Hydrometric stations may be influenced by the sea tide, disrupting the stage-discharge relation and making it difficult to estimate discharge through a traditional rating curve. Twin-gauge stage-fall-discharge (SFD) rating curves, based on a flow friction equation and stage and water slope measurements, are a possible alternative, but they were found to perform poorly when the tide effect is strong. To capture the complex flow dynamics, including flow reversal, an approach via a 1D hydrodynamic model is proposed.

To set up the model, the cross-sectional geometry, friction coefficient, upstream discharge and downstream water level are required. In hydrodynamic modelling, the friction coefficients are the main calibration parameters and spatial changes of roughness combined with unsteady flow make their manual calibration difficult. Moreover, the understanding and quantification of uncertainties associated with data and model is an important step of the calibration process. Therefore, an automatic calibration of friction coefficients is proposed via Bayesian inference. In terms of numerical tools, the selected 1D hydrodynamic code is Mage, developed by INRAE, solving the 1D Saint-Venant equations for subcritical, transient flows. Likewise, the Bayesian Modeling (BaM) framework (https://github.com/BaM-tools) is used to specify prior information and estimate friction coefficients and their uncertainty, using stage and discharge observations.

The case study is the Lower Seine River in France, because it comes as a simple hydraulic model with a strong tidal effect with gauging campaigns and stage records available. Discharge time series of the Seine at Poses and of the Eure, the only significant tributary, are specified as upstream boundary conditions.  The downstream boundary condition is the stage time series of the Seine at Saint-Léonard, reflecting the tidal signal. Calibration data include stage records at different stations and times, and ADCP discharge measurements at Rouen during several tidal cycles.

For all reaches, a lognormal distribution with 95% probability interval [33; 49] is used as a prior for the Strickler coefficient. Bayesian estimation then provides their posterior distributions, represented by a large number of samples generated by means of a Markov Chain Monte Carlo (MCMC) algorithm. These samples can be used to identify optimal “maxpost” coefficients (maximizing the posterior density), but also to quantify and propagate their uncertainty. Thereafter, a propagation is performed to estimate the stage and discharge series of all cross-sections along with their uncertainty.

This study aims to provide an alternative solution for the continuous monitoring of discharge from stage records and upstream discharges in tidal rivers in order to improve flood forecasting, warning systems and the understanding of tidal-influence on hydrometric stations.

How to cite: Mendez Rios, F., Le Coz, J., Renard, B., and Terraz, T.: Bayesian calibration of a 1D hydrodynamic model used as a rating curve in a tidal river: Application to the Lower Seine River, France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6587, https://doi.org/10.5194/egusphere-egu23-6587, 2023.

Obtaining real-time water level estimations is crucial for effective monitoring and response during emergencies caused by heavy rainfall and rapid flooding. Typically, this type of monitoring can be a difficult task, requiring river reach preparations and specialized equipment. Moreover, in extreme flood events, standard observation methods may become ineffective. This is why the possibility of developing low-cost, automatic monitoring systems represents a significant advancement in our ability to monitor river courses and allow emergency teams to respond appropriately.

Image-based methods for water level estimation facilitate the development of a low-cost river monitoring strategy in a quick and remote approach. These techniques are faster and more convenient regarding the setup than traditional water stage monitoring methods, allowing us to efficiently monitor the river from different locations with a cost-effective approach. By increasing the density of the observation network, we can improve flood warning and management.

The approach presented involves placing cameras in secure locations to capture images of the river, for which we have previously modelled the terrain in 3D using Structure from Motion (SfM) algorithms supported by GNSS data. With the images obtained every 15 minutes, we perform a Convolutional Neural Network (CNN) segmentation based on artificial intelligence algorithms that allow us to automatically extract the contours of the water surface area. In this study, two different neural network approaches are presented to segment water in the images.

Using a photogrammetric strategy, we reproject the water line extracted by the AI on the 3D model of the scene. This reprojection is also supported by the use of a keypoint detection neural network that allows us to accurately identify the ground control points (GCPs) observed in the images captured by the surveillance camera. This approach allows us to automatically assign to each image the real coordinates of the GCPs and subsequently estimate the camera pose.

This AI segmentation and automatic reprojection into the 3D model has allowed us to generate a robust centimetre-accurate workflow, capable of estimating the water level in near-real time for daylight conditions. In addition, the automatic detection of the GCP has permitted to obtain automatic water level measurements over a longer period of time (one year). This approach represents the basis for obtaining other river monitoring parameters, such as velocity or discharge, which allow a better understanding of river floods and represent key steps for the development of early warning systems for flood events.

How to cite: Eltner, A., Zamboni, P., Hedel, R., Grundmann, J., and Blanch, X.: Image-based methods for real-time water level estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6745, https://doi.org/10.5194/egusphere-egu23-6745, 2023.

The use of image velocimetry techniques for river monitoring has been increasing in the last few years, but there are some limitations to be solved related mainly to natural environmental conditions and operative framework (Dal Sasso et al., 2021). Along with these issues, the need for surface tracking features or homogeneously distributed materials across the cross-section represents one of the challenges for outdoor applications. In a natural environment, flows can present low seeding densities or locally distributed tracer clusters. These conditions can introduce a high variance and underestimate the flow velocity field, especially near the riverbanks.

In this work, the Farnebäck dense optical flow method (Farnebäck 2003) implemented in SSIMS-Flow software (Ljubicic, 2022) was tested and compared with LSPIV technique (Thielicke et al., 2021) to estimate surface flow velocities under different seeding conditions. The application was carried out on the Arrow River (UK) along two meandering river reaches during low-flow conditions. Four different seeding conditions were experimented from low (natural) to high (artificial) seeding density of tracers . Tracers were manually distributed onto the water surface and videos were acquired from DJI Phantom 4 Pro. Seeding metrics were used to estimate seeding conditions including: mean tracer area, seeding density, spatial tracer distribution, and the SDI index (Pizarro et al., 2020). Conventional velocity measurements were used as benchmark purposes along various transects.

This study highlighted the good performances of the two tested image velocimetry methods, with results comparable to traditional techniques. On the one hand, the Farnebäck optical flow method proved to be more sensitive to changing setting parameters (e.g., feature extraction rate) with respect to LSPIV. On the other hand, optical flow showed low sensitivity to seeding density (error reduction 30-40%). This is due to the capacity of the Farnebäck method integrated with an ad-hoc pooling technique for spatial velocity averaging to represent surface velocity under sporadic and uneven seeding (e.g., near the convex bank).

References

Dal Sasso, S. F., Pizarro, A., Manfreda S. (2021). Recent Advancements and Perspectives in UAS-Based Image Velocimetry. Drones 5, 3: 81.

Farnebäck, G. (2003). Two-frame motion estimation based on polynomial expansion, Scandinavian conference on Image analysis. Springer, Berlin, Heidelberg.

Ljubicic, R. (2022). SSIMS-Flow: UAV image velocimetry workbench, https://github.com/ljubicicrobert/SSIMS-Flow

Pizarro, A., Dal Sasso, S.F., Manfreda, S. (2020). Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization. Hydrol. Process. 2020, 34, 5167–5175.

Thielicke, W., Sonntag, R. (2021). Particle Image Velocimetry for MATLAB: Accuracy and Enhanced Algorithms in PIVlab. Journal of Open Research Software, 9, Ubiquity Press, 2021.

How to cite: Dal Sasso, S. F., Ljubicic, R., Pizarro, A., Pearce, S., Maddock, I., and Manfreda, S.: Image-based velocity estimations under different seeded and unseeded river flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6936, https://doi.org/10.5194/egusphere-egu23-6936, 2023.

The measuring of flood events is associated with many challenges. Among them is the determination of flow velocities for the derivation of discharge. Most of the applied methods for velocity determination the disadvantage that they work in direct contact with water. This often makes measuring under critical flow conditions dangerous. Optical measurement methods have a great advantage because they can work remotely, i.e., without water contact.

For representative discharge measurements, flow velocity measurements over the entire width of the river cross-section are required. This is a major challenge in the application of PTV, because visible particles must be present across the entire cross-section, which is not always the case. The potential measurement gaps in the surface velocity distribution have a negative effect on the quality of the discharge determination. Because optical measurement methods are relatively new in hydrology, there is not yet a standardised procedure with which the discharge can be determined. 

The "OptiQ" method presented here is an approach for determining discharge using PTV. This method is based on the continuity equation, which is dependent on two variables, the flow area and the mean flow velocity. The challenge here is to determine the depth-averaged flow velocity, because PTV is used to determine the surface velocity. To get the depth-averaged flow velocities, the PTV results are averaged over a transect and converted using a velocity coefficient. The arithmetic mean, the velocity area method (DIN EN ISO 748:2008-02) and the moving average are considered as averaging methods. A statistical approach was chosen for closing measurement gaps that occurred in the velocity distribution. In this approach, the measurement results with similar discharge conditions in the entire time series, i.e. PTV results for the same water levels, are statistically analysed, filtered and summarised in a lookup table. The gaps in the measurements due to missing particles are filled with the data from the lookup table.

For the data collection, three camera gauges were installed at regular gauging stations of the Saxon State Agency for Environmental and Agricultural Monitoring (BfUL). The camera gauges recorded short video sequences at regular time intervals, which were used to determine the velocity distributions using the FlowVelo tool (Eltner et al., 2020). This resulted in three time series covering a period of 10-15 months. For the validation of the optical discharge time series, the regular water level and discharge measurements of the BfUL are used. 

The application of "OptiQ" shows a significant adjustment of the optically determined discharge data to the reference measurement at all three gauging stations. While acceptable results were determined with the arithmetic mean only at higher discharge, the results with the velocity area method and the moving average are similarly good at all discharges. At the gauging station in Elbersdorf, the average difference from the reference value could be reduced from 29% to 15% with "OptiQ". In the next step, it is planned to further develop the statistical model "OptiQ" by using Deep Learning.

How to cite: Kutscher, A., Grundmann, J., Eltner, A., Blanch, X., and Hedel, R.: Application of optical Particle Tracking Velocimetry (PTV) to determine continuous discharge time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9946, https://doi.org/10.5194/egusphere-egu23-9946, 2023.

The operator effect is a prominent error source in image-based velocimetry methods. The LSPIV method is known to be sensitive to the parameters and choices of the user, as shown in the literature and emphasized by the results of a video gauging intercomparison, the Video Globe Challenge 2020 (VGC2020) (Le Coz et al., 2021). The intercomparison was carried out during the COVID-19 lockdown of spring 2020 and involved 15 to 23 participants using the LSPIV method among other techniques on 8 videos representative of the diversity of river gauging conditions and imaging viewpoints. Each video came with a discharge reference and associated uncertainty.

An in depth investigation of the intercomparison results has been carried out to identify the most sensitive parameter(s) for each video and also to review the common setting mistakes (cf. Bodart et al., 2022). The investigation highlighted the strong impact of the image temporal sampling (extraction framerate) and of the velocity filtering on the discharge errors. The ortho-rectification and the surface coefficient were also found to be impacting in given cases.

Based on these observations, several assistance tools and automated filters are proposed to reduce the operator effect. They are evaluated on the intercomparison dataset. The assistance tools use available information (e.g. transect data) or basic user inputs (e.g. manual spotting of some velocities) to determine the optimal extraction framerate, grid points and searching area (SA) for LSPIV computation. The sequence of automated filters is built for the specific context of discharge measurement: spatial coherency of the velocities in a local neighborhood and temporal coherency of the velocities computed at a point. These velocity filters are systematic and do not require any input from the user.

The application of the assistance tools and automated filters to the intercomparison dataset leads to a significant improvement of the results. On the eight videos, the mean interquartile range of the percent error initially at 17% is reduced to 2% and the mean median of the percent error initially at -9% is reduced to 0.6% with the assistance tools and filters. The results are encouraging and can be implemented in software tools for the operational deployment of the LSPIV method for discharge measurement.

Le Coz, J., Hauet, A., and Despax, A. (2021). The Video Globe Challenge 2020, a video streamgauging race during the Covid-19 lockdown, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2116, https://doi.org/10.5194/egusphere-egu21-2116, 2021

Bodart, G., Le Coz, J., Jodeau, M., and Hauet, A.: Quantifying the operator effect in LSPIV image-based velocity and discharge measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4457, https://doi.org/10.5194/egusphere-egu22-4457, 2022.

How to cite: Bodart, G., Le Coz, J., Jodeau, M., and Hauet, A.: Reducing the operator effect in LSPIV image-based discharge measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5779, https://doi.org/10.5194/egusphere-egu23-5779, 2023.

The moving-boat Acoustic Doppler Current Profiler (ADCP) gauging method is extensively used to measure the discharge of rivers and canals. Inter-comparison of ADCP measurements are necessary not only to validate the instruments and their deployment, but also to study the discharge measurement uncertainty. Uncertainty estimates provided by the propagation methods cannot be validated for in situ conditions because of the complexity of the ADCP data workflow and the uncertainty of discharge references in rivers and canals. To solve this issue, a complementary approach to uncertainty propagation methods is the repeated measures experiments, also known as inter-laboratory comparisons. ADCP inter-comparisons have been done for decades and with very different conditions. These data sets are precious in order to test and validate uncertainty propagation methods.

The OURSIN ADCP uncertainty analysis is validated using empirical uncertainty estimates on inter-comparison experiment. This propagation method has been implemented in the QRevInt software which provides an ADCP data quality review. QRevInt is developed by Genesis HydroTech LLC (Mueller, 2021) with the guidance and contributions from an international board of hydrological agencies. QRevInt helps to clean ADCP measurements from avoidable errors and to homogenize the discharge computations irrespective of the instrument manufacturer and model.

However, post-processing inter-comparison results is a long and complicated process particularly if users want to determine and quantify uncertainty sources. There are as many practices as there are hydrometric services. Uncertainty is an indispensable component of discharge measurement and should be estimated for as many measurements as possible. To popularize these practices and homogenize them, a user-friendly tool has been developed.

From raw ADCP measurements, it applies QRevInt post-process quality analysis, the OURSIN uncertainty propagation method, and the empirical uncertainty computation based on the repeated-measures experiment. The tool applies Grubbs and Cochran statistical tests to validate the measurement selection. It returns tables with a row for each measurement with information, such as, discharge and uncertainty decomposition from QRevInt. It also returns an overview of the inter-comparison with graphs of the discharge and its uncertainty among measurements, computed uncertainty, and empirical uncertainty. The tool allows replaying data with homogeneous parameters and users can manually exclude a measurement if it does not seem consistent. The tool will be open source and freely available.

Beyond the operational application, it could be used to replay historical inter-comparisons. With an inter-comparison database, it will be possible to study diverse types of rivers to improve and validate uncertainty estimation in various conditions. A first synthesis is proposed from one inter-comparison data set and will be extended to as much data as possible in the future.

How to cite: Calmel, B., Le Coz, J., Alexandre, H., Aurélien, D., and Mueller, D.: Comparing ADCP inter-comparison results using an automated post-processing tool, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7073, https://doi.org/10.5194/egusphere-egu23-7073, 2023.

In the last years, several methods to establish surface flow velocities and river flow from camera videos have been developed and codified into software. Together with a hardware setup, these may be used to establish near real-time observations of river flow. The hardware setup used and associated quality of the camera, methods to pre-process, process and post-process the videos may all result in errors, and uncertainties. In this contribution we assess what the main sources of uncertainty are, and under what conditions these may appear, focusing on both hardware and processing methods. We do this by co-locating two different camera setups, and using two different software processing methods. For camera setups we use a very simple and low cost FOSCAM FI9900EP running at its maximum of 4Mbps and a much better quality Vivotek IB9367-EHT running at 20Mbps. As systems we use the DischargeKeeper and pyOpenRiverCam.

The cameras were co-located over a significantly long period at a site in Limburg in The Netherlands, and footage analyzed with 15-minute intervals. Videos were treated with as much as possible the same settings, reprojection resolution and window. Results were compared in terms of the ability to resolve velocities (amount and quality) and the impact of post-processing. Integrated flow over a cross-section is also compared. We assess under what conditions flow and velocity estimates are robust and similar and under what conditions these diverge focusing on the platform used, light conditions, and flow conditions.

Keywords: River flow monitoring, stage-discharge relationships, OpenRiverCam, DischargeKeeper, computer vision

The work leading to these results has received funding from the German Federal Ministry of Education and Research (BMBF) and the CLIENT II program (Drought-ADAPT, FKZ: 01LZ2002B) and the European Horizon Europe Programme (2021-2027) under grant agreement no. 101086209 (TEMBO Africa). The opinions expressed in the document are of the authors only and no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

How to cite: Winsemius, H., Peña-Haro, S., Annor, F., Hagenaars, R., Luxemburg, W., Van den Munckhof, G., Grimmeisen, F., and Van de Giesen, N.: Sources of uncertainty in video-based flow observations, revealed by co-location experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14234, https://doi.org/10.5194/egusphere-egu23-14234, 2023.

Measuring the volumetric water flow in ephemeral streams, typical of semi-arid climates, in which water rarely flows, is challenging since water only flows some days per year and some times it is in the form of flash floods. In this type of conditions it is important to detect when there is water in the stream. For this, we have implemented a machine learning algorithm for water detection and for stream gauge measurement.

Machine learning was used to differentiate pixels of the image that contains water from those those that do not via image segmentation. Different segmentation models have been proposed, but in our case we used an encoder-decoder DNN architecture based on DeepLabV3. To train the model, we used the ArtificiaL And Natural waTer-bodIes dataSet (ATLANTIS) data-set. However not all the images were used since these data-set includes classes that are not representative for our application, hence the total number of images used for training was 685. Additionally the original defined classes were merged to reduce the problem to a semantic binary segmentation problem, since our objective is to simply detect the presence of water on the stream. In addition to those images, we have used other images recorded by fix cameras looking at some ephemeral streams to improve the training.

The trained network was used to analyze 50 images with different water levels or no water. To evaluate its performance and indicator was defined which considered the number of pixels classified as water inside the image area covered by the stream over the total number of potential pixels having water, and a 60% threshold was used to determine if there is water in the stream. From the 50 images analyzed, only 3 were wrongly classified giving promising results.

This work has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017861 and also by projects RTC2019-007159-5 and Ramon y Cajal Grant RYC2018-025580-I, funded by MCIN/AEI/10.13039/501100011033, “FSE invest in your future” and “ERDF A way of making Europe”

How to cite: Peña-Haro, S., Hernandez, D., and Cecilia, J. M.: Machine learning for water detection in ephemeral streams, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14752, https://doi.org/10.5194/egusphere-egu23-14752, 2023.

Human settlements and infrastructure in alluvial floodplains face erosional risk due to the lateral migration of meandering rivers. There is a large body of scientific literature on the dominant mechanisms driving river migration. However, it is challenging to make accurate forecasts of river meander evolution over multiple years. This is in part because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. In this work, we suggest a workflow to generate river-migration risk maps using probabilistic modeling. Forecasts for river channel position over time are generated by Monte Carlo runs, using a distribution of model parameter values inferred from satellite data, enabling us to make risk maps for river migration. We demonstrate that such risk maps are more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. 

How to cite: Wani, O., Noh, B., Dunne, K., and Lamb, M.: Generating risk maps for river migration using probabilistic modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17240, https://doi.org/10.5194/egusphere-egu23-17240, 2023.

In recent years the changing climate has resulted in an increased prevalence of extreme weather, with corresponding extreme precipitation and surface flow events. Adapting management of water and other natural resources to these conditions requires accurate and robust tools to measure water flow, and in particular the development of non-contact measurement methods.

Once such method is Infrared quantitative image velocimetry (IR-QIV), which is a large scale surface velocimetry method that uses infrared imagery to calculate the mean and instantaneous velocity at high resolution in space and time, over large spatial areas (Schweitzer & Cowen, WRR 2021). IR-QIV can operate continuously for extended periods (days, weeks, etc.) without requiring artificial illumination or particle seeding of the flow. The high resolution, continuous, measurement capabilities of IR-QIV make it particularly well suited to applications where the spatial and temporal variance of the flow must be resolved, such as fishery management, air-water heat and gas exchange, and flow-structure interaction studies.

We present metrics of turbulence, estimates of gas transfer rates, and other hydrodynamic properties calculated from velocity measurements conducted by IR-QIV at the surface of several rivers in California, and Michigan, USA. The measurements were made as part of fishery management projects, motivated by efforts to better understand and manage the interaction of migrating fish and the hydrodynamic environment. Results are validated by comparison with acoustic velocity measurements. 

How to cite: Schweitzer, S. and Cowen, E. A.: Turbulence metrics at the surface of rivers, measured by Infrared Quantitative Image Velocimetry (IR-QIV), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16919, https://doi.org/10.5194/egusphere-egu23-16919, 2023.