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Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

In recent years, technological advances have been observed in environmental monitoring field, leading to a rapid spread of innovative technologies overcoming many historical challenges. In river monitoring field the use of image-based techniques provides non-intrusive measurements ensuring the best safety conditions for operators. The most used optical methods are the Large-Scale Particle Image Velocimetry (LSPIV) and the Large-Scale Particle Tracking Velocimetry (LSPTV).In LSPIV and LSPTV techniques a floating tracer is introduced on the water surface and its motion is recorded by commercial devices (e.g. digital cameras). Resulting videos are then processed by free and open source software which applies a statistical cross-correlation analysis to provide the instantaneous surface velocity field.The aim of this work is to investigate the performance of the most widely used LSPIV software in estimating the surface velocity field taking into account the presence of turbulent structures. Indeed a typical feature of natural river is the presence of turbulent eddies which makes the tracer patterns above the water surface difficult to predict. The evaluation of tracer particle displacement is further complicated by the negative phenomenon of aggregation; it influences cross-correlation causing an incorrect estimation of the velocity vectors.The study of the hydraulic turbulence of a natural river has been tackled from a numerical point of view. PANORMUS (Parallel Numerical Open-Source Model for Unsteady Flow Simulations) package (Napoli, 2011) has been used by adopting a LES (Large Eddy Simulation) scheme. PANORMUS is a numerical tool coded to solve the 3D momentum equations for incompressible flows (Navier-Stokes and Reynolds equations) using the Finite-Volume Method (FVM). The analyses were carried out on real cases modelled with PANORMUS-LES package. The hydraulic reconstructed domains are characterised by regular cross sections, accurately derived from real topographic survey campaigns, and low river-bed roughness (smooth concrete surface).Synthetic sequences of tracer motion were derived from the hydraulic model and then processed by using LSPIV software.The results of such numerical analyses have allowed an evaluation of LSPIV performance assessing the errors in terms of mean value of the surface velocity field and velocity along transverse transects.

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&ndash, 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.

The relevance of hydrological surface processes, such as drainage formation, landscape's evolution, pollutant diffusion, and sediment transport, has recently fostered the development of an array of novel sensing systems. In this context, the deployment and observation of enhanced fluorescent particles is studied in [Tauro et al., 2012] to non- intrusively estimate surface flow velocities and travel times. Such insoluble and buoyant fluorescent particles are not affected by adsorption issues, thus requiring minimal amounts of tracing material to be released in the environment, and are detected through commercially available inexpensive digital cameras. In this paper, we complement previously developed unsupervised particle detection tools with Large Scale Particle Image Velocimetry (LSPIV) analysis for surface flow estimations in natural settings. Specifically, we conduct several proof of concept experiments by deploying few grams of fluorescent particles in a natural brook and analyzing acquired videos with both unsupervised image analysis tools and LSPIV. Results from both methodologies suggest their complementary use for enhanced surface flow monitoring. In addition, the use of environmentally friendly highly visible fluorescent particles would sensibly improve the joint performance of such non-intrusive sensing systems.