Displays

The common streamgauging methods (ADCP, current-meter or tracer dilution) generally require expensive equipment, with the notable exception of volumetric gaugings and floats, which are however often difficult to implement and limited to specific conditions. The following work aims at testing and validating a reliable, easy-to-deploy and low-cost gauging method, at a cost typically below 40 € each.

The “velocity-head rod” firstly described by Wilm and Storey (1944), made transparent by Fonstad et al. (2005) and improved by Pike et al. (2016) meets these objectives, for wading gauging with velocities greater than 20 cm/s typically. The 9.85 cm wide clear plastic rod is placed vertically across the stream to identify upstream and downstream water levels using adjustable rulers. The difference in level (or velocity head) makes it possible to calculate the average velocity over the vertical, using a semi-empirical calibration relationship.

Experiments carried out in INRAE’s hydraulic laboratory and in the field have enabled us to find a calibration relationship similar to that proposed by Pike et al. (2016) and confirm the optimal conditions of use. The average deviation to a reference discharge has been found to be close to 5 % except for very slow-flow conditions. The influence of the width of the rod on the velocity-head was studied in the laboratory. The uncertainty of the velocity due to the reading of water levels has been estimated. It increases at low velocity due to decreasing sensitivity, and increases at high velocities due to water level fluctuations that are difficult to average.

Several improvements were tested in order to facilitate and improve the measurement operations, without increasing the cost too much: magnetic ruler, removal of a graduated steel rule (expensive), plastic ruler with water level and velocity graduations, reading the depth with another ruler, spirit level, electrical contact (so the operator has not to bend to the surface of the water). An operational procedure and a spreadsheet for computing discharge are proposed. The method being extremely simple and quick to apply is well suited for rapid estimates of flow (instead of floats), training or demonstrations, citizen science programs or cooperation with services with limited resources.

Acknowledgments: The authors thank Q. Morice, J. Cousseau, Y. Longefay (DREAL) who were involved in this study by carrying out field tests.

How to cite: Despax, A., Le Coz, J., Pernot, F., Buffet, A., and Berni, C.: Low-cost river discharge measurements using a transparent velocity-head rod, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5684, https://doi.org/10.5194/egusphere-egu2020-5684, 2020.

Image-based methods for measuring surface flow velocities in rivers have several advantages, one of them being that the sensor (camera) is not in contact with the water and its mounting position is very flexible hence there is no need of expensive structures to mount it. Additionally, it is possible to measure the whole river width. On the other hand, environmental factors, like wind, can affect the surface velocity and the have an impact on the accuracy of the measurements.

Herein we present an analysis of the wind effect on the image based surface velocity at Rhine river, at the border between Switzerland and Austria. At this location the river width is of approximately 100 meters under low flow conditions, while the width of its floodplain is of about 200 m. An ATMOS 22 ultrasonic anemometer was installed at the site to measure the wind intensity as well as its direction.

A time series of flow velocities and wind from May to October 2019 was analyzed. During this period, the average discharge was 320 m³/s and the average flow velocity 1.7 m/s. While the average wind velocity was of 2.3m/s which roughly follows the same direction of the river flow.

A rating curve following a power law function was fitted to the image based surface flow measurements. It was found that for maximum wind speeds of 10 m/s, blowing in the opposite direction of the river flow, there was a deviation of 8%. For the average wind speed of 2.3m/s, the deviation was found to be 3%.

How to cite: Peña-Haro, S., Lüthi, B., Lukes, R., and Carrel, M.: Wind effect on image-based river surface velocity measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9943, https://doi.org/10.5194/egusphere-egu2020-9943, 2020.

In current fluvial remote sensing approaches, there exists a certain dichotomy between the analysis of small channels at local scales which is generally done with airborne data and the analysis of entire basins at regional and national scales with satellite data. One possible solution to this challenge is to use low-altitude imagery from low-cost UAVs to provide sub-metric scale class information which can then be used to train fuzzy classification models for entire Sentinel 2 tiles. The fuzzy classification approach can allow for sub-pixel information and when extended to entire Sentinel 2 tiles, the method therefore develops information at a resolution of less than 10 meters (the best spatial resolution of Sentinel 2 bands) at regional scales. In this contribution, we present such a method where UAV imagery is used as the training data for the fully fuzzy classification of Sentinel 2 imagery. We partition the fluvial corridor in three simple classes: water, dry sediment and vegetation.  Then we manually classify the local UAV imagery into highly accurate class rasters. In order to augment the value of the Sentinel 2 data, we use an established super-resolution method that delivers 10 meter spatial resolution across all 11 Sentinel 2 bands. We then use the sub-metric UAV classifications as training data for the 10 meter super-resolved Sentinel 2 imagery and we train fuzzy classification models using random forests, dense neural networks and convolutional neural networks (CNN). We find that CNN architectures perform best and can predict class membership within a pixel of a new Sentinel 2 tile not seen in the training phase with a mean error of 0% and an RMS error of 18%. Crisp class predictions derived from the fuzzy models range in accuracy from 88% to 99%, even in the case of tiles never seen in the training phase. With this approach, it is now possible to deploy a low-cost UAV in order to train a transferable CNN model that can predict fuzzy classes at very large scales from freely available Sentinel 2 imagery. This approach can therefore serve as the basis for multi temporal classification and change detection of the Sentinel 2 archives.

How to cite: Carbonneau, P., Belletti, B., Micotti, M., Casteletti, A., Mariani, S., and Bizzi, S.: UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10659, https://doi.org/10.5194/egusphere-egu2020-10659, 2020.

Environmental Flow Release monitoring can be an expensive undertaking in active watercourses normally suitable for run-of-river hydropower projects.  In order to attain acceptable (<10%) uncertainty in the derived flow series, it is necessary for a Qualified Professional (QP) to make several site visits to measure a range of flows in order to calibrate a stage-discharge (rating) curve.  With climate change, the need to measure drought conditions and respond appropriately is crucial for habitat health and to prevent fish stranding.  The current study employs a Water Quality Mixing Model (WQMM) to estimate flows at a downstream site from an existing hydropower plant using a modified constant rate mixing model.  This is an independent estimate of flow entirely distinct from the stage-discharge curve.  The method can be employed anywhere there is a sufficient mixing length and sufficiently distinct WQ traits.  The method can reduce both maintenance costs and flow uncertainty where Environmental Flow Release Monitoring is required.

How to cite: Sentlinger, G.: Water Quality Mixing Model (WQMM) for Environmental Flow Release Monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11759, https://doi.org/10.5194/egusphere-egu2020-11759, 2020.

Correct streamflow measurements are of fundamental importance for hydrology. Mountain catchments are particularly complex systems to obtain reliable discharge time series and several challenges have to be overcome. For example, turbulent flow of mountain streams leads to unstable streambed conditions by erosion and sedimentation and the irregular stream profile makes any streamflow measurements through the velocity-area method difficult. The salt dilution method provides reliable streamflow estimation for specific injection times. We can construct rating curves when these and river stage data are available. However, this relationship entails intrinsic uncertainties that derive from experimental errors as well as from extrapolation outside the measured range. In this work, we provide a rigorous quantification of the uncertainty of discharge measurement based on rating curves using error propagation techniques. During multiple field campaigns in 2019, we collected 74 streamflow measurements for nine sites over three high Alpine catchments (Horlachtal, Kaunertal and Martelltal). We then consider also continuous measurements of water level, water temperature and electrical conductivity. The aim is not only to get more information about the hydrological processes and response of these catchments but also to use this information to construct more robust and less uncertain rating curves. Our results show the high uncertainty affecting measured discharges in Alpine catchments and they are relevant for model applications as well. In fact, the uncertainty in river discharge observations affects the optimal value of the model objective function (e.g., Nash-Sutcliff Efficiency).

How to cite: Hofmeister, F., Rubens Venegas, B., Disse, M., and Chiogna, G.: Uncertainty quantification of continuous streamflow monitoring in high elevation Alpine catchments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13832, https://doi.org/10.5194/egusphere-egu2020-13832, 2020.

Accurate flow data form the basis for describing hydrological runoff processes and extremes. While the continuous measurement of the water level is a standard task in hydrometry, the continuous measurement of flow velocity is more complex and often involves greater effort. Videometric methods like LSPIV (Large Scale Particle Image Velocimetry) allow a contactless acquisition of surface velocity distribution in open channels. Ready-to-use instrumentation for that purpose is hardly available and requires permanent electricity supply.
Therefore, a simple self-made measuring system, consisting of a data logger with camera and a distance sensor, is introduced. It enables not only the detection of the water level but also the recording and remote transmission of video data. Based on an Arduino microcontroller and a Raspberry Pi Single Board Computer the battery-powered data logger is freely programmable with open source software and supports the operation of various sensors with digital interface at low power consumption. 
The measuring system with its wide angle camera is intended to be mounted on bridges or steep banks with longitudinal or transverse to flow camera alignment. The water level is detected by an ultrasonic range transducer, a raspberry pi camera module with wide angle lens records videos in 1080p resolution.  The water level data and the videos are remotely transmitted via cellular network to a server that provides the data to the subsequent LSPIV analysis. The LSPIV analysis enables a high-resolution measurement of the velocity distribution at the water surface and in combination with the known channel geometry and the height of the water level it offers an accurate discharge determination.
Particularly with regard to extreme events the use of video data brings considerable advantages as it allows a visual on-site inspection of the situation. Information such as the condition of the local vegetation, icing or disturbing influences at the gauge site can be derived and included in the flow rate determination.

How to cite: Eichendorff, P. and Schlenkhoff, A.: Continuous measurement of open channel discharge using a video data logger and subsequent LSPIV analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18413, https://doi.org/10.5194/egusphere-egu2020-18413, 2020.

Unmanned Aerial Systems (UAS) are offering an extraordinary opportunity to improve our ability to monitor river basins. The wide use of UAS leaded to a significant grow of the number of applications and methodologies developed for specific scopes of environmental monitoring. For this reason, there is a serious challenge to harmonise and provide standardised guidance applicable across a broad range of environments and conditions. In this context, a network of scientists is cooperating within the framework of a COST (European Cooperation in Science and Technology) Action named “Harmonious - Given the wide use of UAS within environmental studies”. The intention of “Harmonious” is to promote monitoring strategies, establish harmonised monitoring practices, and transfer most recent advances on UAS methodologies to others within a global network. The working groups of Harmonious are currently working on the definition of practical guidance for environmental studies identifying critical processes and the interconnection of each step for a successful workflow. Given the number of environmental constraints and variables, it is impractical to provide a protocol that can be applied universally under all possible conditions, but it is possible to systematise the fragmented knowledge on this topic identifying the best-practices to improve the overall quality of the final products. Preliminary results of the HARMONIOUS COST Action will be given.

How to cite: Manfreda, S. and the HARMONIOUS TEAM: Use of Unmanned Aerial Systems for Hydrological Monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21652, https://doi.org/10.5194/egusphere-egu2020-21652, 2020.

Image velocimetry (IV) is a remote technique which calculates surface flow velocities of rivers (or fluids) via a range of cross-correlation and tracking algorithms. IV can be implemented via a range of camera sensors which can be mounted on tri-pods, or Unmanned Aerial Systems (UAS). IV has proven a powerful technique for monitoring river flows during flood conditions, whereby traditional in-situ techniques would be unsafe to deploy. However, little research has focussed upon the application of such techniques during low flow conditions. The applicability of IV to low flow studies could aid data collection at a higher spatial and temporal resolution than is currently available. Many IV techniques are under-development, that utilise different cross-correlation and tracking algorithms, including, Large Scale Particle Image Velocimetry (LSPIV), Large Scale Particle Tracking Velocimetry (LSPTV), Optical Tracking Velocimetry (OTV), Kanade Lucas Tomasi Image Velocimetry (KLT-IV) and Surface Structure Image Velocimetry (SSIV). Nevertheless, the true applications and limitations of such algorithms have yet to be extensively tested. Therefore, this study aimed to conduct a sensitivity analysis on the commonly relatable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate (or sub sampled frame rate).

Fieldwork was carried out on Kolubara River near the city of Obrenovac in Central Serbia. Cross-sectional surface width was relatively constant, varying between 23.30 and 23.45m. During the experiment, low flow conditions were present with a discharge of approx. 3.4m³ s^-1 (estimated using a Sontek M9 ADCP), and depths of up to 1.9m. A DJI Phantom 4 Pro UAS was used to collect video data of the surface flow. Artificial seeding material (wood-mulch) was distributed homogenously across the rivers’ surface, in order to improve the conditions for IV techniques during slow flows. Two 30-second videos were utilised for surface velocity analysis.

This study highlighted that KLT, SSIV, OTV and LSPIV are the least sensitive algorithms to changing parameters when no pre- or post-processing of results are conducted. On the other hand, LSPTV must undergo post-processing procedures in order to avoid spurious results and only then, results may be reliable. Furthermore, KLT and SSIV highlighted a slight sensitivity to changing the feature extraction rate, however changing the particle identification area did not affect significantly the outputted surface velocity results. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area provided a higher variability in the results, whilst changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area.

This analysis has led to the conclusions that during the conditions of sampling with surface velocities of approximately 0.12ms^-1, and homogeneous seeding on the rivers surface, IV techniques can provide results comparable to traditional techniques such as ADCPs during low flow conditions. All IV algorithms provided results that were, on average, within 0.05ms^-1 of the ADCP measurements.

How to cite: Pearce, S., Ljubicic, R., Pena-Haro, S., Perks, M., Tauro, F., Pizarro, A., Fortunato Dal Sasso, S., Strelnikova, D., Grimaldi, S., Maddock, I., Paulus, G., Plavsic, J., Prodanovic, D., Manfreda, S., Corbett, M., and Everard, N.: An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using Unmanned Aerial Systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-324, https://doi.org/10.5194/egusphere-egu2020-324, 2020.

Unmanned Aerial Systems (UASs) can monitor streams and rivers also in remote, inaccessible locations during extreme hydrological events. Image cross-correlation analysis techniques, such as Particle Image Velocimetry (PIV), applied to videos acquired using UASs can provide estimates of water surface velocity (WSV) in rivers. However, estimation of discharge from WSV is not trivial: it requires water depth and the mean vertical velocity (U_m). Scientific studies show that U_m is generally between 70% and 90% of WSV; however, an accurate estimation of U_m from WSV requires assumptions on the full vertical velocity profile. We developed a new method for estimating WSV applying PIV techniques on UAS-borne videos. This method does not require any Ground Control Point (GCP), because the conversion of the velocity field from pixels into meters is performed by using a camera pinhole model where the distance from the pin-hole to the water surface is measured by an on-board radar altimeter. For approximately uniform flow conditions, U_m becomes a function of Gauckler–Manning–Strickler roughness coefficient (Ks) and WSV. Our method can be used to jointly estimate Ks and discharge by informing a non-linear system of 2 equations and 2 unknowns (Ks and discharge): i) Manning equation ii) mid-section method equation for computing discharge from U_m, which is a function of WSV and ks. This approach merely relies on bathymetry knowledge, on UAV-borne measurements of WSV and water surface slope.  Our approach was extensively validated in 27 case studies, in multiple Danish streams with different hydraulic conditions. Compared to discharge measured with a multi-depth electromagnetic velocity probe, PIV-estimates of discharge showed a mean absolute error of 18% and a mean bias error of -9%. The underestimation of discharge is caused by inaccuracies in WSV_, by deviations from the uniform flow assumption and by the assumption of constant Ks coefficient for the entire cross section.

How to cite: Bandini, F., Lüthi, B., Peña-Haro, S., and Bauer-Gottwein, P.: A drone-borne contactless method to jointly estimate discharge and Manning’s roughness in rivers , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4229, https://doi.org/10.5194/egusphere-egu2020-4229, 2020.

Discharge measurement using salt dilution is an old method, but it has been recently more and more used thanks to the development of new sensors making it possible to measure conductivity and compute discharge in real-time. Salt dilution is very well suited for turbulent rivers, such as mountain streams. The ISO standard ISO 9555 propose a normative framework to estimate uncertainty, but it was published in 1994 and is now obsolete for new sensors and computational capabilities. In this article, we propose a complete framework to compute the uncertainty of a salt dilution gauging following the GUM (Guide to the expression of uncertainty in measurement) method that take into account the following error sources:  (i) the uncertainty in the mass of salt injected, (ii)  the uncertainty in the measurement of time, (iii) the uncertainty in the Conductivity to Concentration law, (iv) the uncertainty if a measurement conductivity is out of the range of the Conductivity to Concentration law, (v) the uncertainty in the computation of the area under the conductivity curve, (vi) the uncertainty due to a not perfect mixing of the tracer if the mixing length between injection and the probes is not reached (vii) the uncertainty due to a loss or a gain of tracer between the injection and the probes if tracer can be adsorbed for example and (viii) the uncertainty due to unsteadiness of the flow  i.e. variation of discharge during the measurement. The method for computing each uncertainty source is presented and the new framework is applied to a set of real measurements and compared to the expertise of field hydrologists.

How to cite: Hauet, A., Florvaag-Dybvik, K., Dahl, M.-P. J., Kvernhaugen, F. T., Møen, K. M., and Sentlinger, G.: Uncertainty of discharge measurement using salt dilution , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4661, https://doi.org/10.5194/egusphere-egu2020-4661, 2020.

Establishing reliable rating curves and thereby reliable streamflow monitoring records is fundamental to much of hydrological science and water management practice. Cost-effective methods that enable rapid rating curve estimation with low uncertainty are needed given diminishing monitoring resources and increasing human-driven changes to the water cycle. Traditional power-law rating curves rely on numerous gaugings to estimate rating curves and their associated uncertainty. Hydraulically-modelled rating curves are a promising alternative to power-law methods as they rely on fewer gaugings, but they are associated with additional uncertainty sources in the hydraulic knowledge (bed slope, roughness, topography and vegetation), which need to be assessed.

Our aim with this study was to compare power-law and hydraulic-model based methods for estimating rating curves and their uncertainty. We focused on assessing their accuracy as well as the costs and time required for establishing rating curves. We compared the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework with the Bayesian power-law method BaRatin. The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference to incorporate information from both hydraulic knowledge and the calibration gauging data. We applied both methods to the 584 km² River Röån station in Sweden under nine different gauging strategies associated with different costs. The gauging strategies differed in the number and flow magnitude of the gaugings used as well as the probability of observing the gauged flows.

We found that rating curves with low uncertainty could be modelled with fewer gaugings using the RUHM framework compared to BaRatin. As few as three gaugings were needed for RUHM if these gaugings covered low and medium flows, making the estimation both cost effective and time efficient. When using all the gaugings available (i.e. a high-cost gauging strategy), the uncertainty for RUHM and BaRatin was similar at the Röån station. Furthermore, we found that BaRatin needed gaugings with lower probability of occurrence (i.e. covering a larger part of the flow range) than needed when using hydraulic modelling (low and middle flow gaugings with high probability of occurrence gave good results). The results for this Swedish site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. In particular, it is useful for previously ungauged or remote sites, or at stations where there have been major temporal changes to the stage–discharge relation.

How to cite: Westerberg, I., Mansanarez, V., Lyon, S., and Lam, N.: Comparison of rating-curve uncertainty estimation using hydraulic modelling and power-law methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6779, https://doi.org/10.5194/egusphere-egu2020-6779, 2020.

The natural conditions of surface water bodies and groundwater aquifers in many densely populated river basins have been altered in order to satisfy various human water demands, such as drinking water supply, irrigation, power generation and navigation. The North China Plain (NCP) accounts for about 24% of the country's population, and the huge water demand makes it one of the regions with the strongest artificial intervention in the water cycle. China has promoted the South-to-North Water Diversion (SNWD), which diverts surplus water from the Yangtze River Basin to the water-deficient North. Since the central line project of SNWD has become fully operational in 2014, more than 16 km³ of water have been supplied to the NCP, which has had a significant impact on water resources in the regions along the route. Monitoring the recent dynamics of surface and sub-surface water storage is essential for water resources management and sustainable use of ongoing and forthcoming SNWD water transfers. Multi-mission satellite earth observation methods provide timely and spatially resolved datasets for monitoring inland water bodies, which have been validated over the last two decades. In this study, first, we evaluate the influence of SNWD on the Terrestrial Water Storage (TWS) monitored by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) over the NCP. The results indicate that the significant downward trend during 2002 – 2014 period, has stopped in the past 5 years, since the implementation of the central line project of SNWD. Second, Sentinel-3 radar altimetry and Sentinel-1 SAR missions were used to monitor the water surface extent and water surface elevation of surface water bodies. Sentinel-1 with its newly available Synthetic Aperture Radar (SAR), high spatial resolution and short temporal baselines shows potential for monitoring surface water area variations. Sentinel-3 benefits from the new Sentinel Ku/C Radar Altimeter (SRAL) and a modified on-board tracking system and shows great potential for monitoring inland water surface elevation (WSE) variations for several large and medium reservoirs and canals in this region. We show that, along with other policy measures, the SNWD transfers have had a significant impact on the water balance of the NCP region as evident from multiple satellite earth observation missions.

How to cite: Liu, J., Jiang, L., Bandini, F., Zhang, X., and Bauer-Gottwein, P.: Impacts of water resources management on the North China Plain revealed in multi-mission earth observation datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8890, https://doi.org/10.5194/egusphere-egu2020-8890, 2020.

Monitoring extreme events with high accuracy and consistency is still a challenge, even by using up-to-date approaches. On the one side, field campaigns are in general expensive and time-consuming, requiring the presence of high-qualified personnel and forward planning. On the other side, non-contact approaches (such as image velocimetry, radars, and microwave systems) have had promising signs of progress in recent years, making now possible real-time flow monitoring. This work focuses on the estimation of surface flow velocities for streamflow monitoring under particle aggregation, in which tracers are not necessarily uniformly distributed across the entire field of view. This issue is extremely relevant for the computing stream flows since velocity errors are transmitted to river discharge estimations. Ad-hoc numerical simulations were performed to consider different levels of particle aggregation, particle colour and shapes, seeding density, and background noise. Particle Tracking Velocimetry (PTV) and Large-Scale Particle Image Velocimetry (LSPIV) were used for image velocimetry estimations due to their widely used worldwide. Comparisons between the theoretical and computed velocities were carried out to determine the associated uncertainty and optimal experimental setup that minimises those errors.

How to cite: Pizarro, A., Dal Sasso, S. F., and Manfreda, S.: Image-velocimetry techniques under particle aggregation for streamflow monitoring: a numerical approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15525, https://doi.org/10.5194/egusphere-egu2020-15525, 2020.

In the last years, new technologies have been developed to monitor rivers in a real-time framework opening new opportunities and challenges for the research community and practitioners. Acquiring data in open flow conditions can be performed through the use of Unmanned Aerial System (UAS) to derive surface velocity fields and in consequence, river discharge. Significant work has been done to investigate the reliability of image-velocimetry techniques using numerical simulations and laboratory flume experiments, but, to date, the effects of environmental factors on velocity estimates are not addressed adequately. In this context, a critical variable is represented by the number of particles transiting on the water surface (defined as seeding density) during field surveys and their challenging dynamics along the cross-section, on both time and space. Seeding density has a significant effect on surface velocity estimation and river discharge accuracy. The goal of this study was, therefore, to evaluate the accuracy and feasibility of LSPIV and PTV techniques under different seeding and flow conditions using several footages acquired employing UASs. To this purpose, the seeding behaviour during the whole acquisition time was examined for each case study focusing on the quantification of essential variables such as seeding density, average tracers’ dimension, coefficient of variation of tracers’ area, and spatial dispersion of them in the field of view. For each case study, both image-velocimetry techniques have been applied considering several different sets of images to locally measure the accuracy of velocity estimations in challenging seeding conditions. Results show that the local seeding density, tracers’ dimension and their spatial distribution can strongly influence the reconstruction of velocity fields in natural stream reaches. Therefore, prior knowledge of seeding characteristics in the field can deal with the choice of the optimal image-velocimetry technique to use and the related setting parameters.

How to cite: Dal Sasso, S. F., Pizarro, A., and Manfreda, S.: On the characterisation of open-flow seeding conditions for image-velocimetry techniques using UASs , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16011, https://doi.org/10.5194/egusphere-egu2020-16011, 2020.

We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.

The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01 m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31 % (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.

Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.

How to cite: Eltner, A. and Grundmann, J.: FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17773, https://doi.org/10.5194/egusphere-egu2020-17773, 2020.

There is growth in evidence of intensification of the global hydrological cycle over the past few decades, possibly due to changing climate and/or land-use landcover associated with anthropogenic forcing. For sustainable water management, an efficient and effective streamflow network is essential as it facilitates accurate monitoring of spatio-temporal variations of surface water. However, in recent years a remarkable decline in stream gauge density is observed in both developed and developing countries, possibly due to economic constraints and changing government priorities. World Meteorological Organization recommends periodic reviewal of stream gauge networks (accounting for changes in budgetary, data and end user’s needs) to improve the database for better assessment of hydrological uncertainty. However, there is a dearth of such attempts in India. Entropy theory (specifically Shannon entropy-based method (SEBM)) has gained wide recognition over the past few decades for the optimal design of hydrometric networks owing to its advantages. However, the SEBM has some limitations, which include (i) lack of fixed upper bound for entropy when a uniform distribution is considered to determine the probability and (ii) loss of information due to discretization of data in analysis with continuous variables. In this backdrop, there is a need to locate feasible alternatives to the Shannon entropy method. There are various methods for entropy estimation and data discretization, but there is a lack of information on their relative performance. This study is envisaged to investigate these aspects and to propose a novel fuzzy approach for optimal design and performance assessment of a stream gauge monitoring network. The proposed methodology does not require the choice of bin size for the discretization of data to estimate entropy measures/indices. Therefore, it alleviates the associated uncertainty which is a concern in analysis with SEBM and its related theoretical improvement EEBM (exponential entropy-based methodology). This is demonstrated through case studies on 16 river basins of Peninsular India encompassing more than 600,000 km² by considering objectives as prioritization of existing gauges, identification of gauge deficient zones and devising options for expansion of the existing stream gauge networks. Further, the effect of choice of bin size on entropy estimates obtained using SEBM and EEBM is demonstrated by considering nine bin size determination methods. Flows in ungauged catchments were simulated using SWAT (Soil and Water Assessment Tool) and optimization of the existing stream gauge network is performed using a Fast-Non-Dominated Sorting Genetic Algorithm (NSGA-II). The study indicated that all the stream gauge networks in peninsular India are inadequate for effective monitoring of flows and there is a growing need for their expansion.  This study is first of its kind which evaluates the potential of different entropy-based methods in stream gauge network design. The proposed methodology could be readily considered for the evaluation of networks monitoring other hydro-meteorological and hydrological variables, and water quality parameters.

How to cite: Vijay, S.: A Comparative Study of Entropy-based Methods for Optimal Design of Streamgauge Monitoring Networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-398, https://doi.org/10.5194/egusphere-egu2020-398, 2020.

Previous studies have established the ability to map river channel bathymetry accurately in clear water, shallow wadeable streams using imagery from Unmanned Aerial Vehicles (UAVs), Structure-from-Motion (SfM) photogrammetry and the application of refraction correction. However, because standard rotary-winged UAVs geotag imagery at a relatively low accuracy, there has been a need to use Ground Control Points (GCPs) to georeference the Digital Elevation Model (DEM). This is problematic in that is requires the operators to navigate around the site to place, survey and collect the GCPs which can be very time consuming and/or hazardous. A potential solution lies with the recent introduction of lower cost rotary-winged drones fitted with higher accuracy on-board RTK GPS sensors. These have raised the possibility of conducting UAV surveys with the use of very few or no GCPs across the survey site, saving time and removing the need to access all areas for GCP placement.

To test this possibility, we flew a 250 metre reach of the River Teme (max depth ~1m) on the English-Welsh border at 40m in July 2019 with two drones, i.e. a DJI Phantom 4 RTK UAV and base station and a DJI Phantom 4 PRO (non-rtk). The Phantom 4 RTK UAV was flown three times, i) using the flight program’s 2D option (nadir only and one flight path) ii) using the 3D option (camera angled at 60° and flown in two directions) and iii) using the RTK off option and then using post-processing (PPK) to correct the image locations. 20 GCPs were placed across the site and their locations surveyed with a Trimble R8 dGPS and an additional 20 Independent Validation Points (IVPs) were surveyed along the floodplain for terrestrial validation points and 100 points within the channel were surveyed submerged area validation points.

Imagery was processed with Agisoft Metashape (v1.5.5). A total of 28 DEMs were produced using the imagery from the two drones, different flight paths and different combinations of numbers and location of GCPs. These included reducing the number of GCPs from 20, to 10, 5, 3, 1 and 0. When using three GCPs, DEMs were produced by having them i) spread throughout the reach and ii) clustered close to one another. The bed heights of the submerged locations were corrected using the simple refraction correction first used by Westaway et al (2001) and then compared to the measured heights in the field. Accuracy was quantified using linear regression.

The results of this analysis demonstrated the ability to obtain accurate surveys of bathymetry in depths upto 1m using a DJI Phantom 4 RTK UAV and base station and a significantly reduced number of GCPS, combined with the application of refraction correction. This study confirms that considerable time saving in terms of fieldwork can be gained from the use of an RTK rotary-winged drone and base station. This technology can also be beneficial for obtaining accurate survey data in locations where it may be unsafe or impossible to place GCPs due to the hazardous nature of the terrain.

How to cite: Maddock, I. and Lynch, J.: Assessing the accuracy of river channel bathymetry measurements using an RTK rotary-winged Unmanned Aerial Vehicle (UAV) with varying Ground Control Point (GCP) number and placement, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1534, https://doi.org/10.5194/egusphere-egu2020-1534, 2020.

In this research, we conducted LSPIV (Large Scale Particle Image Velocimetry) measurements to measure river surface velocity based on images recorded by mobile phone. The realization of this research is based on the developments of two products. The first one is the digital camera, which has been combined with the mobile phone after several years of development. The second one is the three-axis accelerometer, which can measure the attitude of the object. A three-axis accelerometer is one of the necessary parts of the mobile phone nowadays, as many functions of the mobile phone, such as step counting, Do Not Disturb mode, games, require the detection of attitude.

In LSPIV, there are nine parameters of the collinear equation. Three of parameters are the coordinates of the perspective center in the image space (focus distance d and image center position (u, v)), which can be determined in advance in the laboratory; the other three parameters are the coordinates (x, y, z) of the perspective center in real space, which can be set to (0, 0, 0); the last three parameters are the attitude of the camera (i.e., the mobile phone), which is determined by the depression angle, the horizontal angle, and the left-right rotation angle and can be measured by three-axis accelerometer. Therefore, river surface velocity could be analyzed by LSPIV with not only continuous images captured by a camera of the mobile phone but also the acceleration values obtained by the three-axis accelerometer when each image was captured.

In the present study, Yufeng gauging station, which is in the upstream catchment of the Shihmen Reservoir in Taiwan, is selected as the study site. Two other measurement methods were used to measure the river surface velocity and the comparison was conducted. One is using a handheld digital flow meter and another is using LSPIV with control points to calculate the parameters for measuring the river surface velocity.

How to cite: Liu, W.-C. and Huang, W.-C.: Large scale particle image velocimetry measurement of river surface velocity based on images captured by a camera of the mobile phone, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1760, https://doi.org/10.5194/egusphere-egu2020-1760, 2020.

Robust predictions and forecasts of flood risks and hazards are reliant on accurate estimates of stream flow data.  However, the stage-discharge relationship is subject to substantial uncertainties from a range of error sources, particularly for out-of-bank flows where measurements are scarce and flows are often extrapolated.  Hydraulic modelling can be used to produce more reliable stage–discharge relationships beyond the range of observed measurements, but, these methods are often data intensive requiring topographical, bathymetric, calibration data etc. restricting their use across large samples of gauges.    

In this study, we present an automatable framework that can estimate out-of-bank discharge uncertainty using a hydrodynamic model and readily available national datasets.  The framework utilises LiDAR data, in-bank stage-discharge measurements and gauged river flows to calibrate a 1D/2D hydrodynamic model (LISFLOOD-FP) of a river reach and make predictions of river flow and rating curve uncertainty beyond bankfull.  A particularly novel aspect of this framework is the use of national LiDAR datasets of water surface elevation returns to estimate the bathymetry and friction in the channel using an inversion solver. 

The framework was applied to produce models of two gauged river reaches in the UK, the River Severn at Montford in Shropshire, and the River Tweed at Norham in Northumberland. Bathymetry estimates were consistent with observations, considering that the channel was simplified to rectangular below the LiDAR water surface, while Manning’s channel friction estimates were between 0.03 and 0.035. The model predictions showed a close fit to the official rating curve and out-of-bank stage-discharge measurements, with the model-predicted uncertainty bounds able to contain 89.5% and 100% of the out-of-bank flow measurements for Montford and Norham respectively. This holds promising results for quantifying out-of-bank discharge uncertainties across large samples of catchments to enable robust national flood risk assessment.

How to cite: Coxon, G., Milsom, R., and Neal, J.: Estimating out-of-bank discharge uncertainties using a hydrodynamic model and nationally available datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3066, https://doi.org/10.5194/egusphere-egu2020-3066, 2020.

Typhoon is the most frequent natural disaster that causes widespread damage during summer and autumn in Taiwan. On average, each year the island suffers four typhoons, which result in disastrous flash floods and losses in a short time because of steep terrains and intense rainfall. The Tamsui River Basin is located in northern Taiwan about 2,726 square kilometers and inhabited by eight million people. During flooding events, the emergency managers rely on accurate flood forecasting to take proper actions for damage reductions. The flood forecasting and warning system based on hydraulic models play an important role in flood risk management. This study first establishes river stage routing model based on dynamic wave theory. Then, both the real-time observed river stages and the least squares method are used to adjust the model currently flow conditions as the data assimilation. Finally, The Ensemble Kalman Filter method carries out the data correction with the computation of minimum error-covariance between the model prediction and the observation. The simulation results found the root-mean-square error of forecasted river stage using the data assimilation at the gauged stations of Taipei Bridge and Tudi-Gong-Bi for 1-3 hours lead time is 0.862m, 0.892m, 0.903m, and 0.281m, 0.326m, 0.345m, respectively. When the Ensemble Kalmen Filter is added in the model, the root-mean-square error reduces to 0.191m, 0.375m, 0.612m, and 0.062m, 0.090m, 0.145m at described gauged stations. It is found that the data assimilation and the Ensemble Kalmen Filter give reliable forecast water stages with a small root-mean-square error which successfully corrects the forecasted river stage at each time step of the flood routing process. The results reveal that the integrated model gains a better accuracy of the water-stage profiles with probabilistic uncertainties. The model provides reliable forecasts of the water-stage profiles for 1–3 hours lead time along the Tamsui River for specific locations in emergency response for flood risk management.

How to cite: Hsu, M., Fu, J.-C., Tsao, M.-C., and Kimura, N.: Application of Data Assimilation and Ensemble Kalman Filter for Flood Forecast in Tamsui River, Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4300, https://doi.org/10.5194/egusphere-egu2020-4300, 2020.

River bathymetric investigation has a long tradition as river-bed morphology is a crucial geomorphological variable that also has implications for river ecology and sediment management. In one sense, this is becoming more straightforward with the development of UAV platforms and SfM-MVS photogrammetry. Mapping inundated and exposed areas simultaneously has proved possible either by adopting two media refraction correction or by using some form of the Beer-Lambert Law. However, both of these approaches rely upon the bed being visible which becomes restricted to progressively shallower zones as stream turbidity increases. Traditional survey techniques to collect bathymetric data for inundated zones (e.g. total station or differential GPS systems) are time consuming and require a trade-off between point density and the spatial extent of survey. In this study we test a simple hypothesis: it is possible to generalize the likely depth of water in a shallow braided stream from basic planimetric information and use such statistical relationships to reconstruct the bathymetry of inundated zones. This is based upon the principle that a suite of planimetric variables (e.g. distance from stream banks, river channel width, local curvature magnitude and direction, streamline convergence and divergence) can be used to model the spatial distribution of water depths. We attempt to do this for a shallow braided river with high suspended sediment concentrations using orthoimages and DEMs derived from application of SfM-MVS photogrammetry to UAV-based imagery. We develop separate calibration and validation relationships to train and to assess the statistical models developed. These are then applied to the stream to produce bathymetric maps of flow depth for integration with SfM-MVS derived data from exposed areas. The method produces a point specific measure of uncertainty and tests suggest that the associated uncertainties are sufficiently low that after propagation into DEMs of difference reliable data on braided river dynamics and erosion and deposition volumes can be obtained.

How to cite: Mancini, D., Antoniazza, G., and Lane, S.: Bathymetric mapping in turbid braided mountain streams using SfM-MVS photogrammetry and statistical approaches, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5296, https://doi.org/10.5194/egusphere-egu2020-5296, 2020.

The hydraulic jump occurs depending on conditions of upstream and downstream and makes large vortexes in itself of which flow is complex and fluctuates. Recently, the abnormal climate and gain of the impervious area increase the variation in river discharge. It can result in exerting the pressure that is over the acceptable load at the bottom in the downstream of a weir and increasing the fluctuation of the pressure due to the hydraulic jump. Those can provoke damages because of negative pressure, erosion of materials, local scour, and excess of the design load. Thus, this study aims at making use of the design in river-bed maintenance structures such as riprap and an apron considering by the pressure fluctuations. We simulated the hydraulic jump phenomenon through a hydraulic model experiment and examined the relationship between hydraulic factors and the pressure in the range of the hydraulic jump. Specifically, the hydraulic jump is generated by installing a weir upstream in the channel and measured the velocity of the flow by using particle image velocimetry (PIV) and bubble image velocimetry (BIV) to identify the characteristics of turbulence in the section of the hydraulic jump. Also, this study measured the pressure at the bottom along to the flow. As a result, the main factors of the pressure fluctuations are derived by statistical analysis such as determining the correlation between the pressure and the factors. In the subsequent study, it will be suggested to expect the pressure fluctuations at the bottom by using surrounding hydraulic factors in hydraulic jump through an elaborate analysis.

Acknowledgement

"This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20AWMP-C140010-03)."

How to cite: Choi, S. H., Kim, H. S., and Park, M.: A study on turbulence flow and pressure due to hydraulic jump, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6483, https://doi.org/10.5194/egusphere-egu2020-6483, 2020.

Human beings always wanted to protect themselves from hazards associated with rivers and streams. Wether we talk about low flow, pollution or flooding, streams very quickly interested scientists and engineers for their wealth and abilities.

EDF (Electricité de France) is a french company dealing with energy production. Dealer or owner operator of electricity production structures, the company is responsible for their operation in safe conditions. Thus, the knowledge of parameters such as streamflow discharge or streamflow velocities is one of its priorities to better respond to three key issues which are plant safety, compliance with reguatory requirements and optimization of the means of production.

The present work consists in showing how to use ADCP (Accoustic Doppler Current Profiler) to accurately measure streamflow volocities in complicated conditions (tide cycle, complex flow, bubbles, factory in operation…). Such device can be coupled with GPS to precisely geolocalize the measured velocities to make them usable for models calibration. By showing a case study, this work aims at underlining how field work using ADCP with onboard GPS can create input data for the adaptation and the calibration of physical models.

How to cite: Morlot, T., Oustriere, P., Leclercq, F., and Scheepers, H.: ADCP with onboard GPS for streamflow velocity measurement usable for physical models calibration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8858, https://doi.org/10.5194/egusphere-egu2020-8858, 2020.

River discharge is a key variable for hydrological studies and water resource management, but acquiring high-quality measurement remains challenging in mountain environments and in particular for mountain torrents. Extreme discharge variations between summer and winter, negative temperatures and intense sediment transport are the main issues for sensors (that get easily clogged, frozen or stucked out of the water) as well as for cross-section stability (a pre-condition for using a rating curve approach). 
In this presentation, we discuss what we learned from streamflow observations in the experimental Vallon de Nant catchment (13,4 km²), located in the Swiss Alps, which serves as a field laboratory for environmental research, ranging from plant ecology to snow hydrology and sediment transport to stream-C02 exchange with the atmosphere. We discuss here 4 years of optical height gauge records at the outlet (1200 m a.s.l.), obtained from a single VEGA-PULS WL-61 sensor measuring the water height above a concrete trapezoidal shaped cross-section (base width 5.3 m), designed primarily for sediment transport observations (with 10 geophones mounted flush on the concrete weir). There was no low flow channel within the cross-section. At least four other similar gauging stations are currently in use for hydrologic research in Switzerland, with or without low flow channels. The relevance of a discharge quality study at this site is twofold: i) to understand the reliability of flow measurements during low flow and during sediment-influenced high flow events and ii) to compile recommendations for similar discharge observation settings. 

At the Vallon de Nant study site, the absence of a low-flow channel in the weir, combined with the limitation of having a single river stage measuring point resulted in significant over- and under-estimation of the river stage at low-flows, caused by the fluctuation of the river bed position relative to that of the measuring point. Even if the flow covers the entire width of the weir crest, single clast deposits near to the crest can significantly disturb stage observations. We performed a validation of the data using hourly pictures taken during daytime with a low-cost camera at the outlet, and used the photographic evidence to identify periods when the river was partially or totally frozen, sediments were distorting the river stage measurements, and river channelization was occurring below or next to the river height sensor. Concurrent monitoring of temperature, conductivity or turbidity failed to identify these distortions. Consequently, significant error in discharge calculation would arise without a concurrent photographic observation. The key conclusion is that despite the growth of automation in measurements at gauging stations, there remains a need for observation of those stations, and if humans are no longer doing these, other digital technologies such as imaging need to be used instead. Our approach could be extended to night-time situations and locations that will go for extremely long periods without access.

How to cite: Michelon, A., Antoniazza, G., Ceperley, N., Lane, S., and Schaefli, B.: Poking holes in discharge time series with photographic evidence, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13153, https://doi.org/10.5194/egusphere-egu2020-13153, 2020.

Globally, access to hydrometric data of adequate record length, quality and geographical coverage to answer research questions and manage freshwater systems remains a major issue. The UK National River Flow Archive (NRFA) provides stewardship of river flow data from over 1,500 locations across the UK. Data are acquired and displayed as ‘provisional’ in real-time for 500 stations, however the NRFA also undertake a full update to the quality controlled dataset on an annual basis. Upon submission, river flow records are subject to both automated data screening and manual methods of quality control by a team of trained hydrologists to ensure the data disseminated by the Archive to its broad user community are of the highest quality and fit-for-purpose for a range of applications. In the 1990s, an increasing number of gaps in river flow records and emerging declines in data quality resulted in the introduction of a Service Level Agreement (SLA) in 2002 to protect the UK’s hydrometric network and resulting data. Here, we present the results from 15 years application of the SLA system through the use of a set of quantifiable indicators of data quality, completeness and provision. The improvements shown demonstrate the benefits of such a system to the overall utility of the nationally archived river flow data and an example of quality control and performance indicator systems that can be used as a best practice model for other monitoring networks around the world. They also demonstrate one method of helping to ensure hydrological databases provide information of high quality to meet pressing research and water management needs today and into the future.

How to cite: Muchan, K., Tindall, I., Dixon, H., Turner, S., Sefton, C., and Hannaford, J.: Evidence of long-term improvements in the quality and completeness of UK river flow data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18930, https://doi.org/10.5194/egusphere-egu2020-18930, 2020.

Riverbank erosion is a ubiquitous, natural process. Typically, it occurs during larger flood events when the applied forces exerted by the flowing water on a bank exceed some erosion-resistance threshold. Riverbank protection may be needed when critical infrastructure is present or planned near eroding banks, which requires the quantification of the risk of infrastructure failure by bank erosion. Similarly, renaturalization of many European streams, for example through removal of bank protection measures, necessitates the quantification of expected river width adjustment. Unfortunately, we have been unable to accurately quantify bank erosion rates to date. Limitations exist in characterizing both the applied and resisting forces. For example, bank roughness co-evolves with erosion, which makes it difficult to adequately resolve the forces acting on the bank material. Bank material erosion-resistance of fine-grained soils varies significantly, that is over orders of magnitude, both spatially and temporally. Moreover, existing techniques to measure bank material erosion-resistance do not always produce repeatable results. As a consequence, existing bank erosion models, such as the widely used Bank Stability and Toe Erosion Model (BSTEM), require extensive calibration and validation. This is often unsatisfactory to river engineering professionals that have to make decisions on where to place bank protection measures and the level of protection required. The decision-making process could benefit from a risk-based analysis that quantifies the uncertainty in calculated bank retreat rate. Recent enhancements to the BSTEM model allow users to input probability density functions of (measured) bank roughness and bank material erosion-resistance properties. A Monte Carlo analysis then quantifies the effects of both variability and uncertainty in these parameters on bank retreat. We will present how the shape of different probability density functions affect the probability density function of bank retreat. Results will be further presented of application of the new model to assist in prioritizing riverbank restoration measures along the Lower American and Sacramento Rivers, CA, USA, to prevent failure of levees that protect the City of Sacramento from flooding.

How to cite: Langendoen, E. and Ursic, M.: Quantifying the uncertainty in riverbank erosion for risk-informed river engineering, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19427, https://doi.org/10.5194/egusphere-egu2020-19427, 2020.

In diverse developments such as hydropower potential assessment, flood mitigation studies, water supply, irrigation, bridge and culvert hydraulics, the magnitude of stream or river flows is a potential design input. Several methods of flow measurement exist; some basic and some more sophisticated. The sophisticated methods use equipment which, although they provide more accurate and reliable results, are invariably expensive and unaffordable by many institutions that depend greatly on flow records to plan and execute their projects. The need for skilled expertise in the use of these equipment and the associated maintenance problems preclude them from consideration in most projects developed and executed in developing regions such as Africa. For countries or institutions in these regions, there is a need for less expensive, but relatively reliable methods for stream or river flow measurement to be investigated; methods that require no equipment maintenance schemes. One such method is the float method in which the velocity of an object thrown in a river is measured by recording the time taken for the object to traverse a known distance and multiplying the velocity by the cross-sectional area of the river or stream. This method looks simplistic, but when flows obtained from it are correlated with those obtained from the more accurate and conventional methods, reliable results can be obtained. In this study, flow measurements were done at 42 different stream sections using the float method and a more reliable and generally accepted but expensive flow measurement method using a current meter. A statistical relationship was then developed between the flows obtained by the two methods by fitting a linear regression model to the set of data points obtained at the 42 locations on several reaches of selected streams in the western area of Freetown.  The study was conducted on streams with tranquil or laminar flow with flow magnitudes in the range of 0.39 m3/s to 4 m3/s in practically straight reaches with stable banks. The material of the stream beds was laterite soil. Thirty-two data sets were used to develop and calibrate the model and the remaining ten data sets were used to verify the model. The current meter method flows were regressed on the float method flows. For a significance level of 5%, the predicted flows of a current meter, given a float method flow, showed a high level of agreement with the observed current meter flows for the tested data set. 

How to cite: Kanu, I.: Stream Flow Measurement: Development of a Relationship between the Float Method and the Current Meter Method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21719, https://doi.org/10.5194/egusphere-egu2020-21719, 2020.

The lost Saraswati has been described as a large perennial river which was 'lost' in the desert towards the end of the 'Indus-Saraswati civilisation'. It has been suggested that this paleo river flowed in the Sutlej-Yamuna interfluve, parallel to the present-day Indus River. Today, in this interfluve an ephemeral river- the Ghaggar flows along the abandoned course of the ‘lost’ Saraswati River. We examine the hypothesis given by Yashpal et al. (1980) that two Himalayan-fed rivers Sutlej and Yamuna were the tributaries of the lost Saraswati River, and constituted the bulk of its paleo-discharge. Subsequently, the recognition of the occurrence of thick fluvial sand bodies in the subsurface and the presence of a large number of Harappan sites in the interfluve region have been used to suggest that the Saraswati River was a large perennial river. Further, the wider course of about 4-7 km recognised from satellite imagery of Ghaggar-Hakra belt in between Suratgarh and Anupgarh in the Thar strengthens this hypothesis.

            In this study, we have developed a methodology to estimate the paleo-discharge and paleo-width of the lost Saraswati River. In doing so, we rely on the hypothesis which suggests that the ancient Saraswati River used to carry the combined flow or some part thereof of the Yamuna, Sutlej and Ghaggar River catchments. The paleo-discharge of the river would compare with that of some of the large river of the Himalayan Foreland. These alluvial rivers are often called self-formed rivers, as they flow on the loose sediment and are subjected to erosion and deposition of channel bed and banks. The geometry of rivers such as width (W), depth (D) and slope (S) are primarily controlled by water discharge (Q) and catchment area (A). Various functional relationships have been developed to scale the alluvial rivers, which we have used to obtain the first-order estimate of the river discharge of the ‘lost’ Saraswati. A scaling relationship was established between the catchment area-channel width for 31 rivers and catchment area-discharge at 26 different locations on the rivers presently flowing on the Himalayan Foreland from Indus in the west to the Brahmaputra in the East. We found the width and discharge of all the Himalayan rivers scale in a similar way when they are plotted against their corresponding catchment area. Using these regime curves, we calculate the width and discharge of paleochannels of the Sutlej, Yamuna and Ghaggar rivers by measuring their corresponding catchment area from satellite images. Finally, we add the discharge and width obtained from each of the contributions of individual catchments (Yamuna, Sutlej and Ghaggar River) to estimate the paleo width and paleo discharge respectively of the Saraswati River. Our regime curves provide a first-order estimate of the paleo-discharge and paleo-width of the lost Saraswati ~2500 cumec and ~1000 m respectively. We also suggest that the 4-7 km channel width observed earlier on the satellite image corresponds to the channel belt width of the lost Saraswati River.

How to cite: Beg, Z., Gaurav, K., and Kumar Tandon, S.: Estimation of paleo-discharge of the lost Saraswati River, north west India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21212, https://doi.org/10.5194/egusphere-egu2020-21212, 2020.

The development of new image-based techniques is allowing a radical change in the environmental monitoring field. The fundamental characteristics of these methods are related to the possibility of obtaining non-intrusive measurements even in adverse circumstances, such as high flow conditions, which may seriously threaten the operators’ safety conditions in traditional approaches.

Optical techniques, based on the acquisition, analysis and elaboration of sequences of images acquired by digital cameras, are aimed at a complete characterization of the river instantaneous surface velocity field, through the analysis of a floating tracer, which may be naturally present or artificially introduced. The growing availability of a new generation of both low-cost optical sensors and high-performing free software programs for image processing, is a key aspect explaining the rapid development of such techniques in recent years. The best known optical techniques are the large scale particle velocimetry (LSPIV) and the large scale particle tracking velocimetry (LSPTV).

This work is aimed to analyze and compare the performance of the two most common free software packages based on LSPIV (i.e. the PIVlab and the FUDAA-LSPIV), which use different cross-correlation algorithms. The test is carried out by analyzing several sequences of both synthetic images and real frames acquired on natural rivers under different environmental conditions (with tracers artificially introduced). An image sequences generator has been implemented ad-hoc with the aim to create, under fixed configurations, synthetic sequences of images, simulating uniformly distributed tracers moving under controlled conditions. The various configurations are characterized by different parameterization in terms of: (i) flow velocity (S=slow or F=fast flow conditions, according to a logarithmic transverse flow profile); (ii) tracer particles size (CON= disks of constant diameter; VAR=disks of variable size with given mean diameter); (iii) seeding density per frame (density: low -LD, medium -MD, high -HD).

The synthetic sequences are processed by the two software packages together with the real sequences, analyzing the errors in terms of mean value of the surface velocity field and velocity along a transverse transect, with respect to a benchmark velocity (i.e. that imposed in the image sequence generator for the synthetic sequences and that deriving from the use of traditional sensors, i.e. ADCP, for the real sequences).

How to cite: Pumo, D., Alongi, F., Ciraolo, G., and Noto, L.: On the use of LSPIV-based free software programs for the monitoring of river: testing the PIVlab and the FUDAA-LSPIV with synthetic and real image sequences, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10155, https://doi.org/10.5194/egusphere-egu2020-10155, 2020.

How to cite: Bertulessi, M., Bianchini, P., Boschini, I., Chiarini, A., Ferrario, M., Mazzon, N., Menduni, G., Morosi, J., and Zambrini, F.: Conceptualization of an anti-erosion sensing revetment for levee monitoring: experimental tests and numerical modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22495, https://doi.org/10.5194/egusphere-egu2020-22495, 2020.