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Operator choices, both in acquiring the video and data and in processing them, can be a prominent source of error in image‐based velocimetry methods applied to river discharge measurements. The Large Scale Particle Image Velocimetry (LSPIV) is known to be sensitive to the parameters and computation choices set by the user, but no systematic comparisons with discharge references or intercomparisons have been conducted yet to evaluate this operator effect in LSPIV. In this paper, an analysis of a video gauging intercomparison, the Video Globe Challenge 2020, is proposed to evaluate such operator effect. The analysis is based on the gauging reports of the 15 to 23 participants using the Fudaa‐LSPIV software and intents to identify the most sensitive parameters for the eight videos. The analysis highlighted the significant impact of the time interval, the grid points and the filters on the LSPIV discharge measurements. These parameters are often inter‐dependent and should be correctly set together to strongly reduce the discharge errors. Based on the results, several automated tools were proposed to reduce the operator effect. These tools consist of several parameter assistants to automatically set the orthorectification resolution, the grid and the time interval, and of a sequence of systematic and automatic filters to ensure reliable velocity measurements used for discharge estimation. The application of the assisted LSPIV workflow using the proposed tools leads to significant improvements of the discharge measurements with strong reductions of the inter‐participant variability. On the eight videos, the mean interquartile range of the discharge errors is reduced from 17% to 5% and the mean discharge bias is reduced from −9% to 1% with the assisted LSPIV workflow. The remaining inter‐participant variability is mainly due to the user‐defined surface velocity coefficient α. Key Points: Video‐based river discharge measurements are sensitive to both measuring conditions and user‐defined parameters and optionsThe sensitivity of Large Scale Particle Image Velocimetry discharge computations to operator choices is quantified through a video streamgauging intercomparisonProposed automatic settings and spurious velocity filters efficiently reduce discharge biases and inter‐operator variability [ABSTRACT FROM AUTHOR]

Satellite‐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. Plain Language Summary: Videos of the Earth surface recorded by satellites can enable us to observe and characterize dynamic moving features, such as floods, that would otherwise be very difficult or dangerous to investigate from the ground. Hydrologists often rely on using physics‐based computer models to simulate flood events, but require observational data to make sure these reflect reality accurately. We use artificial intelligence techniques to automatically detect flood extents from satellite video, and track surface features from frame to frame in order to measure how fast the water surface is flowing. Satellite video was collected during opportunistically clear skies in January 2022, along a 6.5 km length of the River Darling in Australia. The flood extent and flow velocities were used to improve numerical model predictions of the flood event. Our findings demonstrate the considerable promise of satellite video to complement existing flood mapping and modeling approaches, and to provide insight into the earth's hydrosphere, particularly in remote locations and during extreme conditions. Key Points: Satellite video derived flood extents and velocities successfully validate 2D hydraulic model predictionsTest‐time augmentation during deep learning inference improved flood extent delineation and enhanced 2D model validation metricsIncorporating characterization of discharge uncertainty into hydraulic model predictions resulted in more accurate model validation [ABSTRACT FROM AUTHOR]

The modification of the surface velocity field of the weakly undulating flow in a reach of the Suquía River (Córdoba city, Argentina) due to the placement of a bridge was investigated in a physical model. Measurements using Large-Scale Particle Image Velocimetry (LSPIV) allowed evaluating how the hydraulic structure changes the size, shape, wavelength, and relative position to river centerline of the wave train downstream of the first ridge of the weak undular jump. Analysis and discussion of results in terms of the dimensionless parameters reported in the literature (obtained from laboratory studies) are offered. Results show that LSPIV is a reliable, fast low-cost tool to gather valuable information regarding the surficial flow field at the physical model scale in the presence of a weakly undular flow. [ABSTRACT FROM AUTHOR]

The accuracy of discharge measurements derived from surface velocities are highly dependent on the accuracy of conversions from surface velocity u s to depth-averaged velocity U. This conversion factor is typically known as the 'velocity coefficient', 'velocity index', 'calibration factor', 'alpha coefficient', or simply 'alpha', where α = U / u s . At some field sites detailed in situ measurements can be made to calculate alpha, while in other situations (such as rapid response flood measurements) alpha must be estimated. This paper provides a review of existing methods for estimating alpha and presents a workflow for selecting the appropriate method, based on available data. Approaches to estimating alpha include: reference discharge and surface velocimetry measurements; extrapolated ADCP velocity profiles; log law profiles; power law profiles; site characteristics; and default assumed values. Additional methods for estimating alpha that require further development or validation are also described. This paper then summarises methods for accounting for spatial and temporal heterogeneity in alpha, such as 'stage to alpha rating curves', 'site alpha vs. local alpha', and 'the divided channel method'. Remaining challenges for the accurate estimation of alpha are discussed, as well as future directions that will help to address these challenges. Although significant work remains to improve the estimation of alpha (notably to address surface wind effects and velocity dip), the methods covered in this paper could provide a substantial accuracy improvement over selecting the 'default value' of 0.857 for alpha for every discharge measurement. [ABSTRACT FROM AUTHOR]

In hydro-environmental and natural resource studies, Alaska is characterized by sparse hydrologic data. The state's hydrologic data set could be increased by using a nonintrusive gauging method, large-scale particle image velocimetry (LSPIV), which is limited in its current application to a single camera. This study seeks to assess the feasibility of using multiple cameras to diversify the conditions in which LSPIV may be applied. Using specialized software and the deployment of multiple cameras, we compared multiple-camera LSPIV discharge measurements with those determined by using an RDI River Pro acoustic Doppler current profiler and accepted single-camera practices used with LSPIV. The results indicate the feasibility of using multiple cameras, with additional work. Furthermore, the data indicate the possibility of an empirical relationship between the velocity index (α) and aspect ratio (B/H, width divided by average depth). [ABSTRACT FROM AUTHOR]

Measuring flow discharge in rivers and open channels has always been one of the most important concerns of water sciences experts. The most common flow measurement method in open channels is velocity- area approach. An approach recently considered as a non-contact option for measuring flow is the use of surface flow image velocimetry methods. Recently, the standard PIV method has been used to measure the velocity at larger scales and on the flow surface, which is referred to as the LSPIV (Bieri et al., 2009).The most common way of converting the surface velocity to the depth-averaged velocity is to use a coefficient called the Velocity Index (VI) which is in fact the ratio of the depth-averaged velocity to the surface velocity. In the literature, the value of the Velocity Index for river flows and laboratory flumes, which were mainly studied for subcritical conditions, is believed to be equal to 0.85, which seems to be an accepted value for this index among the hydraulics communities.In this research, an image velocimetry technique was used to study the flow characteristics, determine the Velocity Index and investigate the surface flow pattern. The instantaneous surface velocity field was measured using the LSPIV method and then a two-dimensional time-averaged velocity map was obtained for different experiments providing the possibility of comparing the flow pattern in different scenarios. Furthermore, by means of spatial averaging of the time-averaged velocity map, the double averaged surface velocity was obtained using which and the cross-sectional mean velocity the Velocity Index was calculated. Therefore, the effect of channel slope and relative submergence on the Velocity Index was investigated and relationships were proposed to estimate the VI in steep slopes.

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]

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. [ABSTRACT FROM AUTHOR]

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The large-scale particle image velocimetry (LSPIV) technique was regarded as a novel approach for estimating dam breach discharge in recent years. In order to investigate its advantages and disadvantages in the estimation of the dam breach hydrograph, the landslide dam breach experiments were conducted in a large width flume (4 m wide), and the relationship between the breach discharge and the surface velocity during the dam break process was also discussed. It was found that the LSPIV approach showed improved accuracy over the conventional volume conservation method in the estimation of the dam breach hydrograph, and the LSPIV approach was capable of capturing the prompt response of the hydrograph to the abrupt breach channel enlargement caused by the lateral collapse. To eliminate the error induced by the logarithmic approximation, the discharge obtained by the LSPIV approach at each time step should multiply a uniform factor of 1.08, which was proposed based on the total mass conservation concept. The methodology established in this study can be extended to measure the hydrograph of a prototype-scale dam breach flow, which is important for disaster mitigation but only difficult to measure in the field. [ABSTRACT FROM AUTHOR]

A novel aerial tracer particle distribution system has been developed. This system is mounted on an unmanned aerial vehicle (UAV) and flown upstream from where surface velocimetry measurements are conducted. Tracer particles can be rapidly deployed across the full channel width and distributed in a crescent shape (for larger channels), so that they reach the measurement cross section at a similar time. This enables surface velocimetry techniques to be applied in rivers and channels lacking sufficient natural tracer particles or surface features. Lack of tracers is a common problem during low flows, in lowland rivers, or in artificial channels. Techniques for colouring tracer particles with biodegradable dye have also been developed, along with methods for extracting them from Red Green Blue (RGB) imagery in the Hue Saturation Value (HSV) colour space. The use of coloured tracer particles enables flow measurements in situations where sunglint, surface waves, moving shadows or dappled lighting on riverbeds can interfere with and corrupt results using surface velocimetry techniques. These developments further expand the situations where surface velocimetry can be applied, as well as improving the accuracy of the results. The tracer particle distribution system has been successfully deployed in multiple rivers and canals in New Zealand and has enabled surface velocimetry measurements where they were not previously possible due to a lack of natural tracers. [ABSTRACT FROM AUTHOR]

The frequency of droughts and floods is increasing due to the extreme climate. Therefore, water resource planning, allocation, and disaster prevention have become increasingly important. One of the most important kinds of hydrological data in water resources planning and management is discharge. The general way to measure the water depth and discharge is to use the Acoustic Doppler Current Profiler (ADCP), a semi-intrusive instrument. This method would involve many human resources and pose severe hazards by floods and extreme events. In recent years, it has become mainstream to measure hydrological data with nonintrusive methods such as the Large-Scale Particle Image Velocimetry (LSPIV), which is used to measure the surface velocity of rivers and estimate the discharge. However, the unknown water depth is an obstacle for this technique. In this study, a method combined with LSPIV to estimate the bathymetry was proposed. The experiments combining the LSPIV technique and the continuity equation to obtain the bed elevation were conducted in a 27 m long and 1 m wide flume. The flow conditions in the experiments were ensured to be within uniform and subcritical flow, and thermoplastic rubber particles were used as the tracking particles for the velocity measurement. The two-dimensional bathymetry was estimated from the depth-averaged velocity and the continuity equation with the leapfrog scheme in a predefined grid under the constraints of Courant–Friedrichs–Lewy (CFL). The LSPIV results were verified using Acoustic Doppler Velocimetry (ADV) measurements, and the bed elevation data of this study were verified using conventional point gauge measurements. The results indicate that the proposed method effectively estimated the variation of the bed elevation, especially in the shallow water level, with an average accuracy of 90.8%. The experimental results also showed that it is feasible to combine the nonintrusive imaging technique with the numerical calculation in solving the water depth and bed elevation. [ABSTRACT FROM AUTHOR]

Les précipitations courtes et violentes observées sur la Côte d’Azur conduisent souvent à des crues-éclairs sur les bassins versants côtiers : lors de la crue du 2 octobre 2015, un débit de pointe compris entre 185 et 295 m3/s a été estimé sur la Brague à Biot vers 20h00, alors que le débit y était d’environ 1 m3/s à 17h30 . Si les mesures de tel débits sont très importantes (pour l’analyse statistique des crues, la modélisation des crues, la conception d’ouvrages hydrauliques), elles sont dangereuses lorsqu’elles nécessitent qu’un opérateur manipule un instrument dans ou à proximité de la rivière. Des méthodes alternatives peuvent être utilisées, comme l’analyse de vidéo. Dans ce travail, nous avons appliqué deux algorithmes différents de vélocimétrie d’images (LSPIV) (Fudaa-LSPIV et OpyFlow) à plusieurs vidéos des inondations du 23 novembre 2019 sur le bassin versant de la Brague, afin d’estimer les débits. Les valeurs de débit obtenues sont ensuite comparées aux débits mesurés, ainsi qu’aux résultats de la modélisation pluie-débit sur les mêmes sections. Les estimations LSPIV, les simulations de débits et les observations sont cohérentes sur les sections étudiées, montrant l’intérêt de combiner des techniques aussi différentes et indépendantes pour estimer les valeurs de débit de crue.

VISION is open-source software written in MATLAB for video stabilisation using automatic features detection. It can be applied for any use, but it has been developed mainly for image velocimetry applications in rivers. It includes a number of options that can be set depending on the user’s needs and intended application: 1) selection of different feature detection algorithms (seven to be selected with the flexibility to choose two simultaneously), 2) definition of the percentual value of the strongest features detected to be considered for stabilisation, 3) geometric transformation type, 4) definition of a region of interest on which the analysis can be performed, and 5) visualisation in real-time of stabilised frames. One case study was deemed to illustrate VISION stabilisation capabilities on an image velocimetry experiment. In particular, the stabilisation impact was quantified in terms of velocity errors with respect to field measurements obtaining a significant error reduction of velocities. VISION is an easy-to-use software that may support research operating in image processing, but it can also be adopted for educational purposes.

Deflected flow, as one of undesirable flow patterns, may scour river beds and banks seriously. Experiments are conducted at a symmetric multi-gate sluice model with different large expansion ratios and Froude numbers. The LSPIV (Large-Scale Particle Image Velo- cimetry) technique is adopted to collect the velocity data of free surface downstream. This study aims at the flow deflection features on the condition of symmetric inflow and outflow. The results show that the velocity distribution at the cross sections display an S shape. The flow dynamic axis is deflected obviously. It is classified into three reaches according to the differences of the dominant factors on the whole deflected flow zone. In general, as Froude number increases, the reach dominated by the total outflow width tends to shrink, while the reach influenced by the enlarged boundary width downstream tends to expand. The deflected ratio of flow is a function of Froude number and expansion ratio by the method of regression analysis, and the deflected ratio increases with Froude number and expansion ratio within limits. The maximum of deflected angle is less than20°. These achievements are instructive to putting forward the improvement measures of undesirable flow patterns. [ABSTRACT FROM AUTHOR]

The use of image based velocimetry methods for field-scale measurements of river surface flow and river discharge have become increasingly widespread in recent years, as these methods have several advantages over more traditional methods. In particular, image based methods are able to measure over large spatial areas at the surface of the flow at high spatial and temporal resolution without requiring physical contact with the water. However, there is a lack of tools to understand the spatial uncertainty in these methods and, in particular, the sensitivity of the uncertainty to parameters under the implementer's control. We present a tool specifically developed to assess spatial uncertainty in remotely sensed, obliquely captured, quantitative images, used in surface velocimetry techniques, and selected results from some of our measurements as an illustration of the tool's capabilities. The developed software is freely available via the public repository GitHub. Uncertainty exists in the coordinate transformation between pixel array coordinates (2D) and physical coordinates (3D) because of the uncertainty related to each of the inputs to the calculation of this transformation, and additionally since the transformation itself is generally calculated in a least squares sense from an over determined system of equations. In order to estimate the uncertainty of the transformation, we perform a Monte Carlo simulation, in which we perturb the inputs to the algorithm used to find the coordinate transformation, and observe the effect on the results of transformations between pixel- and physical- coordinates. This perturbation is performed independently a large number of times over a range of the input parameter space, creating a set of inputs to the coordinate transformation calculation, which are used to calculate a coordinate transformation, and predict the physical coordinates of each pixel in the image. We analyze the variance of the physical position corresponding to each pixel location across the set of transformations, and quantify the sensitivity of the transformation to changes in each of the inputs across the field of view. We also investigate the impact on uncertainty of ground control point (GCP) location and number, and quantify spatial change in uncertainty, which is the key parameter for calculating uncertainty in velocity measurements, in addition to positions. This tool may be used to plan field deployments, allowing the user to optimize the number and distribution of GCPs, the accuracy with which their position must be determined, and the camera placement required to achieve a target level of spatial uncertainty. It can also be used to estimate the uncertainty in image-based velocimetry measurements, including how this uncertainty varies over space within the field of view.

Abstract In order to investigate the relationship between water surface velocity and breach hydrograph, a series of dam breach experiments with a generalised landslide dam were conducted in an open channel of 50 m × 4 m × 2 m. The large‐scale particle image velocimetry (LSPIV) technique was applied to measure the time history of water surface velocity during the dam breach process, and the hydrography was obtained by integrating the surface velocity along the water depth. The influence of different vertical velocity profile approximation and suspended sediment concentration on the peak breach discharge were analysed and discussed. The results showed that the water depth over the breach crest can be described as a function of water surface velocity using d¯t=k22u¯surf2/k1g. A simple formula based on surface velocity and breach width for the estimation of breach discharge was further proposed and verified.

Despite widespread recognition that confluences are characterized by complex hydrodynamic conditions, few studies have mapped in detail spatial patterns of flow at confluences and variation in these patterns over time. Recent developments in large‐scale particle image velocimetry (LSPIV) have created novel opportunities to explore the spatial and temporal dynamics of flow patterns at confluences. This study uses LSPIV to map two‐dimensional flow structure at the water surface at a confluence and to examine variation in this structure over time. Results show that flow within the confluence is characterized by a large region of flow stagnation at the junction apex, a region of low velocities at the downstream junction corner, and a region of merging of the two flows along a mixing interface within the center of the confluence. Interaction between the incoming flows varies over time in the form of episodic pulsing in which one of the two tributary flows first decelerates and then subsequently accelerates into the confluence. The cause of this pulsing remains uncertain, but it may reflect unsteadiness in the water‐surface pressure‐gradient field as the two flows compete for space within the confluence. No large‐scale vortices are evident within the mixing interface for the particular flow conditions documented in this study, but such vortices do occur along the margins of the stagnation zone where shearing action between fast‐moving and slow‐moving fluid is strong. The results of the study provide insight into the time‐dependent dynamics of the spatial structure of flow at stream confluences. Key Points: Large‐scale particle image velocimetry (LSPIV) reveals spatial and temporal variation in confluence flow structure at high spatial resolutionInteraction of incoming flows within a confluence is characterized by episodic pulsing of flow from the main tributaryFluid motion in a region of flow stagnation varies in direction over time and is influenced by large shear‐generated vortices [ABSTRACT FROM AUTHOR]

In order to investigate the relationship between water surface velocity and breach hydrograph, a series of dam breach experiments with a generalised landslide dam were conducted in an open channel of 50 m × 4 m × 2 m. The large‐scale particle image velocimetry (LSPIV) technique was applied to measure the time history of water surface velocity during the dam breach process, and the hydrography was obtained by integrating the surface velocity along the water depth. The influence of different vertical velocity profile approximation and suspended sediment concentration on the peak breach discharge were analysed and discussed. The results showed that the water depth over the breach crest can be described as a function of water surface velocity using d¯t=k22u¯surf2/k1g. A simple formula based on surface velocity and breach width for the estimation of breach discharge was further proposed and verified. [ABSTRACT FROM AUTHOR]

Image-based methods for surface velocity measurements are notable due to the monitoring requirements during flood events. This method is a low-cost and secure approach that has already been commonly utilized to monitor and quantify flood events. This study applied image-based velocimetry below a Japanese dam in the Naka River basin to record the evolution of flow and sediment movement near a sediment replenishment (SR) site. SR is a typical technique of sediment management that requires high flow to erode the emplaced sediment. Consequently, in the field, three significant flow patterns, including surface flow velocity, discharge, and water depth were monitored over long periods during various flushing hydrographs to investigate SR dynamics. Hence, Large-scale particle image velocimetry (LSPIV) and Space-time image velocimetry (STIV) were utilized to accurately estimate the surface velocity distribution during two recent flood events in 2021. The flow discharge was computed based on the area velocity method at different flood magnitudes. The corresponding water depth was calculated by combining the surface velocity and dissipation rate of the turbulent kinetic energy (TKE). Finally, the results illustrated that image-based approaches are suitable for flow pattern monitoring during high-erosion periods of placed sediment. TKE utilization is a relevant approach to quantify the water depth and investigate the impacts of bed topography on the developed flow fields. This nonintrusive method with simple devices and inexpensive placement can be adopted to record flow and sediment erosion for SR purposes. • Utilizing Image-based approach to measure flow patterns during sediment replenishment implementation. • Collecting data by a PTZ camera with solar panel and 4G internet. • Performance of LSPIV and STIV approaches to calculate flow velocity and discharge at various stages of flood. • Water depth estimation based on distribution of surface flow velocity using turbulent structure. [ABSTRACT FROM AUTHOR]

Los raudales/avenidas en el Paraguay causan a menudo grandes problemas en las calles de las ciudades, no solamente importantes daños materiales, si no también causan decesos humanos, arrastrando vehículos y personas. Esta alarmante situación, nos llevó a estudiarla, pero no sólo como un número estadístico más, si no a entender la física de los fenómenos hidrodinámicos del flujo que escurre por nuestras calles tras un raudal. Para ello se eligió parametrizar las velocidades y los niveles encontrados en las calles durante fenómenos reales, con precipitaciones comunes mediante el procesamiento de imágenes de filmaciones por LSPIV (Large Scale Particle Image Velocimetry) y complementando con simulaciones numéricas tridimensionales CFD (Computational Fluid Dinamic), y luego compararlos con estudios realizados por otros autores en laboratorios. Los resultados encontrados arrojaron valores de velocidades y niveles críticos para transeúntes y vehículos, confirmando el peligro que se presenta en la vía pública durante estos eventos.

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.

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. [ABSTRACT FROM AUTHOR]

Large-Scale Particle Image Velocimetry (LSPIV) has emerged as a reliable technology to measure river surface flow velocity distribution and can be applied to estimate river discharge. Fewer studies have explored the capability of surface turbulence measurements using LSPIV. In this paper, LSPIV is applied to evaluate statistics of surface turbulence of a natural river. Turbulence measurements including velocity fluctuation, velocity spectra and the dissipation rate of turbulent kinetic energy (TKE) are validated by comparing with those measured by an Acoustic Doppler Velocimeter (ADV). Traditionally, estimation of stream discharge through LSPIV needs a secondary measurement to determine river bathymetry and water depth. A new method is presented here to demonstrate that for a fully developed and channel-controlled flow, the cross section geometry can be estimated from the combined measurements of surface mean velocity and the dissipation rate, following the Manning-Strickler formula. Therefore, river discharge can be estimated with LSPIV along with a calibrated Manning's roughness, without additional bathymetry survey. The proposed new method is applied to measure discharge in Milwaukee River (Milwaukee, Wisconsin, U.S.A.), which agreed well with data obtained from a nearby streamgage station. • LSPIV is applied to measure surface turbulence structures in a natural river. • Surface turbulence velocity spectra demonstrated a −5/3 power law. • River depth can be estimated from surface turbulence measured by LSPIV. • River discharge can be measured by LSPIV without channel geometry survey. [ABSTRACT FROM AUTHOR]

Abstract We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow , which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see [18] for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods. Highlights • The imaged river free surface could be described by the Transport Equation (TE) augmented with a weighted diffusion term. • Dense displacement vector fields could be used to derive other useful results like streamlines or divergence fields. • Trajectory reconstruction of passive particles of interest is a great tool to assess estimated displacement vector fields. [ABSTRACT FROM AUTHOR]

En los últimos años, la probabilidad de ocurrencia de inundaciones urbanas ha aumentado significativamente. Recientemente se han publicado diversos artículos científicos-técnicos relacionados a problemáticas específicas generadas por escurrimientos superficiales durante inundaciones urbanas como, por ejemplo, efectos nocivos sobre infraestructura urbana por anegamiento e impactos de altas velocidades de flujo sobre personas, calles y vehículos. La mayoría de los trabajos publicados se basan en investigaciones realizadas utilizando modelos numéricos y modelos físicos a pequeña escala (estos últimos presentan efectos de escala). En este trabajo se presenta una metodología experimental para la cuantificación de caudales y velocidades de flujo durante inundaciones urbanas utilizando una nueva técnica de medición disponible en la actualidad para realizar mediciones de velocidades superficiales de flujo a gran escala (LSPIV por sus siglas en ingles). Además se propone adaptar esta tecnología para su implementación con videos digitales no profesionales, generalmente realizados por vecinos durante eventos de inundación utilizando distintos dispositivos electrónicos (teléfonos celulares, cámaras digitales, etc.). El método permite, a partir de las imágenes digitales registradas en estos dispositivos, calcular los campos superficiales de velocidad del flujo, la distribución transversal de velocidades, el caudal escurrido, etc. Los resultados que se presentan en este trabajo corresponden a diferentes inundaciones urbanas repentinas ocurridas en el área urbana del Gran Asunción, Paraguay (Arroyo Lambaré y otros). Finalmente se propone utilizar los resultados generados con las mediciones in situ para el diseño futuro de medidas estructurales y no estructurales requeridas para mitigar el riesgo hídrico.

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.

Abstract Particle tracking velocimetry (PTV) is a promising image-based approach for remote streamflow measurements in natural environments. However, most PTV approaches require highly-defined round-shaped tracers, which are often difficult to observe outdoors. PTV-Stream offers a versatile alternative to cross-correlation-based PTV by affording the identification and tracking of features of any shape transiting in the field of view. This nearest-neighbor algorithm is inherently thought for estimating surface flow velocity of streams in outdoor conditions. The procedure allows for reconstructing and filtering the trajectories of features that are more likely to pertain to actual objects transiting in the field of view rather than to water reflections. The procedure is computationally efficient and is demonstrated to yield accurate measurements even in case of downsampled image sequences. Highlights • Particle tracking velocimetry approaches are promising to monitor surface stream flow but are affected by feature shape. • PTV-Stream is a nearest-neighbor algorithm designed for tracking features of any shape. • Trajectory-based filtering guarantees tracking of actual objects in the field of view. • Based on outdoor tests, PTV-Stream is as efficient as traditional PTV. [ABSTRACT FROM AUTHOR]

Although past field work at stream confluences has relied on velocity information at specific cross sections to examine flow structure, detailed characterizations of spatial and temporal variations in the hydrodynamics of confluences are lacking. This study uses large‐scale particle image velocimetry (LSPIV) obtained from small unmanned aerial systems (sUAS), a method evaluated in a companion paper, to map surficial patterns of mean flow and turbulent structures at two small stream confluences in unprecedented levels of detail. LSPIV reveals two‐dimensional flow patterns within different hydrodynamic zones in each confluence as well as similarities and differences in hydrodynamic conditions between the confluences. As expected based on extant conceptual models of confluence hydrodynamics, the spatial arrangement of characteristic hydrodynamic zones varies with confluence planform geometry and with changes in incoming flow conditions. However, local morphological features, such as bars and irregularities in channel banks, also exert a strong influence on the spatial structure of flow and in some cases influence confluence hydrodynamics to an extent comparable to changes in incoming flow conditions. The usefulness of sUAS‐based LSPIV is demonstrated by the correspondence between patterns of flow curvature revealed by this method and patterns of helical motion documented at cross sections within the confluences using acoustic Doppler velocimetry. The method can also be used to characterize the structure of turbulent vortices within the mixing interface between confluent streams under appropriate conditions. Hydrodynamic mapping using sUAS‐based LSPIV enriches the interpretation of traditional in‐stream velocity data acquired in the field and provides information on surface velocity patterns in rivers at a resolution similar to that of numerical models. Key Points: sUAS‐derived LSPIV provides unprecedented spatial detail of 2‐D surface flow patterns at confluencesPatterns of 2‐D surface flow at an asymmetrical and symmetrical confluence show similarities to, but also important differences from, conceptual models of flow at confluences2‐D sUAS‐derived LSPIV can enhance interpretations of 3‐D flow measurements and yields information density comparable to that of CFD models [ABSTRACT FROM AUTHOR]

Quantifying flow in rivers is fundamental to assessments of water supply, water quality, ecological conditions, hydrological responses to storm events, and geomorphological processes. Image-based surface velocity measurements have shown promise in extending the range of discharge conditions that can be measured in the field. The use of Unmanned Aerial Systems (UAS) in image-based measurements of surface velocities has the potential to expand applications of this method. Thus far, few investigations have assessed this potential by evaluating the accuracy and repeatability of discharge measurements using surface velocities obtained from UAS. This study uses large-scale particle image velocimetry (LSPIV) derived from videos captured by cameras on a UAS and a fixed tripod to obtain discharge measurements at ten different stream locations in Illinois, USA. Discharge values are compared to reference values measured by an acoustic Doppler current profiler, a propeller meter, and established stream gauges. The results demonstrate the effects of UAS flight height, camera steadiness and leveling accuracy, video sampling frequency, and LSPIV interrogation area size on surface velocities, and show that the mean difference between fixed and UAS cameras is less than 10%. Differences between LSPIV-derived and reference discharge values are generally less than 20%, not systematically low or high, and not related to site parameters like channel width or depth, indicating that results are relatively insensitive to camera setup and image processing parameters typically required of LSPIV. The results also show that standard velocity indices (between 0.85 and 0.9) recommended for converting surface velocities to depth-averaged velocities yield reasonable discharge estimates, but are best calibrated at specific sites. The study recommends a basic methodology for LSPIV discharge measurements using UAS that is rapid, cost-efficient, and does not require major preparatory work at a measurement location, pre- and post-processing of imagery, or extensive background in image analysis and PIV. [ABSTRACT FROM AUTHOR]

An automated discharge imaging system (ADIS), which is a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all of the measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP) and surface velocity radar (SVR), respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan) in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that the components till (t) and roll (0) of the camera are most sensitive parameters to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events. [ABSTRACT FROM AUTHOR]

In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

Flash floods in wadi systems discharge large volumes of water to either the sea or the desert areas after high-intensity rainfall events. Recently, wadi flash floods have frequently occurred in arid regions and caused damage to roads, houses, and properties. Therefore, monitoring and quantifying these events by accurately measuring wadi discharge has become important for the installation of mitigation structures and early warning systems. In this study, image-based methods were used to measure surface flow velocities during a wadi flash flood in 2018 to test the usefulness of large-scale particle image velocimetry (LSPIV) and space–time image velocimetry (STIV) techniques for the estimation of wadi discharge. The results, which indicated the positive performance of the image-based methods, strengthened our hypothesis that the application of LSPIV and STIV techniques is appropriate for the analysis of wadi flash flood velocities. STIV is suitable for unidirectional flow velocity and LSPIV is reliable and stable for two-dimensional measurement along the wadi channel, the direction of flow pattern which varies with time.

LSPIV (Large Scale Particle Image Velocimetry) and LSPTV (Large Scale Particle Tracking Velocimetry) are used as relatively low-cost and non-intrusive techniques for water-surface velocity analysis and flow discharge measurements in rivers or large-scale hydraulic models. This paper describes a methodology based on state-of-the-art tools (for example, that apply classical PIV/PTV analysis) resulting in large-scale surface-flow characterization according to the first operational version of the RIVeR (Rectification of Image Velocity Results). RIVeR is developed in Matlab and is designed to be user-friendly. RIVeR processes large-scale water-surface characterization such as velocity fields or individual trajectories of floating tracers. This work describes the wide range of application of the techniques for comparing measured surface flows in hydraulic physical models to flow discharge estimates for a wide range of flow events in rivers (for example, low and high flows). [ABSTRACT FROM AUTHOR]

River system measurement and mapping using UAVs is both lean and agile, with the added advantage of increased safety for the surveying crew. A common parameter of fluvial geomorphological studies is the flow velocity, which is a major driver of sediment behavior. Advances in fluid mechanics now include metrics describing the presence and interaction of coherent structures within a flow field and along its boundaries. These metrics have proven to be useful in studying the complex turbulent flows but require time-resolved flow field data, which is normally unavailable in geomorphological studies. Contactless UAV-based velocity measurement provides a new source of velocity field data for measurements of extreme hydrological events at a safe distance, and could allow for measurements of inaccessible areas. Recent works have successfully applied large-scale particle image velocimetry (LSPIV) using UAVs in rivers, focusing predominantly on surficial flow estimation by tracking intensity differences between georeferenced images. The objective of this work is to introduce a methodology for UAV based real-time particle tracking in rivers (RAPTOR) in a case study along a short test reach of the Brigach River in the German Black Forest. This methodology allows for large-scale particle tracking velocimetry (LSPTV) using a combination of floating, infrared light-emitting particles and a programmable embedded color vision sensor in order to simultaneously detect and track the positions of objects. The main advantage of this approach is its ability to rapidly collect and process the position data, which can be done in real time. The disadvantages are that the method requires the use of specialized light-emitting particles, which in some cases cannot be retrieved from the investigation area, and that the method returns velocity data in unscaled units of px/s. This work introduces the RAPTOR system with its hardware, data processing workflow, and provides an example of unscaled velocity field estimation using the proposed method. First experiences with the method show that the tracking rate of 50 Hz allows for position estimation with sub-pixel accuracy, even considering UAV self-motion. A comparison of the unscaled tracks after Savitzky-Golay filtering shows that although the time-averaged velocities remain virtually the same, the filter reduces the standard deviation by more than 40% and the maxima by 20%. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We use pairs of parallel mounted laser profile scanners to measure main debris-flow variables in two debris-flow channels in central and southern Switzerland. The scanners measure the instantaneous cross-sectional geometry of debris flows at rates of 25-100 Hz, and we apply large-scale particle image velocimetery to estimate velocity. The scanners also provide direct measurements of flow depth. From these data, we were able to estimate debris-flow depth, velocity and discharge for 16 out of 17 events. These results are consistent with discharge estimated from a system of geophones and a radar gauge for two available datasets. We also investigated debris-flow geometry to quantify rheology-controlled cross-flow convexity and found that four events manifest strong surface convexity at their surge fronts where we expect the largest boulders and low pore-fluid pressures. The scanners provide a completely new view of debris-flow dynamics and channel morphology and present novel opportunities to measure discharge and investigate debris-flow geometries. [ABSTRACT FROM AUTHOR]

The probability of the occurrence of urban flash floods has increased appreciably in recent years. Scientists have published various articles related to the estimation of the vulnerability of people and vehicles in urban areas resulting from flash floods. However, most published works are based on research performed using numerical models and laboratory experiments. This paper presents a novel approach that combines the implementation of image velocimetry technique (large-scale particle image velocimetry-LSPIV) using a flash flood video recorded by the public locally and the estimation of the vulnerability of people and vehicles to high water velocities in urban areas. A numerical one-dimensional hydrodynamic model has also been used in this approach for water velocity characterization. The results presented in this paper correspond to a flash flood resulting on November 29, 2012, in the city of Asunción in Paraguay. During this flash flood, people and vehicles were observed being carried away because of high water velocities. Various sequences of the recorded flash flood video were characterized using LSPIV. The results obtained in this work validate the existing vulnerability criterion based on the effect of the flash flood and resulting high water velocities on people and vehicles. [ABSTRACT FROM AUTHOR]

Stream velocity and flow are very important parameters that must be measured accurately to develop effective water resource management plans. There are various methods and tools to measure the velocity but, nowadays, image-based methods are a promising alternative that does not require physical contact with the water body. The current study describes the application of a low cost unmanned aerial vehicle that was selected in order to capture a video over a specific reach of Aggitis River in Greece. The captured frames were analyzed by three different software (PIVlab, PTVlab, and KU-STIV) in order to estimate accurately the surface water velocity. These three software also represent three different image-based methodologies. Although there are differences among these three methods, the analysis produced similar trends for all. The velocity ranged between 0.02 and 3.98 m/s for PIVlab, 0.12 and 3.44 m/s for PTVlab, and 0.04 and 3.99 m/s for KU-STIV software. There were parts, especially in the existing vegetation, where differences were observed. Further applications will be examined in the same or different reaches, to study the parameters affecting the analysis. Finally, the image-based methods will be coupled with verification measurements by a current meter to produce more rigorous results.