Orals: Fri, 2 May | Room 3.16/17
A novel approach has been developed to facilitate streaming gauging in shallow rivers with frequently changing geomorphologies. This effort, developed in partnership with the United States Geological Survey (USGS), aims to ultimately generate more accurate, real-time discharge estimates at previously ungauged locations in remote areas.
This presentation will introduce the StreamScope, designed for automated bathymetry retrievals. It is a low-power laser scanning instrument that uses a Class II 620 nm laser and an ultrasonic sensor to remotely measure cross-sectional geometry and generate real-time stage-area ratings. By utilizing onboard automation and clustering algorithms, StreamScope can accurately measure channel bathymetry using multiple angles, determine stream width and stage, providing data to enable real-time discharge estimates from noncontact sensors. Results from laboratory experiments will be presented to evaluate the laser’s efficacy under different solar radiation conditions, varying turbidity levels at different water depths, different bottom substrates, and at varying heights above the water’s surface. Notably, the experiments revealed that the laser's distance retrievals may serve as a proxy for turbidity, offering a potential new method for assessing water clarity in real-time.
How to cite: White, B., Gourley, J., Duarte, J., Kirstetter, P., and Wasielewski, D.: StreamScope: Fixed-mount laser scanning instrumentation for remote stream gauging in shallow rivers with frequently changing geomorphologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7488, https://doi.org/10.5194/egusphere-egu25-7488, 2025.
Riverbank analyses are crucial for understanding fluvial dynamics, evaluating environmental risks, and promoting the sustainable management of river catchments. The monitoring, assessment, and governance of river basins, as prescribed by EU Directives 2000/60/EC (Water Framework Directive) and 2007/60/EC (Floods Directive), are declined by Member States with their own guidelines and methodologies. This makes it difficult to develop globally applicable calculation tools for analyses.
To address these challenges, we developed a new toolkit of QGIS-based model scripts called QGIS Riverbanks Tools, advancing global riverbank analysis, management, and classification for various applications and serving as a foundational step toward a comprehensive suite for the assessment of the historical evolution of watercourses and the prediction of future tendencies. The scripts are specifically designed to support river analysis and risk assessment procedures, such as those outlined in the Italian IDRAIM methodology (Rinaldi et al., 2014), in particular, the developed tools are:
- Confined Valley Index (CVI): This tool quantifies the confinement of a river within its valley by calculating the ratio between the valley bottom width and riverbank width. It provides critical insights into river confinement, aiding in the identification of areas influenced by geomorphologic or hydrological constraints.
- River Banks Distance (RBD) and River Banks Distance Comparison (RBDC): These scripts calculate the distances between the river centerline and its banks using transects along a defined path. They facilitate the comparison of riverbank distances across different time periods (in any temporal scale), supports historical trend analyses and quantitative assessments of riverbank erosion.
- River Banks Segments Cutter (RBSC): This model segments riverbanks into discrete sections based on predefined stretches of the river centerline. Each segment inherits attributes from the centerline, facilitating localized analyses and improving data granularity.
- River Banks Safety Bands Tool (RBSBT): By calculating buffer zones around riverbanks based on annual erosion rates and user-defined multiplicative factors, this tool generates safety bands. These zones are critical for risk assessment and the planning of mitigation measures.
The QGIS Riverbanks Tools have been effectively applied in hydrological and hydraulic studies across more than 15 Sicilian rivers, yielding significant results in flood risk management and river morphology monitoring. By providing a standardized framework for analysis, these tools enhance accuracy in risk assessment and river segment classification and facilitate comparative analyses across diverse hydrodynamic fluvial contexts. Users can define parameters such as transect width, segmentation step, and erosion rate factors, adapting the models to various river systems. All tools generate many outputs with geospatial layers with rich attribute tables, enabling immediate visualization and in-depth analyses. Detailed guidance on using each tool, including descriptions of input parameters, variables, and output data, is embedded within the models as in-built help documentation in the QGIS processing tools.
This first-of-its-kind toolkit provides a comprehensive solution within QGIS, empowering hydrologists to conduct in-depth, granular analyses of riverbanks across a wide range of fluvial system assessment and management approaches. Future development will prioritize integrating these tools into a user-friendly QGIS plugin and incorporating near-real-time hydrological data to enhance predictive capabilities.
How to cite: Di Pietro, G., Stagnitti, M., Pennisi, V., Foti, E., and Musumeci, R. E.: Qgis RiverBanks tools suite for morphological river analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15570, https://doi.org/10.5194/egusphere-egu25-15570, 2025.
The changing climate and increasing anthropogenic pressures on hydrological systems emphasize the need for continuous river flow observations to support water resource management and hydrological research. Conventional monitoring methods, despite their long history, are expensive, intrusive, labor-intensive, and require significant maintenance, making them impractical for remote locations. In recent decades, non-intrusive image velocimetry techniques have emerged as an alternative, enabling surface flow velocity analysis from sequential image frames typically captured by commercial digital cameras. However, these methods have primarily been validated over short durations and have rarely been applied to northern latitude rivers, where hydrology is influenced by seasonal ice and snow cover. Furthermore, the reliance of optical imaging systems on visible wavelengths of light limits their usability in the low-light conditions typical of these regions.
This study employed hourly video data from statically installed thermal infrared cameras to analyze seasonal variations in surface flow velocities and discharges over two years of ice-free flow seasons in two hydrologically distinct northern latitude rivers in Finland. The methodology involved video frame pre-processing with photogrammetric re-projection, surface flow velocity detection using Space-Time Image Velocimetry (STIV) and Large-Scale Particle Image Velocimetry (LSPIV), and validation against in-situ Acoustic Doppler Current Profiler (ADCP) measurements. River discharges were computed using the mid-section method with bathymetry data derived from aerial laser scanning and ADCP datasets, and compared with national hydrological observations based on conventional stage-discharge relationships.
Validation of surface flow velocities obtained using the STIV technique showed strong agreement with near-surface ADCP measurements, consistent with findings from earlier studies. In contrast, results from the LSPIV technique were unreliable and insufficient for accurate discharge computations. Daily averaged discharges computed from STIV velocities effectively captured the seasonal flow dynamics at both sites and corresponded acceptable with conventional stage-discharge observations. These findings demonstrate that image velocimetry techniques, particularly STIV, can be used for near-continuous flow observation over extended periods, even in challenging northern latitude conditions. With further refinement to address existing uncertainties, remote sensing observation systems could offer a viable alternative to traditional hydrological monitoring.
How to cite: Heiskanen, H., Lotsari, E., and Välimäki, J.-M.: Assessing seasonal flow characteristics of two northern latitude rivers using static thermal infrared video data and image velocimetry methodology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16586, https://doi.org/10.5194/egusphere-egu25-16586, 2025.
High-resolution hydrometric monitoring of rivers is crucial as climate change significantly impacts the frequency and intensity of extreme events, leading to rapidly evolving flood and drought risk profiles. However, hydrometric data is often limited, with insufficient spatial resolution and coverage, especially in remote or hard-to-access rivers in alpine, arctic, and tropical regions.
Traditional hydrometric monitoring relies on station-based, in-situ measurements. Parameters such as water surface elevation, flow velocity, bed geometry, and river discharge are typically recorded using sensors installed directly in or near the flow.
In this study, we analyzed velocity measurements obtained using range-Doppler radar and compared them to Doppler radar experiments. Our findings demonstrate that range-Doppler radar is more effective for measuring surface velocity from a moving platform. Here, we discuss the methodology and rationale for adopting this innovative approach in future research on water observation systems. By utilizing range-Doppler radar, we aim to achieve high-resolution, extensive spatial coverage for key hydrometric variables like surface velocity.
How to cite: Grubesa, S., Drmic, L., Orlic, N., and Grubeša, T.: Advancing River Monitoring: High-Resolution Surface Velocity Measurement Using Range-Doppler Radar from Moving Platforms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1140, https://doi.org/10.5194/egusphere-egu25-1140, 2025.
Forecasting the magnitude and frequency of floods has become progressively important under climate change. Flood inundation, determined by flow depth and velocity, results from the interplay between gravitational and frictional forces. Flow resistance, a critical factor in determining velocity, is often parameterized using the roughness coefficient Manning’s n. Despite its importance in geomorphology and hydrology, constraining n in natural rivers remains challenging, as synoptic data on riverbed geometry and roughness are sparse. Topo-Bathymetric LiDAR (TBL) data open up new opportunities to constrain the spatial variation of n in rivers by providing detailed and accurate measurements of riverbed and water surface geometry. This study presents a novel, iterative approach to estimate spatially variable n values across a Digital Elevation Model (DEM), given that the channel bed, water surface, and total discharge are independently constrained. The method adjusts n iteratively until the best agreement between predicted and measured water depth is achieved. The model is first validated on artificial, homogeneous reference surfaces to establish statistical criteria for convergence to an optimal solution. We demonstrate the model's ability to resolve complex n distributions by introducing different n patches with varying patch sizes and testing how backwater effects influence model accuracy across varying channel slopes, n patch sizes, and river discharges. Finally, we apply our approach to a 1 m resolution DEM created from a high-resolution TBL dataset covering the 25 km-long Ardèche Gorge, France. This application highlights the method's effectiveness in natural environments, emphasizing its potential to enhance flow resistance parameterization linked to morphological characteristics when channel bed, water surface, and discharge data are available.
How to cite: Nativ, R., Steer, P., Guerit, L., Davy, P., Gailleton, B., Leroy, P., Godard, V., Cattin, R., and Lague, D.: Inverting the Friction Coefficient of Heterogeneous Riverbeds Using 2D Hydraulic Simulations and Fluvial Topo-Bathymetric LiDAR Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5794, https://doi.org/10.5194/egusphere-egu25-5794, 2025.
The new WMO Expert Team on Hydrometry and its mandate
Prepared for EGU 2025 in Vienna (April 2025) and HS1.2.1 Session on Innovative Technologies and Approaches in Hydrological Monitoring
Libor Ducháček, Salvador Peña-Haro, Elizabeth Jamieson, Tommaso Abrate, Jérôme Le Coz
The World Meteorological Organization (WMO) seeks to provide the framework for international cooperation to advance meteorological, climatological, hydrological, and related environmental services, to improve well-being of all. Within are several working groups and expert teams like the newly established Expert Team on Hydrometry (ET-Hydrometry).
The Expert Team on Hydrometry evolved from Project X (the short name for the WMO group titled the Assessment of the Performance of Flow Measurement Instruments and Techniques), which was established in 2008 and focused on assessing flow measurement instrumentation and measurement methodologies, through the development of literature reviews, the collection of data and reports, intercomparison events and activities, etc., and to make the relevant outputs (reports, guidance, best practices, software, etc.) available to Hydrological Services around the world.
In 2024, the Expert Team on Hydrometry (ET-Hydrometry) was established (to replace Project X) under the direction of the Chair of the Standing Committee for Measurement Instrumentation and Traceability (SC-MINT), under the WMO Commission for Observation, Infrastructure and Information Systems (INFCOM).
The overall objectives of ET-Hydrometry remains the same as the former Project X, but with an expanded scope beyond flow measurement instrument and techniques to encompass a broad number of hydrometric (water level and flow) activities and parameters. As well, with the growing need to support at a practical and operational level the implementation of new innovative technologies (particularly those coming from the WMO HydroHub initiative), there is an important role for ET-Hydrometry to play with establishing assessment methodologies and technology transition pathways for the validation and adoption of new technologies and methods. Furthermore, ET-Hydrometry will encourage and promote guidance material and standardized approaches that are freely available and accessible to all wherever possible. Both established and innovative hydrometric instrumentation and methodologies are to be considered, including lower cost and lower tech alternatives to traditional approaches.
In 2025, the primary objective of the expert group is to finalize guidance for organizing acoustic Doppler current profiler (ADCP) regattas and similar hydrometric intercomparison events. These events are highly valuable to National Hydrological Services (NHSs) as they facilitate the mutual comparison and verification of commonly used instruments for streamflow measurements under natural (field) conditions. These intercomparison events also aim to foster collaboration and encourage the exchange of technical knowledge and fieldwork expertise among participants. The results from these events can also provide valuable datasets for the advanced analyses of instrument performance and determining discharge measurement uncertainty.
How to cite: Ducháček, L. and Peña-Haro, S. and the Expert Team on Hydrometry: The new WMO Expert Team on Hydrometry and its mandate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17550, https://doi.org/10.5194/egusphere-egu25-17550, 2025.
Salt Dilution (SD) is an accurate, safe, relatively inexpensive and easily employed method to measure water flow in turbulent streams and rivers. It has been used in some form for over 100 years and continues to experience a renaissance with refined methods and improved technologies. However.. SD is challenging in less turbulent flows without “complete” lateral mixing, and also requires a continuous estimate of water level or other proxy to generate a continuous hydrograph. Image Velocimetry (IV, Large Particle IV or Space Time IV), on the other hand, has been used for more than 20 years to estimate the flow in more placid rivers and streams without making contact with the water, using high resolution video to measure the surface velocity. However.. apriori estimates of the surface (VS) to bulk (VB) velocity ratio (k) is required, as well as the channel wetted area (A).
In this research we examine whether we can marry the two technologies to create a comprehensive automated flow measurement system to span all flow regimes from turbulent to placid, by removing the need for apriori knowledge in the case of IV, and using continuous imagery as the proxy flow estimate in the case of SD. SD measures Q; IV measures VS; this method combines the two using the equation Q = VS*k*A to estimate continuous Q, as well as an estimate of surface to bulk velocity ratio (k), and wetted area (A).
The method/system has the potential to replace conventional stations that rely on expensive and dangerous site visits and error prone water level sensor proxies. The results of our preliminary investigations are presented for 3 test stations.
How to cite: Sentlinger, G., Poisson, J.-C., Patalano, A., and Mackay, S.: Automated Salt Dilution Instream Q (ASDIQ) with Image Velocimetry (IV): It Looks Like a Salty Marriage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12905, https://doi.org/10.5194/egusphere-egu25-12905, 2025.
Deep learning methods have been shown to achieve state-of-the-art velocity estimation across synthetic computer vision benchmarks and particle image datasets. Images acquired in real environments however, present additional challenges such as seeding sparsity, time-varying seeding morphology, imperfect lighting, camera stability and orientation. Therefore, we evaluate the performance of deep learning based velocity estimation methods across a range of real hydrodynamic images and compare with classical methods. We employ a hydrodynamics laboratory dataset featuring a variety of flow types and two open-source aerial river footage datasets from field campaigns. Our investigation explores three deep learning approaches which utilise different operating principles; recurrent all-pairs-field transforms (RAFT), a physics-informed approach and an unsupervised learning approach (UnLiteFlowNet-PIV). Additionally, we demonstrate the applicability of the unsupervised method for environmental flow velocimetry, where ground truth data sources are unavailable for supervised model training.
How to cite: Tlhomole, J., Hughes, G., Zhang, M., and Piggott, M.: Deep learning for surface flow velocimetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18048, https://doi.org/10.5194/egusphere-egu25-18048, 2025.
Digital Twins (DT) are a dynamic virtual representation of a system and have been widely used in engineering and industry. A key advantage of DT technology is its ability to quickly capture and visualize large spatially disparate data sources and to combine them with numerical modelling to replicate systems in real time as well as provide near time forecasts and predictions. Here we present a pilot DT, FLOODTWIN, built for water-related hazard forecasting and decision-making in the first instance for Hull and the East Riding of Yorkshire (UK), a region heavily impacted by several hydrometeorological hazards including groundwater, surface water, river and coastal flooding. This federated cyber-physical infrastructure ecosystem was conceptualized using interconnected systems including a programme of Earth Observation (EO), sensor and network integration, modelling, data infrastructure development and stakeholder engagement. Significant outcomes of FLOODTWIN include the integration of EO and sensor data, a combined ground/surface water model geared towards decision making, development of a real-time digital hub for assessing, analysing, storing, passing and serving data and longitudinal professional stakeholder engagement through co-creation of project tools. This interdisciplinary study helps to improve the efficiency, resilience, and sustainability of a new evidence-base to underpin improved multi-agency decision-making in flood risk management - with possible foci including past flood review, nowcasting and future planning.
How to cite: Panchanathan, A., Novellino, A., Mansour, M., Watson, C., Scheidegger, J., Barkwith, A., McEwen, L., Underhill, H., Becker, R., Buytaert, W., and Coulthard, T.: Digital Twins for Hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19184, https://doi.org/10.5194/egusphere-egu25-19184, 2025.
Information on river shape, bed morphology and sediment load are critical to help inform research and management issues related to river channels. However, such information is not easily accessible and/or available in the public domain. RIMORPHIS (River Morphology Information System) fills this information gap by providing a web platform for aggregating, storing, sharing and analyzing river related scientific data. Additionally, it serves as a clearing house for river morphology data to help improve our overall understanding of rivers’ health using scientifically-rendered datasets. This presentation will provide an overview of RIMORPHIS, including its capabilities to access publicly available data in usable form, process and visualize river morphology data, interact with other river data repositories and interoperate with other community resources such as CUAHSI HydroShare. RIMORPHIS provides several tools for scientific analysis, including coordinate transformation of bathymetry points from cartesian coordinates to channel fitted coordinates, creating optimal configuration of cross-sections from irregularly spaced bathymetry points, generating bathymetry mesh, and creation of synthetic bathymetry using conceptual and deep learning models. Overall, RIMORPHIS aims to advance river morphology research by not only providing data to the community but also tools to process the data and produce new information.
How to cite: Merwade, V., Muste, M., Demir, I., Cox, A., and Minear, T.: RIMORPHIS – A platform for discovering and processing river morphology data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20007, https://doi.org/10.5194/egusphere-egu25-20007, 2025.
Posters on site: Fri, 2 May, 10:45–12:30 | Hall A
With the increasing frequency of extreme weather events, such as river flooding, there is a growing need for more cost-effective and efficient methods for hydrometric river monitoring. Moreover, traditional in-situ hydrometric surveys often face challenges when applied to remote or hard-to-access river locations. Therefore, we investigated the potential of using Unoccupied Aerial Systems (UAS) hydrometry surveys to develop a hydraulic model for extracting rating curves, which can then be used to derive discharge from satellite altimetry-based Water Surface Elevation (WSE) measurements.
This study employed UAS-borne Water Penetrating Radar (WPR) to map river bathymetry, while Digital Elevation Models (DEM) were used to extract the non-submerged portions along the WPR cross section. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receiver provided ground truth WSE measurements. Additionally, the pixel cloud data product of the Surface Water and Ocean Topography (SWOT) satellite mission was used to extract WSE along the river. Furthermore, based on the cross sectional geometry information, we constructed two one-dimensional hydraulic models, one using a steady gradually-varied flow solver and the other using the MIKE+ hydrodynamic solver.
The study site is located along the Torne River in northern Scandinavia, which forms part of the national border between Sweden and Finland. From September 3rd to 9th, 2024, surveys were conducted at 23 field sites distributed across two areas of interest along the river. Manning's numbers for the river reaches were calibrated against WSE observations derived from the SWOT pixel cloud dataset using the steady gradually-varied flow solver. Hydraulic models were employed to construct rating curves at chainage locations where observations from the Sentinel-3 satellite mission were available at two defined virtual stations: Övertorneå and Pello. These rating curves were subsequently used to convert WSE observations by the SWOT pixel cloud and Sentinel-3 to discharge, enabling the construction of a river discharge time series.
How to cite: Zhou, Z., Damgaard Christensen, F., Flendsted Jensen, V., Andreas Pedersen, M., Nielsen, S., Wennerberg, D., Fagerström, V., Gustafsson, D., Cendagorta, D., Jose Escorihuela, M., and Bauer-Gottwein, P.: Virtual station rating curves derived from hydraulic models informed with UAS hydrometry and SWOT WSE , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5749, https://doi.org/10.5194/egusphere-egu25-5749, 2025.
River monitoring is of particular importance in river engineering due to decision-making related to protecting life and property from water-related hazards, such as floods and water resources management.
In this direction, three cross-sections (XSs) were surveyed along a 10 km stretch of the Rönne River in Sweden. Ground-truth surface velocity measurements were obtained using an electromagnetic velocity sensor (OTT MF Pro). Additionally, videos captured by a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques (Zhou et al., 2024). The bathymetry data for all cross-sections were recorded by the water penetrating radar.
The Entropy model was applied to the three different selected sites to estimate the two-dimensional cross-sectional velocity distribution by exploiting the available data, with the aim to estimate river discharge. Specifically, the surface velocities and bathymetry data for each section were considered as input for the Entropy model (Bahmanpouri et al., 2022a, b). The phenomenon of the velocity dip induced by the secondary current was also implemented in the estimation of the vertical velocity distribution where, for aspect ratios (river width/flow depth) lower than 5, the maximum velocity was observed below the water surface. Secondary currents result in a vertical shift in momentum, enhancing the turbulence and shear stress near the bed. Finally, the discharge rate was calculated for each cross-section using the mean velocity of the section and the observed flow area. The results highlighted the potential of the combination of the UAS‐Borne Doppler Radar and the theoretical Entropy model to estimate the velocity distribution and flow discharge with high accuracy. The suggested methodology would be of particular benefit in estimating the velocity distribution and flow discharge for inaccessible locations especially during high flow conditions where there are in-situ dangers for operators to measure flow characteristics. The work is funded by the European Union's Horizon Europe research and innovation programme as part of the UAWOS project (Unoccupied Airborne Water Observing System).
Keywords: Entropy, Velocity distribution, Velocity dip, Flow discharge, UAS‐Borne Doppler Radar, Rönne River
Bahmanpouri, F., Barbetta, S., Gualtieri, C., Ianniruberto, M., Filizola, N., Termini, D., & Moramarco, T. (2022). Prediction of river discharges at confluences based on Entropy theory and surface-velocity measurements. Journal of Hydrology, 606, 127404.
Bahmanpouri, F., Eltner, A., Barbetta, S., Bertalan, L., & Moramarco, T. (2022). Estimating the average river cross‐section velocity by observing only one surface velocity value and calibrating the entropic parameter. Water Resources Research, 58(10), e2021WR031821.
Zhou, Z., Riis‐Klinkvort, L., Jørgensen, E. A., Lindenhoff, C., Frías, M. C., Vesterhauge, A. R., ... & Bauer‐Gottwein, P. (2024). Measuring river surface velocity using UAS‐borne Doppler radar. Water Resources Research, 60(11), e2024WR037375.
How to cite: Bahmanpouri, F., Barbetta, S., Hu, X., Zhou, Z., Wennerberg, D., Tarpanelli, A., and Bauer‐Gottwein, P.: Applying the Entropy theory to estimate river flow using the surface velocity by UAS‐Borne Doppler Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4136, https://doi.org/10.5194/egusphere-egu25-4136, 2025.
The measurement of streamflow in the world’s rivers is critical to the management of water as a resource and to predicting and managing the impacts of potentially damaging hydrological events such as major floods. Aerial drones capable of capturing high-resolution digital video have shown enormous potential to improve observations of river and floodplain flows and to benefit science and research projects where streamflow must be measured. However, their effectiveness, operational readiness, and the accuracy of observations in UK rivers is at present largely unknown.
This research closes this knowledge gap by undertaking a thorough assessment of the performance and usability of aerial drones over a wide range of locations and conditions.
Furthermore, as the technique has yet to fully transition from the research domain to operational use, there remain a number of practical challenges and uncertainties over how and where drone-based methods can be applied. This project will identify and address limitations and create further opportunities for the research community to help refine the methods to become effective for widespread operational use.
Low-cost consumer-grade aerial camera drones were deployed at a range of sites in England and Scotland and the resulting river discharge results compared against reference values obtained with Acoustic Doppler Current Profilers (ADCPs) to assess their potential for making accurate measurements of river discharge. A total of 45 comparisons were made at 28 sites, almost all of which were hydrometric river flow gauging stations. At some sites, measurements were made at more than one location.
The drones used were low-cost consumer-grade models available from electronics and photography stores for between €500 and €1200 – orders of magnitude cheaper than traditional discharge measurement tools and equipment.
How to cite: Everard, N. and Shaw, A.: The UKCEH DroneFlow project: Assessing the potential of drone-based velocimetry across a range of UK river types and flow conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17286, https://doi.org/10.5194/egusphere-egu25-17286, 2025.
River discharge plays a crucial role in hydrologic studies and water resource management. Accurate discharge estimations enable significant advancements in scientific research and water-related decision-making processes. Given that discharge is the product of flow area and flow velocity, traditional in situ measurements of river discharge typically require detailed data on river water level, river bathymetry, and the bulk velocity of the river cross-section. However, such manual measurements are time-consuming and impractical in certain situations, such as rivers in remote, hard-to-access regions or those experiencing extreme high-flow events. With the increasing availability of technical and computational resources, remote sensing offers significant potential for improving discharge estimation. Among these technologies, Unmanned Aerial Systems (UAS) have emerged as a valuable solution for improving discharge estimation. Their low cost, high accuracy, and ability to cover large areas make them particularly effective for monitoring in remote or hard-to-access locations. While numerous studies have developed and demonstrated the feasibility of UAS-based discharge estimation algorithms, their evaluations are often limited to specific sites. Thus, questions remain regarding the adaptability of these algorithms across diverse river systems.
Funded by European Union's Horizon Europe project UAWOS (Unoccupied Airborne Water Observing System), This work focuses on evaluating the performance of UAS-based discharge estimation algorithms across a diverse set of cross-sections to enhance their generalizability. Specifically, this work seeks to address the following key questions: 1, how well the discharge algorithm performs based on UAS velocimetry, bathymetry and water surface elevation across different cross-sections? 2, which input datasets and river characteristics may limit the performance, and how sensitive they are? 3, how can we improve the discharge estimation strategies?
To answer the questions above, we applied several bulk velocity estimation models on UAS hydrometry parameters to calculate the river discharge among various types of rivers: Rønne Å River (Sweden), Isar River (Germany), Po River (Italy), Orco River (Italy), and Torne River (Sweden). We utilized various in situ discharge measurements to assess the accuracy of our algorithms and investigated how specific cross-sectional properties affect performance. We further systematic analysed the uncertainty from the inputs and models, and discovered strategies to optimize the discharge estimation results by utilizing Bayesian inference. Overall, this study shows that advanced UAS hydrometry technique is an accurate and reliable way for river discharge estimation, providing valuable insights for hydrological studies and water resource management.
How to cite: Hu, X., Zhou, Z., Bahmanpouri, F., Tuo, Y., Tarpanelli, A., Barbetta, S., Gustafsson, D., Daniel, W., Broich, K., Merk, F., Disse, M., and Bauer-Gottwein, P.: Contactless river discharge surveying with UAS hydrometry: Performance evaluation using a large and diverse set of river cross sections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19896, https://doi.org/10.5194/egusphere-egu25-19896, 2025.
In this study we explore the potential to inform a nation-wide high-resolution hydraulic model used for flood risk forecasting, using Unoccupied Aerial Systems (UAS) hydrometry surveys and satellite altimetry. Drone-based data on bathymetry, water surface elevation, slope and discharge was collected along a 50 km flood-prone part of the Torne river, located on the border between Sweden and Finland during a low flow period in September 2024. Cross-section profiles of bathymetry, water surface velocity and elevation were sampled at about 1 km distance. Along river profiles of water surface elevation were collected both with the UAS surveys, as well as from the SWOT satellite mission for the survey period and previous historic data back to April 2023.
The Lisflood-FP model was previously set up at a 5x5 m2 resolution for all rivers in Sweden with an upstream area larger than 50 km2. To make this possible, the model setup was split into around 13000 sub-models based on the existing sub-basin delineation of the hydrological model used for discharge predictions (S-HYPE). Each Lisflood-FP sub-model was calibrated separately using observations of water level along the local river reach derived from the national laser-scanning data and the corresponding discharge predicted by the hydrological model at the dates of the laser-scanning of the area. In most sub-models, this meant that calibration was made using only one set of discharge and water level data. Additionally, several assumptions were made in lack of more information regarding the river bathymetry and downstream boundary conditions.
Based on the UAS and satellite altimetry data, we will demonstrate the potential to improve the previously setup hydraulic model with regard to flood risk assessment, in particular the ability to predict a recent flood event during the spring flood 2023. The UAS and altimetry data is used to improve the representation of river bathymetry, downstream boundary condition (slope), as well as impact of additional along river water surface elevation data for model calibration.
How to cite: Gustafsson, D., Greve Villaro, C., Petersson Wårdh, L., Wennerberg, D., Fagerström, V., Zhou, Z., Damgaard Christensen, F., Nielsen, S., Cendagorta, D., Escorihuela, M. J., and Bauer-Gottwein, P.: Use of UAS and space born hydrometric data to improve flood modelling along the Torne river in northern Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17836, https://doi.org/10.5194/egusphere-egu25-17836, 2025.
With the rapid development of UAV technology, the challenge of selecting appropriate sampling areas and flight altitudes to ensure that collected data is both representative and accurate has gained increasing attention in the field of sedimentology. This study specifically investigates UAV-based sampling methods for riverbed grain size analysis, focusing on the critical task of determining optimal flight altitudes and sampling areas that can accurately capture the distribution of riverbed sediments.
By comparing systematic sampling with other traditional methods, this research aims to validate the precision and reliability of data collected through UAV imaging. The results show that under carefully selected conditions, such as a 5m×5m area with a flight altitude of 20 meters, it is possible to balance the need for detailed resolution with the goal of reducing errors caused by abnormal grain size distributions. This study further contributes to the optimization of UAV sampling methods, providing guidelines for practitioners seeking to enhance the accuracy and representativeness of their sediment research.
The findings of this research not only offer practical recommendations for UAV-based sediment analysis but also introduce strategies to improve sampling methodologies in river systems. These strategies aim to reduce biases and improve the reliability of UAV-generated data in riverbed sediment studies, ultimately contributing to more robust environmental monitoring and management practices.
How to cite: te wei, W.: UAV image analysis of particle size distribution in rivers , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3383, https://doi.org/10.5194/egusphere-egu25-3383, 2025.
Discharge evaluations in spring-fed headwater streams are crucial for understanding hydrological processes and improving water resource management. Small streams, however, pose challenges due to low flow and turbulent conditions that limit the reliable application of traditional methods such as impeller devices, electromagnetic sensors and acoustic doppler profilers.
This study tested a variety of discharge measurement methods in two catchments with differing hydrological and physical attributes. The tested methods included impeller and electromagnetic current meters, volumetric gauging, a floating method, chemical and optical tracers and an innovative thermal imaging technique. The thermal imaging method involved introducing hot water into the stream and observing its heat dispersion using a thermal imaging camera.
Results highlighted the strengths and limitations of each approach under varying conditions. At sites with very low discharge of 0.1 to 0.3 L s-1 or highly turbulent flows of 15 to 22 L s-1, discrepancies between methods reached up to ±45%. In contrast, measurements at sites with moderate discharge of 2 to 6 L s-1 and smoother riverbeds, showed error margins mostly below 10%. The novel thermal imaging approach proved to be reliable, easy to use, minimally invasive, and particularly effective for small or hydrologically complex spring systems.
How to cite: Schubert, C., van Geldern, R., Visser, A.-N., Gossel, W., and Barth, J. A. C.: Critical Assessment of Discharge Measurement Approaches in Small Streams: A Comparison of Traditional Methods and the Novel Thermal Imaging Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8779, https://doi.org/10.5194/egusphere-egu25-8779, 2025.
Introduction
EDF (Électricité de France) is one of the world's largest electricity generators, with an installed capacity of about 130 GW. Streamflow velocity analysis plays a critical role within its hydroelectric and nuclear activities in ensuring the safety of facilities, optimizing the use of natural resources and meeting environmental requirements.
The ADCP (Acoustic Doppler Current Profiler) is a key technology for measuring flow rates and velocity profiles along depths. Based on the Doppler effect, this method detects frequency shifts generated by the movement of particles in the water. The collected data is then processed using specialized software such as VMT (Velocity Mapping Toolbox [1]) or the MAP tool integrated into the QRevInt software [2] to compute 3D streamflow velocity. However, the ADCP technology has significant limitations due to its "local" perspective, which focuses only on transects. To understand larger-scale flow patterns, such as recirculation zones and water pathways, a broader spatial coverage is required. The ADCP struggles to provide this coverage due to deployment time constraints and operational conditions related to stable flow rates. Generally, only a limited number of transects can be carried out.
To complement ADCP data, LSPIV (Large-Scale Particle Image Velocimetry) can be used. This method analyzes image sequences of the flow. By detecting visible tracers such as plant debris, bubbles, or turbulence patterns, it estimates the 2D surface velocity field. The Fudaa LSPIV software, developed by INRAE and EDF [3], is particularly well-suited for large-scale applications and easy to use, especially when combined with images from aerial drones. This makes it highly useful for rapid measurements over large areas, providing a comprehensive understanding of the steady-state flow patterns. It effectively addresses the limitations of ADCP technology, offering a complementary solution for more complete hydrodynamic analyses.
Practical example : study of Bazacle
- Context
A practical example of this complementarity is an operational study conducted at EDF's Bazacle hydroelectric plant on the Garonne River in Toulouse, France. By combining a Teledyne RDI RioPro 1200kHz ADCP mounted on a Pario2 aquatic drone from RiverDrone with LSPIV technologies using an aerial drone, a comprehensive analysis of velocity profiles was performed to design a solution focused on optimizing fish ecological continuity in the studied area.
- Results
For this study, about ten transects were conducted at varying distances from the intake of the turbine and the Bazacle weir. The observed surface velocities ranged between [0;140 cm/s]. They were higher on the right bank, immediately upstream of the water intake screen, and downstream the weir. Velocities at depth ranged between [0;60 cm/s] upstream of the weir and between [0; 120 cm/s] downstream.
References
[1] Parsons, D. R., Jackson, P. R., Czuba, J. A., Engel, F. L., Rhoads, B. L., Oberg, K. A., Best, J. L., Mueller, D. S., Johnson, K. K., & Riley, J. D. (2012). https://onlinelibrary.wiley.com/doi/abs/10.1002/esp.3367
[2] Lennermark, M., & Hauet, A. (2022). https://meetingorganizer.copernicus.org/EGU22/EGU22-9379.html
[3] Le Coz, J., Jodeau, M., Hauet, A., Marchand, B., Le Boursicaud, R. (2014) River Flow.
How to cite: Perriaud, T., Hauet, A., Morlot, T., and Bodart, G.: Analysis of velocities along depths : complementarity of ADCP and LSPIV technologies for hydrometric studies , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8317, https://doi.org/10.5194/egusphere-egu25-8317, 2025.
Reliable discharge data is a key requirement of hydrological studies, yet previous research has primarily focused on detecting sensor errors and outliers. Undetected changes in stage discharge relationships and the resulting discrepancy between the actual and measured discharge have received significantly less attention. The present study aims to contribute to closing this research gap by developing a detection routine for unnoticed changes in stage discharge relationships. In a first step, classical statistical methods are tested. In a subsequent step, a machine learning approach is evaluated and contrasted with the statistical methods. The study is conducted on the two Swiss rivers, Aare and Reuss, which comprise 41 gauged subcatchments.
How to cite: Meyer, B., Horton, P., and Schaefli, B.: Preliminary study on the detection of unnoticed changes in stage discharge relationships, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18081, https://doi.org/10.5194/egusphere-egu25-18081, 2025.
Riverine landscapes are shaped by the feedbacks between hydrological, geomorphological and ecological processes. These feedbacks occur across multiple scales, from the scale of single plants modifying the hydraulic forces around it to the formation of landforms like islands which in turn lead to the emergence of specific river types such as braided or anastomising. Over the past years, the field of biogeomorphology has significantly improved the understanding of the interaction of vegetation and hydro-morphological processes. Despite recent scientific progress, research gaps remain. In particular, it is still poorly understood, how processes happening on small scales, such as sedimentation in the lee of an individual plant or a piece of large wood, lead to the emergence of landforms and reach scale river types and how – vice versa – the specific landform pattern within river types foster small scale processes. The concept of Panarchy considering a number of adaptive cycles linking the different scales of the fluvial biogeomorphic system is a promising candidate for analyzing cross-scale vegetation-hydromorphology feedbacks. However, developing methods for quantitative studies is still an ongoing challenge in biogeomorphological research.
We introduce an empirical approach for filling this research gap driven by a combination of field mapping and state-of-the-art remote sensing taking the Naryn River, a large free flowing river in Kyrgyzstan, as a case study. In the field, we map vegetation traits and geomorphic characteristics and link them to the stages of the biogeomorphic succession concept. Then, we utilize the computational potential of the “Terrabyte” cloud computing platform of the German Aerospace Center (DLR) to analyze temporally dense time series from the Sentinel-1 and -2 archives. To map vegetation and hydro-geomorphic characteristics (vegetation height, density, biomass, share of bare sediment, grainsize, duration of inundation) and to assess how these biogeomorphic traits change over time, we make use of the capabilities of the recently available foundational deep learning model “Clay” as state-of-the-art artificial intelligence method in earth observation. This enables river corridor scale analysis of the spatial-temporal dynamics of hydro-geomorphic disturbance, rejuvenation potential (windows of opportunity), vegetation growth as well as the emergence of biogeomorphic feedback windows and therefore tracking the biogeomorphic succession. This gives us the possibility to study adaptive cycles on different scales and construct Panarchies for different river types occurring along the Naryn River. Our approach is a significant step towards the quantification of biogeomorphic feedbacks across multiple scales and advances the empirical understanding of the role of scale dependence of biogeomorphic feedbacks which lead to the emergence of riverine landscape pattern.
How to cite: Betz, F., Lauermann, M., Arisoy, B., Becker, I., Egger, G., and Kulikov, M.: Leveraging the potential of satellite time series, cloud computing and artificial intelligence to quantify fluvial biogeomorphology across multiple scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13312, https://doi.org/10.5194/egusphere-egu25-13312, 2025.
Accurate bathymetric mapping is essential for a wide range of applications, including coastal management, navigation, hydrodynamical modeling, and environmental monitoring. Traditional methods such as sonar and LIDAR surveys, while precise, are often cost-prohibitive, time-consuming, and limited in spatial coverage, particularly for remote or inaccessible areas. This study explores the application of Sentinel-2 satellite imagery (about 10m resolution) combined with the band-ratio algorithm as a high-resolution and cost-effective approach to estimating bathymetry in riverine environments. The Rhone river, a critical waterway in the Mediterranean region, has been selected as case study due to its environmental and economic significance.
The band-ratio algorithm utilizes the differential attenuation of light in the blue and green spectral bands to estimate water depth. Sentinel-2’s high spatial resolution and multispectral capabilities make it an ideal source for this method. A key aspect of this study was the evaluation of several atmospheric correction techniques to preprocess the satellite images by mitigating atmospheric interference and ensuring accurate reflectance values. The tested correction methods included QGIS Dark Object Subtraction (DOS), ACOLITE Dark Spectrum Fitting (DSF), ACOLITE Exponential Rayleigh (EXP), and the C2RCC processor in ESA’s SNAP software. These methods were compared to identify the optimal approach for handling the optically complex waters of the study area.
EMODnet bathymetry data in the shelf off the Rhone river mouth was used to train the band-ratio algorithm through regression models that related the computed band-ratio index to observed water depths. The accuracy of the derived bathymetry was assessed using statistical metrics, including root mean square error (RMSE), correlation coefficient (R²), mean bias, and mean absolute error (MAE).
A subset of the Sentinel-2 images has been selected based on cloud cover, water clarity, and temporal relevance to the study period and among them the data acquired on September 11, 2022, provided the most accurate results. This image achieved an R² value of 0.8, an RMSE of 0.79 meters, and an index of agreement of 0.88 for depths ranging from 0 to 10 meters. These results demonstrate that the combined use of Sentinel-2 imagery (after proper atmospheric correction) and the band-ratio algorithm can yield reliable bathymetric estimates in shallow, moderately turbid river environments.
How to cite: Viola, F., Saleem, A. A., and Verri, G.: Cost-Effective and high-resolution Bathymetric Mapping in Rivers: Leveraging Sentinel-2 and the Band-Ratio Algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17606, https://doi.org/10.5194/egusphere-egu25-17606, 2025.
