Fluorescent particle tracers for surface hydrology

Surface water processes control downstream runoff phenomena, waste and pollutant diffusion, erosion mechanics, and sediment transport. However, current observational methodologies do not allow for the identification and kinematic characterization of the physical processes contributing to catchment dynamics. Traditional methodologies are not capable to cope with extreme in-situ conditions, including practical logistic challenges as well as inherent flow complexity. In addition, available observational techniques are non-exhaustive for describing multiscale hydrological processes. This research addresses the need for novel observations of the hydrological community by developing pioneer flow characterization approaches that rely on the mutual integration of traditional tracing techniques and state-of-the-art image-based sensing procedures. These novel methodologies enable the in-situ direct observation of surface water processes through remote and unsupervised procedures, thus paving the way to the development of distributed networks of sensing platforms for catchment-scale environmental sensing. More specifically, the proposed flow characterization methodology is a low-cost measurement system that can be applied to a variety of real-world settings spanning from few centimeters rills in natural catchments to riverine ecosystems. The technique is based on the use of in-house synthesized environmentally-friendly fluorescent particle tracers through digital cameras for direct flow measurement and travel time estimations. Automated image analysis-based procedures are developed for real-time flow characterization based on image manipulation, template-based correlation, particle image velocimetry, and dimensionality reduction methodologies. The feasibility of the approach is assessed through laboratory-designed experiments, where the accuracy of the methodology is investigated with respect to well-established flow visualization techniques. Further, the transition of the proposed flow characterization approach to natural settings is studied through paradigmatic observations of natural stream flows in small scale channel and riverine settings and overland flows in hillslope environments. The integration of the proposed flow sensing system in a stand-alone, remote, and mobile platform is explored through the design, development, and testing of a miniature aerial vehicle for environmental monitoring through video acquisition and processing

An automatic ANN-based procedure for detecting optimal image sequences supporting LS-PIV applications for rivers monitoring

River flow monitoring has recently experienced rapid development due to advancements in optical methods, which are non-intrusive and enhance safety conditions for operators. Surface velocity fields are obtained recording and analyzing displacements of floating tracer materials, artificially introduced or already present on the water surface. River discharge can be assessed coupling the surface velocity fields with geometric data of a cross section. The accuracy of optical techniques is strongly affected by different environmental and hydraulic factors, and software parameterization, with tracer features that often play a prominent role. An adequate density and spatial distribution of tracer is required to ensure a complete characterization of surface velocity fields. In practical applications such conditions might occur only for a limited portion of the entire acquired images sequence. This work proposes an automatic procedure for identifying and extracting the best portion of a recorded video in terms of seeding characteristics and demonstrates how LS-PIV software performances can be enhanced through this approach. The procedure is implemented through a data-driven empirical approach based on an Artificial Neural Network, trained using data collected during an extensive measurement campaign across different rivers in Sicily (Italy). Performances are evaluated in terms of error in reproducing surface velocity profiles along specific transects, where benchmark profiles derived using an Acoustic Doppler Current Profiler are available. The procedure, also tested via numerical simulations on synthetic image sequences, outperformed an approach based on an existing metric for seeding characterization and represents a simple and useful tool for LS-PIV based applications

Optical sensing for stream flow observations: a review

Images are revolutionizing the way we sense and characterize the environment by offering higher spatial and temporal coverage in ungauged environments at competitive costs. In this review, we illustrate the major image-based approaches that have been lately adopted within the hydrological research community. Although many among such methodologies have been developed some decades ago, recent efforts have been devoted to their transition from laboratories to operational outdoor settings. Sample applications of image-based techniques include flow discharge estimation in riverine environments, clogging dynamics in irrigation systems, and flow diagnostics in engineering infrastructures. The potential of such image-based approaches towards fully remote observations is also illustrated through a simple experiment with an unmanned aerial vehicle

VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers

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

An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

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

Steep Bedload‐laden Flows: Near‐Critical?

International audienceSteep gravel-bed rivers sometimes experience floods that dramatically rework river bed structure and topography. Hazard assessments and paleo-event reconstructions require better knowledge of such phenomena. This paper explores morphodynamic evolution of steep channels carrying bedload-laden flows, using a generic Froude-scaled model. Bedload-laden floods were introduced in a narrow flume and spread into a five-times wider unconfined area with a 0.1-steep slope (m/m). Image analysis enabled measurements taken at an unprecedented level of accuracy on unconfined flows laden with bedload. A flow reconstruction procedure was used to compute depth, Froude (F r) and Shields (τ *) numbers on millions of pixels based on a friction law and measurements of surface velocity, slope, and roughness. Despite the steep slope, Froude numbers proved to be mostly subcritical in all but the regions experiencing the most active sediment transport. Competent flows, identified by the transport stage higher than unity (τ * /τ *cr > 1), were near-critical and seldom had F r > 1.3 − 1.5. This demonstrates that, providing that bed width and structure can adjust, hydraulic features such as standing waves, hydraulic jumps and lateral shock waves dissipate energy very efficiently in addition to adjusting channel features. These competent flows also tend to rework channels to approach the energy minimum of near-critical flows. This hypothesis was postulated by Gordon Grant (1997, Wat. Resour. Res. 33(2):349-358), but demonstrated here for the first time at this scale. Considering near-critical flows during discharges high enough to be clearly competent in laterally unconfined reaches seems reasonable as a first approximation in steep channels

River flow monitoring: LS-PIV technique, an image-based method to assess discharge

The measurement of the river discharge within a natural ort artificial channel is still one of the most challenging tasks for hydrologists and the scientific community. Although discharge is a physical quantity that theoretically can be measured with very high accuracy, since the volume of water flows in a well-defined domain, there are numerous critical issues in obtaining a reliable value. Discharge cannot be measured directly, so its value is obtained by coupling a measurement of a quantity related to the volume of flowing water and the area of a channel cross-section. Direct measurements of current velocity are made, traditionally with instruments such as current meters. Although measurements with current meters are sufficiently accurate and even if there are universally recognized standards for the current application of such instruments, they are often unusable under specific flow conditions. In flood conditions, for example, due to the need for personnel to dive into the watercourse, it is impossible to ensure adequate safety conditions to operators for carrying out flow measures. Critical issue arising from the use of current meters has been partially addressed thanks to technological development and the adoption of acoustic sensors. In particular, with the advent of Acoustic Doppler Current Profilers (ADCPs), flow measurements can take place without personnel having direct contact with the flow, performing measurements either from the bridge or from the banks. This made it possible to extend the available range of discharge measurements. However, the flood conditions of a watercourse also limit the technology of ADCPs. The introduction of the instrument into the current with high velocities and turbulence would put the instrument itself at serious risk, making it vulnerable and exposed to damage. In the most critical case, the instrument could be torn away by the turbulent current. On the other hand, considering smaller discharges, both current meters and ADCPs are technologically limited in their measurement as there are no adequate water levels for the use of the devices. The difficulty in obtaining information on the lowest and highest values of discharge has important implications on how to define the relationships linking flows to water levels. The stage-discharge relationship is one of the tools through which it is possible to monitor the flow in a specific section of a watercourse. Through this curve, a discharge value can be obtained from knowing the water stage. Curves are site-specific and must be continuously updated to account for changes in geometry that the sections for which they are defined may experience over time. They are determined by making simultaneous discharge and stage measurements. Since instruments such as current meters and ADCPs are traditionally used, stage-discharge curves suffer from instrumental limitations. So, rating curves are usually obtained by interpolation of field-measured data and by extrapolate them for the highest and the lowest discharge values, with a consequent reduction in accuracy. This thesis aims to identify a valid alternative to traditional flow measurements and to show the advantages of using new methods of monitoring to support traditional techniques, or to replace them. Optical techniques represent the best solution for overcoming the difficulties arising from the adoption of a traditional approach to flow measurement. Among these, the most widely used techniques are the Large-Scale Particle Image Velocimetry (LS-PIV) and the Large-Scale Particle Tracking Velocimetry. They are able to estimate the surface velocity fields by processing images representing a moving tracer, suitably dispersed on the liquid surface. By coupling velocity data obtained from optical techniques with geometry of a cross-section, a discharge value can easily be calculated. In this thesis, the study of the LS-PIV technique was deepened, analysing the performance of the technique, and studying the physical and environmental parameters and factors on which the optical results depend. As the LS-PIV technique is relatively new, there are no recognized standards available for the proper application of the technique. A preliminary numerical analysis was conducted to identify the factors on which the technique is significantly dependent. The results of these analyses enabled the development of specific guidelines through which the LS-PIV technique could subsequently be applied in open field during flow measurement campaigns in Sicily. In this way it was possible to observe experimentally the criticalities involved in applying the technique on real cases. These measurement campaigns provided the opportunity to carry out analyses on field case studies and structure an automatic procedure for optimising the LS-PIV technique. In all case studies it was possible to observe how the turbulence phenomenon is a worsening factor in the output results of the LS-PIV technique. A final numerical analysis was therefore performed to understand the influence of turbulence factor on the performance of the technique. The results obtained represent an important step for future development of the topic

Bedload transport analysis using image processing techniques

Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder to understand this notoriously complex phenomenon. In this study, a novel, image-based approach, the Video-based Bedload Tracker (VBT), is implemented to quantify gravel bedload transport by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in a laboratory flume, with future field adaptation as an overall goal. VBT offers a full statistics of the individual velocity and grainsize data for the moving particles. The paper introduces the testing of the method which requires minimal preprocessing (a simple and quick 2D Gaussian filter) to retrieve and calculate bedload transport rate. A detailed sensitivity analysis is also carried out to introduce the parameters of the method, during which it was found that by simply relying on literature and the visual evaluation of the resulting segmented videos, it is simple to set them to the correct values. Practical aspects of the applicability of VBT in the field are also discussed and a statistical filter, accounting for the suspended sediment and air bubbles, is provided

2D experiments and numerical simulation of the oscillatory shallow flow in an open channel lateral cavity

Steady shallow flows past an open channel lateral cavity can induce the excitation of an eigenmode of a gravity standing wave inside the cavity, called seiche, which may be coupled with the shedding of vortices at the opening of the cavity. The presence of the seiche is of fundamental interest as it enhances the mass exchange between the main channel and the cavity. Measurements of the time evolution of the water surface are not often found in the literature for this type of flows. In this work, an experimental and numerical study of a shallow flow past a channel lateral cavity is carried out. The main novelty is the use of a pioneering non-intrusive experimental technique to measure the water surface at the channel-cavity region. This optical technique offers high resolution 2D data in time and space of the water surface evolution, allowing to determine the relevant features of the seiche oscillation. Such data are supplemented with Particle Image Velocimetry measurements. Furthermore, the experiments are numerically reproduced using a high-resolution depth-averaged URANS shallow water model, under the assumption that shallow water turbulence is mainly horizontal. The experimental and numerical results are analyzed in the frequency domain. High-resolution two-dimensional amplitude oscillation maps of the seiche phenomenon, as well as velocity fields, are presented. The high quality of the experimental data reported in this work makes this data set a suitable benchmark for numerical simulation models in order to evaluate their performance in the resolution of turbulent resonant shallow flows