Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. The twin pressures of population growth and a rapidly changing global climate act as multipliers of water’s value and of water-related hazards.
River streamflow is one of the most crucial hydrological variables for ecology, for people and industry, for flood risk management and for understanding long term changes to the hydrological regime. However, despite significant efforts, long-term, spatially dense monitoring networks remain scarce, and even the best monitoring networks can fail to perform when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.
Happily, a number of new technologies and techniques are emerging which show great potential to meet these challenges. In this context, this session focuses on:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) Remote sensing for hydrological & morphological monitoring;
4) Measuring extreme conditions associated with a changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.
Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.
Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.
This session is sponsored by the COST Action CA16219, Harmonisation of UAS techniques for agricultural and natural ecosystems monitoring (HARMONIOUS).
vPICO presentations: Tue, 27 Apr
Several studies have been carried out to evaluate image-based solutions for velocity measurement and discharge determination in river. However, these studies are limited because it is difficult to know the reference surface velocity field accurately. These data are usually extrapolated from measurement within the water column or integrated over a cross-section to determine the discharge to be compared with a reference, which is uncertain itself. Measurement uncertainties are difficult to quantify and cannot be neglected usually.
The only solution that arises to get a flow with a known surface velocity reference is synthetic imaging: we generate artificial images on which particles movements are known everywhere. However, these generators must allow a comparison between simulations and measurements for a wide range of conditions representative of the situations observed in the natural environment. Several Synthetic Image Generators have been designed for laboratory PIV but the generated images are made of white particles moving on a dark background. Such images are not representative of river applications with turbulence figures, foam, debris, sunlight effects but also some homogeneous areas with poor contrast where we can sometimes see the river bed through.
We propose a novel method to generate images from a synthetic river scene with accurate surface velocity references. It is based on the 3D computer graphics tool Blender which integrates a dedicated fluid simulation tool, Mantaflow. Blender allows many different configurations by playing on the modeling of the river, the surrounding objects, the textures and optical properties of the materials but also on the lighting and the camera settings and position. Mantaflow is then used to model and extract the characteristics (velocities, positions in time) of a flow that looks similar to real-life situations. The first synthetic videos obtained were used to study the sensitivity of the velocity results to the image-based velocimetry algorithm, its parameters and user choices.
How to cite: Bodart, G., Le Coz, J., Jodeau, M., and Hauet, A.: Generating videos of synthetic river flow for the evaluation of image-based techniques for surface velocity determination, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10778, https://doi.org/10.5194/egusphere-egu21-10778, 2021.
The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.
The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).
The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.
Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.
Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.
Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.
Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.
Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.
How to cite: Schmid, B.: An improvement to the ANN-enhanced flow rating method, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-221, https://doi.org/10.5194/egusphere-egu21-221, 2020.
Salt Dilution flow measurement is relatively accurate and easy way to measure flow in turbulent waterways. However, it’s accuracy and precision are governed by the Signal to Noise (SNR) Ratio, which can be very low in urban, sub-urban, and rural waterways due to a highly variable BackGround specific Electrical Conductivity (BG ECT) signal. Conventionally, more salt is added to the waterway to overcome the noise in the BG ECT. The “noise” is a combination of random noise, which is amplified by the typically high BGECT (>500 uS/cm), but also lower frequency noise that changes on the same time scale as the salt breakthrough curve. To compensate for the changing BG ECT, we have employed a 3rd UpStream (U/S) probe to track the BG ECT, along with algorithms to transform the signal in 3 domains: magnitude (ECT offset), time (transit time of pulse), and frequency (to compensate for storage in the waterway). Additionally, we have tested the use of a 3rd DownStream (D/S) probe to measure cross-channel variance when mixing is not complete in order to achieve a reasonable flow estimate. Results are compared and discussed.
How to cite: Sentlinger, G.: The use of a 3rd U/S or D/S sensor in Salt Dilution Flow Measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1558, https://doi.org/10.5194/egusphere-egu21-1558, 2021.
Over the past few years, smartphone devices have become so powerful that in your pocket, not only do you have a device which can communicate with people across the world, the sheer power of these devices has now also brought a new frontier in scientific measurements. In this presentation, we present our smartphone app 'flowonthego', a technology which allows users to determine flow velocities, in almost real-time, from simple video footage. The instantaneous velocity fields are calculated by solving the Lucas-Kanade solutions to the optical flow equations and tracking naturally occurring features. The app also harnesses the potential of augmented reality, making calibration reference and the need tape measures a thing of the past. Furthermore, the app also packs an arsenal of post-processing tools in which users can understand basic statistics. From preliminary our studies we have found 'flowonthego' is able to match the statistics of commonly used ADCP's while also providing instantaneous full vector fields allowing users to better understand dynamical processing.
How to cite: Higham, J. and Plater, A.: `Flowonthego' - flow tracking technology on your smartphone , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5902, https://doi.org/10.5194/egusphere-egu21-5902, 2021.
The strict lockdown imposed by the Covid-19 health crisis motivated the French-speaking hydrometry network Groupe Doppler Hydrométrie (GDH) to organise a new type of hydrometric intercomparison, based on video gauging. Between 15 April and 10 May 2020, the Video Globe Challenge 2020 was run in 8 stages corresponding to 8 videos taken from the ground (5 cases) or from a drone (3 cases), each coming with a reference discharge measurement (6 ADCP gauging, 1 dilution gauging, 1 calibration curve). These eight cases present various flows, measurement conditions and operating difficulties. The data were provided by EDF, DREAL Auvergne-Rhône-Alpes, NVE (Norway) and DNRME Queensland (Mark Randall, Australia).
For each stage, around 25 competitors participated by submitting their discharge result, their surface velocity coefficient (a.k.a alpha) estimate and their parameters, with the hope of getting as close as possible to the reference discharge. Several velocimetry techniques and software tools were used: from visual spotting and manual processing, in Flowsnap (Tenevia), Excel or Barème, to specialised software, mainly Fudaa-LSPIV (EDF/INRAE) but also SSIVSuite (Photrack), PIVlab, and Opyf (EPFL/INRIA, local optical flow). The general classification (smaller sum of percentage deviations to discharge references), points classification (smaller sum of ranks), sniper classification (best visual velocimetry) and young rider classification (for students) awarded the yellow, green, polka dot and white jerseys, respectively.
The Challenge 2020 has been rich in lessons, notably by illustrating several important sources of error for video gauging and the possible parries that the user can deploy (or not...). The exercise was as useful for training and coaching the participants (often beginners) as it was for identifying the improvements to be expected in procedures and software. The results highlight some operator-related error sources which need to be minimized by developing more guided or automated parameter settings, and more robust velocimetry algorithms. They also illustrate the typical uncertainty levels of such measurements.
The cultural aspects were not left out, revealing historical facts and hydrometry-related feats about the rivers visited, e.g. Julius Caesar wading the river to join the druids in the sanctuary of Seranos, Viking Stør Åne the Blue breaking the ice cover to prevent rating shift, or Sir Herbert inventor of the anti-crocodile waders. The official history of hydrometry conceals many unsuspected mysteries that have yet to be revealed...
How to cite: Le Coz, J., Hauet, A., and Despax, A.: 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.
When two mega rivers merge the mixing of two flows results in a highly complex three-dimensional flow structure in an area known as the confluence hydrodynamic zone. In the confluence zone, substantial changes occur to the hydrodynamic and morphodynamic features which are of significant interest for researchers. The conﬂuence of the Negro and Solimões Rivers, as one of the largest river junctions on Earth, is the study area of the present research. During the EU-funded Project “Clim-Amazon” (2011-2015), velocity data were collected using an ADCP vessel operating under high and low flow conditions in different locations at that confluence (Gualtieri et al., 2019). By applying the Entropy theory developed by Chiu (1988) for natural channels and simplified by Moramarco et al. (2014), the two-dimensional velocity distribution, as well as depth-averaged velocity, were calculated at the different transects along the confluence zone, using only the surface velocities observation. The estimated data yielded 6.6% and 6.9% error percentage for the discharge data related to high and low flow conditions, respectively, and 8.4% and 8.3% error percentage for the velocity data related to high and low flow conditions, respectively. Regardless of the flow condition, these preliminary results also suggest the potential points at the confluence zone for the maximum local scouring. The findings of the current research highlighted the potential of Entropy theory to estimate the flow characteristics at the large river’s confluence, just starting from the measure of surface velocities. This is of considerable interest for monitoring high flows using no-contact technology, when ADCP or other contact equipment cannot be used for the safety of operators and risks for equipment loss.
Keywords: Amazon River, Negro/Solimões Confluence, Entropy Theory, Velocity Distribution, Local Scouring
Gualtieri, C., Ianniruberto, M., Filizola, N. (2019). On the mixing of rivers with a difference in density: the case of the Negro/Solimões confluence, Brazil. Journal of Hydrology, 578(11), November 2019, 124029,
Chiu, C. L. (1988). “Entropy and 2-D velocity distribution in open channels”. Journal of Hydrologic Engineering, ASCE, 114(7), 738-756
Moramarco, T., Saltalippi, C., Singh, V.P. (2004). “Estimation of mean velocity in natural channels based on Chiu’s velocity distribution equation”. Journal of Hydrologic Engineering, ASCE, 9 (1), pp. 42-50
How to cite: Bahmanpouri, F., Barbetta, S., Gualtieri, C., Ianniruberto, M., Filizola, N., Termini, D., and Moramarco, T.: Estimating the hydrodynamic and morphodynamic characteristics using Entropy theory at the confluence of Negro and Solimões Rivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10330, https://doi.org/10.5194/egusphere-egu21-10330, 2021.
We use satellite altimeter data to estimate average monthly discharge at seven different locations in the middle and lower parts of the Ganga River. We have obtained the water level from different satellite altimeter mission ERS-2 (1995 - 2007), Envisat (2002 - 2010), and Jason-2 (2008 - 2017) through publicly available databases Hydroweb and DAHITI. To make the water level comparable with the gauge stations, we applied the datum and offset correction to the altimetry datasets. The corrected water level data well accord with the ground measurements with RMSE values in a range between (22 - 71) cm.
We then established stage-discharge rating curves from the water-level derived from satellite altimeter and the corresponding discharge measured at the nearest gauge station. We use these rating curves to estimate discharge of the Ganga River in the middle (Kachla bridge, Kanpur, Shahzadpur, Prayagraj and Mirzapur) and lower (Azmabad and Farakka) reaches from the water-level from satellite altimeter. Our estimates of discharge compare with the monthly average discharge recorded at the nearest ground station.
We observed that the uncertainty in the discharge estimate is relatively high in the middle than the lower reaches of the Ganga River. This is probably associated with the low discharge and shallow flow depth of the Ganga River in the middle reaches as compare to the high flow depth and discharge in the lower reaches. Overall performance analysis of statistical parameters (NSE, RSR, PBIAS, and R2), suggests that except for the Kanpur station, our estimates of discharge can be categories into "good" to "satisfactory".
How to cite: Rai, A. K. and Gaurav, K.: Satellite altimeter to estimate discharge of the Ganga River, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14199, https://doi.org/10.5194/egusphere-egu21-14199, 2021.
Highly intermittent rivers are widespread on the Tibetan Plateau and deeply impact the ecological stability and social development downstream. Due to the highly intermittent rivers are small, seasonal variated and heavy cloud covered on the Tibetan Plateau, their distribution location is still unknown at catchment scale currently. To address these challenges, a new method is proposed for extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau. The proposed method first determines the proper time scale of extracting highly intermittent river, based on which the statistical features are calculated to amplify the difference between land covers. Subsequently, the synoptic cumulative distribution location is extracted through Random Forest model using the statistical features above as explanatory variables. And the precise result is generated by combining the synoptic result with critical flow accumulation area. The highly intermittent river segments are derived and assessed in an alpine catchment of Lhasa River Basin. The results show that the the intra-annual time scale is sufficient for highly intermittent river extraction. And the proposed method can extract highly intermittent river cumulative distribution locations with total precision of 0.62, distance error median of 64.03 m, outperforming other existing river extraction method.
How to cite: Fei, J. and Liu, J.: Extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4040, https://doi.org/10.5194/egusphere-egu21-4040, 2021.
Splosh, gurgle, burble are all terms that can be used to describe how a river sounds as we stand on the bank. We have developed a new approach that uses the passive sound generated by a river, to gauge the current stage of the river, and generate (sono)hydrographs from the safety of the river bank. Our approach offers a cost-effective, power-efficient and flexible means to install flood monitors. We have developed a method of how to take the sound from around a river and translate it into a useful gauging tool without the need to listen to individual recordings. Using an internet of things approach we have developed a system of sound monitors that can be placed anywhere in the vicinity of a river. We aim to target the lesser studied parts of a river catchment, the headwaters, which are often data scarce environments. These environments are an opportunity to identify the real time responses of sub-catchments. The ultimate goal of our research is to enable community level flood monitoring, in areas that may be susceptible to river flooding, but are not yet actively gauged.
We hypothesise that the sound generated by a river is a direct response to the obstacles found within the channel and the turbulence they cause. Sound is generated by the increase of energy available in the channel, being transformed into sound energy through turbulence generating structures, i.e. boulders. Data gathered over a winter season from several rivers in the North East of England, during Storm Ciara and Dennis, has shown sound to be a reliable method for determining rapid changes in river stage and is comparable to what the official Environment Agency gauges measured. Through an innovative approach, we have begun to understand the limits on sound data and the calibration of sound to the channel properties. Utilising a 7.5 m wide flume at a white water course we have recreated controlled environments and simulated different discharges and their effect on sound.
Overall, we have found that sound is an opportunity to be taken to measure river stage in areas that are seldom studied. We have identified that sound works during extreme conditions, and being placed on the banks of the channel our monitors have a lower risk of being damaged during storm events and are easy and safe to install. We present the first means of using sound from a river to actively gauge a river and the full workflow from collection, analysis and dissemination of results.
How to cite: Osborne, W. A., Hodge, R., Love, G., Hawkin, P., and Hawkin, R.: Innovative method for gathering river stage data using only the sound of the water, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-136, https://doi.org/10.5194/egusphere-egu21-136, 2020.
Floods are natural inundations affecting rivers, lakes, coasts and the open sea. Due to anthropogenic impacts those floods can be modified with pollutants. The pollutants are then transported or dislocated during flood events and can then harm humans and life, society and other. The main objective of the project is to understand the complex and non‐linear processes, effects and long‐term impacts of “Toxic Floods” including the various influences of changing natural and anthropogenic boundary conditions from past to future.
As water/environmental engineers we aim to understand flood-related dispersion of contaminants and contaminated sediment. Therefore, we have a deep look at processes concerning sediment transport, fate and load during flood events. In a further step, it is aimed to describe and quantify the anthropogenic impact in floodplains and medium size river catchments. This knowledge will help to simulate toxic floods and define different scenarios and their effect on the environment.
In collaboration with an interdisciplinary team we can synthesize all outcomes and will be able to develop e.g. smart flood monitoring plans.[CB1]
How to cite: Brüll, C., Schüttrumpf, H., and Hollert, H.: Understanding Toxic Floods - Develop monitoring strategies for affected areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7513, https://doi.org/10.5194/egusphere-egu21-7513, 2021.
The world has entered an era of immense water-related threats due to climate warming and human actions. Changing precipitation patterns, reducing snowpack, accelerating glacial melt, intensifying floods and droughts have made the need for timely hydrometric information indispensable. Climate change thus introduced requirements for adaptive management and timely water resource information at the municipal, regional and national levels. Over the last 10 years, it became evident that demands from users had moved towards best available hydrometric data in near real-time. As with most hydrometric services around the world, the WSC was a legacy and archive-driven organization that published approved data on an annual basis. Real-time data was an after-thought simply equated with the application of rating curves onto telemetry water levels, while hydrographers remained focused on approving data months after the facts. To address this challenge, the Meteorological Service of Canada‘s National Hydrological Services, and specifically the Water Survey of Canada (WSC) has developed a near real-time continuous data production system to meet the evolving needs of stakeholders. To meet this challenge, WSC developed solutions where data would be improved as field-measurements were being acquired. Corrections to data and rating curves are applied within hours of field discharge measurements, allowing for near-real time publication of corrected discharge information. Moreover, station conditions and performance are constantly monitored with “eyes-on-data” production tools that allow the program to optimize its field visits, costs and data publication. These tools were developed in-house to enable effective network time-management while communicating important information with partner agencies. This was made possible with a cloud-based hydrometric data production system and modern telecommunications tools. As a result of this work, the improved near real-time data became the catalyst to revamp a multi-decade approach to final data approval. This improved overall efficiency and is now leading to less delays in the approved data production cycle. This paper describes the design and implementation of the continuous data production system adopted at WSC and highlights some of the benefits noted since program implementation. This paper also identifies future investments that could help the sustainability of this new system in the long term.
How to cite: Rainville, F., Pietroniro, A., Bouchard, A., Brown, A., and Stiff, D.: A Continuous Data Production Approach to Flow Estimation in Canada, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8897, https://doi.org/10.5194/egusphere-egu21-8897, 2021.
Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.
Dal Sasso S.F., Pizarro A., Manfreda S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sensing, 12, 1789 (doi: 10.3390/rs12111789), 2020.
Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., …Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 – 1559, 2020.
Pizarro A., Dal Sasso S.F., Manfreda S., Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.
Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020), 2020.
How to cite: Dal Sasso, S. F., Pizarro, A., Pearce, S., Maddock, I., Perks, M. T., and Manfreda, S.: Seeding metrics for image velocimetry performances in rivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9229, https://doi.org/10.5194/egusphere-egu21-9229, 2021.
Urbanization is the dominant force shaping social, economic, and environmental life in the 21 century. Urban areas will become essential to achieve the Sustainable Development Goals (SDGs) established by the United Nations in their 2030 Agenda. Local governments must identify the vulnerable ecosystems to make cities inclusive, safe, and resilient (SDG 11). In Latin America, urban rivers are vulnerable ecosystems, negatively impacted by rapid urbanization. Furthermore, detailed geospatial information of urban rivers is not updated frequently, therefore available data doesn’t reflect changes occurring due to rapid urban development processes affecting the quality of water, sediments, or vegetation health. This research uses a GIS-based multicriteria decision analysis (GIS-MCDA) for the environmental assessment of the Pesqueria River as a decision tool to facilitate mitigation focused strategies. The developed method has used the pixel to pixel data from socio-economical, environmental, topographical, geological, and hydrological factors affecting the environmental health of urban rivers. Census data, geological formation or soil type were obtained from official information; reflectance indices and vegetation height were obtained using aerial photogrammetry with near-infrared and red bands; terrain and hydrological analysis used digital elevation models derived from LIDAR; land cover was created using a SENTINEL 2 image; and water quality data was obtained from field sampled raised and analyzed with traditional laboratory analysis of Chemical Oxygen Demand and validated also with official data. Results implied the generation of the thematic maps with ranges from 1 (very low quality) to 5 (very high quality) according to the environmental quality assessment. For the GIS-MCDA, the values of each map were converted to the same scale, each criterion was weighted in function of its importance according to the literature review and the objective of this research, and there were aggregated by the way of a lineal combination. The result is a map that shows the level of mitigation or conservation priority along the river. This map can offer information to the stakeholders in a relatively short time and accelerate the actions aimed to protect the quality of this important urban ecosystem.
How to cite: Mireles Soria, D. L., Yépez Rincón, F. D., Ramírez Serrato, N. L., Ortiz Martínez, M. G., Ferriño Fierro, A. L., and Guerra Cobián, V. H.: GIS-based multicriteria decision analysis for the environmental assessment of the Pesqueria River in Northeast Mexico, using UAS and multi-spectral imagery , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10378, https://doi.org/10.5194/egusphere-egu21-10378, 2021.
In recent years, technological advances have been observed in environmental monitoring field, leading to a rapid spread of innovative technologies overcoming many historical challenges. In river monitoring field the use of image-based techniques provides non-intrusive measurements ensuring the best safety conditions for operators. The most used optical methods are the Large-Scale Particle Image Velocimetry (LSPIV) and the Large-Scale Particle Tracking Velocimetry (LSPTV).
In LSPIV and LSPTV techniques a floating tracer is introduced on the water surface and its motion is recorded by commercial devices (e.g. digital cameras). Resulting videos are then processed by free and open source software which applies a statistical cross-correlation analysis to provide the instantaneous surface velocity field.
The aim of this work is to investigate the performance of the most widely used LSPIV software in estimating the surface velocity field taking into account the presence of turbulent structures. Indeed a typical feature of natural river is the presence of turbulent eddies which makes the tracer patterns above the water surface difficult to predict. The evaluation of tracer particle displacement is further complicated by the negative phenomenon of aggregation; it influences cross-correlation causing an incorrect estimation of the velocity vectors.
The study of the hydraulic turbulence of a natural river has been tackled from a numerical point of view. PANORMUS (Parallel Numerical Open-Source Model for Unsteady Flow Simulations) package (Napoli, 2011) has been used by adopting a LES (Large Eddy Simulation) scheme. PANORMUS is a numerical tool coded to solve the 3D momentum equations for incompressible flows (Navier-Stokes and Reynolds equations) using the Finite-Volume Method (FVM). The analyses were carried out on real cases modelled with PANORMUS-LES package. The hydraulic reconstructed domains are characterised by regular cross sections, accurately derived from real topographic survey campaigns, and low river-bed roughness (smooth concrete surface).
Synthetic sequences of tracer motion were derived from the hydraulic model and then processed by using LSPIV software.
The results of such numerical analyses have allowed an evaluation of LSPIV performance assessing the errors in terms of mean value of the surface velocity field and velocity along transverse transects.
How to cite: Alongi, F., Ciraolo, G., Napoli, E., Pumo, D., and Noto, L. V.: Large-Eddy Simulation in LSPIV techniques: the study of surface turbolence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15468, https://doi.org/10.5194/egusphere-egu21-15468, 2021.
Water resources in Myanmar are increasingly affected by anthropogenic pressure and climate change related impacts. At the Inle Lake a unique village is located on the water in close proximity to intense fishing/farming activities. The nearby floating gardens provide invaluable resources for local communities, who are highly vulnerable to changes to water quality in the lake. Diversely, within the city of Yangon, the Kandawgyi lake is a popular recreational area which has become heavily affected by excessive algae proliferation. The deterioration of water quality Is likely caused by uncontrolled untreated wastewater, and poses a risk to the citizens. Finally, rivers such as the Pan Hlaing River, flow through industrial zones and collect waste water discharges.
Monitoring in these regions is scarce and limited to a few point-sampling locations. Local stakeholders lack adequate tools to monitor the needed parameters and are in need of reliable and updated baseline water quality data to support them in setting-up sustainable water management strategies. Tools such as aquatic drones and in-situ sensors are innovative ways of monitoring water quality and ecology that could contribute for effectively gathering valuable environmental data.
In this project, aquatic drones (both underwater and surface) were equipped with water quality sensors and cameras for low-cost and rapid assessment of surface water quality at high spatial resolution. The drones are able to navigate autonomously through way-points while collecting geo-referenced data. This study aims at field-testing of two affordable aquatic drones with sensors to map water quality parameters in different types of water systems (large lake, urban lake, river). This study reports the challenges encountered, and evaluates the resulting dataset/maps are in relation to the cost and value for the local stakeholders (ongoing research).
At the Inle Lake, results show varying concentrations of the different parameters that were measured. Low dissolved oxygen levels were found within the villages and underneath floating gardens, while chlorophyll-a and cyanobacteria levels were low across the whole lake. Underwater images show the presence of fish and provide insights into the aquatic ecosystems. At the Kandawgyi Lake, the generated water quality maps illustrate the spatial distribution of the different parameters, and two main areas of contamination could be identified (high algae content, low dissolved oxygen, high E-coli concentrations). At the Pan Hlaing river, the plotted data show degrading levels of dissolved oxygen concentrations, indicating potential effects caused by industry outlets.
The water quality maps that were generated with this data are very illustrative of the condition of the water bodies and the location of contaminations hotspots. The measurement process was accompanied by stakeholders and local universities, which contributed to stimulate capacity building and to create awareness for water quality related problems. As follow-up activities, these results will be used to draft a long-term water quality monitoring plan for local Myanmar students to continue collecting water quality data at these lakes. The detected issues are being discussed with local stakeholders, as well as the possibilities for establishing a larger scale monitoring campaign using this type of monitoring tools.
How to cite: Pedroso de Lima, R., Bogaard, T., and De Lange, R.: Mapping surface water quality in Myanmar using aquatic drones, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15531, https://doi.org/10.5194/egusphere-egu21-15531, 2021.
The development of Airborne Infrared Thermal sensing (TIR) is an example of how technological advancements and the field that they focus on have fostered one another. The pace at which global change is occurring has fed the demand for better understanding of the thermal behaviour of rivers. In turn, the improvement of remote sensing and data processing techniques has provided researchers and managers with new tools to apprehend such aspects at ever larger scales. Still, recent studies have mostly focussed on rivers showing little human alteration, with a particular interest on groundwater–surface water interactions. Lowland streams are scarcely considered when it comes to the study of temperature despite their widespread occurrence, their relatively high degree of disturbance and the risks that they face in the light of temperature rising following climate change. Some of these streams already display critically high maximum summer temperatures and their state is likely to worsen in the future, putting all compartments of biota at risk.
The aims of this project were twofold. We first tested the applicability of airborne TIR to study lowland, slow-flowing stream reaches draining agricultural catchments, some of which being particularly narrow and sinuous. We then sought to understand the role of different environmental factors, observed in such context, on driving river temperature during the warmest days of the year. A number of anthropogenic actions such as clear-cutting of riparian trees, stream rectification and the construction of weirs are likely to influence the longitudinal temperature profile of such streams. By choosing rivers with no or limited groundwater inputs, we were able to quantify the relative role of each of the three tested factors and identify stream sections showing critically high maximum temperature over the summer.
A final step was proposed to upscale these results in order to identify sections of streams showing high risks of reaching critically high summer temperature at a regional network scale. To do so, we used a combination of high resolution land-cover data, digital elevation models and other existing databases (e.g. national inventory of weirs). Identification of the risks in relation with the relative contribution of the different factors is key to process-based river management. This type of output is valuable to river basin managers and decision makers as it can be used to implement targeted restoration initiatives or remediation actions in areas where these have higher chances of being effective.
How to cite: Marteau, B., Chandesris, A., Cernesson, F., Michel, K., Vaudor, L., and Piégay, H.: Riverscape-scale airborne TIR assessment of weirs and riparian cover effects on lowland river temperature , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7134, https://doi.org/10.5194/egusphere-egu21-7134, 2021.
We present a velocimetry method, which we refer to as Infrared Quantitative Velocimetry (IR-QIV), that uses images of thermal patterns, captured in the infrared, on the surface of rivers or other water bodies, to calculate the time-resolved instantaneous two-dimensional surface velocity field. The method works in all natural light conditions (day or night), and under most weather conditions, by tracking thermal patterns in the surface of the water, and is therefore suitable for a large range of flows and environments. The method, is a form of remote sensing and has significant advantages over traditional (visible-light) PIV (Particle Image Velocimetry) or LSPIV (Large Scale PIV) methods for non-contact measurement of water surface velocity field, as it requires no particle 'seeding' or contact with the water.
Measurements of instantaneous water flow velocity, from which turbulence metrics are calculated, are important for advancing the understanding of river hydrodynamics beyond fundamentals such as discharge and mean velocity. However, most velocity measurement methods used in the field are capable of measuring at a point, or along a transect, but not over a two-dimensional area. Additionally, tools such as ADCPs generally require temporal and spatial averaging, and therefore can not resolve instantaneous velocities.
Image-based velocimetry methods, including IR-QIV and LSPIV, measure at the surface of the water and over a large area. However, methods that utilize visible-light imagery, such as LSPIV, require external illumination at night, and are challenged by the relatively homogeneous appearance of the water surface, often requiring either naturally occurring, or added 'seeding' particles, that are advected by the flow. Due the intermittent availability of seeding or surface texture, spatial or temporal averaging is often required, limiting the technique to mean velocity measurements.
These limitations do not apply to IR-QIV since under natural conditions a rich texture of temperature differences exist at the surface of the water due to spatially heterogeneous air/water heat exchange. IR-QIV is capable of calculating the instantaneous velocity at high accuracy and resolution, in space and time (centimeter scale, several Hz), over large areas—up to thousands of square meters. The instantaneous velocity measurements can be used to calculate metrics of turbulence to inform applications such as the study of river and other surface water dynamics; small-scale hydrodynamics near flow features such as water diversions, junctions, obstacles, and river bends; fishery management; gas transfer measurement; non-contact estimation of bathymetry, discharge and bed stress, and more.
We present instantaneous velocity and turbulence metrics measured at sites in the Sacramento River, (California, USA,) made using IR-QIV. Additionally, we discuss issues related to uncertainty analysis in velocimetry techniques using oblique camera viewing angles, and pattern tracking in images containing gradients of intensity (not discrete particles), as well as effects of camera noise. These considerations are relevant to all types of large scale image-based velocimetry, regardless of wavelength of image collection (visible-light or IR), and can be used to inform and improve measurements from both fixed and mobile platforms such, as UASs.
How to cite: Schweitzer, S. and Cowen, E.: Large-Scale, Accurate, High Resolution, Measurements of River Surface Velocity and Turbulence Metrics Using Thermal Infrared Images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13623, https://doi.org/10.5194/egusphere-egu21-13623, 2021.
Streamflow measurement is of great importance in hydrological research, water management and water infrastructure design. Traditional measurement methods typically employ intrusive techniques, and under certain conditions, obtaining accurate streamflow data with these techniques can be challenging because of safety concerns, especially in some critical circumstances, such as during flood flows. The advent of new instrumentation and technologies, and in particular advances in digital imagery, has led to the emergence of non-intrusive novel image-based technologies that can be used to estimate surface velocity, which in turn can be used to estimate streamflow. Image based technologies, most of which are based on correlation between consecutive images, have the potential for remote and on demand measurements and can provide data when the application of other traditional methods are not possible, reliable or safe. In this study, we present a novel machine learning based optical flow algorithm for streamflow surface velocimetry estimation. The developed algorithm is tested in different flow conditions and using drone and fixed photogrammetry. This method appears to outperform all the other available image-based surface velocimetry approaches (i.e. correlation based and classical optical flow methods). Moreover, this method requires the least user involvement for velocity estimation and thus reduces the impact or arbitrary choices linked to user expertise.
How to cite: Ansari, S., Rennie, C. D., Jamieson, E. C., Seidou, O., and Clark, S. P.: Machine Learning Based Surface Velocimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9825, https://doi.org/10.5194/egusphere-egu21-9825, 2021.
River flow observations are notoriously difficult to sustain. A site’s setup,operation and maintenance requires expensive equipment, repetitive field work, and physical contact of instruments and people with water. These issues compounded with the fact that rivers may change their course and behaviour in time, and sites are mostly bound to river crossings such as bridges, make equipment susceptible to theft and vandalism. Over the last decade, several contributors in science have pioneered the use of computer vision methods such as Particle Image Velocimetry, Particle Tracking Velocimetry, and Dense Optical Flow to measure stream velocities, and interpret river flow from short movie snapshots. This has resulted in research oriented software such as FUDAA-LSPIV and a limited set of proprietary software aimed for operational use.
In this contribution we will share and demonstrate the first version of OpenRiverCam, a new fully open-source, user-friendly, low cost and sustainable web-software stack with API to establish and maintain river rating curves (relationships between geometry and river discharge) in small to medium sized streams based on the aforementioned computer vision methods. The software is co-designed with practitioners from The Netherlands (Waterboard Limburg and KNMI) and Tanzania (Wami - Ruvu Basin Authority and TMA) with the principle that organizations should be able to establish and maintain operational flow monitoring sites and networks at low costs. We demonstrate it through operational feeds from two first sites (Geul River, Limburg - The Netherlands and Chuo Kikuu - Dar es Salaam, Tanzania).
The software stack will allow a practitioner in hydrology to monitor discharge and maintain a rating curve at low cost with simple yet robust equipment. The required set-up contains a permanent camera providing a view of the river surface and a permanent staff gauge for water level readings. Occasionally a bathymetric survey of the river’s cross section is required that can be performed with standard surveying equipment. The open source software stack is available at no costs and contains a separate python library for processing in case a researcher wishes to use the stack. The software operates with a web-client that connects to a locally or globally deployable server stack (laptop, desktop, local server or cloud) with database, front-end server and workers, so that scalability is warranted. Other than existing software, OpenRiverCam offers: adding and maintenance of sites and cameras; automated retrieval and processing of movies and rating curve analysis, all in a fully open-source code base. The software can therefore be operated with local people, local devices and open software at any scale leading to job creation and locally sustainable services for National Meteorological and Hydrological Services (NMHS) and their service providers. We plan to extend the software with operational water level measurements and possibly other relevant environmental parameters such as sediment deposit segmentation.
How to cite: Winsemius, H., Annor, F., Hagenaars, R., Luxemburg, W., Van den Munckhoff, G., Heeskens, P., Dominic, J., Waniha, P., Mahamudu, Y., Abdallah, H., Verver, G., and Van de Giesen, N.: OpenRiverCam, open-source operational discharge monitoring with low-cost cameras, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5880, https://doi.org/10.5194/egusphere-egu21-5880, 2021.
Unmanned Aerial Vehicles (UAV) have become a commonly used measurement tool in geomorphology due to their affordable cost, flexibility, and ease of use. They are regularly used in fluvial geomorphology, among other fields, because the high spatiotemporal resolution of UAV data makes it possible to assess the continuum rather than relying on single samples.
In this study, UAV data are used to hydro-morphologically describe three different river reaches of lengths between 150 and 1000 m. Specifically, the surface flow velocity and bathymetry of the rivers were reconstructed. The flow velocities were calculated using the Particle Tracking Velocimetry (PTV) method applied to UAV video sequences. In addition, UAV-based imagery was acquired to perform 3D reconstruction above and below the water surface using SfM (Structure from Motion) photogrammetry, taking into account refraction effects as well as frame processing to increase the visibility of underwater features. Reference data for flow velocities were generated at selected positions using current meters as well as ADCP (Acoustic Doppler Current Profiler) readings. The image-based calculated bathymetry was compared with RTK-GNSS sampling depth measurements and also ADCP data.
The developed workflow enables rapid and regular measurement of hydrological and morphological data of river channels. This ultimately enables multi-temporal assessment and significantly improves hydro-morphodynamic modelling, in particular their calibration.
How to cite: Eltner, A., Bertalan, L., and Lotsari, E.: Hydromorphological monitoring of individual river reaches with UAV-data – image-based measurement of bathymetry and flow velocity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14276, https://doi.org/10.5194/egusphere-egu21-14276, 2021.
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