The accurate and timely measurement of streamflow is therefore more critical than ever to enable the management of water for ecology, for people and industry, for flood risk management and for understanding changes to the hydrological regime. Despite this, effective monitoring networks remain scarce, under-resourced, and often under threat on a global scale. Even where they exist, in-situ observational networks are increasingly inadequate when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.
This session aims to tackle this problem by inviting presentations that demonstrate new and improved methods and approaches to streamflow monitoring, including:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) UAS and satellite remote sensing techniques for hydrological & morphological monitoring;
4) Measurement in extreme conditions associated with the changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.
Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.
Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.
Posters on site: Wed, 6 May, 16:15–18:00 | Hall A
Whether we talk about safety reasons, energy production or regulation, water resources management is one of EDF’s (Électricité De France, French hydropower company) main concerns.
The range of water-based activities is steadily increasing : paddleboarding, canoeing, float-tube fishing, these floating crafts are now widely available at low cost and are becoming popular on rivers. EDF’s hydroelectric facility operators are regularly faced with the intrusion of swimmers and watercraft near the structures. This occurs, for example, beyond the restricted zone upstream of hydroelectric installations. This zone, usually marked by a line of buoys, is intended to prevent drowning accidents that could result from the start-up of a turbine or any other system capable of creating a suction current. Similar risks exist downstream of the structures, where currents and depths attract swimmers and floating crafts.
Given the risky behavior of river users, EDF’s challenge is to secure the vicinity of its installations by assessing the danger and proposing appropriate countermeasures. In this context, EDF aims at determining current thresholds beyond which the swimming ability and maneuvering capacity of floating crafts (paddleboards, canoes, float-tubes) for different user profiles (children, adults, athletes) are no longer sufficient to escape the danger of being sucked in or swept away. This study therefore only concerns areas where users cannot stand in the river. Such work will enable the company to implement the necessary measures to secure zones considered hazardous near hydroelectric structures.
To carry out this work, tests in collaboration with members of the SDIS 81 Water Rescue team were conducted at the Millau whitewater stadium to determine the current speeds beyond which swimmers and light watercraft can no longer escape danger. These trials, carried out under controlled and safe conditions, involved scenarios of swimming and maneuvering floating crafts in currents ranging from weak to strong at the Millau water sports facility. The objective was to assess the swimming and mobility capacities of swimmers and non-motorized watercraft (paddleboards, canoes, float-tubes) across different current speed ranges.
All the tools routinely used by EDF hydrometric teams to measure flow velocities were deployed (ADCP, current meter, SVR radar, LSPIV) to better characterize the different current speeds tested.
The results obtained made it possible to identify the threshold values sought, based on current speed and the presence or not of turbulence. Finally, a theoretical approach based on Newton’s second law helped corroborate the empirical results obtained during the tests.
How to cite: Morlot, T., Belleville, A., and Le Brun, M.: Risk near hydroelectric structures – Determination of critical velocity thresholds for river users (swimmers and floating crafts), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3833, https://doi.org/10.5194/egusphere-egu26-3833, 2026.
River discharge is a key indicator for water resources management and flood forecasting; however, the traditional single stage–discharge rating curve used for its estimation produces systematic errors under unsteady flow conditions due to hysteresis. In this study, field-measured Manning’s roughness coefficients (n) are first estimated at Naju Bridge on the Yeongsan River by combining H-ADCP–measured discharges with water-surface slopes derived from upstream and downstream stage observations, using the continuous slope–area (CSA) framework in inverse form. The resulting 10-min n time series for the 2020 flood events is then segmented by stage to represent cross-sectional controls on roughness. These stage-wise n segments are subsequently applied to the CSA method to compute discharge time series for the 2019 and 2021 flood events, and the estimates are validated against observations. The estimated n exhibits a consistent stage-dependent pattern, including a rapid decrease at low stages, convergence at intermediate stages, an inflection point near the onset of rapid cross-sectional expansion, and an increase at high stages, reaching n ≈ 0.08–0.09 during extreme floods—values higher than those from conventional empirical formulas and design criteria. Using the measured and stage-segmented n, the CSA-based discharge estimates successfully reproduce hysteresis across six flood events, achieving R² ≥ 0.94 and peak error ≤ 3%, although nRMSE exceeds 10% under low-flow conditions. Overall, applying field-derived roughness substantially improves CSA discharge estimation and supports the practical use of roughness monitoring and CSA-based computation in rivers subject to unsteady flow.
How to cite: Ikhan, L., Dongsu, K., Dohyeon, K., Gibeom, S., and Jonghyeon, Y.: Field Estimation of Manning’s Roughness and Its Application to CSA Discharge Computation during Flood Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4747, https://doi.org/10.5194/egusphere-egu26-4747, 2026.
Sediment transport is a key process in fluvial geomorphology, particularly in gravel-bed rivers, as it controls channel morphology and has important implications for river management and restoration. This process occurs in three main phases: entrainment, movement, and deposition, and is influenced by factors such as particle size, shape, density, angularity, imbrication, and the ratio between transported sediment size and bed material. Although field data on sediment transport are essential, they are often difficult to obtain. To overcome this, a wide range of monitoring techniques has been developed, including direct samplers such as the Helley-Smith sampler and bedload traps, acoustic sensors such as geophones and hydrophones, and laboratory experiments that allow sediment dynamics to be studied under controlled conditions. In parallel, sediment transport models rely strongly on grain size distribution, either using the full distribution or representative metrics such as D50 or D84. Since the 1990s, sediment tracking has become increasingly important, with gravel tagging emerging as a widely used method for analysing sediment mobility and travel distances. Technological advances have significantly improved recovery rates, particularly through the use of electronic tags. Passive Integrated Transponder (PIT) tags are now commonly used due to their small size, low cost, long lifespan, and passive operation. Active tags, such as VHF and UHF, enable continuous tracking via fixed antennas but are larger and more expensive. In this work, we used active tags to estimate the volume of sediment mobilised by deploying RFID‑tagged gravels, thereby improving our understanding of sediment movement through the concept of virtual velocity. Tag seeding was carried out at four sites upstream of the first fixed antenna. Preliminary results show that gravels in the 45–181 mm size range (85% of all seeded material) have been mobilised over distances of approximately 700 m and detected by the antennas, whereas gravels in the coarsest size classes have not yet been recorded. In conclusion, higher‑magnitude events are required for the coarsest particle sizes to become mobile and be detected by the first antenna. Although several low‑magnitude events have occurred, very few of the mobilised gravels have been detected by the second antenna located downstream of the first, suggesting that they may have become buried or trapped in pools.
ACKNOWLEDGMENTS: This work is funded by the European Research Council (ERC) through the Horizon Europe 2021 Starting Grant program under REA grant agreement number 101039181 - SEDAHEAD.
How to cite: Juez, C. and Rabanaque, M. P.: Sediment mobility in a gravel-bed river (Aragón Subordán River, Central Spanish Pyrenees) assessed using active RFID tags, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7104, https://doi.org/10.5194/egusphere-egu26-7104, 2026.
Ensuring the safety of spillways during flood events is a critical challenge for dam operators and public authorities. Accurate knowledge of flow velocities along spillways and downstream of ski jumps is essential for assessing the erosive potential of high‑velocity flows and preventing structural damage. However, intrusive velocity measurement techniques are unsuitable in such configurations due to limited accessibility and the very high flow speeds involved. Image‑based velocimetry offers an attractive alternative, providing instantaneous, spatially distributed velocity fields from a single viewpoint. Yet, conventional image-based techniques rely on the assumption of a planar free surface, which becomes invalid for curved flows such as those encountered on spillway chutes or nappe flows. Surface curvature induces geometric distortions in ortho‑rectified images, leading to significant velocity errors.
Stereo‑vision systems can be used to reconstruct non‑planar free surfaces, but their deployment on full‑scale spillways is complex and costly as it requires synchronized cameras with high spatial and temporal resolution. To overcome these limitations, we propose OrthoCyd, a novel single‑camera orthorectification method dedicated to flows whose free‑surface geometry is known a priori and corresponds to a right‑cylindrical surface (i.e., a planar surface which is curved along the longitudinal dimension). This approach is well suited to spillway chutes, with the assumption that the free surface follows the underlying curved geometry. OrthoCyd enable consistent displacement measurements with any image-velocimetry method (block matching, tracking, optical flow, spatio-temporal approach). This method extends the applicability of image‑based velocimetry to non‑planar free‑surface flows while maintaining the simplicity and practicality of single‑camera acquisition systems.
Two applications illustrate the method: a laboratory experiment on a free‑nappe flow, and a field application on an operating spillway. These examples demonstrate that OrthoCyd provides reliable velocity measurements in both controlled and full‑scale conditions.
How to cite: Bodart, G., Hauet, A., Le Coz, J., and Jodeau, M.: Applying image velocimetry on curved free‑surface geometries of known shape such as spillways or free-nappe: the OrthoCyd method., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7926, https://doi.org/10.5194/egusphere-egu26-7926, 2026.
Recently, non-contact current meters (radar, image-based) are widely used to measure the discharge of rivers, and research and cases of calculating discharge by applying surface velocity-based index velocity method and velocity distribution method are increasing. To apply index velocity or velocity distribution methods based on surface velocity, the relationship between the surface velocity used as the index velocity and the cross-sectional average velocity must be known. In general, in straight channels with sufficiently large channel width, the ratio of the cross-sectional average velocity to the maximum velocity (ϕ(M)) is known to be between 0.6 and 0.7.
In this study, the relationship between the maximum surface velocity and the cross-sectional average velocity was analyzed. Using 179 flow measurement data from 60 sites, the ratio of the cross-sectional average velocity to the maximum surface velocity (≈ϕ(M)) was calculated. As a result, ϕ(M) was analyzed to have an average of 0.64(correlation coefficient R=0.86). When the relationship between the two elements was established as a linear equation, the slope was calculated to be 0.6306 (R=0.74). The ratio of ϕ(M) and the velocity coefficient α (ratio of reference discharge/surface velocity discharge (α=1 applied)) was calculated to be 0.75 on average (range 0.51 to 0.91), and when the relationship between the two elements was established as a linear equation, the slope was calculated to be 0.7586 (R= 0.68). The correlation coefficient between ϕ(M) and the maximum surface velocity/average surface velocity of the cross-section was calculated to be -0.84, indicating that ϕ(M) shows a strong correlation with the distribution characteristics of the surface velocity. When the relationship between the two factors was established as an exponential equation, the coefficient of determination was calculated to be 0.76, confirming that the value of ϕ(M) can be estimated and used through surface velocity measurements.
keyword : average velocity, surface velocity, index velocity, ϕ(M)
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and development on the technology for securing the water resources stability in response to future change Program, funded by Korea Ministry of Climate, Energy and Enviroment (MCEE)(RS-2024-00336020)
How to cite: Lee, S., Jang, B., and Lee, J.: Relationship between Cross-Sectional Mean Velocity and Surface Velocity in Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8486, https://doi.org/10.5194/egusphere-egu26-8486, 2026.
Most existing developments in camera gauges focus on single-camera configurations supported by ancillary information, such as detailed three-dimensional (3D) channel geometry and ground control points (GCPs). In these approaches, the water surface is delineated from images and reprojected onto a predefined 3D terrain model. However, acquiring accurate and up-to-date 3D models is often challenging or impractical, particularly in dynamic river environments where channel geometry evolves over time. As a result, frequent model updates are required to maintain measurement accuracy. Another key limitation of conventional camera gauges is the limited quantification of uncertainty in water level estimation. Surface velocity is typically derived using image velocimetry or particle tracking velocimetry. While these methods can provide accurate velocity measurements, they are contingent upon the selection of several parameters that must be meticulously chosen for each monitoring site. Furthermore, the performance of these methods can be degraded by challenging camera poses, varying illumination conditions, and flow regimes.
To address these limitations, we introduce a novel low-cost camera gauge system that integrates stereo photogrammetry with artificial intelligence (AI). The system comprises two low-cost cameras connected to a microcomputer capable of capturing, storing, and transmitting images and short video sequences to an online server. An AI-assisted multi-epoch stereo photogrammetry workflow is then applied to estimate camera pose and reconstruct dense 3D model. This process eliminates the need for predefined 3D data of the cross-section and allows us to compute a new and updated 3D model for each image pair. The updated 3D models are the key component of our methodology, from each water level and water surface velocity can measured in scaled values. Additionally, geomorphologic process can be also measured comparing subsequently 3D models. River water surface segmentation is performed using two foundation models, Grounding DINO and the Segment Anything Model (SAM). River waterlines from both images are then matched and projected into the 3D model, from which the water level is retrieved. This approach enables explicit assessment of errors in water level measurements. Particles tracked in video sequences, using a robust AI model, in both images are further projected into the 3D model, enabling scaled estimation of water surface velocity and, subsequently, river discharge.
The proposed methodology provides a robust and scalable remote sensing solution for river monitoring, enabling the observation of hydrological variables and geomorphological processes. Its low cost and reduced reliance on site-specific ancillary data make it well suited for addressing observational data gaps and for densifying hydrological monitoring networks. Moreover, with an appropriate setup, the system can be used for real-time monitoring, making it a valuable tool in scenarios such as flash floods.
How to cite: Zamboni, P., Krüger, R., Bertalan, L., and Eltner, A.: A novel low-cost stereo camera system for river monitoring., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19389, https://doi.org/10.5194/egusphere-egu26-19389, 2026.
Non-contact river discharge measurement techniques, such as radar-based surface velocity sensors, are increasingly applied in hydrological observations due to their advantages in operational safety and accessibility during flood events. However, these sensors directly measure only surface velocity, and reliable discharge estimation therefore requires conversion to depth-averaged velocity using an accurately estimated velocity index (α). In practice, α is often treated as a constant or determined empirically, which can lead to substantial uncertainty, particularly under unsteady flow conditions.
This study proposes a regression-based framework to quantify the velocity index as a function of hydraulic and flow variability characteristics, using field observations from natural rivers. Both Acoustic Doppler Current Profiler (ADCP) measurements and radar-based surface velocity data are employed. First, reliable depth-averaged velocities and velocity profiles are obtained from ADCP observations, from which reference velocity index values are derived. Subsequently, corresponding α values for radar observations are generated using stage–discharge relationships, and the regression dataset is expanded by integrating both ADCP- and radar-based cases.
The velocity index is formulated using a hybrid multiplicative regression model incorporating water surface slope, channel aspect ratio, and the rate of water level change (dH/dt). In particular, the inclusion of the water level change rate explicitly accounts for unsteady flow effects occurring during rising and falling stages of flood events. Model performance and robustness are comprehensively evaluated using adjusted coefficient of determination, root mean square error, mean absolute percentage error, and variance inflation factor to assess both predictive accuracy and multicollinearity.
Results indicate that the three-variable model consisting of water surface slope, channel aspect ratio, and water level change rate achieves the most favorable balance, exhibiting the lowest prediction errors while maintaining low multicollinearity. The incorporation of dH/dt is shown to effectively represent hysteresis effects in the relationship between surface velocity and depth-averaged velocity during flood conditions, significantly improving model stability.
The regression model proposed in this study is developed based on an integrated dataset combining ADCP and radar observations and provides a velocity index formulation that is applicable across a wide range of hydraulic conditions, including unsteady flood flows, without dependence on a specific sensor type. The results confirm that the proposed model contributes to improving the reliability and consistency of depth-averaged velocity estimation in non-contact river discharge measurements.
Keywords : Non-contact river discharge measurement, Velocity index (mean velocity conversion coefficient), Surface velocity, Unsteady flow conditions, Regression model
How to cite: Lee, T. H., Chun, J. H., Park, S. H., Ok, T. W., and Kim, W. J.: An Integrated Regression Model for Estimating the Velocity Index in Non-Contact River Discharge Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8722, https://doi.org/10.5194/egusphere-egu26-8722, 2026.
Accurate measurement of river surface velocity is essential for hydrological monitoring, flood forecasting, and water resource management. In contrast with traditional in-situ point measurements using electromagnetic current meters, remote sensing techniques offer significant advantages for river surface velocity estimation, including rapid data acquisition, lower operational costs, and contactless operation. Based on Unmanned Aerial Systems (UAS) equipped with Doppler radar becomes more attractive due to it is suitable for real-time velocity determination and has fewer limitations.
The UAS-borne Doppler radar estimates river surface velocity by detecting the frequency shift of backscattered microwaves, with the drone operating at a controlled hover altitude of approximately 4 meters above the water surface during field measurements. However, the propeller wash (propwash) generated by the UAS distorts the radar return signal, resulting in a composite velocity measurement that combines the true river surface flow with the locally induced airflow velocity. To isolate the true river velocity, we introduce a bidirectional observation scheme in which the same surface footprint is measured from two opposing directions. The Continuous Wavelet Transform (CWT) algorithm is employed to extract the mixed velocity components from each directional dataset. By analytically reconciling these bidirectional measurements, the downwash contribution is effectively removed, thereby yielding a refined estimate of the true river surface velocity.
In this study, river surface velocity was analysed across more than 50 cross-sections spanning four distinct rivers, covering a broad velocity range from 20 cm/s to 250 cm/s. To validate the velocity estimates obtained from the UAS-Doppler radar and CWT method, comparisons were made against in-situ measurements collected using instruments such as the OTT MF Pro, Acoustic Doppler Current Profilers (ADCP), and flow trackers. Quantitative analysis confirmed that the UAS-Doppler radar system provides reliable river flow velocity measurements while offering enhanced efficiency in post-processing workflows.
How to cite: Zhou, Z., Hu, X., Merk, F., Disse, M., Caccamo, E., Barbetta, S., Giordan, D., Tarpanelli, A., Flendsted Jensen, V., Andreas Pedersen, M., Nielsen, S., Wennerberg, D., Fagerström, V., Gustafsson, D., and Bauer-Gottwein, P.: River surface velocity estimation using UAS-borne Doppler radar and continuous wavelet transform, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9919, https://doi.org/10.5194/egusphere-egu26-9919, 2026.
This study investigates surface-to-depth-averaged velocity relationships for discharge estimation across a wide range of river scales and flow conditions. The analysis focuses on conversion factors used in surface-velocity-based discharge estimation and their hydraulic controls. A total of 172 ADCP datasets collected from Korean rivers between 2016 and 2018 were reprocessed using an updated version of QRev to obtain consistent and reliable velocity and discharge estimates. Surface velocities were estimated using QRev-based extrapolation and power-law velocity profile methods, and conversion factors were calculated as the ratio of depth-averaged velocity to surface velocity.
The mean conversion factor was 0.88 for the extrapolation method and 0.85 for the power-law method, with most values ranging between 0.80 and 0.90. The estimated uncertainty was approximately 7–8%. Analysis of hydraulic variables showed that conversion factors increased with water surface width and mean depth, whereas weak negative trends were observed with mean velocity and shape factor. Correlation coefficients were generally below 0.5, indicating substantial scatter and limitations in generalizing conversion factors based on single hydraulic parameters.
Acknowledgements
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change program, funded by Korea Ministry of Climate, Energy and Environment (MCEE) (RS-2024-00336020)
Keywords : Surface velocity, Depth-averaged velocity, Conversion factor, ADCP, Natural rivers
How to cite: Lee, K., Lee, S., and Yang, S.: Characteristics of Surface-to-Depth-Averaged Velocity Conversion Factors Based on ADCP Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15987, https://doi.org/10.5194/egusphere-egu26-15987, 2026.
Salt Dilution (SD) is an accurate, safe, relatively inexpensive, and easily employed method to measure water flow in turbulent streams and rivers. Often, Temperature Compensated Electrical Conductivity (EC.T) sensors are deployed continuously in Automated Salt Dilution (AQ) flow measurement systems. Recent studies (Cano-Paoli, K., Chiogna, G., and Bellin, A. 2019) have examined how well a continuous EC.T record can be used in regression analysis to estimate the Discharge. EC.T has the benefit of not requiring a stable Pressure Transducer (PT) elevation and avoids other complications of a stage-discharge station, such as shifting hydraulic controls. However, EC.T can be impacted by sediment fouling, aeration, and seasonal/storm event-related changes to the relationship with Q. This study examines how robust a combined stage-discharge-EC.T time series can be for the generation of a maximum likelihood flow hydrograph. This relationship can be useful for infilling missing data and determining hydraulic control shifts in the stage-discharge relationship. Examples are presented from several mountainous catchments.
How to cite: Sentlinger, G., Hofmeister, F., Combatti, M., Gentile, A., Chiogna, G., Weijs, S., Hauet, A., Mindham, D., and Mahannah, R.: Using Electrical Conductivity as a Proxy for Q: A Madman’s Delusion or Elusive Science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11565, https://doi.org/10.5194/egusphere-egu26-11565, 2026.
River flow conditions at high latitudes show strong seasonal variability due to changes in hydrological conditions throughout the year. During winter, ice cover reduces flow velocity. In spring, snowmelt increases discharge and often results in flooding. During summer and autumn, rainfall can cause temporary increases in discharge. High-latitude fluvial environments are particularly sensitive to climate change, which has been found to have a stronger impact in these regions than in rivers at lower latitudes. This study investigated seasonal flow dynamics in a single meander bend of the Oulankajoki River in Finland during winter, spring, and autumn over one hydrological year using an Acoustic Doppler Current Profiler (ADCP). Vertical ADCP surveys provided detailed cross-sectional velocity profiles and flow directions throughout the water column in each field campaign.
Results showed significant variation between seasons. Winter ice cover significantly reduced near-surface velocities and shifted the high-velocity core (HVC) to mid-depth, whereas in open-water conditions the highest velocities occurred closer to the surface. In open-channel conditions, peak velocities were observed in the shallow upstream and deep downstream sections of the bend, while in winter flow decelerated downstream of the bend apex. During winter and autumn, the HVC was located near the inner bank in the upstream section of the meander bend and gradually shifted to the outer bank before the bend apex. Downstream of the apex, the HVC migrated from the outer bank toward the center of the channel. Unlike in winter and autumn, the spring flood caused the HVC to migrate from the upstream of the meander, flow directly across the point bar, and shift toward the outer bank at the apex.
The study is now being extended by integrating continuous monitoring of flow conditions using a side-looking Doppler current meter. This enables long-term, high-resolution observations, including ice-covered periods. Combining vertical and horizontal ADCP measurements is expected to provide a rare, spatially comprehensive and temporally continuous dataset. This integration enables improved characterization of flow variability and seasonal dynamics in high latitude rivers, thereby enhancing hydrological analyses and process-based modeling.
How to cite: Korkiakoski, K.: From short-term vertical ADCP measurements to continuous Side-Looking current meter monitoring in a high-latitude river, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7246, https://doi.org/10.5194/egusphere-egu26-7246, 2026.
We present RUHM (Rating curve Uncertainty estimation using Hydraulic Modelling), a new open-source tool for rapid estimation of stage–discharge rating curves and their uncertainty at river discharge monitoring stations. A rating curve models the relation between stage (water level) and discharge at a river location and is used to derive discharge time series from water level at most discharge monitoring stations worldwide. However, many stage-discharge field gaugings are needed to estimate rating curves and their uncertainty using traditional approaches. RUHM combines a 1D hydraulic model with Bayesian inference and uncertainty estimation techniques to more rapidly estimate a rating curve and its associated uncertainty with a minimum of three low to middle flow gaugings. The data needed to use RUHM can be effectively collected using drone/UAV (Unmanned Aerial Vehicle) surveys, reducing field efforts compared with traditional approaches. Our open-source implementation of RUHM is written in Python and features a graphical user interface, a user manual, two workflows for pre-processing UAV-derived and/or traditionally surveyed data to generate the RUHM input data files, and example datasets with UAV data. Applications of RUHM to Swedish stations show well-constrained and robust uncertainty estimates where the 1D-flow assumption holds, and wider uncertainty intervals where it does not.
How to cite: Westerberg, I., Karlsen, R., Mansanarez, V., and Lavenius, A.: RUHM: An open-source tool for rapid stage–discharge Rating curve Uncertainty estimation using Hydraulic Modelling and UAV data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17351, https://doi.org/10.5194/egusphere-egu26-17351, 2026.
Discharge observations mainly rely on gauged water levels through rating curves (RC), whose reliable establishment requires long term measurements that are often unavailable due to high maintenance costs, complex terrain, and political reasons. As a result, many basins worldwide remain ungauged, making RC estimation particularly challenging. Recent advances in remote sensing, including satellite altimetry, provide new opportunities for discharge and RC estimation in ungauged basins. However, several challenges remain, including parameter equifinality in discharge inversion from water level, oversimplified assumptions of channel resistance and cross-sectional instability in morphologically active rivers. While Unmanned Aerial Systems (UAS) enable retrieval of channel geometry in complex and hard-to-reach river reaches which imposes an important constraint to mitigate parameter equifinality in hydrodynamic modeling, a systematic assessment of how UAS and remote sensing observations can be combined to reliably estimate rating curves in fully ungauged basins remains lacking.
Funded by European Union's Horizon Europe project UAWOS (Unoccupied Airborne Water Observing System), this study presents a RC estimation framework specifically for ungauged basins using multisource remote sensing, UAS data, and a coupled lumped rainfall–runoff and one-dimensional hydrodynamic model. The model is fully forced and calibrated using remote sensing and UAS observations only. To address parameter equifinality, we first perform a temporal-scale dependent parameter sensitivity analysis to assess the identifiability of model parameters given availability of different remote sensing observations. Based on the sensitivity results, a multi-staged Bayesian calibration strategy is introduced, in which each observation type constrains only the parameter subspace supported by its information content. Isar River, Germany was chosen to test and evaluate the feasibility of the proposed methodology.
Overall, the proposed framework provides a transferable theoretical and technical pathway for estimating RC in ungauged river basins, demonstrating the potential of combining UAS and remote sensing data to derive RC, without relying on prior discharge measurements and offering implications for estimation of ungauged catchments.
How to cite: Hu, X., Zhou, Z., Anwar, F., Tuo, Y., Nielsen, S., Merk, F., Bauer-Gottwein, P., and Disse, M.: Rating curve estimation in ungauged basins using coupled hydrological–hydraulic modelling and multi-source remote sensing and UAS data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18005, https://doi.org/10.5194/egusphere-egu26-18005, 2026.
Ensuring high-quality river discharge measurements is fundamental for flood protection, forecasting and assessing water availability under changing climate conditions. While modern technologies such as Acoustic Doppler Current Profilers (ADCP), camera-based surface velocimetry and emerging sensor technologies offer advanced capabilities, they also present new challenges in terms of quality assurance compared to traditional instruments such as rotating-element current meters.
Although the modern measurement methods mentioned above are already in use in many countries, standardized testing procedures are still lacking. Coupled with the complexity of device application and data evaluation, and the absence of standardized operating protocols in these areas, the accuracy of these methods may be compromised compared to conventional techniques. Addressing these issues requires innovative, internationally coordinated approaches to quality assurance that can adapt to future developments.
Several strategies have proven effective in improving data reliability:
- Routine device checks and maintenance to guarantee functionality and detect early signs of malfunction.
- Regular intercomparison measurements to identify systematic and random errors, highlight operational differences, and ensure comparability of results.
- Continuous training and experience exchange to reduce operational errors and strengthen technical expertise.
- Development of independent software solutions and adoption of open data principles to reduce black box solutions, enable robust data analysis and foster innovation.
- Creation of guidelines and institutionalization of standards for measurement methods, intercomparison programs and device testing.
- Promotion of research and development to advance measurement techniques and adapt to emerging technologies.
Our work highlights effective measures and underscores the need for coordinated international initiatives to establish common standards. By combining research, operational experience, and international collaboration, we can collectively strengthen the reliability of river monitoring worldwide.
How to cite: Kainz, S., Hasenhündl, M., and Büchen, S.: Ensuring Data Quality in River Discharge Measurements: Strategies and Future Directions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10110, https://doi.org/10.5194/egusphere-egu26-10110, 2026.
Fluvial ice significantly impacts river hydrodynamics by increasing flow resistance, which leads to altered water levels, flow velocities, and turbulence characteristics. River ice also has socio-economic implications as it impacts energy production, flood risk management, and transportation. These effects have remarkable spatial extent: approximately one third of the Earth’s landmass is drained by seasonally freezing rivers. At the same time, the Earth system is experiencing rapid and dramatic changes due to changing climate and these changes are intense in northern areas due to even faster warming and fragile ecosystems. Despite the importance of understanding these changes, obtaining detailed information of ice-covered hydrodynamics remains challenging and potentially dangerous, resulting in limited data availability even under current conditions.
In this research, more comprehensive insights into fluvial ice and ice-covered hydrodynamics in Finland are achieved by integrating novel approaches in physical modelling, numerical modelling, and field data acquisition and processing. Information of current conditions in the studied subarctic rivers is gathered using conventional methods, such as flow velocity measurements, as well as emerging technologies including underwater drones. The study includes ice growth calculations under projected climate and flow conditions. Flume experiments in an indoor flume with proxy ice and bed topography and pressurised conditions are conducted to observe ice-covered hydraulics in a controlled environment. New methodologies and integration of different approaches allow for gathering more comprehensive information on seasonally freezing rivers and help in predicting future changes in response to climate change.
How to cite: Vaahtera, R., de Vet, M., Veijalainen, N., and Lotsari, E.: River ice under climate change: integrating modelling and data acquisition methods for detecting changes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19665, https://doi.org/10.5194/egusphere-egu26-19665, 2026.
