Nowadays, the sustainable management of water resources requires a holistic approach, which attains to the soil, vegetation and all the living things interacting with the water.
The transition from the mere monitoring of the processes related to water systems to the wider concept of “water habitats”, implies the study of such ecological interactions in various possible scenarios, which are often characterised by a strong relationship between natural and anthropogenic contexts.
In this challenging framework, research activities aimed at developing efficient monitoring technologies and management strategies are encouraged to embrace a highly multidisciplinary approach. Here, water management meets noticeable ecological, economic and social implications, and the public awareness of such implications is rapidly growing.
Accordingly, scientific/technological advancements have to go beyond the observation of water bodies and their related processes and infrastructures, by extending the scope to the water habitats and the many measurable indicators of their functions and health status, directly or indirectly related to water, such as water quality, biodiversity, plant ecophysiology, and resilience to environmental extremes.
This session welcomes contributions related to the monitoring of water systems and their characteristic habitats about:
• design of field measurement instrumentation
• development of new sensing techniques, innovative field experiments
• application of remote sensing products
• advancements in sensor networks
• Integration between sensor systems and computational tasks
• Investigations about data science aspects, e.g. geospatial analyses, big data and AI applications.
Contributions may regard (but are not limited to) rivers & lakes, wetlands, irrigated areas, forests and natural habitats, coastal zone, urban habitats and water infrastructures, including distribution networks. Both qualitative and quantitative assessments are appreciated.
Studies regarding groundwater monitoring and management and its interaction with surface processes are also relevant to this session and are very encouraged.
Orals: Fri, 8 May, 14:00–15:45 | Room -2.62
As access to drinking water is a major public health issue worldwide, many technologies have emerged for water harvesting from alternative sources. Among these, active atmospheric water harvesting technologies, known as atmospheric water generators (AWGs), are attracting growing interest as a decentralised water production system. However, the water quality they produce is known to be influenced by the ambient air pollution, but scientific data on air-to-water transfer is limited, stressing the need for assessment tools to support monitoring and management strategies. This difficulty is exacerbated by the complexity of atmospheric chemistry and the large number of compounds present in the air, which far exceeds the number of compounds regulated in drinking water. To address this challenge, we present the first systematic methodology for risk assessment of air-to-AWG water transfer and apply it to the Greater Paris area. First a bibliographic inventory of the compounds found in the air in the region of interest and of their maximum reported concentration was created. For each compound, empirical (when available) or theoretical air-to-water transfer models were applied to determine the upper concentration expected in AWG water. The risk level of each compound was determined based on the ratio between this concentration and experimental or extrapolated guideline values for ingestion toxicity. In the Greater Paris area, while as many as 193 air pollutants were inventoried with quantified ground-level atmospheric concentrations over the last 15 years, only about half of them presented a risk of being present in AWG water above the set thresholds. Of these, around 20 - a much more manageable number of species to monitor - may reach concentration levels two orders of magnitude or more above the threshold values and may require priority consideration. These include ammonium, Polycyclic Aromatic Hydrocarbons -PAHs- (e.g., phenanthrene), pesticides (e.g., prosulfocarb), organic acids (e.g., acetate), phenols (e.g., benzenediol), and aldehydes (e.g., acrolein). The presence of some of these species linked to vehicle emissions was studied experimentally in the water of an AWG exposed to varying levels of diesel emissions through integrated water and air quality monitoring, both in-situ and in Sense-City climatic chamber (https://sense-city.ifsttar.fr/). A large number of species were discovered for the first time in AWG water, notably numerous PAHs and acrylamide, while several were observed to exceed EU regulatory thresholds (pH, ammonium, nitrite, Cu, Al, Mn, Pb, Ni, benzo(a)pyrene, benzene and acrylamide), some of them for the first time (Cu, acrylamide). The composition of raw AWG water was found to be directly correlated with exhaust levels through NOx and TVOC concentrations with turbidity, total organic content, nitrite, BTEX, several metals and most PAHs. Acrylamide concentration also featured correlation with the exhaust pollution, a surprising, as of yet unreported, finding in air or water that thus needs to be extensively confirmed. Overall, the study confirms the strong influence of air pollution on AWG water but its viability despite extreme pollution conditions.
How to cite: Merlet, T., Doggaz, A., Ulanowski, Y., Laporte, S., Abid, M. A., and Lebental, B.: Assessment of chemical contamination of condensed water from Atmospheric Water Harvesting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3118, https://doi.org/10.5194/egusphere-egu26-3118, 2026.
Rivers are under pressure from many different sources, including farming and rural land use, wastewater treatment, towns and transport. In England, very few rivers achieve good ecological status, and none achieve good chemical status. This comes after many years of exploiting our freshwater systems. In 2024 there were more than 450,000 combined sewer overflow discharges in England, totalling over 3.5 million hours of spills. This sewage has direct implications on ecological and human health, increasing the environmental contaminant load in rivers. In response, Section 82 of the Continuous Water Quality Monitoring Programme mandates continuous monitoring of freshwater systems, with scope for future expansion of monitored parameters.
Current water quality monitoring relies heavily on infrequent spot sampling, often missing key impact events, with limited spatiotemporal context. The MaD-OPS project has developed a novel sensing network for continuous monitoring of biological, chemical, and physical water quality parameters. A key focus is to demonstrate the value of a new fluorescence-based optical sensor for detecting organic pollution and bacterial contamination within a demonstrator catchment, with the potential to reveal underlying biogeochemical cycling processes.
To isolate different pollution sources, sensor nodes have been deployed at multiple points along a river. Alongside continuous sensor data, regular spot sampling is being carried out for faecal indicator organisms, BOD₅, nutrient analysis, and microbial community profiling to provide robust ground-truthing.
The project aims to develop a user-friendly dynamic Water Quality Index (WQI) that integrates high-frequency sensor data with machine learning, for real time assessment of river health that can be used by citizen scientists, community groups, and regulators alike. Using a novel dynamic baseline approach, the WQI will assess each sensor node relative to the least impacted section of the river at any given time.
Preliminary results demonstrate that continuous monitoring captures point source pollution and hydrological events that are not detected through spot sampling alone. Comparison between the dynamic headwater baseline and downstream sensor nodes highlights the direct impact of point source events on river health.
We present progress in deploying the sensing network, early insights into river health derived from high-frequency data, and how these findings are informing the development of the WQI framework.
How to cite: Tulloch, C., Perrett, R., Coombs, M., Stanton, I., Attridge, J., Thorn, R., Smith, L., and Reynolds, D.: MaD-OPS: Monitoring & Detection of Organic Pollution from Sewage: Implementation of an agile sensing network for informing river health, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18033, https://doi.org/10.5194/egusphere-egu26-18033, 2026.
Contaminants of emerging concern (CECs), including pharmaceuticals, pesticides, and PFAS, have attracted increased attention due to their potential to affect the environment and human health. At the same time, environmental DNA (eDNA) can detect and monitor biological communities and can complement chemical monitoring to give a more comprehensive picture of ecosystem status. The simultaneous sampling of CECs and eDNA presents significant technical and logistical challenges and requires very sensitive techniques. Autonomous surface vehicles (ASVs) offer a flexible platform for monitoring coastal water systems, particularly when repeated or prolonged sampling is required. Their use is increasingly relevant for supporting emerging biological and chemical monitoring techniques. Despite its potential, few studies investigate seawater ecosystems using this combined approach.
This work involves innovative monitoring of Irish coastal waters using an interdisciplinary approach that integrates expertise in engineering, chemistry, and biology. Research involving an ASV capable of reliable dynamic positioning during extended sampling operations will be shown alongside sensitive analytical techniques for investigating CECs and eDNA in seawater matrices. Results will show strategies to address a key challenge for ASV-based eDNA sampling of maintaining precise station for adequate periods while water is actively pumped through our filtration systems. Study observations include methods for sample handling to overcome the challenge of low target analyte concentration degradation, and contamination.
How to cite: Sale, N., Regan, F., Parle-McDermott, A., Wosinski, M., Dooly, G., Griffin, L., Duraibabu, D. B., Prodöhl, P., and Cadena-Aizaga, M. I.: Monitoring Contaminants of Emerging Concern and eDNA off the Coast of Ireland Using Autonomous Surface Vehicles: A Spatiotemporal Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18591, https://doi.org/10.5194/egusphere-egu26-18591, 2026.
Monitoring and maintaining functioning freshwater habitats is increasingly challenging, despite the widespread implementation of European and international freshwater quality monitoring frameworks. With the complexities of climate change, there is a need for data with higher spatial and temporal resolution. In this context, citizen science initiatives have emerged as a valuable complement to official monitoring programs. These initiatives are particularly important in small river basins and remote rural areas, where data from environmental agencies is often sparse or unavailable. However, concerns regarding the reliability and consistency of citizen-generated data persist, highlighting the need for novel technological solutions capable of improving the quality of in situ measurements collected by volunteers.
We present a low-cost fluorometer for field measurements of phytoplankton biomass, through the measurement of chlorophyll-a, featuring a multivariate turbidity correction algorithm and automated online data upload. This open-source device aims to advance monitoring by integrating cutting-edge optical sensing with IoT connectivity and citizen science.
The sensor is integrated in a 3D-printed case and comprises an optical system with two light sources: an 820 nm LED for turbidity measurements and a 430 nm SMD LED for chlorophyll-a excitation, coupled with a long-pass optical filter. The voltage signal from the photodiode is acquired via a 16-bit analog-to-digital converter and transmitted to a microcomputer (Raspberry Pi Zero 2 W), which powers and controls the system.
Laboratory and field evaluations demonstrated that the sensor delivers accurate and reproducible measurements, achieving higher resolution and precision than measurements without turbidity correction. For ease of replication, the 3D enclosure CAD model, software, and user guidelines are openly accessible online.
How to cite: Cirrone, R., Vesprini, F., Boldrini, A., Polvani, A., Liu, X., Galgani, L., and Loiselle, S.: Open-Source Fluorescence Sensing with a Turbidity Correction Model for Community-based Freshwater Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19151, https://doi.org/10.5194/egusphere-egu26-19151, 2026.
Algal blooms represent a significant challenge for the sustainable management of freshwater habitats, strongly affecting water quality, biodiversity, ecosystem functioning, and human activities. Their occurrence is often driven by complex interactions between natural processes and anthropogenic pressures [1–3]. Consequently, there is a growing demand for monitoring strategies capable of capturing the spatial and temporal variability of algal dynamics while supporting a holistic assessment of water habitat health. Traditional monitoring approaches typically rely on point-scale in situ measurements or satellite remote sensing products, which, although essential, are often limited by spatial resolution, revisit frequency, operational costs, or deployment constraints [4]. In this context, low-cost, image-based sensing systems represent a promising complementary solution, enabling continuous and visually explicit observations at local to regional scales.
This contribution presents preliminary results from an in situ monitoring system based on cost-effective optical imaging cameras combined with deep learning-based image analysis. The proposed approach is developed within the framework of the WINCA4TI (Water Interactions with Nature, Climate and Agriculture for Ticino) Interreg project, which aims to foster cross-border innovation in environmental monitoring through low-cost sensing technologies and data-driven methods. The system is designed to complement high-end in situ instrumentation and satellite observations by providing flexible, scalable, and cost-effective monitoring capabilities, with a specific focus on the automatic characterization of algal bloom phenomena to support near-real-time detection and decision making.
The monitoring system relies on compact cameras and optical sensors operating in the visible and near-infrared spectral ranges, deployed on fixed platforms suitable for long-term observations and on-site (edge) processing. Image data are initially combined with in situ measurements to build a reliable reference dataset, which is subsequently exploited to enable image-only monitoring. The computational workflow integrates image preprocessing, including illumination normalization and water surface masking, with deep learning–based image segmentation to derive spatial and temporal indicators of algal presence, surface coverage, and bloom dynamics.
Preliminary results demonstrate the capability of the proposed approach to capture fine-scale spatial and temporal patterns of algal blooms, bridging the gap between localized field measurements and large-scale remote sensing products. The findings suggest that low-cost image-based monitoring systems can enhance the responsiveness and resilience of water management strategies, particularly where traditional monitoring is constrained by cost, logistics, or spatial coverage.
- Strigaro D., Capelli C. (2024). An open early-warning system prototype for managing and studying algal blooms in Lake Lugano. https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-143-2024
- Bosse K. R., Fahnenstiel G. L., Buelo C. D., Pawlowski M. B., Scofield A. E., Hinchey E. K., & Sayers M. J. (2024). Are harmful algal blooms increasing in the Great Lakes? https://doi.org/10.3390/w16141944
- Zeng K., Gokul E. A., Gu H., Hoteit I., Huang Y., & Zhan P. (2024). Spatiotemporal expansion of algal blooms in coastal China seas. https://doi.org/10.1021/acs.est.4c01877
- Ogashawara I. (2019). Advances and limitations of using satellites to monitor cyanobacterial harmful algal blooms. https://doi.org/10.1590/S2179-975X0619
How to cite: centazzo, A., strigaro, D., primerano, C., cannata, M., and capelli, C.: Preliminary results of a cost-effective optical imaging and deep learning system for algal bloom monitoring in Lake Lugano , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11204, https://doi.org/10.5194/egusphere-egu26-11204, 2026.
Chlorophyll-a (Chl-a) is commonly used as an indicator of phytoplankton biomass and eutrophication in inland waters, as it reflects changes in primary productivity and nutrient availability. Turbidity describes the optical effect of suspended particles in the water column and, in oligotrophic lakes, is typically low but highly responsive to external factors such as wind-induced mixing and precipitation. Analyzing Chl-a and turbidity together in relation to atmospheric conditions is therefore crucial for evaluating water quality and identifying potential pressures on aquatic ecosystems.
In this study, Lake Bolsena was investigated as a representative oligotrophic system to evaluate how atmospheric conditions influence Chl-a concentration and turbidity. The analysis was conducted over the lake surface and an additional surrounding land buffer of approximately 15 km, selected to account for meteorological and atmospheric processes that are not confined to the water body itself but can indirectly affect its optical and biological properties.
Chl-a and turbidity were derived from the data set (version 2.1) of the ESA Lakes_cci project based on the processing of OLCI images for the period 2016-2022. Meteorological variables considered include wind speed at 10m, 2-m air temperature, surface pressure, boundary layer height, precipitation, and solar radiation, all derived from the ERA5 reanalysis dataset (Hersbach et al., 2020). In oligotrophic lakes, wind speed regulates water column mixing and sediment resuspension, while air temperature and solar radiation influence thermal stratification and the energy available for phytoplankton growth; precipitation contributes to suspended material modifying surface optical properties. Boundary layer height and surface pressure provide additional information on atmospheric stability and mixing conditions that modulate air–water exchanges.
Aerosol Optical Depth (AOD) retrieved using the MAIAC algorithm was also included, although it is not available directly over the lake surface but only in the surrounding area (Lyapustin et al., 2018). AOD was used as a proxy for regional aerosol loading to investigate its potential indirect effects on the lake through dry and wet deposition of particulate matter and nutrients, which may alter water transparency and, over time, phytoplankton dynamics even under oligotrophic conditions.
Correlation analysis revealed significant seasonal variability throughout the studied period. Chl-a is particularly influenced by multiple atmospheric forces in autumn, while turbidity is primarily driven by meteorological factors in summer. Both water quality parameters exhibit variable but significant dependencies in spring; on the other hand, atmospheric influence is less relevant in winter.
References
Hersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018.
How to cite: Terenzi, V., Bresciani, M., Giardino, C., Greife, A. J., Pinardi, M., Tratzi, P., Fois, F., and Bassani, C.: Towards an Assessment of Atmospheric Forcing on Chlorophyll-a and Turbidity in an Oligotrophic Lake: Lake Bolsena Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19435, https://doi.org/10.5194/egusphere-egu26-19435, 2026.
Monitoring lakes is traditionally expensive, but satellite technology offers a more affordable solution. Human activities are currently damaging water bodies worldwide, including Egypt's coastal lakes. This study focuses on Burullus Lake, Egypt’s second-largest lake in the northern Mediterranean. Researchers used Remote Sensing and Geographic Information Systems (GIS) to track changes in the coastline and land use. The authors analyzed Landsat images from 1984 to 2019 and compared 2019 Landsat data with Sentinel-2A imagery. They also performed field visits to confirm their findings. Using a supervised classification method, they identified eight categories, including seawater, urban areas, and fish farms.
The results show significant changes between 1984 and 2019: the lake’s open water decreased by 16%, and floating plants dropped by 52%. Conversely, agricultural land expanded by 648 km^2, and fish farms grew by 290 km^2. These updated maps help officials identify where human activity is most harmful. This data is essential for restoring the lake and meeting Sustainable Development Goals (SDGs).
Keywords: Remote Sensing & GIS, Environmental Monitoring, Land Use/Land Cover (LULC), Change Detection, Burullus Lake, Egypt,
How to cite: Abdelsadek, E., Elbeih, S., and Negm, A.: Coastal and Land Use Variations of Burullus Lake, Egypt Using Remote Sensing , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14117, https://doi.org/10.5194/egusphere-egu26-14117, 2026.
Offshore Freshened Groundwater (OFG) is increasingly recognized as an important component of continental shelf hydrogeology, yet its physical structure, geochemical evolution, and preservation mechanisms remain poorly documented in polar settings. This study characterizes OFG systems on the Ross Sea shelf using borehole porewater data from IODP (Integrated Ocean Drilling Program) Sites U1522 and U1524, with the aim of resolving their vertical structure, origin, and diagenetic state.
Depth-resolved porewater samples were analyzed for chloride, stable water isotopes (δ¹⁸O, δ²H), major cations and anions, and redox-sensitive species. Lithological information was used to assess stratigraphic controls on fluid distribution. A groundwater transport model was applied to evaluate the relative roles of diffusive and advective processes in shaping present-day porewater profiles.
Both sites host vertically stratified OFG systems comprising a saline, marine-influenced upper unit, an intermediate transition zone, and a deeper freshened interval preserved beneath finer-grained sediments. Downcore decreases in chloride and progressive depletion of δ¹⁸O and δ²H indicate dilution by a non-marine water source, while elevated Br/Cl ratios and smooth concentration gradients support long residence times and limited modern exchange. Redox profiles show sulfate depletion, ammonium enrichment, and methane production at depth, indicating active diagenetic alteration of the fluids. The transport model demonstrates that diffusion is the dominant control on present-day tracer distributions, with only minor or negligible vertical flow patterns.
The Ross Sea OFG systems at Sites U1522 and U1524 are therefore laterally extensive, vertically stratified, and geochemically evolved bodies, preserved through stratigraphic confinement and diffusion-dominated transport. Their characteristics reflect long-term isolation and water-rock interaction rather than active recharge phenomena, highlighting OFG as a stable subsurface reservoir and an archive of past hydrogeological conditions on polar continental shelves.
How to cite: Chidichimo, F., Thomas, A. T., De Biase, M., Straface, S., and Micallef, A.: Characterization of Offshore Freshened Groundwater systems on the Ross sea shelf, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10394, https://doi.org/10.5194/egusphere-egu26-10394, 2026.
Machine learning (ML) models have become essential tools for monitoring water system dynamics, enabling accurate prediction of water levels, discharge patterns, and responses to meteorological forcing. However, their operational deployment remains constrained by limited interpretability and the challenge of translating numerical outputs into actionable insight, particularly when assessing system anomalies, regime shifts, and potential impacts on aquatic and riparian habitats.
This study introduces a novel framework that integrates large language models (LLMs) as a semantic interpretation layer within ML-based hydrological monitoring systems. Building on established time-series ML architectures for water level prediction, model outputs are coupled with statistical anomaly detection techniques to identify atypical hydrological behaviour, threshold exceedances, and periods of elevated system stress relevant to near-real-time monitoring. These quantitative signals, together with meteorological drivers and system metadata, are subsequently processed by an LLM to generate structured, contextual natural-language explanations.
The proposed framework is demonstrated using historical water monitoring datasets, with particular emphasis on extreme events and hydrological anomalies. When such events are detected, the LLM synthesizes information across multiple data streams to articulate observed patterns, plausible hydro-meteorological drivers, and potential implications for water system functioning and associated habitats. Rather than replacing process-based understanding or predictive models, the LLM acts as an intelligent synthesis component that contextualizes ML outputs and supports their interpretation.
Results indicate that LLM-enhanced monitoring outputs can substantially improve transparency, interpretability, and communicability compared to conventional numerical monitoring approaches, thereby facilitating improved situational awareness and decision support during critical periods. By embedding natural-language reasoning within data-driven monitoring workflows, this work establishes a pathway toward interpretable, stakeholder-centred hydrological monitoring that aligns advanced artificial intelligence methods with practical environmental observation and management needs.
Keywords
- Hydrological monitoring
- Machine learning interpretability
- Large language models
- Water system intelligence
How to cite: slaimi, A. and Scriney, M.: Explainable Hydrological Monitoring: Large Language Models as Semantic Interpreters of Machine-Learning-Based Water System Intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19125, https://doi.org/10.5194/egusphere-egu26-19125, 2026.
This paper examines the connectivity and beyond-connectivity requirements essential for water data ecosystems, highlighting the critical role of advanced communication technologies, such as 5G/6G, in enabling rapid, reliable data transmission for real-time monitoring and decision-making. Optimizing communication protocols supports robust infrastructure, interoperability among diverse sources, including environmental sensors, weather data, and utility-provided information, and seamless data integration and utilization, while promoting innovation, efficiency, and sustainability in water management.
The study is structured in two main parts. First, it identifies specific connectivity and beyond-connectivity requirements, focusing on the integration of various water data sources and evaluating the efficacy of communication protocols to support dynamic data integration, including capabilities like artificial intelligence, sensing, and sustainability. This forms the foundation for subsequent analysis. Second, it analyzes and optimizes connectivity services offered by 5G/6G and beyond technologies to meet these requirements, considering factors such as energy efficiency, reliability, scalability, and integrated services like sensing, AI, and computation.
The study aims to demonstrate that addressing these requirements enhances the integration and utilization of diverse water data, facilitating access to information, development of new solutions, improved understanding of water management challenges, and innovation in water supply through enhanced prediction models and more efficient, sustainable solutions. It identifies and optimizes key performance indicators (KPIs) as well as services derived from standardization bodies, tailored to water-related use cases such as leak detection, wastewater monitoring, and resource efficiency. These include ultra-low latency for critical alerts, high reliability for infrastructure control, energy efficiency in sensor networks, scalability for IoT-dense environments, and integrated AI for dynamic data processing. Anticipated insights reveal how water data ecosystems can overcome challenges like demand-supply gaps through efficient data collection, sharing, and utilization, while addressing barriers such as limited data availability and regulatory constraints. This necessitates clear visions, effective data-sharing mechanisms, and scalable architectures to drive innovation and reduce water loss.
The proposed framework facilitates informed strategies and new opportunities for stakeholders in water utilities and related sectors. This study advances the understanding of digitalization in critical infrastructure, demonstrating how optimized connectivity can promote efficiency and sustainability in water management.
How to cite: Kharbouch, A., Monemi, M., Taskinen, P., and Rasti, M.: Advancing Water Data Ecosystems: Identifying and Optimizing Connectivity and Beyond-Connectivity Requirements with 5G/6G Technologies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21301, https://doi.org/10.5194/egusphere-egu26-21301, 2026.
Posters on site: Fri, 8 May, 16:15–18:00 | Hall X4
The distinction of lentic (still) and lotic (flowing) inland waters is fundamental for understanding ecosystem functions, hydrodynamic behavior, nutrient cycling, and biogeochemical exchanges across terrestrial and aquatic interfaces. These systems influence carbon storage, sediment balance, biodiversity support, water residence time, and regional climate regulation, making accurate separation essential for large-scale hydrological assessments. However, existing classification approaches often depend on site-specific information, manual interpretation, or large training datasets, and commonly struggle to classify inland waters smaller than 3 hectares due to resolution limitations and insufficient annotated samples. This work presents an Automated Data Efficient Morphometric Approach (ADEMA) for classifying inland waters down to 0.09 ha (single LANDSAT pixel) using multi-dimensional morphometric interpretations derived using the Global Surface Maximum Extent (GSMW) dataset. The approach was trained and validated using 17,391 expert-labeled samples from 66 geographically diverse locations across multiple climate zones, varied topographies, and hydrological regimes. Further, ADEMA was benchmarked against optimized machine learning, deep learning, and global classification products. Results showed that across all size classes (small: <10 ha, medium:10-1,000 ha, and large: >1,000 ha), ADEMA provided comparable F1 scores (94%) to machine and deep learning models with minimal omission (2%), demonstrating its ability to achieve reliable classification with significantly lower computational and data requirements. A multi-decadal evaluation from 1991 to 2021 showed stable accuracy, highlighting temporal ADEMA’s robustness (F1 score = 92%). When compared to global classification products, ADEMA achieved substantially higher accuracy (average F1 score: 97% vs. 62%), especially for small and medium inland waters that are often underrepresented in global datasets. The method offers a data-efficient and automated solution suitable for regional to global hydrography. However, the framework excludes inland waters >10,000 ha to maintain computational feasibility, limiting coverage of large systems. Single-pixel detections (~0.09 ha) are less reliable due to noise, vegetation, and GSMW uncertainty, with accuracy stabilizing above ~0.5 ha. With further advancements, ADEMA could improve global open-water inventories, guide conservation strategies, and strengthen our understanding of how small inland waters collectively shape hydrology and ecosystem resilience across different environments.
How to cite: Sharma, A., Narayanan, M., and Ilampooranan, I.: An Automated Morphometric Approach for Global Lentic and Lotic Classification of Inland Waters , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1257, https://doi.org/10.5194/egusphere-egu26-1257, 2026.
Saltwater intrusion threatens coastal ecosystems and water resources globally, intensified by
climate change. Rising sea levels and reduced river flows disrupt the water balance in estuarine
zones, allowing seawater to penetrate upstream into rivers and coastal aquifers. Studies predict a
9.1% global average increase in saltwater intrusion under high emissions scenarios, with extreme
events becoming up to 25 times more frequent [2]. The Po River Delta exemplifies this
vulnerability. The Ferrara area, characterized by minimal slopes and elevations mostly below sea
level, is particularly exposed. Projections indicate the Po di Goro estuary could experience up to
63% annual increase in saltwater intrusion, reaching 120% in summer [6]. The 2022 drought
demonstrated system fragility, with saltwater compromising irrigation and domestic water
supplies. Groundwater aquifers face additional stress from excessive extraction and reduced
natural recharge [1], affecting drinking water quality, agriculture, natural habitats, and soil
integrity. Traditional monitoring relies on point measurements of electrical conductivity using
boat-mounted probes, providing inadequate spatial and temporal resolution. Geophysical
methods—particularly electrical resistivity tomography (ERT) and electromagnetic (FDEM)
surveys—offer rapid, high-resolution alternatives. Previous research demonstrated the
effectiveness of combined ERT-FDEM approaches in the Po di Goro for monitoring saltwater
wedge advancement [4]. Integration of multiple geophysical techniques enables multi-scale
characterization [3].
This PhD project develops an integrated monitoring and predictive modeling system for saltwater
wedge intrusion in Ferrara, combining advanced geophysical methods with machine learning.
Building on long-term FDEM monitoring (2022-2025) in the Po di Goro, the project extends to
other Ferrara rivers and incorporates additional methods (ERT, GPR).
Expected outcomes include: (1) precise mapping of saltwater wedge extent, depth, and temporal
evolution; (2) machine learning-based predictive tools to forecast intrusion evolution; (3) decision-
support tools for sustainable water resource management, agriculture, and territorial planning,
with methodologies transferable to other estuaries globally.
The project addresses a critical gap: the absence of systematic monitoring systems and reliable
predictive tools. Increasing salinization frequency underscores the urgency for robust predictive
capabilities enabling preventive interventions. The project responds to the 2022 Po River basin
water crisis, offering practical solutions through informed policy on coastal defense, flood
mitigation, subsidence reduction, and intrusion control [5].
References
[1] Crestani, E. (2022). Large-Scale Physical Modeling of Salt-Water Intrusion. Water, 14(8), 1183.
[2] Lee, J., et al. (2025). Global increases of salt intrusion in estuaries. Nature Communications, 16,
3444.
[3] Mansourian, D., et al. (2022). Geophysical surveys for saltwater intrusion assessment. Journal
of the Earth and Space Physics, 48(3), 331–341.
[4] Rizzo, E., et al. (2023). DC and FDEM salt wedge monitoring of the Po di Goro river. EGU23-
5297.
[5] Simeoni, U. (2009). A review of the Delta Po evolution. Geomorphology, 107(1–2), 64–71.
[6] Verri, G., et al. (2024). Salt-wedge estuary's response to rising sea level. Frontiers in Climate, 6,
1408038.
How to cite: Fongo, F. and Rizzo, E.: Monitoring of the saline wedge in the rivers of the Ferrara province (Emilia Romagna region,Italy)., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7210, https://doi.org/10.5194/egusphere-egu26-7210, 2026.
The evolution of cryospheric components (snow cover, ice, and meltwater) plays a fundamental role in regulating energy exchanges between the atmosphere and ice shelves and represents a key indicator of climate change impacts in remote polar regions. Within the framework of the HOLISTIC (Holistic Overview of the supraglacial Lake–Ice–Snow Timing and Climate causality) project, funded by the Italian National Antarctic Research Program, we present an advanced multi-sensor assessment of supraglacial lake (SGL) dynamics over the Nansen Ice Shelf (Victoria Land, Antarctica).
We adopted a synergistic remote sensing approach, aimed at integrating active microwave observations from satellite SAR and radar altimetry missions with optical imagery. This multi-frequency and multi-platform strategy investigates the possibility of detection, mapping and temporal monitoring of SGL position and extent and spatial distribution under all-weather conditions and across different spatial and temporal scales. HH-polarized SAR data proved effective in identifying surface meltwater signatures and characterizing seasonal lake evolution, despite polarization limitations, while optical data provided complementary constraints on lake morphology and surface hydrology during cloud-free periods. The seasonal melt and refreezing processes of SGL units were further investigated by leveraging the combined revisit time of operational sensors such as Sentinel-2 and Landsat, together with dedicated tasking missions like PRISMA, providing a more comprehensive understanding of lake dynamics over time.
A dedicated processing chain for Sentinel-3 altimetry L1A individual echoes was implemented using the PISA algorithm (Abileah and Vignudelli, 2021, https://doi.org/10.1016/j.rse.2021.112580). This allowed the retrieval of localized elevation anomalies associated with bright targets, mountainous targets and supraglacial water bodies, and the characterization of surface roughness changes presumably linked to melt and drainage processes, as well as to changes in snow density and surface slope.
The combined analysis highlights the strong coupling between snowpack evolution, surface energy feedback, and the formation and drainage of SGLs, providing new insights into ice-shelf surface hydrology and its seasonal to interannual variability. The results represent a step forward in quantifying SGL properties using active microwave and passive/active optical techniques and offer a valuable testbed for existing and future altimetry missions, such as NASA's ICESat-2 and ESA’s CRISTAL missions, aimed at directly retrieving snow depth. The capabilities of high-resolution satellite-born SAR sensors are also expected to benefit from this study, in detecting and monitoring snowpack changes, particularly those resulting from surface snow melt and the formation of supraglacial lakes. Supraglacial lakes emerge as particularly suitable targets for assessing visible light as well as Ka-, Ku- and C-band scattering contributions and for advancing the understanding of snow–ice–water interactions in polar environments.
How to cite: De Biasio, F., Vignudelli, S., Zecchetto, S., Zucchetta, M., Valentini, E., Salvadore, M., and Salzano, R.: Advances in supraglacial lake detection and characterization on the Nansen Ice Shelf from active microwave and visible light satellite remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13495, https://doi.org/10.5194/egusphere-egu26-13495, 2026.
Synthetic dyes released from textile and related industries are a major source of aquatic pollution and can pose risks to ecosystem and human health. Methylene blue (MB), a widely used cationic thiazine dye in industrial dyeing and pharmaceutical applications, is of particular concern because it can persist in water and affect photosynthetic activity, aquatic biodiversity, and water quality. However, monitoring dye contamination often relies on laboratory-based analytical techniques that are costly and time-consuming, limiting rapid assessment in field conditions.
In this study, yeast powder (a low-cost and renewable bio-precursor) was converted into fluorescent carbon dots (C-dots) using a simple one-pot hydrothermal synthesis route. The as-prepared C-dots showed excitation-dependent fluorescence emission with a clear red shift from 360 to 460 nm. Structural and chemical characterisation using UV–Vis, TEM, XPS, XRD, FTIR and Raman spectroscopy confirmed quasi-spherical particles with an average size of 3–8 nm and an amorphous carbon structure enriched with oxygen-containing functional groups. The C-dots exhibited high stability across a wide range of pH and salinity (NaCl), under prolonged UV exposure and during storage.
The C-dots were then applied as a fluorescence-based sensor for rapid and selective detection of methylene blue in water. A strong decrease in fluorescence intensity was observed upon addition of MB, with a linear response in the range of 1 ppb to 1 ppm. The sensor achieved a limit of detection (LOD) of 73.9 ppb and a limit of quantification (LOQ) of 246.4 ppb, demonstrating high sensitivity. The sensing mechanism was attributed to fluorescence quenching dominated by FRET, supported by experimental spectroscopy and computational investigations. Theoretical analysis further indicated that π–π stacking and hydrogen bonding interactions between MB molecules and the C-dot surface contribute to strong binding and enhanced selectivity.
Finally, the developed sensor was successfully applied to real water samples, showing satisfactory recoveries between 96% and 116%. Overall, this work demonstrates a green, cost-effective and highly sensitive fluorescent nanosensor for MB monitoring, offering strong potential for real-time water quality assessment and pollution control in freshwater and wastewater systems.
How to cite: Chauhan, N., Krause, S., Singh, M., and Toor, A. P.: Selective Fluorescence Sensing of Methylene Blue Dye Using Yeast-Based Carbon Dots: Experimental and Computational Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18415, https://doi.org/10.5194/egusphere-egu26-18415, 2026.
Within the framework of proximal sensing, monitoring early-stage seagrass colonization in turbid waters presents challenges due to the spectral similarity between the dwarf eelgrass Zostera noltei and ephemeral macroalgae. A preliminary study previously demonstrated the utility of high-resolution Unmanned Aerial Vehicle (UAV) imagery for general monitoring through visual inspection (Mistri et al., 2025). However, the reliance on manual detection and known transplantation coordinates limits the scalability of the approach. In this study, we take a further step to overcome these limitations by applying pixel-based supervised classification to high-resolution orthomosaics. This allows for precise and quantitative tracking of the spatial evolution of seagrass meadows over time.
Ultra-high-resolution aerial surveys were conducted in the Caleri Lagoon (Po River Delta, Italy) using a DJI Air 2S UAV flown at an altitude of 7 meters, achieving a theoretical ground sampling distance of 0.2 cm/pixel. The collected imagery was processed into georeferenced orthomosaics and analyzed using a supervised Maximum Likelihood Classification algorithm based on Bayes’ theorem. To isolate the spectral signal of the target seagrass, the probabilistic framework incorporated 40 regions of interest for each of five classes: seagrass, green algae, red algae, shadow, and background. To reduce high-frequency 'salt-and-pepper' noise, a post-classification Sieve filter (20×20 pixel window) was applied, refining patch segmentation based on neighborhood mode.
Multitemporal analysis revealed a distinct non-linear expansion trajectory within the 0.5-hectare study area. Starting from a planted footprint of just 2.5 m² (~0.05% of the study area) in August 2023, the seagrass colonies expanded to 60 m² (1.2%) by June 2024, reaching approximately 716 m² (14%) by October 2025.
These results demonstrate that combining low-altitude UAV photogrammetry with probabilistic classification offers a highly repeatable and scalable framework for quantifying restoration dynamics. This methodology effectively overcomes the limitations of manual monitoring, enabling the detection of the subtle, non-linear growth patterns typical of early-stage colonization.
How to cite: Albéri, M., Abdelkader, M., Cozzula, C., Cunsolo, F., Elek, N. I., Esen, E. C., Hasnain, G., Mantovani, F., Mistri, M., Munari, C., Paletta, M. G., Pezzi, M., Raptis, K. G. C., Sfriso, A. A., Sfriso, A., and Strati, V.: Quantifying early seagrass growth with UAV imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18973, https://doi.org/10.5194/egusphere-egu26-18973, 2026.
Mercury contamination in freshwater ecosystems is of major concern due to its persistence, toxicity, and bioaccumulation potential. This preliminary study investigates mercury dynamics in Lake Maggiore by integrating surface water mercury analyses with high-resolution assessments of phytoplankton structure and chlorophyll concentrations along the water column and surface water. Monitoring activities were conducted using FluoroProbe probe (BBE Moldaenke GmbH) to characterize algal groups with particular focus on the horizontal spatial distribution of both chlorophyll and mercury in proximity to tributaries and lake outlets. Surface water samples were analyzed for mercury concentrations using a Lumex RA‑915+ portable atomic absorption spectrometer, equipped with the dedicated attachment for dissolved and total mercury determination in water, while FluoroProbe profiles were used to quantify algal group composition and chlorophyll distribution. Results reveal marked spatial heterogeneity in mercury concentrations, closely associated with tributary zones and changes in chlorophyll patterns, suggesting coupling between hydrological inputs, phytoplankton dynamics, and mercury behavior. This integrated monitoring approach improves understanding of mercury–ecosystem interactions in large lake systems and supports the development of effective monitoring and management strategies for freshwater environments.
How to cite: Austoni, M., Fantozzi, L., and Luciano, G.: Spatial Dynamics of Mercury and Phytoplankton in Lake Maggiore: An Integrated Monitoring Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22477, https://doi.org/10.5194/egusphere-egu26-22477, 2026.
The sustainable management of water resources in anthropogenic contexts requires a holistic transition from mere monitoring to comprehensive assessments of water habitats. Urban reservoirs are particularly vulnerable ecosystems, where pressures such as agricultural runoff and untreated sewage discharge drive eutrophication, compromising water quality. Traditional monitoring methods often lack the spatiotemporal resolution required to capture the complex dynamics of these environments. To address this gap, this study evaluates the efficacy of close-range remote sensing combined with Machine Learning (ML) and Explainable Artificial Intelligence (XAI) for estimating optically active water quality parameters (turbidity, chlorophyll-a, and phycocyanin). The research was conducted at the Ibirité Reservoir (Minas Gerais, Brazil), a highly eutrophic urban system serving as a petrochemical industry water supply. Ten monthly field campaigns (August 2024 to May 2025) were conducted, covering both dry and wet seasons, to capture seasonal variability. Data acquisition employed Unmanned Aerial Vehicles (UAVs) equipped with two distinct multispectral sensors: the DJI Phantom 4 Multispectral (P4M – 5 bands) and the MicaSense RedEdge Dual-P (MSR – 10 bands). This setup allowed for a comparative analysis of spectral resolution impacts on model performance. The methodology tested three ML algorithms: Random Forest, CatBoost, and XGBoost. To ensure physical consistency, SHAP (SHapley Additive exPlanations) values were used to interpret the models. This ML-XAI approach assessed: (1) the comparative performance of each sensor and algorithm; and (2) the robustness of the models by identifying the most influential spectral bands for each parameter. Results indicate that ensemble learning algorithms, specifically Random Forest and CatBoost, consistently outperformed others across datasets. The MSR sensor achieved the highest overall accuracy, particularly for Phycocyanin estimation using Random Forest (R² = 0.93), compared to the P4M's best result for the same parameter (R² = 0.90). Explainable AI analysis revealed the physical drivers behind this performance: for Phycocyanin, the MicaSense models relied heavily on the specific 717 nm and 705 nm (RedEdge) bands. This explains the superior performance, as these narrow bands better resolve the specific spectral features of cyanobacteria compared to the single RedEdge band available on the DJI sensor. Conversely, for Chlorophyll-a, the NIR (842 nm) and Red (650 nm) bands were the dominant predictors. Since both sensors possess these bands, the performance gap was narrower (R² = 0.79 for MSR vs. 0.77 for P4M), validating the cost-effectiveness of the 5-band sensor for general pigment monitoring. However, for Turbidity, the additional spectral resolution of the MSR (specifically the 717 nm band) proved decisive, raising accuracy to R² = 0.84 compared to 0.78 for the P4M. Findings demonstrate that integrating high-resolution multispectral sensing with interpretable ensemble learning offers a scalable and physically consistent tool for monitoring water habitat health, supporting data-driven decision-making in complex urban environments.
How to cite: Mello, C., Salim, D., Souza, B., Pereira, G., and Amorim, C.: Benchmarking UAV multispectral sensors and machine learning for water quality estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14855, https://doi.org/10.5194/egusphere-egu26-14855, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 1b
EGU26-13852 | ECS | Posters virtual | VPS21
Monitoring Shallow Water Depths: A Review of Satellite-Derived Bathymetry MethodsMon, 04 May, 14:12–14:15 (CEST) vPoster spot 1b
Satellite-Derived Bathymetry (SDB) offers a cost-effective alternative to traditional shipborne surveys for mapping large coastal areas. This technique utilizes optical remote sensing data from multispectral sensors to estimate water depth. The fundamental principle relies on the behavior of light as it travels through the water column; as depth increases, light intensity decreases due to absorption and scattering. Different wavelengths penetrate to varying degrees, with blue light reaching the greatest depths while red light is absorbed quickly. By analyzing these spectral features, researchers can calculate underwater topography. Currently, SDB techniques are categorized into two primary groups: physically based (analytical) models, which simulate light propagation without needing local in-situ depth calibration, and statistical (empirical) models, which correlate satellite data with known depth measurements from nautical charts, ship-based acoustic surveys or airborne LiDAR.
While both approaches provide extensive spatial coverage at a lower cost, they are generally limited to clear, shallow waters, typically reaching depths of less than 20 meters. Analytical models are highly accurate but complex and data-intensive, whereas empirical models are more accessible but rely heavily on the quality of reference data. Recent advancements in machine learning have significantly improved the automation and performance of these empirical methods. This study evaluates the core concepts, advantages, and limitations of various SDB approaches, with a focus on Landsat-8 and Sentinel-2 data. Furthermore, the research details essential processes for empirical model calibration, validation, and detecting model bias. The findings emphasize that rigorous evaluation and bias correction are critical for ensuring the reliability of depth data in diverse coastal environments.
Keywords: Satellite-Derived Bathymetry, Remote Sensing, Empirical Models, Stumpf Algorithm, Coastal Waters, Model Bias Detection and Correction.
How to cite: Abdalla, M. H., Elhalawany, H., Abdelrahman, S. M., Negm, A., and Scozzari, A.: Monitoring Shallow Water Depths: A Review of Satellite-Derived Bathymetry Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13852, https://doi.org/10.5194/egusphere-egu26-13852, 2026.
EGU26-22084 | ECS | Posters virtual | VPS21
Monitoring Groundwater Quality and Improvement in the Kima Area, AswanMon, 04 May, 14:15–14:18 (CEST) vPoster spot 1b
Groundwater in arid regions is highly sensitive to human activity, especially when untreated wastewater interacts with shallow aquifers. This study evaluates the hydrogeochemical response of the Kima aquifer in Aswan, Egypt, following the Kima Drain Covering Project. The research uses an integrated framework of field measurements, geospatial analysis, and multi-criteria decision-making. The team analyzed groundwater samples from 2020 and 2025. They tested eleven physicochemical parameters and six irrigation indices. Spatial interpolation through Inverse Distance Weighting (IDW) helped map temporal variations and identify contamination hotspots. To classify water suitability, the study standardized values according to WHO and Egyptian guidelines. The Analytical Hierarchy Process (AHP) was used to determine the importance of various drinking and irrigation indicators. Finally, a Weighted Linear Combination (WLC) generated composite Groundwater Quality Index (GWQI) maps. The results show a significant improvement in groundwater quality after the drain was covered. Levels of TDS, chloride, sulfate, sodium, and magnesium decreased substantially across the area. The ionic balance shifted toward a more favorable calcium-magnesium-bicarbonate facies. Irrigation indices also improved, with most parameters falling into safe or excellent ranges. The 2025 GWQI map reveals a transition from "good–permissible" to "excellent–safe" zones. This confirms that eliminating direct seepage from the drain had a positive environmental impact. This integrated AHP–GIS–IDW approach is an effective tool for monitoring groundwater changes. It provides a robust decision-support system for managing water resources in arid urban environments.
How to cite: Khairy, M., S. Nour-Eldeen, A., Hossen, H., Abd-Elaty, I., and Negm, A.: Monitoring Groundwater Quality and Improvement in the Kima Area, Aswan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22084, https://doi.org/10.5194/egusphere-egu26-22084, 2026.
EGU26-21793 | Posters virtual | VPS21
Hydrological Modelling of the Upper Senegal River Basin Using SWAT: Assessing the Impact of Multi-Source Precipitation Data on Model PerformanceMon, 04 May, 14:18–14:21 (CEST) vPoster spot 1b
The Upper Senegal River Basin is a strategic water resource system supporting agriculture, hydropower generation, and essential ecosystem services in West Africa. However, a comprehensive understanding of its hydrological dynamics remains constrained by the limited availability of in situ hydroclimatic observations. This study applies the Soil and Water Assessment Tool (SWAT) to simulate hydrological processes in the basin, with a particular emphasis on the influence of precipitation data sources on model performance and uncertainty. Hydrological simulations were conducted at six representative gauging stations (Bakel, Kayes, Gourbassy, Oualia, Bafing Makana, and Daka Saidou) over the period 1983–2021, using a combination of ground-based observations, satellite precipitation products, and reanalysis datasets (ERA5, MERRA-2, PERSIANN, and CHIRPS). Model calibration demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values reaching up to 0.74 at upstream stations, while reduced performance was observed downstream. Validation results showed a moderate decline in model efficiency, highlighting the sensitivity of SWAT outputs to precipitation inputs and data uncertainty. The comparative analysis of precipitation datasets reveals substantial variability in simulated streamflow and water balance components, underscoring the importance of precipitation data selection in data-scarce regions. These findings highlight the need for robust, multi-source hydroclimatic data integration to improve hydrological modelling reliability and support informed water resource management decisions.
Keywords: Upper Senegal River, SWAT, Hydrological modelling, Precipitation uncertainty; Satellite rainfall; Reanalysis data.
How to cite: Boussabou, S. M., Taia, S., El Mansouri, B., Kebd, A., Mohamedou Idriss, A., Legsabi, H., and Erraioui, L.: Hydrological Modelling of the Upper Senegal River Basin Using SWAT: Assessing the Impact of Multi-Source Precipitation Data on Model Performance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21793, https://doi.org/10.5194/egusphere-egu26-21793, 2026.
EGU26-5340 | ECS | Posters virtual | VPS21
A Multi-Objective Cost Minimization Framework for Managed Aquifer Recharge Integrating Pareto Optimization and Least-Cost Path AnalysisMon, 04 May, 14:21–14:24 (CEST) vPoster spot 1b
In arid and semi-arid regions, pressure on groundwater resources has reached critical levels. Long-term over-pumping has depleted many aquifers, and climate change is intensifying this process. Rising temperatures increase evaporation from rivers and reservoirs, reducing the amount of surface water available for infiltration and natural recharge. Under these conditions, the use of surface water during periods of availability and its storage underground represents a key mechanism of managed aquifer recharge, effectively avoiding evaporation losses.
In this study, a practical framework is developed and tested to identify feasible ways to transfer accumulated surface water toward stressed aquifers. Rather than relying on complex ranking approaches, the locations of existing water infrastructure specifically wells and traditional khettara systems are used as reference points. These features indicate where aquifers are accessible and provide realistic spatial anchors for planning recharge at the regional scale.
The method combines satellite imagery to map surface water, geographic information systems (GIS) to identify cost-effective transfer pathways across the landscape, and multi-objective optimization to evaluate trade-offs between competing objectives. Feasibility is assessed through a cost function that accounts for terrain slope, elevation differences, transfer distance, pumping energy requirements, infrastructure costs, and potential water treatment needs.
The approach is applied to the Draa Oued Noun Basin in southern Morocco, a region strongly affected by water scarcity, high evaporation rates, and declining groundwater levels. Several surface water sources are examined, and feasible conveyance routes toward aquifers supplying key wells and khettara systems are identified.
The results show substantial variations in cost between water sources. Available water volume, transfer distance, and especially elevation lift emerge as the main cost drivers. Trade-off analysis helps identify the most cost-effective projects under limited budgets. The results also highlight opportunities for cost reduction: where gravity-driven transfer is possible, costs are significantly lower, and where pumping is required, solar energy offers a viable option for reducing long-term operational expenses.
Overall, this work provides a spatially explicit and realistic basis for planning artificial groundwater recharge, while respecting economic constraints and supporting sustainable groundwater management in highly water-stressed regions.
How to cite: Fri, R., Scozzari, A., Haida, S., Kili, M., Chao, J., Mridekh, A., and El Mansouri, B.: A Multi-Objective Cost Minimization Framework for Managed Aquifer Recharge Integrating Pareto Optimization and Least-Cost Path Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5340, https://doi.org/10.5194/egusphere-egu26-5340, 2026.
EGU26-13783 | Posters virtual | VPS21
Monitoring Land Cover Dynamics in Bahr Qarun District, Egypt, via Remote Sensing DataMon, 04 May, 14:24–14:27 (CEST) vPoster spot 1b
Accurate monitoring of land cover is essential for sustainable environmental management and urban planning in arid regions. However, rapid changes in land use often make it difficult to distinguish between different surface types, such as urban areas and bare soil, using standard satellite data alone. This research examines land-use changes in the Bahr Qarun district of Fayoum, Egypt, during 2019, 2021, and 2023. The study used Sentinel-2 and Landsat OLI 8 satellite images taken each April to ensure data consistency. We applied the Maximum Likelihood (ML) method to classify Sentinel-2 images. They used 30 training samples for each land category to guide the process. The results achieved a Kappa coefficient above 75%, indicating a reliable level of accuracy. We measured vegetation using the Normalized Difference Vegetation Index (NDVI) and urban areas using the Normalized Difference Built-up Index (NDBI). A comparative analysis revealed that NDVI results were closely aligned with those obtained from supervised classification, reflecting its strong capability in accurately identifying vegetated areas. In contrast, NDBI exhibited a tendency to overestimate urban extent, primarily due to spectral confusion between built-up surfaces and bare soil within individual pixels. The study concludes that NDVI is an effective tool for mapping the green cover in this area.
Keywords: Land Cover Change, Sentinel-2, Landsat OLI 8, Supervised Classification, Spectral Indices (NDVI & NDBI), Bahr Qarun, Egypt.
How to cite: Elsehsah, A., Negm, A., Ashour, E., and Elsahabi, M.: Monitoring Land Cover Dynamics in Bahr Qarun District, Egypt, via Remote Sensing Data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13783, https://doi.org/10.5194/egusphere-egu26-13783, 2026.
