virtual baselines much lower than the equivalent baseline corresponding to the decorrelation time of raw data preserving data coherence. The performance analysis, conducted on live airborne full-polarimetric SAR data, highlights the effectiveness of the proposed approach in providing reliable river surface velocity estimates without the need of multiple passes on the observed scene.

River-ice and water velocities using the Planet optical cubesat constellation

Abstract. The PlanetScope constellation consists of ∼150 optical cubesats that are evenly distributed like strings of pearls on two orbital planes, scanning the Earth's land surface once per day with an approximate spatial image resolution of 3 m. Subsequent cubesats on each of the orbital planes image the Earth surface with a nominal time lag of approximately 90 s between them, which produces near-simultaneous image pairs over the across-track overlaps of the cubesat swaths. We exploit this short time lag between subsequent Planet cubesat images to track river ice floes on northern rivers as indicators of water surface velocities. The method is demonstrated for a 60 km long reach of the Amur River in Siberia, and a 200 km long reach of the Yukon River in Alaska. The accuracy of the estimated horizontal surface velocities is of the order of ±0.01 m s−1. The application of our approach is complicated by cloud cover and low sun angles at high latitudes during the periods where rivers typically carry ice floes, and by the fact that the near-simultaneous swath overlaps, by design, do not cover the complete Earth surface. Still, the approach enables direct remote sensing of river surface velocities for numerous cold-region rivers at a number of locations and occasionally several times per year – which is much more frequent and over much larger areas than currently feasible. We find that freeze-up conditions seem to offer ice floes that are generally more suitable for tracking, and over longer time periods, compared with typical ice break-up conditions. The coverage of river velocities obtained could be particularly useful in combination with satellite measurements of river area, and river surface height and slope.

Filtering Continuous River Surface Velocity Radar Data

In this study, the prediction interval method was used in simple regression models to filter continuous river surface velocity microwave radar data. To evaluate the model performance, two data sets from monitoring stations with mild and steep channel slopes were used. A human–machine interface software program developed in LabVIEW was used to sample data from big continuous data for establishing the relationships between surface velocity and water level, two surface velocities, and their prediction intervals. Filtering by coupled relationships detected the most noise in the surface velocity and the original data, and the results for different cases were compared. The results were also compared with widely used modern smoothing methods. It was found that raw data cannot always be post–processed using these smoothing methods. Moreover, peaks become distorted. This study provides a method for filtering noise signals in continuous river surface velocity data without data contamination, which makes the surface velocity data more reliable and applicable for advanced studies, such as machine learning applications, and can be applied for the quality control of surface velocity data in the future.

Efficient and accurate estimation of water surface velocity in STIV

In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space–time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105–114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space–time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

The hydropower potential and feasibility analysis of Sombriero River, Rivers State, Nigeria

This paper examines the Sombriero River with a view of ascertain its discharge rate, potential hydro power can be derived and to provide a RETScreen-based financial viability analysis of adopting the river for a Micro Hydro Power Scheme. The river discharge was measured via the “Float Method Technique” while the river surface and mean velocities, derivable power as well as the scheme’s viability were computed. The Sombriero river has the capacity to deliver 51kW power, it would be a viable venture at a tariff price of 127.61$/MWh and would be most suitable for Run-Off-River hydro scheme considering that it has low water head and substantial discharge. http://dx.doi.org/10.4314/njt.v37i2.1 9

Development of Aerial Space Time Volume Velocimetry for Measuring Surface Velocity Vector Distribution from UAV

Due to the remarkable development of unmanned aerial vehicle (UAV) in recent years, its application in river engineering increases widely mainly for the measurement of ground topography such as by the technique Structure from Motion (SfM) using a series of high-resolution static images. However, although UAV usually installed a high density video camera, the use of the movie is limited just for watching and observing the geometrical feature of the ground. In the light of such a present status, the authors have developed an aerial space-time image velocimetry (STIV) technique to measure streamwise river surface velocity distributions. However, as STIV is insensitive to the change of flow direction, the aerial space-time volume velocimetry (STVV) technique, which is an extension of STIV, was developed in this research. STVV examines the change of volumetric texture within a space-time volume (STV) instead of examining the change of image intensity on a line segment as in STIV. The performance of STVV was investigated during the measurement of snowmelt flood of the Shinano River by comparing it with those obtained by the other techniques such as STIV, LSPIV and ADCP. It was made clear the aerial STVV has a great advantage over the existing image-based techniques.