There is a pressing need to monitor, measure, understand and mitigate causes of anthropogenic activities on our aquatic environments. In situ water monitoring sensors are vital tools for decision support and risk mitigation, pollution source tracking, and regulatory monitoring. To be fit-for-purpose, sensors must withstand the challenging marine environment and provide data at an acceptable cost. If networks of sensors are to become not only a reality but common place, it is necessary to produce reliable, inexpensive, rugged sensors integrated with data analytics.

In this context, this work presents the design, development and testing of an affordable, robust and reliable optical sensor for continuous measurement of chemical and physical parameters in aquatic environments. Sensor electronics are housed in a marine grade watertight housing; the optical components are mounted inside a custom designed 3D-printed optical head which joins with the sensor housing.The sensor uses multiple optical detection modes (absorption, scatter and fluorescence) over a broad spectral range (280 nm to 850 nm) to measure parameters like turbidity, fluorescent dissolved organic matter (fDOM), Chlorophyll a (Chl a) and petroleum. Sensor analytical performance was established in the laboratory using analytical standards and in the field by comparison with a commercially available multi- parameter probe. The laboratory and field trials demonstrate that the sensor is fit-for-purpose and an excellent tool for catchment monitoring where rainfall-impacted soils can cause ecological disruption in the surface water. The sensor provides high frequency time-series data with unattended operation in-situ for extended periods of times.

How to cite: Regan, F., Power, S., Briciu, C., Richards, C., Delgado, A., and Free, L.: Low-cost autonomous in-situ multiparameter optical sensor for climate-impacted catchment monitoring , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8784, https://doi.org/10.5194/egusphere-egu24-8784, 2024.

Monitoring systems are used to understand and predict natural phenomena so that science-based wise decision can be opportunely taken to preserve natural resources and ecosystems. Today, thanks to IoT technologies, in-situ sensors allow to collect unprecedent number of observations of several environmental parameters at high frequency and at high spatial resolution so that fast evolving phenomena, that cannot be derived from satellite information, can be detected and analysed. The management of these data pose new challenges and requires a paradigm shift to fully exploit their potential by moving from time-based to event-based management. For this reason, we are redesigning istSOS, a long-lasting Open-Source project implementing the Sensor Observation Service: an interoperable Open Standards from the Open Geospatial Consortium. The new solution, that is currently under intense development, is based on the following individuated requirements: (i) Open Source software to guarantee the forever free usage for everyone with full open rights; (ii) Interoperability to offer a standard interface for data and metadata; (iii) reliability of adopted solutions to guarantees a solution to be used in production; (iv) microservices based, so that specialized tasks can be performed atomically using the best technologies; (v) reactive approach that enable real-time and asynchronous exploitation of information; (vi) choreographed architecture which permits single tasks to be autonomously executed without any external coordinator. To respond to the individuated requirements the new system is based on the OM:Observation standard as a core element to describe observations and Apache Kafka as reliable, scalable, durable and high performing solution for data stream management. Based on these two core elements, several microservices for observation consuming, archiving, processing and notification have been created and integrated in the architecture which is available as containerized composed solution. The solution is being tested in production environment for the management of the hydro meteorological monitoring data for the Canton Ticino in Southern Switzerland. With this work we share our results and our experience and discuss individuated challenges and barriers. 

How to cite: Cannata, M., Strigaro, D., Primerano, C., Antonovic, M., and Pozzoni, M.: Toward an event-driven infrastructure for in-situ monitoring systems , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15694, https://doi.org/10.5194/egusphere-egu24-15694, 2024.

Global warming is rising the sea level, which increases the saltwater intrusion in coastal aquifers and the  penetration into delta systems. The delta systems are sensitive areas due to complex hydrogeological dynamics between fresh and salt water. Such iterations have a significant impact on ecosystems and might lead to environmental problems, such as triggering processes of desertification and the increased vulnerability of soils. Moreover, seawater intrusion may have an economical and social impact due to scarce  water resources and damages to agriculture that are caused by irrigation with salt water contaminated groundwater. Therefore, it is important to examine the spatial extent and the evolution of the salt wedge intrusion inland over time. Such intrusion involves the upward movement of saltwater along the riverbed correlated with both a scarce pressure of the river water and an increase of the upstream of the mixing zone in the surface waters. This phenomenon requires a new monitoring system that has to provide fast information for a manager boarding which plans the groundwater use. One of the chemical-physical parameters to define the water quality is the electrical conductivity (EC), which increases in saline water, with groundwater being commonly associated to much lower values. Hence, surveys using a moving boat with a multiparameter probe able to measure salinity and temperature is to date used as monitoring system with punctual collections. This approach is limited in its spatio-temporal resolution as it is time consuming and may lack the required resolution of the salt wedge for monitoring  long rivers (> 5 km). To overcome these limitations, we propose the application of geophysical electromagnetic (EM) methods using fast acquisition system and high resolution. This work describes results for surveys collected along the Po di Goro River in the Po Delta system using frequency domain (FDEM) and time domain (TDEM) electromagnetic measurements. The FDEM method was able to detect the saltwater flow front observed during the summer of 2022, when a large salt water wedge contaminated the Po Delta system for several kilometres (around 20-25 kms from the sea). During the summer of 2023, an integrated survey with FDEM and TDEM system was used to obtain the distribution of EC along the Po di Goro River. Even if the salt wedge penetration was less intensive, it was observed close to the Goro village (ca. 7 km from the sea). The FDEM data were inverted using the  EMagPy open-source software, that highlighted the EC distribution section along the investigated river path; while an open-source library based on empymod and pyGIMLi was used for the inversion of the TDEM data. The two methodologies were compared and integrated, to improve the EC distribution model of the Po di Goro River. Our results demonstrated the ability of both methods to detect the saltwater wedge effect due to the tidal phenomenon. The results emphasize the significant potential of the proposed geophysical approach to monitor the salt wedge phenomenon during crisis period when fast and high-resolution information are necessary for hydrodynamical monitoring decisions.

How to cite: Boldrin, P., Aigner, L., Flores Orozco, A., and Rizzo, E.: Evaluation of waterborne electromagnetic methods to delineate the salt wedge on Po di Goro river, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7392, https://doi.org/10.5194/egusphere-egu24-7392, 2024.

Fiber optic measurement techniques such as Raman Distributed Temperature Sensing (DTS) are beneficial for applications in boreholes, as they provide continuous measurements over long distances with all measurement equipment outside of the borehole. Efforts have been made for at least a decade to utilize these temperature measurements for indirect velocity measurements. Active DTS measurements refer to a setup, where the fiber is embedded in a hybrid cable, which itself is heated by Joule heating. Such Active DTS processing takes advatange of known heat transfer phenomena to determine velocitiy information.

When the heated cable is placed in a saturated porous medium, groundwater fluxes perpendicular to the cable's axis were quantified with low uncertainties in a controlled lab experiment [1]. Heated fiber placed in the free flow of a borehole was applied to identify active zones of groundwater flow and highly fractured zones [2]. A very similar setup of a heated fiber in a borehole was applied to measure vertical flow exploiting the heat transfer law of a cylinder in flow parallel to its axis  [3]. Utilization of this heat-transfer law was shown to be difficult as a thermal boundary layer builds up in the flow direction, thus influencing the downstream sections. This effect may be modelled, but additionally, the boundary layer mixes behind every centralizer and therefore enhances the heat transfer locally. The latter cannot easily be modeled and was removed using a postprocessing filter [3]. Even though only limited quantitative comparability with reference flowmeter measurements was possible the results rendered the approach a promising strategy, since the correct order of magnitude and moreover similar trends have been identified.

Inspired by the heat transfer of a cylinder in cross flow as state of the art in aerodynamics velocimetry [5], it has been demonstrated that the convective heat transfer of a heated cable in free flow can be better utilized if the cable axis is positioned perpendicular to the flow to take advantage of the heat transfer law of a cylinder in cross flow [4].

The objective of our research is to build an active DTS-based free stream flowmeter to monitor pump flows in arbitrarily deep boreholes. The system shall be scalable with an arbitrary number of point measurement flowmeters, which are connected to a single glass fiber and one heating cable. At the current state of the research, the flowmeter consists of a point flowmeter, which is a helically wound, heated glass fiber. The sensitivity in the predicted measurement range was verified and the temperature distribution along the cable cross-section was investigated. Presently, the major challenge is a precise reproducible DTS temperature measurement. Therefore, water baths and a new prototype were built to achieve measurement uncertainties within the range of the water bath reference sensors (cp. [7]).

[1] Simon et al. https://doi.org/10.1016/j.jhydrol.2023.129755 

[2] Banks et al. https://doi.org/10.1111/gwat.12157

[3] Read et al. https://doi.org/10.1002/2014WR015273

[4] Rautenberg et al. https://doi.org/10.1007/s00348-023-03741-5

[5] Örlü, Vinuesa, Chapter 9 Thermal Anemometry https://doi.org/10.1201/9781315371733-12  

[6] Giesen et al. https://doi.org/10.3390/s120505471

How to cite: Rautenberg, D. and Kriegseis, J.: Active Distributed Temperature Sensing to determine flow velocities in boreholes based on a cylinder in cross-flow approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9310, https://doi.org/10.5194/egusphere-egu24-9310, 2024.

Ambient seismic noise has become a valuable tool in seismology, playing a crucial role in environmental seismic studies. This research explores the Horizontal-to-Vertical Spectral Ratio (HVSR) method's applicability to groundwater studies in two distinct cases: the Río de la Plata Coastal Plain, Argentina, and the Maltese archipelago.

In the hydrogeology of the Río de La Plata region, partially interconnected coastal porous aquifers within sedimentary formations prevail. Employing HVSR analysis on ambient seismic noise reveals two prominent peaks. The low-frequency peak is associated with the sediment-basement interface, while the higher frequency is linked to a shallower stratigraphic discontinuity. Temporal analysis unveils cyclical patterns in mean frequency and amplitude, correlating with estuarine levels. This suggests a compelling connection between variations in subsurface mechanical properties and tidal dynamics, supported by phreatic and piezometric measurements.

In Malta, with primary aquifers developed on limestone rocks, a distinct HVSR peak related to a claystone-limestone stratigraphic boundary is observed. Preliminary results indicate changes in HVSR shape associated with seasonal variations in groundwater. This study underscores the potential of ambient seismic noise analysis as a non-invasive and cost-effective approach to studying aquifers and gaining insights into groundwater dynamics.

This work has been supported by DEMUWA project which is financed by the Malta Council for Science and Technology through the Space Research Fund (Building Capacity in the Downstream Earth Observation Sector) a program supported by the European Space Agency. Funds were also made available through the IPAS (Internationalisation Partnership Awards Scheme) funded by the Malta Council for Science and Technology.

How to cite: Galone, L., Panzera, F., Colica, E., Fucks, E., Carol, E., Cellone, F., Rivero, L., Agius, M. R., and D'Amico, S.: Utilizing Ambient Seismic Noise in Hydrogeology Studies , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18527, https://doi.org/10.5194/egusphere-egu24-18527, 2024.

According to the European Parliamentary Research Service, agriculture is a major user of ground and surface water in the Mediterranean region. Agriculture accounts for more than 40% of water use in the EU and most freshwater abstraction is for agricultural use. Water applied as irrigation enables crop production in arid regions and replenishes soil moisture in humid regions when rainfall during the growing season is insufficient. It helps to increase crop productivity, but it also poses a threat to the conservation of water resources. The issue of water scarcity therefore requires careful consideration of the trade-off between increased agricultural productivity and the degradation of water resources. Ensuring food security in the face of climate change requires improved water management capacity.

Nowadays, the interest in estimating the average soil moisture content (SM) and its variability is a cross-cutting issue in many areas of scientific research in the natural sciences. The water contained and transiting in the vadose zone is involved in and plays a central role in many natural processes related to plant physiology and agriculture, soil microbial activity, groundwater pollution and, more generally, eco-hydrological and bio-geochemical processes.

SM depends either on soil characteristics, i.e. hydraulic conductivity, porosity, soil texture, etc., or on meteorological forcing, i.e. precipitation, temperature, evapotranspiration, etc. Knowing the soil characteristics, a numerical model for unsaturated flow (Hydrus-1D) can be calibrated using time-lapse measurements of meteorological forcing and SMs obtained by IoT enabled sensors. After the calibration, the numerical model can generate a very large number of SMs for many meteorological forcings. With these data, a WEF Nexus tool, based on a machine learning approach, integrates the SM and meteorological IoT data to estimate crop water demand.

This research aims to develop and test a building block for possible future water demand estimation tools. As a future perspective, further development and integration may lead to new tools with user-friendly interfaces.

How to cite: Straface, S., Brunetti, G. F. A., Maiolo, M., Brunetti, G., and Scozzari, A.: A WEF NEXUS tool for integrating soil moisture and meteorological IoT data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8914, https://doi.org/10.5194/egusphere-egu24-8914, 2024.

How to cite: Strigaro, D., Capelli, C., and Cannata, M.: Developing a replicable and cost-effective solution for monitoring algal concentrations in lake Lugano to facilitate timely decision-making, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15863, https://doi.org/10.5194/egusphere-egu24-15863, 2024.

Trihalomethanes (THMs), which may be harmful to human health if ingested or inhaled, are produced when organic matter reacts with chlorine. Hence, their formation during potabilization requires to be controlled to ensure safe drinking water.

In this study, the predictive capacity of a Multiple Linear Regression (MLR) and an Artificial Neural Networks (ANN) models have been compared with real-time field-scale data of the THM formation potential (THM FP) from a Spanish Drinking Water Treatment Plant (DWTP). Spectral absorbance data obtained with Spectro::lyser® probes, installed in several treatment steps of the plant were the independent variables used to construct the models. Variable selection was based on the Stepwise Selection (SS) procedure.

Following the fitting of the investigated models, ANN demonstrated precise goodness of fit (R² = 0.92; RMSE = 0.77), clearly outperforming the MLR model (R² = 0.35; RMSE = 1.65). Severe multicollinearity among wavelengths is responsible for the model's accuracy difference. Even though it was reduced by a prior study on the Variance Inflation Factor (VIF), it was still very high for some of the remaining wavelengths. As a result of this effect, large fictitious correlations were produced, which adversely impacted the MLR model's prediction performance (R² = 0.30 in the validation set). While R² reduced, indicating perhaps a slight overtraining of the ANN, the resulting R² in the validation set (0.72) was still very high

This study proved that Machine Learning models such as Artificial Neural Networks based on spectral absorption data can enhance the ability of operators to respond to critical events, becoming a decisive component of the daily management of drinking water in DWTP when needed.

How to cite: Pla-Castellana, M., Gutierrez, O., Raich-Montiu, J., and Gernjak, W.: Comparative analysis of Machine Learning models for predicting the trihalomethanes formation potential in a Drinking Water Treatment Plant in Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10984, https://doi.org/10.5194/egusphere-egu24-10984, 2024.

Accurate hydrological prediction faces challenges due to diverse datasets and the absence of universally applicable models. This study investigates meta-learning's role in identifying optimal models for hydrological time series forecasting in specific contexts. We explore limitations in model identification and introduce meta-learning as a solution.

The proposed methodology formalizes the model selection process by using meta-features from a dataset that includes time series, geology, and the experimental configuration and results of predictions . These features are crucial for empowering the meta-learner to make informed model selections.

The study encompasses localized and national analyses, offering insights into the meta-learner's performance across distinct geographic regions.

In this initial phase of the experiment, we present the results of the Meta-Learners within the context of a localized evaluation in Ireland, we evaluated the Neagh Bann River Basin District ( NB RBD) which covers an area of around 5740 km2. It includes all of County Armagh, large parts of Counties Antrim, Londonderry, Down and Tyrone and a small area County Fermanagh in Ireland.  Using the NB RBD dataset which comprises 16 monitoring stations, we constructed the details of 12  prediction models. 

The second phase of the experiment widens its scope by encompassing data from multiple locations across Ireland. This broader approach allows us to draw upon a more extensive and diverse dataset, comprehensively evaluating meta-learner performance within a national context. By incorporating data from various regions in Ireland, we aim to capture a more holistic understanding of the meta-learners' adaptability and effectiveness in a nationally diverse landscape.  We used data from 249 hydrometric stations in Ireland, each corresponding to a distinct geographical location. This comprehensive dataset encompasses various locations across the country, offering a rich and diverse perspective for our analysis.

Evaluation results demonstrate the performance of 12 models. Models like Random Forest and K-Nearest Neighbors show promise, while Support Vector Machine struggles consistently. Addressing class imbalance through resampling techniques proves effective, underscoring the importance of tailored model selection strategies.

Key findings highlight model performance variability, concerns about overfitting, and the significance of appropriate resampling strategies. The meta-learner's application showcases its value in leveraging strengths across classifiers and mitigating weaknesses.

In conclusion, this study contributes to advancing adaptive model selection methodologies in hydrological time series forecasting. 

Keywords: Meta-learning, model selection, hydrologic time-series prediction.

How to cite: slaimi, A., Scriney, M., Hegarty, S., Regan, F., and E. O’Connor, N.: A  Meta-Learning Approach for Adaptive Model Selection in Hydrological Time Series Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20870, https://doi.org/10.5194/egusphere-egu24-20870, 2024.

Like many other capital cities in Latin America and Asia, the water supply of Mexico City is intermitted, which has negative effect on water availability and quality. The long term vision of Mexico City is to make drinking water of best quality continuously available through the pipeline system to every single citizen. Major investments into water infrastructure are necessary to achieve this long term goal.

Until this can be achieved, during a transition phase of 20 to 30 years, this project wants to help making water as accessible, secure, and of best quality as possible, at a small fraction of infrastructure investment costs.

The plan is to apply a least cost strategy with the best short term effect on water accessibility and water quality, by investing into smart water technology (sensors, actuators, communication, central data management), which goes parallel to repairing the water infrastructure, but can be achieved much faster.

The project’s DNA constitutes of two main ideas: Collecting and managing water data from a dense network of smart sensors; and making these data transparent and accessible for a broader public.

The project team will deploy hundreds (later thousands) of smart sensors initially in the unam university network, and later at private households all over Mexico City, to measure mainly water pressure and water flow; later also quality parameters to feed into the same (cellular) network. Data will be collected and analyzed in the open and independent Water Transparency Portal (WTP), which consists of the Data Collection and Management System (DCMS), and will be made transparent to the public via the Data Publication System (DPS).

The WTP will visualize water data for anyone interested and address a broader public on 3 different user levels, including water consumers, scientists, managers, politicians, journalists, social media, and so on. The data will be transformed into numerical and graphical formats that can be understood by everybody, just like traffic data in Google maps.

The project is being set up as a collaboration between the Water Transparency Foundation (WT), the engineering institute of the largest Mexican university (unam), young Mexican start-up companies in the fields of sensors and data management, and some water related authorities, and allies.

Some merits of the project will be: Cost efficient and same time reliable pressure and flow data; for “first flush” management to optimize quality during the given time windows of water pressure; for network optimization and balancing of pressure zones; for leakage and loss detection support; for transparency of the water system to everybody .

Socio-political targets are to connect to water customers through a strong “citizen science” approach; bring young water consumers on board by virtualization of water on smart phone apps and motivate to reduce water consumption by interaction via these apps; increase customer trust and satisfaction with the final target to motivate to use network water for all purposes inclusive drinking; develop a core social water network of the first 500 enthusiastic users, tell their stories as a reference for Mexico City.

How to cite: Weingartner, A. and Castillo, A.: Better management of intermitted water supply based on smart sensors and transparency of data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19313, https://doi.org/10.5194/egusphere-egu24-19313, 2024.

In data-scarce watersheds, hydrological models are often calibrated by using only streamflow observations. This approach may overlook crucial landscape characteristics, which, instead, may significantly affect the runoff. This study explores the integration of parameters derived from remotely sensed data, focusing on evapotranspiration, soil moisture, and runoff, to enhance the overall accuracy of the Soil and Water Assessment Tool (SWAT) model. Four calibration scenarios were implemented: S1 (streamflow only), S2 (streamflow and evapotranspiration), S3 (streamflow and soil moisture), and S4 (all variables). Results showed that S2 achieved high scores for streamflow, outperforming S1, with slight improvements observed in some cases. However, scenarios incorporating root zone soil moisture (S3 and S4) negatively impacted the streamflow estimates. Nevertheless, S2 exhibited slightly better evapotranspiration simulation, while S3 and S4 improved soil moisture representation. Hydrograph comparisons highlighted satisfactory streamflow simulations in S1 and S2, while S3 and S4 overestimated flow peaks. The results of this investigation show that embedding remotely sensed data in the SWAT model, particularly evapotranspiration and soil moisture, may not necessarily improve runoff estimations, thus a careful analysis is required to determine the role of these parameters. In fact,  these parameters play a pivotal role in enabling hydrological models to achieve a more comprehensive and accurate representation of the water balance within a watershed.

How to cite: Taia, S., Scozzari, A., Erraioui, L., Chao, J., and El Mansouri, B.: Leveraging remotely sensed evapotranspiration and soil moisture data for enhanced watershed modelling with the SWAT model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9363, https://doi.org/10.5194/egusphere-egu24-9363, 2024.

Yield improvement of the smart and greenhouse agriculture necessities to adopt the recently developed technologies. Therefore, sensors and the Internet of Things (IoT) and/or the Internet of Underground Things (IOUT) should be included. The Internet of Underground Things (IOUT) communication network consists of a large number of sensors or actuators deployed in a geographical area. In IOUT networks, the data is collected by multiple sinks and sent over to the central processing unit. The special needs for this type of network pose great challenges for network architecture and implementation. Accurately modeling the information generated by each sensor device is essential for designing a smart system for precision agriculture. Successful design leads to improving the whole performance of the communication network and increases the productivity of the plants by early detecting hazards and diseases of the plants. This research aims to model and measure the performance of communication networks of underground sensor devices and above-ground networks with different scenarios. The traffic of each device in the network is modeled by truncated power tail distribution to represent the real burst of the collected information traffic. The impact of burst traffic on the performance is investigated analytically and by simulation. The model compares two common types of traffic models using truncated power tail distribution. Analyzing all the collected information could lead to adjusting the environmental conditions (including soil moisture and water feed) to set then to meet the optimal conditions of the plants to improve the smart and greenhouse agriculture productivity to produce more food to help support the related SDGs.

Keywords:  Traffic Modeling, Performance Modeling, Communication network, Greenhouse agriculture, Smart/precision agriculture

How to cite: Negm, A., Esmaiel, H., and Agamy, A.: Performance Analysis of Internet of Underground Things Network in Smart and Greenhouse Agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12277, https://doi.org/10.5194/egusphere-egu24-12277, 2024.