Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment and protection of water resources.
This session focuses on measurement techniques, sensing methods and data science implications for the observation of water systems, given the strong link between measurement aspects and computational aspects, especially in the water sector.
This session aims at providing an updated framework of the observational techniques, data processing approaches and sensing technologies for water management and protection, giving also attention to today’s data science aspects, e.g. data analytics, big data, cloud computing and Artificial Intelligence.
We welcome contributions about field measurement approaches, development of new sensing techniques, low cost sensor systems and measurement methods enabling crowdsourced data collection also through social sensing. Therefore, water quantity and quality measurements as well as water characterization techniques are within the scope of this session.
Remote sensing techniques for the monitoring of water resources and/or the related infrastructures are also welcome.
Contributions dealing with the integration of data from multiple sources are solicited, as well as the design of ICT architectures (including IoT concepts) and of computing systems for the user-friendly monitoring of the water resource and the related networks.
Studies about signal and data processing techniques (including AI approaches) and the integration between sensor networks and large data systems are also very encouraged.
vPICO presentations: Fri, 30 Apr
The use of drones to monitor water quality is relatively new. Although drones and lightweight cameras are readily available, deriving water quality parameters is not so straightforward. It requires knowledge of the water optical properties, the atmospheric contribution and special approaches for georeferencing of the drone images. We present a cloud-based environment, MAPEO-water, to deal with the complexity of water surfaces and retrieve quantitative information on the water turbidity, the chlorophyll content and the presence of marine litter/marine plastics.
MAPEO-water supports already a number of camera types and allows the drone operator to upload the images in the cloud. MAPEO-water also offers a protocol to perform the drone flights and allow efficient processing of the images. Processing of the drone images includes direct georeferencing, radiometric calibration and removal of the atmospheric contribution. Final water quality parameters can be downloaded through the same cloud platform. Water turbidity and chlorophyll retrieval are based on spectral approaches utilizing information in the visible and Near Infrared wavelength ranges. Marine litter detection combines spectral approaches and Artificial Intelligence. Visible, Near Infrared and Short Wave Infrared wavelengths are used to detect marine litter but also discriminate marine litter from turbid water plumes and surface features such as glint and white caps. First tests have also been performed to apply a Convolutional Neural Network (CNN) for the automatic recognition of the marine plastic litter.
How to cite: Knaeps, E., Moelans, R., and De Keukelaere, L.: Drone image processing for water quality in the cloud, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-652, https://doi.org/10.5194/egusphere-egu21-652, 2021.
Harmful Algae Blooms (HAB) are now a topic of increasing interest due to the consequences they trigger on the quality of aquatic ecosystems and human health. Cyanobacterial (blue-green algae) proliferation, recurrence and distribution in water bodies around the world is caused by the sum of different climatic and anthropic factors. Manual sampling techniques are not sufficient to satisfy an adequate monitoring; hence, new strategies are needed to its continuous monitoring and possible prediction in affected areas. Real-time sampling techniques provide continuous recording and immediate data reception, which facilitates HABs monitoring with a very fine spatial-temporal resolution. However, these emerging tools are in their very early development stage and some relevant issues still constrain their applicability by many water management agencies. The objective of this work is to implement the same Remote Monitoring System (RMS) architecture in two different water bodies in Iberian Peninsula and to test its suitability for HABs monitoring. To this end, we deployed two plug-and-play nodes based on YSI technologies, two customised nodes based-on Libelium Waspmote and one Libelium weather station in the freshwater As Conchas reservoir, in NW Spain, and the shallow L'Albufera brackish water lagoon in Eastern Mediterranean shoreline. After that, we evaluate the representativeness of the collected data by performing a Pearson correlation test between the deployed nodes and satellite images. The results show that the more heterogeneous the environment is, the more nodes must be deployed in different areas for a longer time to obtain a realistic view of the water body status. Therefore, this study provides critical and empirical data to implement a profitable and effective real-time monitoring system in other HAB-affected areas.
Acknowledgements: This work was supported by the Spanish Fundación Biodiversidad, Ministry for Ecological Transition and the Demographic Challenge (CianoMOD Project, CA_CC_2018).
How to cite: Pascual-Aguilar, J. A., Morón-López, J., Rodríguez-Sánchez, C., Carreño, F., Vaquero, J., Pompa-Pernía, Á. G., and Mateos-Fernández, M.: CIANOMOD Project. A data gathering and analysis structure for the remote monitoring of algae blooms in inland waters based on Internet of Things, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1122, https://doi.org/10.5194/egusphere-egu21-1122, 2021.
Long term time-lapse photography has proven to be a key tool in monitoring changes in coastal environments, particularly in terms of morphology. The present study adapts and simplifies the approach of some precedents, such as the Argus and CoastSnap systems, to remotely monitor tidal inundation on a sandy beach at Magilligan on the north coast of Northern Ireland. Such a system could prove essential in the study of the effect of waves and tides on groundwater flow and saline intrusion in coastal aquifers, its consequences for sensitive subsurface infrastructure (such as water supply wells), and in the reconciliation of continuous data from same. Photographic data in this study have been gathered using a remote, solar powered time-lapse camera over a six-month period, capturing full neap and spring tidal cycles. Images are captured at hourly intervals and automatically uploaded to the cloud for remote access. The camera is located just 25 metres from the high water mark, overlooking the beach and perpendicular to the sea. This setup contrasts with previous studies where there is a need to find an elevated location at greater distance from the area of investigation. The extent to which a tide inundates up a sandy beach is governed primarily by astronomical effects, which are considered in this study, but also beach slope and atmospheric conditions. It is known that the beach at Magilligan has both a shallow grade (0.02 m) and a high tidal variation (> 150 m between spring and neap tides). Profiles of beach slope are gathered using a differential GPS, while a solar weather station on site, which also uploads data to the cloud, is used to gather atmospheric data. For tidal reference, a traditional tide gauge measuring tide levels at a pier 15 km east of the site is used. Captured images are post-processed using image analysis techniques based around characterising the tidal front against the visual contrast between pixels of sand and pixels of seawater using a routine in MATLAB®. From this analysis, a numerical value for tidal inundation is extracted. Analysis of these data indicates that the tide times (timing of high and low tides) correspond well with those measured at the nearby tide gauge, however important differences exist in terms of magnitude. In comparing these differences with atmospheric data from the site, it is possible to align larger and smaller inundation events with shifts in wind direction and speed. The calibration process involved in digitising the captured images is time-consuming, however, it may be possible to predict tidal inundation from a site using only a remote weather station — knowing how a change in wind speed or direction will affect inundation on the beach. It has already been shown that such instrumentation can be used to detect changes in beach morphology (as a key element in tidal inundation), this research therefore represents an important development in the low-cost remote monitoring of tidal inundation, particularly in locations where regular ground surveying is challenging.
How to cite: McDonnell, M., Fernández Águila, J., Hamill, G., Flynn, R., Etsias, G., Rowan, T., and Benner, E.: Tidal monitoring on sandy beaches using perpendicular time-lapse photography, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2802, https://doi.org/10.5194/egusphere-egu21-2802, 2021.
Coastal flooding is a sudden and abrupt inundation of a coastal environment caused by a short-term increase in sea level due to a storm surge and extreme tides. Although the coastal flooding is generally a natural process and constitutes an important part of the natural coastal dynamics, in areas with human activities it can constitute a major challenge and lead to loss of infrastructures and lives. For these reasons, an Integrated Coastal Zone Management (ICZM) approach, which spans over the main aspects of the coastal region (from prediction to protection, from engineering to ecosystems, from tourism to sustainability) seems essential to mitigate the mentioned negative impacts. At this aim, during the conference a methodology will illustrate based on the combination of X-band Marine radar with a numerical solver (NSWE), which allows predicting the beach inundation . Specifically, the elaboration of the X-band raw data sequence by means of a dedicated data processing based on the solution of an inverse problem, allows us to reconstruct of the local sea state parameters in terms of peak wave direction, peak wavelength, peak wave period and significant wave height as well as the seabed depth . Such reconstructed data are then exploited for the generation of both initial and boundary conditions, to be used to feed the NSWE model. The initial condition consists of the reconstructed bathymetry (e.g., referring to seabed depths within 5m and 9m) which is extended up to the coast using either an existing survey or an equilibrium-profile-based bathymetry. The reconstructed wave characteristics are used to generate, following ’s method, the random time series of free-surface elevation, which characterizes the boundary condition of the flood simulations.
Two different wave spectra, which mimic the actual storm conditions occurring along the coast of Senigallia (Adriatic Sea, central Italy), have been simulated. The beach inundations obtained from baseline and flood tests related to both storm conditions are compared. The results confirm that good predictions can be obtained using the combined of X-Band Radar and NSWE simulations . Such findings demonstrate that, for practical purposes, this methodology provides suitable beach-inundation predictions and may represent a useful tool for public authorities dealing with the coastal environment, e.g. for hazard mapping or warning purpose.
-  Postacchini, M.; Ludeno, G. Combining Numerical Simulations and Normalized Scalar Product Strategy: A New Tool for Predicting Beach Inundation. J. Mar. Sci. Eng. 2019, 7, 325
-  Ludeno, M. Postacchini, A. Natale, M. Brocchini, C. Lugni, F. Soldovieri, F. Serafino; Normalized Scalar Product Approach for Nearshore Bathymetric Estimation from X-band Radar Images: an Assessment Based on Simulated and Measured Data, IEEE Journal of Oceanic Engineering, doi: 10.1109/JOE.2017.2758118
-  Liu, Z.; Frigaard, P. Generation and analysis of random waves. Technical report, Aalborg Universitet, 1999.
How to cite: Ludeno, G. and Postacchini, M.: A methodology based on the coupling of marine radar and numerical modeling for beach-inundation prediction., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10930, https://doi.org/10.5194/egusphere-egu21-10930, 2021.
Abstract After the construction of the Grand Ethiopian Renaissance Dam (GERD), Nasser Lake (NL)became one of the most challenging hot spots at both local and global level. It is one of the biggest manmade reservoirs in the world and the most important in Egypt. It is created in the southern part of the Nile River in Upper Egypt after the construction of Aswan High Dam (AHD). The water level in NL might fluctuate between 160 to 182 m above the mean sea level. Monitoring NL water depth is an expensive and time-consuming activity. This work investigates the possibility to use information from the Sentinel missions to estimate the depth of NL as an inland water body, in the frame of estimating storage variations from satellite measurements. In this preliminary study, we investigated the relationship between the radiance /reflectance of optical imagery from two instruments SLSTR and OLCI instruments hosted by the Sentinel-3A platform and in situ water depth data using the Lyzenga equation. The results indictaed that there was a reasonable correlation between Sentinel-3 optical data and in situ water depth data. Also, Heron's formula was used to estimate water storage variations of NL with limited in situ data. In addition, equations governing the relationship between water level and both surface area and water volume were worked out. This study is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.
Keywords: Sentinel, SLSTR, OLCI, Inland water body, Nasser Lake, Egypt, Water Depth, GERD, AHD, Egypt
How to cite: Khairy, M., Hossen, H., Elsahabi, M., Ghaly, S., Scozzari, A., and Negm, A.: Feasibility of Using Sentinel-3 in Estimating Lake Nasser Water Depths, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11958, https://doi.org/10.5194/egusphere-egu21-11958, 2021.
This study deals with the quantitative estimation of the accumulated sediment capacity within the period from the initiation of the storage process of Lake Nubia in 1964 until 2012, by using field measurements and remote sensing data.. The bed levels of the study area related to year 1964 were extracted from a tri-dimensional model of the lake derived from a topographic map, based on observations anterior to lake filling. This map was compared with the bed levels estimated for the year 2012, which were extracted from remote sensing data, with the aim to estimate the sediment capacity. The utilized technique for estimating the bathymetric data (depths) from satellite images relies on establishing a Multiple Linear Regression (MLR) model between in situ measurements and reflectance data from multi-spectral optical satellite observations. The Multiple Linear Regression (MLR) model showed good results in the correlation between field measurements and remote sensing data. The current approach provides flexibility as well as effective time and cost management in calculating depths from remote sensing data when compared to the traditional method applied by Aswan High Dam Authority (AHDA). This study is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy, which is still running.
How to cite: Negm, A., Hossen, H., Elsahabi, M., Makboul, O., and Scozzari, A.: Estimation of sediment capacity of Aswan High Dam Lake utilizing remotely sensed bathymetric data: Case Study Active Sedimentation portion of Lake Nubia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13628, https://doi.org/10.5194/egusphere-egu21-13628, 2021.
The geological and petrophysical properties of the Vadose Zone (VZ) play a major role in the reactive transport of contaminants and fluid dynamics in fractured media and karstic hydrosystems. The mass and heat transfers through the VZ are governed by numerous complex and coupled processes, which control the fate of pollutants and influence the quality of groundwater resources. The current scientific research aimed at deciphering these hydrological and biogeochemical processes thanks to multi-scale laboratory experimentations and field observations. In order to acquire observation data over a wide range of spatial (nm- to km-) and temporal (minutes to decades) scales, an Observatory of transfers in the Vadose Zone (O-ZNS) is being developed at Villamblain (Orléans, France) in an agricultural field. The O-ZNS project consists of an access well with a diameter of 4 m and a depth of 20 m surrounded by several boreholes which will provide access to the entire VZ of the Beauce aquifer. The main target of the O-ZNS project is to acquire high resolution data on the reactive transfers of mass and heat in the VZ, in order to follow in situ and in real time the highly coupled physical, chemical, and biological processes taking place over the long term and at different various scales.
To meet the scientific objectives of the project, several sensors and environmental monitoring techniques are being considered as part of the instrumentation of the O-ZNS site. The preliminary geological, geochemical, and hydrogeological investigations conducted at the laboratory scale and coupled with a multi-methods geophysical sounding undertaken at the field scale generated valuable information on the lithological and hydraulic properties of the highly heterogeneous VZ facies. Based on these results, a first estimation of the mean water travel time of 29 years for an inert solute to reach the water table level (15 m deep) was given.
In this context, three distributed optical fiber sensors (temperature “DTS”, deformation “DSS”, and acoustic “DAS”) were installed in July 2020 along three boreholes surrounding the main observatory well and were connected to a data center. These sensors will allow the monitoring of fluid circulation, the rock fractures characterization, and the micro-movements detection in the VZ of the Beauce aquifer. Many innovative hydrogeological solutions are also being considered for the monitoring of fluids and solutes transport in VZ. These sensors include: water content probes for deep VZ materials, multi-level water sampler systems, and new generation of lysimeters allowing the study of contaminants transfer at an intermediate scale between laboratory and field. A latest-generation multiparameter probe will also be installed in February 2021 in the O-ZNS piezometer to monitor the variations of the water level and the quality of the groundwater. To complete these sensors, geophysical imagery will be deployed at different scale to link all parameters together in a 3D model.
This whole set of devices will provide a better understanding of the mass and heat transfer processes within the whole VZ column of the Beauce aquifer and some of the key compartments of the critical zone.
How to cite: Abbar, B., Isch, A., Mallet, C., Jodry, C., Gautier, L., Abbas, M., and Azaroual, M.: Observatory of Transfers in the Vadose Zone “O-ZNS” (in Orléans, France): Instrumentation strategy and installation of fiber optic sensors (DTS, DAS and DSS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8639, https://doi.org/10.5194/egusphere-egu21-8639, 2021.
In this presentation we provide a brief overview on the strategic selection of representative groundwater wells and lessons learned.
The inter-disciplinary project “Integrative Groundwater Assessment”, looks into the effects of extreme hydro-meteorological events on the quantity and the chemical and biological quality of groundwater. Focus is on the Austrian Mur catchment, an area reaching from its alpine spring (~2000 m asl) down to the Slovenian border (~200 m asl). More than 500 state operated groundwater observation wells are available over the 400 km of the river’s course, taking private wells not into account. For state operated wells, time series for water levels are publicly available which allows for simply using all the data i.e. using big data approaches [1, 2, 3] – albeit with some issues .
However, for water quality, such time series rarely exist and if so, they often do not cover all specific parameters one needs, asking for targeted sampling campaigns. The availability of hundreds of wells seems like a benefit. However, the identification of wells that are representative and suitable for sampling regarding both chemical and biological parameters is a challenging task
In consequence, we went through a multi-step process of planning a sampling campaign that should fulfill the following requirements:
Coverage of the entire stream section from alpine to lowland regions
Coverage of different land uses in the river valley
Realization of well transects from the river through the complete local aquifer
Wells allow sampling of groundwater for the analysis of physical-chemical and biological parameters
Historical data of groundwater quantity and quality aspects are available
Assessing the available metadata and taking into account the very helpful advice of stakeholders, already reduced the number of representative wells considerably. In order to obtain a consistent data set, another set of wells had to be dismissed, to allow for the same sampling and monitoring procedures at every location. Finally, out in the field, wells that were found damaged or out of order, led to a further reduction. Thus we ended up with only 45 wells suitable for our specific purposes, <10% of what seemed available at the beginning.
However, using specific strategies for data analysis as outlined in  and  and application of a novel groundwater ecological assessment scheme (D-A-C Index ) showed that even the substantially reduced number of wells provides a very good coverage of the various regions in the Mur catchment. In a further step, the results from two sampling campaigns and subsequent data analysis will be used to select an even smaller subset of wells where novel multi-parameter spectral dataloggers are going to be installed, enabling us to monitor various quality data in an very high temporal resolution.
 Haas et al (2020)
Tiny steps towards Big Data - Freud und Leid der Arbeit mit großen Grundwasserdatensätzen.
Tagungsband 2020. Grundwasser und Flusseinzugsgebiete - Prozesse, Daten und Modelle.
How to cite: Haas, J. C., Retter, A., Birk, S., and Griebler, C.: Selection of representative groundwater monitoring wells – A compromise between site characteristics, data history, stakeholder interests and technological limitations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12895, https://doi.org/10.5194/egusphere-egu21-12895, 2021.
Beyond high frequency monitoring : an optimised automatic sampling
Mougin Jérémy, Superville Pierre-Jean, Cornard Jean-Paul, Billon Gabriel
In order to improve the representativity of samples when monitoring a water body, efforts have been made these last years to develop new methodologies to replace grab samples. Passive samplers have allowed to have measurement averaged over several days and represented a first step. High frequency monitoring (usually one measure per hour), either in situ or on-line, led to the observations of daily cycles or transitory phenomena that were not suspected beforehand.
However, such method is usually difficult to implement for some trace analytes (e.g. trace metals or pesticides) or for some specific analysis (e.g. size exclusion chromatography on natural organic matter). Automatic sampling and analysis in the lab can be a solution, but it becomes very labor intensive as soon as the sampling frequency is high. Luck is also needed as a long sampling period can sometimes lead to very few variations if the water system is stable. In order to optimise the automatic sampling, a new methodology has been developped in this project.
A multiparameter probe measuring general parameters (temperature, pH, turbidity, ORP, conductivity, dissolved oxygen and two fluorometers for organic matter) was coupled with an automatic filtering sampler. The data from the probe are processed on-line and an algorithm decides if the geochemical situation in the water body seems new enough to trigger the sampling, based on previously sampled waters. The aim of this device is to collect the right number of samples with the best representativeness of phenomena taking place in the environment.
This method will be tested over a year in 2021 in order to monitor the dissolved organic matter in a small stream with both rural and urban contamination. These high-frequency measurements and samplings could make it possible to better define the sources and dynamics of the organic matter that has a strong impact on the quality of watercourses.
How to cite: Mougin, J.: Beyond high frequency monitoring: an optimised automatic sampling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1969, https://doi.org/10.5194/egusphere-egu21-1969, 2021.
Today in the water sector there are many methods for generating values in big databases. Everything should be connected in the cloud and everyone should access everything anytime. The data is changing very fast in every level and layer and should be controlled anytime. For better quality more methods in measuring data should be applied in parallel and so improve the quality, but this point is very complicated. You can not always compare different data measured by different methods, there are a lot of other enrivonmental factors in most cases. Many systems in different countries exists with a lot of methods and values (eg. ph,chlorine,nitrate,germs,pathogens,viruses,micro/nano materials(plastic,metall,glass,wood,...),minerals(salt connections,...).
How to cite: Rübig, P.: (Waste) Water quality sensor requirement and (too) high standards, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2611, https://doi.org/10.5194/egusphere-egu21-2611, 2021.
Bioelectrochemical systems (BES) are devices that transform the chemical energy of organic and inorganic substrates into an electric current. BES represents a particularly interesting biosensor technology for monitoring the performance of remote/isolated wastewater treatment facilities (such as constructed wetlands). The work presented here aimed to assess the potential use of the electric signal produced by low-cost, membrane-less BES systems as an indicator of the operational conditions and treatment performance of natural-based wastewater treatment systems. For this purpose, several BES configurations and operation modes working under real domestic wastewater conditions were monitored.
Results showed that the electric current produced by the BES significantly correlates with key parameters in biological-based wastewater treatment systems such as microbial activity and biomass, water COD or solids accumulation. Therefore, our work demonstrates the feasibility of applying bioelectrochemical-based low-cost biosensors for the improvement and control of natural-based wastewater treatment systems.
Keywords: bioelectrochemical systems, wastewater, microbial activity, organic matter, low-cost, biosensor
How to cite: Fernandez-Gatell, M., Sanchez-Vila, X., and Puigagut, J.: Low-cost biosensors for continuous performance assessment of natural-based wastewater treatment systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9800, https://doi.org/10.5194/egusphere-egu21-9800, 2021.
The continuous monitoring of water services is an important task for the reduction of cost associated with the maintenance of buried utility infrastructure and to prevent environmental and security risks for the territory. To this aim, ICT based solutions seem to be good candidates for the development of innovative and effective technologies for sensing and monitoring. Although several aspects are involved in a complete and accurate monitoring of water services (e.g., planning, optimization and modelling of the distribution system), we focus on the detection of possible failures in terms of pipes and sewers leaks through Ground Penetrating Radar (GPR)  and microwave imaging (MWI) techniques .
Over the past decades, GPR has become one of the most popular non-invasive subsurface imaging techniques, but the complex nature of the environment in which utility infrastructure detection surveys are carried out makes the interpretation of GPR radargrams difficult to accomplish. Accordingly, the idea is to exploit MWI and, in particular, a frequency domain microwave tomography to obtain accurate images of the investigated scenario by solving an inverse scattering problem (ISP) .
The ISP is a non-linear and ill-posed problem, so effective solution strategies and regularization techniques are needed to achieve meaningful solutions. In this respect, we exploit a reconstruction approach based on a linearized model of the electromagnetic scattering, that is the Born approximation (BA), in a multifrequency framework. More in detail, we consider the presence of pipes or sewers in our model and look for possible leaks, thus resulting in a distorted BA inversion approach . A truncated Singular Value Decomposition is finally used for the inversion of the relevant linear operator.
Note that even though being an approximated model, the adoption of a linearized model offers practical advantages in terms of computational burden still achieving useful information on the location/size of anomalies. More in detail, once the data have been collected, few seconds are required for the signal-processing step, thus making this kind of approach very appealing for real-time monitoring. Moreover, ‘false solutions’ affecting non-linear techniques are avoided.
Numerical examples concerning different investigated scenarios and failures will be shown at the Conference.
 D. J. Daniels, Ground penetrating radar. John Wiley & Sons, Hoboken, NJ, 2005.
 M. Pastorino, Microwave Imaging, John Wiley & Sons, Hoboken, NJ, 2010
 Crocco L., et al, “Early-stage leaking pipes GPR monitoring via microwave tomographic inversion", Journal of Applied Geophysics, 67.4 (2009): 270-277.
Acknowledgment: The authors would like to thank the SMART WATERTECH project “Smart Community per lo Sviluppo e l’Applicazione di Tecnologie di Monitoraggio e Sistemi di Controllo Innovativi per il Servizio Idrico Integrato” by which the present work has been financed.
How to cite: Palmeri, R., Catapano, I., Soldovieri, F., and Crocco, L.: Buried pipes monitoring via tomographic imaging in a multifrequency distorted Born approximation framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7908, https://doi.org/10.5194/egusphere-egu21-7908, 2021.
To define the performance characteristics of turbines in Hydropower Plants (HPP) accurate hydraulic, mechanical and electrical quantities are needed. The discharge is the most difficult quantity to measure and assess its uncertainty (Adamkowski, 2012). Traditionally, during field acceptance tests the discharge is measured using velocity-area method. Often, no direct flow measurements are possible and only index methods are used, with flow coefficients obtained during physical model testing. In the non-standard situations, with adverse flow conditions this may lead to unpredicted flow uncertainty.
The system used at the Iron Gate 2 HPP for control flow measurement at the inlet of bulb turbine is presented in this paper. The HPP is situated on a Danube river, between Serbia and Romania and is operational from 1985. The HPP is equipped with 20 horizontal Kaplan low head bulb turbines. The physical model experiments (JČInstitute, 2006) have concluded that due to the upstream flow conditions, the incident water flow direction is not parallel to the turbines (depending on operating conditions and can be up to 40o) as was assumed during the turbine’s model tests, raising the question of used Winter-Kennedy’s method accuracy.
To perform a control flow measurement, a modular velocity-area system was designed. The system can be installed at the intake of any turbine, upstream of the trash rack. It consists of the 14.5x3.1 m steel frame, shaped to minimize flow disturbances, which can be traversed vertically through the flow cross section (28 m). Due to the high incident angles and large vortices in the front of the trash rack, propeller current meters were not suitable. The novel spherical 3D electromagnetic velocity meter (EMVM) was developed (Svet Instrumenata), enabling fast and continuous measurements of all the velocity vector components, with low flow disturbance. The 15 EMVMs were mounted on the frame and connected into the measurement network. Redundant velocity measurement was done using 2 Nortek “Vector” ADVs (Nortek). The measurement network also comprises of 2 water level pressure transducers and 2 steel frame position transducers (UniMeasure). All measurements were synchronized with HPP’s SCADA, so turbine’s operational parameters were downloaded off-line and merged.
During the 2020, measurement system was used on the two turbines. The velocity profile was measured using two strategies: incrementally, the steel frame was raised from the bottom (average depth of 26 m) in increments of ~1.0 m and kept for at least 10 min in fixed position, and continuous where the steel frame was traversed through the flow cross-section with a constant speed of 0.05 m/s. Uncertainty assessment procedure, specifically tailored for this application, yielded discharge measurement uncertainties between 1.02 % and 2.00 % for incremental, and between 1.65 % to 2.79 % for continuous regime.
Adamkowski, A. (2012). Discharge measurement techniques in hydropower systems with emphasis on the pressure-time method. Hydropower-practice and application.
Jaroslav Černi Institute (2006). Scale model investigation of turbine runner inflow at an unfavorable angle at HPP „Đerdap II“, SDHI (in Serbian)
Svet Instrumenata: http://www.si.co.rs/index-e.html
How to cite: Ivetic, D., Prodanovic, D., and Vojt, P.: Velocity field and discharge measurements at the turbine inlet of Iron Gate 2 hydropower plant, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8380, https://doi.org/10.5194/egusphere-egu21-8380, 2021.
The development and field testing of autonomous robots are complex tasks for a number of reasons. The involved logistics are often quite complicated, and the risk of damaging the equipment under evaluation is very high; this could, on occasion, represent a considerable time and monetary overload. Such difficulties are greatly magnified in the development and testing of the underwater platforms designed to operate in hazardous environments, such as underground flooded mines, the main focus of the UNEXUP project, funded under EIT Raw Materials, and its predecessor, the horizon 2020 UNEXMIN project. These unique field working conditions involve a high risk of permanent loss of the platform in case of failure or error and a high risk of casualties in rescue attempts entailing direct human actions.
In contrast, software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing paradigms are powerful tools for preliminary system validation and algorithm benchmarking where troubleshooting is simplified through the use of a controlled environment. They provide a time- and cost-effective solution for testing, thus having a crucial role in the development of advanced autonomous robotic platforms. These two paradigms are possible thanks to the evolution of simulation models, which have achieved astonishing completeness and can realistically simulate not only system dynamics but also all the operational components of a robotic platform, such as their sensor readings, even corrupted with realistic noise. SIL experiments involve software uniquely, and are thus performed without the real robot platform but its simulation model, making them an ideal tool for testing specific algorithms or software modules. On the other hand, HIL experiments involve the real robot’s hardware, either the complete robotic platform or only parts of it, thus providing a more realistic testing environment.
In this work, we illustrate the vast range of aspects during the development of a robotic platform that can dramatically benefit from the use of the combination of SIL and HIL testing, especially in those technical applications where field trials show severe operational difficulties. We show how these testing paradigms provide a solid basis for evaluating parts or modules of a system, which, besides being convenient for the development of advanced robotic platforms by multiple teams, also bridges the gap between algorithm design and the testing of a complete platform. Then, we show how SIL and HIL can substitute parts of real environments, and enrich aspects of real data to focus on specific testing situations not easily controllable, or even dangerous, in field tests. We demonstrate how we can create augmented environments by introducing virtual obstacles and even complete virtual maps into the available experimental setup, such as a real submersible inside a water tank, thus testing complex maneuvers and simulating possible real scenarios with minimum risk of compromising the equipment. We show how these environments are beneficial not only when developing autonomous submersibles but also when training human operators of non-autonomous ones in a so-called human-in-the-loop configuration.
How to cite: Milosevic, Z., Suarez Fernandez, R. A., Dominguez, S., Rossi, C., Zoltan Papp, R., and van Moerkerk, H.: Software-in-the-loop and hardware-in-the-loop paradigms and their use for research and development of autonomous underwater vehicles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13476, https://doi.org/10.5194/egusphere-egu21-13476, 2021.
Good water quality status in rivers and lakes is vital for both human well-being and biodiversity conservation and requires efficient monitoring and restoration strategies. This is reflected in an increasing number of International and National legislations which enforce water resources management and monitoring at a basin scale.
For this purpose, state-of-the-art monitoring schemes have been developed by using low-cost, technologically advanced sensors and Internet of Things (IoT) infrastructure. Remote sensing offers also a good water monitoring alternative but is more appropriate for medium to large water bodies with less dynamic character in comparison to small scale, temporary rivers.
Recent technological advances in sensors technology, energy supply, telecommunication protocols and data handling, facilitate the use of automated monitoring stations, but still, deployment of extended networks with readily available data remains far from common practice. Installation and operational costs for the development of such monitoring networks are among the most commonly faced challenges.
The main aim of this effort is to present the development of a network of automatic monitoring stations that measure in near real time water level and physicochemical parameters in several Greek rivers. This infrastructure has been developed under the project “Open ELIoT” (Open Internet of Things infrastructure for online environmental services - https://www.openeliot.com/en/), which was funded by the Greek National Structural Funds. It includes a low cost and easy to produce hardware node, coupled with commercial sensors of industrial specifications, as well as an IoT data platform, elaborating and presenting data, based on open technologies.
During its initial operation phase, the system has been deployed in sites with different hydrological regimes and various pressures to water quality, including (a) an urban Mediterranean stream (Pikrodafni stream), and (b) the urban part of a continental river running through an agricultural area (Lithaios stream).
Preliminary data on the continuous monitoring of sites (a) and (b) are presented here, reflecting the differences in pressures to the respective water bodies. Pikrodafni stream which is located close to the center of Athens – Greece and receives a lot of pressure from urban waste, illustrates Dissolved Oxygen (DO) concentration with a heavily skewed distribution towards low values (mean value: 2.15 mg/l and median: 0.93 mg/l). On the contrary, in Lithaios stream, which is more affected by agricultural runoff, dissolved oxygen data approach a normal distribution (mean value: 6.93 mg/l and median: 7.03 mg/l). The 25th and 75th percentiles in Pikrodafni stream are: 0.1 mg/l and 3.47 mg/l respectively while in Lithaios stream are: 5.6 mg/l and 8.45 mg/l. The average water temperature is similar to both streams (18.8 oC in Pikrodafni and 16.2 oC in Lithaios). Therefore, the significant differences in DO concentrations between the two streams indicate the need for continuous monitoring of data that facilitates the identification of pressures and enables stakeholders to respond to pollution events in time.
How to cite: Dimitriou, E., Poulis, G., and Papadopoulos, A.: Development of a water monitoring network based on open architecture and Internet-of-Things technologies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13460, https://doi.org/10.5194/egusphere-egu21-13460, 2021.
In the research project Iot.H2O, which is funded under the Water JPI Joint Call 2017 IC4WATER, the potential of the Internet of Things concept is investigated for monitoring and controlling water distribution systems. Smart sensors are used which send data among others via LoraWAN to gateways which are connected to the Internet. The aim of the project is to use low-cost sensors and open-source software.
In the presentation, results of a range test with the developed LoraWAN devices are reported. One important factor is the antenna design. Results of tests with 6 different antennas will be presented among them are two antennas which are printed on a PCB and 4 commercially available antennas.
The TTN mapper App is used for recording the signals of the IoT devices in an urban and an rural environment.
How to cite: Roclawski, H., Krätzig, T., and Sterle, L.: Influence on antenna design on range of LoraWAN devices – a practical test, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4583, https://doi.org/10.5194/egusphere-egu21-4583, 2021.
Advanced technologies have proven to deliver significant outcomes in the water management sector. New technologies provide the capability to collect and correlate the information from remote devices, introducing smart tools that can leverage augmented intelligence for interpreting structured and unstructured, text-based or sensory data. However, most of the single feature or non-sequential prediction machine learning methods for understanding water quality achieve poor results due to the fact that water quality information exists in the form of multivariate time-series datasets.
At the catchment scale, there are many layers where relevant data needs to be measured and captured. For that, data warehouses play an essential role in decision support systems as they provide adequate information.
In this paper, we started by extracting, transforming, cleaning and consolidating data from several data sources into a data warehouse. Then, the data in the warehouse was used to develop a computer tool to predict river water level using Artificial Neural Networks (ANNs), in particular, Long Short-Term Memory networks (LSTM). As the prediction performance is significantly affected by the model inputs, the feature selection step, which considers the multivariate correlation of water quality information in terms of similarity and proximity, is particularly important. The features obtained from the previous steps are the inputs to the prediction model based on LSTM, which naturally takes the time sequence of water quality information into account.
The proposed method is applied to two different catchments in the island of Ireland. Experimental results indicate that our model provides accurate predictions for water levels and is a useful supportive tool for water quality management.
Ultimately, digitised representations of water environments will guarantee situational awareness of water flow and quality monitoring. The digitalisation of water is no longer optional but a necessity to solve many of the challenges faced by the water industry.
Keywords: Water digitalisation, water quality, data warehouse, machine learning, predictive model, LSTM.
How to cite: Slaimi, A., Hegarty, S., Regan, F., Scriney, M., and O’Connor, N.: Machine learning-based tools for water digitalisation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13092, https://doi.org/10.5194/egusphere-egu21-13092, 2021.
The Hydro-telemetric Network, HYDRO-NET, is a pilot streamflow monitoring network established and operated by the Institute for Environmental Research and Sustainable Development of the National Observatory of Athens (NOA) within the HYDRO-NET Project (2018-2021): Hydro-Telemetric Network of Surface Waters: Gauging instruments, smart technologies, installation and operation. An aim of the project is that HYDRO-NETs’ principles of design, installation and operation will guide establishing of hydrometric networks in the Hellenic territory. HYDRO-NET provides a comprehensive framework for collection, transmission, handling and free use of data that combines technological innovations and advanced scientific methods with efficient use of resources. It particularly responds to the need of estimating the discharge at cross-sections of streams where no prior data exist by inexpensive means.
Technological innovations concern the design and construction of a prototype hydro-telemetry system that combines custom built firmware and intelligent measuring technologies with telecommunication at low cost, ~50% the price of a commercial station. This prototype is equipped with an ultrasonic sensor for measuring stage, a thermometer, a GPRS modem, a camera and a data logger (it can also receive input from a rain gauge), and is powered by a solar panel; data and photos are transmitted to NOA’s server via mobile internet. The systems’ additional advantages are flexibility in programming, low maintenance costs, and the possibility of extending its monitoring capabilities with additional sensors (e.g. for monitoring water quality, video camera).
Progress in streamflow estimation is achieved through the development of a maximum-entropy based method that calculates the discharge, at a cross-section of known bathymetry, using measurements of water stage and surface velocity by SVR (Surface Velocity Radar) and/or video cameras. Rating curves at monitoring stations can be thus constructed by inexpensive field campaigns, and safely under flooding.
HYDRO-NET currently operates 16 hydro-telemetric stations, six of which are of NOA’s design, in the Peloponnese and in Attica, Greece. Measured data are transmitted to NOAs’ Server, where they are automatically processed (Quality Controlled) and stored in a Data Base; the data are freely available to users through the OpenHi.net platform (openhi.net), or upon request (firstname.lastname@example.org). A prime service prospect of the HYDRO-NET system, with its real-time observations, is Flood Warning.
Acknowledgment: The Hellenic General Secretariat for Research & Technology has provided financial support, under the National Strategic Reference Framework (2014-2020), for the project HYDRO-NET: Hydro-Telemetric Networks of Surface Waters: Gauging instruments, smart technologies, installation and operation, as a part of the Hellenic Integrated Marine and Inland Water Observing, Forecasting and Offshore Technology System, HIMIOFoTS (MIS5002739) (https://www.himiofots.gr/).
How to cite: Mazi, K., Koussis, A. D., Lykoudis, S., Vitantzakis, G., Dimitriadis, P., Kappos, N., Psiloglou, B., Katsanos, D., Koletsis, I., Rozos, E., and Kopania, T.: HYDRO-NET: Hydro-telemetric Network for surface waters – Innovations and Prospects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14956, https://doi.org/10.5194/egusphere-egu21-14956, 2021.
Plant transpiration accounts for about half of all terrestrial evaporation (Jasechko et al., 2013). Plants need water for many vital functions including nutrient uptake, growth, maintenance of cell turgor pressure and leaf cooling. Due to the regulation of water transport by stomata in the leaves, plants lose 97% of the water they take via their roots, to the atmosphere. They can be viewed as transpiration-powered pumps on the interface between the soil and atmosphere.
Measuring plant-water dynamics is essential to gain better insight into their role in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for transpiration measurement and highlight several promising innovations for monitoring plant-water relations. Some of the techniques we will cover include stomata imaging by microscopy, gas exchange for stomatal conductance and transpiration monitoring, thermometry for water stress detection, sap flow monitoring, hyperspectral imaging, ultrasound spectroscopy, accelerometry, scintillometry and satellite-remote sensing.
Outlook: To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques is required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly solve for spatial heterogeneity as well as temporal variability, dense network sampling is needed.
In PLANTENNA (https://www.4tu.nl/plantenna/en/) a team of electronics, precision and microsystems engineers together with plant and environmental scientists develop and implement innovative (3D-)sensor networks that measure plant and environmental parameters at high resolution and low cost. Our main challenge for in-situ sensor autonomy (“plug and forget”) is energy: we want the sensor nodes to be hyper-efficient and rely fully on (miniaturised) energy-harvesting.
Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial water fluxes dominated by transpiration. Nature, 496(7445), 347-350.
Plantenna: "Internet of Plants". (n.d.). https://www.4tu.nl/plantenna/en/
How to cite: ten Veldhuis, M.-C., van den Berg, T., van der Ploeg, M., Kaiser, E., Dutta, S., Moene, A., Vialet-Chabrand, S., and van Emmerik, T.: Plants, vital players in the terrestrial water cycle, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3085, https://doi.org/10.5194/egusphere-egu21-3085, 2021.
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