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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. 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 Video‐based river discharge measurements are sensitive to both measuring conditions and user‐defined parameters and options The sensitivity of Large Scale Particle Image Velocimetry discharge computations to operator choices is quantified through a video streamgauging intercomparison Proposed automatic settings and spurious velocity filters efficiently reduce discharge biases and inter‐operator variability

Sediment transport is a key process that affects the morphology and ecological habitat diversity of rivers. As part of a gravel augmentation program to mitigate sediment deficit below a dam, gravel mobility in the Ain River in Eastern France was investigated by tracking of a large amount (n= 1,063) of PIT‐tagged gravels in the field, conducting a probabilistic approach based on published tracer studies, and performing two‐dimensional (2D) numerical modeling of flow and bedload transport. This comparative study highlights the strengths, weaknesses, and complementary aspects of the three approaches to the understanding of river gravel mobility. Thanks to recent technological improvements, PIT‐tagged gravels provide an increasingly reliable and accurate representation of bedload movement in the field, although it remains limited in spatio‐temporal resolution. Based on an exponential distribution, the probabilistic approach correctly reproduces the average trend in travel distances by the different classes of particles over hydrological periods, including one or several significant floods. Furthermore, the 2D numerical modeling accounts for the variability of local hydrodynamic conditions and can simulate realistic displacement distributions for the different classes of particles with high spatio‐temporal resolution. Numerical modeling is a very encouraging approach, which makes our study original because it is the first time that the estimation of mean travel distances, the application of an exponential distribution, and the comparison with a hydrodynamic model are combined. A more effective modeling strategy involves incorporating a probabilistic transport model in the 2D numerical model to reproduce the observed scatter of the individual particle trajectories. PIT‐tagged gravels are used to track gravel mobility in the restored Ain RiverThe results of field tracer surveys are compared with probabilistic and two‐dimensional numerical modelingThe three approaches are found to be meaningful and complementary PIT‐tagged gravels are used to track gravel mobility in the restored Ain River The results of field tracer surveys are compared with probabilistic and two‐dimensional numerical modeling The three approaches are found to be meaningful and complementary

Stage measurement errors are generally overlooked when streamflow time series are derived from uncertain rating curves. We introduce an original method for propagating stage uncertainties due to two types of stage measurement errors: (i) errors of the stage read during the gauging and (ii) systematic and nonsystematic (independent) errors of the recorded stage time series. The error models are generic and can be used for any probabilistic rating curve estimation method that provides an ensemble of rating curves. The new method is applied to a range of six contrasting hydrometric stations in France. Uncertainty budgets quantifying the contribution of various error sources to the total streamflow uncertainty are computed and compared for streamflow time series averaged at time intervals from hour to year. A sensitivity analysis is conducted on the stage time series error model to identify the most sensitive parameters. The results are site specific, which illustrates the key role played by the properties of both the hydrometric site and the gauged catchment. Across the range of sites, stage errors of the gaugings are found to have limited impact on rating curve uncertainty, at least for gaugings performed in fair conditions. Nonsystematic errors in the stage time series have a negligible effect, generally. However, systematic stage errors should not be neglected. Over the six hydrometric stations in this study, the 95% uncertainty component reflecting stage systematic errors (from ±0.5 cm to ±6.8 cm) alone ranged from 4% to 12% of daily average streamflow, and from 1% to 3% of yearly average streamflow as sensors were assumed to be recalibrated every 30 days. Perspectives for improving and validating the streamflow uncertainty estimation techniques are eventually discussed. Water discharges in rivers are monitored by measuring the water level (known as stage) every 15 minutes or so and by converting it to discharge with a calibrated stage‐discharge model (known as rating curve). We introduce an original method for computing the uncertainty of water discharge time series due to the measurement errors of the stage records used as inputs of the stage‐discharge model. Systematic errors in stage measurements, for instance due to sensor drift or calibration errors, were found to produce non negligible discharge uncertainties. Over six hydrometric stations with varied conditions, the corresponding discharge uncertainty ranged from 4% to 12% of daily average discharge, and from 1% to 3% of yearly average discharge. Stage errors in gaugings and in stage time series affect streamflow computationSpecific error models are introduced and used to compute streamflow time series uncertaintiesEffect of stage errors depends on site characteristics and averaging time intervals, systematic stage errors being the most impactful

Irstea mesure depuis 2009 les flux de sédiments en suspension sur le bassin versant de l’Arc en Maurienne, très producteur en sédiments, au moyen de quatre stations turbidimétriques chacune associées à une station débitmétrique. La mesure en continu de la turbidité est convertie en concentration en Matières En Suspension (MES) dans le cours d’eau par le biais d’analyses d’échantillons et par l’établissement de courbes d’étalonnage [MES] = f(turbidité) ; de même que les hauteurs d’eau sont converties en débit par courbes de tarage Q = f(h) établies par le biais de jaugeages.

Various image‐based velocimetry methods have been developed and increasingly used for surface velocity and discharge measurements in rivers. Their evaluation is challenging as in situ comparisons are limited and affected by uncertainties, and synthetic flow images used to test laboratory velocimetry methods are not representative of outdoor applications. A novel method is proposed to generate synthetic river flow videos with known surface velocities. The method is based on open‐source computer graphics tools: Blender and Mantaflow. The synthetic videos represent realistic situations where the scene configuration, for example, riverbed and riparian areas, water aspect, camera location and orientation or lighting can be easily modified. Two cases studies emphasize the strengths and the limitations of the proposed method. The synthetic datasets generated will allow the improvement of different image‐based velocimetry methods, notably through collaborative evaluations or intercomparisons. An original method is proposed to generate synthetic videos of realistic river flow scenes with spatially‐distributed velocity referenceThe synthetic videos are more suited than available datasets to study environmental error sources of outdoor image‐based surface velocimetryThe strengths and limitations of the method are illustrated through case studies An original method is proposed to generate synthetic videos of realistic river flow scenes with spatially‐distributed velocity reference The synthetic videos are more suited than available datasets to study environmental error sources of outdoor image‐based surface velocimetry The strengths and limitations of the method are illustrated through case studies

Stage‐fall‐discharge (SFD) rating curves are traditionally used to compute streamflow records at sites where the energy slope of the flow is variable due to variable backwater effects. We introduce a model with hydraulically interpretable parameters for estimating SFD rating curves and their uncertainties. Conventional power functions for channel and section controls are used. The transition to a backwater‐affected channel control is computed based on a continuity condition, solved either analytically or numerically. The practical use of the method is demonstrated with two real twin‐gauge stations, the Rhône River at Valence, France, and the Guthusbekken stream at station 0003⋅0033, Norway. Those stations are typical of a channel control and a section control, respectively, when backwater‐unaffected conditions apply. The performance of the method is investigated through sensitivity analysis to prior information on controls and to observations (i.e., available gaugings) for the station of Valence. These analyses suggest that precisely identifying SFD rating curves requires adapted gauging strategy and/or informative priors. The Madeira River, one of the largest tributaries of the Amazon, provides a challenging case typical of large, flat, tropical river networks where bed roughness can also be variable in addition to slope. In this case, the difference in staff gauge reference levels must be estimated as another uncertain parameter of the SFD model. The proposed Bayesian method is a valuable alternative solution to the graphical and empirical techniques still proposed in hydrometry guidance and standards. A Bayesian method with hydraulically interpretable parameters for stage‐fall‐discharge rating curvesSensitivity analyses to available prior information and gaugingsApplication to three twin‐gauge stations, with distinct hydraulic configurations

Fuel/air mixing is analysed on an optical gasoline direct-injection engine. The fuel concentration field is measured using a laser-induced fluorescence technique. Three conditions, which vary injection timing while keeping constant the ignition timing, are investigated. A qualitative correlation is made between the local fuel/air ratio near the spark plug with ignition and combustion in chamber. It is found that the mean concentration at the ignition point is high when the delay between injection and ignition is short. In some cycles, liquid fuel covers the electrodes resulting in frequent misfires. The best operating point is achieved with near-stoichiometric conditions at the ignition point, and a moderate concentration cyclic variability. On the contrary, early injection timing gives lean conditions and large cyclic variations at the ignition point. Moreover, the mixture is more dilute in the chamber, and combustion is slow and unstable. Cycle to cycle correlation between fuel concentration near the spark gap and combustion initiation shows that locally lean conditions give very slow combustion.

Irstea mesure depuis 2009 les flux de sédiments en suspension sur le bassin versant de l’Arc en Maurienne, très producteur en sédiments, au moyen de quatre stations turbidimétriques chacune associées à une station débitmétrique. La mesure en continu de la turbidité est convertie en concentration en Matières En Suspension (MES) dans le cours d’eau par le biais d’analyses d’échantillons et par l’établissement de courbes d’étalonnage [MES] = f(turbidité) ; de même que les hauteurs d’eau sont converties en débit par courbes de tarage Q = f(h) établies par le biais de jaugeages.Il apparaît cependant pour notre site d’étude qu’une relation moyenne n’est pas applicable à l’ensemble des évènements hydrologiques. En effet, pour une même concentration, la sensibilité du capteur de turbidité varie d’un événement à l’autre. L’erreur commise sur les flux événementiels peut ainsi atteindre 30 % typiquement et ponctuellement dépasser 80 % par rapport à un flux calculé avec une relation moyenne, tandis que l’erreur sur le flux annuel moyen reste acceptable (inférieure à 15 % typiquement).Une des hypothèses régulièrement avancées pour expliquer ces changements de relations [MES] = f(turbidité) estla variation de la granulométrie des sédiments. Ce document présente un retour d’expérience sur la mise en évidence de la variation de sensibilité de la sonde turbidimétrique en fonction de la fraction granulométrique étudiée. Celle-ci est trouvée proportionnelle à l’inverse du diamètre médian de la fraction étudiée.La stratégie de gestion des courbes d’étalonnage turbidimétrique revêt donc autant d’importance que celles des courbes de tarage pour l’estimation de flux dont l’incertitude usuelle est moindre surtout à l’échelle évènementielle.

The aDcp (acoustic Doppler current profiler) is an efficient tool increasingly used for stream gauging when deployment is possible. Most often, aDcp discharge measurements are in good agreement with standard methods. However, the requirements of correct operation are not fully reported yet and practices are far from unified. Consequently operators, hydrometric network administrators, engineers and research scientists from different French organizations have decided to share ideas about aDcp river discharge measurement, beyond technical support from manufacturers. Within this ?Doppler Group?, the sharing of varied knowledge and experience, of technological watch, of documents and procedures is a first step towards common culture. The group plans to investigate the uncertainty factors in the aDcp method and to define methodological guidelines through user documents and field operation notes.

For the last fifteen years, acoustic Doppler current profilers (aDcp) have been increasingly used to measure river discharge. Originally designed for oceanographical applications, aDcp can provide vertical profiles of 3D instantaneous flow velocity, referenced in space and time at a sampling rate of up to about 5?Hz. ADcps emit ultrasonic waves whose rated frequency typically ranges from 300 to 3,000 kHz. Water velocity is measured by Doppler effect, so that no local parameter calibration is required. Due to some technical restrictions it is impossible to measure velocity in certain parts of the river cross-section. Uncertainty assessment is made difficult by the complexity of the measurement process and potential errors that stem from field deployment. Consequently practitioners are looking for guidelines and corrective methods in order to assess the quality of discharge measurement, especially in case of moving bottom or complex flows (eddies, recirculating flows, secondary currents, etc.). Besides, today much experimental effort is dedicated to making the most of this versatile and very slightly intrusive device?: bed topography, velocity field measurements, estimate of bed load velocity or suspended solid fluxes. This variety of data can be obtained rapidly and economically even in tough field conditions. Therefore Doppler profilers appear as promising tools for the study of river systems.

Improving the quantification of the transverse mixing coefficient in rivers is essential for a better estimation of pollutant dispersion. This coefficient is evaluated from tracing experiments or from semi‐empirical equations assuming uniform flow conditions. This assumption, verified for laboratory experiments, is questionable for river studies. For example, piedmont rivers exhibit nonuniform flows due to their longitudinal morphological variations. This work aims at studying the longitudinal variability of the transverse mixing coefficient in a river with longitudinally varied morphology and the overlooked impact of the flow nonuniformity on the coefficient’s values. Tracing experiments were conducted in the Durance River in France using slug injections and concentration measurements made at the cross‐sections delineating homogeneous sub‐reaches. Using the well‐known 1D transverse diffusion model, values of the transverse mixing coefficient are determined for all consecutive sub‐reaches. These values substantially vary along the studied river, with stronger mixing efficiency at pools and complex riffles and weaker mixing efficiency in straight sub‐reaches with emerging obstacles. The dimensionless transverse mixing coefficient is strongly related to a parameter quantifying the deviation from flow‐uniformity, i.e., indicating flow acceleration, deceleration or a succession of both within the sub‐reaches. While usual formulas from the literature fail to predict our empirical results, a new equation including the flow nonuniformity parameter fits our experimental data with high accuracy. After further validation, such equation may be used for predicting the transverse mixing coefficient in rivers with longitudinally varied morphology. Tracing experiments in a shallow, gravel‐bedded river yield highly variable transverse mixing coefficientsUsual predictive formulas fail to predict reach‐averaged transverse mixing coefficients in a river with longitudinally varied morphologyAn equation based on a flow nonuniformity parameter is fitted to our experimental data Tracing experiments in a shallow, gravel‐bedded river yield highly variable transverse mixing coefficients Usual predictive formulas fail to predict reach‐averaged transverse mixing coefficients in a river with longitudinally varied morphology An equation based on a flow nonuniformity parameter is fitted to our experimental data

Abstract: A survey of zooplankton physiology on the northern coast of Brittany (France) was carried out over a one-year period by comparing two estuarine areas, one oil-polluted area (Aber Benoit) following the oil spill by the tanker “Amoco Cadiz” and one non-oil-polluted area (Rade de Brest). A new approach to an ecological survey was made by describing trophic relationships using analysis of digestive enzyme equipment (amylase and trypsin) of zooplankton organisms, mesoplankton populations and some selected species. These measurements allowed determination of (a) groups of populations with homogeneous trophic and faunistic characteristics and (b) groups of species with homogeneous trophic characteristics. The study of the appearance of these groups over a one-year period revealed the succession of populations and their adaptation to the environment on the basis of biochemical analysis. These phenomena observed in the compared areas showed marked differences in the most polluted areas during the productive spring period. Specific treatment of the data using unusual correlations between digestive enzymes is discussed in terms of the immediate effect on the whole population and on a copepod (Anomalocera patersoni) living in the upper 10 cm.

The stage‐discharge rating curve is subject at many hydrometric stations to sudden changes (shifts) typically caused by morphogenic floods. We propose an original method for estimating shift times using the stage‐discharge observations, also known as gaugings. This method is based on a recursive segmentation procedure that accounts for both gaugings and rating curve uncertainties through a Bayesian framework. It starts with the estimation of a baseline rating curve using all available gaugings. Then it computes the residuals between the gaugings and this rating curve with uncertainties. It proceeds with the segmentation of the time series of residuals through a multi‐change point Bayesian estimation accounting for residuals uncertainties. Once the first set of shift times is identified, the same procedure is recursively applied to each sub‐period through a “top‐down” approach searching for all effective shifts. The proposed method is illustrated using the Ardèche River at Meyras in France (a typical hydrometric site subject to river bed degradation) and evaluated using several synthetic data sets for which the true shift times are known. The applications confirm the added value of the recursive segmentation compared with a “single‐pass” approach and highlight the importance of properly accounting for uncertainties in the segmented data. The recursive procedure effectively disentangles rating changes from observational and rating curve uncertainties. For many hydrological and hydraulic issues, such as flood forecasting, a reliable river discharge estimate is needed. In general, discharge is derived from the recorded water level (stage) through a stage‐discharge relation (rating curve). This relation is calibrated using direct observations (gaugings). Unfortunately, the rating curve is not only uncertain but it can also be subject to sudden changes or shifts due for example to intense floods that modify the river bed geometry. One solution to identify periods of rating curve stability is to apply a segmentation procedure to the gaugings. We propose in this paper an original recursive segmentation procedure that accounts for both gaugings and rating curve uncertainties. We propose a method for detecting rating shifts through the segmentation of residuals between the gaugings and a reference rating curveThe method accounts for observational and rating curve uncertainties and expresses change points in terms of time (rather than position)The method recursively re‐estimates the reference rating curve to improve the detection of small shifts We propose a method for detecting rating shifts through the segmentation of residuals between the gaugings and a reference rating curve The method accounts for observational and rating curve uncertainties and expresses change points in terms of time (rather than position) The method recursively re‐estimates the reference rating curve to improve the detection of small shifts

Managing stage‐discharge relations at hydrometric stations affected by seasonal aquatic vegetation is challenging. Their ratings vary continuously in time so that establishing the streamflow time series is difficult. To the best of our knowledge, no rating curve model exists to deal with a vegetated channel control, only manual adjustment methods. To address this issue, a temporal rating curve model accounting for transient changes due to vegetation is developed. The model is built on basic concepts from open‐channel hydraulics and plant physiology. In the model, the flow resistance varies through time as a function of the plant development (growth and decay) and of the plant ability to reconfigure. Model parameters and uncertainty are estimated through Bayesian inference combining prior knowledge on the hydraulic controls at the station and observational data. In addition to the traditionally used stage‐discharge gaugings, the model allows the use of qualitative observations on the plant development state (e.g., no plant, growth, and decay). The model is tested and validated for a French hydrometric station where frequent gaugings (twice a month) and written comments about vegetation are available over more than 20 years. Relative errors between the simulated discharges and the observed discharges mostly range between ±20% for the temporal rating curve. In contrast, they are higher than ±50% with a standard model with no vegetation module. This case study highlights the importance of using observations about plant development to predict water discharge. We finally discuss some possible improvements and extensions of the model and recommend methods for data collection. A rating curve model accounting for aquatic vegetation was developed and estimated through Bayesian inferenceInformation on the vegetation development state should be used to estimate the rating curveDischarge prediction in presence of aquatic plants was improved using the new model (relative errors decrease from ±50% to ±20%) A rating curve model accounting for aquatic vegetation was developed and estimated through Bayesian inference Information on the vegetation development state should be used to estimate the rating curve Discharge prediction in presence of aquatic plants was improved using the new model (relative errors decrease from ±50% to ±20%)

Predicting mixing processes, especially transverse mixing, downstream of river confluences, is necessary for assessing and modeling the fate of pollutants transported in river networks, but it is still challenging. Typically, there is a lack of transverse mixing solutions implemented in 1‐D hydrodynamical models widely used in river engineering applications. To investigate the mixing processes developing downstream of a medium‐sized river confluence, three high‐resolution in situ surveys are conducted at the Rhône‐Saône confluence in France, based on geolocated specific conductivity and hydroacoustic measurements. Contrasting mixing situations are observed depending on hydrological conditions. In some cases, the two flows mix slowly due to turbulent shear at their vertical interface. This can be modeled by an analytical solution of the advection‐diffusion equation. In other cases, the waters from one of the two tributaries move under the waters of the other tributary. The induced local circulation enhances transverse mixing but not vertical mixing and the flow remains stratified vertically, which may be missed when surface or satellite images are analyzed qualitatively. Stratification may be predicted by comparing the time scales for shear and density‐driven adjustment. Shear‐dominated transverse mixing of depth‐averaged concentrations can be predicted analytically and implemented in 1‐D hydrodynamical models. However, the initiation of apparently rapid transverse mixing due to density‐driven circulation remains to be better understood and quantified. Predicting how waters mix downstream of river confluences is necessary for assessing and modeling the fate of pollutants transported in river networks, but it is still challenging. Typically, there is a lack of transverse mixing solutions implemented in models widely used in river engineering applications. To investigate the mixing processes developing downstream of a medium‐sized river confluence, three high‐resolution in situ surveys are conducted at the Rhône‐Saône confluence in France. Contrasting slow or rapid mixing situations are observed depending on hydrological conditions. The transverse mixing of depth‐averaged concentrations can be predicted analytically and implemented in 1‐D hydrodynamical models. However, the initiation of rapid transverse mixing due to difference in fluid density remains to be better understood and quantified. Three high‐resolution surveys of mixing are conducted downstream of a medium‐sized, symmetrical, low‐angle river confluenceShear‐dominated transverse mixing can be predicted analytically and implemented in 1‐D hydrodynamical modelsVarious criteria are tested to predict vertical stratification due to density effects, inducing apparently rapid mixing