Long series of river discharge data are essential for developing improved river and water management strategies and for coping with water-related hazards such as floods. However, continuous direct measurement of river discharge is practically infeasible. Recently developed electromagnetic and ultrasonic methods can be used for automated (or direct) river discharge measurements; however, they are not widely used because they are expensive and are prone to damage during high flows.

At most gauging sites around the world, a rating curve is used to convert the measured stage into discharge. However, using rating curves is fraught with difficulties, including (a) hysteresis effect during unsteady flow, (b) extrapolation error during high flows, (c) need for regular updating due to change in hydraulic resistance and channel geometry. More recently, methods have been developed for dynamic river discharge estimation by solving governing equations of river flow i.e., shallow water equations (SWE). However, these methods (a) solve SWE in its conservative form, (b) are most suitable for prismatic channels with no lateral flow, (c) require one flow value, and (d) assume channel roughness or calibrate it by using observed stage data from two or three gauging locations. Although, stage data from two or three gauging locations are theoretically sufficient to calibrate channel roughness, in practice error margins are still high due to sub-optimal positioning of gauging stations, and coarse temporal resolution of existing measurement networks.

Therefore, motivated by a need to surmount the limitations in existing methods, we have developed a non-contact, robust, and cost-effective approach for dynamic river discharge estimation. We use an array of bespoke sensors to monitor the river stage at high resolutions and use these stage data to estimate river discharge. We present a methodology to calibrate a hydraulic model of a river reach by only using stage data from a network of such sensors. We use freely available HEC-RAS software as the solver for SWE. We have developed python scripts to control and automate HEC-RAS simulations and estimate river discharge dynamically.

How to cite: Sah, N., Buytaert, W., D. Paul, J., De Stercke, S., and Paschalis, A.: Non-contact, Low-cost Sensor Network for River Stage Monitoring and Dynamic Discharge Estimation , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2867, https://doi.org/10.5194/egusphere-egu22-2867, 2022.

Climate change, together with other natural and anthropogenic drivers lead to changes in streamflow patterns that are now occurring with increasing frequency. At the same time traditional streamflow monitoring methods are time-consuming and costly so that it typically takes many years of significant field efforts to establish reliable streamflow data for a new location or for stations with major temporal changes to the stage—discharge relation. To provide timely and reliable streamflow data to tackle these changes to the hydrological regime and their impacts on society’s water management requires new cost-effective monitoring methods that can rapidly produce data with low uncertainty. Hydraulically modelled rating curves are a promising alternative to traditional power-law methods as they need much fewer calibration gaugings, but they are associated with additional uncertainty sources in the hydraulic knowledge and these need to be assessed.
We present the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework which was developed to rapidly estimate rating curves and their uncertainty. The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference to incorporate information from both hydraulic knowledge and the calibration gauging data. In this study we compare RUHM and the Bayesian power-law method BaRatin in application to a Swedish site using nine different gauging strategies associated with different costs. We compare results for the two methods in terms of accuracy, cost and time required for establishing rating curves. 
We found that rating curves with low uncertainty could be modelled with fewer gaugings for RUHM compared to BaRatin. As few as three gaugings were needed with RUHM if these gaugings covered low and medium flows, whereas high flow gaugings were not necessary. This makes the RUHM method both cost effective and time efficient as low and medium flows occur more frequently than high flows. When using all gaugings (i.e., a high-cost gauging strategy), the uncertainty for RUHM and BaRatin was similar. The results for this Swedish site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. Finally, we discuss the potential of using RUHM together with drone-derived data to make field efforts even more efficient.

How to cite: Westerberg, I., Mansanarez, V., Lyon, S., and Lam, N.: The RUHM framework for rapid rating curve uncertainty estimation: comparison to power-law methods and potential using drone-derived data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13348, https://doi.org/10.5194/egusphere-egu22-13348, 2022.

Non-contact and automated flow measurement in open channels is becoming more popular as techniques improve to measure surface velocity, reducing costs and risk to hydrographers.  However, these methods rely on estimates of bulk-to surface ratio estimates, as well as channel wetted area.  This study considers the accuracy and application of paired Up and Downstream Water Quality (WQ) measurements to estimate the Transit Time (TT) and average bulk velocity.  Combined with results from both the Automated Salt Dilution (AutoSalt) and Water Quality Mixing Model (WQMM) systems, we can calibrate the waterway for wetted area at a given water level, and hence estimate discharge from transit time velocity on a continuous basis using only temperature and conductivity insitu sensors.  This low-cost method can used to build or validate rating curves, measure peak and low flow events, and conduct cost-effective hydrological assessments over large regions for any size waterway,  to support climate change study and adaptation.  This method also has application to flood wave propagation and Initial Dilution Zone (IDZ) studies. Results from a large and a small waterway, along with uncertainty, is discussed.

How to cite: Sentlinger, G.: Water Quality Transit Time (WQTT) for Continuous Velocity/Discharge Measurement in Large and Small Waterways, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10795, https://doi.org/10.5194/egusphere-egu22-10795, 2022.

Streamflow measurement is essential in hydraulic engineering to develop and manage water resources and ensure they are managed correctly and adequately. Two primary parameters for discharge measurements in natural rivers, namely, the mean flow velocity and cross-sectional flow area at the measurement site, are requisites. The cross-sectional area of the section could be measured using river bathymetric surveys or by using advanced and modern methods such as Acoustic Doppler Current Profiler (ADCP). For mean velocity, numerous ways and tools are available depending on the fact, whether the measurements are taken from a distance (non-contact) or using a contact method (traditional approach). Nowadays, non-contact velocity measurement approaches are becoming more popular as they are less time-consuming and user‑friendly to deal with high flows and rough weather. In contrast, the entropy-based concepts (such as Shannon entropy, Tsallis entropy and Renyi entropy) are utilized to obtain the discharge from the non-contact measurements, which gives better results than the traditional approaches such as the velocity area method. Entropy-based velocity distribution depends on the crucial parameter called entropy parameter (a function of the mean and maximum velocity), which is linked to the channel characteristics such as channel roughness and bed slope. Due to a lack of concrete evidence regarding its variation with the channel characteristics, the entropy parameter was surmised as constant. In this study, the experimental velocity data was collected from a rectangular flume fitted with a mechanical apparatus to change the bed slope. The obtained velocity data was employed to comment on the actual variation of the Shannon entropy parameter for the one such channel characteristic, i.e., channel bed slope. The velocity data analysis depict only a slight variation in entropy parameter. In addition, the discharge error analysis provided a substantial justification for using a unique constant value of the entropy parameter for the whole cross-section can be utilized instead of individual values for each channel bed slope condition.

How to cite: Singh, G. and Khosa, R.: Effect of Channel Bed Slope on Shannon Entropy-Based Velocity Distribution in Open Channel Flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-139, https://doi.org/10.5194/egusphere-egu22-139, 2022.

High mountain environments have shown substantial geomorphological changes forced by rising temperatures in recent decades. As such, paraglacial transition zones in catchments with rapidly retreating glaciers and abundant sediments are key elements in high alpine river systems and promise to be revealing, yet challenging, areas of investigation for the quantification of current and future sediment transport. In this study, we explore the potential of semi-automatic image analysis to detect the extent of the inundation area and corresponding inundation frequency in a proglacial outwash plain (Jamtal valley, Austria) from terrestrial time-lapse imagery. We cumulated all available records of the inundated area from 2018-2020 and analysed the spatial and temporal patterns of flood flows. The approach presented here allows semi-automated monitoring of fundamental hydrological/hydraulic processes in an environment of scarce data. The pixel classification based on greyscale values from oblique hourly recordings returned plausible results of the spatial and temporal variability of surface runoff in the investigated glacier forefield. The image sets, processed in ImageJ, allowed geo-rectification to produce inundation frequency maps. Meteorological and discharge data from downstream measuring stations was consulted to interpret our findings. Runoff events and their intensity were quantified and attributed to either pronounced ablation, heavy precipitation, or a combination of both. We also detected an increasing degree of channel concentration within the observation period. The maximum inundation from one event alone took up 35% of the analysed area. About 10% of the observed area presented inundation in 60-70% of the analysed images. In contrast, 60-70% of the observed area was inundated in fewer than 10% of the analysed period. Despite some limitations in terms of image classification, prevailing weather conditions and illumination, the derived inundation frequency maps provide novel insights into the evolution of the proglacial channel network.

How to cite: Hiller, C., Walter, L., Helfricht, K., Weisleitner, K., and Achleitner, S.: Flood flow in a proglacial outwash plain - quantifying spatial extent and frequency of inundation from time-lapse imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7517, https://doi.org/10.5194/egusphere-egu22-7517, 2022.

A lot of different methods are used to estimate flood risk worldwide. The method that performs better depends on the catchment features and dimension, data time resolution and availability and uncertainty level required. Remote sensing approaches are more and more common, but because of the limited periods covered by time series derived by this new methodology, in-situ data integration is still required. A new methodology is proposed, based on a case-study of different reaches of Basento river, Basilicata (Southern Italy). Starting from hourly rainfall time series (covering not less than 20 years), for each pluviometric station taken into account into the catchment area, Intensity-Duration-Frequency (IDF) curves are computed (fitting a power law), in order to calculate the rainfall maximum at a certain percentile (typically 90° or 95° percentile are used) during the concentration time. Thiessen polygon method is used to divide the catchment area into smaller areas, each one corresponding to a pluviometric station, with the purpose of calculating weighted  rainfall values for each station area. A Digital Terrain Model is used to extract multiple cross sections of the river-bed, spanning different morphologies, from braided to meandering channels. For each cross section, starting from bankful level, it is possible to estimate diverse hydraulic parameters such as river stage, hydraulic radius, section’s surface area (using image analysis) and the mean velocity of the current, using the logarithmic law profile of the turbulent flow. Sediment size analysis is carried out as to estimate the river bed roughness for each cross section. The mean velocity value V can be used to estimate the concentration time t=L/V, where L is equal to the distance between the cross section and the hydraulically further point into the catchment area. The concentration time value t is used into the equation of the IDF curves, in order to link the corresponding rainfall height to the river stage reached at the cross section, eventually to estimate the rainfall value that, if exceeded, can cause flood. A FLO-2D model has been then used to run simulations with the aim to detect flood-prone areas, finding an overall good matching between the values of current mean velocity, discharge and river stage estimated in the cross sections.

How to cite: Roseto, R., Capolongo, D., and Dellino, P.: A new approach for flood risk estimation integrating remote sensing and in-situ data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7229, https://doi.org/10.5194/egusphere-egu22-7229, 2022.

The handling of natural disasters, especially heavy rainfall and corresponding floods, requires special demands on emergency services. The need to obtain a quick, efficient and real-time estimation of the water level is critical for monitoring a flood event. This is a challenging task and usually requires specially prepared river sections. In addition, in heavy flood events, some classical observation methods may be compromised.

With the technological advances derived from image-based observation methods and segmentation algorithms based on neural networks (NN), it is possible to generate real-time, low-cost monitoring systems. This new approach makes it possible to densify the observation network, improving flood warning and management. In addition, images can be obtained by remotely positioned cameras, preventing data loss during a major event.

The workflow we have developed for real-time monitoring consists of the integration of 3 different techniques. The first step consists of a topographic survey using Structure from Motion (SfM) strategies. In this stage, images of the area of interest are obtained using both terrestrial cameras and UAV images. The survey is completed by obtaining ground control point coordinates with multi-band GNSS equipment. The result is a 3D SfM model georeferenced to centimetre accuracy that allows us to reconstruct not only the river environment but also the riverbed.

The second step consists of segmenting the images obtained with a surveillance camera installed ad hoc to monitor the river. This segmentation is achieved with the use of convolutional neural networks (CNN). The aim is to automatically segment the time-lapse images obtained every 15 minutes. We have carried out this research by testing different CNN to choose the most suitable structure for river segmentation, adapted to each study area and at each time of the day (day and night).

The third step is based on the integration between the automatically segmented images and the 3D model acquired. The CNN-segmented river boundary is projected into the 3D SfM model to obtain a metric result of the water level based on the point of the 3D model closest to the image ray.

The possibility of automating the segmentation and reprojection in the 3D model will allow the generation of a robust centimetre-accurate workflow, capable of estimating the water level in near real time both day and night. This strategy represents the basis for a better understanding of river flooding and for the development of early warning systems.

How to cite: Blanch, X., Wagner, F., Hedel, R., Grundmann, J., and Eltner, A.: Towards automatic real-time water level estimation using surveillance cameras, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3225, https://doi.org/10.5194/egusphere-egu22-3225, 2022.

Image-based monitoring of rivers is a growing field of research and is being popularized as a technical alternative for discharge, erosion and flood risk estimation applications. Surface velocimetry can also be a way to characterize the turbulence structure of shallow flows, making possible the remote determination of quantities of interest such as dissipation and integral length scales. To evaluate velocimetry methods and data processing workflows, a laboratory facility emulating a river reach was assembled at IST, and monitored using commercial grade cameras, in field-like conditions. In this work the results of estimates of turbulent dissipation and integral length scales using multiple methods are provided, along with a discussion on the differences among methods and possible applications of the derived data in hydrodynamic model parameter calibration and data assimilation. LSPIV and PTV display similar results with regard to velocity estimation and vortex detection. In the estimation of integral lengths, the longitudinal scales are most affected by limitations in the measurement setup, whereas for the dissipation and turbulent viscosity estimates, spectrum methods seem to be less reliable than simpler methods based on dimensional analysis and integral length scales.

How to cite: Zandonadi Moura, L., Ferreira, R., and Aleixo, R.: Turbulence Metrics from Surface Image Velocimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3049, https://doi.org/10.5194/egusphere-egu22-3049, 2022.

The importance of optical measurement methods in hydrology is increasing in the last years. In contrast to conventional gauging techniques, they can be applied remotely, making the measurement safe for humans and equipment, even under difficult measurement conditions. One important hydrological parameter to measure is discharge. Deriving discharge with remote sensing can be done by applying particle tracking velocimetry (PTV) in combination with the velocity area method (VAM). VAM is a standardized and established method in hydrology. For reliable discharge results with the VAM, surface flow velocity measurements and thus trackable particles in the case of PTV usage are required across the entire width of the river cross section, which is not always the case in natural observation conditions. To fill these data gaps several statistical methods were investigated that incorporate information provided at different measurement times but with similar discharge conditions.

In this study, data were collected over longer time periods with different cameras at a gauging station of a medium scale river in Saxony, Germany. Stationary cameras recorded short videos, which are used to estimate the velocity distribution at the water surface using PTV incorporated in the FlowVelo tool (Eltner, 2020), and afterwards, to estimate the discharge using VAM. The obtained discharge time series from different cameras and camera positions were used to analyse the performance of different gap filling approaches. The results were compared to discharge and water level measurements of the official gauging station maintained by the federal measuring agency. They show, that the adjustment to the data of the reference measurements increases significantly by application of the gap filling methods. Next steps are to enhance the presented methods by using targeted data filtering and deep learning.

Keywords: velocity area method, particle tracking velocimetry, camera based discharge estimation

How to cite: Kutscher, A., Grundmann, J., Eltner, A., Blanch, X., and Hedel, R.: Determination of continuous discharge time series based on the optical Particle Tracking Velocity (PTV), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5967, https://doi.org/10.5194/egusphere-egu22-5967, 2022.

Debris flows and flash floods occur frequently in Chile due to geology, geomorphology and weather, costing human lives and impacting settlements, infrastructure and economic activities. One of the problems relates to the lack of adequate monitoring technology in remote areas with limited connectivity. We have developed a low cost system that processes acquired lidar and image data in-situ with a Raspberry Pi obtaining flow level and velocity and transmits near real time via satellite (or cellular network if available). The low implementation cost allows to replicate the system in the many hazardous sites, as well as advance towards early warning systems in locations with limited communication networks. The velocimetry method consists of two steps: first obtaining the images, and then a brightness filter and normalized cross correlation. To eliminate outliers a flow direction filter is used, and velocities are obtained by tracking of flow surface elements. Also the flow level is measured with a lidar also connected to the R-Pi. We will present both laboratory and field test results.

How to cite: Dussaillant, A., Sepúlveda, N., Aguilar, F., Valencia, J., Ancan, J., Cotroneo, J., Herrera, R., Leiva, N., Peña, C., Alfaro, A., Fernández, J., and Muñoz, A.: In-Situ, Near Real Time and Low Cost Image Velocimetry for Debris Flows and Flash Flood Monitoring in the Chilean Andes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10797, https://doi.org/10.5194/egusphere-egu22-10797, 2022.

Monitoring volumetric flow in arid and semi-arid regions is a major challenge due to their harsh and continuously changing environment (e.g. extreme temperature, severe sand storms). In these regions, hydrological events such as rainfall storms and flash flood events occur intermittently, time between major events may take years. Drainage water courses in these areas are often referred as Wadis, which are ephemeral drainage courses. Wadis are normally dry except after a rain event, often resulting in flash floods events with flood peak values occurring in the first few minutes of the event.

Monitoring the volumetric flow under these environments requires robust devices which record continuously at relatively short recording intervals, e.g. minutes or less, to be able to capture the steep ramp of the flood peak.

On April 2021 a DischargeKeeper, an image-based system for flow monitoring, with a PTZ camera was installed at the Wadi Naqab located in northern United Arab Emirates. The Wadi is approximately 50m wide and has been dry for most of the time. One event occurred at the beginning of January 2022, reaching a peak discharge of 78 m³/s just 15min after water started flowing. In this session we will show the system, its challenges and the results of the event.

How to cite: Peña-Haro, S., Lüthi, B., Düster, R., Hansen, I., Vogel, K., Gad, M., and Magdy, M.: Non-contact volumetric flow monitoring in a semi-arid regions’ Wadi, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6198, https://doi.org/10.5194/egusphere-egu22-6198, 2022.

The gauging of open channel flows in waterways provides the foundation to monitor, understand and manage the water resources of our built and natural environment. Several methods are available for measuring the flow, with each of these methods having its own advantages and limitations. For a significant economic and environmental cost, hydraulic control structures can be built to measure the flow using analytical relationships with water height often by measuring the pressure head invasively in the water. Another common approach using the proxy measurement of water height without a hydraulic control structure is the expensive development and maintenance of a discharge rating table relating the measured water height to an estimated flow which has been manually measured at a previous time by acoustic instruments with technically proficient operators. Whilst these approaches are typically able to reasonably estimate flow within their measurement range, the safety risks in monitoring high flow events and the ongoing costs involved are prohibitive to increasing the spatial coverage of these approaches. As water resources become increasingly vulnerable to climate variability, modification of waterways, and increased extraction, there is a critical need to develop monitoring tools that can be flexible, cost-effective, and safe.

Much research has been undertaken into optical non-contact methods to estimate flow in waterways by measuring surface velocities without intrusive instruments or structures. However, to date, these surface velocimetry methods are limited to a narrow operational window of certain stream types and flow velocities due to inherent challenging optical variability in stream environments. A cost-effective stereographic camera-based stream gauging device has been developed for rapid stream gauging through the remote sensing of water height and stream velocities to estimate flows and employ the learning of an adaptive discharge rating envelope. The device includes embedded edge computing capabilities, local app connectivity for setup, and online cloud fleet management with a data dashboard for streamlined deployment and ongoing operational monitoring. Automated analysis is performed reconstructing the point cloud of the scene in front of the camera out to 40 m in order to estimate the water level without any instream equipment. An optical flow algorithm is passed over the short videos collected, generating an array of net motion in the scene which is projected out of the image plane onto the assumed water surface plane using the water level estimation combined with the accelerometer and the embedded intrinsic camera properties. The optically measured motions which are out of the plane of the waterway surface are then able to be automatically filtered and integrated into a water level indexed learning surface velocity distribution which generates an updating adaptive discharge rating envelope for the site. With over 100,000 videos recorded and analysed across 20 sites, the computer vision stream gauging approach has achieved discharge measurements within 15% RMSE of traditional acoustic gauging. This work evaluates this innovative approach across sites on the east coast of Australia and demonstrates the potential to improve the operational reliability and performance of surface velocimetry stream gauging.

How to cite: Hutley, N., Wagenaar, D., Beecroft, R., Soutar, J., Pimble, L., Edwards, B., Grinham, A., and Albert, S.: A stereo computer vision approach to automated stream gauging, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11030, https://doi.org/10.5194/egusphere-egu22-11030, 2022.

OpenRiverCam is a fully open-source, user-friendly, low cost and sustainable web-software stack with API to establish and maintain river rating curves (relationships between geometry and river discharge) in small to medium sized streams based on Large Scale Particle Image Velocimetry (LSPIV). The software is co-designed with practitioners from The Netherlands (Waterboard Limburg and KNMI) and Tanzania (Wami - Ruvu Basin Authority and TMA) with the principle that organizations should be able to establish and maintain operational flow monitoring sites and networks at low costs. A user only requires to establish a temporary or permanent camera site; a simple field survey to measure river cross sections and several control points; and feeding operational videos into the dashboard of the software.

In July 2021, a severe flood event hit several Western European countries including parts of Germany, France, The Netherlands and Belgium. Also the Geul river, a tributary to the Meuse river was severely affected. One of our camera setups was operational at the Geul, near the village Hommerich during the event. The camera recorded 10 second videos every 15 minutes. Through the recordings of this single event, we were able to reconstruct flows and prepare a large number of rating points over a wide diversity of flow domains within a period of less than 12 hours. In this presentation we will share the results of our analysis, and validation against formal flow observations of the Waterboard Limburg. We plan to extend the software with improved pre-processing and allow use of less precise smart phone videos.

How to cite: Winsemius, H., Annor, F., Hagenaars, R., Luxemburg, W., Van den Munckhof, G., Heeskens, P., and Van de Giesen, N.: Capturing the Europe July 2021 flood event flows with an IP camera and OpenRiverCam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11627, https://doi.org/10.5194/egusphere-egu22-11627, 2022.

The changing climate and corresponding increased variability in weather events globally have made clear the need for accurate measurements of streamflow, and the ability to respond quickly to conditions as they occur.

We present Infrared Quantitative Image Velocimetry (IR-QIV), a nearfield remote sensing method that uses infrared imagery of the surface of a river or other body of water to accurately calculate the surface flow field at high resolution in space (~10cm resolution) and time (>1Hz), accurately and continuously, over large areas (1,000s of m^2), for extended periods of time.

IR-QIV is similar to LSPIV (Large Scale Particle Image Velocimetry) and other image-based velocity measurement methods, however, it does not require any illumination or tracer particles since it uses thermal infrared images. IR-QIV has the advantages of being able to measure instantaneous velocity, in addition to mean velocity, and hence makes it possible to calculate metrics of turbulence, from which additional hydrodynamic properties of the flow can be found, including estimates of local bathymetry and bed stress, which allow estimation of discharge from a single, non-contact, measurement.

Since IR-QIV can be used to measure a wide range of flows, can operate day or night and in most weather conditions, and can continuously and robustly measure at high spatial resolution over large areas, it is particularly of use where high accuracy and resolution measurements are required, such as for fish management applications, near hydraulic structures or at other locations with complex hydrodynamics, or at locations where physical access to the water is restricted or dangerous. Because measurements can be set up relatively quickly and without requiring contact with the water, we expect IR-QIV to increasingly become an important tool in responding to changing environmental conditions.

IR-QIV was developed in a partnership between Cornell University, the California Department of Water Resources (DWR), and the US Geological Survey (USGS) for applications including monitoring flow and discharge, and high resolution hydrodynamic measurements near fish guidance structures and barriers. In this presentation we will present an overview of the method, and discuss its capabilities and applications, including considerations that are relevant for any image-based velocity measurement methods, regardless of the imaged wavelengths (thermal, or visible-light).

Figure 1. IR-QIV example: Velocity calculated by IR-QIV (black arrows), plotted over an infrared image of the water surface at Sutter Slough, Califiornia, USA, superimposed on an aerial image.  From: Schweitzer, S. A., & Cowen, E. A. (2021). Instantaneous river-wide water surface velocity field measurements at centimeter scales using infrared quantitative image velocimetry. Water Resources Research, 57, e2020WR029279. https://doi.org/10.1029/2020WR029279

How to cite: Schweitzer, S. and Cowen, E.: Infrared Quantitative Image Velocimetry (IR-QIV): Instantaneous River-Wide Water Surface Velocity  Measurements at Centimeter Scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10350, https://doi.org/10.5194/egusphere-egu22-10350, 2022.