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.

The volumetric flow rate in rivers is essential to analyze hydrological processes and at the same time it is one of the most difficult variables to measure. Image based discharge measurements possess several advantages, one of them being that the sensor (camera) is not in contact with the water, it can be placed safe of floods, its mounting position is very flexible and there is no need of expensive structures/constructions. During the last years several image-based methods for measuring the surface velocity in rivers and canals have been proposed and successfully tested under different conditions. However, these methods have been used and configured to perform well under the particular conditions of a single recording or single site. The objective of this paper is to present a system which has reached a Technology Readiness Level (TRL) 9. The system is able to measure the volumetric flow under different conditions day and night and all year long, the system is able to perform in rivers or canals of different sizes and flow velocities and under different conditions of visibility. In addition, the system is capable of measuring the river stage optically without the need of a stage, but it can also integrate external level sensor. Important for a wide set of customers, the system must be able to interface with the various common signal input and output standards, such as 4–20 mAmp, modbus, SDI-12, ZRXP, and even with customer specific formats. Additionally, the developed technology can be implemented as an edge or as a cloud system. The cloud system only needs a camera with Internet connection to send videos to the cloud where they are processed, while the edge systems have a processing unit installed at the site where the processing is done. This paper presents the key aspects needed to move from prototype with TRL5-7 and lower toward the presented field proven system with a TRL 9.

Image-based methods for measuring surface flow velocities in rivers have several advantages, one of them being that the sensor (camera) is not in contact with the water and its mounting position is very flexible hence there is no need of expensive structures to mount it. Additionally, it is possible to measure the whole river width. On the other hand, environmental factors, like wind, can affect the surface velocity and the have an impact on the accuracy of the measurements.Herein we present an analysis of the wind effect on the image based surface velocity at Rhine river, at the border between Switzerland and Austria. At this location the river width is of approximately 100 meters under low flow conditions, while the width of its floodplain is of about 200 m. An ATMOS 22 ultrasonic anemometer was installed at the site to measure the wind intensity as well as its direction. A time series of flow velocities and wind from May to October 2019 was analyzed. During this period, the average discharge was 320 m3/s and the average flow velocity 1.7 m/s. While the average wind velocity was of 2.3m/s which roughly follows the same direction of the river flow.A rating curve following a power law function was fitted to the image based surface flow measurements. It was found that for maximum wind speeds of 10 m/s, blowing in the opposite direction of the river flow, there was a deviation of 8%. For the average wind speed of 2.3m/s, the deviation was found to be 3%.

Despite the availability of systems to detect water like those based on electric resistance, monitoring ephemeral and intermittent streams is still a challenge. For this reason we developed an image-based system to detect the presence of water in streams using Machine Learning and a micro-controller. The system main components are a micro-controller and a coordination unit. The micro-controller is equipped with a 2MP color camera which supports Machine Learning and Micro Python. The coordination unit is used to control relays and transmit data via LoRa. The system is low-power and can be powered with a battery making it a standalone system for analyzing and processing images on the edge. Using machine learning, we trained a model to specifically identify the presence of water on ephemeral streams. More-over, and using relays, this system can activate a more complex and power-consuming device to measure the discharge of the stream only when it detects the presence of water. Making this monitoring system more efficient for ephemeral stream monitoring. This work has been supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 1010178

Hydrological models need continuous streamflow data for calibration, however these data are not always available due to the lack of monitoring stations or properly functioning ones. Mobile Crowdsensing (MCS) can help alleviate this problem. MCS relies on data collection from mobile sensing devices, it is inexpensive and its spatial coverage can be outstanding. Moreover, it can be an effective way of increasing local and citizen awareness of socio-environmental issues. Herein, we introduce the MCS strategy developed in SMARTLAGOON, a H2020 project (grant agreement No. 10101786). The main study area of the project is the Mar Menor in Spain, which is the largest hypersaline coastal lagoon in Europe, it is under many pressures which are compromising the ecosystem stability. A SWAT model will be built which needs streamflow data for calibration. It is expected that DischargeApp (http://photrack.ch/dischargeapp.html) can provide some of the lacking information. DischargeApp is a user-friendly smart-phone application which provide fast and accurate volumetric flow measurements in rivers and canals. For this, citizens are invited to use their mobile devices to voluntarily monitor stream flow. Fisrt, users located potential sites through the watershed and uploaded the calculated pixel displacement to the cloud using the DischargeApp. A selection of final sites was done based on the flow velocity (pixel displacement) and the cross section. Finally, those sites were fully setup for participatory discharge measurements. Additionally, the DischargeApp is connected to a cloud data-based which can be accessed via a web platform, any user can see the data recorded making the data transparent to everyone. We also propose the use of social sensing to leverage user-contributed data from social media. This mode of MCS considers participants as “social sensors”, i.e., agents that provide information about their environment through social-media. We use sensingTools.com, a framework for the development of applications based on social sensing. The main objective of using this strategy is threefold; to obtain videos of affected areas in the Mar Menor for discharge measurements, to locate sites of interest to subsequently in situ infrastructures and to identify potential users who can collaborate in citizen science activities.