The establishment of the European Open Science Cloud (EOSC) places renewed emphasis on the role of national e-infrastructures in enabling standards-based, interoperable, and reusable research workflows in the EU. Within the context of Ireland’s EOSC Node, there is particular interest in demonstrating how European-scale open-data services can be digested by national research clouds, transformed into analysis-ready assets, and made available for both open research and applied industry use-cases. Earth Observation (EO) provides a strong test case, given the volume and complexity of the data involved, and its growing role in scalable environments that support operational decision-making.

This pilot project, QuarryLink, presents a Phase-1 study focused on building a reproducible EO data ingestion workflow that connects the Copernicus Data Space Ecosystem with the HEAnet Research Cloud, operating on the SURF Research Cloud platform. Through a real-world quarry case-study in the Dublin region (Ireland), the work demonstrates how EOSC-aligned principles, including auditable machine-readable workflows, can be applied from the outset of the EO research process. We will demonstrate how precise spatial boundaries can be defined and validated; how modern OAuth-based authentication mechanisms can be integrated into research cloud workflows; and how Sentinel-2 Level-2A products can be programmatically discovered, retrieved, and prepared for downstream analysis using current Copernicus services.

By executing the ingestion workflow on the HEAnet Research Cloud using open-source geospatial tooling, the pilot aims to establish an analytics-ready foundation for working with Sentinel-2 data in a reproducible research cloud environment. The resulting data products are structured to support downstream analysis, with compute resources accessed dynamically through the HEAnet Research Cloud workspace as required. Building on this foundation, Phase 2 will focus on developing time-series analyses, EO data cubes, and derived environmental indicators to support both research-driven investigation and applied monitoring scenarios in European quarry environments.

More broadly, the pilot seeks to illustrate how EOSC-aligned integration across data ingestion and compute layers can support open research practices while enabling scalable, real-world EO-enabled industrial applications, providing a practical pathway for national EOSC Nodes to translate open data into shareable analytics and societal impact.

How to cite: Neff, F., Sabatino, R., Arreba, A., and Sweeney, J.: An EOSC Node Ireland Pilot Study: Bridging European and National e-Infrastructures for Reproducible Sentinel-2 Data Ingestion in Quarry Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21965, https://doi.org/10.5194/egusphere-egu26-21965, 2026.

Modern digital technologies and geoinformatics have experienced rapid growth, offering powerful tools to bridge the gap between scientific communities and society in landscape assessment and mapping. This research details the application of a crowdsourcing scheme that utilizes a dedicated mobile application to facilitate direct public participation in quantifying perceptions of urban landscapes and architecture. Initially developed as an educational tool, the methodology has been tested by university students across Italy, Greece, and France, providing a foundational phase for assessing landscape quality and urban typologies. Building upon these educational pilot studies, the work explores the evolution of this methodology into a broader, multicultural citizen science initiative designed to improve the quality and quantity of available landscape perception data.

A significant technical advancement in this research involves the integration of automated image analysis to process the novel data generated by participants from any location. The photographic material was examined using stochastic image analysis based on climacograms, in which images are treated as two-dimensional grayscale intensity fields and analyzed across multiple spatial scales. The method enables the comparison of image patterns based on the visual complexity of the uploaded photographs. A primary challenge addressed was the algorithm's performance when processing real-world, non-curated smartphone images. The analysis began an assessment on how the methodology handles environmental noise, such as sky, trees, and unconventional capture angles, which are inherent to bottom-up crowdsourcing schemes.

The early results indicate that the method can reveal group-level tendencies associated with differing architectural characteristics, particularly in relation to visual complexity, while not supporting reliable classification at the level of individual image. In detail, the findings indicate a trend towards two categorizations: firstly, between modernist-type movements, characterized by minimal elements, and secondly between eclectic or decorative movements, which exhibited higher measured complexity; however, this this behaviour was not observed universally on all analyzed movements The stochastic analysis also indicated theoretical overlaps between certain movements, such as Postmodernism and Eclecticism, based on shared decorative patterns. While the results highlight that environmental factors can influence the analysis of individual photographs, the method utilized presents potential for distinguishing movement trends with logical consistency even from unfiltered data.

Scientifically, this yield of quantitative data sets the groundwork for improved research in the humanities and culture, showing a strong correlation with established landscape quality indices. Socially, the project provides a scalable model for participatory mapping that fosters critical thinking about urban quality, creating new conditions for communication between universities and the broader public. Overall, the presented work reports on the early-stage results of this methodological exploration and aims to evaluate the combined use of participatory mobile data collection and exploratory image-based analysis for landscape and architectural studies, while identifying key challenges related to data quality, interpretation, and future methodological refinement.

How to cite: Kopelia, S., Tepetidis, N., Tzortzi, J. N., Sargentis, G.-F., and Ioannidis, R.: Integrating Participatory Perception-Mapping Data and Stochastic Image Analysis for Urban Landscape Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13270, https://doi.org/10.5194/egusphere-egu26-13270, 2026.

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.

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.

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.

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.

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.

Modern Earth system models increasingly hit I/O limits—not only in performance, but also in reproducibility, maintainability, and developer productivity. As data volumes and workflows evolve, tightly coupled, file-centric I/O approaches can become hard to scale and hard to extend.

We present the design and lessons learned from introducing an asynchronous, modular I/O server concept in the Modular Earth Submodel System (MESSy). I/O operations were decoupled from the Fortran-based scientific core and implemented as separate Python services, where the communication between the two components was implemented using the Yet Another Coupler (YAC) library. This architecture was chosen to improve flexibility and long-term maintainability, while enabling heterogeneous workflows and evolving storage backends.

Using MESSy as a case study, we discuss practical decision criteria for selecting an I/O concept in large models (e.g., scaling behavior, accessibility for developers, testing and CI strategies, and reproducibility).  We conclude with lessons learned from bridging Fortran and Python communities and from lowering entry barriers for user-developers in a large modeling system.

How to cite: Mitic, A., Jöckel, P., Kerkweg, A., Hartung, K., Kern, B., and Hanke, M.: Choosing an I/O approach for Earth system models: lessons learned from a modular I/O server for MESSy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11154, https://doi.org/10.5194/egusphere-egu26-11154, 2026.

Data-driven global weather models, such as GraphCast, have revolutionized medium-range forecasting but often exhibit systematic limitations in quantitative precipitation forecasting (QPF). Specifically, these models tend to produce over-smoothed blurry rainfall fields and underestimate localized extremes , primarily due to the inherent uncertainties in their reanalysis training data (e.g., ERA5) and the use of mean-squared-error-based loss functions.

To bridge the gap between coarse-resolution global AI forecasts and the need for precise, high-impact weather prediction, we introduce SynQPF-Net, a deep learning framework designed to synergize GraphCast’s dynamical background fields with high-resolution observational analyses. The model employs a dual-stream spatiotemporal encoder to process heterogeneous inputs: the 0.25^o dynamical forecasts from GraphCast and the 0.0625^o precipitation analyses from the China Meteorological Administration Land Data Assimilation System (CLDAS) . A specialized hybrid loss function, combining classification (Dice) and regression (Weighted MSE) objectives, is utilized to jointly optimize the spatial structure and intensity of precipitation.

Evaluated on warm-season events in Southern China, our approach demonstrates significant skill improvements. SynQPF-Net effectively sharpens the forecast, doubling the Critical Success Index (CSI) for heavy rainfall (>=10 mm) at the 6-hour lead time compared to the raw GraphCast output. Crucially, interpretability analysis reveals that the model learns physically consistent meteorological principles: it predominantly relies on extrapolating recent observational patterns for short lead times (<=12 h) and dynamically shifts its focus to large-scale circulation and moisture variables (e.g., 700 hPa specific humidity) as the forecast horizon extends. This work provides a validated pathway for correcting and downscaling global AI weather models, offering a robust solution for short-range extreme precipitation forecasting.

How to cite: Chen, D.: Bridging Global AI Models and Local Extremes: A Dual-Stream Framework for Correcting and Downscaling GraphCast Rainfall Predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1413, https://doi.org/10.5194/egusphere-egu26-1413, 2026.

Urban heat risk assessments increasingly require land surface temperature (LST) and near-surface air temperature at spatial scales that resolve microclimatic drivers such as material heterogeneity, shading, and complex terrain. While satellite thermal products and stationary air temperature observations provide essential regional and temporal context, their spatial resolution and coverage, as well as satellite revisit frequency, limit the quantification of surface thermal variability within urban blocks and campus-scale environments. Unmanned aerial vehicle (UAV) thermal imagery can bridge this scale gap, but quantitative LST retrieval remains sensitive to radiometric calibration, emissivity assumptions, local viewing geometry, geolocation accuracy, and acquisition-time atmospheric conditions.

This contribution develops and demonstrates a reproducible UAV thermal remote sensing workflow that converts raw thermal imagery into georeferenced LST mosaics over complex urban surfaces. Using a DJI Matrice 4T thermal sensor over a university campus in Sheffield, UK, thermal data were collected through multiple field surveys combining UAV flights with ground measurements collected alongside the flights. UAV flights were conducted in late June 2025, with flight planning targeting approximately 80% forward and side overlap. Raw thermal imagery derived from UAV was batch-converted using documented acquisition parameters informed by on-site conditions. Key factors include target distance, relative humidity, emissivity, and reflected apparent temperature,  applied consistently within each survey to support cross-frame comparability.  This research: (1) converts raw thermal imagery to georeferenced thermal outputs using ground-informed acquisition parameters (i.e. distance, humidity, emissivity, and reflected apparent temperature) to stabilise cross-frame temperature consistency; (2) reduces spatial distortions through co-registration with high-resolution basemaps, with a digital terrain model (DTM) used as an additional terrain reference; (3) accounts for surface emissivity variability by integrating land use/land cover and material proxies derived from complementary geospatial datasets, with high-resolution RGB orthomosaics used to derive land cover or material proxies (e.g., vegetation and pavements) that inform thermal processing parameters and support consistent interpretation of microscale thermal patterns.

The workflow delivers thermal remote sensing products at centimetre-level ground sampling distances and is designed to be transferable to other urban sites using standard UAV surveys and widely available geospatial datasets. By foregrounding calibration, emissivity handling, and quality control, this study strengthens the methodological basis for integrating UAV thermal observations into environmental remote sensing in urban settings, enabling more robust cross-scale interpretation of urban thermal patterns and supporting evidence-based decision making.

How to cite: Wang, J.: UAV thermal remote sensing for land surface temperature mapping in complex urban environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21586, https://doi.org/10.5194/egusphere-egu26-21586, 2026.