Orals: Thu, 1 May | Room -2.15
Microwave links from cellular communication networks have been proposed as an opportunistic source of precipitation data more than two decades ago. The first scientific studies demonstrating the potential of this ground-based remote sensing technique, in particular for areas around the world lacking dedicated rainfall observation networks, were published more than 15 years ago. Since then, a small but dedicated community of scientists and engineers working at universities, national meteorological services, engineering firms, mobile network operators and telecommunication equipment manufacturers has been making significant progress in turning this promise into a reality. In the meantime, numerous papers and reports have been published, conference presentations have been given and courses have been delivered. However, real-time access to high-resolution rainfall information from commercial microwave link networks over large continental areas is still a dream. How far have we come after more than 20 years of research and development? What does the future have in stall for the hydrological and meteorological communities? What should be done to turn this dream into a reality? Finally, which other hydrometeorologically relevant variables could potentially be retrieved using received signal levels from commercial microwave links? This sollicited presentation will attempt to provide some preliminary answers to these questions.
How to cite: Uijlenhoet, R.: Hydrometeorological Monitoring using Microwave Links from Cellular Communication Networks: Opportunities and Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20151, https://doi.org/10.5194/egusphere-egu25-20151, 2025.
Freshwater quality continues to decline despite the adoption of the Water Framework Directive (WFD) almost twenty five years ago with the recovery of water quality in Europe plateauing since the 2010s (Haase et al., 2010). Pollution from diffuse sources, particularly from agriculture remains a key challenge to restoring water quality to at least ‘good status’ under the WFD (EEA, 2018) compounded by water quality declines due to increased frequency and intensity of hydrodynamic events (van Vliet et al., 2023).
Assigning accurate WFD classes and detecting changing trends in water quality have been challenging where traditional low frequency monitoring approaches have been implemented (Skeffington et al., 2015). Higher monitoring frequency and spatial coverage is required to effectively identify improvements in water quality (Westerhoff et al., 2022) particularly when detecting changes over shorter time periods (Mcdowell et al., 2012). High frequency monitoring is required to identify temporal water quality changes linked to rainfall driven pollutant transfer from land to waters (Métadier and Bertrand-Krajewski, 2012) with monitoring over multiple events required to capture the variability in pollutant concentrations and pollutant loads across events (Kozak et al., 2019). Furthermore, 50% of surface waterbodies in the EU are impacted by multiple pressures (EEA, 2018), with increased urbanisation requiring a more complex, multi-pollutant approach to assessing impacts on river quality (Strokal et al., 2021).
The aim of this research was to identify if rainfall driven transient pollution events were occurring at two monitoring stations in a river catchment, and if continuous instream monitoring of turbidity and other water quality parameters could be used to capture changes in water quality and potential instances of such events. One of the objectives was to identify if continuous monitoring could create a site-specific water quality profile that could be used to identify early warning indicators of rainfall driven or other transient pollution events.
Results from this study indicate that changes in water quality are happening during rainfall events and that turbidity alongside other parameters can be used to track such events, trigger alarms when a probable event is occurring and automatically activate more intense monitoring during these events. The integrated monitoring approach adopted allows for the tracking of water quality changes across temporal and spatial scales for multiple pollutants and allows for temporal fluctuations, and variation in pollutant loads during hydrodynamic events to be determined.
The significant advantages of this approach are it’s suitability for remote deployments with no requirement for permanent infrastructure, the use of site specific water quality profiles to identify potential water quality events at individual sites and to activate further monitoring if required, the ability to tailor the monitoring for pollutant screening or more specific pollutants of concern, and the cost effectiveness of moving the integrated monitoring station between different water bodies.
How to cite: Cronin, L., Taylor, C. M., Briciu-Burghina, C., Regan, F., and Lucy, F. E.: Near real-time, water quality event monitoring in small rivers, in the context of increasing frequency and intensity of hydrodynamic events due to climate change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10582, https://doi.org/10.5194/egusphere-egu25-10582, 2025.
Natural hazards such as floods, storms, and earthquakes present significant threats to urban infrastructures, particularly water supply and distribution networks. These events can severely impact the quality and quantity of water resources, leading to serious consequences for public health and social security. Factors such as unplanned urban development and non-compliance with engineering standards further increase the vulnerability of these systems. Recent advancements in technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) have enabled real-time monitoring and data analysis of these critical infrastructures. IoT-based smart sensors capture essential information, including flow rate, water quality, corrosion, leakage, and pipeline ruptures. These data are processed using machine learning and deep learning algorithms to identify anomalies. Such systems can enhance monitoring capabilities and support effective decision-making in crisis situations. This study explores key criteria for selecting optimal locations for sensor deployment. These criteria include connection points, infrastructure accessibility, water quality, natural hazard risks, and historical incident data. For example, evaluating the location of connection points and their impact on water flow and distribution can help identify optimal routes, reducing costs and response times. Easy access to infrastructure facilitates sensor installation and maintenance, improving system efficiency. Monitoring water quality at various points in the distribution network is also critical to identifying sensitive locations and ensuring water safety. Additionally, identifying areas prone to natural hazards helps prioritize vulnerable regions for monitoring and improve system resilience. Historical data on anomalies and past incidents provide patterns that highlight risk-prone areas and help refine monitoring strategies. Based on these criteria, a multi-criteria decision-making approach is applied to propose the most effective locations for sensor placement. This method suggests prioritizing locations that have the highest impact and accessibility. These recommendations aim to enhance system efficiency and improve response capabilities during emergencies.
Ketwords: Smart Infrastructures, Internet of Things, MCDM, Artificial Intelligence, Natural Hazards
How to cite: Malekmohammadi, B., Rahimi, M., Kerachian, R., Singh, V. P., Falconer, R. A., Noori, R., and Bahmanpouri, F.: An Approach for IoT-Based Smart Sensors Placement in Urban Water Networks Under Natural Hazards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13576, https://doi.org/10.5194/egusphere-egu25-13576, 2025.
Critical raw materials (CRMs) are crucial for technological advancements and the global energy transition, especially in sectors such as renewable energy, electronics, and electric mobility. The sustainable and secure management of these materials is increasingly important. Geothermal springs represent a promising source of CRMs, offering valuable materials such as lithium, magnesium, strontium, and boron in addition to clean energy. Depending on where they come from geologically, geothermal springs can have lithium levels that are at least 10 times higher than seawater (0.18 mg/L) and about the same as salt lakes (0.04–3 g/L). The moderate Mg2+/Li+ molar ratio (~35) also shows that the two elements might be better separated, which would allow for more Mg2+ recovery. This study introduces a novel method for the recovery of CRMs from geothermal brines, combining Reverse Osmosis (RO), Nanofiltration (NF), and Membrane Distillation (MD) for efficient separation of water and valuable materials. The experiments are conducted using a synthetic laboratory-reproduced geothermal spring solution, which accurately replicates the pH, temperature, and ionic composition typical of natural geothermal waters. This experimental approach ensures that the results reflect real-world conditions, which is critical for evaluating the feasibility and scalability of the proposed method. The process begins with RO and NF to concentrate the brine and selectively separate multivalent ions (e.g., Mg) from monovalent ions (e.g., Li), leveraging differences in ionic valence. Following this, MD is applied to reduce brine volume and minimize thermal energy consumption, thereby optimizing both water recovery and the concentration of CRMs. A key innovation of this work is the exploitation of the elevated temperature of geothermal brines (> 35°C), which allows the use of MD with minimal external heating. This significantly reduces energy requirements and operational costs. The process minimizes Specific Thermal Energy Consumption (STEC), highlighting its efficiency and sustainability. This method not only enhances the recovery of lithium and magnesium from geothermal springs, but it also offers a cleaner, more sustainable approach to CRM extraction by utilizing renewable geothermal heat.
How to cite: Inzillo, B. M., Santoro, S., Curcio, E., and Straface, S.: Innovative Geothermal Mining through Membrane Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19565, https://doi.org/10.5194/egusphere-egu25-19565, 2025.
Distributed hydrological models are crucial for flood prediction, drought analysis, and water resource monitoring. They are typically calibrated using streamflow observations at the watershed outflow to determine the best parameter values within their common ranges. These models are then applied to analyze management and climate scenarios. However, accurately representing hydrological complexities is challenging due to limited knowledge, data availability, and imprecise measurements. Uncertainties in these models arise from parameters, model structure, calibration processes, and data, especially in regions with scarce data. Consequently, hydrological models require extensive hydro-meteorological data for calibration and validation, which can be costly and time-consuming. Recently, remote sensing techniques advanced hydrological modeling by providing regular sampling of essential variables like precipitation, soil moisture, and evapotranspiration. However, thanks to technological advancements, numerous global and regional remote seeing products for the same variable have become freely available. These products vary in their algorithms, approaches, spatial and temporal resolutions, leading to diverse datasets for the same variable. Therefore, different products can perform differently in terms of parameter estimation, model robustness, and water balance predictions within the same area. However, each product may introduce biases or uncertainties, necessitating modelers to assess their performance and carefully choose the most suitable product for their study objectives. This research reviews commonly used remotely sensed products and the techniques and approaches for integrating them into distributed and semi-distributed hydrological models. Additionally, this review examines the uncertainties associated with different existing products and their performance within hydrological models.
How to cite: Taia, S., Ait Brahim, Y., Hssaisoune, M., Scozzari, A., and El Mansouri, B.: Enhancing Hydrological Models with Remote Sensing: A Review of Products, Techniques, and Uncertainties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19593, https://doi.org/10.5194/egusphere-egu25-19593, 2025.
To meet European (WFD) and International objectives (SDGs), there is a growing demand for water quality data with elevated spatial and temporal resolution. This has been an ongoing process, achieved by integrating data from governmental agencies with community-based monitoring initiatives (crowdsensing). Community-based monitoring has proven effective in addressing information gaps in managing and monitoring aquatic ecosystems, particularly in small rivers that often lack agency monitoring. However, there are still challenges regarding the reliability of such data. To fill this gap, there is an urgent need to develop affordable, reliable, and open-source instrumentation for water quality monitoring. These instruments should also comply with the recent European guidelines on the use of toxic substances in technology development.
This study presents the development and validation of a RoHS directive-compliant, open-source, low-cost optical sensor for detecting nitrates and phosphates in community-based monitoring initiatives. The sensor setup takes advantage of light-emitting diodes (LED) as light sources and a commercial ambient light detector. A second light sensor positioned at a 90° angle is employed for scattering correction. All components are managed by a Raspberry Pi Zero W microcomputer and housed in a custom 3D-printed poly(lactic acid) case. The device enables data collection, including GPS coordinates, with results stored offline or transmitted in real-time through Wi-Fi. The sensor’s analytical performance was evaluated in both laboratory and field conditions using reference materials and river samples. Results demonstrated accurate and repeatable measurements which were shown to increase resolution and precision compared to standard colorimetric methods. To promote accessibility and replication, the 3D-box CAD model, software, and usage guidelines are freely available online.
How to cite: Cirrone, R., Boldrini, A., Polvani, A., Liu, X., and Loiselle, S.: Advancing Community-Based Water Quality Monitoring through Low-Cost Open-Source Optical Sensors and Data Integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19717, https://doi.org/10.5194/egusphere-egu25-19717, 2025.
Landslides, alongside earthquakes and floods, are among primary natural phenomena that are responsible for significant social and economic losses. Their impact poses an increasing threat worldwide, particularly in marginal and degraded contexts, affecting urban areas, infrastructures, environmental, historical, and cultural heritage, and, in severe cases, resulting in human casualties. In recent years, the number of infrastructure collapses or severe structural damages due to landslide movements has risen significantly, hindering the functionality of infrastructures, and highlighting the urgent need to deeply understand their interactions. Landslides can endanger roads, bridges, and railways, compromising the accessibility and inclusivity, and exacerbating social and economic exclusion in affected areas. Critical infrastructures are often located in challenging areas, where the susceptibility to landslides and natural hazards is significantly elevated. These sites demand advanced monitoring technologies to ensure infrastructure safety and mitigate the social and economic impacts of landslides. This study explores an innovative approach that integrates Interferometric Synthetic Aperture Radar (InSAR) data with numerical Finite Element Modelling (FEM) to address these challenges. The proposed method was applied to a case study involving a partial interaction between a slow-kinematic landslide, documented in the Inventory of Landslide Phenomena in Italy (IFFI), and a bridge along a highway section in the Liguria Region. Leveraging high-resolution satellite-based data from the Copernicus European Ground Motion Service (EGMS), the InSAR analysis provided spatial and temporal monitoring of ground displacements. Satellite remote sensing offers a wide spatial and temporal coverage over multiple regions, enabling for the detection of extensive or hard-to-access areas with millimetric precision in deformation velocity, ensuring high efficiency at a favourable cost-benefit ratio. However, while InSAR analysis can precisely measure ground motions, it lacks the ability to provide insights into the physical mechanisms under varying loading conditions. To address this limitation, FEM modelling was used to simulate the three-dimensional landslide mechanical behaviour under hydraulic loading, offering a deeper understanding of the slope stability and infrastructure deformations. InSAR data post-processing enabled the estimation of transverse and vertical components of the actual displacement vector, aligning with the observed landslide deformations and facilitating the numerical model validation. Simultaneously, FEM results highlighted significant displacements downstream of the landslide area, indicating a slope stability close to the limit equilibrium condition. Quantitative analysis also revealed relevant deformations at the base of bridge piers located within the landslide, caused by horizontal forces impacting the foundations. The integration of InSAR observations and FEM calculations demonstrated consistency in the identified movement, validating the efficacy of the combined method in identifying critical zones in landslide-prone regions. This study highlights how advanced remote sensing technologies, when coupled with numerical simulations, can enhance the monitoring and maintenance of critical infrastructure, particularly in marginal or extensive contexts. By identifying vulnerable areas and supporting the maintenance strategies, this methodology can contribute to hydrogeological risk management and promote inclusivity in regions where social and economic disparities exacerbate natural hazards impacts.
How to cite: Salciarini, D., Vitaletti, A., Cernuto, E., and Ubertini, F.: Integrating Remote Sensing Technique with 3D Numerical Modelling for Enhanced Maintenance of Critical Infrastructure in Landslide-Prone Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12996, https://doi.org/10.5194/egusphere-egu25-12996, 2025.
Rural areas, i.e. areas with a low population density and a small number of anthropogenic environments, represent a significant resource in the pursuit of a green and sustainable development of the European Community [1]. This development involves not only the ecological and balanced use of agriculture and forestry resources, but also policies devoted to environmental protection and monitoring. In this context, drone-based technologies offer valuable opportunities because they facilitate the effective and non-invasive surveillance and monitoring of wide and inaccessible places. These technologies, indeed, allow surface and subsurface explorations, while concomitantly reducing the financial and logistical demands associated with investigation missions.
The present contribution is focused on Unmanned Aerial Vehicle (UAV)-Ground Penetrating Radar (GPR) technology and the potential of UAV-GPR technological solutions in subsurface prospecting [2]. The discussion encompasses the collection and processing of data, emphasising the efficacy and sustainability of the technology. The contribution will address the development of guidelines for the design of the flight grid and the formulation of an effective imaging strategy that can account for deviations in motion relative to the nominal trajectory.
[1] Bizottság, E. (2024). The long-term vision for the EU’s rural areas: key achievements and ways forward. Report from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Brüsszel, The long-term vision for the EU’s rural areas: key achievements and ways forward, Report from the Commission [Letöltve: 2024.06. 20.].
[2] Noviello, C., Gennarelli, G., Esposito, G., Ludeno, G., Fasano, G., Capozzoli, L., Soldovieri, F., & Catapano, I. (2022). An Overview on Down-Looking UAV-Based GPR Systems. Remote Sensing, 14(14), 3245. https://doi.org/10.3390/rs14143245
Acknowledgements: The communication has been funded by EU - Next Generation EU Mission 4 “Education and Research” - Component 2: “From research to business” - Investment 3.1: “Fund for the realisation of an integrated system of research and innovation infrastructures” - Project IR0000032 – ITINERIS - Italian Integrated Environmental Research Infrastructures System - CUP B53C22002150006.
The authors acknowledge the Research Infrastructures participating in the ITINERIS project with their Italian nodes: ACTRIS, ANAEE, ATLaS, CeTRA, DANUBIUS, DISSCO, e-LTER, ECORD, EMPHASIS, EMSO, EUFAR ,Euro-Argo, EuroFleets, Geoscience, IBISBA, ICOS, JERICO, LIFEWATCH, LNS, N/R Laura Bassi, SIOS, SMINO.
How to cite: Catapano, I., Esposito, G., Gennarelli, G., and Soldovieri, F.: Drone based radar technologies for wide rural areas resources exploration: potentialities and challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17346, https://doi.org/10.5194/egusphere-egu25-17346, 2025.
Volcanic eruptions and earthquakes present significant challenges to developing countries, where limited monitoring infrastructure restricts effective risk mitigation efforts. Satellite remote sensing observations offer essential information, including surface deformation and thermal anomalies, for hazard assessment, early warning, and emergency response. These satellite-based observations enable comprehensive spatial and temporal monitoring, utilising both publicly available medium-resolution and commercial high-resolution datasets. Over the past decade, Sentinel radar and optical observations have been employed in areas with limited in-situ measurement capabilities[1]. Nonetheless, the utilisation of these datasets in developing countries is frequently hampered by insufficient computational and analytical resources.
This study examines the role of remote sensing in strengthening disaster risk management within resource-constrained contexts. We propose a collaborative framework that utilises satellite remote sensing data processing Centres in developed countries to assist developing nations in analysing pre-, during, and post-crisis events. Moreover, we advocate for engaging with space agencies to enhance satellite tasking during crisis observation, thereby improving our understanding of the event’s driving mechanisms. We highlight the critical role of remote sensing through a case study of recent seismic and volcanic activity in the Main Ethiopian Rift, specifically between the Fentale and Dofen volcanoes[2]. While national seismic and geodetic networks provide data on large and medium-magnitude earthquakes and significant deformations, they cannot detect low-magnitude precursory events or local deformations due to their proximity to volcanic centres. Furthermore, the installation of temporary monitoring facilities is often constrained by various limitations. Remote sensing bridges this gap by offering detailed data to support local research, inform timely decision-making, and strengthen crisis management. The crises have impacted under-resourced regions, the primary import-export corridor, and nearby urban centres, including Addis Ababa, where rapid urbanisation has raised safety concerns. This study underscores the necessity of integrated remote sensing solutions and international collaboration to enhance resilience and mitigate risks in disaster-prone areas.
Keywords: Sentinel, Main Ethiopian Rift, Fentale Volcano, Developing Countries, Emergency Management
Acknowledgements
The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.
References
[1] Tessema, T. T., Biggs, J., Lewi, E., & Ayele, A. (2020). Evidence for active rhyolitic dike intrusion in the northern Main Ethiopian Rift from the 2015 Fentale seismic swarm. Geochemistry, Geophysics, Geosystems, 21, e2019GC008550. https://doi.org/10.1029/2019GC008550
[2] Derek Keir, Alessandro La Rosa, Carolina Pagli, et al. (2024). The 2024 Fentale Diking Episode in a Slow Extending Continental Rift. ESS Open Archive DOI: 10.22541/au.172979388.80164210/v1
How to cite: Tessema, T., Lewi, E., and Tosti, F.: Remote Sensing for Volcanic Eruptions and Earthquake Emergency Management Strategies in Developing Countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18926, https://doi.org/10.5194/egusphere-egu25-18926, 2025.
Posters on site: Fri, 2 May, 08:30–10:15 | Hall X4
Urban areas need to rethink their policies to strengthen their capacities to prepare for and respond to hazards and become more resilient, intelligent and inclusive. In this context, one of the objectives is to ensure the resilience of their services and systems against multi-hazard scenarios, where the effect of local hazards combines with global challenges such as climate change and pandemics. Moreover, the concept of inclusiveness is becoming crucial, as highlighted during COVID, which showed that the most vulnerable population is the one living in sparsely and densely populated areas, where the level of social and physical services is often inadequate [1].
In this context, one possible response to this need is the development of monitoring and surveillance approaches [2]. The present contribution will focus on three aspects
The first is that resilience must be addressed as a whole, since services and networks are interconnected and interdependent (e.g. health system, transport, energy and water distribution, air quality, protection from extreme weather events, etc.). The main consequence of these interconnections is that the complete collapse of services (blackout) may become a realistic possibility.
The second aspect is that resilience can only be achieved in the presence of continuous and detailed monitoring of both the structures/infrastructure/services and the territory on which they insist, and that without such a monitoring it is impossible to correctly define the interventions to be carried out and their priorization.
The third aspect concerns the development of new monitoring systems based on Earth observation, positioning, navigation, and ICT technologies that exploit the citizen as a sensor and the so-called ‘non-sensors’, i.e. sensors that provide useful information for monitoring even if they are not designed for this purpose. All this ‘sensory’ data must be integrated to obtain a complete and reliable awareness of the scenario; hence the need to process and systematize large amounts of information that can only be processed by AI and HPC.
[1] V. Cuomo F. Soldovieri F. Bourquin, N. -E. El Faouzi, J. Dumoulin. The necessities and the perspectives of the monitoring/surveillance systems for multi-risk scenarios of urban areas including COVID-19 pandemic. Proceedings of the TIEMS Annual Conference, 18-20 November 2020, Paris, France, ISBN: 978-94-90297-19-0, vol. 27
[2] Cuomo V., Soldovieri F., Ponzo F.C., Ditommaso R. (2018). A holistic approach to long-term SHM of transport infrastructures. The International Emergency Management Society (TIEMS) Newsletter 33, pp. 67-84.
How to cite: Soldovieri, F., Cuomo, V., and Dumoulin, J.: Monitoring for sustainable and inclusive urban areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9193, https://doi.org/10.5194/egusphere-egu25-9193, 2025.
Among Non-Destructive Testing (NDT) methods, Ground Penetrating Radar (GPR) and magnetic surveys are among the most widely used techniques for various applications, including geo-environmental, archaeological, geotechnical, and engineering purposes. Their success is attributed to factors such as cost-efficiency, versatility, and data collection capabilities. Additionally, both methods enable the detection of buried targets through their respective magnetic and electromagnetic properties. Integrating the results from these two methodologies can yield excellent outcomes for an in-depth analysis of the investigated environment and significantly enhance the detection capabilities for anomaly sources.
This study presents preliminary results on the integration of simulated GPR and magnetometric data for a representative scenario. Advanced imaging techniques, including the Depth from Extreme Points (DEXP) method for magnetic data and the microwave tomography approach for GPR data, were applied to produce an initial high-resolution visualization of the simulated target.
Building on these results, an arithmetic integration approach was used to merge the two datasets into a single image, enhancing the interpretation of the anomaly source, including its morphology, position, and depth.
These preliminary results demonstrate the potential of this workflow, based on the arithmetic integration of these datasets, to provide more accurate and detailed subsurface models. This approach paves the way for real-world applications, and further developments aim to refine it for broader geophysical purposes.
Acknowledgments: the project ITINERIS "Italian Integrated Environmental Research Infrastructure Systems" (IR0000032), which funded the research
How to cite: Mercogliano, F., Barone, A., Vitale, A., Esposito, G., Tizzani, P., and Catapano, I.: Towards the Integration of GPR and Magnetic Data for the Study of Urban and Rural Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20136, https://doi.org/10.5194/egusphere-egu25-20136, 2025.
Coastal regions are crucial for human settlements and economic development. However, their distinctive environmental characteristics, particularly in deltas, bays, and gulfs, render them highly vulnerable to threats such as erosion phenomena and pollution. The effective management of these areas depends on the accurate predictions of wave dynamics and their interactions with the shoreline and seabed. Reliable forecasts require numerical wave propagation models to be initialized with precise data and detailed bathymetric representations, and their accuracy depends on calibration operations using high-quality sea state observations.
Sea state data are typically collected through in-situ sensors, such as buoys and drifters, or remote sensing devices, including radars and video-monitoring systems [1]. Remote sensing technologies are often preferred due to their ability to provide both spatial and temporal information. Among these, ground-based radar systems like High-Frequency and X-band radars have proven effective in retrieving wave spectra and coastal sea state information. However, these systems face notable limitations, including difficulties in acquiring data near the shoreline. Additionally, they are bulky, heavy, and cumbersome, which complicates the deployment stage.
To address these challenges, the Italian PRIN-PNRR 2022 Project SEAWATCH—Short-Range K-Band Wave Radar System Close to the Coast—was launched on November 30, 2023. SEAWATCH focuses on developing an innovative, portable, short-range K-band radar prototype specifically designed for sea state monitoring in nearshore zones. Thanks to its compact size, lightweight design, and low power requirements, the system enables flexible, on-demand surveys, meeting critical safety and environmental management needs in harbors and coastal zones.
This communication outlines the key activities and initial results achieved during the first year of the SEAWATCH project. This last is organized into six milestones, supported by a robust collaboration between research units to ensure efficient knowledge sharing and steady progress. Preliminary here results shown highlight the radar prototype potential to overcome traditional limitations, offering enhanced spatial resolution and real-time monitoring capabilities near the coastline [2]-[4].
Future efforts will focus on further refining the radar prototype and validating its performance across diverse coastal environments.
References:
- P. Neill, M. Reza Hashemi, Chapter 7 - In Situ and Remote Methods for Resource Characterization, Editor(s): Simon P. Neill, M. Reza Hashemi, In E-Business Solutions, Fundamentals of Ocean Renewable Energy, Academic Press, 2018, Pages 157-191.
- Afolabi, L. A., et al. (2025). Underestimation of Wave Energy from ERA5 Datasets: Back Analysis and Calibration in the Central Tyrrhenian Sea. Energies, 18(1), 3.
- Ludeno, G., Antuono, M., Soldovieri, F., & Gennarelli, G. (2024). A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar. Remote Sensing, 16 (2), 261.
- Ludeno, G.; Esposito, G.; Lugni, C.; Soldovieri, F.; Gennarelli, G. A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data. Mar. Sci. Eng.2024, 12, 1609.
Acknowledgment: This work was supported and funded by the European Union—NextGenerationEU PNRR Missione 4 “Istruzione e Ricerca”—Componente C2 Investimento 1.1, “Fondo per il Programma Nazionale di Ricerca e PRIN—SEAWATCH—Short-rangE K-bAnd Wave rAdar sysTem Close to tHe coast CUP B53D23023940001, and partially funded by the research project STRIVE—La scienza per le transizioni industriali, verde, energetica CUP B53C22010110001.
How to cite: Ludeno, G., Contestabile, P., Vicinanza, D., Antuono, M., Lugni, C., Catapano, I., Esposito, G., Noviello, C., Soldovieri, F., and Gennarelli, G.: SEAWATCH Project: A year of advancements in Short-Range K-Band Radar for Coastal Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18196, https://doi.org/10.5194/egusphere-egu25-18196, 2025.
In recent years, there was a notable technological advancement in geophysical sensors. In the case of magnetometry, several sensors were used having the common feature to be miniaturized and lightweight, thus idoneous to be carried by UAV in drone-borne magnetometric surveys. Moreover, such sensors have the common feature to be very cheap, so that it is in principle very easy to have the resources to combine two or three of them to form gradiometers. Nonetheless, another common feature is that their sensitivity ranges from 0.1 to about 200 nT, thus not comparable to that of alkali vapor, standard flux-gate or even proton magnetometers. However, their low-cost, small volume and weight remain as very interesting features of these sensors. In this communication, we want to explore the range of applications of small tri-axial magnetometers commonly used for attitude determination in several devices. We compare the results of ground-based surveys performed with conventional geophysical instruments with those obtained using these sensors.
How to cite: Accomando, F. and Florio, G.: Applicability of cheap and lightweight magnetic sensors to geophysical exploration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18356, https://doi.org/10.5194/egusphere-egu25-18356, 2025.
Presently, three fundamental methods are employed for sea cucumber aquaculture: pond culture, dam culture, and submarine seedling culture. However, these methods are susceptible to environmental water quality degradation due to factors such as sea cucumber feces, excessive feed, and climate change, which can impede sea cucumber growth and affect yields.
In order to address the issues outlined above, this study presents the intelligent circulating water system (ICWS), which is composed of a composite low-energy physical liquid-solid separator and a multi-mixed biofilter. A detailed description of these components is provided below.
- Composite Low Energy Physical Liquid-Solid Separator
The liquid-solid separator uses minimal energy because of its innovative composite type. It extracts the contaminant source from the aquatic environment, reducing biofilter bed load and energy demand.
- Multi-mixed Biofilter
The configuration of hybrid arrangement structures increases the specific surface area of the biofilter, leading to a reduction in its volume. The structure controls flow rate, hydraulic residence time, and hydraulic loading, which can be used to regulate temperature, salinity, pH, dissolved oxygen, and ammonia levels. This ensures the provision of high-quality water that meets the needs of sea cucumbers.
The innovative low-energy-consuming water recycling system outlined in this project has the theoretical potential to achieve complete water recycling without the necessity of replenishing the source water. This scenario presents a mutually beneficial opportunity for the sustainable utilization of Earth's water resources and the realm of commercial aquaculture, exhibiting no inherent incompatibility.
How to cite: Chien, K.-H., Huang, W.-S., Chen , J.-C. . ., and Ren , X. .: Development of an intelligent recirculating water system for land-based sea cucumber aquaculture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2316, https://doi.org/10.5194/egusphere-egu25-2316, 2025.
The Acoustic Doppler Current Profiler (ADCP) is one of the most commonly used instruments for measuring river discharge by utilizing the Doppler effect of acoustic waves. However, its reliance on a single transducer introduces certain limitations. During the transition between transmission and reception of the acoustic signal, the returning signal cannot be captured, resulting in an inability to measure discharge near the sensor.
Additionally, side-lobe interference generated by acoustic waves reflects off the riverbed and contaminates measurements near the bottom. To mitigate this, discharge data within 5% of the water depth from the bottom are typically excluded from results. Furthermore, in shallow areas where the unmeasured regions near the sensor and near the bottom overlap, discharge cannot be accurately measured.
To address these gaps, discharge in the unmeasured regions of ADCP measurements is typically extrapolated using data from the measured sections or calculated using empirical equations. In this study, a method to improve the measurement accuracy in the unmeasured regions of the ADCP was developed and evaluated.
Acknowledgements
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and development on the technology for securing the water resouces stability in response to future change Program, funded by Korea Ministry of Environment(MOE)(RS-2024-00336020)
How to cite: Kim, J. and Kim, D.: Improving Discharge Measurement in Unmeasured Zones of ADCPs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4974, https://doi.org/10.5194/egusphere-egu25-4974, 2025.
The König-Project, funded by the German state, is a long-term project focused primarily on developing a multi-scale wave measurement laboratory to improve flow measurements in an industrial context. Part of this project researches the propagation of ultrasound waves inside a moving fluid. A wide variety of flow scenarios are considered, and new methods for ultrasonic flow measurement can be developed and optimized. One experimental scenario includes the determination of volume fraction and drop size distributions of air dispersed in water using ultrasonic waves.
For this purpose, a modular system is used as an initiative to integrate manufacturer-independent measurement components with open-source software for the acquisition and processing of ultrasound signals. The modular system equipment consists of a multichannel system, which allows the positioning of several transceivers to send and receive ultrasonic waves from different directions along the experimental zone of interest. The concentration of dispersed air in water will be determined by measuring the reduced transit time caused by the added compressibility of the air phase.
Characterizing multiphase flows using other techniques can be time-consuming and the accuracy can fall short as the complexity of the fluid grows. The use of ultrasound to characterize fluid flows has many advantages such: as a non-invasive method that doesn’t alter the fluid path, real-time data acquisition, and high-temporal resolution, it is cost-effective and can be used on opaque fluids. Therefore this technique is gaining more attention in several industrial applications, including oil and gas, hydrogen, and geothermal energy generation. The results of this investigation will be validated and compared with the output of a numerical simulation, in which the boundary conditions and the flow characteristics will be similar to the experimental setup.
How to cite: Calderon, J., Dormann, M., Branß, T., Balcewicz, M., Aberle, J., and Saenger, E.: Advanced Ultrasound Techniques for Investigating Air-Water Two-Phase Flow: An Experimental Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10725, https://doi.org/10.5194/egusphere-egu25-10725, 2025.
The König-project is funded by the German state with the aim to develop a calibrated and virtual measurement laboratory to enhance methods based on ultrasound measurements that find application in the determination of flow velocity or particle movement. By comparing the results of controlled laboratory and real-world experiments with numerical simulations, the understanding of the interaction between ultrasonic waves and fluid flow is intended to be improved. The amount of scatterers within a fractured medium directly affects the effective velocity of elastic waves. Thus we investigate, if the effects found in solid media can be transferred to fluids. We ran a series of numerical experiments, simulating ultrasound transmission measurements for multiple concentrations of bubbles of varying diameter dissolved in a stationary water layer. For the simulation of elastic wave propagation, we used a rotated staggered finite-difference scheme. We investigate the relation between the effective wave speed and the bubble concentration and compare those to results of laboratory experiments. Future research will then expand to moving fluid-gas mixtures.
How to cite: Dormann, M., Calderon, J., Finger, C., Balcewicz, M., and Saenger, E. H.: Numerical Study to Determine Water-Air Dispersion with Ultrasound Waves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10832, https://doi.org/10.5194/egusphere-egu25-10832, 2025.
Salar de Uyuni is a salt desert in Bolivia, spanning approximately 10,000 km2. During the wet season a thin layer of rainfall water covers the salt flats, making its surface mirror-like and earning it the title of “the largest natural mirror in the world”. The surface reflects the sky like a mirror, and attracts tourists who document this effect only from its outer perimeter. No evidence is documented in the interior, accessible only during the dry season. The only frequent observations of the Salar surface are from satellites, particularly altimetric radars, which are specifically designed to measure topography. Originally developed to measure sea level [1], they have recently been used, with a different metrics, to describe how emitted radar pulses are reflected by the surface, measuring the intensity of the reflected echo, and thus the Radar Cross Section (RCS) of the surface [2]: higher RCSs correspond to smoother surfaces. RCS was initially estimated in [1] with a an approximate method. Later EUMETSAT shared a better estimate by solving the radar equation with satellite parameters that were previously unknown to us [3]. In this study we used Sentinel-3A and 3B RCS measurements over the Salar flats, along six ground tracks, to describe for the first time the evolution of the Salar surface smoothness in space and time. A field campaign (16th - 20th of February 2024) was also conducted to validate the interpretation of radar measurements during the Sentinel-3A overpass on the track 167. At the field site, in a water depth of 1.8 cm (horizontal wind 4.5-3.4 ms-1), we measured a null vertical surface displacement to within ±0.5 mm, which classifies the surface as electromagnetically smooth at the radar frequency. The RCS values near the site were around 120 dBsm, as expected for radar return from a smooth surface. Three peaks are observed on the statistical distribution of the RCS: 87 (dry), 101 (intermediate) and 120 dBsm (wet season).The wet season, characterized by values above 101 dBsm, begins in December, peaking from late January to early March. February thus ensures the highest chance to observe mirror-like effects. Rainfall climatology from Uyuni city meteorological station reflects such statistics. The spatial and temporal evolution of RCS over the Salar, however, do not describe this place like a uniform mirror at the radar frequency, and so it is unlikely to observe such effect at shorter wavelengths, contrary to what is believed in the literature. Finally, satellites can help tourism stakeholders in programming the most enjoyable experience for travellers.
[1] Vignudelli et all. 10.1007/s10712-019-09569-1
[2] Abileah and Vignudelli, 10.1016/J.Rse.2021.112580
[3] Dinardo and Lucas, EUM/RSP/TEN/23/1376566
How to cite: De Biasio, F., Vignudelli, S., Abileah, R., and Pacheco Mollinedo, P.: Radar Altimetry Reveals the Smoothness of the Surface: the Case of Salar de Uyuni, Bolivia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10867, https://doi.org/10.5194/egusphere-egu25-10867, 2025.
Technological advancements in forest digitization have revolutionized real-time monitoring of tree ecophysiological processes. Direct measurement sensors, such as dendrometers, sap flow sensors, and spectrometers, enable high-resolution insights into tree function and growth. Here, we present a novel dendrometer designed to monitor radial stem increment using a Hall effect-based linear magnetic encoder system integrated into an IoT-enabled platform.
The dendrometer employs a commercially available linear magnetic encoder chip (AMS OSRAM GmbH) that operates without physical contact, ensuring low power consumption and long-term monitoring suitability. Key design components include a linear arm, sensor housing, rail, magnetic tape, and chip braces. Calibration was conducted using a stepper motor for linear movements at 0.1 mm increments, capturing 100 data points per step in four replicates. Regression analysis demonstrated high accuracy, with an R² of 0.99 and an RMSE of 0.05 mm. Temperature sensitivity tests (0–40°C) revealed minimal impact on sensor performance.
Field tests over one growing season involved four dendrometers installed on specimens of spruce (Picea abies (L.) H.Karst)) and silver fir (Abies alba Mill.). Seasonal radial growth patterns captured by the devices aligned closely with established static UMS D1 diameter belt measurements, demonstrating their capacity to detect both long-term trends and short-term diel stem oscillations.
This study highlights the potential of an IoT-driven dendrometer for capturing high-resolution radial growth data, offering insights into tree physiology and forest responses to environmental changes. Future development should focus on enhancing measurement precision through design optimization and improved access to power width modulation components in the AS3511 chip. This dendrometer represents a promising tool for advancing forest monitoring and understanding the impacts of climate change on tree growth dynamics.
How to cite: Belelli Marchesini, L., Yates, J., Renzi, F., and Valentini, R.: Building a Smart Dendrometer: Calibration and Field Deployment of a linear magnetic driven IoT Sensor for Real-Time Radial Growth Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20810, https://doi.org/10.5194/egusphere-egu25-20810, 2025.
Wetland ecosystems exhibit large spatial and temporal variability in terms of greenhouse gas (GHG) fluxes, necessitating new technologies to ensure that they are well-monitored. Both manual and automated chamber-based approaches are currently costly and thus limited either in spatial or temporal resolution. Following on from Wang et al. (2022), we propose a new, inexpensive autochamber (TraceCatch) for long-term outdoor installation. Costs for one unit are less than 800€ in total, making it affordable and scalable for long-term ecological research, also in lower income countries such as the global south. The system is based on gathering gas samples over two weeks into four gas bags on a high-frequency sampling schedule. TraceCatch is controlled using an Arduino Uno, connected to a peristaltic pump for sampling of chamber headspace air as well as a number of sensors for air temperature and humidity (SHT-41), air pressure (BMP280), and CO2 concentrations (K30FR; 0–5,000 ppm, 30 ppm resolution). The latter are used to track the sealing condition of the chamber. We validated the system using defined injection amounts of technical gas (100% CO2). In addition, the system was applied to measure GHG fluxes from three wetland cores placed inside three ecotrons (UGT EcoLab flex, manufactured by Umwelt-Geräte-Technik GmbH, Germany). Gas samples were collected 4 times a day for 2 weeks during a 1 hour chamber closure time at t0, t20, t40, t60 and subsequently analyzed using gas chromatography (Nexis GC-2030, manufactured by Shimadzu Corporation, Japan). Average GHG fluxes determined over the two-week period were then compared to single measurements obtained using multi-gas sensors (LI-COR LI-7820 and LI-7810 analyzers, manufactured by LI-COR Biosciences, USA). If adopted, the system’s low cost, scale and robustness for permanent field deployments could help improve wetland GHG monitoring, offering a cost-efficient and practical alternative to traditional methods for global-scale biogeochemical cycle assessments.
How to cite: Kretzschmar, M. S., Dubbert, M., Hoffmann, M., Bielcik, M., Bergmann, J., and Dubbert, D.: A Cost-Effective Automatic Chamber for Permanent CH4 and N2O Assessments inWetland Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20884, https://doi.org/10.5194/egusphere-egu25-20884, 2025.
The Earth is a complex ecosystem and each element is strictly linked with the others. It is often required to collect data on multiple aspects variously related to the main phenomenon in order to understand its mechanism. Moreover, the increasing use of machine learning algorithms requires the creation of new, reliable and extensive dataset in order to obtain significant results. The increasing demand for accurate, real-time monitoring of tree ecophysiological parameters presents challenges in developing affordable and efficient technologies, in particular in difficult environments such as mountains. TreeTalker Cyber, an innovative IoT platform, addresses these needs by integrating multiple sensors into a single, cost-effective device capable of measuring radial growth, radiation intensity below the canopy across 26 spectral bands, sap-flow, microclimate data, and trunk inclination. This presentation explores its capabilities, practical applications, and potential to transform forest monitoring globally. The use of a single platform to collect all the aforementioned parameter greatly reduces the cost of the equipment per collected parameter providing at the same time all main information required to evaluate the status of a tree, improving the maintenance of the network at the same time. The device is equipped with an NB-IoT or LoRaWAN transmission module to transmit collected data and make them available remotely. A comprehensive description of the platform and real field data are presented along with the technologies used for data transmission and storage with their strength and weaknesses. The OGC SensorThings API is also briefly described along with FROST (FRaunhofer Opensource SensorThings-Server) as an alternative to efficiently store IoT data and make them compliant with the FAIR principles, making them usable by both scientific and public communities. The creation of a dataset of trees ecophysiological parameters will help deepening the knowledge and understanding of forests around the world. TreeTalker Cyber lays the groundwork for advancing forestry research, providing fine-scale data as ground truth for forestry models and a starting point for future scenarios predictions, in particular when based on machine learning algorithms.
How to cite: Renzi, F., Yates, J., Coppola, V., Riggi, S., Chiriacò, M. V., and Valentini, R.: TreeTalker Cyber: A Multi-Sensor, Low-Cost IoT Platform for Real-Time Monitoring of Tree Ecophysiology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21862, https://doi.org/10.5194/egusphere-egu25-21862, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 4
EGU25-20324 | ECS | Posters virtual | VPS19
Using Remote sensing and geographic information system for delineating suitable sites for artificial groundwater recharge: A multi-criteria decision-making approach.Tue, 29 Apr, 14:00–15:45 (CEST) vPoster spot 4 | vP4.1
The semi-arid region of Deraa Oued Noun in Morocco faces significant challenges related to water scarcity, which greatly affects the availability of groundwater resources. With recurring droughts and periods of water shortage, it is imperative to address these challenges and implement effective measures for sustainable groundwater resource management. Artificial groundwater recharge has proven to be a viable solution for alleviating water scarcity issues. By capturing and storing excess water during periods of heavy precipitation or surface water availability, artificial recharge can replenish depleted aquifers and provide a reliable water source during drought periods. However, the success of recharge projects depends on identifying suitable sites that meet specific criteria and maximize the efficiency of the recharge process.
The identification of suitable sites for artificial groundwater recharge in Daraa Oued Noun, through the integration of remote sensing, GIS (Geographic Information System), and MCDM (Multi-Criteria Decision Making) techniques, offers a promising solution to address water scarcity challenges in the context of climate change. The proposed research project aims to provide valuable and spatially explicit information for strategic groundwater resource management.
This study was conducted in the Deraa Oued Noun district, where water shortages have been observed over the years. The research utilized geology, soil, land use, stream data, and Sentinel-2 and DEM images to develop thematic layers, including lithogeology, soil, slope, lineament density, land use, stream density, and water surface. Additionally, data on the vadose zone thickness were incorporated to enhance the analysis.
By integrating GIS and image processing techniques, these thematic layers were utilized to prepare groundwater recharge maps of the area through a weighted overlay method on a GIS platform. The results revealed that artificial recharge potential was high in the northern and western parts of the study area.
By following a systematic and rigorous methodology, including data collection, remote sensing analysis, MCDM evaluation, and site validation, this project aims to contribute to the successful implementation of artificial recharge projects in the region. By maximizing the efficiency of the recharge method, these projects will help ensure sustainable water supply, mitigate the impacts of drought, and promote long-term water security in Derâa Oued Noun and similar semi-arid regions.
How to cite: Fri, R., Scozzari, A., Haida, S., Kili, M., Erraoui, L., Chaou, J., Mridekh, A., Goumghar, L., and El Mansouri, B.: Using Remote sensing and geographic information system for delineating suitable sites for artificial groundwater recharge: A multi-criteria decision-making approach., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20324, https://doi.org/10.5194/egusphere-egu25-20324, 2025.
EGU25-19489 | ECS | Posters virtual | VPS19
Model-Agnostic Meta-Learning for Data Integration Across Heterogeneous Hydrological DatasetsTue, 29 Apr, 14:00–15:45 (CEST) | vP4.2
Integrating heterogeneous hydrological datasets remains a significant challenge in environmental modelling due to variations in feature spaces, data distributions, and temporal and spatial scales across sources. This study introduces a Model-Agnostic Meta-Learning (MAML) approach to address the challenge of integrating heterogeneous hydrological datasets, leveraging a collection of datasets compiled from diverse sources. These datasets, characterized by varying features, distributions, and temporal and spatial scales, provide an ideal basis for evaluating MAML's ability to handle real-world data heterogeneity.
MAML’s unique capability to learn shared representations across datasets with minimal feature overlap and significant variability allows it to effectively transfer knowledge between subsets, offering a flexible and scalable solution for integrating hydrological data with diverse characteristics.
The proposed approach trains a base model on one subset of the data while utilizing MAML's meta-learning capabilities to adapt and transfer knowledge to other subsets with differing feature distributions. To test the model's adaptability, we simulate scenarios with varying degrees of feature overlap. Model performance is assessed using metrics such as mean squared error (MSE), both before and after fine-tuning on unseen data subsets.
Preliminary results demonstrate that MAML effectively learns shared representations across datasets, achieving significant improvements in prediction accuracy. Fine-tuning further enhances the model's adaptability, particularly for datasets with minimal feature overlap. These findings highlight MAML's potential as a powerful and flexible tool for integrating and predicting across heterogeneous hydrological datasets.
This study bridges the gap between advanced meta-learning techniques and hydrological applications, providing new insights into scalable and adaptable data integration methods for environmental sciences.
Keywords: Model-Agnostic Meta-Learning, hydrological datasets, data integration, heterogeneous data, meta-learning, environmental modelling, machine learning.
How to cite: Slaimi, A. and Scriney, M.: Model-Agnostic Meta-Learning for Data Integration Across Heterogeneous Hydrological Datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19489, https://doi.org/10.5194/egusphere-egu25-19489, 2025.
