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Image‐based methods have compelling, demonstrated potential for characterizing flow fields in rivers, but algorithms like particle image velocimetry (PIV) must be further tested and improved to enable more effective use of these techniques. This paper presents a framework designed for this exact purpose: Simulating Hydraulics and Images for Velocimetry Evaluation and Refinement (SHIVER). The approach involves coupling a hydrodynamic model with a synthetic particle generator to advect particles between frames, as dictated by local velocity vectors and thus construct a plausible image sequence specific to the reach of interest. The resulting time series can then be used as input to a velocimetry algorithm to compare image‐derived estimates with known (modelled) velocities to perform an exhaustive, spatially distributed accuracy assessment. As an example application of SHIVER, we examined the effects of interrogation area (IA) size, frame rate, flow velocity, and image sequence duration on the performance of a standard PIV algorithm. This analysis indicated that image‐derived velocities were generally in close agreement with those from the flow model (root mean square error <10% and mean bias <3%), except when small IAs were coupled with low frame rates. Velocity estimates were most accurate for the lowest modelled discharge (R2=0.97 at baseflow) and became less reliable as the mean flow velocity increased (R2=0.92 for an intermediate discharge and R2=0.86 at bankfull). Accuracy was essentially independent of image sequence duration, implying that long occupations might not be necessary. Errors were concentrated along channel margins, where PIV‐based velocities tended to be greater than those from the flow model. Small IAs led to underpredictions of velocity, while larger IAs led to overpredictions. SHIVER is highly modular and could be updated to make use of different hydrodynamic models or image simulators. The framework could also facilitate more thorough sensitivity analyses and comparison of various velocimetry algorithms. [ABSTRACT FROM AUTHOR]

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions. • New filtering method for image velocimetry in rivers handles heterogeneous seeding. • The method enhances surface velocity estimations for more accurate flow measurement. • The methodology was validated in a wide range of environmental and seeding conditions. • This approach minimizes the need for subjective choices during data analysis. [ABSTRACT FROM AUTHOR]

Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s-1. The imagebased discharge was estimated with deviations to ADCP references of up to 5%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models. [ABSTRACT FROM AUTHOR]

Video‐based hydrometry continues to develop for contactless discharge measurements, automated flood gauging stations and the use of crowd‐sourced flood videos for discharge reconstruction. Irrespective of the velocimetry algorithm used (LSPIV, STIV, PTV...), orthorectification of the images is necessary beforehand, so that each pixel has the same known physical size. Most times, the orthorectification transformation is a plane‐to‐plane projection from the water surface to the camera sensor. Two approaches are typically used to compute the coefficients of this transformation: their calibration from ground reference points (GRPs) with known image and real‐world coordinates ("implicit calibration") or their calculation from the values of the intrinsic (focal length, sensor size) and extrinsic (position, angles) parameters of the camera ("explicit calibration"). In this paper, we develop a Bayesian method which makes it possible to combine the implicit and explicit approaches in a probabilistic framework. The Bayesian approach can be used from situations suitable for the implicit approach (plenty of GRPs) to situations propitious to the explicit approach (well‐known camera parameters). The method is illustrated using synthetic views of a typical streamgauging scene with known true values of the parameters and GRP coordinates. We show that combining observational and prior information is generally beneficial to get precise estimates. Further tests carried out with a real scene of the Arc River at Randens, France, in flood conditions illustrate the impact of the number, uncertainty and spatial distribution of GRPs on the final uncertainty of flow velocity and discharge. [ABSTRACT FROM AUTHOR]

Image-based hydrological measurement techniques have surged in popularity over the last decade, with the most common type of data obtained being the open-channel surface velocity field and flow rate. The lack of a feature-rich image velocimetry software often forces practitioners to use multiple tools as part of a cumbersome workflow, which restricts the uptake of such methods and reduces the reproducibility of results. We present the free and open-source software SSIMS-Flow, which handles extraction of video frames, removal of geometric distortions, video stabilization, orthorectification, image enhancement, and velocity and flow rate estimation using the Farnebäck optical flow method improved with specific pooling techniques developed in this study. SSIMS-Flow performance is tested in two case studies and compared with known reference values and those obtained using industry-standard PIVlab software. In low seeding and tracerless conditions, SSIMS-Flow demonstrates higher velocity estimation accuracy, while delivering several times faster computational runtime. • SSIMS-Flow is a feature-rich image velocimetry software for open-channel flow. • Novel pooling methods are developed for obtaining time-averaged velocities. • More accurate results are obtained under tracerless conditions than with PIV. • Computational runtime of SSIMS-Flow is several times faster than with PIVlab. [ABSTRACT FROM AUTHOR]

This paper presents a novel non-contact method for simultaneous analysis of pressure and velocity conditions in cavitating flows. The method (implemented in our software ADMflow ) is based on high-speed camera flow visualization and was evaluated in an experiment with ultrasonically induced acoustic cavitation of different intensities. Attached cavitation with clearly visible cavitation structures occurred on the tip of an ultrasonic probe immersed in distilled water. Using the high-speed imaging data, pressure fluctuations were calculated by a computer-aided algorithm based on the Brennen’s theory of cavitation cloud kinematics and a modified version of the Rayleigh-Plesset equation. Reference measurements of pressure pulsations were conducted by a hydrophone installed at the bottom of the liquid container. The analysis of cavitation structure dynamics was complemented by calculation of velocity fields from the imaging data, the algorithm for which is based on the advection-diffusion equation. Calculated pressure fluctuations were analyzed in the spatial, temporal and spectral domain. Presented results indicate a strong correlation between the fields of velocity and pressure fluctuations during the growth and collapse of cavitation structures. A comparison of time series and power spectra demonstrates that our cavitation analysis method is in a reasonably good agreement with results of the reference measurement methods and can therefore be used for non-contact analysis of pressure and velocity conditions in cavitating flows. [ABSTRACT FROM AUTHOR]

This paper presents a non-contact method for velocity field calculation from a series of fluid flow images with illuminated planar layer of the flow and a mixed-in pollutant for flow visualization. The velocity field is calculated using a model similar to optical flow that is based on the advection-diffusion equation. The model is evaluated using a set of synthetic airfoil flow visualization images generated by a combination of computational fluid dynamics and inverse advection-diffusion equation approaches. The calculated velocity fields are in a good agreement with the true velocity fields. [ABSTRACT FROM AUTHOR]