With the rapid development of artificial intelligence (AI), energy consumption and carbon emissions from high-demand computing power have gradually attracted widespread attention in the environmental field. Existing research largely focuses on data centers, which are the infrastructure that directly generates AI-related carbon emissions. However, the users who truly drive computing power demand have long been neglected. A major reason is the difficulty in tracking and accurately locating users, so that AI-related carbon emissions from users’ perspective have lacked systematic identification and discussion so far. It is worth noting that with the wide application of AI, the primary source of carbon emissions has shifted from model training to large-scale and multi-domain usage. This means that understanding the spatial distribution pattern of AI users is crucial to explore demand-side emission reduction in AI, especially during periods of bottlenecks in production-side emission reduction, such as the slow green transformation of the electricity energy mix. Demand-side management can, to some extent, contribute to mitigating AI-related carbon emissions. In this study, we aim to display the spatial distribution of AI users within the city and assess whether variations in usage across different wards may lead to potential spatial inequalities in AI-related carbon emissions. Taking London as a case study, we utilize regional AI penetration rates and AI user profiles to spatially decompose urban AI users at a more granular scale, quantifying the corresponding AI-related carbon emissions and comparing the proportion of AI-related carbon emissions in residents' carbon footprints and potential inequalities. We expect to find spatial clustering of AI-related carbon emissions and a positive correlation with the distribution of educational resources and wealth. Our study may provide an empirical basis for understanding the new environmental inequalities brought about by AI development and offers key references for future green digital governance on the demand side.

How to cite: Yin, Y., Chu, Y., and Chen, Y.: Mapping AI Users and Potential Inequality Pattern: A Spatial Downscaling Study in London, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-895, https://doi.org/10.5194/egusphere-egu26-895, 2026.

Gridded population datasets play a pivotal role in a wide range of contemporary research and development, such as the distribution of aid, public health campaigns, as well as disaster risk management. However, the selection of the appropriate existing population dataset remains a non-trivial task, resulting in many practitioners choosing based on convenience or familiarity, rather than explicit use-case suitability.

In our contribution we present a user-requirement driven review of major gridded population datasets, in particular reviewing the wide array of the WorldPop suite, including their bespoke datasets, LandScan (HD), Kontur, Facebook HRSL, GPW, and GHS-Pop. We first consolidate key requirements of users in applied human-environment research and policy, based both on a literature review as well as key-informant-interviews of practitioners in the Humanitarian sector. Our synthesis reveals barriers to informed dataset choice, including scattered and inadequate documentation, limited uncertainty quantification and communication, and a lack of explicit suitability statements.

We then systematically evaluate, based on Riedler et al., 2025 (under review), how current existing datasets perform with respect to spatial granularity, temporal consistency, sensitivity to input data, the influence of settlement type on accuracy, and transparency of the product.

Based on the combination of both findings, we derive a set of generalised guiding questions for practitioners, as well as a decision tool for use-case specific dataset choice. We, furthermore, illustrate the effects of different dataset choices on down-stream applications and their potential impact on decision-making, as well as discussing alternative methods to establish population estimates and their suitability for studies and policies.

By shifting the perspective from dataset-centric descriptions to user-centred logic our review provides a foundation for operational decision-support and better understanding of gridded population products for domain agnostic users.

How to cite: Klaussner, E. S.: Towards Informed Use of Gridded Population Data: A User-Driven Selection Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1294, https://doi.org/10.5194/egusphere-egu26-1294, 2026.

The present study aims to identify potential areas for the development of sustainable ecotourism in the Naryn Region of the Kyrgyz Republic using geographic information systems (GIS) and weighted overlay methods based on Earth remote sensing data. Ecotourism is one of the most dynamically developing and economically promising sectors oriented toward sustainable territorial development. The Naryn Region possesses significant potential for ecotourism development due to its mountainous terrain, unique natural landscapes, rich biodiversity, and cultural heritage. The weighted overlay method is an effective and visually intuitive tool for comparing multiple thematic layers, whose values are determined based on natural, environmental, and socio-economic factors. The study utilizes open-access geospatial data, including satellite imagery and digital elevation models. Data processing and analysis are carried out using ArcGIS software and specialized remote sensing applications. Seven thematic layers are employed in the analysis: elevation above sea level, land use and land cover, proximity to water bodies, transportation accessibility, population density, proximity to protected areas, and natural and cultural heritage sites. Based on the physical-geographical and socio-cultural characteristics of the Naryn Region, weighting coefficients are assigned to each thematic layer, followed by an integrated suitability analysis. As a result, an ecotourism suitability map is generated and classified into five categories from very high to very low suitability. The results demonstrate the potential of specific areas within the Naryn Region for sustainable ecotourism development while simultaneously accounting for environmental protection constraints.

How to cite: Darylkan kyzy, K., Lehnert, L., and Atyshov, K.: Geospatial Assessment of Sustainable Ecotourism Potential in Naryn, Kyrgyz Republic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3779, https://doi.org/10.5194/egusphere-egu26-3779, 2026.

How to cite: Goncalves Sales, V. and Fujitani, M.: Do disasters speak louder than hazard exposure? Tourism policy and ecosystem protection in coastal destinations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7631, https://doi.org/10.5194/egusphere-egu26-7631, 2026.

Whistler-Blackcomb is a premier ski resort in Canada, attracting approximately 2 million visitors annually and is about a two-hour drive from Vancouver, British Columbia. Whistler-Blackcomb has approximately 3,300 hectares of skiable terrain, a peak elevation of 2,240 meters, and a vertical drop of approximately 1,565 meters. Located at the ski resort are two weather stations: one at 659 meters (the resort Village) and a second at 1,835 meters (Roundhouse Lodge). These weather stations have been collecting daily data on air temperature, snowfall, rainfall, and ground snow depth since the 1970s. The Village weather station data record spans from 1977 to 2025. At this weather station, minimum temperatures, averaged for the winter season, are rising much faster than maximum temperatures (0.44 vs 0.10 °C per decade). Snowfall and rainfall show no noteworthy trends at the Village from 1977 to 2008. Measurements of these two variables were not made from 2009 to 2025. Ground snow depth appears to have declined significantly since 2009. The Roundhouse Lodge weather station data record spans from 1974 to 2025. At this location, average winter minimum temperatures are also rising faster than maximum temperatures (0.22 vs 0.11 °C per decade). No meaningful change in snowfall was observed at Roundhouse Lodge. However, winter rainfall has increased considerably since the early 2000s. Ground snow depth during the winter season shows no trend at the Roundhouse location. Finally, a stochastic weather generator, combined with an eight-member AR6 climate model ensemble (with an equilibrium climate sensitivity of 3.2 °C) and the emission scenarios SSP2-4.5 and SSP5-8.5, is employed to predict how daily minimum and maximum temperatures averaged over the winter season will change from 2030 to 2090.

How to cite: Pidwirny, M.: Changes in Temperature, Snowfall, Rainfall, and Ground Snow Depth Observed in Winter Daily Weather Station Data Collected at 659 and 1835 Meters from the 1970s to 2025 at Whistler-Blackcomb Ski Resort., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8452, https://doi.org/10.5194/egusphere-egu26-8452, 2026.

Urban and suburban forests provide major cultural ecosystem services, yet planning still often relies on destination-based indicators (e.g., nearest forest, simple distance buffers). These measures miss how real access is shaped by corridor continuity, available transport modes, and last-mile connections to entrances. As a result, they can misrepresent both visitation pressure and equity patterns across a metropolitan region.

We analyse forest recreation in the Vienna Metropolitan Area using representative Public Participation GIS (PPGIS) data (n = 3,121). We link anonymised home locations to reported forest destinations and entrances, derive origin–destination (OD) flows, and assess accessibility using both Euclidean distance and mode-specific network travel times for walking, cycling, public transport, and car. To move beyond a destination-only assessment, we apply density-based OD flow clustering (DBSCAN/HDBSCAN) to detect corridor-like patterns and compare clusters by travel time, mode share, and visitation frequency.

We identify six visitor groups (with sub-clusters in the two largest), differing in mobility profiles and spatial structure. We find a clear distance–decay relationship: each additional kilometre to the forest is associated with ~11% fewer annual visits. Importantly, distance alone does not explain use. Corridor structure matters. Multimodal “belts” around the city support access within feasible travel times, while other areas remain underused despite being geographically close, suggesting gaps in connectors and continuity rather than limited forest supply.

This corridor-based perspective complements destination-centric metrics and supports more actionable planning and mitigating environmental impacts. Strengthening gateways and last-mile links, protecting high-performing multimodal corridors, and targeting specific accessibility gaps can improve equity while limiting car dependence.

How to cite: Stefan, F.: Beyond distance: mapping multimodal forest recreation corridors in the Vienna metropolitan area using PPGIS and origin–destination flow clustering, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11718, https://doi.org/10.5194/egusphere-egu26-11718, 2026.

How to cite: Hawker, L., Bondarenko, M., Hilton, J., Noi, E., Tejedor Garavito, N., Priyatikanto, R., McKeen, T., Tanaka, T., and Tatem, A.: FuturePop - Constructing global gridded population maps at multiple scales for SSP scenarios , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15386, https://doi.org/10.5194/egusphere-egu26-15386, 2026.

While ongoing climate change is projected to expand environmentally suitable cropland toward high-latitude regions, the practical utilization of this potential is increasingly shaped by socio-economic constraints. Previous studies have suggested that agricultural workforce availability, as a proxy for socio-economic transitions and interactions, acts as a bottleneck for cropland supply potential. In this study, we assess spatially explicit practical cropland supply potential by incorporating agricultural workforce constraints using gridded population datasets from WorldPop. A weighting map of agricultural workforce distribution was constructed based on national-level minimum distance thresholds between population pixels and cropland pixels, and was used to allocate agricultural labor spatially. Future cropland potential was then derived by applying land-to-labor ratios that represent technological advancement. Within this workflow, urbanization levels were reviewed by comparing WorldPop Global 1 and Global 2 datasets and population projection datasets, all classified based on DEGURBA definitions (EUROSTAT), with national urbanization statistics from the World Bank. In addition, agricultural workforce shares between rural and urban pixels were evaluated through comparison with ILO statistics. Our results indicate that agricultural workforce availability constrains the northward expansion of cultivable land. A southward retreat of workforce-available cropland potential is also projected in some regions, such as Central Asia, despite increasing environmental suitability. Beyond regional projections, this study further demonstrates an application channel through which high-resolution population datasets can be used to constrain and quantify human influences on the Earth system.

Acknowledgment: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2021-NR055516, RS-2025-02312954).

How to cite: Forsell, N., Lee, H., and Kim, H.: Application of a Gridded Population Dataset to the Projection of Cropland Potential under Workforce Constraints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16302, https://doi.org/10.5194/egusphere-egu26-16302, 2026.

The construction of a network for assigning users to essential socio-economic services at the urban level provides a powerful framework to represent the web of functional connections that are exposed to natural hazards. Such a representation is particularly relevant for natural risk assessments, as it enables the analysis not only of direct damages to assets and services, but also of indirect and cascading impacts arising from service disruptions and user reallocation processes (e.g. during flood events). Building this type of network requires an understanding of decision-making factors, both individual and non-individual, which depend on multiple parameters, from economic to social. Despite this complexity, it is possible to reduce the modelling of these mechanisms to the analysis of a limited set of behavioural variables, such as the distance between the service and the user’s residence.

Based on millions of human movements, we highlight how to generate realistic flows of home–essential service users on an urban scale according to a distance-based universal law of service attractiveness. To do this, we incorporate the city road network into the distribution of populated buildings using demographic data, assigning an attractiveness value to the path to the service among all possible choices.

By showing how the universal law of service attractiveness depends on the size of the city, our study demonstrates that the larger the city under analysis, the more rapidly the attractiveness distribution of the service declines, and vice versa. Moreover, we highlight how service attractiveness is influenced by the type of essential service selected, distinguishing those for which people are most willing to travel long distances in order to benefit from them.

Our model, in addition to enabling the generation of a socio-economic network of assigned users to essential services—useful for various areas of research such as epidemiology and urban risk—bridges the gap between distance- and opportunity-based models of human mobility, characterising users’ decision-making mechanisms across multiple spatial scales and for different types of essential services through a distance-based universal law of service attractiveness.

How to cite: Arosio, M., Fidelibus, N., and Starnini, M.: From human mobility to urban service networks: a distance-based model for systemic risk assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19312, https://doi.org/10.5194/egusphere-egu26-19312, 2026.

The global climate crisis poses a growing and multifaceted threat to human health. Assessing and mitigating these climate-related health risks requires spatially explicit understanding of where populations are exposed and vulnerable to climate hazards. Advances in geospatial technologies and the increasing availability of satellite and remote sensing data have enabled the development of high-resolution global gridded population datasets, which have become critical infrastructure for climate-health research. These datasets support the analysis of population exposure and vulnerability across regions and scales, and are increasingly important for scenario-based assessments aligned with future climate and socioeconomic pathways.

This study systematically reviews academic literature published since 2015 to assess how gridded population data are being used in climate change and health research. Specifically, we examine who is using gridded population datasets, in which geographical regions, and for what types of climate-related health analyses. We assess the types of gridded population products used, including their spatial resolution and levels of demographic disaggregation, and how population data are integrated with climate and health information. Where reported, we also evaluate how study results are interpreted and applied to inform policy or decision-making.

The review of 222 academic peer-reviewed studies demonstrates that i) gridded population data have become foundational infrastructure for climate–health research, with a marked increase in publications since 2015; ii) applications span multiple health domains; iii) there is a substantial geographical imbalance; iv) gridded population data enable assessments of population exposure and vulnerability; v) use of age- and sex-disaggregated data is limited.

Overall, the review highlights gridded population data as a crucial bridge between climate science and public health action, emphasising the need for continued dataset development, interdisciplinary collaboration, and integration with future climate and socio-economic scenarios.

How to cite: Woods, D., Esepey, J., and Bonnie, A.: A systematic review of the use of gridded population datasets in the assessment of climate-health risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19353, https://doi.org/10.5194/egusphere-egu26-19353, 2026.

The impact of climate change on human populations is already being felt around the world. Both its effects, and the human populations affected, are unevenly distributed, driving differential exposure and vulnerabilities. To better understand, plan for, and respond to climate change impacts, mapped estimates of population projected under future SSP (Shared Socioeconomic Pathway) scenarios have been developed.

With a growing number of SSP-consistent gridded population datasets being developed - over thirty to date - the comparability of these datasets needs to be understood. If these datasets are used in hazard exposure analyses or vulnerability assessments, the choice of gridded population dataset potentially has a considerable impact on the population estimated to be at risk. Research on the impact of dataset choice in such analyses has been very limited. In this work, we start to address these knowledge gaps. Firstly, we introduce results of a comparative review of existing gridded future population estimates. We explore how differences in: (i) SSP database versions, (ii) downscaling methods, and (iii) classification of built settlement and urban areas, translate into variability at the grid cell level. The results of our comparative analysis show that fundamental differences exist between the various SSP-consistent future gridded population datasets.

Secondly, we focus on the challenges that differences in gridded population dataset bring for downstream data users, with an example of assessing future population exposure to flood hazards in parts of China and Italy. Using flood extents, derived from a high-resolution hydrodynamic flood model, for four time points (2020, 2050, 2070 and 2100), we calculate an estimate of exposed population based on each gridded population dataset. Preliminary results show that flooding exposure estimates vary considerably depending on which gridded population dataset is used. Our results underscore the critical role that accurate future small area population estimates have in robust exposure and vulnerability analyses.

How to cite: Chamberlain, H., Savage, J., and Hawker, L.: The Role of Gridded Population Data in Shaping Future Exposure Estimates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20185, https://doi.org/10.5194/egusphere-egu26-20185, 2026.

The European Commission’s Joint Research Centre (JRC) produces open and free gridded data on human settlements and population at the European and global level. These datasets provide robust sources for decision making, planning, disaster risk management and scientific research. In this talk, we will provide an overview of recent developments and advances with this regard. Specifically, we will highlight ongoing work, novel datasets and underlying methods, including global, gridded future projections (GHS-WUP-POP; 1-km population estimates from 1980 to 2100), historical gridded population data for Europe since the 1960s using spatially-explicit backcasting models and innovative, chain-linking based dasymetric population downscaling, including age-sex disaggregations, as well as global historical gridded population data from 1900 onwards produced by integrating historical, long-term land-use models with data from the Global Human Settlement Layer.

For robust and transparent gridded population data production, uncertainty awareness and -quantification is key. Hence, at the JRC, we explore novel ways to conduct accuracy assessments of gridded population data. For example, we benchmark our datasets against increasingly available authoritative gridded population and other official data reported by national census agencies, and develop new metrics tailored to estimate the accuracy of gridded population data and similar datasets in meaningful and intuitive ways. In our talk, we will highlight recent methodological advances on gridded population data quality assessments and showcase exemplary results of benchmarking and cross-comparing different gridded population datasets. Moreover, we will reflect on pitfalls and caveats that may occur when gridded population data accuracy assessments involve unsuitable data processing or sampling design and highlight the importance of reflected considerations of the fitness-for-use of these datasets.

How to cite: Uhl, J., Schiavina, M., Pigaiani, C., Batista e Silva, F., Alessandrini, A., Freire, S., Krasnodębska, K., Carioli, A., Pesaresi, M., Kemper, T., and Dijkstra, L.: Advances in producing and evaluating gridded population data at the European Commission’s Joint Research Centre, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20475, https://doi.org/10.5194/egusphere-egu26-20475, 2026.

Protecting and restoring natural spaces is critical in the face of climate risks and environmental change, whilst at the same time, access to natural space plays an important role in population health and well-being. Understanding visitation patterns to natural spaces aids planning, maintenance, and land use, and allows us to evaluate the impact of interventions designed to benefit both nature and society. While surveys can provide snapshots of information about visits to natural spaces, robustly measuring visitor patterns on broad scales remains a challenge. Moreover, we lack the tools required to provide visitation estimates under the range of scenarios involved in land use and natural space planning. In this research, we develop scalable tools to predict visitor counts along paths in the UK located in natural spaces using Machine Learning methods, expanding on previous work by the Office for National Statistics. We employ a range of linear, tree-based, and time series models trained on automated footplate counter data and test our models across a range of spatial and temporal scenarios. Our models demonstrate promising ability to replicate historical visitation patterns at many sites, suggesting data-driven methods could offer valuable insights into the sustainable management of natural spaces. We also highlight areas for future improvement, such as improving the spatial generalisability of the models, which could inform future visitation monitoring strategies. Finally, we use Explainable AI approaches to investigate the characteristics of natural space visitation, providing information for planning and interventions which we explore in this study using a storytelling approach.

How to cite: Galloway, E., Chai, Y., Langford, P., and Challenor, P.: Machine learning tools for estimating visitation to natural spaces in the UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20620, https://doi.org/10.5194/egusphere-egu26-20620, 2026.

How to cite: Baldassarre, B., De Luca, C., Giacomelli, M., and Barchetta, L.: Participatory adaptive planning for nature-based tourism in a changing climate: the case of “Anello della Val di Fiastra” hiking path, Marche region, Italy , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20623, https://doi.org/10.5194/egusphere-egu26-20623, 2026.

The majority of the world’s population live in cities, making urban food environments an important driver of global diets and their associated health and environmental impacts. However, only a few dietary and food-system assessments have been conducted at the city level, often with important shortcomings which limit consistent policy planning. Existing studies cover only a few cities and mostly large ones, leaving many smaller cities without estimates. Further, most simply scaled national estimates of food intake – either from food balances or surveys – to city populations. We combined dietary data for urban residences by age and sex, gridded age and sex structures from WorldPop, and urban settlement polygons from the Global Urban Polygons and Points Dataset (GUPPD), to estimate the dietary intake in 5,500 cities with populations over 100 thousand inhabitants. Our estimates indicate that diets in most cities contained greater amounts of foods compared to a country’s average intake in 2020. As a result, cities in most regions were responsible for a larger share of food-related environmental resource use and pollution compared to their share of population. This was mostly driven by increased intake of animal source foods in cities included in our impact assessment. Cities were also responsible for a large share of diet-related health burden and an outsized share of health-related costs, in line with the generally higher cost levels observed in cities. Dietary changes to healthier and more sustainable diets could substantially reduce the environmental, health, and cost impacts associated with city diets, but are dependent on consistent policy approaches and support.

How to cite: Caleffi, S., Springmann, M., Rawden, J., and Auclair, O.: The environmental and health impacts of diets and dietary change in 5,500 cities worldwide, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20901, https://doi.org/10.5194/egusphere-egu26-20901, 2026.

The valorization of intangible cultural heritage represents a cornerstone for developing resilient
and sustainable tourism models in rural areas. Within the framework of the transborder project
CulinaryTrail.eu, this study focuses on the Gagauzia region (Republic of Moldova), a unique
cultural enclave in the Danube basin. Our research aimed to identify, document, and inventory
specific culinary assets that define the identity of the Gagauz community and assess their potential
to catalyze Community-Based Sustainable Tourism (CBST).
The methodology integrated ethnographic field research, semi-structured interviews with local
practitioners, and participatory mapping. The resulting inventory comprises 20 distinct units of
culinary heritage, classified into traditional dishes (e.g., kaurma, gözleme), specific processing
techniques (such as the use of traditional ovens and clay vessels), local beverages, and communitydriven
gastronomic events.
The analysis reveals that these culinary assets are not merely food products, but "living artifacts"
that encode migration history, adaptation to the steppe environment, and social cohesion. We
argue that the systematic integration of this inventory into the Culinary Trail network can:
Redirect tourist flows from oversaturated centers toward the Danubian hinterland—a
territory that remains peripheral yet profoundly authentic;
Ensure economic circularity by establishing direct links between small-scale local producers
and the regional hospitality sector;
Safeguard local biocultural identity by revitalizing authentic recipes and indigenous
ingredients.
The findings presented in this work demonstrate how a data-driven culinary inventory serves as a
vital tool for policymakers and local stakeholders in designing a tourism product that is both
economically viable and culturally respectful.
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How to cite: Căpățînă, L. and Odnostalco, I.: Mapping the Culinary Heritage of the Bugeac Steppe: A Strategic Inventory for Community-Based Sustainable Tourism in Gagauzia(Republic of Moldova), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21259, https://doi.org/10.5194/egusphere-egu26-21259, 2026.

European coastal regions represent a substantial share of the European tourism economy, but they are also among the places where climate change is most likely to be felt by visitors and businesses alike. Rising temperatures, changing precipitation regimes, and altered wind and cloudiness patterns can directly affect thermal comfort and perceived “beach quality,” with implications for visitation, seasonality, and local economies. This study quantifies how climate shapes coastal-beach tourism demand across Europe and translates these relationships into forward-looking, risk-based scenario insights.

Climate suitability is characterized using the Holiday Climate Index for beach tourism (HCI:Beach; Scott et al., 2016), a bioclimatic indicator integrating temperature, precipitation, humidity, wind, and cloudiness to reflect tourists’ stated preferences and destination comfort. This indicator is employed in a monthly panel tourism-demand model estimated on historical regional observations of tourism activity, alongside sector-specific controls and fixed effects. The resulting estimates indicate a statistically significant link between HCI:Beach and tourism demand, and a clear north-south pattern in demand changes in observed , with northern regions benefiting and southern regions experiencing significant reductions, particularly in higher warming scenarios.

To evaluate future impacts, monthly HCI:Beach projections through 2100 are generated using an ensemble of ten regional climate models, and corresponding changes in tourism demand are simulated. Uncertainty is represented from two sources: (i) climate model spread, by sampling across the ensemble projections of the underlying climate variables, and (ii) statistical uncertainty, by repeatedly drawing from the estimated parameter distribution of the demand model. These components are combined in a Monte Carlo framework, producing distributions of future demand outcomes.

Results are reported under two emissions pathways (RCP4.5 and RCP8.5) and, to support policy-relevant interpretation, are also summarized for four global warming levels (1.5°C, 2°C, 3°C, and 4°C). Across European coasts, projections reveal strong spatial and seasonal heterogeneity: climate change can improve suitability in some destinations and months (often in shoulder seasons) while degrading peak-season conditions elsewhere, implying shifts in the timing and geography of demand.

The risk assessment translates probabilistic projections into decision-ready metrics, such as the probability of peak-season demand losses exceeding specified thresholds, the likelihood of shoulder-season demand gains, and the emergence of “high-risk months” in which unfavorable beach conditions become consistently more common. Robust signals are further identified (high agreement across climate models and stable econometric effects) versus deep-uncertainty cases where adaptive strategies should remain flexible.

Finally, building on these findings, adaptation options tailored to regional and seasonal risk profiles are discussed, including spreading demand though season extension and product diversification, or managing heat and comfort through services and information. By integrating a preference-based climate index, econometric demand estimation, multi-model climate projections, and probabilistic risk metrics, this study provides a transparent framework to anticipate where, when, and how European coastal tourism may change.

Keywords: climate change impacts, coastal tourism demand, panel data analysis, HCI:Beach (Holiday Climate Index)

How to cite: Matei, N. A.: Tides of Change: How Climate Will Reshape Coastal Tourism in Europe. Destination Shifts, Economic Impacts, and Adaptation Options, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21281, https://doi.org/10.5194/egusphere-egu26-21281, 2026.

Sources of geophysical anomalies, such as density, susceptibility, conductivity, reflectivity, etc., are not always random as we often assume, but follow a scaling/fractal distribution. This has been demonstrated by analyzing borehole data from the German Continental Deep Drilling Programme (KTB) and other boreholes used for oil exploration. The new scaling spectral method (SSM) was developed to interpret gravity, magnetic, resistivity, and other geophysical measurements, which are better than the conventional spectral method. The application of fractal and scaling approaches in Earth science is widespread across all aspects of geophysics, including the acquisition, processing, and interpretation of geophysical data. The selection criteria for spacing for measurement stations in a 1D survey or grid size for a 2D survey have been suggested. Similarly, processing of non-stationary data is subdivided into stationary data for which the SSM can be applied. Potential field theory has also been studied in the context of fractals or scaling laws and has been found to be worthwhile in inferring the physical properties of the subsurface. The Voronoi tessellation approach using fractional dimension has been applied to model the subsurface from field geophysical data. Here, an attempt is made to discuss the in-depth review of the application of the fractal/scaling approach for qualitative and quantitative interpretation of complex sources of interest. The implications of this study will be beneficial for readers, enabling them to understand the gaps in subsurface source characterization, with practical applications demonstrated through field geophysical examples. 

How to cite: Dimri, V. P.: The Fractal Nature of the Earth: Redefining Geophysical Interpretation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2491, https://doi.org/10.5194/egusphere-egu26-2491, 2026.

Ground deformation fields are widely recognized as key tools for the study of geological phenomena such as volcanic eruptions, which cause displacements in the Earth’s surface and interior. When ground deformation data are available, modeling approaches enable the characterization of deformation sources, such as overpressurized and migrating volcanic or hydrothermal fluids within the crust. Geodetic data modeling is therefore a powerful approach for monitoring volcanic systems, managing alerts, and mitigating possible disasters.

For the characterization of ground deformation, the satellite-based Interferometric Synthetic Aperture Radar (InSAR) technique now plays a significant role, as it provides high-quality spaceborne data with extensive coverage and varying resolution. Moreover, several technological efforts are currently ongoing within the Earth Observation framework to advance SAR sensors and related satellite missions, as well as to refine data systems in order to automatically provide measurements of the Earth’s surface deformation in near real time. However, these advancements have not yet been matched by comparable progress in geodetic data modeling strategies. Indeed, the most commonly used modeling approaches, based on parametric optimization and tomographic inversion algorithms, are often unable to address the inherent issues of inverse problem solutions. In addition, they rarely guarantee a reliable characterization of the volcanic context, as they rely on several assumptions underlying analytical models. Finite Element (FE) approaches can potentially ensure greater reliability, although the number of variables to be managed and the computational cost increase considerably. As a result, modeling strategies may fail to determine a unique solution for source parameters when adequate model constraints are not available.

This research topic aims to address ambiguities in the modeling of volcanic deformation sources in order to ensure the full exploitation of the large amount of available InSAR data. This task requires methods capable of providing unambiguous constraints on source parameters while being fast, computationally efficient, and easy to implement in automatic modeling tools, making them suitable for monitoring systems. Our proposal is based on imaging and multiscale methods of potential fields, which satisfy these requirements, even though the deformation field itself is not formally defined as a potential field.

Here, we demonstrate that, under certain conditions, potential field theory can be applied to analyze deformation fields, which can be expressed through harmonic and homogeneous functions. During the lecture, we present several tests validating the proposed arguments and discuss the usefulness of potential field theory in addressing different real-world cases (e.g., Campi Flegrei caldera, Yellowstone caldera, Okmok volcano, Uturuncu volcano, and Fernandina and Sierra Negra volcanoes), using Multiridge and ScalFun methods to constrain the geometric parameters of magmatic reservoirs, boundary analysis techniques to image medium heterogeneity, and potential function evaluation to reconstruct the three-dimensional displacement field.

The results highlight that the proposed methodological suite meets all the necessary requirements to improve the geodetic modeling of volcanic systems and can be integrated into monitoring facilities as an automatic and efficient tool.

How to cite: Barone, A.: Potential field theory for ground deformation: a new tool for the space-borne monitoring of volcanoes and fluid reservoirs., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12462, https://doi.org/10.5194/egusphere-egu26-12462, 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.

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.

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.

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.

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.

Significant heterogeneity in metadata schemas, vocabularies, and ontologies hinders the discovery, reuse, and integration of European environmental data infrastructures across national and disciplinary boundaries. Recent initiatives have identified semantic interoperability as a vital enabler of FAIR data flows between infrastructures, paving the way for sophisticated, AI-driven, large-scale analyses.

Powered by OntoPortal technology, EarthPortal is a specialised catalogue of semantic resources (ontologies, thesauri and controlled vocabularies) for Earth and environmental sciences. It provides navigation, multi-ontology searching, mapping management, text annotation and recommendation services via web interfaces and REST APIs. These support data catalogues and repositories in an interoperable way.

EOSC LUMEN builds an interoperable discovery ecosystem across multiple domains (including Earth System Science, Social Sciences and Humanities, and Mathematics) to enable cross-platform search and meaningful reuse across communities. Rather than focusing only on metadata aggregation, LUMEN targets the practical enablers of interoperability that make resources discoverable and machine-actionable across infrastructures.

LUMIS (LUMEN Infrastructure for Semantics) is the shared semantic layer of LUMEN. It supports the end-to-end lifecycle of semantic artefacts (ontologies and controlled vocabularies, including SKOS resources) from scoping and requirements to implementation, publication and long-term maintenance. LUMIS focuses on governance, provenance, versioning and quality checks, while adopting an integration-first strategy: it connects and orchestrates established community tools (deployed services and/or API-based components) into coherent workflows, so that semantic resources can be created, aligned, validated and delivered in reusable forms for discovery platforms.

Integrating EarthPortal into LUMIS links a domain-specific semantic catalogue to a cross-domain discovery ecosystem. This enables repositories to annotate metadata using EarthPortal resources, while making use of LUMIS’s lifecycle-driven workflows and FAIR-aligned governance and quality checks.

In this presentation, we will demonstrate how integrating EarthPortal into the LUMIS platform supports more consistent semantic interoperability and FAIR-aligned practices across European Earth System Science infrastructures. We will showcase practical data workflows to enhance interdisciplinary research.

How to cite: Homo, J., Pierkot, C., Darty, K., and Allem, H.: Operationalising Semantic Interoperability for Cross-domain Discovery with LUMIS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19784, https://doi.org/10.5194/egusphere-egu26-19784, 2026.

Modern Earth system research relies on integrating heterogeneous datasets such as reanalysis, satellite observations, in situ measurements, climate model ensembles, and reforecasts, yet these data are often stored in fragmented, inconsistent, and difficult to reuse forms. This limits reproducibility, slows modelling workflows, and constrains the development of operational digital twins for water and climate risk management.

This contribution presents a scalable, FAIR aligned data lake architecture implemented on the EVE high performance computing environment. The system transforms a large, unstructured source pool of more than two million files into a curated, duplication free, metadata rich repository designed for hydrological modelling, machine learning, and climate analytics. The architecture follows a four stage lifecycle: raw, curated, database ready, and ancillary GIS layers, reflecting data governance practices used by major climate centres.

A reproducible ingestion workflow classifies, deduplicates, and standardizes datasets from ERA5, ERA5 Land, MERRA 2, PRISM, E OBS, GPM IMERG, CMIP6, ISIMIP3, ECMWF reforecasts, MODIS, CHIRPS, GFED, GRDC, GSIM, and other sources. A Python based metadata extractor, built on CF convention standards, automatically captures variables, units, dimensions, spatial resolution, temporal coverage, coordinate reference systems, and checksums. Metadata are stored both as dataset level JSON and as a global inventory, enabling transparent provenance tracking and rapid dataset discovery.

The curated data hub is implemented under /data/db/earth_system and organized by scientific domain, temporal resolution, spatial extent, and processing stage. The system supports SLURM based workflows, HPC native processing, and cloud optimized formats such as Zarr.

This work demonstrates how a single researcher can design and operationalize a modern, HPC native data infrastructure that accelerates hydro climate research and forms the backbone of an emerging Digital Hydro Twin. The approach is transferable to institutions seeking to modernize their data ecosystems and improve reproducibility in environmental modelling.

How to cite: Amin, B., Zscheischler, J., Samaniego, L., Peng, J., García-García, A., and Harzendorf, T.: A Scalable, FAIR‑Aligned Data Lake Architecture for Earth System Modelling: From Heterogeneous Raw Archives to Curated, Metadata‑Rich, Analysis‑Ready Climate Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20391, https://doi.org/10.5194/egusphere-egu26-20391, 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.

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.

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.

Hyperspectral image (HSI) classification often struggles with feature interference across different scales and the inherent challenges of data imbalance and sample scarcity. While deep learning models have significantly advanced the field, traditional single-branch architectures often suffer from scale-related noise, where features from different receptive fields interfere with one another. To address this, we propose the Multibranch Adaptive Feature Fusion Network (MBAFFN). Our approach utilizes three parallel branches to independently extract scale-specific features, effectively decoupling the multiscale information to prevent interference. This architecture is enhanced by two specialized modules: Global Detail Attention (GDA) for capturing broad contextual dependencies and Distance Suppression Attention (DSA) for refining local pixel-level discrimination. Furthermore, a pixel-wise adaptive fusion mechanism is introduced to dynamically weigh and integrate these features, prioritizing the most relevant scales for final classification. The performance of MBAFFN was validated on four benchmark datasets: Indian Pines (IP), Pavia University (PU), Longkou (LK), and Hanchuan (HC). Compared to current state-of-the-art methods, our model improved Overall Accuracy (OA) by 0.91%, 1.71%, 0.86%, and 3.16% on the IP, PU, LK, and HC datasets, respectively. The significant improvement on the HC and PU datasets underscores the model’s robustness in scenarios with limited training samples and complex class distributions. These results, supported by detailed ablation studies, demonstrate that adaptive fusion and scale-specific branching are effective strategies for mitigating feature interference in hyperspectral analysis.

How to cite: Li, C. and Du, B.: Multibranch Adaptive Feature Fusion for Hyperspectral Image Classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3080, https://doi.org/10.5194/egusphere-egu26-3080, 2026.

Characterizing the thermal state of urban surfaces is fundamental for mitigating the impacts of the Surface Urban Heat Island (SUHI) effect. This study presents an intensive in-situ thermal infrared monitoring campaign in the high-density urban core of Athens, Greece. Utilizing a calibrated handheld TIR sensor (7.5–14 μm), surface temperatures were recorded across strategic locations in the center of Athens during hot weather conditions. The methodology emphasizes the critical role of material-specific parameterization, where thermographic data were post-processed to account for emissivity (ε) variations and surface temperature, ensuring high-fidelity measurements.

Experimental results reveal extreme thermal stress, with maximum surface temperatures reaching 56.0°C on conventional paving materials, while the mean ambient air temperature was close to 35.0°C during peak solar hours (13:00–18:00LT). Spatial analysis and visualization of the results were performed using QGIS, correlating thermal signatures with urban geometry, shading conditions, and vegetation density. The aim of this study was to highlight the significant cooling potential of specific urban materials and nature-based solutions.

How to cite: Gkountaras, O., Georgakis, C., Velissaridis, T., and Assimakopoulos, M. N.: In-situ Thermal Infrared Monitoring in an Urban Area: A Case Study of Micro-scale Thermal Transitions during Hot Weather Conditions in Athens, Greece., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3363, https://doi.org/10.5194/egusphere-egu26-3363, 2026.

Landslides claim thousands of lives and cause billions in economic losses annually, with impacts disproportionately concentrated in developing regions across Asia, Africa, and Latin America. Paradoxically, the current trajectory of artificial intelligence in geohazard detection—characterized by billion-parameter foundation models requiring substantial computational infrastructure—risks widening, rather than closing, the gap between technological capability and operational deployment where it is needed most. We argue that this paradigm requires fundamental reconsideration, proposing domain adaptation on strategically curated geological datasets as a more equitable and effective path toward globally accessible landslide detection systems.

Foundation models like the Segment Anything Model (SAM), pre-trained on over one billion masks, demand computational resources—312 million parameters, 1,376 GFLOPs per inference, specialized GPU infrastructure—that remain inaccessible to disaster management agencies in resource-constrained regions. Beyond these practical constraints, we contend that the apparent generalization capabilities of such models reflect pattern coverage in training data rather than emergent understanding transferable to geological contexts. The SA-1B dataset, despite its scale, was not curated to systematically represent landslide morphological diversity, creating coverage gaps for rare failure types, unusual triggering mechanisms, and underrepresented terrain configurations precisely where robust detection is operationally critical.

Given these limitations, we propose that effective generalization for geological applications emerges not from architectural scale but from strategic coverage of domain-relevant pattern space. We developed and tested GeoNeXt, a lightweight architecture that exploits the hierarchical transferability of geological features through targeted domain adaptation. Low-level representations (edges, spectral gradients) transfer universally across sensors and terrain; mid-level patterns (drainage networks, slope morphology) require adaptation to local expressions; and high-level configurations (failure geometries, trigger signatures) demand targeted training. Our results showed that this approach outperformed SAM-based methods across three independent benchmarks while requiring 10× fewer parameters (32.2M versus 312.5M) and a 62% reduction in computational cost. Zero-shot transferability to geographically distinct test sites (74–78% F1 score) emerged from the training dataset's systematic morphological diversity rather than parameter count. Inference at 10.6 frames per second on standard hardware, versus 3.0 frames per second for foundation model alternatives, transforms theoretical capability into deployable technology for resource-constrained environments. These findings suggest that strategic domain adaptation, rather than architectural scale, offers the most viable path toward operational landslide detection in vulnerable regions.

How to cite: Uribe-Ventura, R., Viveen, W., Pineda-Ancco, F., and Beltrán-Castañon, C.: Democratizing landslide detection for vulnerable regions beyond resource-intensive foundation models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3619, https://doi.org/10.5194/egusphere-egu26-3619, 2026.

Numerical modeling is a fundamental tool for understanding physically driven processes in geosciences. In multiparametric settings, the Finite Element Method is widely used because it can accommodate irregular geometries and complex boundary conditions. However, this advantage critically depends on the quality of the computational mesh, which must faithfully represent geological features such as faults, stratigraphic interfaces, and wells. In practice, mesh generation remains a major bottleneck, requiring specialized expertise and significant manual effort. We present Geo2Gmsh, an automated, lightweight workflow built on Gmsh (Geuzaine & Remacle, 2009), that generates geological meshes directly from simple text‐based descriptions of topological elements, including surfaces, lines, and points. These elements correspond to geologically meaningful features, allowing users to define faults, horizons, wells, and domain boundaries in a transparent, reproducible, and solver‐independent way. The workflow is demonstrated using two contrasting case studies: (1) Ringvent, an active sill‐driven hydrothermal system in the Guaymas Basin, and (2) the Eastern Llanos Basin, a foreland basin in eastern Colombia. To evaluate solver compatibility, we solved the heat equation in SfePy (https://sfepy.org/doc-devel/index.html) using the Eastern Llanos Basin model as the computational domain. Although the simulation is illustrative and not calibrated to observations, it confirms that meshes produced by Geo2Gmsh can be readily incorporated into numerical solvers. By explicitly embedding wells, faults, and geological interfaces in the mesh, Geo2Gmsh enables boundary conditions to be applied directly to physically meaningful features and allows model outputs to be extracted along them, simplifying both model setup and post‐processing. Meshes can be exported in standard formats (e.g., VTK, MSH, and Exodus via meshio), ensuring broad interoperability. Overall, Geo2Gmsh provides a lightweight, scalable, and reproducible workflow that dramatically lowers the technical barrier to geological mesh generation. This contribution establishes a practical foundation for reproducible, open-source numerical modeling in geosciences, facilitating the integration of geological knowledge into high-fidelity computational simulations.

How to cite: Buitrago, H., Contreras, J., and Neumann, F.: Geo2Gmsh: A Scalable Workflow for Automated Mesh Generation of Geological Models Using Gmsh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6022, https://doi.org/10.5194/egusphere-egu26-6022, 2026.

Many geoscientific datasets, such as those produced by climate and weather models, are stored in the NetCDF file format.  These datasets are typically very large and often strain institutional data storage resources. While lossy compression methods for scientific data have become more studied and adopted in recent years, most advanced lossy approaches do not work easily and/or transparently with NetCDF files. For example, they may require a file format conversion or they may not work correctly with “missing values” or “fill values” that are often present in model outputs.  While lossy quantization approaches such at BitRound and Granular BitRound have built-in support by NetCDF and are quite easy to use, such approaches are generally not able to reduce the data size as much as more advanced compressors (for a fixed error metric), like SPERR, ZFP, or SZ3.

We are particularly interested in reducing the data size of the CONUS404 dataset.  CONUS404 is a publicly available unique high-resolution hydro-climate dataset produced by Weather Research and Forecasting (WRF) Model simulations that cover the CONtiguous United States (CONUS) for 40 years at 4-km resolution (a collaboration between NSF National Center for Atmospheric Research the U.S. Geological Survey Water Mission Area). 

Here, we investigate one advanced lossy compressor, SPERR [1], together with its plugin for NetCDF files, H5Z-SPERR [2], in a Python-based workflow to compress and analyze CONUS404 data.  SPERR is attractive due to its support for quality control in terms of both maximum point-wise error (PWE) and peak signal-to-noise ratio (PSNR), enabling easy experimenting of storage-quality tradeoffs. Further, given a target quality metric, previous work has shown that SPERR likely produces the smallest compressed file size compared to other advanced compressors. It leverages the HDF5 dynamic plugin mechanism to enable users to stay in the NetCDF ecosystem with minimal to no change to existing analysis workflows, whenever a typical NetCDF file is able to be read. And, importantly for our work, the SPERR plugin supports efficient masking of “missing values,” which are common to climate and weather model output.  The support for missing values enables compression on many variables which are not naturally handled by other advanced compressors that rely on HDF5 plugins. Further, because H5Z-SPERR directly handles missing values, they can be stored in a much more compact format (and are restored during decompression), further improving compression efficiency. (Note that built-in NetCDF quantization approaches can work with missing values.) 

Our experimentation demonstrates the benefit of enabling advanced lossy (de)compression in the NetCDF ecosystem: adoption friction is kept at the minimum with little change to workflows, while storage requirements are greatly reduced.

[1] https://github.com/NCAR/SPERR

[2] https://github.com/NCAR/H5Z-SPERR

How to cite: Li, S., Baker, A., and Xue, L.: Application of advanced lossy compression in the NetCDF ecosystem for CONUS404 data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6232, https://doi.org/10.5194/egusphere-egu26-6232, 2026.

Investigations have been carried out into the initiation of the Pangu weather model, initiating the model with both ERA5 data (on which it was trained) and with the Met Office’s Global UM model data. There are many consistent local biases at ground level between these two sets of initial conditions. The geographically local biases are not dissipated by the Pangu model with timestep but instead remain geographically fixed and gradually decrease with lead time. Whilst the Pangu model initiated with UM initial conditions remains further from the ERA5 truth than the ERA5-initiated Pangu model at all timesteps, it initially moves towards the ERA5 truth with timestep, as the geographically static differences in initiation decrease, before moving further away from the ERA5 truth as differences in large-scale systems begin to dominate.

Also investigated was the difference between the Pangu model 24-hour timesteps and 6-hour timesteps; it was found that the 6-hour timesteps were better able to reduce the geographically static initial differences than the 24-hour timesteps.

If time permits, a similar analysis will be made of the FastNet and GraphCast models.

How to cite: Buttery, H.: Investigations into the Reaction of the Pangu ML Weather Model to Different Initial Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7344, https://doi.org/10.5194/egusphere-egu26-7344, 2026.

Solar phenomena such as flares, coronal mass ejections (CMEs), and solar energetic particles (SEPs) are actively monitored and assessed for space weather hazards. In recent years, machine learning has demonstrated considerable success in solar flare forecasting. Accurate SEP forecasting remains challenging in space weather monitoring due to the complexity of SEP event origins and propagation. We introduce SEPNET, an innovative multi-task neural network that integrates forecasting of solar flares and CME summary statistics into the SEP prediction model, leveraging their shared dependence on space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET incorporates long short-term memory and transformer architectures to capture contextual dependencies. The performance of SEPNET is evaluated on the state-of-the-art SEPVAL SEP dataset and compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET achieves higher detection rates and skill scores while being suitable for real-time space weather alert operations.

How to cite: Chen, Y., Yu, Y., Zhao, L., Whitman, K., Manchester, W., and Gombosi, T.: SEPNET: a multi-task deep learning framework for SEP forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11945, https://doi.org/10.5194/egusphere-egu26-11945, 2026.

Quantifying soil properties such as Soil Organic Carbon (SOC), texture, and Calcium Carbonate (CaCO₃) is essential for assessing soil health and ensuring food security. While Visible, Near Infrared, and Short Wave Infrared (VSWIR) remote sensing is a standard operational tool, the Longwave Infrared (LWIR, 8-14 μm) offer complementary information on mineralogy and moisture that are still not yet fully explored for this specific application. This study investigates the synergy between VSWIR and LWIR data that will be available with future hyperspectral satellite missions. Among them, the European Space Agency's Copernicus Expansion missions that will add to the EO capacity the Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM) mission. Alongside are the NASA's Surface Biology and Geology (SBG and SBG-TIR) missions.

The research focuses on Jolanda di Savoia (Italy), an agricultural landscape resulting from land reclamation projects in the late 19th century. Ground truth data were collected during a field campaign on June 22, 2023, providing 59 topsoil samples further analysed for SOC, texture, and CaCO₃. Field campaign was coincident with an airborne survey carried out with the LWIR Hyperspectral Thermal Emission Spectrometer (HyTES) sensor. HyTES captured data across 256 spectral bands from 7.5 to 11.5 μm, providing a pixel size of approximately 2.3 meters.

To evaluate the multi-frequency potential, we developed a workflow combining a soil composite from PRISMA (VSWIR) satellite time-series with simulated SBG-TIR (LWIR) data. The SBG-TIR simulation chain included as input a surface emissivity map derived from the airborne HyTES survey. To cover the LWIR wide spectral range (up to 12 µm), the emissivity spectrum was extended using an autoencoder neural network procedure trained on the ECOSTRESS Soil Spectral Library. Top-Of-Atmosphere (TOA) radiance was then simulated using the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-14) model, incorporating the optical depth and cloud/aerosol optical properties coefficients specific to SBG-TIR. Furthermore, these simulated data were atmospherically corrected to produce the target satellite emissivity products according to the TES algorithm.

Soil properties prediction models were developed using supervised machine learning algorithms. We benchmarked two scenarios: 1) the proposed combined approach using PRISMA and the simulated SBG-TIR L2 emissivity product; and 2) a VSWIR-only approach using PRISMA. A quantitative assessment by 10-fold cross-validation using common literature metrics (R², RMSE, RPD) highlighted the benefits of the multi-sensor approach. For SOC retrieval, the standalone VSWIR (PRISMA) model yielded an R² of 0.55 (RPD = 1.5), while the synergistic integration of PRISMA with simulated SBG-TIR data improved the retrieval accuracy, reaching an R² of 0.65 and increasing the RPD to 1.69. This work indicates that, on the agricultural test site of Jolanda di Savoia, the combined use of SVWIR and LWIR spectral range slightly improves the SOC retrieval. Further validation across diverse agricultural scenarios will be essential to test the real advantage of combining next-generation imaging spectroscopy missions.

How to cite: Rossi, F., Casa, R., Marrone, L., Mirzaei, S., Pascucci, S., and Pignatti, S.: Evaluating the combined potential of VSWIR and Thermal Infrared data for soil characterisation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13611, https://doi.org/10.5194/egusphere-egu26-13611, 2026.

Accurate high resolution wind field prediction is essential for wind resource as-
sessment, renewable energy planning, and regional weather analysis. Although
Numerical Weather Prediction (NWP) models such as the Weather Research
and Forecasting (WRF) model provide physically consistent wind forecasts, their
outputs often suffer from systematic biases arising from uncertainties in surface
characteristics, simplified physical parameterizations, and resolution limitations.
Furthermore, increasing model resolution to the kilometer scale significantly
raises computational cost. To address these challenges, this study presents a
machine learning–based framework for bias correction of WRF-simulated wind
fields over the Southern Tamil Nadu region, with particular focus on the Mup-
pandal wind farm area.
An extensive validation of WRF configurations was first performed using mul-
tiple physics scheme combinations and domain setups, evaluated against ERA5
reanalysis data. The optimal configuration was identified and used to gener-
ate three years (2023–2025) of wind simulations at 3 km × 3 km resolution.
Significant biases were observed in the raw WRF outputs, motivating the appli-
cation of an Artificial Neural Network (ANN) based bias correction approach.
A Random Forest algorithm was employed for feature selection, followed by
Principal Component Analysis (PCA) to reduce dimensionality while retaining
95% of the variance. A feedforward neural network with multiple hidden layers
was trained to correct the U10 and V10 wind components, with the hyperbolic
tangent activation function yielding the best performance. The bias-corrected
wind fields exhibited substantial improvement in mean and extremes, achieving low error metrics and
strong correlation with ERA5 data.
The results demonstrate that combining physically based NWP simulations with
machine learning driven bias correction provides an accurate and computation-
ally efficient approach for generating high-resolution wind fields. This hybrid
framework offers significant potential for wind energy assessment and localized
meteorological applications in data-sparse regions.

How to cite: Pm, V. and Chakravarthy, B.: Bias Correction of Numerical Weather PredictionWind Fields in Southern Tamil Nadu RegionUsing Machine Learning Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16232, https://doi.org/10.5194/egusphere-egu26-16232, 2026.

Early laboratory experiments of shear flow by Thorpe (Thorpe, 2002) provided evidence of Kelvin-Helmholtz Instability (KHI) billow interactions either due to misaligned adjacent billow cores or varying phases along the adjacent billow axes. Similar evidence has been found in the observations of tropospheric clouds, airglow, and Polar Mesospheric Clouds (PMC) imagery data in the mesosphere. Initial High-Resolution Direct Numerical Simulations (DNS) studies performed at Reynolds Number of 5000 (Fritts et al., 2021a, Fritts et al., 2021b) have demonstrated the that misaligned KH billow cores exhibit strong and complex vortex interactions inducing ‘Tubes and Knots’ (T&K) structures (Thorpe, 2002). These T&K structures were observed to accelerate transition to small-scale turbulence in contrast to previously known notable transitional mechanisms such as secondary KHI and convective instabilities emerging in individual KH billows. Also, the KHI T&K dynamics evidently yield intense turbulence dissipation rates contrasting that of secondary KHI and convective instabilities in billow cores.

More recent high-resolution imaging of OH airglow (Hecht et al., 2021) provide concrete evidence of KHI billows with wavelength ranging between 7-10 km modulated by atmospheric Gravity Waves (GWs) of dominant horizontal wavelengths ∼ 30km and oriented orthogonal to KH billow axes and propagate along the billow cores which result in apparent T&K dynamics rapidly driving KH billow breakdown. Similar evidence has been found in recent PMC imaging. This is the central theme of the idealized DNS discussed in this talk.

We conducted DNS studies to demonstrate the turbulence energetics of KHI billow interactions when subject to modulations due to monochromatic atmospheric gravity waves of small perturbation amplitudes and intrinsic frequency of N/5 (where N is the background Brunt-Vaisala Frequency). Preliminary analyses of our DNS results indicate that GW modes with modest amplitudes promote KHI billow misalignments resulting in complex multi-scale T&K dynamics fixed at specific GW phases. An increase in the GW amplitude resulted in noticeable reduction of KHI billow wavelengths further promoting KH billow misalignments. The resulting turbulence is expected to consist of broader scale ranges of intense turbulence dissipation rate and diffusivity.

References
[Fritts et al., 2021a] Fritts, D. C., Wang, L., Lund, T. S., and Thorpe, S. A. (2021a). Multi-Scale Dynamics of Kelvin-Helmholtz Instabilities . Part 1 : Secondary Instabilities and the Dynamics of Tubes and Knots. pages 1–27.

[Fritts et al., 2021b] Fritts, D. C., Wang, L., Thorpe, S. A., and Lund, T. S. (2021b). Multi-Scale Dynamics of Kelvin-Helmholtz Instabilities . Part 2 : Energy Dissipation Rates , Evolutions , and Statistics. pages 1–39.

[Hecht et al., 2021] Hecht, J. H., Fritts, D. C., Gelinas, L. J., Rudy, R. J., Walterscheid, R. L., and Liu, A. Z. (2021). Kelvin-Helmholtz Billow Interactions and Instabilities in the Mesosphere Over the Andes Lidar Observatory: 1. Observations. Journal of Geophysical Research: Atmospheres, 126(1):e2020JD033414. Publisher: John Wiley & Sons, Ltd.

[Thorpe, 2002] Thorpe, S. A. (2002). The axial coherence of Kelvin–Helmholtz billows. Quarterly Journal of the Royal Meteorological Society, 128(583):1529–1542. Publisher: John Wiley & Sons, Ltd.

How to cite: Doddi, A., Fritts, D., and Lund, T.: Rapid Turbulence Evolution Resulting from Stable Shear layer and Atmospheric Gravity Wave Interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4129, https://doi.org/10.5194/egusphere-egu26-4129, 2026.

Socio-economic drought represents the stage at which water stress translates into tangible disruptions to livelihoods, infrastructure, and economic systems, often preceding severe physical water shortages. In India, pronounced climatic variability combined with extreme physiographic heterogeneity leads to strong spatial contrasts in socio-economic vulnerability to drought. Despite this, most drought assessments in the country remain dominated by hydro-meteorological indicators, with limited integration of socio-economic exposure, sensitivity, and adaptive capacity.
This study develops a spatially explicit socio-economic drought risk assessment framework for India by integrating multi-dimensional climatic, environmental, and socio-economic indicators within a Geographic Information System (GIS). Thirteen indicators capturing water availability, agricultural productivity, infrastructure, population pressure, economic activity, and social deprivation are compiled from multi-source datasets and harmonized to a common spatial resolution. The indicators include available soil water, agricultural yield, livestock density, road density, population density, biomass, electricity consumption, Gross Domestic Product (GDP), global surface water availability, digital elevation model, groundwater availability, land use/land cover, and relative deprivation. Indicator weights are objectively derived using the Analytic Hierarchy Process (AHP), with consistency of expert judgments ensured through the consistency ratio criterion (CR < 0.1). A GIS-based weighted overlay approach is then employed to generate a composite socio-economic drought risk index, which is classified into four risk categories to identify spatial patterns and hotspots.
The resulting risk map reveals pronounced regional disparities, highlighting drought-prone agrarian and socio-economically marginalized regions as areas of elevated risk. The proposed framework offers a transferable and scalable decision-support tool for integrating socio-economic dimensions into drought monitoring and preparedness. By explicitly linking water stress to livelihood and infrastructure vulnerability, the study provides actionable insights for risk-informed planning, targeted mitigation, and long-term drought resilience in India.

How to cite: Beerkur, A. K. and Palagiri, H.: A Multi-Criteria GIS Framework for Socio-Economic Drought Risk Assessment across India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4184, https://doi.org/10.5194/egusphere-egu26-4184, 2026.

Scour, defined as the erosion or removal of sediment from around bridge piers due to flowing water, remains one of the primary causes of hydraulic structure failures worldwide. Local scour around bridge piers poses a serious threat to bridge stability, particularly during high-flow events, as the development of downflow, horseshoe vortices, and wake vortices at the pier base leads to intense sediment removal and foundation instability. To address this challenge, the present study investigates the hydrodynamic behaviour and scour reduction performance of tapered submerged vanes installed upstream of a cylindrical bridge pier as an effective countermeasure against local scour. A combined numerical and experimental approach was adopted to evaluate the influence of tapered submerged vanes on flow structure and scour characteristics. Numerical simulations were carried out using FLOW-3D Hydro to analyse the three-dimensional flow field around the pier–vane system under steady clear-water conditions. The simulations focused on assessing velocity distribution, near-bed shear stress, vortex dynamics, and secondary flow patterns generated by the tapered vanes. Particular attention was given to the formation of leading-edge vortices (LEVs) and their role in modifying erosive flow structures near the pier foundation. Based on the numerical insights, a series of physical model experiments were conducted in a laboratory flume to quantify the scour reduction achieved by the tapered vanes. The experiments aimed to optimize the longitudinal and transverse placement of the vanes relative to the pier. The vanes were installed at a fixed longitudinal distance upstream of the pier, while transverse spacing was systematically varied to examine its effect on sediment transport and scour depth. Bed elevation profiles and maximum scour depths were measured after equilibrium scour conditions were attained. The results demonstrate that tapered submerged vanes significantly alter the near-bed flow field by generating localized leading-edge vortices that effectively deflect high-energy flow away from the pier base. This flow redirection weakens the horseshoe vortex and reduces near-bed shear stress in the vicinity of the pier. Among the tested configurations, the vane arrangement with a longitudinal spacing of 1.5D and transverse spacing of 2D exhibited the best performance, resulting in a 56% reduction in maximum scour depth compared to the no-vane case. Additionally, localized sediment deposition was observed upstream and downstream of the pier, indicating favourable redistribution of sediment induced by the vane-generated secondary currents. By integrating numerical modelling with experimental validation, this study provides valuable insights into the flow mechanisms and optimal placement strategies of tapered submerged vanes. The findings highlight their potential as a practical, efficient, and sustainable solution for mitigating local scour around bridge piers in alluvial channels.

Keywords: Scour, Submerged Vane, Horseshoe Vortices, Wake Vortices, Leading-Edge Vortex (LEV)

How to cite: Karmishtha, K., Behera, R. K., and Singhal, G. D.: Performance of Tapered Submerged Vanes in Mitigating Local Scour Around Bridge Piers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4200, https://doi.org/10.5194/egusphere-egu26-4200, 2026.

The Piano Key Weir (PKW) has earned recognition for its adaptability for large discharges across weir types of varying heights and with small footprints. Therefore, it has the potential to be a substitute for linear weirs (space being a factor), Ouamane and Lempérière, (2006). Even with the above-mentioned advantages of PKWs, other geometries leave much to be desired. The rectangular PKW and the trapezoidal PKW illustrate a most common inefficiency example. Standard literature describes construction and operational shortfalls such as flowing separation at the inlet key, varying discharge and uneven velocities along the crest, vortex shedding and formation at the key intersections, dead zones in the inlet-outlet, zones of intensified energy dissipation, and lowering weir versatility at high flows. These challenges are combined to mean loss of efficiency in weir discharge capability. In response to these challenges, the present study introduces the Modified Piano Key Weir (MPKW) to assess its performance using 3D computational hydraulic modeling. The Volume of Fluid (VOF) methodology for free surface tracking and the Reynolds-Averaged Navier Stokes (RANS) for turbulence closure modeling characterize pressure gradients, flow accelerations in the several dimensions, and eddies. A systematic numerical investigation was conducted to compare the discharge efficiency of RPKW, TPKW, and MPKW across a range of steady inflow discharges: 0.030, 0.060, 0.090, 0.120, and 0.160 m³·s⁻¹. The MPKW demonstrated consistently superior discharge efficiency over both RPKW and TPKW for all tested cases, without requiring an increase in structural footprint or crest length. The highest relative improvement was observed at 0.060 m³·s⁻¹, which was therefore selected as a representative discharge for in-depth flow diagnostics. Discharge at 0.060 m³·s⁻¹ was applied to determine vorticity structures, turbulent kinetic energy (TKE), and energy dissipation to better understand the flow mechanisms that explain the efficiency of the weir. The MPKW design, with refined geometry and improved inlet–outlet design, rounded key transitions, and adjustable wall skew, was successful in mitigating flow separation at the key inlets and reducing the large-scale vortex formation at the key junctions. The modified sidewall skewed the internal recirculation, and as a consequence, TKE in the stagnation zones was less, and recirculation was more along the crests of the weir, thereby nullifying turbulent structures. While the breakdown of turbulence resulted in localized energy dissipation, the stabilization of the approach flow was improved because the process converted rotational energy of large eddies with a low energy loss to rapidly decaying eddies which do not sustain and produce a recycling of energy. Thus, less energy was concentrated in the vortex cells at the key junctions, the loss due to flow contraction was less, and the nappe cohesion over the crests was improved. MPKW, relative to other configurations, was characterized by a lower level of turbulence and vorticity at the junctions, a greater effective utilization of the crest, and improved pressure recovery. The results confirm MPKW as a hydraulically efficient and economically feasible solution for both new installations and retrofit applications under head or footprint constraints.

How to cite: Kumar, A., Padhi, E., and Mishra, S. K.: CFD-Based Comparative Analysis of Conventional and Modified Piano Key Weirs for Improved Discharge Efficiency, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4951, https://doi.org/10.5194/egusphere-egu26-4951, 2026.

Groundwater is the second largest reserve of fresh water and is an important resource that supports agriculture, industrial and domestic water supplies. Groundwater is facing unsustainable impacts by human activities over the years in different forms. The situation is aggravated by climate change which aggravates groundwater stress through variable precipitation leading to reduced recharge. Thus, highlighting the importance of assessing aquifer potential for sustainable groundwater management. The analysis was carried out in the Manjra and Maner sub-basins, of Godavari river basin, India where data-driven assessments remain limited. In this regard, the present research employs a Multi-Criteria Decision Analysis (MCDA) framework that integrates Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to define groundwater potential zones (GWPZ) in the Manjra and Maner sub-basins. In a GIS environment, eight thematic layers—geology, land use/land cover, lineament density, drainage density, rainfall, soil, and slope—were examined. These factors were weighted using AHP, and combined using weighted overlay analysis. Area under the Curve (AUC), Receiver Operating Characteristic (ROC) analysis, and groundwater inventory data were used to validate the final GWPZ map. Five classifications of groundwater potential were identified for the research area: very low, low, moderate, high, and very high. The research region's predominance of moderate (45%) to high potential (28%) zones suggests that groundwater availability is generally fair to good. Priority locations for sustainable groundwater development and management are indicated by the high and very high potential zones.

How to cite: Belluti Nanjundagowda, P. and Vema, V. K.: A Geospatial and AHP-Based Approach for Delineating Groundwater Potential Zones in Vulnerable Groundwater Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5098, https://doi.org/10.5194/egusphere-egu26-5098, 2026.

Deep rock masses have complex internal structures, which results in significant discreteness and blocky structures. With the increase in the depth of engineering construction and energy extraction, the unique pendulum-type waves emerge in deep blocky rock masses under the action of dynamic loads from mining and blasting, and they are characterized by low frequency, low velocity, large displacement amplitude and high kinetic energy, distinguishing them fundamentally from conventional seismic waves. Pendulum-type waves can induce alternating stress states of relative compression and separation within blocky rock masses, and may lead to rockburst disasters and even engineering-induced seismicity, thus posing great challenges to the safety of underground engineering such as tunnel construction and mining. In this paper, experimental research is conducted on the mechanical behaviors and typical characteristics of pendulum-type waves of multi-fractured blocky rock masses under static and dynamic loads. Firstly, the strength, deformation and failure mode were analysized based on uniaxial compression tests. The weak structural layers will significantly reduce the uniaxial compressive strength and enhance the ultimate deformation capacity of rock masses. Fractured rock masses have significant nonlinear deformation and may develop macroscopic fractures (vertical splitting failure, with the failure mode transitioning from brittle failure to ductile failure) at the stress level significantly lower than their uniaxial compressive strengths. Subsequently, based on the dynamic impact tests, the dynamic response, overall displacement, wave velocity and the mechanism of anomalously low friction were investigated, and the typical characteristics of pendulum-type waves, including the low frequency (177 Hz and 153 Hz in this experiment, which are much lower than the natural frequency of the rock itself), low velocity (about 900 m/s in this experiment, which is significantly lower than those of P-waves and S-waves), large displacement amplitude (it is more than two orders of magnitude larger than the deformation of an intact rock under an identical load) and high kinetic energy (The total kinetic energy accounts for 40% and 28% of the total energy in this experiment, which has its particularity and cannot be ignored) were quantitatively described. This study holds significant research importance for understanding the nonlinear waves in deep fractured rock masses and their dynamic behaviors, as well as for preventing and controlling engineering disasters in deep rock masses.

How to cite: Jiang, K., Qi, C., and Hu, X.: Research on the mechanical behaviors of multi-fractured blocky rock masses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5765, https://doi.org/10.5194/egusphere-egu26-5765, 2026.

Predictability of river bank erosion in sinuous alluvial channels requires a combined study of the planform processes, hydraulics processes, sediment transportation, and the geotechnical properties of riverbanks. The research paper provides a detailed analysis of the evolution of channels within the Nabadwip-Kalna stretch of the Bhagirathi-Hooghly River (1990-2020). This analysis combines the synthesis of remote sensing, on-field surveys, lab experiments, and numerical model analysis into a multidimensional analysis. GIS was used through the Digital Shoreline Analysis System (DSAS) to measure changes on the bank-lines using historical satellite images of the same period of time. A two-dimensional migration coefficient (MC) model was used to model spatial-temporal changes in channel centrelines, and an RVR Meander was used to develop a model that takes into consideration depth-averaged flow velocity and reach-averaged hydraulic parameters. The characterisation of cross-sectional bathymetry and near-bank hydraulics was based on ADCP. The results of the geotechnical analysis showed that stratified streambanks showed critical shear stresses of 7.1-7.7 kPa, internal frictional angles of soils less than 30°–34°, and were predominantly affected by either cantilever collapse or piping as a result of varying maximum heights of streams between 5.7 and 6.8 metres. Bank stability through both BSTEM and BEHI was assessed, whereas sediment forecasting combined with SWAT to predict overbank flow and a Genetic Algorithm (GA) to estimate the total load. DSAS analysis on bank-line displacement revealed different erosion patterns within 170 transects, showing different RMSE of 0.090 to 0.162 in predicting zone boundaries. The MC method was able to model the 24-year centreline migration patterns, recording changes in the centreline-geometry parameters. Analysis of five cross-sections instrumented found instability and a factor-of-safety ratio of 0.81-0.95, resulting in 4.07-5.85m/yr and 4.35-7.15 km²/yr, respectively, lateral retreat and the eroded areas. Mean collapse rates were 0.125 to 0.198 m/yr, and the failure angle was 81°–87°. The maximum bank-failure mass was 41.24 kg (seasonal maximum), and the calibrated toe-scour mass was 0.28 kg. The GA model was tried using ten parameterisations and demonstrated the best prediction ability with the coefficient set at ten, where R² = 0.96 and mean relative error (MRE) = 42% gave significantly better performance than the traditional regression analysis (R² = 0.87 and MRE = 40%). There were also considerable changes in the area behind sandbar dynamics, that is, Nandai-Hatsimla increased by 11.87 ha in 1990 to 19.05 ha in 2020; Media by 39.7 ha to 57.68 ha; Char Krishnabati by 82.52 ha to 81.07 ha. Land-use/land-cover (LULC) predictions for 2040 indicated settlement expansion from 13.61% (2020) to 20.19%, with validation accuracy (RMSE = 0.253) confirming model reliability. This combined model shows that the combination of remotely sensed, field, laboratory, and model data provides quantitatively sound estimations of fluvial risks and forms the basis of evidence-based management of high-suspended riverine areas. The modular design can be applied to monsoon-dominated alluvial basins throughout the globe, which will promote adaptive land-use planning and long-term infrastructure development in the vulnerable riparian societies.

How to cite: Ghosh, A.: Unveiling integrated geo-hydraulic assessment of river meandering, bank erosion and sandbar dynamics in Alluvial channels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7917, https://doi.org/10.5194/egusphere-egu26-7917, 2026.

Acoustic emission (AE) monitoring is widely applied to track damage development in brittle rock, although relating recorded signals to specific fracture mechanisms can remain uncertain, particularly when comparing thermal and mechanical loadings. This contribution presents a preliminary assessment of AE waveform characteristics measured during two heating-only experiments and two uniaxial compressive strength (UCS) experiments performed on 100 mm diameter basalt specimens containing a central axial circular hole. This geometry provides a consistent configuration that promotes stress redistribution and damage localisation around an opening, allowing fracture processes to be compared within a common specimen form.

Full AE waveforms were acquired throughout each test using broadband piezoelectric sensors coupled to the specimen surface, with pre-amplification and digital acquisition. Event features were extracted in the time and frequency domains, including rise angle, duration, hit counts, average frequency, peak frequency, peak amplitude, and amplitude distributions. Feature-space comparisons were then used to evaluate whether thermally and mechanically induced microfracturing exhibit separable signal characteristics.

The thermal experiments were associated with a single dominant fracture initiating along the shortest ligament between the aperture boundary and the nearest specimen edge. In contrast, UCS loading produced a more complex fracture network consistent with mixed tensile and shear microfracturing. Rise angle versus hits per duration plots indicated that thermal events occupied a more restricted region, whereas UCS events displayed a broader spread, which may reflect greater variability in source processes during complex damage evolution. Frequency-based comparisons further highlighted the differences: thermally induced events clustered mainly within a lower-frequency band (approximately 100-300 kHz), while the UCS tests exhibited an additional higher-frequency population (approximately 400-600 kHz), alongside the lower-frequency component. Amplitude distributions were also differed, with thermal events tending toward a narrower amplitude range relative to the wider distribution observed under UCS loading. Collectively, these observations suggest that the combined time-domain, frequency-domain, and amplitude-based AE features support mechanism-informed discrimination between thermally driven tensile fracture and mechanically driven complex fracture networks providing a basis for subsequent statistical or learning-based classification in coupled thermomechanical experiments

How to cite: De Alwis, A., Serati, M., Dyskin, A., Pasternak, E., Martin, D., and Williams, D.: Waveform signatures of acoustic emission from thermally and mechanically induced microfracture in centrally apertured basalt, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8596, https://doi.org/10.5194/egusphere-egu26-8596, 2026.

Partial blockage in open channels and urban drainage systems is a common issue arising from debris accumulation, sediment deposition, and inadequate maintenance, often resulting in reduced flow capacity and increased flood risk. Despite its practical relevance, the hydraulic effects of partial blockage on flow behaviour are not well quantified through controlled experimental studies. This work aims to investigate the influence of partial blockage on flow characteristics in open channels and explore its implications for urban stormwater drainage systems.Laboratory experiments are carried out in a rectangular open-channel flume under steady flow conditions. Velocity measurements are obtained at multiple depths for unblocked conditions and for different partial blockage configurations. Blockages of varying size and location are introduced manually to represent realistic obstructions commonly observed in urban drains. The changes in velocity distribution, water depth, and flow-carrying capacity due to partial blockage are analysed to understand the hydraulic response of the system.

Based on these observations, relationships between blockage extent and hydraulic performance are developed to identify critical blockage conditions.The study framework is applied to urban stormwater drainage networks using SWMM modelling to extend the experimental findings to real-world applications. Blockage scenarios are simulated in selected channels to assess their impact on system performance and flooding behaviour.

The outcomes of this study provide experimental insight into blockage-induced hydraulic effects and highlight the importance of considering partial blockage in urban drainage analysis. The combined experimental and modelling approach offers a practical basis for improving flood risk assessment and maintenance planning in urban stormwater systems.

How to cite: Mishra, A. K., Kumar, H., and Vinnarasi, R.: Assessment of Partial Blockage in Urban Drains for Flood Risk Reduction , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8813, https://doi.org/10.5194/egusphere-egu26-8813, 2026.

Rising flooding, which is exacerbated by both climate change and human behavior, demands proper identification of vulnerable zones. Conventional hydrological analysis can neglect geographical variability. In this study, a combined geospatial and decision-making process is used to determine the levels of vulnerability and risk of flooding in the Koshi River Basin in the state of Bihar.  The research work has developed a susceptible, vulnerable and risk map by integrating GIS, Remote Sensing and AHP. Weightings of eleven physical and hydrological factors and five socio-economic indicators were carried out in a systematic manner using a multi-criteria decision-making framework that allowed appropriate consideration of their relative contributions to flooding. Flood susceptibility, vulnerability and risk maps were created using the GIS environment's Weighted Overlay technique. According to the analysis, population density (41.6%) and literacy rate (24%) are controlling factors for flood vulnerability in the basin, whereas rainfall (23.9%), elevation (14.7%) and drainage density are the main elements that influence flood susceptibility. The Koshi basin is largely covered by the low and moderate classes of flood susceptibility, whereas a very minor amount (0.03%) comes under the high susceptibility classes, according to results from flood susceptibility maps. A significant section (42.87%) of the basin has moderate flood susceptibility due to a combination of exposure and socioeconomic characteristics, according to the results of the flood vulnerability analysis. According to the flood risk results, a significant amount of the basin (84.18%) has moderate flood risk, while a tiny portion has high flood risk in the low-lying, heavily inhabited areas close to the basin's riverbanks.  ROC-AUC for model validation yielded an accuracy of 66.3% and proved that the proposed GIS-AHP model was a reliable. Conclusion from this study underscore an integrating role in both physical and socio-economic considerations with prospects of enhancement through climate scenarios in flood mitigation and planning/early warning maps.

How to cite: Chaudhary, P. and Padhi, E.: Flood Hazard Analysis and Risk Assessment of Koshi River, Bihar (India) using Remote Sensing, GIS and AHP Techniques , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8975, https://doi.org/10.5194/egusphere-egu26-8975, 2026.

Block elbowing, the process in which rotating blocks push neighbouring blocks apart, influences both geological deformation and the stability of mining excavations in blocky rock masses. A clearer understanding of elbowing is essential for improving rock mass modelling and maintaining the safety of engineering structures. To this end, we analyse a chain of stiff blocks connected by springs, with one or two end active (driving) blocks – the blocks whose rotation is externally induced. All other - passive blocks - have translational and rotational degrees of freedom. The results show that block rotation is sequential (starting from driving blocks) producing a rotational wave with strongly configuration-dependent rotational patterns.

Opposite to a single driving block system, a double-driving block system exhibits more complex behaviour, as the active blocks may rotate in the same direction (Case I) or in opposite directions (Case II). In Case I passive blocks can exhibit anticlockwise rotation that is opposite to the clockwise rotating driving blocks, while in Case II all passive blocks do not rotate at all.

Further deformation patterns arise from block geometry, introduced by varying block corner rounding to represent spheroidal weathering. The results reveal a transition from reversible to irreversible passive block kinematics. Reversible responses include either clockwise rotation followed by full recovery or no rotation. The boundary between these types of block behaviour is defined by a linear relationship between the active-passive and passive-passive contact friction coefficients, with the intercept related to block corner rounding. In contrast, irreversible kinematics characterised by residual rotation emerge only for highly rounded blocks. This irreversible behaviour is restricted to short block chains and disappears in chains of five blocks suggesting a critical size of the Cosserat like zone with independent rotational degrees of freedom. This study provides new insights for modelling the stability and long-term evolution of blocky rock masses.

How to cite: Zhang, M., Dyskin, A., and Pasternak, E.: Non-linear rotational waves and complex rotation patterns in a chain of blocks with elbowing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8985, https://doi.org/10.5194/egusphere-egu26-8985, 2026.

The subject of this study is the process of hydraulic stimulation of a tectonic fault, leading to induced seismicity. We consider a scenario in which fluid injected near ​​an existing fault, causing a localized change in pore pressure and a reduction in effective stresses. This, in turn, initiates slippage of the fault segments and the formation of a slip zone, the size and slip velocity of which determine the magnitude of the resulting seismic events. The goal of this study was to develop a relatively simple model for estimating the potential magnitude of induced seismic events based on a limited set of governing parameters. The primary objectives of the study were to identify the key factors that have the greatest impact on the characteristics of the slip zone and to determine how fluid injection parameters (rate and injected fluid volume) affect earthquake magnitude by changing slip dynamics. The model obtained is based on the results of a series of numerical experiments analyzing the hydromechanical behavior of the fault under various injection conditions. The modeling was performed using a two-parameter rate-and-state friction law, which, unlike a single-parameter model, allows for a wider range of slip regimes to be simulated and accurately describes the transition from stable slip to dynamic failure.

The functional relationships were established between the initial system parameters and the key obtained slip characteristics. It was shown that the final slip zone length is almost linearly related to the length of the initial unstable zone, and the maximum slip velocity increases exponentially with increasing pore pressure rate. At the same time, in the area of high loading rates, the saturation of the sliding velocity is observed at a characteristic level, which leads to a limitation of the possible magnitudes of earthquakes induced by fluid injection.

How to cite: Turuntaev, S., Baryshnikov, N., and Riga, V.: Estimation of potential magnitudes of induced seismic events based on direct numerical simulation of fluid injection near an active tectonic fault., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11339, https://doi.org/10.5194/egusphere-egu26-11339, 2026.

Efficient water use in agriculture is crucial for sustainable water resource management, especially in areas experiencing increasing water scarcity. A critical yet often oversimplified component of irrigation planning is the estimation of water storage soil profile depth, commonly assumed to be 1-1.5 m as the root-zone depth based on practitioner experience rather than validated soil-water dynamics. Such assumptions introduce uncertainty and limit the reliability of irrigation scheduling decisions. This study presents a novel framework for estimating soil profile depth to store maximum water by integrating Richards’ equation, geotechnical soil column concepts, and the Soil Conservation Service Curve Number (SCS-CN) technique to derive an optimal soil profile depth that maximizes storage capacity based on measurable hydraulic and retention soil properties. By linking the water storage soil column depth with the SCS-CN parameter, for practical field applications such as irrigation scheduling and planning. The proposed framework improves model reliability and interpretability by replacing fixed-depth assumptions with soil-specific storage behaviour, thereby reducing uncertainty in irrigation water estimation. It enables consistent evaluation of field capacity, average soil moisture content, and maximum storage potential across soil types, leading to improved irrigation efficiency. By emphasizing physically constrained model selection, data-informed parameterization, and transparent decision-making metrics, this work enhances the reliability of hydrologic modeling and supports robust irrigation management under water-scarce conditions.
Keywords:  Water storage soil profile depth, Richards’ equation, Irrigation water management, Data-informed parameterization, SCS-Curve Number method.

How to cite: Sharma, D., Mishra, S. K., and Pandey, R. P.: From Empirical Assumptions to Data-Informed Decisions: A Reliable Water Storage Soil Depth Estimation Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13831, https://doi.org/10.5194/egusphere-egu26-13831, 2026.

The energy cascade rate (ε) depicts the energy transfer in a turbulent system. In incompressible magneto-hydrodynamic (MHD)  turbulence, ε is linked to the third-order structure function (Yaglom vector) via the Yaglom/Politano–Pouquet law in the inertial range. In this study, we compare three estimators of ε in anisotropic MHD turbulence: (1) the lag polyhedral derivative ensemble (LPDE) technique that reconstructs the divergence of the Yaglom vector via tetrahedral linear gradients; (2) a directional-averaged third-order estimator that evaluates the Yaglom vector along a finite number of lag directions and averages over solid angle; and (3) the Yaglom vector on 60 degree with respect to the mean magnetic field direction.  To ensure a fair comparison in more realistic MHD turbulence, we emulate a multipoint virtual mission within anisotropic three-dimensional MHD simulations with a guide field B₀ along the z-axis. This work illuminates the reliable regime for LPDE and directional-averaging methods, and also tests whether 60 degree Yaglom vector is an accurate estimate of ε, providing practical guidance in both simulation and observational turbulence analysis.

How to cite: Gao, Z., Yang, Y., Jiang, B., and Pecora, F.: Anisotropic energy transfer rate quantified by LPDE and directional averaging methods in MHD turbulence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15102, https://doi.org/10.5194/egusphere-egu26-15102, 2026.

Digital elevation models (DEMs) play a fundamental role in hydrological modeling by controlling watershed delineation, stream networks and runoff generation processes. This study assess the impact of global DEM product provided by Shuttle Radar Topography Mission SRTM and the Indian national CartoDEM developed by ISRO-Bhuvan (Indian Space Research Organisation-Bhuvan) on streamflow simulation using the Soil and Water Assessment Tool (SWAT) in the Ong River watershed (4650 sq. km), India. The study area is characterized by forest and cropland. Both DEMs, resampled to 30m resolution, were used as inputs to SWAT, along with meteorological data (IMD), land use/land cover data (Sentinel-2), and soil data (FAO). Streamflow data was sourced from Global Flood Awareness System discharge data (GloFAS). Model calibration (2011-2017) and validation (2018-2020) were performed using SWAT-CUP with the SUFI2 algorithm. Model performance was evaluated using Willmott's index of agreement, Nash-Sutcliffe Efficiency (NSE), R², PBIAS, and RSR. Results showed that both DEMs performed satisfactorily, with CartoDEM exhibiting slightly better performance (higher NSE and R², lower PBIAS and RSR) during both calibration and validation periods. Sensitivity analysis revealed that the runoff curve number was the most sensitive parameter, highlighting the impact of DEM selection on surface runoff simulation. The study concluded that CartoDEM is a preferable choice for hydrological modeling in similar catchments, though further research on stream accuracy and catchment delineation in diverse topographies can be explored.

How to cite: Prashant, P., Kumar Mishra, S., and Kumar Lohani, A.: Assessing the Impact of Digital Elevation Model Selection on Hydrological Predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16403, https://doi.org/10.5194/egusphere-egu26-16403, 2026.

Accurate prediction of sediment mobility in open channel flows is essential for effective river engineering and sediment management. This study examines the combined influence of flow depth and sediment grain size on near bed hydraulics and sediment mobility using high-resolution Acoustic Doppler Velocimeter (ADV) measurements in a controlled laboratory flume. Experiments were conducted over uniform sand beds with median grain sizes of d₅₀ = 0.321 mm and d₅₀ = 0.81 mm under four different flow depths (12cm, 15cm,18cm,21cm) and a range of flow velocities. Three dimensional velocity components were measured at multiple vertical locations throughout the flow depth, while water surface elevations were continuously monitored. Depth resolved ADV data were used to compute mean streamwise velocity, Reynolds shear stress, friction velocity, and turbulent kinetic energy for each sediment size and flow depth. Sediment mobility was assessed using the Shields parameter, estimated from ADV-derived bed shear stress, and compared with the critical Shields parameter at multiple velocity points for each depth. The results indicate that coarser sediment beds exhibit increased near-bed turbulence intensity and higher friction velocity across all flow depths, while yielding lower Shields parameter values relative to finer sediment beds. Comparisons across the four flow depths reveal that sediment mobility transitions from stable to mobile conditions depending on the combined effects of flow depth, sediment size, and local velocity magnitude. At lower velocities, Shields parameter values remain below the critical threshold, indicating stable bed conditions, whereas higher velocities at the same depth result in Shields values exceeding the critical limit, signifying active sediment motion. Depth wise velocity and turbulence profiles demonstrate that both flow depth and sediment roughness significantly modify near-bed hydraulic structure and bed shear stress distribution. The findings highlight the importance of accounting for depth-dependent flow structure and sediment characteristics when evaluating sediment mobility. This study provides a robust experimental framework for identifying stable and mobile sediment regimes and estimating sediment transport potential using high-resolution ADV measurements without direct sediment transport observations.

How to cite: Banothu, J. and Devi, K.: Effects of Flow Depth and Sediment Size on Near Bed Hydraulics and Sediment Mobility in Open Channel Flow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18007, https://doi.org/10.5194/egusphere-egu26-18007, 2026.

Urban flooding poses a growing challenge for rapidly urbanizing cities, where climate change–driven increases in extreme rainfall, expanding impervious surfaces, and limited drainage capacity collectively exacerbate the frequency and severity of surface water inundation. In this context, understanding urban surface flood resilience, defined as the capacity of stormwater drainage systems to withstand, convey, and recover from intense rainfall events, remains essential for effective flood risk management and climate adaptation planning. The present study investigates urban surface flood resilience in Janakpur Sub-Metropolitan City, Nepal, a fast-growing urban center increasingly exposed to pluvial flooding. The study develops an integrated modelling framework using a 3-way coupled MIKE+ hydrodynamic model, integrated with intense spatial analysis using GIS, to evaluate the performance of the existing stormwater drainage system under extreme rainfall conditions. The model represents the urban drainage network and surface flow processes using drainage infrastructure data obtained from field surveys, terrain information derived from a high-resolution digital elevation model, and delineated urban catchments. To characterize rainfall extremes, the analysis employs long-term observed hourly rainfall records spanning 25 years to generate design storm events corresponding to multiple return periods. The modelling framework simulates system response for a representative extreme rainfall event and quantifies inundation dynamics across the urban landscape. The results shows that the coupled approach effectively captures critical flood hazard characteristics, including inundation depth, flow velocity, and the depth–velocity product, allowing for the spatial identification of highly vulnerable catchments and drainage bottlenecks. The findings provide actionable insights into the limitations of existing stormwater infrastructure and support the development of targeted adaptation strategies aimed at enhancing urban surface flood and drainage resilience. Overall, the study underscores the value of integrated hydrodynamic modelling for resolving location-specific flood behaviour and strengthening urban flood resilience assessments under evolving climatic and urbanization pressures.

How to cite: Singh, P., Deopa, R., and Mohanty, M. P.: Assessing urban surface flood resilience using hydrodynamic modelling under extreme rainfall conditions in urban catchment of Nepal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18481, https://doi.org/10.5194/egusphere-egu26-18481, 2026.

We present a one-layer global energy balance climate model with highly parameterized radiation, convection, and large-scale atmosphere/ocean macroturbulence. Planetary heat content is parameterized by a 2D in latitude-longitude layer characterized by a temperature field and a uniform constant heat capacity. Radiation is parameterized by mean-annual zonal average top-of-atmosphere solar irradiance. Radiative heating and cooling are parameterized by a uniform constant albedo and Stefan-Boltzmann emission with uniform constant emissivity. Convection is parameterized by a temperature threshold for convection which restricts the layer from warming beyond the threshold, effectively cooling the layer. Macroturbulence is parameterized by 2D barotropic turbulence forced at small scales and damped by Rayleigh friction. Energy conservation is maintained by balancing the convective cooling of the layer with the turbulent kinetic energy forcing, resulting in tropical forcing, while the frictional loss of kinetic energy is balanced by frictional heating of the layer. The parameterized energy transforming processes are characterized by timescales, which, for Earth-like planets, are ordered as t_radiation > t_{macroturbulence} > t_convection.

We investigate the model’s equilibrium climate state in terms of the meridional heat transport (MHT), the resulting zonally averaged temperature profile, and their fluctuations by simulating the system over many radiation times. For Earth-like parameters, despite the model’s extremely simplified dynamics, our simulations reveal a MHT profile comparable to the observed, annually averaged MHT on Earth, featuring a maximum in the mid-latitudes of approximately 5PW, a form of Bjerknes compensation. 

How to cite: van Kan, A., Weiss, J., and Knobloch, E.: Global climate dynamics in a highly parameterized radiative-convective-macroturbulent energy balance model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18820, https://doi.org/10.5194/egusphere-egu26-18820, 2026.

High-resolution climate projections are essential for climate impact, vulnerability, and adaptation studies, particularly over regions with strong spatial heterogeneity such as Brazil. Although CMIP6 global climate models (GCMs) provide valuable information on future climate change, their coarse spatial resolutions, typically ranging from 100 to 200 km, limit their direct application at regional and local scales. Statistical downscaling techniques offer computationally efficient alternatives to dynamical downscaling, but their relative performance and added value remain insufficiently assessed over Brazil.

In this study, we compare two statistical downscaling approaches applied to a subset of CMIP6 models previously evaluated by Bazanella et al. (2024) – 10.1007/s00382-023-06979-1 – and identified as skillful in representing Brazilian climate: (i) a bilinear interpolation method followed by percentile-to-percentile bias correction, and  (ii) machine learning–based downscaling approaches. The original GCM outputs are interpolated to a common high-resolution grid of 10 km × 10 km using bilinear weights, providing a physically consistent reference framework. In parallel, ML-based models are trained using historical GCM predictors and high-resolution reference climate datasets to learn nonlinear relationships and generate high-resolution climate fields.

The performance of both approaches is evaluated for the historical period in terms of mean climatology, spatial patterns, and variability. Future projections under the SSP2-4.5 and SSP5-8.5 scenarios are then analyzed to assess regional climate change signals and associated uncertainties. Results assess the extent to which ML-based downscaling provides added value relative to bilinear interpolation, particularly for variables with strong spatial heterogeneity, such as precipitation and temperature extremes, while also evaluating the ability of the approach to preserve the large-scale climate signals projected by the driving CMIP6 models. This comparative analysis provides insights into the applicability, robustness, and limitations of statistical and ML-based downscaling methods for regional climate assessments over Brazil.

How to cite: Jatobá Santos, D., Goracci, G., Alves Martins, M., and Schneider, R.: From bilinear interpolation to machine learning: a comparative assessment of statistical downscaling methods for CMIP6 projections over Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19471, https://doi.org/10.5194/egusphere-egu26-19471, 2026.

Accurate estimation of evapotranspiration (ET) is critical for various applications in hydrology and agricultural water management. However, direct observations of ET, specially its spatial variation, in time-consuming and cumbersome, thus necessitating the need to use of indirect methods for its estimation. In this study, stomatal conductance data is used in conjunction with bio-physical parameters of wheat crops for deriving the spatially varied estimates of ET (ET_SC) for different irrigation treatments using the Penman-Monteith equation. For this, five treatments, including drip (DI) and flood (FI) irrigated treatments were used in the study, namely fully irrigated (DI)), 50% MAD (maximum allowable deficit) (DI), 50% MAD (FI), farmer fields replication (FI) and rain-fed treatment.

The ET_SC estimates are also compared to the ET estimates derived using a method based on field water balance (ET_WB). When compared with the ET_WB values, the ET_SC estimates compared well particularly for the irrigated treatments. The average root mean square error (RMSE) of ET_SC estimates in comparison to ET_WB values are 0.11, 0.2, 0.23 and 0.26 mm/day for fully irrigated, 50% MAD (FI), 50% MAD (DI) and farmers field replication treatments, respectively. The corresponding RMSE value (0.47 mm/day) for the rain-fed treatment are found significantly higher than the irrigated treatments indicating the limitation of the approach in high water stress conditions. The differences between ET_SC andET_WB values also increase significantly during the end-season stage when the wheat crop is close to maturity. Overall, the results demonstrate the robustness of the proposed approach in estimating the spatial variation of ET using the Penman-Monteith method in conjunction with the on-field field stomatal conductance observations.

How to cite: Upreti, H. and Yadav, M.: Evaluation of Penman-Monteith estimates of evapotranspiration derived using field-collected stomatal conductance observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20031, https://doi.org/10.5194/egusphere-egu26-20031, 2026.

Understanding the spatial distribution and intensity of climate-related hazards is essential for effective risk assessment and adaptation planning.  This study presents a comprehensive analysis of climate hazard indices applied across all IPCC reference regions, using all the available CMIP5-driven regional climate model (RCM) simulations at 25 km resolution over the CORDEX domains, together with Euro-CORDEX simulations at 12 km resolution. The objective is to identify climate hazard hot spots through the formulation of a composite hazard index. 

A subset of hazard indicators representing key climate extremes is selected. Temperature- and heat-stress–related hazards are characterized using TX90p (extreme maximum temperature), TN90p (extreme minimum temperature), and the NOAA Extended Heat Index (HI). Heavy precipitation and drought-related hazards are represented by RX1DAY (maximum 1-day precipitation), P99 (99th percentile of precipitation), and CDD (consecutive dry days).

The composite index integrates both the frequency and intensity of extremes and is computed at both regional and grid-point levels. A normalization approach is used to ensure comparability across regions with diverse climatic characteristics. Results reveal pronounced spatial heterogeneity in hazard intensity, highlighting regions where multiple hazards converge and amplify overall risk. This framework enables systematic identification of global and regional climate hot spots, offering insights into areas that may face heightened climate stress under current and projected conditions. By providing a consistent, region-wide assessment of hazard exposure, this study aims to support comparative climate risk analyses and inform policy-relevant decision-making for climate adaptation and resilience strategies at multiple scales.

How to cite: Zazulie, N., Raffaele, F., and Coppola, E.: Global Hot Spots of Climate Extremes from Composite Hazard Indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21173, https://doi.org/10.5194/egusphere-egu26-21173, 2026.

Reliable river discharge simulation generally relies on observed streamflow data for model calibration; however, such observations are often uncertain or unavailable in data-scarce regions, limiting the applicability of conventional hydrological models. This study presents a hybrid modeling framework that uses soil moisture as an alternative calibration variable to improve discharge simulations in the absence of reliable streamflow observations. The framework couples a two-layer version of the daily lumped MISDc (Modello Idrologico Semi-Distribuito in continuo) hydrological model with a Feedforward Neural Network (FFNN), which is employed to enhance parameter calibration by exploiting soil moisture dynamics. The proposed approach is evaluated across three contrasting basins: Tahanaout in semi-arid Morocco, and Colorso (Italy) and Bibeschbach (Luxembourg) in temperate climates. Both in situ and ERA5-Land soil moisture datasets are used as calibration inputs. Model performance is assessed using multiple hydrological metrics, including Mean Absolute Error (MAE), Kling-Gupta Efficiency (KGE), and the correlation coefficient (R). Results show that the hybrid MISDc-FFNN framework substantially improves river discharge simulations compared to the traditional model. Across all basins, MAE is reduced by up to 61%, KGE increases by more than 200%, and R improves by up to 87%, with consistent performance gains observed for both observed and reanalysis-based soil moisture. These findings demonstrate the potential of soil moisture driven calibration strategies to enhance hydrological modeling in data-scarce environments, offering a viable pathway for improved water resources assessment and flood risk management where discharge observations are limited or unreliable.

Keywords: Soil moisture; river discharge simulation; hydrological modeling; machine learning; ERA5-Land; data-scarce regions; feedforward neural network

How to cite: Ait Naceur, K., El Khalki, E. M., Brocca, L., Hadri, A., Jaffar, O., Rachdane, M., Simonneaux, V., Saidi, M. E. M., and Chehbouni, A.: Soil Moisture Based Calibration of a Hybrid Hydrological-Neural Network Model in Data Scarce Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21830, https://doi.org/10.5194/egusphere-egu26-21830, 2026.

Present-day practices of bridge piers design often employ group arrangements of piers in various configurations to modify flow dynamics and mitigate subsequent scour formation around the piers. These group arrangement configurations may vary in aspects of spacing ratio, number of piers, and orientations to alter the flow-structure interaction, and hence the scour development. Investigating the turbulent flow behaviour around various common group arrangements has been a topic of interest for researchers for a few years now. This study presents an experimental investigation aimed at comparing the equilibrium scour depth caused by various four-pier group arrangements. To assess the impact of spacing, the face-to-face distance between piers (G) was taken to values of D, 2D, and 3D, where D refers to the diameter of the circular pier. The scour patterns reveal that the maximum scour depth occurred when spacing G was equal to D. The equilibrium scour depth decreased with an increase in the pier spacing to 2D and 3D, corresponding to an approximate flow intensity of 0.9. The scour contours exhibit the impact of neighbouring piers and how it differs with an increase in pier spacing. Instantaneous velocity data were collected to derive the flow characteristics in the flow field. Velocity vectors depict the influence of different configurations on the flow pattern. The study provides an insight into the spacing effects on equilibrium scour, which can be useful in the design of pier group arrangements.

How to cite: Sahu, C.: Spacing Effect on the Equilibrium Scour and Flow Pattern around Four-Pier group in Different Configurations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22153, https://doi.org/10.5194/egusphere-egu26-22153, 2026.

How to cite: Thurler Rondon da Fonseca, J. F., Schertzer, D., da Silva Rocha Paz, I., and Tchiguirinskaia, I.: Analysis of Vector-Field Multifractal Cascades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23043, https://doi.org/10.5194/egusphere-egu26-23043, 2026.

Accurate three-dimensional modeling of buildings and cultural heritage objects is crucial for applications such as modeling, engineering, and documentation. Traditional methods, such as Terrestrial Laser Scanning (TLS), provide high precision. The recent incorporation of LiDAR sensors into consumer smartphones, like iPhones, presents a cost-effective and accessible alternative. However, the accuracy and limitations of freely available mobile LiDAR applications have not been sufficiently quantified.

This research aims to quantitatively assess the geometric accuracy of iPhone LiDAR as a low-cost alternative for 3D modeling. Three free iPhone LiDAR applications, Modelar, 3D Scanner, and SiteScape, were evaluated across various study cases, focusing on small-scale heritage statues, indoor corridors, and exterior façades of a building. A geodetic reference network was established using a total station, leveler, and RTK GNSS to achieve high absolute accuracy for detailed comparison of the 3D point clouds. Tracking drift was minimized via standardized scanning procedures, maintaining a distance of less than five meters and moving slowly and methodically. The acquired point clouds were processed and compared using CloudCompare, incorporating noise filtering, control point alignment, Iterative Closest Point (ICP) refinement, and multiscale model-to-model (M3C2) analysis.

The results indicate RMS errors ranging from 1.34 cm for small heritage objects to 4.6 cm for building façades, with the Modelar application achieving the highest overall accuracy. Significant errors were concentrated around reflective surfaces such as glass windows, and the removal of these points improved geometric consistency by approximately 50%. All three applications produced point clouds suitable for small to medium-scale indoor and outdoor mapping; however, the 3D Scanner and SiteScape applications exhibited greater deviations, particularly in large or complex environments.

Freely available iPhone LiDAR applications, particularly Modelar, constitute a practicable low-cost option for expeditious centimeter-level 3D modeling in Building Information Modeling (BIM) and heritage documentation. Limitations persist for large-scale architectural elements and reflective materials, wherein TLS remains the benchmark for maximal precision. These results delineate the capabilities and constraints of iPhone LiDAR relative to geodetic references.

Issa Loghmanieh is funded by the Stipendium Hungarian scholarship under the joint executive program between Hungary and Iran.

How to cite: Hamdan, A., Loghmanieh, I., and Bertalan, L.: Comparative accuracy analysis of iPhone LiDAR applications for high-resolution 3D reconstruction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2752, https://doi.org/10.5194/egusphere-egu26-2752, 2026.

Field-based hyperspectral standoff measurements with a spectroradiometer encounter temporal limitations due to satellite overpass and optimum solar illumination requirements. Efficient sampling protocols, accurate targeting of the measured area, geolocation, repeatability, and rapid data acquisition are critical to the quality of field measurements. In this study we evaluate Spectral Evolution’s SensaProbe accessory used in conjunction with a high-resolution NaturaSpec Plus UV-Vis-NIR spectroradiometer. This accessory integrates an inclinometer, a laser for accurate targeting and distance calculation of the measured area, as well as a video camera for live visualization of the field-of-view of the target. Metadata such as GPS coordinates, solar angle, distance to target, inclination of the optic to the ground and picture of the field of view are automatically captured alongside hyperspectral data over the range of 350 to 2500nm. These capabilities are particularly valuable for satellite and airborne sensor validation, where precise spatial alignment and consistent acquisition geometry are essential for robust ground-truth comparisons. Our findings show that the consolidation of these measurements within one accessory reduced operator errors, enhanced metadata collection, streamlined acquisition workflows, and reduced the time required for accurate field measurements. These improvements suggest that integrated standoff systems like the SensaProbe can meaningfully enhance the quality and efficiency of hyperspectral datasets across many research fields such as environmental monitoring, precision agriculture, and remote sensing research.

How to cite: Woodman, M. and Venjean, N.: Innovative solution to improve the accuracy, repeatability, and efficiency of ground-based hyperspectral measurements with a spectroradiometer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2840, https://doi.org/10.5194/egusphere-egu26-2840, 2026.

Auxiliary seismic stations of the International Monitoring System (IMS) are an important part of the Comprehensive Nuclear-Test-Ban Treaty Organization’s (CTBTO) global monitoring network. While these stations contribute significantly to international monitoring capabilities, responsibility for their routine operation and sustainment rests with the host states. Many of these stations have limited national resources, making long-term sustainment challenging. They face issues such as ageing equipment, harsh environmental conditions, and evolving technical requirements. International collaboration is essential to ensure reliable operation. With financial support from Member States voluntary contributions (EU, Italy, Germany), CTBTO is able to work closely with national station operators to carry out site-specific upgrades and provide valuable training across multiple auxiliary stations.

The work achieved in auxiliary seismic stations in Bangladesh, Indonesia and Senegal demonstrates that international collaboration, combined with targeted instrumentation and infrastructure improvements, can significantly enhance the sustainability, reliability, and resilience of auxiliary seismic stations within the IMS network. Such collaborations also strengthen station operators’ sense of ownership and contribute meaningfully to capacity building. By supporting the CTBTO’s nuclear test detection and verification capabilities, these upgrades also reinforce global efforts in nuclear non-proliferation and disarmament under the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

How to cite: Sundod, C., Bianchi, I., Pereira, J., Parithusta, R., and Aktas, K.: International collaboration for sustaining CTBTO International Monitoring System auxiliary seismic stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3050, https://doi.org/10.5194/egusphere-egu26-3050, 2026.

We present a miniaturized, highly integrated MEMS optical accelerometer based on a diffraction-grating interferometric readout, targeting low-frequency, weak-motion measurements relevant to seismic observation. A novel mechanical architecture combined with a “sandwich” assembly approach enables a low fundamental resonance of 15.1 Hz while preserving a compact and robust package. At the system level, the laser source, MEMS interferometric module, and photodetector are integrated into a single enclosure measuring 4.5 cm × 3.5 cm × 3 cm, reducing alignment complexity and supporting field deployment.

Noise characterization demonstrates a self-noise of 2 ng/√Hz, indicating nanogram-level sensitivity in a small-form-factor instrument. We will describe the device concept, integration strategy, and dynamic/noise test methodology, and discuss how this accelerometer can complement existing seismic sensors for applications such as microtremor monitoring, dense temporary deployments, and near-field ground-motion characterization where size, power, and self-noise are critical constraints.

How to cite: sun, Z., zhou, W., cheng, Y., and yang, B.: A miniaturized, highly integrated MEMS diffraction-grating accelerometer with 15.1 Hz resonance and 2 ng/√Hz self-noise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4815, https://doi.org/10.5194/egusphere-egu26-4815, 2026.

Landslide analysis in vegetated and inaccessible areas represents a key challenge for hazard assessment and risk mitigation. This study presents the application of a UAV-borne low-frequency Ground Penetrating Radar (GPR) system to investigate the internal structure and deep geometry of an earth slide located in southern Italy. The GPR system is based on a Cobra Plug-in Sub-Echo 70 antenna operating in the 20–140 MHz frequency range and mounted on a multirotor UAV. The survey consisted of an east–west transect crossing the landslide body, flown at approximately 1 m above ground level due to dense vegetation about 60–70 cm high. To ensure precise navigation and stable flight altitude, the UAV was equipped with a high-precision GNSS system and a radar altimeter, enabling accurate terrain-following acquisition in complex topographic conditions.  The acquired radargram shows a coherent subsurface response down to an investigation depth of approximately 15 m. A laterally continuous, high-amplitude reflector is clearly visible at around 10 m depth and is interpreted as the main sliding surface controlling the gravity-driven process. Above this surface, zones of chaotic and heterogeneous reflections indicate disturbed stratigraphic units and reworked debris involved in deformation processes across the main body. The analysis highlighted a roto-translational kinematics in the upper part of the source area, as often observed in this type of landslide, evolving downslope into a predominantly translational movement. The central portion of the radargram exhibits more continuous sub-parallel reflectors, suggesting partial preservation of the original stratigraphic organization within the displaced material.  Despite the limited acquisition geometry, our results demonstrate that UAV-borne low-frequency GPR provides critical subsurface information for understanding gravity-driven processes. The identification of depth and geometry of landslide failure  surfaces  represents a key contribution to landslide susceptibility analysis, hazard evaluation for definition of effective risk mitigation and monitoring strategies.

How to cite: Famiglietti, N. A., Massa, B., Revellino, P., Testa, G., Memmolo, A., Migliazza, R., and Vicari, A.: Identification of the Sliding Surface of a Dormant Landslide Using UAV-Borne Low-Frequency Ground Penetrating Radar: The Melizzano Case Study (Southern Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6892, https://doi.org/10.5194/egusphere-egu26-6892, 2026.

How to cite: Padrón, E., Bañares, L., Montero, C., Pérez, N. M., Melián, G. V., Tabares, F., Asensio-Ramos, M., Hernández, P. A., Recio, P., and Cachón, J.: Design and experimental development of a portable instrument for measuring diffuse helium efflux in active volcanic systems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8191, https://doi.org/10.5194/egusphere-egu26-8191, 2026.

Crustal movement and deformation monitoring are important methods for reflecting changes in crustal stress. Crustal deformation data can be used to accurately describe the movement and deformation characteristics of active blocks and their boundary zones, providing effective data constraints for earthquake prediction and scientific research. Crustal deformation monitoring mainly includes crustal movement monitoring (such as Global Navigation Satellite System (GNSS) observations), surface strain monitoring (such as borehole strain observations), and surface tilt monitoring (such as vertical pendulum tilt observations). The high-precision and high temporal resolution data generated are widely used in the study of slow earthquakes, volcanic activity, and earthquake precursors. Given the close relationship between geophysical instruments and observation environments, crustal deformation monitoring instruments can not only record structural signals, but also interference signals caused by changes in surrounding loads. This article is based on the analysis displacement solution caused by the point load model, and derives formulas for calculating the surrounding tilt field and strain field, providing a theoretical basis for the quantitative calculation of the influence of surrounding loads in crustal deformation monitoring. In addition, this article also proposes a method for calculating the strain effects of two-dimensional and three-dimensional irregular shaped loads. Finally, based on the four component borehole strain observation data from Guza borehole strainmeter and the observation data of the surrounding river water level, the applicability of this analytical solution in quantitatively calculating the degree of influence of irregular load models in the surrounding area was verified under the conditions of setting the surrounding medium parameters (elastic modulus and Poisson's ratio). The results indicate that: (a) for the two-dimensional irregular shaped load model problem, vector superposition calculation can be performed after load scattering; (b) For the problem of three-dimensional irregular shaped load models, different weights can be assigned to scattering points after load scattering, and the two-dimensional irregular shaped load method can be used for calculation. The convergence process during vector superposition proves the correctness and feasibility of this method. This study provides a research foundation for the quantitative analysis of the influence of surrounding load interference in crustal deformation monitoring; (c) There is a high possibility that the data disturbance information of the four component drilling strain observation data at Guzan Station in summer is affected by the disturbance of the water level data of nearby rivers. This research work can quantitatively explain the degree of influence of load type interference factors on high-precision geophysical observation data, providing a quantitative interpretation scheme for the extraction of earthquake precursor anomalies.

Fund support: Ningxia Natrual Science Foundation Project (2024AAC03436); National Natural Science Foundation of China (41704062); National Key Research and Development Program of China (2021YFC3000705-06).

Figure 1 Example of the influence of irregularly shaped loads in the near field. In the figure: (a) represents the positional relationship between the Guzan borehole strainmeter and the Dadu River; (b) Representing the comparison of the changes in actual observed data with the results of irregular load model calculations.

How to cite: Yan, W., Li, Z., Niu, A., Tang, L., Lu, X., and Zhai, L.: Analytical solution of the influence of irregularly shaped loads in the near field on crustal deformation monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9036, https://doi.org/10.5194/egusphere-egu26-9036, 2026.

Unmanned aircraft vehicle (UAV) and airborne magnetometers have recently emerged as new technology to gather, in a productive and economical way, high-resolution magnetic data. In volcanic environments, taking advantage of the strong magnetization contrasts of adjacent rock formations, magnetic field measurements can detect and characterize the main subsurface structural features and indicate areas of hydrothermal alteration, or highlight thermal anomalies. Magnetometer-equipped drones have advantages in high maneuverability and hover ability and are able to carry out large-scale magnetic surveys in areas that are difficult to access due to complex ground conditions and large topographical fluctuations or that would pose a potential hazard to operators.

Between 2024 and 2025, aeromagnetic surveys were conducted by UAV within the IRGIE project for the first time at Aeolian Islands (Italy) to identify possible areas of different magnetization potential related to hydrothermal fluid circulation. In particular, the most significant geochemistry sites have been investigated at Lipari, Salina, Panarea and Vulcano Islands. The airborne magnetic surveys were conducted using the Matrice 300 UAV with a MagArrow sensor, a laser pumped cesium total field scalar magnetometer, collecting magnetic data at a 1000 Hz sample rate synchronized on-board GPS (1 Hz sample rate). The high spatial resolution offered by the use of low-altitude drones proved essential for mapping the magnetic anomaly in detail and deducing the distribution of magnetization intensity in the investigated regions. The upcoming interpretation of acquired magnetic data, to be integrated with geophysical and geochemical data will may contribute to the development of conceptual models of geothermal circulation in the investigated areas.

How to cite: Napoli, R., De Beni, E., Cantarero, M., Sicali, A., Currenti, G., Cantucci, B., and Procesi, M.: Drone magnetic surveys of Aeolian Islands (Italy) hydrothermal areas within IRGIE project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9092, https://doi.org/10.5194/egusphere-egu26-9092, 2026.

Maritime anomaly detection with Orthogonal Time-Frequency Space (OTFS) sensing commonly involves assessing individual delay-Doppler (DD) maps using set thresholds, Constant False Alarm Rate (CFAR) methods, or detectors that learn frame by frame. However, sea clutter is naturally unstable, changing with sea conditions, wind, and movement of the platform. It frequently generates brief bursts that may seem like anomalies in a single DD image, particularly when Doppler is spreading. Consequently, false positives can change, and detection accuracy becomes unreliable as conditions change. A significant drawback of many OTFS sensing systems is that they handle each DD map in isolation, failing to utilize the temporal consistency of actual anomaly signatures. This makes it hard to differentiate between lasting anomalies and temporary clutter changes in tough situations.

We introduce a spatio-temporal OTFS sensing system that detects objects using a brief series of delay-Doppler maps, instead of just one frame. The receiver creates a DD "cube" by arranging L successive OTFS frames (usually L = 3-100) and uses motion-sensitive Doppler alignment. This involves estimating the general clutter Doppler shift for each frame (for instance, by tracking the Doppler centroid or ridge) and compensating for this shift before combining the frames temporally. The aligned DD cube is then analyzed by a spatio-temporal detector, such as a streamlined 3D U-Net (a 3D convolutional encoder-decoder) or a 2D U-Net enhanced with a Convolutional Long Short-Term Memory (ConvLSTM) bottleneck or temporal attention. Training employs standard DD representations with Binary Cross-Entropy (BCE) plus Dice supervision, along with a temporal-consistency regularizer to reduce flickering detections. Potential peaks are identified using Non-Maximum Suppression (NMS) and confirmed using a track-before-detect persistence gate (for example, requiring detections in 2 of the last 3 frames along a physically reasonable drift), boosting dependability without needing manual adjustments. The suggested method, which considers both space and time, should offer small but reliable enhancements in challenging environments compared to single-image detection. These environments include situations with a poor signal-to-noise ratio, significant Doppler spread, and changes in sea conditions. In these cases, we aim for an increase in the F1-score of +0.01 to +0.03 (in situations where performance is not already at its peak) and a relative decrease of 15–25% in false positives while maintaining the same recall rate. This is mainly achieved through motion-compensated temporal fusion and persistence validation. Additionally, across different sea conditions, we seek a 10–20% relative decrease in performance variation, which would indicate greater resilience and more consistent operational performance.

The suggested system enhances OTFS maritime sensing in the face of Doppler-dispersive, nonstationary sea clutter by integrating motion-compensated temporal alignment with DD-cube detection and persistence-based confirmation. This leads to more consistent anomaly detection, avoiding the need for threshold adjustments for each specific situation.

Acknowledgment: This research was supported by the Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS 2021-KS211502, RS-2022-KS221620).

How to cite: Yoo, J. and Hussain, K.: Motion-Compensated Spatio-Temporal Delay–Doppler Cube Detection for Robust Maritime OTFS Sensing under Nonstationary Sea Clutter, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9510, https://doi.org/10.5194/egusphere-egu26-9510, 2026.

This study is based on samples recovered from an archaeological context dating back to the 6th century BC, belonging to a Tartessian sanctuary identified at the La Bienvenida site (Almodóvar del Campo, Ciudad Real, Spain). The aim of the research is to determine whether there was anthropic use of Almadén cinnabar at this time at a site that, centuries later, would become the location of the city of Sisapo. This city was mentioned by Pliny the Elder (Nat. Hist. 33, 118) as the main supplier of cinnabar to Rome.

The analyses were performed using Zeeman effect atomic absorption spectrometry, following pyrolysis of the samples. We analysed the archaeological pieces, as well as the patina of detrital materials on their surfaces. The results show that Hg is present in these detrital materials, reaching concentrations of up to 350 mg kg^-1. This material is undoubtedly a clear indication of the widespread distribution of the element in the environment of the area, as it corresponds to the adhesion of local soil to the pieces due to the collapse of the building and its burial. In conclusion, we can deduce that, during the corresponding historical period, Hg extracted from the Almadén mine was processed in this locality.

How to cite: Higueras, P., Zarzalejos, M., Mansilla, L., Esteban, G., Avalos, O., and Hevia, P.: Presence of environmental mercury in a former Tartessian sanctuary: La Bienvenida (Almodóvar del Campo, Spain)., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9720, https://doi.org/10.5194/egusphere-egu26-9720, 2026.

Ground Penetrating Radar (GPR) [1-2] is a well-established geophysical technique for high-resolution subsurface investigations and is increasingly integrated with UAV platforms to enable rapid, flexible, and non-invasive surveys [3]. This contribution presents a comparative analysis of two three-dimensional microwave tomography (MWT) approaches for processing UAV-GPR multimonostatic data. The approaches differ for the adopted scattering model: the Interface Reflection Point 3D (IRP3D) model [4-5] and the Equivalent Permittivity 3D (EP3D) model, previously proposed for 2D imaging in [6]. The approaches are compared in terms of spatial resolution by inverting full-wave simulated data referred to a point like scatterer and the effect of data spacing as well as the irregular distribution is examined. Experimental data collected in laboratory-controlled conditions are considered to validate the expected performance. Furthermore, results referred to on field data acquired at the Altopiano di Verteglia (Avellino, Italy), are provided. The reconstruction capabilities are quantitatively assessed through image-based metrics, including image contrast and entropy, highlighting strengths and limitations of each approach. The results provide insights into the suitability of advanced 3D MWT algorithms for UAV-supported GPR surveys and contribute to the development of robust processing workflows for drone-based subsurface imaging.

[1] D. J. Daniels, Ground Penetrating Radar. Hoboken, NJ: Wiley, 2005.

[2] R. Persico, Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing. Hoboken, NJ: Wiley, 2014.

[3] C. Noviello, G. Gennarelli, G. Esposito, G. Ludeno, G. Fasano, L. Capozzoli, F. Soldovieri, I. Catapano, “An Overview on Down-Looking UAV-Based GPR Systems,” in Remote Sensing, 2022, 14(14):3245. https://doi.org/10.3390/rs14143245.

[4] G. Gennarelli, C. Noviello, G. Ludeno, G. Esposito, F. Soldovieri and I. Catapano, "Three-Dimensional Ray-Based Tomographic Approach for Contactless GPR Imaging," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 2000614, doi: 10.1109/TGRS.2023.3250740.

[5] G. Esposito, G. Gennarelli, F. Soldovieri and I. Catapano, "Effective 3-D Contactless GPR Imaging: Experimental Validation," in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 3509005, doi: 10.1109/LGRS.2024.3451729.

[6] G. Ludeno, L. Capozzoli, E. Rizzo, F. Soldovieri, I. Catapano, “A Microwave Tomography Strategy for Underwater Imaging via Ground Penetrating Radar,” in Remote Sensing, 2018, 10, 1410. https://doi.org/10.3390/rs10091410.

How to cite: Catapano, I., Esposito, G., Ludeno, G., Noviello, C., Soldovieri, F., and Gennarelli, G.: A comparison of 3D MWT Reconstruction Models for UAV-based GPR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10503, https://doi.org/10.5194/egusphere-egu26-10503, 2026.

OGS has been developing and producing cost-effective GNSS technology devices and systems in collaboration with the private sector since 2015. These systems, called LZER0, have been efficiently applied to landslide monitoring, also in collaboration with local administrations and the Regional Civil Protection. In recent years, the development of LZER0 has received new impetus from the PNRR Geoscience IR project.

We have developed a new adaptation of the LZER0 device for temporary monitoring, called LZER0-MOB. It addresses the specific needs of temporary monitoring, which may be carried out in emergency conditions, such as user-friendliness and reduced consumption and weight.

We have implemented a mobile infrastructure ready to be activated in case of emergencies due to geological hazards, consisting of a pool of LZER0-MOB stations. This infrastructure, which benefits from the low cost of these devices, will expand monitoring capabilities during emergencies to support civil protection activities and will be integrated into existing infrastructures. It will also enable continuous monitoring of areas of specific interest after the emergency phases.

In the first prototypes, the usability of LZER0 was limited by configuration management and data distribution, which were based on command line interaction. In addition, each station had to be managed individually. For this reason, we have improved the user interface for managing the cost-effective devices (as single stations or as station networks) and for querying and viewing data in real time. We have also developed an automatic system to manage the initial installation of software and its remote updating. These software improvements will enhance the usability of these systems for end users.

The technical diagrams of the instruments and the developed software are released under the CC BY 4.0 license through the repository of LZER0 on github (https://github.com/OGS-GNSS/LZER0) and the Geosciences IR research infrastructure portal (https://geosciences-ir.it/infrastruttura/).

Acknowledgements

Funded by European Union - NextGenerationEU - Mission 4 “Education and Research” - Component 2 “From Research to Business” - Investment 3.1 “Fund for the realization of an integrated system of research and innovation infrastructures” - Project IR0000037 - GeoSciences IR - CUP I53C22000800006. We thank Lunitek SRL for the collaboration provided in the creation of the instrumentation.

How to cite: Zuliani, D., Compagno, A., Fabris, P., De Giorgi, F., Galvi, S., Magrin, A., Magrin, E., Pastorutti, A., Tunini, L., Ziani, P., and Zorba, S.: LZER0-MOB: cost-effective GNSS for temporary monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11805, https://doi.org/10.5194/egusphere-egu26-11805, 2026.

Precision agriculture is increasingly using rapid and non-invasive methods to characterise soil properties and monitor field status, with the aim of enabling efficient and sustainable management. Electromagnetic induction (EMI) can be used to rapidly measure the electrical conductivity of the soil and thereby provide information about the soil complexity, water content dynamics, and nutrient availability. The use of Unmanned Aerial Vehicles (UAVs) allows measurements of soil properties on cultivated land and makes the method independent from field conditions.
We developed a lightweight, scalable EMI sensing platform with a fast data acquisition for deployment on hexacopters in the 25 kg class. The system has one transmitter and four receivers with variable coil pair distances of 1.5 m, 1.9 m, 2.3 m, and 2.7 m. The EMI system weighs less than 5 kg and allows a measurement time of approximately 15 minutes with a fully charged 100 Wh battery from a DJI600 UAV. The apparent conductivity values are recorded at a measurement rate of 10 Hz. WLAN communication, MQTT-based protocols, a TimeScale database and a web-based measurement interface enable real-time display of the data.
During the initial test and evaluation phase, EMI measurements are carried out using a UAV at several defined measurement points and at multiple elevation levels across the test field near Jülich, Germany. The goal of the test was to (a) identify the UAVs electromagnetic interference on EMI measurements at different distances from the drone, to (b) assess the feasibility of vertical measurements at different altitudes, to (c) determine reference conductivity values using a commercial EMI system at several positions on the ground, to (d) record corresponding housekeeping data from DGPS and onboard position sensors and to (e) perform a potential offset calibration based on the acquired datasets.

How to cite: Dick, M., Mester, A., Zimmermann, E., Wüestner, P., Ramm, M., Scherer, B., Bernard, J., Dogar, S. S., Montzka, C., Brogi, C., Huisman, J. A., and Natour, G.: Drone Based Field Measurements of a Lightweight Electromagnetic Induction System (SELMA-TF) for Agricultural Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12457, https://doi.org/10.5194/egusphere-egu26-12457, 2026.

Optimizing the informational return of measurements along existing road networks is a big challenge for real life data acquisition. Here, informational return describes the effectiveness of a survey in capturing the spatial variability of the target variable, ensuring that measurements provide maximal knowledge and minimize uncertainty when used to generate spatial maps or inform predictive models. In this study we develop a two-stage survey design framework that fuses auxiliary spatial data with road network data and formulates route planning as a combinatorial optimization problem. By integrating a fuzzy-clustered representation of the survey area heterogeneity with the road network, we identify map grid nodes reachable by a vehicle. Information values are assigned to individual road segments using fuzzy membership values and Shannon Entropy. Informative segments are selected, and the most informative pathways between them are constructed using Dijkstra’s Algorithm. An Information-rich initial route is then generated using Ant Colony Optimization (ACO).

To further economize this initial route, spatial coverage is characterized by computing a convex hull in the auxiliary data space. A subset of map grid nodes is heuristically selected to preserve most of the convex hull volume while keeping the computational cost manageable. The shortest paths between road segments covering these nodes are determined with the A* algorithm.  The ACO is applied to construct the final economized route that is both information-rich and distance-optimized.

The framework is evaluated using a large-scale case study based on mobile Cosmic Ray Neutron Sensing (CRNS) soil moisture measurements over a 4500 km² area in northeastern Germany. Compared to an empirically designed route of similar length, the optimized route substantially reduces uncertainty in regression-based soil moisture regionalization (i.e., map generation) while significantly improving spatial coverage of the survey area, data collection efficiency, and data quality. The proposed approach provides a systematic alternative to convenience-based sampling strategies commonly used in Earth and environmental sciences.

How to cite: Topaclioglu, C., Trinkle, L., Anders, J., Landmark, S., Schrön, M., Dietrich, P., and Paasche, H.: A Route Optimization Framework for Vehicle-Based Mobile Remote Sensing , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12703, https://doi.org/10.5194/egusphere-egu26-12703, 2026.

Volcanic eruptions shape the landscape. While some events may locally cause mass loss following e.g. gravitational failure, explosive excavation or collapse following magma withdrawal, constructive processes are by far the most abundant: volcanic deposits can create new land, cover the landscape and create new cones around the vents.

The 2021 Tajogaite eruption (La Palma, Spain, 19 September - 13 December 2021) produced a cinder cone that was initially (January 2022) 187 m taller than the pre-eruption topography. The cone is likely welded in some parts but is covered by unconsolidated deposits. This volcanic edifice offered a unique opportunity to monitor its shape evolution after the eruption.

To this end, we are using six datasets of UAS surveys with optical cameras between March 2022 and July 2024. Structure-from-Motion (SfM) photogrammetry allowed us to produce high-resolution (up to 0.2 m/pixel) DSMs (Digital Surface Models) and orthophotomosaics (up to 0.1 m/pixel). This unprecedented and unique dataset, moreover, allows us to constrain these changes at high temporal and spatial resolution. Over the course of our observation period, our conservative approach reveals that the cone "shrank" by more than 10 m in height and lost almost 1*10⁶ m³ of volume. The rate of these changes was highest at the beginning (6,2 cm height loss per day between 28 January and 21 March 2022) and declined exponentially. Towards the end of the observation period reported here (7 August 2023 to 18 July 2024), the average rate was 0,3 cm per day. These quantifications showed that surface processes (wind, rain) accounted for approximately 10% of volume loss, with approximately 75*10³ m³ being redeposited at the base of the cone. Satellite data show that there is no significant westward movement of the entire cone. Accordingly, most of the observed shape change of the Tajogaite cone is due to intrinsic processes, such as 1) decrease of magmatic pressure, 2) volume loss due to outgassing and cooling and 3) compaction of tephra deposits. The contribution of these individual processes will be discussed.

How to cite: Civico, R., Ricci, T., Kueppers, U., Stoiber, W., Ortega-Ramos, V., Cabrera-Pérez, I., Przeor, M., Taddeucci, J., Scarlato, P., and D'Auria, L.: Uas-Based Multitemporal Remote Sensing Of The 2021 Tajogaite Eruption (La Palma Island, Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12843, https://doi.org/10.5194/egusphere-egu26-12843, 2026.

Low-cost seismic instruments, such as Raspberry Shake sensors, are increasingly deployed to complement traditional seismic networks and enable applications ranging from network densification and education to low-cost forensic seismology monitoring and local early warning; however, systematic performance comparisons with broadband stations remain essential.

The Cadí station, part of the CA Catalan network and operated by LEGEF-IEC, is located in the eastern Pyrenees, a region of moderate seismicity that is of particular relevance for seismic hazard assessment and emergency planning in Catalonia. CADI is installed inside an abandoned tunnel that provides stable thermal conditions and protection from atmospheric effects; despite this isolation, the site exhibits medium-to-low ambient noise levels due to the presence of nearby transportation infrastructure. In general this emplacement provides favorable conditions for instrument comparison.

In this study, we assess the data quality and detection capabilities of a Raspberry Shake RS3D short-period sensor through a direct comparison with broadband instrumentation (Guralp CMG3T and Centaur digitizer) at the CADI site. Before the comparison period, only the low-cost sensor was active due to broadband maintenance; afterward, both instruments ran together, allowing direct comparison and validation of the prior CADI recordings

Continuous seismic data recorded between September 2024 and April 2025 were analyzed and compared using cumulative Power Spectral Density (PSD) spectra and Root Mean Square (RMS) amplitude estimates, using part of the SeismoRMS free software (Lecocq et al., 2020). Broadband data were compared with Raspberry Shake recordings during their overlapping operational period to assess noise levels, frequency response, and sensitivity across relevant seismic bands. PSDs were evaluated relative to the New Low and High Noise Models to characterize baseline performance.

Results show that both instruments exhibit comparable spectral behavior above 0.1 Hz, capturing similar noise patterns and anthropogenic signals in the high-frequency band (10–40 Hz). However, the broadband sensor demonstrates superior performance at lower frequencies, reliably recording signals below the narrowband instrument’s response range.

This difference becomes critical for the detection of teleseismic events, which are only clearly recorded by the broadband station, while both sensors adequately capture local and regional earthquakes. These findings highlight the strengths and limitations of low-cost seismic instrumentation and confirm that Raspberry Shake sensors can effectively complement broadband networks for local and regional monitoring, while broadband stations remain essential for comprehensive seismic observations.

Reference

Lecocq, T., Massin, F., Satriano, C., Vanstone, M., & Megies, T. (2020). SeismoRMS - A simple python/jupyter notebook package for studying seismic noise changes (1.0). Zenodo. https://doi.org/10.5281/zenodo.3820046

How to cite: Ladero, J., Tapia, M., and Suriñach, E.: Performance of a Broadband Seismic Station Versus a Co-located Raspberry Shake: Implications for Low-Cost Seismic Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13189, https://doi.org/10.5194/egusphere-egu26-13189, 2026.

How to cite: Restelli, F., Lindsey, J., Kerkenyakova, A., Calver, J., Kitka, K., Watkiss, N., and Hill, P.: The Güralp Stratis: A Commercial Six-Degree-of-Freedom Seismometer for Ground Motion and Structural Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17894, https://doi.org/10.5194/egusphere-egu26-17894, 2026.

X-ray interferometry holds the potential to image astronomical objects with unprecedented, microarcsecond (μas) resolution, where 1 μas corresponds to 4.8 prad. This target resolution imposes extreme requirements for the accuracy of the spacecraft‘s attitude measurement when operating the X-ray interferometer.

Typically, the orientation of the spacecraft is measured using three single-axis gyroscopes that measure the Euler angles (yaw, pitch and roll) in a spacecraft-fixed coordinate frame. These measurements can be complemented by a star tracker as an absolute reference. Gyroscopes that are capable of determining the orientation with the required accuracy and stability needs to outperform the current high-performance navigation-grade gyroscopes by several orders of magnitude.

High-accuracy rotation angle and rotation rate measurements become increasingly important in many scientific fields: (1) Seismologists want to observe the local rotation from elastic and non-elastic deformation caused by earthquakes to fully observe the seismic wavefield. (2) The next generation of gravitational wave detectors relies on high-precision rotation measurements for enhanced active seismic noise isolation and Newtonian noise mitigation. (3) Geodesists want to measure the Earth’s rotation rate and its variations with Earth-bound instruments. All these applications require gyroscopes with a sensitivity in the range of 1 prad/s/√Hz to 1 nrad/s/√Hz, covering a frequency range from below 0.01 Hz to up to 100 Hz.

This contribution presents (1) a compilation of requirements for a sensor suite to comply with the mission needs, (2) an assessment of the state-of-the-art gyroscope technologies (e.g. fiber-optic gyroscopes, ring-laser gyroscopes, cold atom gyroscopes and mechanical gyroscopes), comprising their scaling parameters as well as technological gaps, and (3) a road map to an ultra-high performance sensor suite for 2030+.

How to cite: Brotzer, A., Bernauer, F., Guattari, F., Jaroszewicz, L. R., Kurzych, A. T., Garrido Alzar, C. L., Landragin, A., Piot, D., and De Raucourt, S.: Ultra-high performance gyroscopes for future X-ray interferometer missions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18427, https://doi.org/10.5194/egusphere-egu26-18427, 2026.

Magnetic surveying is a key geophysical technique for detecting subsurface structures due to its sensitivity to lithological and structural variations. Recent advances in lightweight, high-sensitivity magnetometers have enabled their integration with UAV platforms, allowing rapid, high-resolution data acquisition in complex terrains. This approach improves logistical flexibility, reduces survey times, and ensures safe, non-invasive investigations in areas otherwise difficult to access.

Within the PRORIS initiative (https://www.proris.it/), our team conducted UAV-based magnetic surveys to identify and characterize lava tubes in volcanic environments, focusing on Mount Etna and Mount Vesuvius. These campaigns aimed primarily at testing and validating innovative geophysical methodologies through collaboration among research institutions.

The surveys employed different magnetometric systems, including MagArrow and Magnimbus (in gradiometric configuration), mounted on UAVs. Multiple acquisition strategies were explored, such as varying flight altitudes and sensor-to-platform distances, to assess their impact on signal quality and anomaly resolution. UAV deployment proved essential for achieving dense coverage and safe operations in steep, inaccessible areas.

Preliminary results revealed distinct magnetic anomalies consistent with subsurface lava tubes, some confirmed by historical speleological data. However, interpretation was complicated by partially collapsed tubes and sediment infill, which often share magnetic properties with surrounding rock, reducing anomaly contrast. These challenges highlight the importance of optimizing sensor configurations and survey design.

The primary goal of these initial campaigns was methodological: evaluating the effectiveness of different magnetometric setups and acquisition approaches for lava tube detection. Future work will focus on 3D modeling of detected structures using magnetic inversion techniques and integrating magnetometry with complementary geophysical methods, particularly ground-penetrating radar (GPR). This multi-sensor approach is expected to enhance resolution and reliability of subsurface models, supporting applications in volcanic studies and analog environments.

By combining UAV-borne magnetometry with advanced processing strategies and other geophysical tools, this research contributes to the development of robust remote sensing techniques for subsurface exploration. These efforts expand the capabilities of geophysical investigations in challenging terrestrial settings and provide a foundation for future applications in planetary analog studies.

How to cite: Tizzani, P., Accomando, F., Barone, A., Vitale, A., Pepe, S., Solaro, G., and Castaldo, R.: Drone-based Magnetic Surveys for Lava Tube Detection in Volcanic Terrains: Preliminary Results from Etna and Vesuvius, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19517, https://doi.org/10.5194/egusphere-egu26-19517, 2026.

In the framework of the planetary missions, the human exploration of the Martian surface is becoming a priority. The pathway to Mars is rooted through the development of various space missions of increasing complexity, which will lead to a long-term and sustainable human presence on the Moon. The Moon is therefore an intermediate and fundamental step for testing most of the new technologies required for sustainable human exploration of deep space.

We here present an overview of two Italian National projects dedicated to the development of geophysical technologies for planetary exploration, including the “PROgramma di RIcerca Spaziale di base (PRORIS)” project, funded by the Italian Ministry of University and Research (MUR) and whose management has been assigned to the National Research Council of Italy (CNR) and the National Institute for Astrophysics (INAF), and the “Martian Analysis of Resources and Structures: Lava tubes, neAr-surface ice and aquifers Visibility and Assessment (MARS-LAVA)” project, led by CNR and INAF.

The PRORIS project is mainly related to the Moon exploration with different activities for the development of methodologies and research prototypes, and the validation of the developed technologies in terrestrial analog environments. The MARS-LAVA project instead aims at developing, optimizing and testing a suite of geophysical sensors with high potential for Mars exploration, and to develop analysis techniques based on data fusion/integration and modeling, Both the projects involve the use of magnetometric and electromagnetic geophysical methods.

In this contribution,  we dedicate a particular focus to the multiscale analysis of magnetometric data for studying lava tubes, presenting the preliminary results of magnetometric surveys carried out at Italian lava tubes test-sites.

These results underline the potential of the multiscale analysis of magnetometric data in the framework of the planetary exploration and the challenges that need to be overcome.

How to cite: Barone, A., Vitale, A., Accomando, F., Mercogliano, F., Castaldo, R., Solaro, G., Pepe, S., Catapano, I., Cortesi, U., Orosei, R., and Tizzani, P.: Multiscale analysis of geophysical data in the framework of PRORIS and MARS-LAVA projects., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19724, https://doi.org/10.5194/egusphere-egu26-19724, 2026.

In geophysics, it is common to employ multiple complementary geophysical exploration techniques to gather as much information as possible about the subsurface. In this framework, the concept of “data integration” emerges, referring to the combination of different datasets to extract more insights than those derivable from individual datasets alone, thereby enhancing information content and reducing interpretative ambiguities. While the goal of data integration is widely recognized, defining an optimal approach for the effective combination of different datasets remains an open research topic, strongly dependent on the acquired data and on the geophysical techniques considered.

In this work, we present a preliminary workflow for the integration of data from two main geophysical exploration techniques: Ground Penetrating Radar (GPR) and magnetic method. GPR is an active method sensitive to dielectric permittivity contrasts, while magnetic method is passive and sensitive to magnetic susceptibility contrasts. These two methods, despite being fundamentally different, are often used together in several contexts, enabling the detection and localization of buried targets through the analysis of electromagnetic and magnetic anomalies within the investigated domain.

Moreover, both methods benefit from advanced imaging techniques, such as Microwave Tomography (MWT) for GPR and Depth from EXtreme Points (DEXP) for magnetic data, which further enhance their potential in detecting and localizing anomalous bodies.

Specifically, the proposed workflow aims at the quantitative integration of GPR and magnetic data exploiting the results obtained from their respective MWT and DEXP imaging techniques, yielding a single composite result, which enhances interpretability and improves the characterization of anomalous targets in terms of morphology, position, and depth.

The workflow was validated through its application to simulated GPR and magnetic datasets for a common representative scenario, as well as to real datasets. Both simulated and real GPR and magnetic data were processed via MWT and DEXP, respectively, and subsequently their arithmetic integration was performed. The obtained results demonstrate the potential of the proposed workflow in obtaining a single result that outperforms the ones from individual methods, overcoming their limitations and yielding more accurate and detailed subsurface models.

Acknowledgments. This research has been founded by EU - Next Generation EU Mission 4, Component 2 - CUP B53C22002150006 - Project IR0000032 – ITINERIS - Italian Integrated Environmental Research Infrastructures System.

How to cite: Mercogliano, F., Barone, A., Esposito, G., Castaldo, R., Tizzani, P., and Catapano, I.: Arithmetic Integration of GPR and Magnetic Data Based on Microwave Tomography (MWT) and Depth from EXtreme Points (DEXP), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19830, https://doi.org/10.5194/egusphere-egu26-19830, 2026.

The integration of Unmanned Aerial Systems (UAS) into scientific research has established a crucial link between regional-scale satellite observations and high-resolution local-scale measurements. This study illustrates the operational framework and multi-disciplinary capabilities of GAIA iLAB (CNR), a laboratory designed to provide a structured guide for experimental activities in the geo-agro-environmental and geophysical sectors. By utilizing advanced technologies such as UAS, rovers, and in-situ acquisitions, GAIA iLAB addresses complex challenges ranging from precision agriculture to deep geophysical prospecting.

The laboratory manages a diverse UAS fleet, including DJI Matrice 300 RTK and Matrice 600 PRO platforms, which serve as versatile vectors for a wide array of specialized sensors. The research topics covered by GAIA iLAB are structured into four primary pillars of equal scientific priority:

• Geophysics and Near-Surface Sensing: The lab conducts high-resolution magnetic and electromagnetic surveys. Utilizing drone-borne magnetometers (MagArrow) and vertical gradiometers (MagNimbus), the group investigates magnetization contrasts for archaeological research, geological-volcanological studies, and the search for buried structures. This is complemented by a Low-Frequency GPR system (Zond Aero LF) for urban geophysics and sub-surface investigations up to 10 meters deep.
• Geo-Environmental Monitoring: Using LiDAR (DJI Zenmuse L1) and high-resolution RGB cameras, GAIA iLAB performs detailed topographic reconstructions (DSM/DTM) to monitor hydrogeological instability, landslide movements, and seismic-tectonic processes.
• Advanced Agro-Environmental Research: The laboratory employs multispectral (Micasense Red-Edge M) and hyperspectral sensors (Senop HSC-2) to analyze vegetation health, crop water stress, and canopy temperature via thermal radiometric imaging (FLIR Vue Pro 640R).
• Electromagnetic Induction (EMI): Focused on the "Near Surface Zone," the lab utilizes FDEM sensors (CMD Explorer 6L) to characterize soil apparent electrical conductivity (ECa) and resistivity, essential for understanding soil-atmosphere interactions and anthropogenic impacts.

The GAIA iLAB workflow ensures high-quality scientific output through rigorous flight planning (UgCS), strict adherence to EASA/ENAC regulations, and advanced data post-processing using SfM photogrammetry and LiDAR360 analysis. This integrated approach demonstrates the laboratory's potential to provide innovative solutions for environmental management and geophysical exploration.

How to cite: Pepe, S., Buonanno, M., Barone, A., Vitale, A., Accomando, F., Castaldo, R., Iannuzzi, A., Mercogliano, F., Bonfante, A., and Tizzani, P.: Multi-scale and multi-sensor UAS-based approach for Geo-Agro-Environmental and Geophysical applications: the GAIA iLAB experience., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19930, https://doi.org/10.5194/egusphere-egu26-19930, 2026.

Southern Italy is a tectonically active region of major geodynamic significance, where long-lived convergence, post-collisional extension, slab rollback, and crust-mantle decoupling generate strong lateral and vertical heterogeneity in lithology, temperature, fluids, and deformation. Here, we present an integrated 3D Finite Element (FE) thermo-rheological model developed within the TRHAM project activities, aimed at reconstructing the regional thermal and mechanical architecture of the crust within a physically consistent, data-constrained framework. The FE geometry synthesizes a large body of published geological and geophysical constraints, integrating surface geology, regional structural interpretations, and deep wellbore information, complemented by gravity and magnetic evidence. The thermal field is computed under coupled conductive-convective regimes by solving the fully coupled Fourier heat-conduction and Darcy-flow equations in porous media. Boundary conditions include an altitude-dependent surface temperature, prescribed basal heat flow at Moho depth, and laterally adiabatic conditions. Key thermal parameters are calibrated through a bounded optimization strategy against independent thermal observables, while explicitly accounting for resolution limits and non-uniqueness. Rheological calculations combine a frictional failure criterion for brittle deformation and power-law creep for ductile flow, incorporating spatially variable pore-fluid pressure ratios derived from the thermo-hydraulic solution. Strain-rate scenarios are guided by regional geodetic strain-rate constraints and GNSS-informed kinematic parameters. The resulting strength envelopes and yield-stress distributions show strong spatial variations in effective crustal strength and in the depth and geometry of the BDT, both along the Apennine belt and from the Tyrrhenian side to the Adriatic foreland. The model highlights the mechanical impact of inherited crustal architecture and fluid-assisted weakening, and reproduces a systematic contrast between the Apulian foreland and the Apenninic wedge consistent with regional deformation and seismicity patterns. Explicit fluid flow further emphasizes how crustal geometry modulates hydraulic connectivity and hydrological decoupling between Apulian and Apenninic domains, focusing infiltration/discharge and shaping surface heat-flow patterns.

Acknowledgments

The activities are supported by the projects “Relation between 3D Thermo-Rheological Model and Seismic Hazard for Risk Mitigation in the Urban Areas of Southern Italy”, funded under the PRIN2022 PNRR initiative (code: P202299L2C), PRIN2022 PNRR, EU NextGenerationEU.

How to cite: Castaldo, R., Perrini, M., Accomando, F., Tizzani, P., De Landro, G., Gola, G., Fedi, M., Carafa, M. M. C., Kastelic, V., Di Naccio, D., Falcone, G., and Taroni, M.: 3D Thermo-Rheological Modelling of the Southern Apennines (Italy): Insights from the TRHAM Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20018, https://doi.org/10.5194/egusphere-egu26-20018, 2026.

The Mefite site in the Ansanto Valley (Southern Apennines, Italy) is a unique geological system characterized by intense low‑temperature gas emissions, primarily carbon dioxide (CO₂) and hydrogen sulfide (H₂S), emanating from a small sulfurous pond. Unlike typical geothermal or volcanic settings, these emissions occur in a non-volcanic environment and are associated with pseudo-volcanic processes linked to deposits formed during the Messinian salinity crisis. Mefite d’Ansanto is considered the largest natural source of low‑temperature CO₂-rich gases in a non‑volcanic setting on Earth, with an estimated daily emission of around 2000 tons. The gas discharge is supplied by a deep reservoir consisting of permeable limestone sequences overlain by low‑permeability clay layers, which help channel fluids toward the surface. In May 2025, we conducted a UAV‑based LiDAR survey followed by a magnetic survey to better characterize the subsurface structures guiding fluid flow in the Mefite area. The LiDAR dataset produced high‑resolution Digital Terrain (DTM) and Digital Surface Models (DSM) over 1.2 km², providing detailed topographic information essential for planning a terrain‑following magnetic survey with constant altitude relative to the ground. The UAV used for both LiDAR and magnetic acquisitions was a DJI Matrice 300 RTK. Magnetic data were collected using the Geometrics MagArrow magnetometer, equipped with Micro Fabricated Atomic Magnetometer (MFAM) sensors. These sensors have a sensitivity of 1 pT/√Hz and operate at a 1000 Hz sampling rate. MFAM technology is affected only by a polar dead zone, where the signal weakens when the sensor aligns within ±35° of the Earth’s magnetic field vector. A drone‑based magnetic surveys were performed on the main Mefite emission pond, was surveyed in May 2025, covering 350 × 450 m with 10 m line spacing; here, the MagArrow was suspended 3 m below the UAV. The surveys maintained an altitude of 35 m above ground level and a flight speed of 4 m/s. Magnetic data processing included corrections for heading errors and high-frequency rotor-induced noise, ensuring the isolation of true geophysical signals. The local geomagnetic parameters (declination 4°, inclination 57°) were used for reduction‑to‑the‑pole processing. The final magnetic map revealed an ellipsoidal anomaly (60–70 nT) centered on the CO₂ pond and a second, stronger anomaly northeast of the main vent. These anomalies are modelled to investigate the responsible source may related to magnetic minerals transported by deep fluids and precipitated near the emission vents.

Acknowledgments

The activities are partially supported by the projects “Relation between 3D Thermo-Rheological Model and Seismic Hazard for Risk Mitigation in the Urban Areas of Southern Italy”, funded under the PRIN2022 PNRR initiative (code: P202299L2C) and FRACTURES PRIN-MUR 2022 (grant no. 2022BEKFN2), both supported by the European Union-Next Generation EU.

How to cite: Castaldo, R., Barone, A., Accomando, F., Tizzani, P., Pepe, S., Buonanno, M., Mercogliano, F., Stabile, T. A., Napoliello, A., and Grimaldi, S.: Multiscale modelling of Magnetic field at Mefite D'Ansanto (Southern Apennines, Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20236, https://doi.org/10.5194/egusphere-egu26-20236, 2026.

Shallow subsurface geology, often referred to as the critical zone, is fundamental to groundwater evaluation, aquifer recharge, environmental site investigations, and mining applications. Accurate characterization of this zone is required to identify infiltration pathways, low-permeability barriers, buried valleys, and channel structures, and to support decisions related to well placement, remediation, and resource management. Conventional methods such as electrical resistivity imaging (ERI) and ground conductivity meters are widely used but can be constrained by limited survey speed, discontinuous spatial coverage, sensitivity to near-surface disturbances, and logistical complexity in the field.

To overcome these limitations, a new transient electromagnetic (TEM) system, TEM2Go, has been developed for rapid, high-resolution shallow subsurface characterization. The system is optimized for depths from the surface to approximately 50–75 m while providing continuous lateral coverage along survey profiles. Acquisition speeds of 15–20 minutes per kilometre enable efficient mapping of large areas at high spatial density. A distinguishing feature of TEM2Go is real-time data processing and inversion, allowing near-instant visualization of subsurface conductivity during field operations. This enables adaptive survey design, where line spacing, follow-up measurements, and data density can be adjusted immediately based on observed results.

TEM2Go is the result of several years of research and development and incorporates multiple hardware innovations aimed at maximizing data quality while maintaining field practicality. The system design balances transmitter moment, receiver bandwidth, transmitter turn-off characteristics, and pulse repetition rates to achieve high resolution in the shallow subsurface. Both transmitter and receiver coils measure 65 × 65 cm and are designed for backpack-mounted operation. Each fully assembled unit weighs less than 12 kg, allowing deployment by a small field crew and enabling surveys in areas with limited or no vehicle access.

During data acquisition, the transmitter–receiver offset is continuously monitored, with a recommended operational offset of 15–20 m. This configuration supports continuous profiling while maintaining sufficient depth of investigation and resolution for near-surface applications. Real-time processing is fully integrated into the acquisition workflow. Recorded voltage decay curves are processed and inverted on-site, and conductivity models are displayed directly in the control software. Immediate access to inversion results improves quality control by revealing coupling effects, cultural noise, or offset inconsistencies as they occur, and provides rapid geological context to guide ongoing survey decisions.

The conference presentation describes the research and development process behind TEM2Go, highlighting key design choices and performance trade-offs. Case studies from collaboration with the Central Denmark Region are presented, where TEM2Go was used to map complex geology associated with point-source contamination. These examples demonstrate how rapid, high-resolution TEM profiling can improve identification of conductive pathways and geological controls on contaminant transport, supporting more targeted site characterization and remediation planning.

How to cite: Maurya, P. and Auken, E.: A lightweight small-loop TEM instrument for rapid near-surface mapping: Development and Case Studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21366, https://doi.org/10.5194/egusphere-egu26-21366, 2026.

Our regional aeromagnetic study of La Palma Island (Canary Islands), based on conventional airborne data, successfully imaged the island's large-scale magnetic structure. While the magnetic data provided a robust regional framework, they lacked the spatial resolution required to investigate shallow volcanic structures and post-eruptive thermal anomalies following the 2021 eruption. Therefore, a high-resolution, drone-based aeromagnetic survey was conducted over the Tajogaite volcano, targeting the area most affected by the eruption.

The survey was carried out in June 2024 and March 2025 using the DJI Matrice 210 RTK and DJI Matrice 300 RTK with a dual-sensor fluxgate magnetometer system sampling at 200 Hz. Constant-altitude flights covered an area of ~7 km², with N-S-oriented survey lines spaced 30–60 m apart and tie lines spaced 150–200 m in the perpendicular direction. The drone flew in a lawnmower pattern, and we acquired high-altitude calibration flights in low magnetic gradient conditions to quantify and correct the magnetic measurements.

Data curation involved several processing stages, including removing inconsistent flight tracks and compensating for platform-induced noise using the calibration data. The total magnetic intensity map and anomaly were obtained by applying gridding and smoothing to the signal and then removing the IGRF model.

The resulting high-resolution magnetic anomaly map provides critical detail of the shallow magnetic structure of the Tajogaite volcanic edifice, allowing the identification of fault-controlled anomalies, low-susceptibility zones (after the 3D modelling of the data) related to high temperatures, and a potential shallow magma pathway.

Additionally, we acquired new magnetotelluric data along a North-South oriented profile, perpendicular to the inferred still-hot dyke direction. This enabled us to construct a 3D electrical resistivity model that correlates with the magnetic model to further analyse this area, which is likely to be related to the final stage of magma ascension.

This multidisciplinary research emphasises the importance of drone-based surveys in investigating active volcanic environments and post-eruptive dynamics on a local scale.

How to cite: Romero-Toribio, M. C., Martín-Hernández, F., Giménez-Berenguer, C., Piña-Varas, P., Martí, A., Marcuello, A., Queralt, P., and Ledo, J.: Drone-based aeromagnetics combined with magnetotellurics to investigate volcanic structures at the Tajogaite volcano, La Palma Island., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21459, https://doi.org/10.5194/egusphere-egu26-21459, 2026.

Wind simulation is a critical step for wind turbine lifetime assessment: to accurately represent turbine behaviour across its lifespan, we need accurate wind scenarios. In this work, we propose a methodology for generating synthetic full-field turbulent wind scenarios from sparse, high-frequency SCADA (Supervision Control and Data Acquisition) collected across multiple wind farms.

The proposed methodology is a two-stage process. First, a physics-driven stochastic model learns wind data characteristics from low-frequency measurements extracted from high-frequency sparse SCADA. Second, the pipeline generates a low frequency signal that reproduces the observed spectral content, marginal distribution, and autocorrelation.

We build upon this first stage with a high-frequency turbulence generated via *PyConTurb* [1], which implements IEC/Kaimal coherence models to produce spatially coherent 3D velocity fields across the rotor plane. Our tool is first calibrated using learnt parameters from raw high-frequency SCADA data, then blended with the first signal, which is used as a constraint.

The pipeline outputs TurbSim-compatible BTS files [2] , enabling use in SeaHowl [3] aeroelastic simulations. This industry-standard output enables a ready-to-use wind scenario in both simulation pipelines and machine learning analysis tools.

[1] (Rinker, J. M. (2018). PyConTurb: an open-source constrained turbulence generator. _Journal of Physics: Conference Series_, _1037_, 062032. https://doi.org/10.1088/1742-6596/1037/6/062032) 
[2] (Jonkman, B J (2006). TurbSim User's Guide. https://doi.org/10.2172/891594) 
[3] (De Lataillade, T., Yu, W., Pallud, M., & Capaldo, M. (2024). SEAHOWL: Partitioned Multiphysics and Multifidelity Modelling of Wind Turbines with Monolithically Coupled Elastodynamics. _Journal of Physics: Conference Series_, _2767_(5), 052051. https://doi.org/10.1088/1742-6596/2767/5/052051) 

How to cite: Malbois Le Borgne, B., Gatti, F., and Capaldo, M.: Synthetic wind scenario generator from on-site SCADA for wind turbine digital twin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21977, https://doi.org/10.5194/egusphere-egu26-21977, 2026.

Fin whales (Balaenoptera physalus) produce low-frequency vocalisations that propagate efficiently through the ocean and seafloor, making them detectable on broadband ocean bottom seismometers (OBS). While primarily deployed for seismic studies, OBSs offer a unique and cost-effective opportunity for passive acoustic monitoring (PAM) of marine mammals in remote regions over extended periods. Traditional detection and classification of whale calls have relied on energy thresholding, cross-correlation, or matched filtering techniques. These approaches, however, may falter in performance in high-noise environments typical of OBS datasets and often require extensive manual post-processing, making them a labour-intensive process. These limitations motivate automated, noise-robust approaches capable of exploiting the growing volume of seismic data now available.

We present a deep learning framework for detecting fin whale calls from broadband OBSs surrounding the São Jorge Island in the Azores, as well as up to twenty stations of the wider UPFLOW array spanning the Azores–Madeira–Canaries region. Our method uses a semantic segmentation model that operates on spectrogram representations between 12–35 Hz, a frequency band encompassing the classic ‘20-Hz’ fin whale note and the lower frequency ‘backbeat’. The model architecture includes a ResNet-18 encoder pretrained on ImageNet with a U-Net decoder to identify calls in both time and frequency. Training was conducted on a dataset comprising of ~6 days of manually annotated spectrograms and an additional ~6 days of background-only spectrograms. Performance was evaluated using mean Intersection-over-Union and F1-score, achieving 0.65 and 0.80 respectively.

Once validated, the model was applied to months- to year-long OBS records across the region. Fin whale calls were detected at all stations, with clear seasonal patterns showing peak calling activity between October and February, consistent with known migratory patterns in the North Atlantic. Spatial differences in call characteristics and temporal patterns further revealed potential regional variations in vocal behaviour, offering insights into song plasticity and complexity.

By applying a deep learning-based detector on OBS data, we show that machine learning provides a powerful and efficient approach to automating fin whale call detection at scale. Our method processed hundreds of thousands of hours of OBS recordings and identified nearly a million calls across all stations. This large-scale detection unlocks detailed analyses of vocal behaviour, spatial distribution, and seasonal trends, deepening our understanding of their behaviour in the north-east Atlantic. Our findings not only highlight the interdisciplinary value of OBS datasets, but also the potential of machine learning in supporting PAM efforts for the conservation and management of wide-ranging marine species.

How to cite: Japnanto, J., Saoulis, A., Romagosa, M., Leitão, R., Silva, M. A., Graham, M., and Ferreira, A. M. G.: Detecting Fin Whale Calls from Ocean-Bottom Seismometer Data with Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-304, https://doi.org/10.5194/egusphere-egu26-304, 2026.

This presentation outlines a comprehensive framework of multi-scale digital solutions designed to address Africa's pressing water challenges. We explore the integration of advanced physical modelling with a diverse suite of next-generation hydrologic observations from remote sensing and in-situ networks to crowd-sourced data. The core of our approach lies in automated systems for data fusion, processing, and assimilation, leveraging machine learning and hybrid techniques to enhance model accuracy. Critically, we incorporate robust uncertainty quantification to ensure reliable outputs. These integrated components enable the development of actionable, real-time forecasting and decision support systems for water resources allocation and disaster management. We will demonstrate practical applications, including autonomous processes and embedded devices, showcasing a transformative pathway towards proactive, data-driven water governance across the African continent.

How to cite: Berchie Morfo, S. and Bamfo, N. K. O.: An Integrated Digital Framework for Multi-Scale Water Security in Africa., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-716, https://doi.org/10.5194/egusphere-egu26-716, 2026.

The Gross Calorific Value (GCV) indicates coal quality by measuring the total heat released during the complete combustion of the coal. Accurate GCV estimation is crucial for efficient pricing, processing, and energy performance assessment in industries. Conventional oxygen bomb calorimetry, though precise, is relatively slow and expensive for large-scale analyses. Since coal’s organic and elemental composition strongly affects its heating value, understanding this relationship can help with reliable GCV evaluation. In this study, we analyzed the mid-infrared FTIR spectra of coal and selected 56 absorption bands associated with the relevant organic and elemental constituents of coal. These were used as input features for various machine learning (ML) models to predict the GCV of coal from the Johilla coal basin in India. The ML models tested included piecewise linear regression (PLR), partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), artificial neural networks (ANN), and extreme gradient boosting regression (XGB). By combining the predictions from the three models (PLSR, RFR, and XGB) through a simple average, we achieved the highest accuracy (R² = 0.951, RMSE = 19.05%, MBE = 1.42%, MAE = 4.053 cal/g), indicating strong agreement between the predicted and measured values. Overall, the FTIR-based method yields results that match or surpass those of traditional laboratory techniques reported in earlier research. The GCV values predicted from the FTIR models were statistically tested using t-tests (test for mean) and F-tests (test for variance) at a 1% significance level and were found to be statistically similar to the results from the standard bomb calorimeter method. The study demonstrates that the FTIR-based approach is independent and reliable and can be used as a faster and more convenient alternative method for determining GCV, making it highly useful for quick coal quality analysis in industry.

How to cite: Vinod, A., Prasad, A. K., and Varma, A. K.: A novel method for rapid and reliable estimation of Gross Calorific Value (GCV) of Coal using mid-infrared FTIR Spectroscopy and a multi-model Machine Learning Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1075, https://doi.org/10.5194/egusphere-egu26-1075, 2026.

Magnetic and gravity surveys remain among the most cost-effective geophysical tools for investigating the subsurface. They provide information on rock geometry and bulk properties at regional to deposit scale, and they have long been used to guide mineral exploration. However, turning geophysical anomalies into reliable three-dimensional property models requires inversion, a process that is inherently non-unique: multiple subsurface distributions can explain the same anomaly. Conventional approaches, such as least-squares or Bayesian inversion, can produce valuable results; however, they remain computationally demanding for large 3D models and require strong regularization choices that may bias geological interpretation.
Over the last decade, geoscientists have explored machine learning as an alternative approach. Instead of repeatedly solving forward equations, machine learning methods learn a mapping between geophysical anomalies and subsurface properties using large training libraries of synthetic examples. Early work with convolutional neural networks (CNNs) and U-Net architectures showed the concept is viable for electromagnetic and seismic data. More recent studies have shown that deep neural networks can recover magnetic susceptibility distributions from magnetic data and, in some cases, perform joint inversion of gravity and magnetic observations. Nevertheless, purely convolutional architectures often struggle to preserve long-range spatial relationships in fully three-dimensional volumes, resulting in blurred boundaries and reduced geological interpretability.
Recent advances in deep learning offer new opportunities to address these limitations. Emerging models are designed to capture long-range dependencies and preserve sharper boundaries. They have been effective in other 3D volumetric fields, such as medical imaging and seismic interpretation, but have yet to be explored for potential-field inversion.
In this study, we develop a deep-learning-based inversion method for magnetic and gravity data aimed at critical mineral exploration. The approach targets mineral systems with distinct geophysical signatures, with a focus on volcanogenic massive sulfide (VMS) environments. By combining data-driven learning with physics-informed training, the method produces reproducible three-dimensional susceptibility and density models that reduce ambiguity in subsurface interpretation. The workflow is tested using data from the Flin Flon VMS district in Manitoba, Canada, demonstrating its potential to improve targeting of buried copper-zinc mineralization and to support the integration of advanced AI methods into geoscience workflows.

How to cite: Tirdad, S., Bellefleur, G., Yrro, F., Bavand Savadkoohi, M., and Gloaguen, E.: Toward Robust Three-Dimensional Magnetic and Gravity Inversion Using Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1760, https://doi.org/10.5194/egusphere-egu26-1760, 2026.

Monitoring oil and gas wells is essential for assessing environmental degradation and long-term impacts such as methane emissions from abandoned and orphaned wells. Satellite imagery combined with machine learning offers scalable capabilities for detecting and characterizing oil and gas infrastructure, yet progress remains constrained by the lack of multimodal, multiple-choice (MCQ) vision-language datasets that enable structured evaluation and post-training of vision-language models (VLMs) for oil well scene grounding. Existing resources are predominantly visual-only and therefore provide limited support for image grounding from satellite imagery.

To address this gap, we introduce SatWellMCQ, a vision-language dataset of expert-verified satellite imagery paired with natural-language descriptions and multiple-choice supervision for image-grounded identification and localization of oil wells. SatWellMCQ uses high-resolution multispectral Planet imagery (RGB and infrared) and text annotations that describe well type and spatial context. Each sample includes one expert-verified correct description and three semantically plausible distractor descriptions drawn from other samples, enabling structured MCQ evaluation. All samples were manually verified by a senior domain expert with 100% intra-expert agreement, ensuring accurate alignment between images, labels, and text. The dataset covers four categories relevant to oil well monitoring: active wells, suspended wells, abandoned wells, and control samples without visible wells, yielding a balanced distribution for training and evaluation. We publicly release SatWellMCQ to support research on image grounding and vision-language adaptation in satellite imagery of oil wells.

We evaluate SatWellMCQ across state-of-the-art VLMs in zero-shot and supervised fine-tuning (SFT) settings. In the zero-shot setup, performance is moderate only for large-scale models, with the best result achieved by Qwen3-VL-235B at 0.670 accuracy. Compact models transfer poorly in zero-shot evaluation (e.g., Granite~3.3~2B at 0.422 and Phi-4-multimodal-instruct~6B at 0.376), highlighting the difficulty of domain-specific oil well analysis without targeted supervision. Supervised fine-tuning on SatWellMCQ yields substantial gains for compact models: Granite~3.3~2B improves to 0.722 and Phi-4-multimodal-instruct~6B reaches 0.730, surpassing all zero-shot baselines. These results show that SatWellMCQ poses a challenging benchmark for current VLMs while enabling effective domain adaptation through structured MCQ supervision.

Overall, SatWellMCQ provides a resource for post-training and benchmarking VLMs on image grounding of oil wells in satellite imagery and supports  geoscientific monitoring tasks relevant to environmental impact assessment and methane mitigation.

How to cite: Emam, A., Alrowili, S., Eswaran, M. K., Kinzler, R., and Samih, Y.: SatWellMCQ: A Vision–Language Satellite Datasetfor MCQ-Based Image Grounding of Oil Wells, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3405, https://doi.org/10.5194/egusphere-egu26-3405, 2026.

Around the globe we experience a significant biodiversity loss, mainly driven by direct anthropogenic exploitation, land use changes, and climate change. The most effective strategy to limit biodiversity loss is the designation and management of protected areas. Consequently, the European Union has adopted the EU Biodiversity Strategy for 2030, aiming to protect 30% of aquatic and terrestrial ecosystems by 2030. However, a consistent framework to designate protected areas across all EU member states is lacking. Additionally, the monitoring of biodiversity is challenged by the dynamic nature of the biological system, exacerbated by ongoing climate change, putting additional pressure on the member states in the identification of suitable areas for conservation. 

In contrast, the increasing amount of detailed geospatial and climatic data contains valuable information that can be used to optimise protected area designation. Recent developments in artificial intelligence and machine learning now provide us with powerful tools to best utilise these vast amounts of data. In this study, we develop a transparent and reproducible framework to prioritise protected areas in forests. Here we apply the CAPTAIN framework based on reinforcement learning (RL) to identify valuable forest habitats for conservation in the federal state of North Rhine-Westphalia (NRW), Germany. First, we model habitats of ten forest bird indicator species across the period of 2016-2024. Second, we use the changing habitat patterns to train a RL model that identifies 30% of the most valuable forest sites in the federal state. Finally, we model valuable forest sites under different policies (e.g., including or excluding opportunity costs for nature conservation) to illustrate how potential limitations of nature conservation management can be addressed. Our results indicate that forest sites in the south-east of NRW are most suitable for conservation. Furthermore, we find that including opportunity costs for nature conservation in the model predictions produces similarly strong outcomes for safeguarding the most endangered bird species. The framework makes use of open-source data and can be applied to any other region or country to support strategic nature conservation management.

How to cite: Horn, K., Silvestro, D., Wallis, C., Leitao, P. J., Daldaban, E., and Rudolph, A.: Identifying valuable forest habitats for conservation in north-western Germany using AI and citizen science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4011, https://doi.org/10.5194/egusphere-egu26-4011, 2026.

Geological mapping in complex metallogenic provinces often relies on band ratios and thresholding techniques. While effective for simple targets, these traditional methods struggle to capture non-linear spectral associations inherent in natural mineral mixtures and require significant prior knowledge of the target mineralogy. This study introduces a novel, data-driven unsupervised pipeline for mineral target generation, applied to the Aït Saoun region in the Moroccan Anti-Atlas, a strategic zone characterized by polymetallic occurrences (Cu, Co, Fe, Mn).

We leverage the full spectral topology of ASTER satellite imagery (VNIR-SWIR bands) rather than reduced indices. Our approach integrates topological manifold learning to reduce the high-dimensional spectral space, followed by density-based spatial clustering to delineate mineral clusters. This combination allows for the preservation of local data structure and the automated rejection of noise without human supervision.

The pipeline successfully identified spatially coherent clusters corresponding to specific hydrothermal alteration zones. It autonomously distinguished between structural iron-manganese anomalies and lithology-controlled copper mineralization a nuance often missed by standard linear ratios. The metallogenic relevance of these spectral clusters was rigorously validated through field mapping and geochemical analysis using Atomic Absorption Spectroscopy (AAS). Results confirmed economic grades in the predicted zones, yielding Copper concentrations up to 2.60% in propylitic alteration zones and Iron-Manganese oxide grades (21.94% Fe, 1.80% Mn) in tectonic corridors. Furthermore, the detection of distal barite anomalies highlights the method’s capability to map complete hydrothermal zonations.

These findings demonstrate that topological machine learning offers a robust, superior alternative to conventional remote sensing techniques for vectoring exploration targets in arid environments. By converting raw spectral data into validated metallogenic maps, this pipeline provides a scalable tool for de-risking early-stage mineral exploration in the Anti-Atlas.

How to cite: Elomairi, M. A. and El GAROUANI, A.: Automated Mineral Cluster Detection in ASTER Data Using Topological Machine Learning: A Novel Data-Driven Approach for Geological Exploration in Ait Saoun, Anti Atlas, Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4179, https://doi.org/10.5194/egusphere-egu26-4179, 2026.

Defining subsurface soil conditions in complex coastal settings requires the use of both geophysical and geotechnical datasets, each with different resolution and sensitivity. This study combined helicopter-borne electromagnetic (HEM) data, where large areas are spatially covered with limitations to vertical resolution, with cone penetration test (CPT) data, where high resolution can be achieved while the spatial resolution often is very sparse due to drilling associated costs. Τo formulate a continuous three-dimensional model of subsurface soil properties for levee risk assessment, these datasets were integrated. HEM data provides extensive covering resistivity profiles, while CPT provides high resolution, spatially limited measurements of mechanical soil behaviour.
It is known that resistivity as a soil property depends on many parameters (mostly water quality and soil type), and there is no straightforward method to directly translate it to soil, hence the use of ML. To deal with these complexities, we employed machine learning methods – Random Forests and neural networks – to merge heterogeneous datasets and predict continuous soil behaviour indices and discrete lithological types. We propose the use of multiple features, such as spatial coordinates, depths, distance from coast, soil types and local geological conditions. After pre-processing, machine-learning models were trained to fuse the datasets to ensure spatial consistency in the coastal environment. Afterwards, the Soil Behaviour Type Index (SBT) (Robertson, 1990) was calculated using the CPT measurements and then was discretized into lithological units.
A classical machine learning algorithm (Random Forest) and a PyTorch-based neural network were trained for regression (predicting the continuous SBT index) and classification (predicting soil types) tasks, and their performance was evaluated using standard statistical and visual metrics. Final models were retrained on the full dataset to increase generalizability and robustness. The final product is to map 𝐼𝑐 values and lithological classes at every HEM point and ultimately to make a 3D subsurface soil model. The outcome for each process was validated against an 80%-20% test to ensure reasonable results.
While regression models had similar RMSE scores, classification models generally produced models with greater accuracy of dominant soil types but captured fewer underrepresented mixed lithologies. This work focuses on the interpretability of soil models through integrating data (i.e., not just purely statistical but spatial output) and ultimately continuity in the spatial domain (where engineers are most concerned). The goal of this study is to develop a framework where continuous geophysical data, collected either by helicopters or drones can be combined with additional geological boreholes and CPTs and other geotechnical information, to enable us to image the subsurface beyond resistivity. One of the products of this study serves to represent an approach to providing a better product to those grappling with levee design and safety.

How to cite: Madelis, D., Karaoulis, M., and De Smedt, P.: Electromagnetic & Cone Penetration Test Data Fusion on Soil Characterization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4956, https://doi.org/10.5194/egusphere-egu26-4956, 2026.

How to cite: Jaeger, J., Barquero, J. I., López-Gómez, J. A., and Higueras, P.: Application of Gaussian Mixture Models for Geochemical Anomaly Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5687, https://doi.org/10.5194/egusphere-egu26-5687, 2026.

This study presents a comparative analysis of time-series forecasting models to predict caisson tilt using early-stage monitoring data. To establish a training dataset that accounts for inherent geotechnical uncertainty, 1,000 2D numerical simulations were performed using PLAXIS2D, based on an actual design case in South Korea. To incorporate spatial variability, the subsurface was discretized into 61 independent zones: Deep Cement Mixing (33 zones), foundation rubble (6 zones), backfill rubble (10 zones), and underlying heaving soil (12 zones). Geotechnical parameters including elastic modulus (E), undrained shear strength (S_u), and interface strength reduction factor (R_inter), were varied by up to 50% of their design values. Latin Hypercube Sampling (LHS) was used to assign geotechnical properties to each zone. Each case simulated a 28-stage construction sequence, with caisson tilt extracted at each stage to generate time-series data.

Four forecasting models such as ARIMA, LSTM, Temporal Convolutional Network (TCN), and an encoder-only Transformer, were evaluated. The dataset was split into 680 simulations for training, 170 for validation, and 150 for testing. Forecasting performance was assessed across varying initial observation lengths (cut = 3, 5, 10, 15, and 20 stages) to predict all remaining future stages. Results indicate that while the statistical baseline (ARIMA) showed consistently high errors regardless of observation length, with RMSE values of approximately 0.09 at cut = 3 and 0.08 at cut = 10. In contrast, deep learning models exhibited clear error reductions as more initial observations became available. Among the tested models, the TCN achieved the highest accuracy, with RMSE values of approximately 0.006 at cut = 10 and 0.004 at cut = 15. The encoder-only Transformer model also maintained stable performance for cut ≥ 10, with RMSE values below 0.01.

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C1007635).

How to cite: Kim, S., Hong, J., Huh, I., and Youn, H.: Forecasting Offshore Caisson Tilt via Deep Learning: A Numerical Simulation-Based Approach Accounting for Geotechnical Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6107, https://doi.org/10.5194/egusphere-egu26-6107, 2026.

Bayesian inversion provides a rigorous framework for uncertainty quantification in geophysics， but is often computationally prohibitive due to the reliance on Markov Chain Monte Carlo (MCMC) sampling, which requires massive numbers of forward simulations. While deep learning surrogate models offer acceleration, existing architectures (e.g., CNNs, FNO, DeepONet) often struggle with fixed discretization constraints and cannot flexibly handle the irregular observation coordinates typical in field surveys.

To address these challenges, we propose the General Geophysical Neural Operator (GGNO), a novel Transformer-based architecture designed for mesh-independent operator learning. This design fulfills three fundamental requirements for forward solvers in the context of practical inversion: (1) Discretization-invariant, allowing the processing of input models with different mesh resolutions; (2) Prediction-free, enabling direct solution querying at arbitrary spatio-temporal coordinates; and (3) Domain-independent, decoupling input and output discretizations. 

We validate GGNO on Magnetotelluric (MT) forward modeling, demonstrating exceptional generalization while achieving accuracy two orders of magnitude higher than traditional methods. By integrating GGNO into a Bayesian framework, we achieve highly efficient MCMC sampling, reducing the computational time from tens of days to a few minutes, which allows for a comprehensive exploration of the posterior distribution. Applied to field data, this approach successfully recovers complex subsurface resistivity structures with rigorous uncertainty bounds. These results highlight GGNO's potential to enable high-precision subsurface imaging and robust probabilistic interpretation for complex geophysical exploration.

How to cite: zhang, H. and xu, Y.: Rapid Bayesian Geophysical Inversion Using General Geophysical Neural Operator, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6766, https://doi.org/10.5194/egusphere-egu26-6766, 2026.

Satellite Image Time Series (SITS) are a cornerstone of Earth observation, enabling long-term monitoring of environmental processes such as vegetation dynamics, land-use change, and natural hazards. However, optical satellite time series, including Sentinel-2, are frequently irregular and incomplete due to cloud cover, atmospheric effects, and acquisition constraints, which strongly limit their usability in operational monitoring systems. In contrast, Sentinel-1 Synthetic Aperture Radar (SAR) provides regular observations for any weather condition and offers complementary information for mitigating optical sensor limitations. Generating dense and reliable Sentinel-2 time series from multi-sensor observations therefore remains a critical challenge.

This work investigates Gaussian Process (GP) based statistical models for the reconstruction and densification of Sentinel-2 image time series by jointly exploiting Sentinel-1 and Sentinel-2 data. Gaussian Processes offer a flexible Bayesian framework for pixel interpolation and extrapolation. We explore GP formulations capable of handling irregular temporal sampling, multi-output dependencies, and latent variable structures, enabling the fusion of heterogeneous optical and radar observations.

An in-depth analysis of the state-of-the-art is conducted, covering multi-output Gaussian Processes, sparse and variational approximations for scalability, latent variable models (including hierarchical GP-LVMs), and inverse GP approaches based on shared latent spaces. These methods are evaluated with respect to three key challenges: ensuring spatio-temporal coherence of reconstructed images, fusing asynchronous multi-sensor observations, and maintaining computational tractability for large-scale satellite datasets.

To support experimental investigations, a representative multi-regional dataset is constructed over mainland France and overseas territories, capturing diverse climatic patterns, land-cover types, and cloud conditions, including extreme events such as flooding. 

This study establishes the methodological foundations for reconstructing dense Sentinel-2 time series conditioned on Sentinel-1 observations, with explicit uncertainty quantification. By leveraging Sentinel-1 data, the approach effectively imputes missing Sentinel-2 values while providing consistent average pixel estimates with associated uncertainty, which is critical for geoscience applications. The proposed framework contributes toward more robust Earth observation monitoring systems and the development of reliable geospatial digital twins.

How to cite: Nespoulous, B., Constantin, A., Derksen, D., and Defonte, V.: Probabilistic Reconstruction of Sentinel-2 Satellite Image Time Series Using Multi-Sensor Gaussian Process Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7774, https://doi.org/10.5194/egusphere-egu26-7774, 2026.

Sentinel-2 optical image time series are a key source of information for many Earth observation applications, including climate monitoring, agriculture, ecosystem dynamics, and land surface change analysis. Dense and regular observations are essential to accurately capture seasonal patterns, abrupt events, and long-term trends. However, in practice, Sentinel-2 time series are often sparse and irregular due to cloud cover and varying acquisition conditions. These limitations significantly complicate continuous monitoring and the analysis of surface dynamics. Moreover, beyond time series densification, there is a growing need to anticipate future optical observations to support scenario analysis, early warning systems, and predictive environmental monitoring.

To address these challenges, we propose a deep learning–based framework for densifying Sentinel-2 time series by generating plausible optical images at arbitrary past or future dates. The approach relies on multimodal satellite observations, jointly exploiting optical Sentinel-2 and radar Sentinel-1 data. Indeed, SAR measurements are insensitive to cloud cover and provide complementary structural and temporal information. This multimodal setting enables the reconstruction of missing observations and the prediction of future optical states while preserving realistic spatio-temporal dynamics.

From a methodological perspective, the model is explicitly designed to handle sparse, incomplete, and temporally misaligned multimodal time series. It operates on temporal sets of Sentinel-2 and Sentinel-1 images acquired at irregular dates around a target time. A cross-attention mechanism is used to explicitly model interactions across time and modalities, allowing the network to identify and weight the most relevant observations for generating a Sentinel-2 image at a given target date.

In addition, the proposed framework incorporates a probabilistic decoder that estimates not only the predicted Sentinel-2 image but also an associated uncertainty map. This uncertainty estimation provides valuable insight into the confidence of the generated pixels, which is particularly important for downstream applications such as anomaly detection, risk assessment, and decision-making support.

The model is evaluated across multiple geographical regions and land-cover types, demonstrating strong performance in both densification and forecasting tasks. Results show that the proposed approach successfully preserves the temporal dynamics of the scenes, notably by accurately reproducing vegetation phenology as reflected in NDVI time series. Forecasting experiments further highlight the importance of radar information: Sentinel-1 observations close to the target date allow the model to detect surface changes occurring after the last available optical image, thereby improving future predictions. Overall, the proposed method represents a step towards the densification and forecasting of Sentinel-2 time series, offering a promising direction for future methodologies aimed at continuous Earth surface monitoring and predictive analysis.

How to cite: Defonte, V., Derksen, D., Constantin, A., and Nespoulous, B.: Densification and forecasting of Sentinel-2 time series from multimodal SAR and optical satellite data using deep generative models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7860, https://doi.org/10.5194/egusphere-egu26-7860, 2026.

 As similar disasters and accidents continue to occur, public concern about the limitations of existing disaster response systems and the need for institutional improvement is increasing. The National Disaster Management Research Institute of Korea conducts disaster cause investigations as part of its statutory responsibilities, examining problems observed before and after disasters, institutional weaknesses, and public demands for improvement. In this context, news data provide valuable unstructured information that reflects on-site conditions, response activities, policy debates, and public opinion, and thus complement official investigation records in understanding institutional and managerial factors related to disasters.

 This study aims to develop a media analysis framework based on big data and text mining for use in disaster cause investigations. Disaster-related news articles were first collected, and a large language model (Gemini) was applied to identify and extract sentences that describe problems and suggested improvements in the stages of disaster occurrence and response. The extracted sentences were then processed using natural language processing techniques, including stopword removal and the merging of duplicate and semantically similar sentences. Based on semantic similarity, the remaining sentences were grouped to organize major issues. In addition, nouns were extracted and their frequencies were analyzed by year to identify key terms and to examine changes in topics emphasized in media coverage.
 

 Applying the proposed framework to the disaster cause investigation of the 2023 Osong Underpass Flooding Disaster conducted in 2025, we identified 21 problem items grouped into seven categories, such as insufficient pre-closure of the underpass and inadequate maintenance of river embankments. In addition, 17 improvement measures were derived in six categories, including improvements to underpass closure criteria and flood risk grading, as well as the strengthening of river management practices, and were systematically organized and proposed. The results indicate that combining news big data, text mining, and large language models can effectively structure key issues and institutional weaknesses, and can serve as a useful analytical tool for strengthening the evidence base and explanatory power of disaster cause investigations.

How to cite: Kim, J. E., Shin, H., and Choi, S.: A Big Data and Text Mining–Based Media Analysis Framework for Disaster Cause Investigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8542, https://doi.org/10.5194/egusphere-egu26-8542, 2026.

The scarcity and high acquisition cost of field crop samples remain a major bottleneck for applying Artificial Intelligence (AI)–driven supervised learning methods in large-scale geoscientific applications such as crop type mapping. Meanwhile, crop phenology and, consequently, spectra-temporal characteristics of the same crop type present significant interannual and regional variations due to the differences in local conditions and human activities, such as climatic, soil properties and farming practices. This causes the “domain shift” challenge. Therefore, directly applying a classification model trained in a specific region and year to a new region or year inevitably leads to poor prediction performance. The gap between the abundant availability of Earth Observations imagery and the limited accessibility of training crop samples hider efficient mapping of varied crop types across large regions. To address training sample scarcity and cross-region/year domain shift in large-scale crop type mapping, we propose a transferable crop mapping method named Global-Hierarchical-Categorical feature Alignment (GHCA). GHCA integrates unsupervised domain adaptation, contrastive learning, and pseudo-labeling to achieve multi-dimensional alignment between source domain and target domain at global, hierarchical and categorical levels. The developed method enables accurate and transferable crop mapping across diverse agricultural landscapes with minimum field survey requirements. The main contributions of our study can be summarized as follows: (1) A global feature pre-alignment mechanism is introduced by calculating the Multi-Kernel Maximum Mean Discrepancy (MK-MMD) metric across different hierarchical features to align source and target domains in global and hierarchical feature spaces. This mechanism substantially improves the initial reliability of pseudo-labels generated for the target domain, providing a reliable foundation for subsequent fine-grained categorical level feature alignment; (2) A robust pseudo-label generation strategy is developed by jointly considering prediction confidence, prediction certainty, and prediction stability. Reliable pseudo-labels for target domain are selected by calculating model prediction probabilities and predictive uncertainty estimates through teacher-student model. Moreover, the Exponential Moving Average (EMA) strategy is adopted to updated model parameters in the teacher path to enable the acquisition of obtaining more stable pseudo-labels; (3) Category-wise feature alignment is achieved by integrating pseudo-labeling with contrastive learning, which explicitly pulls intra-class feature closer for the same crop types across source and target domains, while pushing inter-class feature apart for different crop types. The effectiveness of the proposed GHCA method for both cross-region and cross-year crop mapping was evaluated across five regions in China and the U.S. over a two-year timeframe. GHCA was compared with a machine learning method (RF), supervised deep learning models (DCM, Transformer, and PhenoCropNet), and transfer learning methods (DACCN, PAN, and CSTN) for cross‑year and cross‑region crop mapping. Experimental results showed that GHCA outperformed other models in most transfer cases, with OA ranging from 0.82 to 0.95 (cross-region) and 0.89 to 0.98 (cross-year), achieving an average OA increase of 6.2% and 3.5% in cross-region and cross-year experiments, respectively. These results highlight the strong potential of advanced AI methodologies to deliver robust, quantitative, and transferable solutions for complex geoscientific problems using large Earth observation datasets.

How to cite: Yang, J., Hu, Q., Belgiu, M., and Wu, W.: A global–hierarchical–categorical alignment framework to address sample scarcity and domain shift in crop mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10174, https://doi.org/10.5194/egusphere-egu26-10174, 2026.

This study introduces an innovative methodology for generating realistic soil prediction maps that visualise the spatial distribution of specific chemicals, achieved through the rigorous evaluation and comparison of advanced modelling techniques, including innovative modelling techniques based on the use of neural networks and multilayer perceptrons (MLPs). The Drava River floodplain was selected as the primary case study based on stringent criteria: a) intensive historical metal ore mining and metallurgical processing activities, which have left a legacy of contamination; b) distinctive geomorphological features, such as dynamic floodplains and sediment deposition zones; and c) diverse geological settings that facilitate reliable model calibration across transboundary reaches. Soil measurements were integrated with diverse geospatial datasets—derived from Digital Elevation Models (DEMs), land cover classifications, and remote sensing imagery—to enable high-resolution mapping of contaminant distributions via sophisticated predictive modelling powered by neural networks and MLPs. A novel, holistic approach was applied to simultaneously reconstruct multiple influencing processes, including erosion, sediment transport, and pollutant dispersion, across the entire study area. This comprehensive framework not only advances contamination mapping practices but also empowers the developed models to trace primary distribution pathways, quantify the true extent of affected zones, enhance data interpretability, and inform evidence-based decisions on land-use planning, remediation strategies, and environmental management in mining-impacted regions.

How to cite: Alijagić, J. and Šajn, R.: Advanced AI Soil Mapping Techniques and Transboundary Risk Assessment for the Drava River Floodplain , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10465, https://doi.org/10.5194/egusphere-egu26-10465, 2026.

Rapid and objective mapping of co-seismic surface ruptures is essential for post-earthquake impact assessment and for improving our understanding of fault geometry, stress transfer, and rupture processes that inform longer-term seismic hazard analyses. However, rupture mapping has traditionally relied on manual interpretation of field observations or remote-sensing data, which is time-consuming and difficult to extend consistently to large spatial extents, multiple earthquakes, and diverse data sources. Here we present an automated deep-learning framework—the Deep Rupture Mapping Network (DRMNet)—a convolutional neural network designed for end-to-end, high-precision detection of co-seismic surface ruptures from multi-sensor imagery. DRMNet is applied to four large continental earthquakes: the 2021 M_w 7.4 Maduo, 2022 M_w 6.9 Menyuan, 2001 M_w 7.8 Kokoxili, and 1905 M_w ~8 Bulnay (Mongolia) events. The framework consistently delineates both primary and subsidiary rupture structures across centimetre-scale drone imagery and metre-scale satellite data. Across diverse tectonic settings, image resolutions, and preservation states, DRMNet achieves precisions approaching or exceeding 90%. By enabling consistent rupture recognition across multiple events, sensors, and timescales, the proposed framework overcomes the event-specific and local-scale limitations of previous approaches, supporting both rapid post-earthquake response and retrospective rupture reconstruction, and laying the groundwork for standardized global surface-rupture inventories.

How to cite: Liu, X., Wang, S., Shi, X., Su, C., Klinger, Y., Delorme, A., Li, H., Pan, J., and Chen, H.: Deep-learning based large-scale automated observation of earthquake surface ruptures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11012, https://doi.org/10.5194/egusphere-egu26-11012, 2026.

• Zircon trace element geochemistry is a pivotal tool for unraveling petrogenesis and the evolutionary history of the Earth’s crust. While two-dimensional (2D) discriminant diagrams are conventionally used to identify parent rock types, the emergence of machine learning (ML) has introduced a transformative research paradigm. ML not only enhances classification accuracy but also resolves the inherent ambiguities found in traditional geochemical diagrams. However, the reliability of current ML models typically depends on the vast archives of labeled samples from the Phanerozoic. When extending research to “deep-time” samples, such as Hadean zircons, the scarcity of labeled data often forces researchers to rely on models trained exclusively on Phanerozoic datasets. This approach is prone to misclassification due to “domain shift,” caused by systematic variations in zircon trace element distributions across different geological eons. To address this challenge, we propose a Domain Adversarial Neural Network (DANN) framework tailored for zircon trace element analysis. By aligning the feature distributions of the source domain (Phanerozoic) and the target domain (Precambrian), the DANN extracts “domain-invariant yet geologically significant” high-dimensional feature representations, effectively mitigating the effects of temporal data bias. Our results demonstrate that DANN significantly outperforms traditional machine learning methods across multiple performance metrics. Furthermore, t-SNE visualization confirms that the source and target domains are effectively aligned within the feature space. When applied to ~4.3 Ga zircon samples from the Jack Hills, the model achieved a classification accuracy of 0.923. This high level of performance underscores the framework’s exceptional generalization capability for identifying unlabeled deep-time samples and its potential for broader applications in Precambrian geology. This study develops a transferable, data‑driven framework for inferring deep‑time geological processes, providing a novel methodology to address the limitations inherent in the traditional principle of uniformitarianism. Furthermore, the framework is extensible to other mineral systems (e.g., apatite, monazite), thereby opening new avenues for quantitatively reconstructing the dynamic evolution of the early Earth.

How to cite: Zhang, M., Chen, G., Kusky, T., Harrison, M., Cheng, Q., and Wang, L.: Identifying zircon provenances using domain-adversarial neural network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12550, https://doi.org/10.5194/egusphere-egu26-12550, 2026.

Land surface temperature (LST) is a key variable for assessing crop thermal stress and supporting precision agriculture. However, thermal satellite products often involve a trade-off between spatial and temporal resolution. Sentinel-3 provides frequent LST observations, but its coarse spatial resolution limits its use for field-scale agricultural monitoring.

This study proposes a spatial downscaling approach for LST based on the fusion of Sentinel-3 thermal data with high-resolution multispectral information from Sentinel-2. The method exploits the inverse relationship between surface temperature and vegetation cover through the Normalized Difference Vegetation Index (NDVI). A linear regression model was developed to estimate LST at a spatial resolution of 10 m using Sentinel-2 NDVI as the primary predictor.

The approach was applied over the agricultural site of El Ghaba in the Marrakech–Safi region (Morocco), covering different crop types, including annual cereals (barley, wheat, and kerenza) and perennial olive orchards. Results show a clear negative correlation between NDVI and LST, confirming the regulatory role of vegetation on surface temperature. The downscaled LST maps reveal fine-scale spatial heterogeneity that is not detectable in the original Sentinel-3 product.

Quantitative evaluation indicates low absolute errors for annual crops (generally below 0.5 °C), demonstrating the robustness of the proposed method, while higher discrepancies observed for olive orchards highlight the complexity of perennial crop thermal behavior. This work enhances the spatial usability of satellite thermal data for agricultural monitoring and crop stress assessment.

How to cite: Boufous, B., Ben zhair, F., and Belaqziz, S.: Spatial Downscaling of Land Surface Temperature Using Sentinel-2 and Sentinel-3 Data Fusion for Agricultural Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13366, https://doi.org/10.5194/egusphere-egu26-13366, 2026.

Automated Mineralogy – the past

The automated classification of mineral phases in rocks has been a mainstay of the Geoscience analytical community for over 40 years. While we have seen great leaps forward in AI in µCT and light microscopy/petrography, the automated capabilities for the SEM have progressed and changed very little in decades, relying heavily on outdated methods that were available at the time.

The technology come with several significant problems moving forward, including excessive hardware-software dependencies, complex mineral libraries and classifications, inconsistent user experience, and difficult workflows outside their intended use.

Recent technological advances

There are two broad shifts that are taking place across a number of microscopy and microanalysis techniques – the acquisition of more quantitative data, and the application of deep learning neural networks. As a general trend this can be thought of as building better datasets, and building bigger datasets.

EDS as a SEM-based technique is fertile territory for both of these shifts. As an analytical technique EDS is commonly applied qualitatively, or as an image based method for distinguishing regions based on chemical maps. In recent years it has become easier than ever before to calibrate systems and detectors for concentration data, meaning the SEM can generate more robust datasets without having to fall back on other techniques.

Deep Learning is a topic that covers a broad range of mathematical applications to everything from the acquisition of microscopy datasets, through to data processing and interpretation across almost all sciences. There are many different flavours of deep learning neural network (DLNN) and each type lends itself to different applications, particularly in the varied data rich environments of microscopy. DLNN are inherently hard to track exactly how they operate, but at their best should be easy to use, and easy to understand how they’ve been applied to a scientific problem.

Automated Mineralogy – the future

The introduction of both quantitative mineral chemistry and DLNN to automated mineral classification is a huge leap forward, solving many of the problems of traditional software. Detaching data acquisition from processing removes software dependencies and frees users to build their ideal system. An DLNN-driven, unsupervised data processing approach can be data led rather than user led, making it more robust and consistent across instruments and facilities. Quantitative analysis can build on the DLNN approach by allowing a “best fit” classification, removing the need for constant modification of mineral libraries, and simply allowing “textbook” globally consistent mineral compositions to drive the labelling of segmented data.

How to cite: Taylor, R.: Why Automated Mineralogy needed an upgrade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13941, https://doi.org/10.5194/egusphere-egu26-13941, 2026.

The increasing volume and complexity of geochemical literature pose major challenges for the sustainable curation of domain-specific databases such as GEOROC (Geochemistry of Rocks of the Oceans and Continents), the world’s largest repository of geochemical and isotopic data from igneous and metamorphic rocks and minerals, aggregating more than 41 million values from over 23,000 publications. Although GEOROC underpins a wide range of geoscientific research, the extraction and harmonization of metadata from publications still relies heavily on manual effort, which significantly limits the scalability.

In this contribution, we present a novel information extraction architecture that moves beyond linear processing pipelines toward an Large Language Model (LLM)-based multi-agent system combining document layout analysis, schema-driven reasoning, and modality-aware extraction. Unlike generic LLM approaches that treat documents as continuous text streams, our architecture adopts a "Visual-First" strategy. We utilize a layout-aware backbone (MinerU, Niu et al., 2025) to decompose PDF manuscripts into a sequence of geometrically grounded primitive blocks, each representing a localized document region with associated visual and typographic features, preserving the geometric grounding essential for interpreting complex data tables. A routing agent subsequently validates and refines the initial layout classification, dynamically dispatching blocks to specialized downstream agents for text, table, or figure processing. This adaptive routing strategy improves robustness against layout variability across journals, publication years, and formatting styles.

Central to the framework is an active schema agent that operationalizes the GEOROC metadata model. Rather than treating the database schema as a static template, this agent continuously provides extraction targets, normalization rules, unit standards, and conflict-resolution policies that guide all subsequent processing steps. Text blocks are handled by an  Optical Character Recognition (OCR) driven information extraction agent, table blocks by a table parsing agent capable of reconstructing complex table structures, and figure blocks by a visual reasoning agent designed to interpret diagrams and digitize plotted values. Each agent produces structured candidate values enriched with confidence estimates and fine-grained provenance, including page-level and bounding-box references to the original document.

The outputs of these modality-specific agents are consolidated by a merge-and-judge agent, which goes beyond simple aggregation. This agent performs cross-modal arbitration, unit harmonization, and deduplication, resolving conflicts between heterogeneous sources according to schema-defined priorities and data-quality criteria. The final result is a machine-readable JSON representation that preserves both extracted values and their evidential context.

By combining layout grounding, adaptive routing, schema-driven reasoning, and judgment-based integration, this system delivers a robust and extensible approach to large-scale metadata extraction. The framework substantially supports the curation process and strengthens GEOROC’s role as a FAIR-compliant reference infrastructure by enabling more efficient reuse of published geochemical data in future geochemical research.

References:

Niu, J., Liu, Z., Gu, Z., Wang, B., Ouyang, L., Zhao, Z., ... & He, C. (2025). Mineru2. 5: A decoupled vision-language model for efficient high-resolution document parsing. arXiv preprint arXiv:2509.22186.

How to cite: Yang, T., Elezabawy, K., Kurzawe, D., Kallas, L., Traun, M., Sarbas, B., Sturm, A., Möller-McNett, S., Willbold, M., and Wörner, G.: Multi-agent Geochemical Literature Data Mining System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14415, https://doi.org/10.5194/egusphere-egu26-14415, 2026.

Quartz grain surface microtextures observed by scanning electron microscopy (SEM) provide important information on sediment transport history, depositional processes and sediment provenance. Traditionally, the interpretation of these features has relied upon qualitative visual assessment—an approach deeply rooted in expert judgement and cumulative experience. While fundamental, this methodology is inherently susceptible to subjectivity and inter-analyst variability. To counter balance this problem, we explore image-based classification approaches (utilizing Deep Learning frameworks) as a tool to support quartz microtextural analysis and assist in the identification of likely depositional environments thus establishing sediment provenance relationships.

A dataset of 3 367 SEM images was compiled, spanning a diverse range of sedimentary contexts: aeolian dunes, beach faces’, alluvial systems, basal sands, and nearshore, alongside with high-energy deposits from storm and tsunami events. Based on this dataset, five classification models were developed. Three were designed to discriminate between the full set of seven depositional classes, while two focused on a reduced classification scheme comprising four classes (alluvial, beach, dune and nearshore). All models were optimised using an increasing number of training epochs to assess the stability and evolution of classification performance. The results obtained were further examined in comparison with SandAI, an existing tool for microtexture classification, to evaluate its behaviour when applied to new sedimentary contexts and datasets acquired under different conditions.

The most consistent classification results were obtained for environments characterised by well-preserved and distinctive mechanical microtextures (e.g. aeolian sediments). Conversely, while environments defined by overlapping processes occasionally yielded higher nominal accuracies in QzTexNet (CNN-based models developed within the scope of this work), this is potentially attributed to their over-representation in the dataset. Analysis of classification outcomes indicates that microtextural overprinting, dataset imbalance and variations in image quality reduced the visibility of diagnostic features, thereby complicating the differentiation of depositional settings. Nevertheless, the data suggests that our models successfully capture sedimentologically meaningful patterns when surface textures remain clear. While SandAI showed stable performance within its original scope, its accuracy was limited, peaking at 47% for its target environments and dropping significantly when faced with complex deposits like tsunami or nearshore grains. In contrast, the newly developed QzTexNet models showed slightly more encouraging results, reaching accuracies of around 55% and demonstrating a steady improvement through successive refinements.

Ultimately, these findings demonstrate that automated classification offers a powerful complement to traditional analysis, particularly in ensuring reproducibility across large-scale datasets. Solely based on our database, it was observed that challenges regarding dataset equilibrium and textural complexity persist, targeted methodological refinements and supervised training hold significant potential. Such advancements represent a promising frontier in sedimentary provenance studies, particularly for the rigorous identification of deposits linked to extreme geological events.

This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020), UID/50019/2025(https://doi.org /10.54499/UID/PRR/50019/2025), UID/PRR2/50019/2025). Finally this work is a contribution to project iCoast (project 14796 COMPETE2030-FEDER-00930000).

How to cite: Marques, N., Costa, P., and Pina, P.: Quartz grain microtexture analysis using Artificial Intelligence: application to tsunami and storm deposits provenance studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14874, https://doi.org/10.5194/egusphere-egu26-14874, 2026.

How to cite: Hogue, T., Moon, C., and Corona, C.: Quantifying Post‑Wildfire Hydrologic Response Using LSTMs: Ecoregion Patterns Across the Contiguous United States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15295, https://doi.org/10.5194/egusphere-egu26-15295, 2026.

Natural (geological) hydrogen refers to molecular hydrogen produced in the subsurface through abiotic and biogenic pathways, which may migrate, accumulate transiently, be consumed by secondary reactions, or escape to the surface. Increasing evidence indicates that such systems could be a strategic low-carbon energy source, but their exploration is limited as regional-scale, data-driven approaches to identify mechanisms of active or fossil migration in geologically complex environments are lacking. Surface expressions such as circular and sub-circular depressions associated with soil and vegetation anomalies have been reported worldwide as indirect indicators of hydrogen migration and leakage. However, their detection remains limited to either local reconnaissance of the field or manual interpretation of remote-sensing data. In this research, we present an AI-assisted remote sensing framework to conduct a regional screening based on the potential for natural hydrogen seepage patterns to enhance early-stage exploration and improve the quantitative characterization of surface indicators linked to subsurface energy systems. Deep-learning–based computer vision models are used to study high-resolution satellite imagery and automatically identify and classify circular and sub-circular geomorphological features that could correspond to hydrogen exudation. The resulting detections are integrated into a GIS framework for the extraction of morphometric and spatial statistics, providing a formal analytical benchmark to relate surface structures to lithology, structural configuration, and the regional tectonic setting. The workflow is applied to the Alta Guajira region (in northern Colombia), a geologically complex segment of the Caribbean margin characterized by accreted oceanic crust, major fault systems, and sedimentary depocenters that may favor hydrogen generation and migration. Using an AI-based approach allows the construction of a regional inventory of candidate seepage-related structures while significantly reducing false positives associated with purely morphology-based analyses. The results support the prioritization of targets for future field verification, geochemical sampling, and subsurface investigations. Beyond its implications for natural hydrogen prospectivity, the proposed methodology demonstrates how artificial intelligence can translate qualitative geological observations into quantitative, reproducible screening tools. By providing a transparent and spatially explicit representation of subsurface energy systems, AI-assisted screening also facilitates communication with stakeholders and local communities, contributing to informed public perception of emerging sustainable subsurface energy resources in data-limited regions such as Alta Guajira.

The researchers thank the SHATKI Research Project (code 110563), Contingent Recovery Contract No. 112721-042-2025, funded by the Ministry of Science, Technology and Innovation (Minciencias) and the National Hydrocarbons Agency (ANH).

How to cite: Monterroza Montes, M. A., San Martín Cañas, S., Lora-Ariza, B., and Donado, L. D.: AI-assisted Remote Sensing Screening of Potential Natural Hydrogen Seepage Features in Alta Guajira, Northern Colombia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15913, https://doi.org/10.5194/egusphere-egu26-15913, 2026.

Cloud cover estimation is of crucial significance in meteorological observations and short-term/long-term weather forecasting, as it directly affects the accuracy of radiation balance assessment, precipitation prediction, and climate change modeling. Ground-based automated cloud quantification observation instruments enable continuous, high-resolution cloud monitoring with spatial-temporal continuity that satellite remote sensing cannot fully achieve, highlighting the immense value of ground-based cloud image processing for practical meteorological applications. However, existing cloud detection methods predominantly rely on supervised training with ground truth masks, which overlook the rich contextual information and inherent regularization constraints embedded in original cloud images. This oversight frequently results in mismatched cloud boundaries, inadequate model interpretability, and poor adaptability to complex cloud morphologies—particularly for thin clouds and cirrus clouds characterized by weak grayscale contrast, sparse texture, and irregular shapes. Consequently, these limitations lead to suboptimal detection performance, including under-segmentation or over-segmentation, and further induce inaccuracies in quantitative cloud cover estimation.

To address the aforementioned issues and achieve accurate cloud cover detection results, this study proposes a model-agnostic refinement method designed to optimize the coarse detection masks generated by any pre-trained cloud detection model. The framework is jointly optimized by three loss functions: a local similarity descriptor, total variation (TV) regularization, and a traditional detection loss (e.g., cross-entropy). Specifically, the local similarity descriptor is defined as the difference between two terms: the average grayscale difference of each pixel and cloud region and background pixels within a local window. This descriptor effectively enhances the discriminability between cloud and non-cloud regions at the local level. The total variation regularization term is introduced to maintain the smoothness of the detection boundary and suppress spurious noise. The cross-entropy loss ensures the overall consistency between the refined result and the ground truth.

Minimizing the combined loss function drives the coarse detection result to evolve adaptively along the actual cloud boundary, thereby achieving more precise alignment with the true cloud contours. Notably, the proposed framework elevates the detection of thin clouds and cirrus clouds, effectively mitigating missed detection areas in these tenuous cloud structures. Furthermore, the integrated loss function enhances model interpretability: the local similarity descriptor explicitly quantifies the differences within local window, and minimizing this term inherently refines the detection by strengthening the distinction between cloud and background regions. Ultimately, the refined detection results substantially improve the accuracy of cloud cover estimation, laying a solid foundation for reliable meteorological observations and weather forecasting applications.

How to cite: Hu, Y., Yang, P., Zhou, Z., Bo, R., Yang, S., and Zhang, G.: Local Similarity-Driven Refinement for Model-Agnostic Ground-Based Cloud Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16953, https://doi.org/10.5194/egusphere-egu26-16953, 2026.

Earth Observation (EO) is an essential source of information for most geosciences. However, high costs, large data volumes, and difficult access constrained its use for decades. Open data programs like Copernicus have reduced costs, and cloud access via the Copernicus Data Space Ecosystem (CDSE) has made local processing largely obsolete. In fact, API (Application Programming Interface)-based cloud access, analysis-ready mosaics and calibrated Copernicus Land Monitoring Service data products have made Sentinel data AI-ready. But despite these advances, the requirement for complex programming skills remained a significant barrier until recently. Here, we demonstrate how cloud-native processing APIs and generative artificial intelligence (AI) are removing this obstacle by enabling the "vibe coding" paradigm shift. Vibe coding is an approach to software development where the researcher focuses on the high-level logic, the functional vision, and the end product, while the syntax and code are generated and refined by AI.
Copernicus Data Space Ecosystem facilitates this transition through three key features: (1) the abstraction of EO analysis pipelines via RESTful APIs, which reduces tasks to a series of mathematical operations on pixel values; (2) the availability of intuitive web browser visualization for rapid prototyping and debugging; and (3) an extensive body of open documentation and code examples that serve as a robust training foundation for generative AI.
On CDSE, the Sentinel Hub API family utilizes "custom scripts" (or "evalscripts") — modular JavaScript files defining data inputs, outputs, calculations, and visualizations. The openEO API uses "process graphs", JSON representations of the processing steps in a unified structure as a series of nodes. Because the backend manages big data optimization and the browser handles rendering, these scripts are concise enough for AI assistants to generate, adapt, and debug effectively. The Sentinel Hub Custom Script Repository, containing over 200 community-contributed scripts, and the openEO community examples repository and CDSE "Algorithm Plaza" have laid the foundation for this approach. Neither of these advances was intentionally created to support AI, but rather to simplify programming for humans; however, combined, they enable a breakthrough in code development. We demonstrate how AI tools can efficiently adapt scripts across different satellite sensors, combine spectral indices into decision trees, and produce scalable quantitative outputs. This allows researchers not specialized in remote sensing to utilize existing code modules and natural language prompts to create meaningful results for their specific fields. Beyond the capabilities of Sentinel Hub, OpenEO supports joint analysis of data from multiple back-ends and the application of user-defined external code, such as biophysical models or pre-trained ONNX deep learning networks. While this added complexity presents a higher technical threshold, it also creates a massive opportunity for AI-driven automation. Ultimately, in combination with the public data space approach, generative AI further democratizes Earth Observation, transforming it from a specialist-only domain into an integrated component of all geoscience research workflows.

How to cite: Zlinszky, A.: From natural language to quantitative satellite imagery analysis: Copernicus Data Space Ecosystem and AI enable vibe coding of custom scripts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18394, https://doi.org/10.5194/egusphere-egu26-18394, 2026.

Fractional Vegetation Cover (FVC) is a key ecological variable for monitoring ecosystem health, land degradation, and vegetation dynamics in dryland environments. While satellite and UAV observations enable scalable FVC estimation over large spatial extents, the accuracy and robustness of these models remain strongly dependent on high-quality field-based reference data for calibration and validation. Traditional in-situ methods, including visual estimates using transect-based surveys, remain widely used but are labor-intensive and inherently subjective. Digital photography has emerged as a practical alternative, typically analyzed using index-based computer vision techniques or deep learning models. However, these methods are highly sensitive to background variability and therefore rely on massive labeled datasets. Recent advances in multimodal large language models (MLLMs) suggest a potential paradigm shift, as these models combine visual perception with high-level reasoning and benefit from diverse pre-training that enables conceptual knowledge transfer across tasks. In this study, we evaluate the feasibility of using MLLMs for direct estimation of FVC from ground-level photographs without task-specific training. We collected and compiled a dataset of more than 1,100 quadrat pictures from across 26 dryland sites in Saudi Arabia, spanning a wide range of surface conditions from bare soil to sparsely vegetated rangelands. Each picture corresponded to a 1 m × 1 m quadrat with FVC estimated independently by two experts, whose average was used as reference data for assessment of model predictions. Six state-of-the-art multimodal large language models, including Qwen2.5-VL, Mistral-Small-3.2, LLaMA-4-Maverick, LLaMA-4-Scout, and two Gemma-3 variants, were evaluated using four prompt designs that varied in length, ecological context, and methodological detail. Across all models and prompts, MLLMs achieved a mean absolute error of approximately 7.8%, demonstrating competitive performance relative to traditional image-based methods. The best-performing model-prompt combinations achieved mean absolute error values below 5%, with low systematic bias. Short and ecologically explicit prompts consistently outperformed more complex prompt designs, achieving an average reduction in mean absolute error (MAE) of approximately 1.3–1.4 percentage points compared to visually guided or highly structured prompts (MAE ≈ 6.9% versus 8.2–8.4%). Overall performance was more sensitive to model choice than to prompt structure, with mean MAE varying from approximately 5.6% to 10.0% across models, compared to a narrower range across prompts. The highest accuracy was obtained using the Qwen2.5-VL model with an ecologically detailed prompt, which achieved a mean absolute error of 4.9%, near-zero bias, and an RMSE of 8.4%. Across all prompt designs, Qwen2.5-VL and Mistral-Small-3.2 consistently delivered the best overall performance, both maintaining mean MAE values below 6% and exhibiting stable behavior across prompt variations, indicating robustness to prompt design. These results demonstrate that MLLMs can provide accurate and scalable FVC estimates directly from field photographs, without requiring specialized training datasets. This approach offers a promising alternative for rapid field surveys and reference data generation, particularly in dryland ecosystems where background complexity and data scarcity limit the effectiveness of conventional methods.

How to cite: Lopez Camargo, O. A., Elias Lara, M., El Hajj, M., Cheng, H., Scilla, D., Angulo, V., Al wahas, A., Johansen, K., and McCabe, M. F.: Evaluating Fractional Vegetation Cover using Multimodal Large Language Models: A Comparative study with Human Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18939, https://doi.org/10.5194/egusphere-egu26-18939, 2026.

Earth system is characterized by intricate interactions between human activities and natural processes, where stochastic dynamics, nonlinear feedbacks, and emergent behaviors collectively determine system evolution and sustainability outcomes. Despite significant advances in Earth system science, two fundamental challenges persist: the insufficient integration of physical process models with observational data, and the lack of interpretable frameworks for simulating coupled human-Earth dynamics and optimizing governance strategies. These limitations critically impede our ability to conduct effective Earth system governance and guide human-environment interactions toward sustainable development pathways. To overcome these challenges, this study proposes an innovative framework that synergistically integrates data assimilation and reinforcement learning to enhance both predictability and decision-making capabilities in the complex Earth system. Data assimilation, as a well-established methodology in Earth system science, systematically combines dynamic models with multi-source observations to improve system observability and forecast accuracy. Reinforcement learning, grounded in the Bellman equation and Markov decision processes, provides a natural paradigm for modeling adaptive human-environment interactions and deriving optimal strategies through sequential decision-making under uncertainty. Building upon these complementary methodologies, we develop a Multi-Agent Deep Reinforcement Learning (MADRL) framework that employs the Markov decision process as the theoretical foundation, integrates agent-based modeling to represent heterogeneous stakeholder behaviors across multiple organizational levels, utilizes deep neural networks to handle high-dimensional state-action spaces, and incorporates data assimilation techniques to continuously update system states and reduce forecast uncertainties. This integrated framework is specifically designed to address fundamental Earth system governance challenges by capturing emergent phenomena arising from complex human-environment interactions, enabling the exploration of intervention mechanisms such as economic incentives, regulatory policies, and cooperative arrangements, and providing interpretable decision pathways that balance economic development with environmental sustainability. Through this integration, our framework offers a systematic approach to tackle classical problems in Earth system governance, from the tragedy of the commons to planetary boundaries, ultimately advancing our capacity to navigate toward sustainable development trajectories in an increasingly coupled human-Earth system.

How to cite: Yuan, S. and Li, X.: Generalizing human-Earth systems modeling and decision-making: A multi-agent deep reinforcement learning framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19409, https://doi.org/10.5194/egusphere-egu26-19409, 2026.

How to cite: Calderón-Delgado, M., D’Auria, L., Álvarez-Hernández, A., García-Hernández, R., Ortega-Ramos, V., M. van Dorth, D., de Armas-Rillo, S., López-Díaz, P., and M. Pérez, N.: Improving the seismic catalogue completeness of Tenerife (Canary Islands, Spain) through deep learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19427, https://doi.org/10.5194/egusphere-egu26-19427, 2026.

Autonomous spacecraft operations are increasingly important as missions grow more complex, ground contact opportunities remain limited, and the number of LEO satellites continue to rise. Reliable onboard orbit determination (OD) and orbit prediction (OP) are essential for mission planning, resource allocation, and communication scheduling. Operational OD/OP typically relies on physics-based models that estimate parameters (initial state, drag coefficient, etc.) from tracking data. However, environmental modeling is not perfect, and uncertainties in atmospheric density can cause prediction errors to grow rapidly. This limits OP reliability.

We present an onboard-oriented hybrid OD/OP concept that augments a classical physics-based OD/OP chain with a lightweight machine-learning (ML) correction module to compensate for systematic OP errors in real time. While data-driven correction of propagator errors has been explored previously, this work emphasizes the tight integration of a compact correction model into an operational workflow under onboard constraints. The implementation is based on the Python OD/OP toolbox Artificial Intelligence for Precise Orbit Determination (AI4POD) and targets deployment within the Autonomous Space Operations Planner and Scheduler (ASOPS) experiment, that is planned for validation on the ATHENE-1 satellite.

The approach is demonstrated using simulated GPS-like tracking data generated with a high-fidelity reference model, while OD/OP are performed with a reduced-complexity model representative of onboard settings. A compact artificial neural network (ANN) is trained to predict OP errors in the RSW frame from available onboard data, reducing the maximum three-day along-track error from ~5 km to ~1.2 km.

To assess operational robustness, we complement the baseline results with a statistical consistency check of the residuals across all prediction cases and outline planned tests with additional ML/DL correction models.

How to cite: Aigner, B., Dallinger, F., Andert, T., and Haser, B.: Onboard Hybrid Orbit Prediction with Lightweight Machine-Learning Error Correction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19439, https://doi.org/10.5194/egusphere-egu26-19439, 2026.

Orbit Determination (OD) is commonly addressed with classical estimators such as Weighted Least Squares, which are statistically well founded but can be sensitive to poor initialization and may degrade when the initial state is weakly known. Physics-Informed Machine Learning offers an alternative by embedding orbital dynamics directly into the estimation process. In this work, Physics-Informed Extreme Learning Machines (PIELMs) are investigated as fast OD models that do not require a high-quality initial guess, since the output layer is obtained from a physics-based training objective that enforces consistency with both measurements and dynamics.

While single-layer PIELMs can achieve high accuracy, they may exhibit reduced stability in regimes with limited measurement support. To improve representational capacity and generalization, the Deep PIELM augments the model with an autoencoder-based feature hierarchy that is pretrained efficiently via the Moore–Penrose pseudoinverse, followed by physics-informed nonlinear least-squares optimization of the final layer.

Comparative results highlight the trade-offs among classical least squares, single-layer PIELM, and Deep PIELM in terms of OD accuracy, robustness under poor initialization, and computational efficiency under sparse optical and range measurements from a limited set of ground stations. For suitable hyperparameter configurations, the multilayer architecture provides improved stability and accuracy over the single-layer variant while retaining low training times, positioning Deep PIELMs as an effective complement to classical least-squares OD when robust performance without reliable initial guesses is required. The presented work is part of the Artificial Intelligence for Precise Orbit Determination project.

How to cite: Dallinger, F., Aigner, B., Andert, T., and Haser, B.: Single- vs. Multilayer Physics-Informed Extreme Learning Machines for Orbit Determination, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19927, https://doi.org/10.5194/egusphere-egu26-19927, 2026.

Recent advances in artificial intelligence and geospatial data analytics have led to an increasing adoption of data-driven approaches in the identification and prediction of mineral deposits. Traditional mineral exploration methods often rely on single data sources or expert-driven interpretations and may therefore be inadequate in regions where geological information is limited or spatially complex. In contrast, artificial intelligence–based approaches enable the quantitative assessment of mineral potential and the identification of spatial patterns associated with mineralization by jointly integrating multi-source geological, geophysical, and remote sensing data. Therefore, the comparative evaluation of different artificial intelligence algorithms using approaches that account for spatial dependence is critical for selecting reliable and interpretable models in early-stage mineral exploration conducted under data-limited conditions.

This study focuses on a comparative evaluation of artificial intelligence algorithms for predicting potential iron (Fe) mineralization under limited geological data conditions in a region with metallic mineralization potential in Türkiye. The study area covers approximately 2,340 km². A total of seven predictor variables were incorporated into the modeling, classified into geological (lithology, geological age, formation type), structural (fault density), geophysical (magnetic anomaly and gravity-tilt features), and remote sensing–based datasets (iron oxide potantial zones derived from ASTER imagery). The mineralization inventory is highly sparse, comprising only 15 iron occurrences and 24 non-iron reference points selected by geologists To address this limitation, a spatially aware hard negative mining strategy was applied, in which negative samples were preferentially selected from areas spatially proximal to known mineralization occurrences. Model performance was evaluated using GroupKFold-based spatial cross-validation to minimize bias arising from spatial autocorrelation, within which the Random Forest (RF) and XGBoost (XGB) algorithms were compared. The obtained results show that the RF and XGB models achieved mean Area Under Curve (AUC) values of 0.85 and 0.89, respectively. According to the generated mineral prospectivity maps, the Random Forest model delineates approximately 207.02 km² of high-potential areas (probability ≥ 0.90), while the XGBoost model identifies high-potential areas covering approximately 404.04 km² at the same probability threshold. These results indicate that there are pronounced differences in the spatial distribution of high-potential areas depending on the algorithm used. Additionally, the feature importance analysis revealed that geological age, magnetic anomaly, formation type, and gravity-tilt features are the primary controlling factors influencing the spatial distribution of iron mineralization.

This study outcomes revealed the importance of algorithm selection and spatially aware validation strategies in artificial intelligence–based mineral exploration. The findings indicate that reliable mineral prospectivity assessments can be achieved even under limited geological data conditions. Furthermore, in early-stage exploration programs, these approaches strengthen effective target area prioritization and decision-support processes and contribute to cost reduction through more efficient planning of exploration activities.

How to cite: Karakas, G., Civci, B., Topal, B., Gokceoglu, C., Ozcan, A., Erbasli, C., Cebeloglu, F. S., Koruyucu, M., and Binal, B. E.: Performance Comparison of Some Artificial Intelligence Algorithms for Metallic Mineral Deposits: A Case from Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20311, https://doi.org/10.5194/egusphere-egu26-20311, 2026.

Cracks on retaining walls and road surfaces can reveal the early warning signs of geohazards such as landslides or slumps in rural areas. However, even today, many governments still rely on manual visual inspection to identify and evaluate cracks, which is time-consuming, subjective, and highly dependent on individual experience. Artificial intelligence (AI) applied to Earth-observation imagery not only enables the detection of potentially dangerous cracks but also makes it possible to quantify their geometric properties, providing a more objective and quantitative basis for infrastructure monitoring and geohazard risk management.

Nevertheless, several key challenges remain. First, although recent studies have developed many advanced algorithms for crack detection and segmentation, methods for measuring crack width, length ,and area are still insufficient. Second, most existing models are designed for road cracks, while cracks on retaining walls present more complex textures, illumination conditions, and background noise, requiring dedicated model fine-tuning. Third, in regions with dense vegetation, branches, leaves, and shadows often produce false detections, making it difficult for AI models to distinguish real cracks from environmental interference.

In this study, we aim to quantify crack geometry from mobile panoramic Earth-observation imagery and to develop an AI model optimized for cracks on retaining walls in complex environments. A multi-stage approach is used to combine YOLO-based crack detection with 3D geospatial information for estimating the length, width, and area of individual cracks. By focusing on real cracks under vegetation-rich and noisy conditions, this approach advances AI-based quantitative analysis of surface degradation. These crack metrics provide a foundation for future retaining wall stability assessment and risk-informed infrastructure management.

How to cite: Chiang, Y.-C., Chu, S.-C., and Lin, G.-W.: AI-Based Quantification of Crack Geometry on Retaining Walls from Mobile Earth-Observation Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20838, https://doi.org/10.5194/egusphere-egu26-20838, 2026.

The Meteosat Third Generation (MTG) mission represents a major step forward in geostationary meteorological observation by combining, onboard Meteosat-12, multiple instruments with highly complementary characteristics. Among them, the Flexible Combined Imager (FCI) provides multispectral images of the full Earth disk every ten minutes with a spatial resolution reaching 1 km at nadir, while the Lightning Imager (LI) observes the same scene at a much higher temporal sampling, but with a coarser spatial resolution of approximately 4.5 km at nadir. Although designed for distinct operational purposes, these two sensors offer a unique opportunity for joint exploitation, as they observe identical atmospheric phenomena under fundamentally different spatio-temporal trade-offs. In this context, Thales investigates the use of artificial intelligence techniques to leverage this complementarity and generate enhanced observation products from existing MTG-I data. 

The core hypothesis of this work is that the high temporal density of LI observations implicitly encodes fine-scale spatial information. In other words, temporal correlations within LI time series can partially compensate for the sensor’s lower spatial resolution. By exploiting these correlations, fine spatial features can be reconstructed from high temporal frequencies. The availability of reference matching high resolution data enables to consider this process without the need for artificially degraded training data. 

To implement this hypothesis, a hybrid deep learning architecture combining convolutional neural networks (CNNs) and Transformers is proposed. CNN components are used to efficiently extract local spatial structures, such as gradients, cloud edges, and internal texture patterns, while Transformer-based attention mechanisms model short- and long-range temporal dependencies across successive LI acquisitions. This combination enables a joint representation of spatial detail and temporal coherence, while remaining compatible with large data volumes and near-operational processing constraints. 

The proposed approach is evaluated along two complementary scientific tasks. The first focuses on spatial super-resolution of LI images using LI temporal sequences alone. The second addresses the fusion of FCI and LI data to generate a product combining high spatial resolution with high temporal frequency. In both cases, the results are conclusive. The use of FCI images as a cross-reference makes it possible to assess the physical consistency of reconstructed features and to prevent the introduction of spurious, non-physical details. The super-resolved products remain radiometrically consistent with the input observations, with low radiance discrepancies (RMSE below 1), while recovering finer spatial structures than those achievable through conventional interpolation methods. Compared to standard SISR (Single Image Super Resolution), CNN + Temporal Conv1D, CNN + sparse Conv3D approaches, the hybrid CNN–Transformer model achieves the best overall performance. 

As a perspective, the proposed method shows strong potential for operational deployment. Its computational efficiency allows approximately one hour of MTG data—corresponding to about sixty full-disk Earth images—to be processed in less than five minutes on standard computing infrastructure with one Nvidia H-100 configuration, paving the way for the routine generation of high-resolution, high-frequency products from existing geostationary missions. 

How to cite: Dublé, N., Tanguy, S., Arsene, L., Poulain, V., Puechmaille, D., Hinojo Comellas, O., and Stoicescu, M.: AETHER: AI Enhancement for Third-gen Earth observing ImageR. Reaching 3x spatial upsampling and 10x temporal upsampling from existing MTG-I products., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22777, https://doi.org/10.5194/egusphere-egu26-22777, 2026.

Extreme windstorms are among the costliest natural disasters in northwest Europe. Traditional wind measurement methods, while reliable, are limited by cost, installation complexity, and sparse spatial coverage, particularly in cluttered urban areas. Higher resolution approaches are therefore needed to monitor near-surface wind dynamics in complex settings.

Motion tracking in videos of foliated trees has reliably captured fine-scale wind variability, showing strong correlations with anemometer measurements, consistent gust estimates across reference objects, and strong temporal coherence; the present work introduces two major advances: (1) the first application of Visual Anemometry (VA) on videos generated from physics-based simulations of trees, and (2) a focus on defoliated trees, enabling mechanistic isolation of branch and trunk responses. Videos are generated from an elastically articulated body model simulating tree responses under mean wind speeds of 7-40 m s⁻¹ (25-144 km h⁻¹).

Results show that input wind speeds are reflected in kinematic tree responses (R² = 0.8), and then that these tree motions are captured in wind speeds estimated from videos by VA (R² ≈ 0.7). Distal and mid-canopy branches dominate the VA response, whereas the stem and inner branches provide only weak contributions, even though informative motion cues may occur anywhere in the canopy. These VA wind estimates were insensitive to camera orientation, confirming that estimation accuracy remains robust across horizontal viewpoints. In this work, we also explore the method's sensitivity to different camera parameters and assess its transferability across different conditions.

Using simulation-based defoliated trees, this work is a step towards a low-cost, scalable alternative to traditional methods, enabling improved detection of extreme gusts, fine-scale hazard mapping, and risk assessment for urban planning and insurance.

How to cite: Kulkarni, S., Hillier, J. K., Bugby, S. L., Marjoribanks, T. I., Bannister, D., and Higham, J.: Seeing the Winds Better: Simulated Videos of Defoliated Tree Motion Capture Extreme Wind Speeds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-254, https://doi.org/10.5194/egusphere-egu26-254, 2026.

The SPRUCE experiment is the primary component of the Terrestrial Ecosystem Science Scientific Focus Area at ORNL focused on terrestrial ecosystems and the mechanisms that underlie their responses to environmental change. As of December 2025, the SPRUCE experiment is ending its planned decadal timeframe. The manipulation evaluated the response of the existing biological communities to a range of warming levels from ambient to +9°C, provided via large, modified open-top chambers. The ambient and +9°C warming treatments were also conducted at eCO2 (in the range of 800 to 900 ppm). Both direct and indirect effects of these experimental perturbations have been analyzed to develop and refine models needed for full Earth system analyses. The instruments and infrastructure needed for these measurements include meteorological tower-based CO2/H2O sampling and analysis systems, air temperature and relative humidity probes, precipitation gauges, wind speed and direction instruments, solar radiation sensors, subsurface soil moisture and temperature sensors, water level and conductivity sensors, continuous vegetation sap flow and dendrometer sensors, and companion dataloggers to record and report the data. The data collection system deployed of approximately 80 dataloggers, 1100 sensors, and associated instruments. Through the 10 years of observations, we collected substantial amount of data and want to present variety of data product available for analysis and scientific investigation. 

How to cite: Krassovski, M., Mayes, M., Velliquette, T., Ruggles, T., Hanson, P., and Riggs, J.: Spruce and Peatland Responses Under Changing Environments (SPRUCE) - 10 years of data collection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1694, https://doi.org/10.5194/egusphere-egu26-1694, 2026.

High spatiotemporal monitoring of near-surface precipitation phase (N-SPP) is essential for weather forecasting, transportation, agriculture, and hydrology. However, operational capability remains constrained as manual observations are being phased out, in situ surface instruments are sparse and often lack representativeness, and discrepancies persist between aloft phase estimates from radar/satellite and actual conditions at the ground. Recent studies have also reported an “upper-limit” effect in radar-based N-SPP products, further underscoring the need for low-cost, automated, and scalable alternatives.

Urban surveillance cameras are ubiquitous in modern cities and continuously capture near-surface scenes at high temporal resolution. As precipitation particles traverse the camera field of view, the resulting videos inherently preserve rich visual–temporal signatures of hydrometeors, providing a viable basis for visually discriminating precipitation phases. Moreover, leveraging existing camera infrastructures requires no dedicated sensor deployment, making camera sensor networks a cost-effective solution for high-resolution N-SPP monitoring. Nevertheless, reliably distinguishing common N-SPP types (i.e., rain, snow, and graupel) in unconstrained videos remains challenging due to subtle inter-class differences and strong sensitivities to illumination changes, complex backgrounds, camera settings, and wind-driven trajectories.

Our previous study (Wang et al., 2025) demonstrated the feasibility of using urban surveillance cameras for N-SPP monitoring. Guided by meteorological, optical, and imaging principles, we identified discriminative cues from both daytime and nighttime videos and developed an efficient framework that couples MobileNetV2-based transfer learning for spatial feature extraction with GRU-based temporal modeling. Using a self-curated 94-hour surveillance video dataset and benchmarking against 24 baseline methods, the proposed approach achieved the best overall performance, with accuracies of 0.9677 on the dataset and 0.9301 in real-world field validation against manually quality-controlled 2DVD measurements. The model also remained stable under variations in camera settings and across day–night conditions, and exhibited satisfactory wind robustness for wind speeds below 5 m/s.

Building on these results, we further move toward operational, city-scale deployment by discussing key challenges in multi-camera collaborative observing, including camera siting and field-of-view geometric constraints, automated camera screening and tiered selection, and quality-control/anomaly-detection procedures to address occlusion, glare, raindrop adhesion or wiper interference, stream frame loss, and camera-parameter drift. These discussions provide practical guidance for constructing a stable and reliable surveillance camera–based N-SPP monitoring network.

Reference:

Wang, X., Zhao, K., Huang, H., Zhou, A., & Chen, H. (2025). Surveillance camera-based deep learning framework for high-resolution ground hydrometeor phase observation. Atmospheric Measurement Techniques Discussions, 2025, 1-38.

How to cite: Wang, X., Zhao, K., and Yang, Z.: Surveillance Camera Sensor Networks: An Emerging Observing System for Near-Surface Precipitation Phase Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2076, https://doi.org/10.5194/egusphere-egu26-2076, 2026.

The health effects of ultrafine particles (UFP) have come under increased scrutiny. In 2021, the Health Council of the Netherlands published the advisory report “Risks of ultrafine particles in the outside air”, which highlighted that our knowledge of UFP exposure and health effects is limited by a lack of structural measurements of UFP concentrations in the Netherlands. The report therefore recommended

• measuring UFP concentrations structurally in the Dutch National Air Quality Monitoring Network and
• performing structural and validated model calculations to obtain a national overview of UFP exposure.

At the subsequent request of the Ministry of Infrastructure and Water Management, RIVM made an inventory of available data, knowledge and measurement equipment, and developed integrated strategies for incorporating structural UFP measurements into the national network and improving UFP models.

The National Air Quality Monitoring Network currently performs indicative UFP measurements with three TSI EPC 3783 instruments. Diurnal and weekly cycles can already be observed in these indicative data, and they have been used to scale an updated empirical map of average UFP concentrations in the Netherlands as well. On a project base, short-term measurements of UFP are performed using more compact equipment.

Newer counting equipment (TSI CPC 3750-CEN10) has been purchased recently to perform stationary UFP concentration measurements according to the latest technical standard (EN 16976:2024). The locations where the new measurement equipment will be placed are selected with the specific aim to improve the yearly average concentration map and obtain better estimates for the exposure of the Dutch population to UFP. The data will be made available to the public.

This presentation will discuss the progress so far, first experiences with the new equipment and next steps, including steps required for compliance with the new EU Air Quality Directive.

How to cite: Batenburg, A., Wesseling, J., Wever, D., van Ratingen, S., Weijers, E., and Stefess, G.: Expansion of UFP measuring capabilities of the Dutch National Air Quality Monitoring Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3864, https://doi.org/10.5194/egusphere-egu26-3864, 2026.

From rapid assessment to real-time warning: short-term rockfall monitoring along the Route d’Anniviers (Valais, Switzerland)

The Val d’Anniviers is one of the major valleys of the canton of Valais in Switzerland. Well known for its winter sports opportunities and for the villages of Grimentz and Zinal, the valley is accessible by a vertiginous road exposed to a wide range of gravitational hazards. Though the Route d’Anniviers is protected by multiple galleries, severe events can still impact the road.In March 2024, a series of rockfalls led to heavy accumulation of debris atop a road gallery at the entrance of the valley. A week later, on 29 March, bigger blocks hit the road and perforated the gallery. To avoid complete isolation, an alternative route estimated to be less safe was used from the other side of the valley. In this context, real-time monitoring became essential to support decision-making and rapid response during emergency operations and gallery rehabilitation. A first emergency phase focused on rapid assessment: Geoprevent was mandated by the local authorities to deploy an interferometric radar to monitor and accurately assess the area above the Croisettes sector (near Vissoie) and check whether significant movement was ongoing. Early observations suggested only limited changes. This initial phase helped establish shared situational awareness and informed the next steps toward an operational warning setup. An alarm system was then needed to be installed to increase the safety of workers during the gallery renovation, as well as to contribute to a longer-term mitigation plan. Geoprevent was mandated on 10 April by the local authorities and installed a rockfall Doppler radar on 22 April. This technology enables real-time detection of rockfall activity. The radar was coupled with traffic lights and alarm horns, allowing rapid road closure and immediate on-site warning in case of events. SMS and email alerts complement local signaling, and both radar data and live footage from a pan-tilt-zoom remotely controllable camera are available through Geoprevent’s online data portal for remote supervision and event review. This contribution  presents the phased monitoring and warning approach implemented at Vissoie—from rapid assessment to operational warning system —and discusses lessons learned for short-term monitoring, emergency installation, data interpretation, and reliable alerting to insure road and people safety in a complex environment.

How to cite: vincent, S., carrel, M., st.pierre, T., vonwartburg, J., stitelmann, O., and wetter, J.: From rapid assessment to real-time warning: short-term rockfall monitoring along the Route d’Anniviers (Valais, Switzerland), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4030, https://doi.org/10.5194/egusphere-egu26-4030, 2026.

The National Center for Atmospheric Research (NCAR), funded by the US National Science Foundation, has been supporting field deployments for atmospheric research since the 1960s, with its Earth Observing Laboratory (EOL) coordinating large-scale programs. EOL’s In-situ Sensing Facility (ISF) was forged out of decades of instrument development, advances in technology and software, and an evolution of services and support in response to the changing landscape of research. Today, ISF has evolved into three measurement systems: (1) Airborne Vertical Atmospheric Profiling System - dropsonde technology, (2) Integrated Sounding System - combined in-situ and remote ground-based profiling instruments, and (3) Integrated Surface Flux System - suite of mesonet, turbulence and energy balance sensors mounted on scalable towers. These requestable observing systems are designed for scalability and flexibility, emphasizing sensor development and integration, data management, and robust deployments to remote or challenging locations. Observations from ISF's measurement systems have guided regional model and forecast development, supported boundary layer meteorology and turbulence studies, and enhanced our understanding of severe weather and convective processes. When configured together ISF forms the foundation of LOTOS (Lower Troposphere Observing System) - a proposed sensor network to sample fundamental state parameters vertically through the boundary layer and horizontally across the surrounding landscape to provide a wealth of data to advance process studies and model algorithm development. We present an overview of our facilities' instrument capabilities and the LOTOS concept. 

How to cite: Witte, J., Brown, W., Vömel, H., Roden, C., Hoch, S., and Hock, T.: The In-situ Sensing Facility: Agile Observation Networks for Field Campaign Success, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4108, https://doi.org/10.5194/egusphere-egu26-4108, 2026.

How to cite: Kociok, T., Kociok, K., Seiffer, D., and Stein, K.: A Dual-Mode Expansion Cloud Chamber for Reproducible Laboratory Studies of Laser Propagation in Fog and Clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4925, https://doi.org/10.5194/egusphere-egu26-4925, 2026.

Cloud radars are powerful tools for investigating cloud formation, radiative processes, and cloud microphysics. In recent years, polarimetric cloud radars have become increasingly common around the world. Since many cloud property retrieval techniques rely on accurately measured reflectivity, ensuring high-quality calibration is essential. The most widely used calibration approach is one based on disdrometer measurements, but this method carries significant uncertainties, particularly due to the vertical variability of rainfall characteristics. Another conventional method is the one based on point target observations, i.e. using hard targets such as corner and sphere reflectors (Toledo et al.,2020), but it is work intensive and difficult to carry out. Another method is the calibration transfer of a radar to those that are not calibrated yet (Jorquera et al.,2023) for e.g. BASTA radar in CCRES. The main disadvantage of the calibration transfer method is the high time consumption. Since the time needed to ship the reference radar to each location and carrying out the calibration is high, in a year maximum 2 or 3 radars can be calibrated. Another method of calibration is by comparison of observations by ground-based cloud radars and space-borne W-band radars in CloudSat and EarthCARE. EarthCARE (CloudSat) flight cycle of 25 (16) days and the requirement in pure ice nonprecipitating clouds during an overpass, makes this method mainly applicable for long-term calibration monitoring. To address all these limitations and complement in cloud radar calibration methods, A. Myagkov et al. (2020) introduced a self-consistency calibration technique that makes use of the polarization capabilities of W-band cloud radars. In this study, we assess the suitability of the self-consistency calibration method and identify the modifications required to make the approach more user-friendly and practical for operation.For this study, we have used 94 GHz cloud radar data operated at 30⁰ elevation angle during days having rainfall rate less than 20 mm/hr. The methodology of self-consistency method consists of 4 steps. 1) Using Rayleigh Plateau detection method (Unal and van den Brule,2024), retrieve propagational (K_dp) and backscattering (δ) components from differential phase (φ) and, differential attenuation (A_dp). 2) Calculate non-attenuated reflectivity Z₀. 3) Calculate K_dp and A_dp using Z₀, δ and the coefficients given in A. Myagkov et al. (2020). 4) Compare the measured and calculated K_dp and A_dp, and find the best fit for calibration coefficient.The major findings of this study are summarized as follows:1) A 30° elevation angle and rainfall rate below 20 mm/hr are not the only criteria required for applying the self-consistency method. The values of differential backscatter phase(δ) and Doppler spectrum width also play important roles. 2) The influence of surface temperature on the method has been examined.3)The criteria for selecting suitable cloud radar observations for the self-consistency calibration approach have been clearly identified.4)In addition to the calibration of reflectivity, the retrieval of the one-way attenuation profile is shown to be another significant output of the self-consistency calibration technique.

How to cite: Nandan, R. and Unal, C.: Applicability of self-consistency calibration method for polarimetric cloud radars, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5380, https://doi.org/10.5194/egusphere-egu26-5380, 2026.

Improving measurements of atmospheric water vapour remains a priority for the atmospheric science community, including for evaluation of satellite retrievals. The Pandonia Global Network (PGN) provides long-term, high-temporal-resolution direct-sun observations of total-column water vapour at many sites worldwide, but the PGN H₂O product has not yet been widely assessed against independent ground-based datasets across diverse conditions.

Here we present preliminary intercomparisons of PGN total-column water vapour with coincident AERONET sun-photometer retrievals at co-located sites, with additional context from comparisons to GRUAN-processed radiosonde profiles where available. We examine sensitivities to temporal matching and to factors such as solar zenith angle, season, and humidity regime and report initial network-wide and site-to-site statistics (bias, scatter, correlation).

How to cite: Weaver, D., Zhao, X., Hanisco, T. F., Gupta, P., Cede, A., Tiefengraber, M., Gebetsberger, M., and Sommer, M.: Global evaluation of Pandonia Global Network total column water vapour using co-located AERONET and GRUAN observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5857, https://doi.org/10.5194/egusphere-egu26-5857, 2026.

In situ atmospheric particulate matter (PM) monitoring networks play a critical role in advancing understanding of air quality dynamics across local to regional scales by providing continuous, site-resolved observations. In contrast to short-term measurement campaigns, sustained monitoring networks enable the characterization of long-term trends, episodic events, and source-specific variability, while providing essential data for model validation and exposure assessment. These capabilities are especially useful in industrial environments, where emissions are spatially heterogeneous, temporally variable, and chemically complex.

We present the design and implementation of the Environmental Monitoring for Industrial Sites (ÉMIS) network, a ground-based, distributed system developed to monitor complex industrial emission environments. The network is designed to operate under challenging conditions, including high-latitude environments with extreme seasonal variability, limited access to grid power, and constrained connectivity. Each station integrates measurements of particulate matter across multiple size fractions (PM_2.5, PM₅, and PM₁₀) and volatile organic compounds using low-cost optical sensors, local meteorology, and visual documentation via a conditionally triggered camera. Stations are additionally equipped with a modified Wilson and Cooke (MWAC) bottle sampler to collect long-term, sector-representative samples for chemical characterization.

Stations transmit data using long-range radio (LoRa) at user-defined intervals (e.g., two-minute resolution) to a hub node, which aggregates and relays data via cellular or satellite communication to an online dashboard. Unlike Bluetooth or Wi-Fi, LoRa enables kilometer-scale data transmission without the cost or infrastructure requirements of cellular modems. This architecture provides near–real-time insight into emission dynamics while supporting long-term data continuity. Emphasis on low-cost, modular instrumentation reduces financial and logistical barriers associated with traditional monitoring systems, enabling high-density deployments accessible to researchers, industries and public stakeholders.

The initial field deployment consisted of fifteen (15) stations distributed across a copper smelter in Quebec, Canada, spanning more than 1 km². This case study demonstrates the network’s ability to resolve spatial gradients and localized emission signals, while dealing with complex topography and climate conditions. Analysis reveals persistent PM hotspots associated with heavy machinery traffic, ore handling operations, and slag cooling. Periodic PM spikes linked to train transport, as well as clear relationships between wind speed, wind direction, and plume occurrence were identified.

The multi-level data output from the ÉMIS network supports a wide range of applications, including PM source attribution, evaluation of emission dynamics, integration with receptor and dispersion modeling, and validation of satellite-derived products. The network also serves as a testbed for sensor development, quality assurance refinement, and cross-network harmonization under real-world industrial conditions. This work highlights how adaptable, cost-effective ground-based monitoring networks can expand observational capacity in industrial environments and support advances in atmospheric science, air-quality management, and community-informed decision-making.

How to cite: King, J., Norris, E., and Hayes, P.: Environmental Monitoring for Industrial Sites (ÉMIS): A Distributed LoRa-based Network for Real-Time Particulate Matter Characterization in Complex Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13103, https://doi.org/10.5194/egusphere-egu26-13103, 2026.

Opportunistic sensing based on microwave satellite downlinks has recently gained attention as a cost‑effective approach for monitoring tropospheric phenomena, exploiting the attenuation experienced by communication signals as they propagate through the atmosphere. While this approach has been successfully applied to meteorological phenomena—particularly rainfall estimation using Ku‑band broadcasting links and, more recently, Ka‑band broadband services—its use for geophysical monitoring remains largely unexplored.  Volcanic emissions, in particular, release atmospheric constituents capable of significantly affecting microwave propagation through absorption and scattering, depending on particle size, concentration, and chemical composition. In regions surrounding active volcanoes, the interaction between ash, gases, and microwave signals offers an opportunity to detect eruptive activity using low‑cost ground receivers.

This work investigates the feasibility of using commercial satellite downlink signals to sense volcanic emissions in real time. The analysis considers the general interaction between microwave signals and volcanic constituents and examines how the Ku‑ and Ka‑band frequencies commonly used by GEO satellites provide different levels of sensitivity to these atmospheric components. Since these signals are continuously available over wide areas, they offer an attractive resource for passive and inexpensive monitoring.

A key aspect in assessing the feasibility of such opportunistic sensing is the geometry of the satellite–receiver link, which strongly influences the detectability of volcanic emissions. As matter of fact, the relative position of the satellite, the ground station, and the volcanic plume determines whether the microwave path intersects the cloud and at which altitude and distance from the vent this intersection occurs. In volcanic regions such as Mount Etna (Italy), the simultaneous visibility of multiple GEO satellites at different azimuths and elevations increases the likelihood that at least one downlink path crosses the plume, enabling the detection of its impact on the received signal.

This preliminary study provides thus a first assessment of the geometric and physical conditions under which satellite downlink signals can be exploited for volcanic emission detection.

The results suggest that existing broadcast satellite infrastructure could be leveraged also as a low‑cost, wide‑area monitoring system, complementing conventional geophysical instruments and motivating future experimental validation.

Acknowledgements: This work was supported by the following projects: Space It Up, funded by Italian Space Agency (ASI) and the Italian Ministry of University and Research (MUR) – Contract 2024-5-E.0 - CUP I53D24000060005; FoReLab (Departments of Excellence), funded by MUR.

How to cite: Giannetti, F., Sciortino, E. M., Gherardini, O., Sapienza, F., and Piras, A.: Preliminary Design of a Passive System for Monitoring Volcanic Emissions Exploiting Microwave GEO Satellite Downlinks , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13621, https://doi.org/10.5194/egusphere-egu26-13621, 2026.

The island of Tenerife (Canary Islands) exhibits the highest volcanic risk in Spain, owing to its long volcanic history combined with high population density. Consequently, an effective volcanic early-warning system is essential to ensure the safety of both the island’s residents and the millions of tourists who visit each year. Tenerife already hosts one of the most advanced volcano monitoring programs worldwide, integrating permanent instrumental networks with periodic geophysical and geochemical surveys.

Continuous microgravity measurements have proven to be a sensitive and reliable tool for detecting changes in magmatic–hydrothermal systems that may remain undetected by other geophysical or geochemical techniques [1]. However, spring-based gravimeters are affected by instrumental drift, which can compromise the interpretation of long-term observations. Superconducting gravimeters, while offering the highest sensitivity, require complex liquid helium refrigeration systems, significantly limiting their operational deployment. In recent years, absolute quantum gravimeters have emerged as the state-of-the-art technology for microgravity monitoring [2]. Time series acquired at active volcanoes, such as Mount Etna [3], demonstrate that these instruments can detect and quantify subsurface mass redistributions associated with volcanic processes. Within the framework of the GEOFIS-CAN project, the Instituto Volcanológico de Canarias (INVOLCAN) has acquired three Exail AQG-A absolute quantum gravimeters to be deployed on Tenerife.

The volcanic system of Tenerife consists of a central caldera (Las Cañadas), which hosts the Teide–Pico Viejo volcanic complex and is characterized by both basaltic and phonolitic activity, and three radial dorsals dominated by fissural basaltic effusive eruptions. The central complex, as well as the northwestern (NW) and northeastern (NE) dorsals, have experienced eruptions within the last 500 years. The three AQG instruments will be installed at: (1) the NW dorsal (Santiago del Teide), the most volcanically active sector of the island; (2) the boundary between the Las Cañadas caldera and the NE dorsal (Izaña); and (3) the southern margin of the caldera (Vilaflor). This network geometry provides comprehensive coverage of all regions potentially affected by future eruptions. Moreover, it enables not only the detection but also the localization of subsurface mass changes within the volcanic edifice. Specifically, this configuration is sufficient to simultaneously and unambiguously constrain both the location and magnitude of the mass variations within the volcanic edifice.

In this work, we present a sensitivity analysis of the proposed gravimetric network configuration, together with preliminary results from the recorded dataset.

References

[1] de Zeeuw-van Dalfsen, E., & Poland, M. P. (2023). Microgravity as a tool for eruption forecasting. Journal of Volcanology and Geothermal Research, 442, 107910.

[2] Ménoret, V., Vermeulen, P., Le Moigne, N., Bonvalot, S., Bouyer, P., Landragin, A., & Desruelle, B. (2018). Gravity measurements below 10− 9 g with a transportable absolute quantum gravimeter. Scientific reports, 8(1), 12300.

[3] Antoni‐Micollier, L., Carbone, D., Ménoret, V., Lautier‐Gaud, J., King, T., Greco, F., ... & Desruelle, B. (2022). Detecting volcano‐related underground mass changes with a quantum gravimeter. Geophysical Research Letters, 49(13), e2022GL097814.

How to cite: D'Auria, L., Pérez, N. M., Álvarez-Hernández, A., de Armas-Rillo, S., García-Hernández, R., López-Díaz, P., M. van Dorth, D., Ortega-Ramos, V., Bertier, P., Faure, L., Gautier, R., and Richard, J.: The Quantum Gravimeter Network of the Canary Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13828, https://doi.org/10.5194/egusphere-egu26-13828, 2026.

Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)²INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios.

How to cite: Chen, J.: Service-Chain-Driven Communication and Computing Integration Networking: A Case Study of Levee Piping Hazard Inspection via Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15440, https://doi.org/10.5194/egusphere-egu26-15440, 2026.

Accurate monitoring of rock movement is essential for understanding rock slope instability and early failure mechanisms. However, conventional monitoring techniques often rely on manual measurements or expensive instrumentation, limiting their practicality for continuous and large-scale deployment. This study presents a low-cost image-based monitoring system that enables fully automatic measurement of rock displacement using a YOLOv8 pose estimation model. The proposed system was evaluated at the Miroir d’Argentine rock wall, a limestone rock flake in the Swiss Alps, Switzerland, using image data acquired from a Futuro BRW comparator monitored by a low-cost digital camera. The model was trained to detect the manual dial indicator and estimate pointer positions, allowing rock displacement to be derived automatically from pictures without manual intervention. To further assess the practical applicability of the proposed system, additional images acquired at different time periods were used for independent validation. The results demonstrate that the proposed approach can reliably estimate rock movement with high accuracy and strong generalization capability under real-world conditions. In addition, the robustness of the method was evaluated under simulated fog and blur degradations. The results show that the system maintains stable performance under light to moderate visual degradation, while performance decreases under severe fog and strong motion blur due to reduced geometric visibility. Overall, the proposed method provides an effective, low-cost, and practical solution for continuous rock movement monitoring and shows strong potential for long-term deployment in challenging alpine environments.

How to cite: Lin, R., Jaboyedoff, M., Derron, M.-H., Laurent, A., and Chalé, A.: A Low-Cost Image-Based Monitoring System for Automatic Rock Displacement Measurement Using YOLO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17517, https://doi.org/10.5194/egusphere-egu26-17517, 2026.

MEMS Tiltmeters are an effective tool in high-precision monitoring of ground and infrastructure but their sensitivity to temperature variations remains a challenge. This can be a prohibiting factor for their application in field monitoring of natural processes, despite the fact that tiltmeters are likely one of the very few technologies that can provide information on minute movements in real-time and at relatively low cost. Temperature drift, if not removed effectively, can completely mask small tilts or lead to wrong interpretations of the monitored process. Up until very recently, the tiltmeter response to temperature change was assumed to be instantaneous, but previous work by the authors has shown that there is a delay of varying duration between the two over time. We present a weighted least square (WLS) - based method that takes this dynamic change of time-lags into consideration. The time-series data is divided into a number of time-windows based on time-lag values, wherein windows with identical lags are grouped together. Time windows longer than a specified duration are subdivided based on an optimal window length. We describe a method for selecting the optimal window size applicable to different monitoring scenarios. Finally, an iteratively WLS is applied to produce values that best represent the temperature drift over time within each time window. To examine the impact of varying window sizes on results, a sensitivity analysis is performed using the Monte Carlo method, enabling the calculation of prediction intervals. Our approach provides a reliable framework for the removal of temperature drift from tiltmeter recordings, enabling the use of tiltmeters for the monitoring of subtle ground movements. This can be crucial for applications such as early warning systems for landslides and monitoring of the ground surface response to hydrological processes at depth.

How to cite: Pytharouli, S. and Qiu, C.: Removal of temperature drift from tiltmeter recordings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18236, https://doi.org/10.5194/egusphere-egu26-18236, 2026.

Long-term, routine monitoring of volatile organic compounds (VOCs) is needed to provide insight into emission sources and patterns, oxidation processes including photochemistry and radical cycling, and the formation of tropospheric ozone and secondary organic aerosol. However, instrumentation that can provide high quality data both in terms of temporal resolution and molecular speciation has historically been cost-prohibitive, and requires advanced users for field deployment, operation, and data analysis.

While the use of proton transfer reaction mass spectrometry, paired with time-of-flight technology (PTR-TOF-MS), has been utilized for the detection of VOCs in a wide variety of environments and measurement platforms; this technique cannot provide molecular structure information and suffers from detection interferences such as fragmentation, cluster formation, and mixed ionization schemes that complicate data analysis and interpretation. Combining in situ gas chromatography (GC) with PTR-TOF-MS is a remedy, offering isomer level resolution and the ability to easily quantify product ion distributions to account for complex detection schemes. The addition of chromatographic pre-separation not only enhances the accuracy of species typically reported by PTR methods, it can also be used to broaden the scope of VOCs quantified using PTR methods, while maintaining low limits of detection (typically 1 ppt) without losing high time resolution data and negating the need for high mass resolving power.

Here, we present a new instrument package for the online, long-term detection of VOCs consisting of an in situ GC system equipped with an integrated thermal desorption (TD) system coupled to a compact PTR TOF-MS. The entire instrument is contained in a 50 x 65 x 55 cm footprint, weighs < 70 kg, and consumes < 600 W for typical operation. Coupling the GC system with the PTR-TOF allows the automated acquisition of both direct, high-time resolution data (e.g. 1 s) and pseudo-continuous molecular speciation data for isomer separation and improved quantification of the direct-PTR data.

A description of the instrument, including its utilization of a new VUV PTR ion source, will be presented along with > 4 weeks of continuous ambient data acquired in Spring 2025 in Thun, Switzerland. This data demonstrates the stability of the system and the value of continuous VOC detection with regular molecular speciation. This compact, easier to operate and maintain system makes it feasible to deploy this technology in many locations, including those with access limitations, for greater spatial resolution to acquire coinciding datasets to elucidate local, regional, and global VOC trends. 

The compact chemical ionization time-of-flight mass spectrometer (CI-TOF-MS) described, here utilizing proton transfer reaction (PTR) ionization, was developed with support from the Beckmann Foundation.

How to cite: Claflin, M., Rohner, U., Yatsyna, V., Lerner, B., Dobrecevich, A., Thornton, J., Canagarantna, M., Lopez-Hilfiker, F., and Jayne, J.: Introducing a compact GC-PTR-TOF-MS: A novel method for the long-term monitoring of volatile organic compounds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20308, https://doi.org/10.5194/egusphere-egu26-20308, 2026.

Ground-based environment monitoring networks provide essential data for climate and air quality research. However, traditional monitoring infrastructure in remote areas often requires high upfront and operational costs. Satellite communication technologies offer a solution to reach these remote areas, but existing commercial systems frequently pair expensive data loggers with costly satellite transceivers, thus limiting their deployment potential. 

We present a novel satellite-enabled data logger designed to maximize compatibility while minimizing both purchase and operational costs. Our custom-designed Printed Circuit Board (PCB) combines data-logging and satellite transmission into a single unit, leveraging low-cost near-real-time bidirectional communication provided by the Astrocast CubeSat constellation. The integration approach helps reducing the hardware costs and the deployment complexity compared to traditional systems. 

The platform is built on an STM32L476 microcontroller (MCU) with 64 MB of internal memory and an integrated real-time clock (RTC), providing low-power operation essential for autonomous field deployments. The board is equipped with an atmospheric pressure sensor, an air temperature and relative humidity sensor, along with multiple communication interfaces for a flexible, sensor-agnostic architecture: UART, I²C, and ADC channels. This design allows seamless integration of different third-party environmental sensors such as atmospheric chemistry, hydrological monitoring or ancillary meteorological measurements, without requiring hardware modifications. 

The system pair the custom PCB with an Astronode S module for satellite data transmission, enabling bidirectional data transmission with 2-3 transmission opportunities every day. The design provides extended operational autonomy through low-power management while maintaining regular access to measurement throughout Astrocast's API and web UI. 

We demonstrate how this design advances the objectives of ground-based monitoring networks by: (1) reducing deployment barriers to remote monitoring through cost-effective satellite connectivity, (2) supporting flexible sensor integration for cross-disciplinary measurement campaigns, (3) providing a scalable foundation for distributed monitoring networks, and (4) offering a validated, replicable platform for future infrastructure development.

How to cite: Tagliacarne, F., Valentini, R., and Renzi, F.: Design and Implementation of a Low-Cost, Satellite-Enabled Environmental Data Logger, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20334, https://doi.org/10.5194/egusphere-egu26-20334, 2026.

Satellite scatterometers are active radar instruments that transmit microwave pulses towards the Earth's surface and measure how much of this energy is reflected back, a quantity commonly referred to as backscatter. Operating at C-band, the ERS-1/2 ESCAT (1991–2011) and MetOp-A/B/C ASCAT (2007–present) missions together provide more than 35 years of global land surface backscatter observations. These data are widely used for monitoring soil moisture, vegetation dynamics, cryospheric processes, and land cover change. Their coarse spatial sampling of 12.5 km enables very high daily global coverage, which reaches about 82 % during the ASCAT era.

A characteristic feature of scatterometer measurements is azimuthal anisotropy, meaning that backscatter depends systematically on the viewing direction. This arises because ESCAT and ASCAT use multiple fixed fan-beam antennas that observe the same surface under different azimuth angles as the satellite passes overhead. Oriented surface structures such as vegetation patterns, agricultural rows, sand dunes, or wind-blown snow features (sastrugi), interact differently with the radar signal depending on their orientation relative to the radar look direction. While physically meaningful, this directional dependence introduces additional variability when measurements from different viewing geometries are combined, complicating the interpretation of collocated time series.

We present an updated azimuthal correction scheme for ESCAT and ASCAT backscatter that aims to improve the temporal consistency of long-term land surface monitoring. Earlier approaches relied on a unique static reference polynomial derived from multi-year data and mixed viewing geometries to implement the desired azimuthal bias correction, thereby harmonizing the data. Our updated method applies yearly reference polynomials instead and explicitly distinguishes between ascending and descending satellite orbits. This approach thus accounts for long-term land cover changes and systematic diurnal differences between morning and evening overpasses, which are preserved in the corrected backscatter. In addition, the right-looking satellite swath is used as the reference geometry, reflecting the single-swath configuration of the legacy ERS scatterometers. Consequently, this also supports a consistent alignment of measurements across diverse satellite missions. Global comparison maps and local examples show that the updated correction effectively reduces azimuthal noise, as indicated by a lower estimated standard deviation of the corrected backscatter. At the same time, genuine geophysical signals, such as systematic morning–evening differences likely linked to moisture variability, are preserved.

Azimuthal anisotropy is generally undesirable for most monitoring applications, as quantities such as soil moisture do not depend on viewing direction. However, we show that the correction polynomials themselves can provide useful information in specific environments, including sand dunes, ice sheets, and areas dominated by strong point scatterers. This can, for example, enable studies of the temporal migration of sand dunes or sastrugi. We also show that even individual buildings can affect the full footprint of a 25 km ASCAT pixel, as metal–glass structures can produce strong corner reflections when aligned with the radar look direction. The proposed approach therefore supports robust long-term monitoring of natural processes by reducing azimuthal noise, while the correction parameters themselves provide physically meaningful directional information.

How to cite: Lindorfer, R., Hahn, S., Wagner, W., Harrison, C. T., and Melzer, T.: One Researcher's Noise is Another's Signal: Dynamic Azimuthal Correction of ESCAT/ASCAT Backscatter for Long-Term Land Surface Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20688, https://doi.org/10.5194/egusphere-egu26-20688, 2026.

The study of atmospheric composition is a major priority for improving the understanding of future climate models; therefore, it is essential to analyze in detail the variations of certain key variables, particularly water vapor, which strongly influences meteorological phenomena and plays a major role in radiative forcing. During my doctoral project, my research focused on studying the variability of water vapor using radiosoundings, an instrumental method for obtaining precise and high-quality measurements down to the UT/LS zone, by improving the quality of measurements through instrumentation. In addition to internal tests aimed at obtaining higher-quality measurements, setting up observation campaigns is a top priority in order to validate the performance of the sondes under real-world conditions, identify seasonal biases, and thus be able to make various corrections if necessary. Therefore, the TRACIS (Tropospheric Research Campaign on Air Moisture Content by Ipral at SIRTA) comparison campaign, in which I participated and which took place at the SIRTA site (Atmospheric Remote Sensing Research Instrumental Site) [48.71331°N, 2.20901°E] from May 12 to June 12, 2025, made it possible to assess the consistency of the data between the different sondes, while comparing these datasets with auxiliary sources such as ground-based observations and satellite measurements. This multi-source approach notably includes measurements from the IPRAL LiDAR, as well as fields from the ERA5 reanalysis model, thus strengthening the comparison, identifying potential systematic biases, and improving the overall interpretation of the results. Preliminary analyses focus on comparisons between the combined working measurement standard (CWS; mean RH M10/RS41) and other convergent datasets (M20, ERA5, and IPRAL LiDAR), allowing an evaluation of the representativeness and consistency of the different observation sources.

How to cite: Poignard, A., Farah, A., Sarkissian, A., Keckhut, P., Alraddawi, D., Khaykin, S., Dupont, J.-C., and Capo, J.: Comparison of TRACIS campaign data with data from radiosonde, IPRAL Lidar and ERA5, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21164, https://doi.org/10.5194/egusphere-egu26-21164, 2026.

Slow-moving landslides (SMLs) are governed by a combination of causative factors such as geology, tectonic setting, and climatic regime, along with triggering and conditioning factors including precipitation, complex groundwater dynamics, river toe erosion, seismic activity, and anthropogenic modifications. In this study, we present results from monitoring and analysis of a slow-moving landslide at Chandmari Village, Gangtok, in the eastern Himalaya. The analysis is based on real-world data obtained from an operational landslide early warning system active at the site since 2018. An integrated, multi-instrument dataset combining subsurface deformation measurements, hydro-meteorological observations, and high-resolution surface mapping is used to investigate landslide behaviour.

Slope movement was monitored using in-place inclinometer strings installed to depths of up to 30 m within the deforming slope mass to capture depth-wise displacement profiles. These measurements were analysed alongside continuous rainfall data recorded at 5-minute intervals from a local rain gauge to examine hydrological controls on deformation rates. In addition, Differential GPS (DGPS) surveys were conducted to map the spatial distribution of tension cracks on the slope surface. The combined dataset enabled assessment of both slip-surface depth and the spatial extent of the landslide body, as well as their response to seasonal rainfall.

Preliminary results indicate that periods of sustained rainfall correspond to increased displacement rates at specific depth intervals, suggesting activation of shear zones rather than a single, well-defined slip surface. Prolonged rainfall episodes further indicate the involvement of deeper slip surfaces. DGPS-based mapping reveals well-defined tension cracks at the head of the landslide body, while time-lag analysis between rainfall accumulation and deformation response highlights delayed slope adjustment controlled by groundwater dynamics.

The study demonstrates the temporal coupling between subsurface deformation and seasonal hydrological forcing and illustrates the effectiveness of multi-instrument monitoring for characterizing slow-moving landslides. The insights gained into deformation mechanisms support the development of improved early warning and mitigation strategies for urban hill-slope environments under long-term climatic and anthropogenic stress.

How to cite: Kumar M, N. and Vinodini Ramesh, M.: Integrated Analysis of Inclinometer, Rainfall, and DGPS Tension-Crack Data for a Slow-Moving Landslide: A Case Study from Chandmari Village, Sikkim, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21501, https://doi.org/10.5194/egusphere-egu26-21501, 2026.

Copernicus Ground-Based Observations for Validation (GBOV) is a project funded by the European Commission and is part of the Copernicus Land Monitoring Service (CLMS). The project has two main purposes: to support yearly validation efforts of core CLMS products (BRF, Albedo, FAPAR, LAI, FCOVER, LST, Soil Moisture, GPP, NPP, LSP and Evapotranspiration), five of which are listed among GCOS Essential Climate Variables (ECV); and to maintain a data portal making these validation products available. GBOV has reached a broad user community, with about 1700 users, including ESA optical MPC.

A large number of ground-based measurement networks disseminate publicly available data such as NEON, ICOS, TERN, BSRN and ISMN. Within GBOV, long-term datasets are needed in order to maximise the number of matchups between ground-based measurements and satellite data.

GBOV produces both ground-based, point-scale observations (the so-called “Reference Measurements”), and Analysis Ready Validation Data (ARVD) which are spatially upscaled to match the resolution of CLMS products (the so-called “Land Products”). Those GBOV products cover more than 150 sites and are made available to the community via a data portal freely accessible on https://gbov.land.copernicus.eu/. Available Land Products variables include Surface Bi-directional Reflectance Factors (BRFs), Surface Albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Vegetation Cover (FCover), Land Surface Temperature (LST), Surface Soil Moisture (SSM). Gross Primary Production (GPP), Net Primary Production (NPP), Land Surface Phenology (LSP) and Evapotranspiration are new products and are not yet available, their release is planned for the end of 2026.

The networks providing GBOV initial input data are unfortunately not evenly distributed. In an attempt to reduce the thematic and geographical gaps, GBOV is developing its own network as part of a collaboration with existing networks. In GBOV phase 1, six ground stations have been upgraded with additional instrumentation. In GBOV phase 2, two ground stations monitoring vegetation variables have been deployed in 2023, one at the Fuji Hokuroku research station in Japan as part of a collaboration with NIES (National Institute of Environmental Studies) and one in 2024 at the Fontainebleau research station (France) as part of a GBOV/ICOS collaboration. In addition, two LST stations were deployed in Fuji Hokuroku and Litchfield (TERN network Australia) in 2024.

Over the past year, several updates have been implemented in the GBOV database to better respond to CLMS and general user requirements. This includes new procedures for specific land cover types in vegetation products, improved uncertainty estimates for soil moisture, and enhanced processing procedures for LST products. Procedures are being developed for the new products: Gross Primary Production, Net Primary Production, Land Surface Phenology and Evapotranspiration.

This presentation will focus on the current status of GBOV products and highlight recent developments and evolutions.

How to cite: Lerebourg, C., Grousset, R., Nativel, S., Carrière, J.-S., Clerici, M., Gobron, N., Muller, J.-P., Song, R., Dash, J., Paramanik, S., James, F., Ghent, D., Anand, J., Shukla, R., and Perez-Hoyos, A.: Copernicus Ground-Based Observations for Validation service (GBOV): overview and latest updates for EO data Cal/Val, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21819, https://doi.org/10.5194/egusphere-egu26-21819, 2026.

Understanding groundwater dynamics in arid environments is challenging due to sparse monitoring networks and strong anthropogenic influences. This study explores the potential of time-lapse microgravity observations to characterize shallow aquifer storage variability in Al-Ain City, United Arab Emirates. Repeated microgravity measurements were conducted at four monitoring wells over a one-year period between 2018 and 2019, alongside continuous groundwater-level observations, to investigate temporal changes in subsurface water mass. Observed gravity variations exhibit pronounced spatial and temporal contrasts across the study area, reflecting heterogeneity in aquifer response. Groundwater-level fluctuations over the monitoring period indicate notable seasonal and inter-annual variability, which is consistently mirrored in the gravity signal. By jointly analyzing gravity anomalies and water-level changes, variations in groundwater storage were quantified under a range of plausible specific yield values representative of shallow arid aquifers. Estimated storage changes indicate substantial redistribution of groundwater mass over the monitoring period, with corresponding volumetric changes on the order of several tenths to more than one million cubic meters, depending on local aquifer properties. The results demonstrate that microgravity monitoring provides an independent and spatially sensitive means of assessing groundwater storage dynamics, particularly in settings where conventional piezometric coverage is limited. This approach offers valuable insights into aquifer behavior under arid climatic conditions and highlights the broader potential of gravity-based methods for groundwater assessment, resource management, and hydrological characterization in data-scarce regions.

How to cite: Darwish, S.: Tracking Shallow Aquifer Storage Variability Using Time-Lapse Microgravity Measurements in an Arid Environment: Evidence from Al-Ain, UAE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22102, https://doi.org/10.5194/egusphere-egu26-22102, 2026.

The University ‘G. d’Annunzio’ of Chieti Pescara Atmospheric Observatory (Ud’A Trabocchi) aims to investigate atmospheric chemistry, greenhouse gases evolution, atmosphere-soil exchanges and marine meteorology within a rural-marine setting. The facility has a 15 m tall tower with seven sample points: each every every 2 m. State-of-the-art instrumentation for long-term continuous measurements of CO₂, CH₄, N₂O and isotopic ratio of CH₄ and CO₂ (Picarro); O₃, NO, NO₂, NO_x, CO and SO₂ (Thermo); NO₂ (CAPS, Teledyne); CO₂ flux (LI-COR); Radon (Mi.lan); Aerosol concentration and size distribution (SMPS, TSI); column observation of O₃, NO₂, SO₂, HCHO (PANDORA 2S), Aerosol optical depth (CIMEL); GNSS reciver (Geoguard and Stonex); Meteorological station (Vaisala); Celiometer (Vaisala CL61). Finally, the station includes a UAV system equipped with meteorological sensors and low-cost sensor to detect CO₂, CH₄, O₃ and NO_x. Data of the first period of observation will be shown and will be discussed possible opportunity of collaborations and synergic activities.

How to cite: Di Carlo, P., Aruffo, E., Mascitelli, A., and Chiacchiaretta, P.: The University ‘G. d’Annunzio’ of Chieti Pescara Atmospheric Observatory (Ud’A Trabocchi): a new supersite for atmospheric observation and research in central Adriatic coast, Italy., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22380, https://doi.org/10.5194/egusphere-egu26-22380, 2026.

How to cite: Pickering, B.: Intelligent All-sky Cameras for Dense Mesoscale Observations: From Field Trials to Operational Pilots, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22806, https://doi.org/10.5194/egusphere-egu26-22806, 2026.

Small watersheds play a crucial role in sustaining river hydrology, ecological flows and local water security. However, they are increasingly threatened by climate change, rapid transformations of land use and escalation of anthropogenic pressures. These problems are worse in areas with little data, where few hydrological observations, sparse monitoring networks, and inconsistent long-term datasets make it hard to accurately assess vulnerability and make plans. To address this critical gap, this study introduces a unique and data-efficient Criteria Importance Through Intercriteria Correlation- Group Method of Data Handling (CRITIC-GMDH) hybrid framework, specifically developed to accurately assess watershed vulnerability in regions where large, continuous, or high-resolution datasets are unavailable. This interpretable decision-support approach integrates CRITIC for objective indicator weighting with the nonlinear modelling capability of the GMDH, enabling robust vulnerability prediction under constrained data conditions, overcoming key limitations of conventional hydrological models and black-box machine learning techniques. The framework incorporates eleven hydro-meteorological, geomorphological, and socio-economic parameters, including rainfall, temperature, runoff, watershed area, watershed length, water quality index, average slope, forest area, impervious area, population density, and highest flood level. The approach is demonstrated across four major river basins in Northeast India, such as Gomati, Haora, Khowai, and Manu, which represent highly sensitive and partially transboundary catchments. Future climate projections from CMIP6 SSP1-2.6 and SSP5-8.5 scenarios were used to compute the Vulnerability Index across decadal periods (2005–2065). Results show a significant escalation in vulnerability, particularly under SSP5-8.5, with Haora and Gomati exhibiting Vulnerability Index > 0.85, indicating extreme exposure to climate extremes, and urbanization stress. Sensitivity analysis identifies rainfall, runoff, and temperature as dominant controlling parameters, and validation through the Falkenmark indicator and green-blue water stress indices confirms emerging scarcity risks. The study provides a scientifically grounded pathway for watershed prioritization and climate-resilient planning, offering an adaptable methodological foundation for sustainable management of small river systems in data-scarce regions.

How to cite: Rudra Paul, A. and Kumar Roy, P.: Climate-Induced Vulnerability Assessment of Small Watersheds Using a CRITIC–GMDH Hybrid Model: A Methodology Tailored for Data-Scarce Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4175, https://doi.org/10.5194/egusphere-egu26-4175, 2026.

Urban roads in fast-growing cities fall apart quickly, and everyone feels the impact—traffic slows down, accidents happen, and the city’s economy takes a hit. The old way of checking roads—sending people out to inspect them on foot—just doesn’t cut it anymore. It’s slow, expensive, and puts workers in harm’s way. So, we’ve built something better: an automated system that uses drones and AI to keep an eye on road conditions.

Here’s how it works. Drones fly over city streets, snapping high-resolution images that pick up everything from big potholes to tiny cracks. We run these images through our analytics pipeline. First, we use classic machine learning to weed out the stretches of road that are still in good shape. That way, the system doesn’t waste time on areas that don’t need attention.

Next, we use a deep learning model—based on YOLO, which stands for “You Only Look Once”—to hunt down and label the actual problem spots. We’ve trained this model using annotated drone photos, so it can handle tricky lighting or weird road surfaces. The model doesn’t just spot the defects—it also nails down where they are, how big they’ve gotten, and how bad the damage is.

But spotting problems isn’t enough. City agencies need to see this info and act on it, fast. So, we’ve built a web portal using OpenLayers and PostGIS that maps out every defect. Maintenance crews can sort issues by type or severity, pull up interactive maps, and even generate reports to plan repairs.

This whole setup is practical, affordable, and scales up easily for any city that wants to take road maintenance seriously. By bringing together drones, AI, and smart mapping, we’re giving city managers the real-time, reliable data they need to keep roads safe and traffic moving. And honestly, this system can help any city make smarter decisions about their roads and urban development.

How to cite: Manu, H. and Bhoopathi, S.: UAV-Based Road Defect Detection Using Hybrid Machine Learning Approach with Web GIS Visualization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6705, https://doi.org/10.5194/egusphere-egu26-6705, 2026.

Root-zone Soil Moisture (RZSM; 10–102 cm) is a critical variable for land–atmosphere interactions, plant water availability, groundwater recharge, and hydrological extremes; however, its reliable estimation at deeper layers over large spatial scales remains challenging. Ground-based monitoring networks such as the International Soil Moisture Network (ISMN) provide accurate multi-depth soil moisture observations, but their utility is constrained by sparse station distribution, high installation and maintenance costs, and limited spatial coverage (Dorigo et al. 2011). In contrast, microwave remote sensing based satellite missions, including Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Sentinel-1, offer frequent and spatially continuous SM observations but are sensitive only to near-surface conditions (top ~5 cm), leaving deeper soil layers unobserved. This disparity between depth-limited in-situ observations and surface-focused satellite measurements motivates the present study to develop a machine learning based framework to estimate RZSM from satellite-derived surface SM by incorporating temporal memory and forcing. This approach effectively captures persistence effects and vertical moisture transfer, which are essential for accurate prediction of deeper SM layers (Pal &Maity, 2019). Multi-depth SM observations from 5 to 102 cm, obtained from ISMN stations and categorized according to USDA Hydrologic Soil Groups (HSG A–D; four stations per HSG), account for differences in soil water movement and retention behaviour (Ross et al. 2018). For each soil group, Support Vector Regression (SVR) and Random Forest (RF) models were trained using a sequential, depth-wise prediction strategy comprising four depth transitions: 5–10 cm, 10–20 cm, 20–51 cm, and 51–102 cm. Model evaluation demonstrates strong predictive performance across all depth intervals (R² = 0.85–0.95 for RF and 0.63–0.95 for SVR at validation sites), indicating that HSG classification effectively captures soil-specific SM dynamics. The trained models successfully generate comprehensive RZSM profiles using satellite-derived SM from the SMAP mission.These profiles are rigorously validated against ground-based observations and demonstrate strong applicability across diverse landscapes lacking direct subsurface measurements.

How to cite: Bakka, S. and Narayanan, S.: Machine Learning-Based Root-Zone Soil Moisture Estimation Using Satellite-Derived Surface Soil Moisture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10741, https://doi.org/10.5194/egusphere-egu26-10741, 2026.

Combined Sewer Overflows (CSOs) in cities can play a significant role in the morphology and hydraulic performance of streams near urban areas. A complete urban drainage system is often modelled by a dedicated hydrological/hydraulic model (e.g., SWMM or Mike+). However, these models must be calibrated against observations. Especially the flow from CSOs is difficult to estimate. The quality of the data and the drainage models depend on the accuracy of the overall mass balance of the drainage system. If it is not possible to estimate the discharge from, for example, a CSO, the results from other parts of the system become unreliable.

This research evaluates flow dynamics through a multi-methodological approach where the CSO is evaluated by theoretical models, CFD models, experimental work in a laboratory, and a new innovative method where the noise from a CSO is analyzed. The sound is both analyzed directly and by training a machine learning model on the laboratory experiments. The result is a hybrid model filtering all the estimates to one flow estimate. CFD has been used to model the specific CSO to take a Q-h relationship into account, and to generate a so-called catalog method. In this method, multiple variations of geometry are simulated in a free-surface CFD model to cover many different geometries, and general equations are extracted from these simulations.

The hybrid approach opens the door to a new way of estimating interactions between the urban water cycle and the receiving waters. Applying edge processing makes it possible to continuously adapt to local conditions that were not present during the calibration and validation of the model. Edge processing involves signal processing and modeling at the measuring point, where the maximum bandwidth of the sensor data is available and can be used for the most accurate data estimation.

How to cite: Rasmussen, M. R., Jackerott, M. U., Nielsen, J. M., Vestergaard, I. K., and Nielsen, J. E.: Combining different hydraulic methods to estimate the discharge from Combined Sewer Overflows (CSO) into streams., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11280, https://doi.org/10.5194/egusphere-egu26-11280, 2026.

This study investigates the coastal-region impacts of climate change on rice yield in Goa, India, a monsoon-driven agroecosystem highly dependent on paddy cultivation and vulnerable to rainfall variability, salinity intrusion, and rising temperatures. The study aims to (i) estimate future crop evapotranspiration (ET_c) and rice yield projections under different Shared Socioeconomic Pathways (SSP 2.6, SSP 4.5, and SSP 8.5), and (ii) assess the effectiveness of adjusting planting dates, along with the integration of drought-resilient cultivars, alternate wetting and drying (AWD) irrigation, and soil management practices, as adaptation strategies to mitigate yield reductions. To achieve these objectives, the CropWat and AquaCrop models were employed, using statistically downscaled CMIP6 CESM2 climate data.

The AquaCrop model was calibrated using data from 1994 to 2004 and validated for the period 2005–2014, demonstrating strong performance metrics (Nash–Sutcliffe Efficiency = 0.86, RMSE = 278.5, r² = 0.93). Our findings indicate that projected climatic changes pose a significant threat to rice yield stability in the region. Rising temperatures and shifting monsoon patterns are expected to elevate evapotranspiration demand by 10–14%, thereby intensifying irrigation requirements even in high-rainfall areas.

In response, adjusting planting dates emerged as a promising adaptation strategy. Specifically, delaying planting by 5 days until 2070 and by 10 days from 2071 to 2099 significantly mitigated yield declines across all SSP scenarios. An optimum 10-day delay in planting was found to recover up to 17% of yield losses under SSP 2.6 and SSP 4.5. Furthermore, compound strategies—including drought-tolerant rice cultivars, AWD irrigation, and improved soil management—provided up to 25% additional yield gains. These integrated approaches not only improved crop water productivity but also stabilized yields under moderate emission pathways. However, under the high-emission SSP 8.5 scenario, yield reductions remained substantial (up to 20%) due to increased temperature stress and shortened grain-filling duration, underscoring the limits of adaptation under extreme climate conditions.

The results highlight the importance of temporally optimized sowing schedules, integrated irrigation management, and improved soil practices for enhancing the resilience of coastal rice systems. This study further demonstrates that reliable data curation, model calibration, and parameter selection are essential to improving predictive accuracy in agro-hydrologic modelling. The findings emphasize the need for consistent methodological frameworks that couple climate projections with process-based crop models to assess adaptation effectiveness under uncertain future conditions.

Overall, the study provides actionable insights for strengthening the accuracy and reliability of water- and climate-based agricultural modelling frameworks. The outcomes contribute to developing climate-resilient strategies for paddy cultivation in coastal India, reinforcing the broader understanding of model validation, uncertainty reduction, and data-driven adaptation in hydrologic and agricultural research.

How to cite: Balvanshi, A., Kv, J., and Desai, V. R.: Evaluating Climate Change Impacts and Adaptation Options for Paddy Yield Using Data-Curated Modelling in Goa, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12001, https://doi.org/10.5194/egusphere-egu26-12001, 2026.

The purpose of the talk is to discuss spatially adapted cross-validation methods that maintain sufficient separation between training and validation sets, thus providing more accurate estimates of model risk. We begin by reviewing various spatial cross-validation techniques, including spatial blocked cross-validation and spatial leave-one-out, under scenarios of low to strong spatial dependence. We then propose a practical framework for determining an optimal “buffer size” for spatial leave-one-out that reduces autocorrelation between training and validation subsets. This framework is further enhanced by a parametric bootstrap approach designed to approximate the true risk in single-realization settings. Simulation experiments confirm that these methods effectively capture the underlying spatial structure, leading to more reliable risk estimation.

How to cite: Chavez Chong, C. O., Hardouin, C., and Fermin Rodriguez, A. K.: Strategies for spatial leave-one-out cross-validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12042, https://doi.org/10.5194/egusphere-egu26-12042, 2026.

This study investigates wave-induced flow behaviour around vertical breakwaters with retreated crown wall using numerical simulations. Previous experimental work has shown that moving the crown wall landward can reduce wave forces, moments, and overtopping. However, the associated flow mechanisms near the wall and trunk region have not been examined in detail. In this work, the open-source CFD model REEF3D is used to simulate regular wave interaction for crown wall retreat configuration. The model solves the Reynolds-averaged Navier–Stokes equations, with a level set method for free-surface tracking and a k–ω turbulence closure. The numerical results are first validated against published experimental data to ensure accuracy. The simulations provide detailed information on velocity fields, vortex formation, and flow separation during wave impact and overtopping. The results show that retreating the crown wall modifies the local flow structure, leading to a redistribution of momentum and a reduction in direct wave impact on the wall. These findings help to clarify the hydrodynamic role of retreated crown wall in vertical breakwater design.

How to cite: Firoj, S. and Afzal, M. S.: Flow Modulation and Wave Impact Reduction by Retreated Crown Walls in Vertical Breakwaters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13261, https://doi.org/10.5194/egusphere-egu26-13261, 2026.

Watershed hydrodynamics is governed by various hydrological flow processes that occur at different spatiotemporal scales. Most hydrological models couple the surface flow solver with the standard empirical infiltration models for flood propagation modeling. However, the empirical infiltration models are not applicable for heterogeneous and anisotropic soils and shallow groundwater tables, which are most vulnerable to waterlogging problems. Hence, simultaneous and integrated modeling of the surface and subsurface flow processes is essential for the continuous monitoring of watershed hydrodynamics. A physically based unified multi-region, multi-process watershed model integrates the various hydrological flow components in different regions through unique coupling mechanisms at the interfaces. The current work presents a Finite Volume (FV) method-based watershed flow model developed using the OpenFOAM^® framework [1]. The developed model framework utilizes the ‘multi-region’ structure from the OpenFOAM^® library to integrate the OpenFOAM^®-based solvers for the individual processes of surface overland flow [2,3] and saturated-unsaturated subsurface flow [4] through the imposition of appropriate interface boundary conditions or addition of source/sink terms at the interfaces of the flow regions. The surface flow component is modeled using the diffusive wave or the zero-inertia (ZI) approximation of the two-dimensional (2D) depth-averaged shallow water equations (SWE). On the other hand, the flow through the variably saturated subsurface media is modeled using the ‘mixed form’ of the 3D modified Richards Equation. The flux exchange between the surface and subsurface regions (infiltration or exfiltration rate) is modeled using a switching algorithm to impose the boundary condition on the interface between the two regions. The algorithm changes the interface to a Dirichlet or a Neumann type boundary condition based on the rainfall intensity and the saturated hydraulic conductivity of the ground surface. A stabilized and adaptive time-stepping algorithm has been implemented to ensure smooth convergence of the iterative technique used for linearizing the nonlinear governing equations. The developed model is equipped with parallelization strategies to be run on multi-core processors, which is essential for increased computational efficiency while solving regional-scale watershed flow problems. The developed watershed model has been verified and validated against the standard benchmark problems on saturation excess and infiltration excess from the literature. Moreover, the applicability of the developed model has been extended to solve complex hydrological problems on exfiltration occurring over natural catchments, yielding satisfactory results.

References

[1] Jasak, H., A. Jemcov, Z. Tukovic. (2007). OpenFOAM: A C++ library for complex physics simulations. In Vol. 1000 of Proc., Int. Workshop on Coupled Methods in Numerical Dynamics,1–20. Dubrovnik, Croatia: Inter-University Center

[2] Dey, S., Dhar, A. (2024). Applicability of Zero-Inertia Approximation for Overland Flow Using a Generalized Mass-Conservative Implicit Finite Volume Framework. Journal of Hydrologic Engineering, 29(1), 04023042.

[3] Dey, S. (2025). zeroInertiaFlowFOAM – a OpenFOAM^®-based computationally efficient, mass-conservative, implicit zero-inertia flow model for flood inundation problems on collocated grid-systems (No. EGU25-17402). Copernicus Meetings.

[4] Dey, S., & Dhar, A. (2022). Generalized mass-conservative finite volume framework for unified saturated–unsaturated subsurface flow. Journal of Hydrology, 605, 127309.

How to cite: Dey, S. and Dhar, A.: An OpenFOAM®-based coupled surface-subsurface flow model for simulating watershed hydrodynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13540, https://doi.org/10.5194/egusphere-egu26-13540, 2026.

Heatwaves are among the most rapidly intensifying climate extremes over India, yet their evolving spatial characteristics under recent and near future climate change remain inadequately quantified. This study examines the spatio-temporal variability of Heatwave Days (HWDs) across India using daily maximum temperature from the India Meteorological Department (IMD) gridded dataset for the historical period 1975-2024 and extends the analysis to the near future (2025-2044) using CMIP6 climate projections. Heatwave days are identified at each grid point using a calendar day based percentile approach, where daily maximum temperature exceeding the local 95th percentile threshold for the same calendar day, computed over a fixed reference period of 1981-2010, is classified as a heatwave day. Grid wise cumulative and decadal HWDs are analysed to assess long-term exposure and spatial redistribution. To objectively identify dominant heatwave regimes, Self-Organizing Maps (SOMs) are employed using multiple HWD metrics, enabling classification of regions with distinct heatwave characteristics and temporal evolution. Observational results indicate a clear reorganization of heatwave patterns over India. During the late 20th century (1975-1994), HWD accumulation is largely limited to north-western and parts of central India, typically ranging between 26 to 50 days per decade, with most eastern and peninsular regions experiencing fewer than 25 HWDs. From the mid-1990s onward, a pronounced intensification and spatial expansion is evident. By 2005-2014, large parts of central and eastern India exhibit decadal HWDs in the range of 51 to 100 days. The most recent decade (2015-2024) shows widespread moderate to high HWDs accumulation across the country, with several regions of central, eastern, and peninsular India experiencing 101 to 150 HWDs, and localized hotspots exceeding 150 days per decade. Future HWDs for 2025-2044 are derived from daily maximum temperature projections of the MPI-ESM1-2-HR model under the SSP2-4.5 scenario. The near-future decadal projections (2025-2034 and 2035-2044) indicate a continued intensification and spatial expansion of HWDs, with extensive areas of north-western, central, and peninsular India experiencing 151 to 250 HWDs per decade, and emerging hotspots exceeding 250 to 350 days, particularly over parts of north-western and southern India. Eastern India also shows a marked transition toward higher HWDs classes, indicating increasing regional vulnerability. Overall, the combined observational and CMIP6 based analysis demonstrates a transition toward widespread and persistent heatwave exposure across India in both recent decades and the near future. The integration of a grid specific, calendar day based percentile definition with SOM based classification provides a robust framework for identifying evolving heatwave regimes and supports improved heat risk assessment, climate adaptation planning, and early warning strategies under continued warming.

How to cite: Bhoopathi, S. and Pal, M.: Reorganization of Heatwave Day Regimes across India under Recent and Near Future Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16374, https://doi.org/10.5194/egusphere-egu26-16374, 2026.

Considering the dearth of gauge-based rainfall observations at desirable resolution, it becomes immensely challenging to quantify and monitor droughts, especially over the developing countries. This can be circumvented by utilizing the high-resolution open-access rainfall products. This study is envisaged with the objective to assess the spatiotemporal variation of meteorological droughts over the Bundelkhand region, India. The multi-source weighted-ensemble precipitation (MSWEP), a blended product of global gauge-based, satellite-based and reanalysis precipitation datasets, is utilized for a period of 44 years (1980-2023). The MSWEP rainfall is bias-corrected with respect to the India Meteorological Department (IMD) gridded observation dataset for the 14 districts in the region. Using the corrected rainfall product, the droughts over each district are characterized by Standardized Precipitation Index (SPI) at three different timescales, i.e., the SPI-3, SPI-6 and SPI-12 are used to model short-term, intermediate-term and long-term droughts, respectively. A drought severity index (DSI) is proposed considering the probability of droughts in different severity classes (i.e., near-normal, moderate, severe and extreme). Further, the trend analysis of SPI at different timescales is carried out using Modified Mann-Kendall (MMK) test. The results reveal the MSWEP dataset’s problems in capturing higher quantiles, which affects the probabilistic distribution used for quantifying drought events. However, the bias-corrected MSWEP product showed an excellent match with the IMD gridded data, thereby substantiating its applicability over the Bundelkhand Region. The region is found to be prone to droughts with an increasing trend of dryness. The novel approach of DSI is found to distinguish the drought severity levels at district-scale, which can be helpful for planning and management of droughts. Overall, this study provides critical insights on the drought characterization using state-of-the-art datasets and innovative approaches, which can also be extended to other drought-prone regions of the world.

Keywords: Bias-correction; Bundelkhand; DSI; MSWEP; MMK; SPI

How to cite: Swain, Dr. S.: A statistical approach of mapping drought severity using bias-corrected blended dataset over a semi-arid region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18814, https://doi.org/10.5194/egusphere-egu26-18814, 2026.

Marine sediments are very important for keeping the coast stable and protecting the shoreline naturally. However, anthropogenic activities can greatly change how sediment moves, making their accurate monitoring essential. In marine settings, understanding bedload sediment transport can be challenging due to conventional methods reliant on visual observations or direct sediment sampling tend to be intrusive, spatially constrained, and inadequate for long-term or continuous monitoring. In this situation, passive underwater acoustics is a promising non-intrusive option that can provide continuous monitoring with high temporal resolution. This study investigates the acoustic signatures related to marine bedload transport, focusing particularly on the sounds generated by interparticle collisions of mobile sediments. A series of controlled laboratory experiments are performed utilising simplified experimental arrangements in which artificial sediments (spherical glass beads) are mobilised under oscillatory motion that simulates wave-induced seabed forcing. We use glass beads of different sizes to create idealised bedload conditions, and we use an oscillating plate to control the movement of the particles. Hydrophones placed close to the sediment bed record acoustic pressure signals. The recorded acoustic signals are analyzed in both the time and frequency domains. Individual particle impacts are characterised by short transient acoustic events, and spectral analyses show clear peak frequencies that are linked to sediment motion. The results indicate that the peak frequency of the acoustic spectrum is predominantly determined by particle diameter and is additionally influenced by the amplitude and frequency of the applied oscillatory motion. These observations align with theoretical models, such as those suggested by Thorne (1985), that explain the generation of pressure waves during underwater particle collisions. To further explore the mechanisms of sound generation, experiments are conducted with both smooth and rough beds below the beads layers. The analysis reveals the existence of sediment-specific acoustic signatures, facilitating the differentiation of particle sizes according to their spectral characteristics. This study illustrates the significant potential of passive acoustic methods for the remote monitoring of marine bedload transport. The study offers novel insights into sound generation mechanisms linked to sediment motion across various particle sizes, motion amplitudes, and bed configurations, utilising a combination of laboratory experiments, theoretical frameworks, and comprehensive spectral analysis, with direct implications for intricate coastal and offshore environments.

How to cite: Dutta, D., Jarno, A., Besnard, H., Morvan, B., and Marin, F.: Passive Acoustic Characterization of Marine Bedload Transport Based on Interparticle Collision Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18906, https://doi.org/10.5194/egusphere-egu26-18906, 2026.

Wildfires in radioactively contaminated regions, such as the Chernobyl Exclusion Zone, pose a growing environmental threat by resuspending long-lived radionuclides into the atmosphere. However, accurately quantifying the redistribution of these radionuclides remains challenging. Existing top-down inversion studies often oversimplify source terms by assuming fixed particle sizes and release altitudes, which hinders the precise evaluation of transport mechanisms and deposition footprints.

To address this gap, this study proposes a novel multi-component source term inversion framework to simultaneously reconstruct the time-varying release profiles of ¹³⁷Cs across multiple particle sizes (0.4, 8, and 16 μm) and seven vertical layers (0-3000 m). We improved the Projected Alternating Minimization with L1-norm and Total variation regularization (PAMILT) algorithm by incorporating a TV-regularized initialization and a Bayesian optimization scheme for hyperparameter tuning to ensure robust convergence. These retrieved source terms were then coupled with the WRF-Chem model using size-resolved microphysics to conduct a high-resolution simulation of the April 2020 Chernobyl wildfires.

Validation results demonstrated exceptional agreement between the simulated and observed concentrations, achieving a Pearson correlation coefficient of 0.996 and reducing maximum relative biases from over 10⁶ to generally below 10². The inversion estimates a total ¹³⁷Cs release of approximately 836 GBq. This release was dominated by fine particles (0.4 μm, ~54%) and low-altitude injections, with 58.1% occurring below 1 km. Crucially, our WRF-CHEM simulations reveal a decoupling between emission abundance and deposition impact. Although fine particles dominate the source term, coarse particles (16 μm) control the near-field deposition flux due to rapid gravitational settling. These coarse particles exhibit a "transport plateau" beyond roughly 800 km, whereas fine particles show a linear growth in transport distance constrained only by meteorological dispersion. Furthermore, we identified distinct deposition signatures. Dry deposition manifests as a continuous spatial accumulation or "creeping" effect. In contrast, wet deposition drives "step-wise" long-range transport, triggering sudden and pulse-like removal events far from the source.

These findings provide critical insights into the complex mechanics of radionuclide redistribution and offer a refined methodology for assessing the environmental impact of future wildfire events in contaminated zones.

How to cite: Xu, Y. and Fang, S.: Unraveling size-resolved 137Cs resuspension and deposition from the 2020 Chernobyl Wildfires via multi-component inversion and WRF-Chem simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2436, https://doi.org/10.5194/egusphere-egu26-2436, 2026.

The discharge of ALPS-treated water from the Fukushima Daiichi Nuclear Power Plant (FDNPP) in August 2023 renewed concerns regarding radionuclide dispersion in the North Pacific, particularly in the waters surrounding Taiwan. This event highlighted the need to assess not only releases from Fukushima but also the cumulative influence of multiple nuclear power plants operating within the region. To investigate potential dispersion patterns under simultaneous multi-source discharges, this study employed a particle tracking model coupled with the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), together with a two-dimensional Gaussian diffusion model, to simulate tritium dispersion in surface seawater from six facilities located in the western North Pacific region during 2023–2024: FDNPP, Wolsong, Qinshan, Fuqing, Daya Bay, and the Maanshan Nuclear Power Plant (NPP3 in Taiwan). Also, the modeled tritium concentrations in the Pacific area were compared with background seawater levels reported in the IAEA Marine Radioactivity Information System (MARIS) database. This comparison provided a baseline consistency check to examine whether the simulated tritium distributions were influenced by large-scale ocean circulation and cumulative multi-source discharges.

To further evaluate potential local impacts around Taiwan, seven representative monitoring sites were selected to capture spatial variability across different coastal sectors and offshore regions, including Kinmen, Matsu, the Tamsui River Estuary, Cijin, the Zhuoshui River Estuary, Guishan Island, and FRI-ST-15 (a Fisheries Research Institute monitoring station). These sites were used to examine seasonal concentration responses associated with eastern, western, northern, and southern waters, as well as offshore island environments. The results indicate that tritium released from multiple sources was transported northward by the Kuroshio Current, reaching southern Japan and extending eastward to approximately 180°E. In the northwestern waters of Taiwan, including Kinmen and Matsu, contributions from Fuqing and Qinshan were dominant. At Kinmen, Fuqing’s contribution reached maximum values immediately after discharge and remained significant into early spring, whereas the contribution from Qinshan was comparatively smaller. At Matsu, Qinshan’s contribution increased approximately one month after discharge, decreased by late winter, and reached a secondary maximum in the subsequent winter, while Fuqing’s contribution increased during late winter and maintained a moderate influence thereafter.

Finally, some sensitivity analyses assuming a 50-fold increase in discharge concentrations were conducted to assess potential variability and relative influence among sources. The results indicated negligible influence from Wolsong and FDNPP, whereas discharges from Qinshan, Fuqing, Daya Bay, and NPP3 produced more pronounced, seasonally modulated signals that diminished with increasing distance from Taiwan.

How to cite: Chiang, Y. and Huang, P.-C.: Modeling the Regional Dispersion of Continuous Multi-Source Tritiated Water Discharges in Surface Waters Around Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3203, https://doi.org/10.5194/egusphere-egu26-3203, 2026.

Since the accident at the Fukushima Daiichi Nuclear Power Station in March 2011, numerous studies have examined the behavior of radioactive cesium (¹³⁷Cs) in the ocean. Recent studies suggest that large amounts of particulate ¹³⁷Cs deposited on land are transported to coastal waters via rivers, becoming a major source of coastal ¹³⁷Cs input. Although numerical simulations and conceptual studies indicate that particulate ¹³⁷Cs entering coastal waters can be transported offshore through sedimentation, resuspension, and lateral transport, long-term, high-frequency observational studies remain limited. In this study, we evaluated the transport of particulate ¹³⁷Cs in coastal area off Fukushima using one year of continuous measurement data from multiple moored systems.

Moored systems were deployed at three sites near the mouth of the Ukedo River, where current velocity, current direction, and turbidity were continuously measured from February 2017 to February 2018. These data were combined with regularly collected measurements of suspended solid concentrations (mg/L) and particulate ¹³⁷Cs concentrations (Bq/L) to estimate hourly lateral fluxes of particulate ¹³⁷Cs (Bq/h). The study area is influenced by ¹³⁷Cs inputs transported via the Ukedo River, and the relationships between particulate ¹³⁷Cs fluxes and seasonal variability, meteorological conditions (waves, precipitation, and wind), and river discharge were analyzed. Furthermore, by focusing on differences in fluxes among the observation sites, we examined the factors controlling riverine input and transport variability from the coastal area toward offshore.

This study uses long-term monitoring data off Fukushima to improve understanding of particulate ¹³⁷Cs transport processes in coastal waters and to provide observational constraints for future numerical modeling studies.

How to cite: Satoh, S., Yoshimura, K., Misonou, T., and Tsumune, D.: Transport of Particulate ¹³⁷Cs in the Coastal Area off Fukushima Based on Long-Term Continuous Measurement, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3623, https://doi.org/10.5194/egusphere-egu26-3623, 2026.

Since the commencement of the ALPS-treated water discharge from Fukushima Daiichi on 23 August 2023, an operational forecasting system developed in Taiwan has been established to provide daily seven-day predictions of tritium dispersion in the North Pacific. The system integrates the real-time CWA-OCM with particle tracking and grid-based concentration diffusion modules, driven by hourly discharge data reported by TEPCO. The computational domain covers the Kuroshio–Kuroshio Extension and adjacent marginal seas, with refined resolution near the outlet to capture dispersion within approximately 3 km. Validation against TEPCO tritium monitoring data at 12 sites across three representative batches (1, 2 and 12) demonstrated that the system successfully reproduced both the spatial distribution and temporal evolution of tritium concentrations, with modeled maxima typically within the observed range of 10–20 Bq/L. However, the model slightly underestimated the peak values, and simulated concentrations decreased more rapidly than observed during the five-day post-discharge period. This discrepancy is likely attributed to the absence of the jet effect in the current model. Therefore, we will continue to refine the model and integrate these improvements into our operational forecasting system.

How to cite: Zeng, H.-T., Teng, J.-H., and Chiang, Y.: Validation of the Refined Daily Forecasting System for ALPS Treated Water Dispersion Against Observation Data near the Fukushima Outlet, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4581, https://doi.org/10.5194/egusphere-egu26-4581, 2026.

The discharge of ALPS treated water from the Fukushima Daiichi Nuclear Power Station into the North Pacific Ocean has necessitated a detailed assessment of long-term dispersion pathways. The transport of this ALPS treated water is primarily governed by the Kuroshio Extension (KE) system. However, the upstream Kuroshio has been experiencing a persistent “Kuroshio Large Meander (KLM)” event since August 2017. Since the variability of the KE is dynamically linked to the path of the Kuroshio south of Japan, understanding how the KLM modulates the downstream flow field is critical for evaluating environmental impacts.

In this study, we investigate the influence of the KLM on the dispersion of tritium-treated water by Lagrangian particle tracking model with a continuous release scheme. The model was forced by ocean current data from the Hybrid Coordinate Ocean Model (HYCOM) to capture the spatiotemporal variability of the Kuroshio Current. We specifically examined the differences in transport patterns during the KLM period (2017–2022) versus non-meander period (2011–2016).

Preliminary results indicate that the presence of the upstream Large Meander induces specific downstream responses in the Kuroshio Extension that distinctively deviate from the reference non-meander period. We focus on how the KLM modulates the stability and position of the KE jet, thereby altering the initial advection pathways of the ALPS treated water. The analysis aims to clarify whether these KLM-induced changes in the KE system act to retard zonal transport or enhance regional retention, creating significant discrepancies in tracer arrival times and concentration fields between the two periods.

This study quantifies these deviations and discusses the implications of the “Kuroshio-Kuroshio Extension coupling” mechanism in determining the dispersion patterns and concentration distributions of passive tracers. The findings highlight the necessity of incorporating low-frequency climate variability into environmental risk assessments for oceanic discharges.

How to cite: Chu, Y.-J., Zeng, H.-T., Teng, J.-H., and Wang, C.-H.: Assessment of the Kuroshio Large Meander’s Impact on the Dispersion Pathways of Fukushima Tritium-treated Water, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5031, https://doi.org/10.5194/egusphere-egu26-5031, 2026.

Overview of the observation and simulation studies regarding radiocesium resuspension from contaminated land surfaces in Fukushima is presented based on our previous papers, Kajino et al., ACP (2016), Kajino et al., ACP (2022), Watanabe et al., ACP (2022). The long-term atmospheric behaviors of radiocesium have been understood based on the long-term measurements of concentration and deposition of radiocesium in Fukushima city (Watanabe et al., 2022) and numerical simulations considering radiocesium resuspension from soil and vegetation (Kajino et al., 2022). However, there is still one unresolved issue remains: exceptionally high monthly cumulative deposition amounts in January in Fukushima city even though the monthly atmospheric concentrations are not very large. We therefore hypothesized that the giant aerosol resuspension due to snow removal work or passing vehicles that carried radiocesium deposited in the vicinity of the observation site into the deposition sampler, but not into the air sampler, since the gravitational velocity of such giant aerosols is too high to collect by the air sampler. This additional source is referred to as secondary resuspension. The numerical assessment and field observations of the secondary resuspension will also be presented at the conference. 

How to cite: Kajino, M.: Resuspension of radiocesium from contaminated land surfaces in Fukushima: source contributions from soil, vegetation, and other sources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8529, https://doi.org/10.5194/egusphere-egu26-8529, 2026.

Chornobyl remains the world’s longest-running field-scale experiment of how societies and ecosystems respond to persistent, spatially heterogeneous contamination. Yet sustainability-relevant synthesis across environmental compartments—soils, forests, surface waters, groundwater, and the evolving exposure landscape—remains fragmented, often separated into radiological, ecological, or regulatory discussions. Here we integrate four decades of observations in and around the Chornobyl Exclusion Zone to evaluate what has changed, what has not, and what this implies for sustainable land and resource use under long-lived hazards. We assess four compartment-linked insights: (i) soils as the primary long-term reservoir of fallout: inventories of ¹³⁷Cs and ⁹⁰Sr have declined but remain highly heterogeneous, while vertical redistribution and particle-associated processes increasingly govern mobility and bioavailability; (ii) forests as both sink and pathway: radionuclides are continuously recycled through litter and biomass, and contrasting within-tree distributions of ¹³⁷Cs versus ⁹⁰Sr impose distinct constraints on wood utilization and circular-economy strategies; (iii) aquatic systems as delayed but persistent exporters: multi-decadal river records exhibit long tails and sensitivity to disturbances (e.g., floods, fires), while groundwater pathways—especially for ⁹⁰Sr—represent enduring, often weakly observed legacy with clear management relevance; and (iv) exposure landscapes that evolve nonlinearly: spatiotemporal changes in dose fields complicate re-zoning decisions that depend on both scientific evidence and societal acceptance. We synthesize these findings into a sustainability framework that links environmental dynamics to governance choices, including conditional resource use, monitoring prioritization, and intergenerational risk trade-offs. These lessons generalize to other nuclear accidents and to broader classes of persistent contaminants where returning to baseline is unrealistic and sustainability must be designed under enduring constraints. 

How to cite: Igarashi, Y., Yoschenko, V., Onda, Y., Protsak, V., Laptev, G., Holiaka, D., Samoilov, D., Kirieiev, S., Konoplev, A., and Smith, J.: Four decades after Chornobyl: long-lived radionuclide legacy and sustainable land and resource use  , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8971, https://doi.org/10.5194/egusphere-egu26-8971, 2026.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident contaminated large areas of the Pacific Ocean with different radionuclides. However, not all areas are studied equally. For example, the Sea of Okhotsk is one of the least studied regions, with almost no measurement data available. In the current study, we applied the Lagrangian particle tracking model Parcels V3.0 to simulate the trajectories of virtual particles containing radionuclides in the Pacific Ocean. Here, the output from the KIOST-MOM circulation model is used. It includes monthly mean climatic data for 3D currents (U, V, W components of water velocity) and vertical diffusivity coefficients. Coefficients for horizontal diffusion are calculated using the Smagorinsky formula.

Virtual particles were emitted at the location of the FDNPP during 31 days (26 Mar to 25 Apr 2011), when 96.6% of the total amount of radionuclides was released directly to the ocean. Each particle initially contained a certain activity of radionuclides (¹³⁷Cs, ¹³⁴Cs, ⁹⁰Sr, ³H, ¹²⁹I) proportionally to the estimated total release of each radionuclide. The activity of each radionuclide inside the particle decreased according to radioactive decay with the corresponding half-life. The atmospheric deposition of radionuclides on the sea surface was not considered here.

Model results were validated on the ¹³⁴Cs concentrations in the Northeastern Pacific in areas with measurement data after 2012, when the impact of atmospheric deposition decreased. For the Sea of Okhotsk, the concentrations of 5 radionuclides were calculated and analyzed. For particles that reached the Sea of Okhotsk, we calculated statistical characteristics based on Lagrangian trajectories: visitation frequency, mean age, and representative trajectory, which demonstrated the pathways of water masses transporting radioactivity from FDNPP to the Sea of Okhotsk.

How to cite: Bezhenar, R., Tateda, Y., Inomata, Y., Kim, K. O., and Kim, H.: Lagrangian trajectories of Fukushima Daiichi NPP originated water, transported by large-scale circulation in the North Pacific Ocean, and reached the Sea of Okhotsk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9903, https://doi.org/10.5194/egusphere-egu26-9903, 2026.

Validating the reproducibility of ocean dispersion models used in prior environmental impact assessments of ALPS-treated water release is essential for evaluating their applicability. In this study, we conducted reproduction simulations using actual release records together with observed meteorological and oceanographic conditions, and quantitatively compared the results with seawater monitoring data.

Model results were compared with tritium monitoring data collected by TEPCO, the Ministry of the Environment, the Nuclear Regulation Authority, and Fukushima Prefecture. The release was assumed to instantaneously disperse within a model grid (147 m × 186 m), with release scenarios prescribed for both the surface layer and the near-bottom layer at a depth of 10 m. As the actual discharged water is expected to rise upward from the seabed, results from the surface-release simulation are mainly discussed. Geometric means were used for model–observation comparisons to reduce the influence of outliers. Since background tritium concentration is not explicitly represented in the model, a constant background of 100 Bq m⁻³ was added to the modeled concentrations to ensure consistency with observations.

For the entire one-year period, the correlation coefficient between annual geometric means of modeled and observed concentrations was R = 0.30, indicating moderate reproducibility of temporal variability. In contrast, the mean log(Model/Obs) was −0.035, corresponding to a Model/Obs ratio of 0.92, demonstrating very good agreement in annual mean concentration levels. When the comparison was restricted to release periods, the correlation improved (R = 0.64), while the mean Model/Obs ratio increased to 1.37, suggesting a tendency toward overestimation associated with uncertainties in local release representation and model resolution near the outlet.

These results indicate that, although the model has limitations in reproducing short-term concentration variability, it reliably reproduces annual mean tritium concentrations that are critical for radiological dose assessment. The present validation demonstrates that the ocean dispersion model used in the prior environmental impact assessment has sufficient reliability for evaluating the dispersion behavior of ALPS-treated water, while highlighting the need for further improvements in the treatment of background concentrations and near-field processes.

How to cite: Tsumune, D., Misumi, K., and Tsubono, T.: Reproducibility of ocean dispersion simulations for ALPS-treated water release off Fukushima: comparison with one-year monitoring data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10166, https://doi.org/10.5194/egusphere-egu26-10166, 2026.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster, triggered by the tsunami after the massive earthquake on March 11, 2011, which led to the accumulation of vast quantities of contaminated water used for emergency cooling. Although containment measures were implemented to prevent leakage, the on-site storage facilities approached full capacity. Consequently, Japan announced plans to disposal the Advanced Liquid Processing System (ALPS) treated wastewater, which removes most radionuclides except tritium. TEPCO officially began discharging the treated water on August 24, 2023, diluted with seawater, into the Pacific Coast via a submerged outfall located 1 km offshore at a depth of 13 meters. This decision raised significant concerns among the publics of neighboring countries regarding marine safety. In response, Taiwan established a specialized task force to monitor and to predict the consequences by developing an operational forecast model system to monitor the discharge and provide daily predictions of radioactive dispersion in the Western North Pacific.

The system integrates a three-dimensional hydrodynamic model (CWA-OCM-FH), an extension of the existing operational model CWA-OCM, with a transport model driven by the simulated currents. In order to capture the influence of the Kuroshio Current and the Extension on the transport of tritiated water, the model domain was expanded to 180°E. An unstructured mesh is employed to resolve complex topographic features. The grid resolution varies from approximately 1 km in the coastal zone to less than 20 meters near the discharge outfall, ensuring a representation of spatiotemporal variations in the near-field flow.

To ensure the reliability of the flow fields driving the dispersion, the hydrodynamic model underwent rigorous validation using tide gauge data and ADCP observations. Harmonic analysis on both the observed and simulated data for data for calibration and verification.

Driven by the verified flow fields, a 3D Lagrangian particle tracking model simulates the dispersion pathways of the tritiated water. These computed trajectories provide the essential spatial distribution data required for calculating subsequent concentration. Simulation results indicate that while the primary transport direction follows the Kuroshio Extension and North Pacific Current eastward, mesoscale eddies induce significant cross-stream transport. Therefore, the contaminated particles could potentially influencing waters near Taiwan. 

The model has been verified with observations utilize quantitative metrics such as the Pearson correlation coefficient (R value), coefficient of determination (R²), and Root Mean Square Error (RMSE) over a period exceeding one year. Validation using data from tide gauge stations, ARGO drifter profiles, AVISO satellite altimetry geostrophic currents, and GHRSST sea surface temperature satellite data will be presented and discussed in the paper.

How to cite: Wang, C.-H., Cheng, H.-Y., Yu, J. C. S., Zeng, H.-T., and Teng, J.-H.: An Operational Modeling System Forecasting the Disposal of Fukushima Tritiated Water and Transport: A Lagrangian Particle Tracking System Driven by High-Resolution Hydrodynamics., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14564, https://doi.org/10.5194/egusphere-egu26-14564, 2026.

The Luzon Strait serves as a critical conduit between the Western North Pacific and the South China Sea (SCS), through which water-mass exchange plays a key role in regulating regional heat budgets and primary productivity. While surface exchange processes have been known well, subsurface intrusion dynamics—particularly those associated with Subtropical Mode Water (STMW)—remain poorly understood. In this study, we investigate the pathways and transport timescales of STMW intrusion through the Luzon Strait by employing radiocesium ¹³⁷Cs released during the 2011 Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident as a transient tracer. A three-dimensional Regional Ocean Modeling System (ROMS) was used to simulate the long-term dispersion of ¹³⁷Cs from the North Pacific into the SCS. The results show that the ¹³⁷Cs within the STMW layer reached the Luzon Strait approximately seven years after the accident, notably earlier than surface circulation. The net flux of ¹³⁷Cs into the SCS exhibits seasonal variability, with enhanced inflow during winter, primarily driven by horizontal advection and variations in Kuroshio intrusion behavior. A comparison of different intrusion modes indicates that the leaking path yields a substantially larger net inflow of radiocesium into the SCS than either the looping or leaping paths. Given that the SCS serves as a gateway to downstream marginal seas—including the East China Sea, Yellow Sea, and Japan/East Sea—these findings provide important insights into basin-scale transport processes of Pacific-derived tracers and their potential ecological implications.

How to cite: Lee, S.-T., Cho, Y.-K., Kim, K. O., and Seo, S.: The Role of Subtropical Mode Water in the Subsurface Transport of Fukushima-derived 137Cs into the South China Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15000, https://doi.org/10.5194/egusphere-egu26-15000, 2026.

The Fukushima Dai-ichi Nuclear Power Station (F1NPS) accident released radionuclide was believed to circulate in the North Pacific Ocean and was suggested to arrive at East China Sea ECS (Aoyama et al., 2022). Temporal fraction of ¹³⁷Cs from F1NPS was estimated to be 0.5 mBq l^-1 at ECS in 2023 (Inomata et al., 2023). Appeared ¹³⁷Cs radioactivity 1.4 mBq l^-1 off Okinawa seawater in 2025 seems to suggest still having contribution of 0.6 mBq l^-1 as F1NPS originated fraction even after 14 years of the accident, compared to assumed global fallout originated level 0.8 mBql^-1 in ECS at 2025 (ENVRDB, 2025). This level was suggested to be caused by recirculation of ¹³⁷Cs within the north western Pacific waters by Subtropical Mode Water (STMW) and Central Mode Water (CMW)(Kumamoto et al., 2025). Similarly, F1NPS-¹³⁷Cs may be brought by North Equatorial Current (NEC) within 10-18 years (Chen et al., 2023). However, in contrast, there is other possibility as depuration delay of global fallout-¹³⁷Cs in surface water by depression of vertical mixing to deeper layer due to high surface water temperature after 2010 as observed global warming. Since F1NPS-orginated ¹³⁴Cs originated F1NPS almost decayed and being difficult to detect, it is still unknown the precise contribution rate of F1NPS-¹³⁷Cs compared to global fallout ¹³⁷Cs fractions. Possible method to derive F1NPS fraction may be using fish muscle which has approximately 50-100 times greater radioactivity in equivalent sample size. Successful detection of F1NPS-originated radio-caesium will is expected not to understand up-to date ocean circulation environment, but also to find the usefulness of bioindicator as oceanic tracer.

How to cite: Tateda, Y., Takata, H., Inomata, Y., Hamajima, Y., and Bezhenar, R.: Possibility of radionuclide originated from the Fukushima accident as oceanic tracer by fish muscle as bio-indicator, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15352, https://doi.org/10.5194/egusphere-egu26-15352, 2026.

Large amounts of radioactive cesium (¹³⁴Cs and ¹³⁷Cs) were released as a result of the Fukushima Daiichi Nuclear Power Plant (F1NPP) accident. Even 15 years after the accident, ¹³⁷Cs concentrations in the marine environment have not returned to pre-accident levels, indicating that leakage from areas outside of the F1NPP site may still be ongoing.[1] Although ¹³⁷Cs concentrations in sandy beach groundwater outside the F1NPP site have been reported to be higher than those in seawater, suggesting groundwater as a major leakage pathway, no observational data are available from areas in close proximity to the plant.[2] Based on these considerations, this study aims to estimate discharge flux of ¹³⁷Cs originating from groundwater in the coastal waters surrounding the F1NPP.

Groundwater-derived ¹³⁷Cs discharge flux (Bq day^-1) was estimated by dividing the inventory (Bq) by the residence time of groundwater (day). Residence times following groundwater discharge to the coastal ocean were estimated using changes in the short-lived radium isotope activity ratios (²²³Ra/²²⁴Ra) between groundwater and seawater. Radium isotopes were selected as groundwater tracers for three reasons: (i) they were scarcely released from the F1NPP, such that the influence of the accident on Ra isotopes can be considered negligible[3]; (ii) radium isotopes (²²³Ra, ²²⁴Ra, ²²⁶Ra, and ²²⁸Ra) exhibit pronounced concentration differences between groundwater and seawater; and (iii) the wide range of half-lives and multiple isotopes enables their application to the estimation of water residence times as well as to the quantification of nutrient fluxes transported via submarine groundwater discharge.[4] In addition, the spatial area representative of ¹³⁷Cs leakage for inventory estimation was defined based on the variability of Ra isotopes and ³H. The mean ¹³⁷Cs concentration within the target domain was determined using seawater sampling data of ¹³⁷Cs concentrations conducted by Tokyo Electric Power Company Holdings, Inc. (TEPCO HD). The ¹³⁷Cs inventory (Bq) was then calculated by multiplying the mean ¹³⁷Cs concentration (Bq m⁻³) by the volume (m³) of the target domain.

The calculated discharge flux is from 2.1×10⁹ to 8.6×10⁹ (Bq day⁻¹). These values are comparable to the flux required to sustain coastal ¹³⁷Cs concentrations (2.0 × 10⁹ Bq day⁻¹)[1], indicating that submarine groundwater discharge may explain why ¹³⁷Cs concentrations in the vicinity of the FDNPP have not returned to pre-accident levels.

[1]Tsumune et al., J Environ Radioact , 2024

[2]Sanial et al., Proc Natl Acad Sci, 2017

[3]Buesselar et al., Ann Rev Mar Sci , 2017

[4]Garcia-Orellana et al., Earth-Science Review, 2021

How to cite: Iino, H., Tsumune, D., Kato, H., Godse, N., Onda, Y., and Otosaka, S.: Quantifying groundwater-derived 137Cs fluxes to surrounding coastal waters using radium isotope, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15358, https://doi.org/10.5194/egusphere-egu26-15358, 2026.

Fifteen years after the Fukushima Daiichi Nuclear Power Plant (F1NPP) accident, ¹³⁷Cs and ³H activities in coastal waters near the plant remain elevated compared to surrounding regions, indicating persistent radioactive inputs. While concentrations within the port are highest, recent estimates suggest that the leakage rate outside the port exceeds that inside, implying the presence of an additional or previously unrecognized source outside the F1NPP site. However, the mechanisms governing these releases remain unclear.

The ³H/¹³⁷Cs activity ratio is a useful tracer for identifying contamination sources, as it remains relatively stable in seawater over short timescales. Since approximately 2016, ¹³⁷Csconcentrations near the FDNPP have shown little decline, while spatial contrasts in the ³H/¹³⁷Cs ratio have become more pronounced. Although both radionuclides’ concentrations peak within the port, the ratio is consistently lower there and higher offshore. This suggests the potential existence of sources outside the harbor governing the distribution pattern of the radionuclides.

To investigate these patterns, we applied a color-classified ³H/¹³⁷Cs ratio analysis and conducted release-rate estimations for the port and adjacent coastal waters. In addition, we collected independent samples of seawater, river water, groundwater, and spring water near the F1NPP. The ³H/¹³⁷Cs ratios of river water, groundwater, and spring water were used in an end-member mixing analysis to evaluate potential terrestrial and subsurface contributions. Preliminary results indicate that the end members for groundwater and spring water (excluding river water) show trends similar to the ³H/¹³⁷Csratio in seawater, potentially explaining the observed increase in the ratio offshore.

This integrated analysis improves constraints on radionuclide sources and transport pathways in the F1NPP coastal environment and contributes to a better understanding of long-term radioactive contamination dynamics.

How to cite: Godse, N. S., Tsumune, D., Kato, H., Iino, H., Onda, Y., and Otosaka, S.: Radionuclide Dynamics in the Coastal Ocean off the Fukushima Daiichi Nuclear Power Plant Using Radioactivity Ratios., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15360, https://doi.org/10.5194/egusphere-egu26-15360, 2026.

 The size distribution (SZ) of radioactive aerosols emitted after nuclear accident at nuclear power plants plays a crucial role in assessment of the subsequent atmospheric transport and deposition. However, in reality this distribution in the source is usually unknown. The SZ of particles in the plume also changes with travel time of the plume, because the coarser particles fall out more rapidly than the finer particles. Hence when the measurements of SZ are undertaken at certain distances from the source the SZ could be already altered by plume travel time while it is SZ in the source which is required by atmospheric transport models (ATMs) for simulation of radionuclides atmospheric dispersion and deposition. Also, SZ measurements are usually not available in real time during the accident. More readily available measurements are airborne concentrations. Hence when concentration measurements are available, the SZ parameters of ATMs could be fitted to achieve better agreement between model and measurements.

 In this work, the inverse problem is stated to identify the optimal set of size distribution parameters of the Fukushima source term – activity-averaged mean aerodynamic diameter (d) and geometric standard deviation (σ) which best fit results of FLEXPART ATM to both, local and global measurements datasets. The problem is formulated as multi-objective optimization in which two objective functions. The first objective function J₁ corresponds to model deviations from measurements in the territory of Japan, while the second objective function J₂ corresponds to model deviations from the global observations of CTBTO measurement stations. The combined cost function J=J₁J₂ , characterizing model deviation against measurements in both datasets was also considered. In this way, the estimate of the unknown SZ parameters, which fits both local and global concentration observations is to be found. The method of finding Pareto solution of such multi-objective optimization problem was developed and preliminary results of comparisons of the estimated SZ parameters with SZ measurements, performed following Fukushima accident were obtained.

 The solution of the stated problem leads to reasonable results. The simulations with small values of 1≤σ≤2 led to excellent agreement of estimated mean aerodynamic diameter d of emitted particles between 2 and 3 μm with available measurements of SZ. At the same time if large values of σ were allowed the resulting estimated mean aerodynamic diameter could significantly deviate from the observed values. The use of the small values of mean aerodynamic diameter (d <1μm) in turn did not allow for the minimization of the combined cost function J.

How to cite: Jung, K. T., Kim, J.-H., and Kovalets, I.: Inverse estimation of size-distribution parameters of emitted aerosols following the Fukushima accident using FLEXPART simulations and measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15588, https://doi.org/10.5194/egusphere-egu26-15588, 2026.

The radionuclides ¹²⁹I and ¹³⁷Cs released during the 2011 Fukushima nuclear accident led to contamination of forested environments. The concentrations of these nuclides in precipitation, as well as their subsequent environmental behavior, are critical for assessing internal radiation exposure. In this study, deposition records of atmospheric ¹²⁹I and ¹³⁷Cs following the accident were reconstructed using a borehole drilled between 2012 and 2014 at Koriyama, located approximately 60 km from the accident site. After subtraction of contributions from global fallout and nuclear reprocessing facilities, the inventories of ¹²⁹I and ¹³⁷Cs in forest soil at Koriyama, integrated to a depth of 50 cm, were estimated to be 4.80 × 10⁵ and 81.7 mBq m⁻², respectively. The ¹²⁹I/¹³⁷Cs radioactivity ratio derived from Fukushima-derived deposition in litter and soil (0–50-cm depth) was 1.71 × 10⁻⁷, which is consistent with the ratio observed in atmospheric aerosols at the time of the accident. The ¹²⁹I/¹³⁷Cs radioactivity ratio in the litter layer was marginally lower than that in the underlying topsoil. This difference is attributed to the higher solubility and mobility of ¹²⁹I relative to ¹³⁷Cs in litter, resulting in preferential washout from the surface layer. It is therefore inferred that a fraction of ¹²⁹I originally retained in the litter layer has migrated from the forest surface toward riverine systems.

How to cite: Ohta, T., Mahara, Y., Matsuzaki, H., Hayami, H., and Tsumune, D.: Deposition of 129I in the forests of Koriyama and 129I/137Cs ratio originated from the Fukushima Daichi Nuclear Power Plant, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16674, https://doi.org/10.5194/egusphere-egu26-16674, 2026.

Following the Fukushima Daiichi Nuclear Power Plant accident, radioactive substances such as radiocaesium (¹³⁷Cs) were widely dispersed and contaminated soils, raising concerns about their transfer from soil to crops. ¹³⁷Cs transfer is primarily regulated by exchangeable potassium (K_Ex), a chemically analogous element, but its effectiveness varies across environmental conditions such as soil type and land-use. Recent studies suggest that soil exchangeable ¹³⁷Cs (¹³⁷Cs_Ex) dynamics and its solid–liquid partitioning play key roles in predicting ¹³⁷Cs transfer irrespective of regional differences. In contrast, current direct methods for measuring ¹³⁷Cs are costly and time-consuming, making them unsuitable for rapid risk assessment. As an alternative approach for risk management, mid-infrared spectroscopy (MIRS) may provide a rapid and cost-effective means of estimating soil properties. Recently, models for predicting soil K_Ex concentrations from spectral data have been reported. However, their applicability to ¹³⁷Cs transfer remains unclear. In this study, we aimed to construct prediction models for the ratio of soil ¹³⁷Cs_Ex to soil total ¹³⁷Cs (¹³⁷Cs_Total) using MIRS spectra and to evaluate the variability of model performance among soil or land-use categories.

1249 soil samples collected in Fukushima Prefecture, Japan, from 2015 to 2020, were analyzed for soil properties, including soil total C, ¹³⁷Cs_Ex, and ¹³⁷Cs_Total, through MAFF and NARO in Japan. Each soil sample was analysed after drying at 37°C for at least 12 hours and being sieved to less than 0.2 mm before measurement. Mid-infrared spectra for these samples were obtained at the FAO/IAEA Soil and Water Management and Crop Nutrition Laboratory over the wavenumber range of 650–4000 cm^–1 using four replicate measurements per sample. Using noise-removed spectral data, partial least squares regression models were developed to predict the ratio of soil ¹³⁷Cs_Ex to ¹³⁷Cs_Total. In addition, prediction models were constructed for different soil types (andosol, brown forest soil, lowland soil, and peat soil) and land-use categories (upland fields and paddy fields), and their differences in model performance were evaluated.

Prediction models were constructed and achieved moderate predictive performance (R² around 0.6). In contrast, by stratifying prediction models by soil type, prediction accuracy improved for all soil types except for peat soil relative to the non-stratified model. In particular, andosol showed the highest prediction accuracy. Comparison of variable importance in projection (VIP) scores among these models showed that the contributions of specific wavenumber ranges to model performance differed among soil types. In andosols, VIP scores were higher in wavenumber ranges associated with carbohydrates, quartz, and clay minerals compared with the model constructed using all data. These results suggest that soil type specific mineralogical composition and carbon content may play roles in improving prediction performance. Furthermore, predictions stratified by land-use showed higher accuracy in upland fields than in paddy fields. Differences of VIP scores between them were also observed in wavenumber ranges associated with carbohydrates and clay minerals. These results suggest that environmental conditions, such as soil redox status, may influence prediction accuracy through their effects on soil minerals and carbon.

How to cite: Murashima, K., Iwai, J., Dercon, G., Vezzone, M., Vlasimsky, M., Albinet, F., Maruyama, H., and Shinano, T.: Performance Variability of Mid-Infrared Spectroscopy–Based Predictions of Soil Radiocaesium Dynamics across Diverse Soil and Land Use Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17315, https://doi.org/10.5194/egusphere-egu26-17315, 2026.

Nuclear accidents contaminate large terrestrial areas with long-lived radionuclides, and river systems play a key role in their redistribution. The concentration of dissolved radiocaesium (¹³⁷Cs) in river water is influenced by catchment-scale physical and geochemical characteristics. After the Chernobyl accident, environmental radionuclide concentrations generally declined over time; however, systematic inter-river comparisons remain limited, and the key factors controlling long-term differences in dissolved ¹³⁷Cs concentrations are still poorly understood.

In this study, we investigated the environmental behavior of dissolved ¹³⁷Cs in river systems affected by the Fukushima Daiichi Nuclear Power Plant accident and compared it with long-term observations from major European rivers impacted by the Chernobyl accident. In Fukushima, river water samples were seasonally collected between 2021 and 2024 from headwater catchments in the Yamakiya and Kuchibuto River basins. Samples were filtered through 0.22 µm membranes, and dissolved ¹³⁷Cs was measured using high-purity germanium detectors. Major ions (K⁺, NH₄⁺), stable ¹³³Cs, and dissolved organic carbon (DOC) were also analyzed. Univariate and multivariate regression analyses were applied to identify dominant release mechanisms. Catchment land cover, topographic gradients, and precipitation were analyzed using GIS, and groundwater residence times were estimated. These results were compared with long-term monitoring data and additional field measurements from nine European river catchments in Ukraine, Finland, Austria, and Italy, incorporating climatic, vegetation, and anthropogenic factors into an international comparison framework.

In Fukushima headwater catchments, dissolved ¹³⁷Cs concentrations increased from summer to autumn, coinciding with rising temperatures, enhanced organic matter decomposition, and increased K⁺ availability. Multiple regression analysis identified ¹³³Cs and K⁺ as significant explanatory variables, indicating that ion exchange plays a key role in ¹³⁷Cs mobilization. In contrast, DOC showed only a weak relationship with ¹³⁷Cs in Fukushima rivers. Comparative analysis of dissolved ¹³⁷Cs trends since 1986 revealed that European rivers have maintained higher concentrations over longer periods. Correlation analysis demonstrated that DOC and ¹³³Cs were significant scaling factors controlling dissolved ¹³⁷Cs concentrations across European river systems, whereas K⁺ and NH₄⁺ contributed little to concentration variability.

These results indicate that differences in the long-term behavior of dissolved ¹³⁷Cs between Fukushima and European rivers are associated with contrasting DOC- and ¹³³Cs-related controls at the catchment scale. This study suggests that accounting for regional variability in biogeochemical controls should be useful for long-term river environment and also can inform environmental modeling of radionuclide transport under nuclear emergency conditions, contributing to improved preparedness and long-term risk assessment.

How to cite: Onda, Y., Igarashi, Y., Smith, J., Sakaguchi, A., Fan, S., and Takahashi, J.: Why Are Dissolved ¹³⁷Cs Concentrations Lower in Fukushima Rivers? A Comparative Study with European Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18568, https://doi.org/10.5194/egusphere-egu26-18568, 2026.

The society acquired vast amounts of data from past major nuclear accidents, then learned the causes of those accidents, the methods to mitigate their adverse effects, and accident-prevention measures. However, it is challenging to store and organize highly technical knowledge related to nuclear accidents and share it in ways that meet our purposes. That is one reason we still do not have a centralized public database of nuclear incidents, despite efforts by international organizations such as the IAEA and academic institutions. Internet searches and AI queries return answers based on publicly available data sources without curation, thereby posing a risk of biased knowledge representation.

We aim to develop a prototype nuclear accident knowledge base using an ontology-based approach to establish the structured management system of nuclear accident-related knowledge. The top-level classes of the Basic Formal Ontology (BFO) are reviewed and utilized to design the base ontology hierarchy of the entities involved in nuclear accidents. The past ontology work in the nuclear and non-nuclear industries is reviewed, and some of their proposed classes and relationships were imported into the nuclear accident knowledge base structure. The classes, entities, and relations among those entities, and data properties relevant to the knowledge base are defined and are entered in protégé ontology editing software, whose ontology design can be shared digitally with interested parties.

During the development of the ontology structure, five knowledge-ambiguity factors were identified as potential focal points for developing the nuclear accident knowledge base. The ambiguity factors include: 1) terminology definition, 2) location definition, 3) temporal change in knowledge needs, 4) contamination definition, and 5) accident cause definition. When sharing nuclear accident knowledge, these factors must be considered to minimize confusion during the user’s knowledge-finding endeavour. By dissecting those ambiguity factors and providing a logical structure for nuclear accident-related data, this prototype knowledge base will assist in developing a public centralized nuclear accident knowledge base that can serve as a trustworthy data depository for preventing future accidents as well as enabling prompt recovery from the adverse effects of those accidents.

References.

Arp, R., Smith, B., Spear, A.D., 2015. Building ontologies with basic formal ontology. Mit Press. https://doi.org/10.7551/mitpress/8743.003.0011

Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., 2021. Introducing the open energy ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 2021; 5: 100074. https://doi.org/10.1016/j.egyai.2021.100074

Rashdan, A., Browning, J., Ritter, C., 2019. Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology. Idaho National Laboratory. https://doi.org/10.2172/2439922

Sorokine, A., Schlicher, B.G., Ward, R.C., Wright, M.C., Kruse, K.L., Bhaduri, B., Slepoy, A., 2015. An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms. Engineering Applications of Artificial Intelligence 43. 157-165. https://doi.org/10.1016/j.engappai.2015.04.010

U.S. Department of Energy, 2022. Environmental Radiological Effluent Monitoring and Environmental Surveillance.

How to cite: Yasumiishi, M. and Bittner, T.: Developing Ontology-Based Nuclear Accident Knowledge Base, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22532, https://doi.org/10.5194/egusphere-egu26-22532, 2026.

Land subsidence has long been a critical environmental hazard along the southwestern coast of Taiwan, with Yunlin County being one of the most severely affected areas. In this study, Long Short-Term Memory (LSTM) neural networks are employed to develop predictive models for land subsidence. Cumulative land subsidence, groundwater-level variations, and lithological layering are considered as input features to investigate the predictive performance of the models from both temporal and spatial perspectives.

As long-term groundwater monitoring data often suffer from missing values, this study further introduces a Cue Wasserstein GAN with Gradient Penalty (CWGAIN-GP) to impute missing groundwater-level data, thereby improving the stability and completeness of subsequent prediction models. Artificial masking experiments, including continuous missing periods ranging from one month to one year and random removal of 10%–50% of the data. The results show that the average Nash–Sutcliffe efficiency (NSE) achieved by the imputation model reaches 0.897.

For temporal prediction, the land subsidence model is trained using different training lengths (one year and seven years) and variable combinations to forecast cumulative land subsidence over the following one to two years. The most recent six months of observations are used as input to predict the monthly land subsidence increment. The results indicate that longer training periods and more comprehensive input variables lead to improved model performance. The coefficient of determination (R²) for the first prediction year reaches 0.945, while for the second year—under conditions of three consecutive months of missing data—the R² remains as high as 0.923.

For spatial prediction, a multi-station training and single-station validation strategy is adopted. When predicting a target station, the three nearest neighboring stations are selected, and their observations from the most recent three months are used as inputs to predict the monthly land subsidence increment at the target station. This increment is then combined with the known cumulative subsidence from the previous month to estimate the current cumulative subsidence. The results show that the average R² for single-month predictions reaches 0.966. Even when cumulative subsidence is estimated iteratively by adding predicted monthly increments over six consecutive months, the average R² remains around 0.90, demonstrating strong spatial generalization capability of the proposed model.

Fig.1 Monthly vertical profiles of cumulative land subsidence at different depths for the Huwei (MW_HWES) station in 2021.

Overall, this study demonstrates that cumulative land subsidence can be effectively predicted by integrating temporally and spatially informed LSTM models with vertically stratified hydrogeological information. Although cumulative subsidence is used as the primary prediction target, the inclusion of groundwater-level variations and lithological layering enables the model to capture the vertical characteristics of aquifer systems and their influence on subsidence processes. The results highlight the importance of incorporating stratified subsurface information when modeling land subsidence and provide a robust framework for spatiotemporal subsidence prediction under realistic data availability constraints.

How to cite: Hsu, Y.-Y., Lo, W., Lee, J.-W., and Huang, C.-T.: Predicting Cumulative Land Subsidence and Its Spatiotemporal Relationship Using Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2950, https://doi.org/10.5194/egusphere-egu26-2950, 2026.

Soil moisture downscaling is a challenging geospatial regression task that requires accurately capturing complex spatiotemporal relationships across scales. In this study, we conduct a preliminary applicability assessment of denoising diffusion probabilistic models (DDPMs) for continuous-value geospatial regression, exploring the potential of generative modeling frameworks for soil moisture downscaling. The model learns the relationships between coarse-resolution soil moisture observations and multi-source auxiliary features, enabling the generation of high-resolution soil moisture estimates.

During training, the model uses 36 km resolution satellite soil moisture data and conditions on auxiliary variables, including normalized difference vegetation index (NDVI), land surface temperature, surface albedo, precipitation, and digital elevation model (DEM). A conditional embedding strategy is introduced to incorporate temporal information, spatial location information, and in-situ statistics into the diffusion network via feature-wise linear modulation (FiLM), enhancing the model’s ability to capture complex spatiotemporal structures while maintaining stability. During inference, a two-stage “generation–correction” pipeline is employed: high-resolution (1 km) auxiliary features are first used to generate initial predictions through the diffusion model, which are subsequently bias-corrected using in-situ station data.

The applicability assessment combines quantitative and qualitative evaluation. Quantitative metrics include unbiased mean squared error (UMSE), root mean square error (RMSE), mean absolute error (MAE), and R², while qualitative evaluation focuses on spatial pattern consistency and temporal trend representation. Experimental results indicate that the diffusion-based generative model produces reasonable, spatially coherent, high-resolution soil moisture results and successfully captures major temporal variations. These findings demonstrate the applicability of generative frameworks for geospatial regression and their potential as a geospatial regression modeling paradigm, providing a foundation for further refinement and evaluation.

How to cite: Yu, X., Hu, L., Su, C., Yan, Y., Wu, S., and Du, Z.: Long-term Soil Moisture Downscaling Based on Diffusion Models: Applicability Assessment of Generative Models for Geospatial Regression Tasks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5255, https://doi.org/10.5194/egusphere-egu26-5255, 2026.

This study presents a comprehensive sensitivity analysis framework to disentangle the drivers of predictive uncertainty in spatial interpolation and how they ultimately affect spatial predictions. Developed within a Global Sensitivity Analysis context, the proposed approach is model-independent and generic, allowing for broad application across diverse spatial interpolation workflows.

The framework is demonstrated using groundwater Sulfate concentration in the Paris Basin, a dataset characterised by sparse and highly clustered sampling across six distinct aquifers according to the French "BD LISA" hydrogeological system (https://bdlisa.eaufrance.fr/). We represent the underlying spatial process as a  Gaussian Random Field, leveraging Integrated Nested Laplace Approximations for computationally efficient Bayesian inference. This allows for a  probabilistic treatment of uncertainty even within complex spatial structures.

We systematically evaluate the impact of several key uncertainty factors related to both data and model configuration: (1) the number of monitoring stations and their spatial distribution; (2) the selection of the environmental covariates  and the functional form of their effects (linear vs. non-linear); (3) the treatment of censored data (values below detection limits); and (4) structural assumptions regarding the spatial covariance function, specifically the estimation of variogram hyperparameters such as range, sill, and nugget effects and their prior specification. By propagating these uncertainty sources through our framework, we derive domain-wide aggregated sensitivity measures. These metrics quantify how specific data topologies—including sampling density, clustering effects, and censoring rates—govern the stability and accuracy of the resulting spatial interpolations.

Finally, the results facilitate an in-depth discussion on the limitations of purely probabilistic methods in data-poor scenarios. We provide an outlook on the potential of extra-probabilistic approaches, such as imprecise or interval-based kriging, to more robustly address the wide range of epistemic uncertainties inherent in environmental monitoring.

We acknowledge financial support of the French National Research Agency within the HOUSES project (grant N°ANR-22-CE56-0006).

How to cite: Perchtold, C., Rohmer, J., Thomas, A., Lions, J., and Wieskotten, M.: Global Sensitivity Analysis of Spatial Interpolation for Sparse, Clustered, and Censored Data: A Case Study of Groundwater Sulfate in the Paris Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8056, https://doi.org/10.5194/egusphere-egu26-8056, 2026.

Predicting building attributes—such as functional classification, socioeconomic status, and energy efficiency—is a fundamental task in urban science. The current paradigm involves leveraging domain knowledge to extract attribute-specific morphological or topological features for supervised modeling. However, this heavy reliance on manual feature engineering often leads to task-specific models where features must be redefined for each attribute. Consequently, the field lacks a unified, generalizable framework capable of multi-attribute building prediction.

Inspired by recent advances in Regression Language Models (RLMs), which cast continuous prediction as a text-to-text task, we propose Buildings as Text (BaT). BaT serializes structured building representations (e.g., GeoJSON) into raw text and enables end-to-end text-to-text regression. To mitigate the spatial sensitivity of building data, we introduce a Topology-Preserved Coordinate (TPC) strategy that removes each building text’s absolute positional information. Specifically, TPC applies a global coordinate shift to the serialized geometry, suppressing absolute-location bias while preserving local shape and topology. By operating directly on raw text, BaT eliminates manual feature engineering and allows the model to learn a “spatial syntax” from the underlying geometric descriptions.

We validated the BaT framework through a case study on informal settlement (slum) classification. The results demonstrate that our model achieves superior performance and higher adaptability compared to traditional morphology-based methods. While validated on slum detection, this research offers a universal and scalable paradigm for urban building analysis, suggesting that Large Language Models can effectively "read" urban forms for diverse prediction tasks beyond specific domains.

How to cite: Wang, C.-C. and Luo, P.: Buildings as Text: A Universal Regression Paradigm for Building Attribute Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8402, https://doi.org/10.5194/egusphere-egu26-8402, 2026.

The archaeological landscapes of northern Oman host thousands of funerary monuments of different periods and morphologies, forming one of the densest and least explored burial regions of the Arabian Peninsula. Within the framework of LAA&AAS (Laboratorio di Archeologia Analitica e Sistemi Artificiali Adattivi) and MASPAG (Missione Archeologica della Sapienza nella Penisola Arabica e nel Golfo), a multidisciplinary project supported by Sapienza University of Rome and the Italian Ministry of Foreign Affairs, we developed a reproducible geo-AI workflow to classify and analyse funerary structures based on remote-sensing and spatial-context information.

The first dataset, encompassing 185 tombs mapped in the Southwestern Cemetery near the village of Muslimat, in the region of Wadi al-Maʿawil (ca. 70 Km southwest of Muscat) was used to test a machine-learning pipeline designed to discriminate between morphological classes (“tombs” vs “non-tombs”, and within-type subclasses) from high-resolution satellite imagery and derived spatial metrics. Two Random Forest models were compared: a geometry-only baseline using shape descriptors (area, compactness, circularity, elongation), and an extended model incorporating spatial-context features such as kernel density, nearest-neighbour distances, Moran’s I local autocorrelation and cluster membership. The integration of these contextual descriptors increased overall accuracy from 59 % to 76 %, improving model reliability and reducing false positives in morphologically ambiguous contexts. The workflow includes systematic feature importance analysis and confusion-matrix evaluation to assess interpretability and class-imbalance effects.

Beyond the single-site test case, this approach aims to address a broader spatiotemporal challenge: learning and transferring morphological–contextual patterns across different archaeological regions. During 2025 field campaign (20 October – 20 December 2025), more than 500 new tombs were surveyed and georeferenced in the area of the Western Cemetery, expanding the available dataset and enabling large-scale testing of model scalability and transferability. This new phase will assess whether models trained in Wadi al-Maʿawil can generalize to nearby valleys with comparable geomorphological and cultural settings, supporting semi-automated mapping and predictive modelling of funerary features.

The presented pipeline, implemented in an open-source environment (Python, QGIS, and scikit-learn), is designed for reproducibility and transparent parameter tracking. All processing steps—from data preparation and feature extraction to model training and evaluation—are logged and versioned, facilitating cross-project reuse. The workflow thus bridges archaeological and geospatial domains, demonstrating how spatially aware machine learning can improve the detection, classification, and interpretation of complex cultural landscapes.

This contribution highlights the potential of AI and ML in managing spatiotemporal archaeological data and in advancing reproducible analytical frameworks. The methodological approach developed for the Omani funerary landscapes can be generalized to other MASPAG regions, supporting comparative analysis of desert landscapes and long-term dynamics of human–environment interaction across the Arabian Peninsula.

How to cite: Meneses Pineda, A. S., Solinas, M., Ramazzotti, M., Musacchio, M., and Buongiorno, M. F.: A preliminary study of the morphology and spatial distribution of funerary elements in Oman, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11193, https://doi.org/10.5194/egusphere-egu26-11193, 2026.

Understanding the spatial dependence of residuals is important for interpreting and diagnosing spatial machine learning models. Spatial autocorrelation in the residuals suggests that the model has not fully captured the data's spatial structure. This may imply that the model is missing crucial spatial context or interactions, and that, in effect, it is spatially biased, leading to underestimation in some areas and overestimation in others.

Moran's I is a commonly used statistic for the diagnosis of spatial autocorrelation in spatial predictions, providing a single-value quantitative measure with a straightforward interpretation. This measure quantifies the degree of spatial autocorrelation, indicating whether similar values are clustered together or dispersed across space. The information provided by Moran's I has been used in various ways in studies applying machine learning: to evaluate model performance, interpret results, understand model limitations, and compare different modeling approaches.

Unlike standard model performance metrics, such as R2 or RMSE, Moran's I depends not only on the values of residuals but also on the spatial context—especially the study area's extent, the sampling strategy used, and the specification of spatial weights. However, there is a lack of a comprehensive understanding of how these factors influence the results of Moran's I calculation in the context of spatial machine learning, and of how to best use this measure for model evaluation and comparison.

Using simulated data with controlled spatial properties, we investigated how testing set size, sampling strategy, and the specification of spatial weights influence Moran's I computed on model residuals. Our results show that Moran's I, calculated based on k-nearest neighbors approach,  primarily reflects the spatial structure of values in the testing set rather than the residual autocorrelation across the full prediction domain, often underestimating fine-scale spatial patterns. These findings have various implications: weight-matrix definitions must be clearly reported, calculations on sparsely distributed or clustered samples should be avoided, Moran's I is generally not directly comparable across studies due to differences in spatial extents and sampling, and its values are inherently scale-dependent.

With this contribution, we aim to present the behavior of Moran's I calculated from residuals of spatial machine learning models under different conditions, outline best practices for selecting and reporting spatial weights, and discuss how to interpret Moran’s I. 

How to cite: Nowosad, J., Meyer, H., and Schmidinger, J.: Using Moran's I for assessing residual spatial autocorrelation in machine learning models , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11342, https://doi.org/10.5194/egusphere-egu26-11342, 2026.

How to cite: Görgen, D. A., Heilig, S., Meyn-Grünhagen, L., Fischer, A., Lederer, J., and Meyer, H.: Area of Applicability for Deep Learning: Exploring Latent Space Geometry of Earth Observation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12275, https://doi.org/10.5194/egusphere-egu26-12275, 2026.

Near-real-time forest monitoring is a critical component for managing climate risk and resource conservation in the Brazilian Legal Amazon (ALB). The DETER program, managed by the National Institute for Space Research (INPE), has played a pivotal role for over two decades by producing warnings of deforestation and forest degradation to support environmental enforcement by agencies such as IBAMA. However, the effectiveness of these warnings is highly dependent on temporal efficiency — the speed at which a disturbance is detected and published after the activity begins.

Recent objective evaluations of DETER’s performance regarding selective logging — a major driver of forest degradation — revealed a significant median delay of approximately 312 days between the start of logging activities and the corresponding warning publication during 2022-2023. This temporal gap highlights the challenge of applying traditional monitoring to complex spatiotemporal datasets, where factors like cloud cover and sensor resolution can hinder early detection.

To address these challenges, this research proposes a novel approach within the framework of AI and Machine Learning in Spatiotemporal Contexts. We leverage Foundation Models and Deep Learning architectures designed to process the complex temporal dynamics of tropical forests using Harmonized Landsat-Sentinel (HLS) time series. A key contribution of using foundation models in this pipeline is their ability to learn robust representations from large-scale data, significantly reducing the requirement for vast volumes of manually annotated samples — a known bottleneck for AI-based remote sensing monitoring systems. By applying these models to HLS data, we aim to improve spatiotemporal predictions and the reliability of the modeling pipeline, facilitating the production of more agile and efficient early warnings.

This work contributes to the development of the next generation of forest monitoring systems, focusing on interpretability and transferability across the Amazonian landscape. By reducing the detection lag of selective logging, this approach seeks to enhance technological sovereignty in environmental monitoring and provide more effective decision-making support for forest preservation.

How to cite: Taquary, E. and Aragão, L.: Enhancing Near-Real-Time Forest Monitoring: Foundation Models and Harmonized Landsat-Sentinel (HLS) Time Series for Selective Logging Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16001, https://doi.org/10.5194/egusphere-egu26-16001, 2026.

The integration of Earth Observation (EO) data with machine learning (ML) has transformed the mapping of Deprived Urban Areas (DUA). Despite these technical advances, persistent disconnect remains between research outputs and their operational uptake by local stakeholders. In parallel, advances in ML and deep learning (DL), together with new satellite missions have improved the extraction of building footprints and urban morphology. Nevertheless, DUA mapping studies, which largely depend on these physical indicators, often prioritize benchmark performance over the robustness, transparency, or usability required in real-world decision-making contexts. One of the main reasons for this gap is spatial data quality (SDQ), which fundamentally limits model performance and generalization. When data quality is poor, due to inaccuracies, incompleteness, or inadequate provenance, models become unreliable, regardless of architectural complexity. Furthermore, many studies rely on validation strategies that ignore spatial autocorrelation, thereby yielding overoptimistic accuracy estimates that mask poor generalization to new local contexts.

To address these challenges, this paper argues for a shift toward a systematic assessment of spatial data quality. We first conduct a scoping review of 50 state-of-the-art DUA mapping studies published between 2017 and 2025. Our analysis reveals a high dependence on very-high-resolution imagery (72%), a widespread lack of publicly accessible data and code (92%), and a critical deficiency in operationalizing semantic definitions of DUAs with 90% of studies failing to provide mapping rules (for visual interpretation) or ground rules (for in-situ collection). Most studies also fail to assess user needs (90%) or do not consider the ethical implications of using DUA data (88%), which is highly sensitive due to risks such as forced evictions. Building on these findings and established international standards from ISO and the OGC, we propose a comprehensive Spatial Data Quality (SDQ) framework tailored to transparently document supervised image classification in DUA mapping. This framework integrates established practices such as adherence to the Findable, Accessible, Interoperable, Reusable (FAIR) principles and assessment of acquisition, measurement and spatial-temporal quality with novel dimensions addressing semantic consistency, sampling representativeness, human factors in annotation, learning shortcut risk, user needs validity, ethical considerations, and transparent reporting of the dataset’s potential failure modes or uncertainties. By operationalizing SDQ as a living, extensible framework, this work aims to better align advances in ML and DL with sustained societal impact, ensuring that DUA mapping products, or any relevant application domain, are fit for use by local communities and decision-makers.

How to cite: Campomanes V, F., Kuffer, M., Stein, A., Dijkstra, A. M., Trento Oliveira, L., and Belgiu, M.: A framework for assessing the quality of spatial data applied in supervised image classification of deprived urban areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19025, https://doi.org/10.5194/egusphere-egu26-19025, 2026.

Greenhouse agriculture has become a crucial element of agricultural practices in Morocco, yet its spatial and temporal evolution remain insufficiently quantified. This study aims to map greenhouse structures at the Souss-Massa region scale in order to assess the progress of covered agriculture and examine its relationship with socio-economic development in Morocco. Using hand-annotated greenhouse data from the Chtouka region as ground truth, we develop a deep learning–based detection framework relying exclusively on open-source tools. Multispectral Sentinel-2 satellite imagery at 10 m spatial resolution is used as input to a U-Net convolutional neural network, which is trained, validated, and tested for greenhouse segmentation. The proposed model achieves an overall accuracy of up to 94%, demonstrating strong generalization capability. The resulting plug-and-play methodology enables scalable, cost-effective, and open-source greenhouse mapping, and provides valuable insights into the dynamics of covered agriculture and its role in Morocco’s agricultural and socio-economic development.

How to cite: El hachemy, S., Aglagal, C., Ait-Ichou, H., Elhaid, I., Zlaiga, J., Hssaisoune, M., Bouchaou, L., and Belaqziz, S.: Satellite imagery for greenhouse mapping in Morocco using U-net model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19057, https://doi.org/10.5194/egusphere-egu26-19057, 2026.

Accurate forecasting of vector-borne diseases such as dengue is often challenged by limited and noisy spatiotemporal data. This study evaluates the effectiveness of data augmentation techniques in enhancing the robustness and predictive accuracy of machine learning models. We assess multiple augmentation strategies applied to weekly dengue case data across countries in South and Central America (2014–2022). Results show that augmentation substantially improves short-term forecasting performance, particularly in regions with sparse or irregular observations, yielding higher R² values and lower relative errors compared to non-augmented baselines. These findings demonstrate that well‑designed augmentation can mitigate data scarcity and strengthen the generalization of graph‑based deep learning frameworks for epidemiological forecasting. Overall, the study highlights augmentation as a practical and scalable approach for improving spatiotemporal ML applications in disease surveillance.

How to cite: Siabi, N., Son, R., Thomas, M., Irrgang, C., and Saynisch Wagner, J.: Improving Dengue Forecasting with Spatiotemporal Data Augmentation and Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19452, https://doi.org/10.5194/egusphere-egu26-19452, 2026.

At continental scale, crop classification needs models that capture phenology through temporal analysis without degrading field boundaries. We introduce a decoupled architecture that uses static foundation‑model features across multi‑sensor time series and fuses them with high‑resolution spatial features. The temporal stream ingests paired multispectral and SAR sequences plus a static DEM and metadata, extracts foundation model token features per timestep, and compresses them with a Perceiver‑style bottleneck that cross attends from a fixed latent bank to the full foundation model token volume. Such heavy compression collapses sequence length by orders of magnitude, which makes longer temporal windows and larger batches ingestible on consumer‑grade GPU memory constraints while preserving the temporal signatures needed to separate crops with similar single‑date appearance.
The spatial stream stays purely static --- it selects a single high‑quality multispectral reference frame and passes it through a high‑resolution backbone to retain fine geometry and crisp boundaries. The two streams are joined in a query‑based decoder, where dynamic queries generated from the compressed temporal latents attend to multi‑scale spatial features, aligning phenological signatures with precise field edges. This fusion mechanism prevents coarse temporal features from blurring geometry and makes delineation robust to shifts in timing or crop management practice. In fact, temporal queries encode crop‑specific growth signatures, while the spatial stream supplies the pixel‑level evidence for boundary localization, whereas the decoder enforces instance‑aware segmentation through iterative cross‑attention and masked refinement.
We evaluate on EuroCrops crop‑class labels, achieving a Micro Recall of 84.1% and a Segmentation Quality of 84.2%. Transferability is tested with a spatial holdout protocol using geographically disjoint train/test regions, reliability is summarized by aggregate metrics on these strict splits, and uncertainty is communicated through per‑class performance variability and label‑noise sensitivity analyses that bound achievable scores.

How to cite: Vinholi, J. G., Sleimi, R., Werner, F., and Abelló, A.: A Two‑Stream Spatiotemporal Architecture with Foundation‑Model Features Applied to Crop Classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19669, https://doi.org/10.5194/egusphere-egu26-19669, 2026.

Spatial Data Infrastructures (SDIs) contain a lot of spatial data from various organizations and data producers. Metadata is intended to enable the discovery of the data, yet finding the relevant data can be challenging. The challenges include rigid keyword-search, complex search interfaces in geoportals, map-based search that require some geographic knowledge, and language differences between user queries and the metadata.

The development of Large Language Models (LLMs) offers new opportunities to improve spatial data discovery. LLMs demonstrate strong language understanding and generation capabilities and have been used in information retrieval tasks. They can overcome semantic differences and language barriers between user queries and the needed information. However, their internal knowledge is limited and they are prone to hallucinations. Unless the datasets in SDIs, or the web pages describing them are indexed by search engines, LLMs with internet search tools cannot find them. 

Retrieval-Augmented Generation (RAG) offers a solution for the knowledge limitations, by connecting an LLM with an external and up-to-date knowledge base. However, RAG mainly works in the textual domain and excels at retrieving external information that is semantically relevant to a user query. Queries for geographic data have a spatial aspect yet the spatial reasoning capabilities of LLMs are limited. For a query like “forest data for Vienna”, RAG can identify the relevant forest data from a pool of metadata, regardless of the language or words used to describe the data. However, identifying datasets that meet the spatial intent is a problem. DCAT metadata, the most popular metadata standard, defines the spatial extent of spatial datasets using bounding box coordinates or as links to gazetteers. Naive RAG is based on semantic similarity approaches.  An LLM can identify “Vienna” as a location, but would struggle to identify datasets relevant to the location, as there is little semantic similarity between the location name and coordinate digits or gazetteer links.  There is thus a need to incorporate spatial indexing techniques for improved spatial reasoning.

With our contribution we present an approach that combines LLMs, RAG, and spatial indexing techniques to overcome existing challenges in discovering spatial data in SDIs, and improve spatial data discovery through natural language queries.

How to cite: Ondieki, J. O., Rieke, M., and Jirka, S.: Using Large Language Models to Enhance Spatial Data Discovery in Spatial Data Infrastructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20221, https://doi.org/10.5194/egusphere-egu26-20221, 2026.

The recent rise of foundation models in Earth Observation (EO) has reshaped how remote sensing tasks are approached, particularly by allowing strong downstream performance with comparatively limited labeled data. These models have reported impressive results in applications such as land cover classification and semantic segmentation. However, performance gains alone do not resolve a central concern: whether the resulting predictions can be trusted. In practical EO scenarios—including disaster response and environmental monitoring—miscalibrated confidence estimates may lead to incorrect decisions even when overall accuracy appears high.

Motivated by this gap between accuracy and reliability, this study focuses on the uncertainty calibration behaviour of fine-tuned EO foundation models. Using TorchGeo for consistent data handling and the Lightning-UQ-Box framework for uncertainty quantification, we construct an evaluation pipeline that contrasts Vision Transformer–based pretrained models with conventional convolutional neural networks trained from scratch. Experiments are conducted across both image classification tasks (e.g., EuroSAT) and dense prediction settings such as semantic segmentation.

Rather than assuming superior representations automatically yield better-calibrated predictions, we explicitly examine how calibration properties change after fine-tuning large pretrained models. In addition, we evaluate a spectrum of uncertainty quantification approaches, from lightweight post-hoc methods like temperature scaling to more computationally demanding techniques, including Monte Carlo Dropout, deep ensembles, and Laplace approximation. Calibration quality is assessed using expected calibration error and reliability diagrams, alongside predictive accuracy.

By analysing the trade-offs between computational cost, accuracy, and calibration, this work provides practical insight into which UQ strategies are most effective for EO foundation models. Our findings aim to support the deployment of remote sensing systems in operational settings where reliable uncertainty estimates are as critical as raw predictive performance.

How to cite: Wei, Y.: Uncertainty Quantification for Earth Observation Foundation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20813, https://doi.org/10.5194/egusphere-egu26-20813, 2026.

Localized rapid urban expansion and global climate change have contributed to land use and land cover (LULC) dynamic modifications, which further links to changed land surface temperatures (LST). This study proposes an integrated approach of machine learning (ML) models in assessing decadal LULC changes and future prediction in a city in the Mekong region. To achieve an accurate LULC map object-based classification strategies were implemented using various ML techniques across observed years with four main land cover categories: built-up areas, water bodies, paddy fields/shrubs, and orchards, together with LST extraction. The findings reveal that Random Forest classifier works superior to other classifiers, achieving the best overall accuracy of 81%. There have been substantial land usage changes, with the percentage of developed areas rising from 8% in 2014 to approximately 12% in 2024. Urbanization is correlated with rising temperatures , while, vegetation, on the other hand, helps alleviate this heat by providing shade and cooling. With an overall accuracy of 85% in the patch-generating land use simulation (PLUS) model, by 2030, under the impacts of both natural and socio-economic drivers, an apparent increase in the proportion of built-up areas to 15% and a slight variation in other categories could be seen in line with planning objectives. The urban expansion could be clearly seen in the highly dense districts with an increase to 42% by 2030 from an initial stage of merely 27% in 2014. The primary forecast conversions in LULC observed were vegetated lands transforming into construction areas for urbanization, yet maintaining agricultural practices for food security. The integrated approach has proven its suitability in intricate land usage patterns evaluation and optimization.

How to cite: Nguyen, L. and Daou, D.: Harnessing an Integrated Machine Learning based Approach in Monitoring and Predicting Dynamic Spatiotemporal Land Use and Land Cover Changes. A case study in a Mekong city , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21002, https://doi.org/10.5194/egusphere-egu26-21002, 2026.

Ecosystems are a key component of biodiversity, providing vital services to humans and the economy. Anthropogenic pressures driving environmental change result in widespread ecosystem degradation and loss. The area and spatial distribution of ecosystem types, referred to as ecosystem extent, provide a critical entry point for assessing ecosystem condition, functioning, and associated services, and therefore require detailed and spatially explicit monitoring.

Despite advances in geospatial analysis, consistent mapping and delineation of ecosystem extent remain challenging. Map products on ecosystem extent should, therefore, be supported by uncertainty assessments, ideally in a spatially explicit manner. According to best practices in related fields, the minimum requirement for uncertainty quantification for thematic maps is the aggregated estimation of per-class accuracy and per-class area uncertainty, following a validation procedure based on independent reference data. However, the standard practice remains spatially implicit. To date, there is no established practice for spatially explicit uncertainty quantification procedures.

This study presents a spatial solution for estimating the uncertainty of maps produced using machine-learning algorithms. The approach builds on the standard map-validation procedure and extends it to pixel-wise assessments using conformal prediction. While conformal prediction can be applied to any machine learning algorithm, ecosystem extent mapping poses domain-specific challenges, including a high-dimensional multi-class setting and hierarchical class structures. This study, therefore, focuses on developing solutions to ensure robust class-specific coverage, exploring different conformal prediction implementation variants, and adapting them from flat to hierarchical mapping scenarios.

To assess the feasibility and applicability of our approach, we tested it on the Oslo-Viken municipality in Norway. In this case study, we developed an ecosystem extent map for 2024 and quantified and mapped its uncertainty at pixel-level. This analysis helped to evaluate the practical application and performance of the approach on real-world cases.

How to cite: Tregubova, P., Clappe, S., Marielle Mienna, I., Smets, B., Buchhorn, M., Remelgado, R., and Meyer, C.: Spatially-explicit uncertainty assessment of ecosystem extent mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21265, https://doi.org/10.5194/egusphere-egu26-21265, 2026.

In this study, multispectral images were used to detect toxic blooms in Villarrica Lake in Chile, using a time series of water quality data from 1989 to 2024, based on the extraction of spectral information from Landsat 8 and 9 satellite imagery. To explore the predictive capacity of these variables, we constructed 255 multiple linear regression models using different combinations of spectral bands and indices as independent variables, with phycocyanin concentration as the dependent variable. The most effective model, selected through a stepwise regression procedure, incorporated seven statistically significant predictors (p < 0.05) and took the following form: FCA = N/G + NDVI + B + GNDVI + EVI + SABI + CCI. This model achieved a strong fit to the validation data, with an R2 of 0.85 and an RMSE of 0.10 μg/L, indicating high explanatory power and relatively low error in phycocyanin estimation. When applied to the complete weekly time series of satellite observations, the model successfully captured both seasonal dynamics and interannual variability in phycocyanin concentrations (R2 = 0.92; RMSE = 0.05 μg/L). These results demonstrate the robustness and practical utility for long-term monitoring of harmful algal blooms in Lake Villarrica.

How to cite: Rodríguez-López, L., Bustos Usta, D., Bravo Alvarez, L., Duran Llacer, I., Bourrel, L., Frappart, F., and Urrutia, R.: Advanced phycocyanin detection in a South American lake using Landsat imagery and remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4, https://doi.org/10.5194/egusphere-egu26-4, 2026.

The Ganges floodplains are among the most flood-prone regions in India, where recurrent inundations cause significant socio-economic and ecological impacts. Understanding the spatial distribution, frequency, and dynamics of flooding is essential for effective floodplain management and enhancing climate resilience. This study examines the flood frequency and spatial extent across a section of the Ganga River floodplains in Bihar, utilising multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data spanning the period from 2016 to 2024. Flooded areas were delineated through an optimal threshold-based classification of VH-polarised backscatter images, with threshold values ranging from -19.5 dB to -22.3 dB. Annual flood extents were mapped, and an inundation frequency composite was generated to identify zones experiencing recurrent flooding. The spatial analysis revealed substantial variability in flood occurrence, with extensive inundation observed in low-lying regions. Several areas were inundated in more than 60% of the study years, indicating chronic flood exposure. The decadal analysis revealed that August and September were the peak months for flooding, with some areas remaining inundated for more than one month, which had an adverse impact on both human settlements and agricultural lands. Validation using optical satellite imagery from Sentinel-2 confirmed a 98% accuracy in the SAR-derived flood extent, reinforcing the reliability of the classification method. The temporal flood frequency analysis provides crucial insights into long-term flood dynamics and helps identify hydrologically sensitive zones. Overall, this study highlights the effectiveness of SAR-based monitoring in understanding floodplain behaviour under changing climatic and hydrological conditions, and supports improved flood hazard mapping, hydrodynamic model calibration, and sustainable flood risk management in the Ganges Basin and other monsoon-affected regions.

Keywords: Flood Inundation, Multi-Temporal, Time-Series, Flood Frequency, Sentinel-1 SAR, Ganges River

How to cite: Sajid, M., Hasan Khan, H., Khan, A., and Ansari, A. A.: Flood Dynamics and Frequency Mapping in the Lower Ganges Floodplain in India Using Multi-Temporal Sentinel-1 SAR Observations (2016–2024), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-125, https://doi.org/10.5194/egusphere-egu26-125, 2026.

Wetlands are very sensitive hydrological ecosystems that are essential for groundwater recharge, flood control, and biodiversity. Climate variability, changed river regimes, and unsustainable anthropogenic pressures are all posing new challenges to their stability. The current work evaluates the two-decade hydro-climatic dynamics of the Haiderpur Wetland (Ganga River, India) by merging optical (Landsat), radar (Sentinel-1), and gridded climate (ERA5, CHIRPS) datasets with GRACE-based groundwater anomalies. On the Google Earth Engine (GEE), processing of time-series Landsat (NDVI, NDWI, LST) and Sentinel-1 (SAR) data to monitor all-weather surface inundation and vegetation structure. To disentangle climatic and anthropogenic drivers, these remote sensing products are statistically correlated against ERA5-Land (Evapotranspiration) and CHIRPS (Precipitation) data, alongside GRACE groundwater anomalies. The findings demonstrated a considerable downward trend in pre-monsoon NDWI and wetland water distribution. This was accompanied by a significant increase in LST and an unexpected increase in NDVI. All-weather Sentinel-1 data validated the drying trend. On the other hand, 'greening' (as indicated by NDVI) in a drying environment suggests a structural shift from native wetland vegetation to more drought-tolerant or invasive terrestrial plants. The study assesses the capability of a multifaceted (optical-radar-climate) GEE strategy to quantify the individual contributions of climatic and anthropogenic factors, while also monitoring wetland development. Furthermore, these findings quantify the hydro-ecological vulnerability of major Ramsar wetlands and emphasize the vital need for coordinated water management to sustain ecosystems in the Ganga River Basin, with far-reaching implications for global wetland conservation.

Keywords: Hydrology, GRACE, Climate Change, SAR, NDVI, NDWI, LST

How to cite: Ansari, A. A., Hasan Khan, H., Khan, A., and Sajid, M.: Hydro-Ecological Vulnerability of  Ganga River Wetland (India): A Multi-Sensor Remote Sensing and GRACE-based Assessment of the Haiderpur Ramsar Site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-147, https://doi.org/10.5194/egusphere-egu26-147, 2026.

Floods are the costliest and most frequently occurring natural disasters. One of the key factors in preventing and reducing losses is providing a reliable flood map. However, the uncertainty associated with either flood inundation model or data, specifically the Digital Elevation Model (DEM), may have adverse effects on the reliability of flood stage and inundation maps. Therefore, a systematic understanding of the uncertainty is necessary. In this study, an attempt is made to assess whether models are more susceptible to the uncertainties or the data itself. In order to do this, a SCIFRIM (Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model) is employed, utilizing a list of DEM datasets to reconstruct the October 2024 Valencia flood event. The modelled flood extents were validated against those derived from multi-sensor remote sensing data. The Critical Success Index (CSI) was calculated to assess the agreement between observed and modelled flood extents, yielding values of 0.49 and 0.59 for October 30th and 31st, respectively, when combining SCIFRIM and Lidar-DEM. Additionally, a multi-model comparison has been performed between SCIFRIM and CaMa-Flood (Catchment-based Macro-scale Floodplain), HEC-RAS (Hydrologic Engineering Center's River Analysis System), and TUFLOW (Two-dimensional Unsteady FLOW), demonstrating its relevance in terms of outputs (flood extent and stage) and model runtime. The findings demonstrate that the proposed modeling framework offers a reliable approach for flood assessment. It has great potential to support rapid assessment and decision-making in data-scarce regions.

How to cite: Tripathi, G., Sarkar, E., and Biswal, B.: Evaluating Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCIFRIM)-based Flood Inundation against multi-satellite observation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-436, https://doi.org/10.5194/egusphere-egu26-436, 2026.

Floods are highly dynamic hazards whose spatial extent can change rapidly within hours. Timely and accurate monitoring is essential for early warning, emergency response, and post-disaster assessment. A major challenge in current Earth Observation (EO) based approaches is the difficulty of capturing the complete evolution of a flood event, including its maximum flood extent. This information is often missing due to temporal gaps in Synthetic Aperture Radar (SAR) acquisitions and cloud cover in optical imagery. Missing the peak extent limits the accuracy of impact assessments and poses challenges for applications such as parametric insurance, which depend on reliable measurements of flood magnitude. Although daily flood products exist, they are often based on large-scale multi-spectral sensors and struggle during persistent cloud cover as well as with resolution for smaller events, creating an urgent need for a more reliable method for daily flood estimation from higher-resolution SAR datasets. To address these challenges, we propose a novel deep learning framework that fuses EO-based coarse dynamic hydrometeorological data with static geospatial datasets to produce high-resolution daily flood extent maps. Our approach integrates static flood conditioning inputs, including elevation, Height Above Nearest Drainage, Urban Development Area, flow direction, Normalized Difference Vegetation Index, Normalized Difference Built-up Index, soil clay and sand content, and pre-flood SAR and multispectral imagery with dynamic hydrometeorological variables such as daily precipitation and soil moisture. The model adopts a multi-stage vision transformer architecture: encoders extract multi-level latent representations from all inputs, which are then fused using cosine similarity, normalization, and temporal attention mechanisms. A decoder reconstructs high-resolution flood extent, followed by a Gaussian filter to reduce high-frequency noise. The framework is fully supervised using the globally available KuroSiwo flood mask dataset, ensuring transferability across diverse geographic regions and climate zones. In addition, this research provides a complete data preparation workflow that converts flood mask shapefiles into standardized image patch datasets, including a modular input selection interface that removes dependence on inputs included in specific datasets, directly suitable for deep learning training, enabling straightforward implementation and practical applicability. The model is trained and evaluated across three distinct climate zones on multiple continents, demonstrating a robust capability to overcome the temporal limitations of SAR data and cloud-induced gaps in optical observations. Held-out region tests with strict geographic separation to minimize spatial autocorrelation induced data leakage, further ensure unbiased evaluation and true transferability. Preliminary tests across multiple continents yield stable performance, with cross-site metric variations remaining within approximately 5-7 percent. This study introduces the first deep learning framework for daily fine-scale flood extent mapping using purely EO data which are globally accessible, providing a scalable and transferable solution for real-time flood monitoring, disaster management, and potential applications in parametric insurance by improving flood mapping cadence and reliably estimating maximum flood extents.

Keywords: spatio-temporal fusion, vision transformer, high-resolution flood mapping

How to cite: Surojaya, A., Kumar, R., and Dasgupta, A.: DeepFuse2.0: Novel Deep Learning-based Fusion of Satellite-based Hydroclimatic Data and Flood Conditioning Factors for Daily Flood Extent Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1047, https://doi.org/10.5194/egusphere-egu26-1047, 2026.

Fully automated, globally applicable flood-mapping systems must earn user trust, which in turn requires systematic testing across diverse environmental conditions to understand performance stability and a clear understanding of model transferability. While some recent studies have evaluated cross-site performance of flood mapping algorithms, the cross-biome transferability of Random Forest (RF) models for SAR-based flood delineation has not yet been thoroughly evaluated. In this study, we assess how well RF classifiers trained for binary flood detection generalize across biomes using primarily Synthetic Aperture Radar (SAR) data. Our feature stack comprises 14 variables, including 9 SAR-derived features (Sentinel-1 VV and VH backscatter and associated temporal-change metrics) which provide information on the flood-induced land surface changes and 4 contextual predictors such as land cover and topographic indices which influence radar backscatter and help to reduce as well as mitigate uncertainties. Experiments were conducted across 18 flood events distributed equally amongst 6 distinct biomes: (1) Deserts and Xeric Shrublands, (2) Tropical and Subtropical Moist Broadleaf Forests, (3) Temperate Broadleaf and Mixed Forests, (4) Temperate Coniferous Forests, (5) Mediterranean Forests, Woodlands and Scrub, (6) Temperate Grasslands, Savannas and Shrublands. Model transferability is evaluated using a two-level nested cross-validation approach. First, intra-biome performance is established through an inner 3-fold Leave-One-Group-Out Cross-Validation (LOGO-CV), in which models are trained on all but one site within a biome and evaluated on the held-out site iteratively. Second, inter-biome transferability is quantified using an outer 6-fold LOGO-CV, treating each biome as a distinct group. In this setup, models are trained on all biomes except one and evaluated on all sites of the held-out biome. Classification performance is assessed using Overall Accuracy (OA), F1-score, Precision, Recall, and Intersection over Union (IoU), with all experiments repeated across 10 independent iterations to capture model structural and sampling variability.

Preliminary results on select biomes show substantial variation in inter-biome transferability. Notably, in some cases, models transferred between biomes outperform those trained within the same biome. These findings highlight the need for comprehensive biome-level transferability assessments to better understand the capabilities and limitations of RF-based flood mapping under globally diverse conditions, ultimately supporting more transparent and trustworthy flood-mapping products for end users.

How to cite: Hosch, P. C. and Dasgupta, A.: Cross-Biome Transferability of SAR-based Flood Mapping with Random Forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1092, https://doi.org/10.5194/egusphere-egu26-1092, 2026.

Flood mapping using machine learning methods such as Random Forests (RF) requires informed feature engineering and selection. Despite feature-importance rankings across different biomes and land covers varying substantially, the stability of these feature rankings has not been evaluated specifically for RF-based flood delineation. In this study, we investigate the consistency of RF feature-importance rankings in a binary flood-classification task primarily based on Synthetic Aperture Radar (SAR) imagery. The feature stack comprises 14 variables, including 9 SAR-based features, Sentinel-1 VV and VH polarizations and their temporal-change metrics which inform the flood extent identification, and 4 contextual features such as land cover and topographic indices which provide information on backscatter uncertainties. The classification task was conducted across 18 flood events spanning six distinct biomes: (1) Deserts and Xeric Shrublands, (2) Tropical and Subtropical Moist Broadleaf Forests, (3) Temperate Broadleaf and Mixed Forests, (4) Temperate Coniferous Forests, (5) Mediterranean Forests, Woodlands and Scrub, and (6) Temperate Grasslands, Savannas and Shrublands. Three feature-attribution methods were evaluated: (1) Shapley Additive exPlanations (SHAP) provides a game-theoretic framework for feature attribution and is widely recognized for its consistency and interpretability; (2) Mean Decrease in Impurity (MDI), computed during tree growth, is the most commonly used importance metric for RF models; (3) Permutation feature importance (MDA) offers a model-agnostic approach that assesses importance by measuring the reduction in model accuracy when feature values are randomly shuffled. Both feature cardinality and feature correlation, which bias the feature rankings for these algorithms in different ways, were considered during interpretation. All experiments were repeated across 10 independent iterations to account for random variability. We first examined feature-importance rankings independently across the three sub-sample studies within each biome to establish baseline intra-biome variability, followed by quantification of inter-biome variability to assess whether feature-importance patterns transfer across different environmental conditions. Preliminary results across select biomes indicate stable rankings for SAR-based features, with VV and VH event polarizations dominating the decision boundary, while contextual descriptors, particularly terrain indices such as Height Above the Nearest Drainage, exhibit greater variability both within and between biomes. Understanding the transferability of feature-importance patterns and feature stacks across biomes is critical for developing an RF-based flood-mapping pipeline that operates reliably under diverse environmental conditions worldwide and ultimately builds user trust in the resulting products.

How to cite: Havakhor, P., Hosch, P., and Dasgupta, A.: Cross-Biome Feature Importance Stability Analysis for SAR-based Flood Mapping with Random Forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1266, https://doi.org/10.5194/egusphere-egu26-1266, 2026.

Waterlogging in agricultural fields is the condition of temporally inundated areas driven by extreme rainfall, rising groundwater or poor drainage, and has been identified as a major issue by Danish farmers. During the inundation period, plants are deprived of oxygen which negatively affects the root development and leads to decreased yields and grain quality. Additionally, these waterlogged areas are a large source of greenhouse gas (GHG) emissions. The issue is expected to exacerbate under current climate projections through wetter winters and rising groundwater levels in Denmark. Hence, an increased understanding of the spatio-temporal dynamics of waterlogging is required to future-proof the management strategies. The research goals are three-fold: (1) to optimise the detection of waterlogging, (2) to reveal inter- and intra-annual patters across Denmark and (3) to investigate the drivers of waterlogging such as climate, topography and bio-physical conditions. We aim to detect waterlogged areas through a deep learning semantic segmentation approach utilising multi-temporal PlanetScope imagery and nation-wide high resolution elevation data. This approach requires a manually delineated reference dataset to train, validate and test the model which needs to be well-balanced spatially, e.g. covering various soil types, and temporally, e.g. including various illumination conditions. Additionally, we will experiment with various model architectures, backbones and covariate combinations to optimise the segmentation performance. Initial tests using a UNET architecture and building upon a published reference dataset by Elberling et al. (2023), show promising results and lay the foundation for the upcoming model development and extension of the existing reference data.

^{Elberling, B. B., Kovacs, G. M., Hansen, H. F. E., Fensholt, R., Ambus, P., Tong, X., ... & Oehmcke, S. (2023). High nitrous oxide emissions from temporary flooded depressions within croplands. Communications Earth & Environment, 4(1), 463.}

How to cite: Kleinsmann, J., Koch, J., Horion, S., Kovacs, G. M., and Stisen, S.: Detecting Waterlogging in Agricultural Fields in Denmark using High-Resolution PlanetScope Time Series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1859, https://doi.org/10.5194/egusphere-egu26-1859, 2026.

Operational flood intelligence for emergency response and insurance, providing a rapid overview of impacted land, population, and economic damages, requires mapping solutions that remain reliable under adverse observational conditions and across diverse landscapes. Although Sentinel-1 SAR provides consistent global, all-weather and day-and-night coverage, automated flood extraction is challenged by speckle noise, land-cover heterogeneity, and confusion between floodwater and permanent low-backscatter surfaces. These limitations highlight the need for approaches that exploit temporal backscatter changes while maintaining global robustness and computational efficiency.

We present SaferSat, a fully automated Sentinel-1 toolbox for flood-extent mapping, water-depth estimation, and impact assessment. SaferSat is part of SaferPlaces (saferplaces.co), a global Digital Twin platform for flood risk intelligence supporting emergency response and insurance applications. Central to the framework is Pr-RWU-Net (Progressive Residual Wave U-Net), a lightweight deep-learning model with 2.6 million trainable parameters, designed to detect flood-induced backscatter changes using VV-polarized SAR imagery. The model uses a three-channel input; pre-event VV, post-event VV, and their radiometric difference, enhancing inundation sensitivity while mitigating VH instability for global deployment.

SaferSat provides end-to-end processing: automated data retrieval, multi-date flood inference, and Maximum Flood Extent generation. To reduce SAR ambiguities, it generates auxiliary layers: a vegetation mask for SAR "blind spots" and a low-backscatter anomaly mask for permanent dark features. Flood extent layers are integrated with the FLEXTH model and GLO-30 or local high-resolution LiDAR DTMs for water-depth reconstruction. The system also analyzes acquisition patterns to predict short-term revisit opportunities. Impact assessment intersects flood extents with JRC GHS-POP and ESA WorldCover datasets.

The Pr-RWU-Net model was trained on the S1GFloods dataset, containing 5,360 paired pre- and post-event Sentinel-1 GRD images across 42 flood events from 2016–2022. Binary flood masks were generated via semi-automated thresholding and expert quality control. Evaluation on the test split achieved an IoU of 90.0%, F1-score 94.6%, Recall 95.6%, Precision 93.8%, and overall accuracy 96.6%.

Operational applicability was demonstrated on three 2025 flood events: Romania, Pakistan, and France. SaferSat flood extents closely matched SAR manual driven flood references (IoU 89–92%) and CEMS products (IoU 85–88%). Water-depth estimation against a reference hydrodynamic model yielded a MAE of 34–40 cm and correlation R of 0.78–0.82. For a 260 km² flood in Romania, the full processing chain completed in ~3 minutes on a standard CPU, demonstrating suitability for rapid, large-scale deployment.

SaferSat is available globally through SaferPlaces, supporting emergency response and insurance applications. Future developments aim to enhance SaferSat globally via integration of commercial satellite data to reduce revisit time and rapid hydrodynamic modeling to address radar limitations.

How to cite: DaliriSusefi, S., Mazzoli, P., Luzzi, V., Renzi, F., Redaelli, T., Renzi, M., and Bagli, S.: SaferSat: The Saferplaces’s  Operational Sentinel-1 Toolbox for Multi-Temporal Flood Extent Mapping, Water-Depth Estimation and Impact Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2995, https://doi.org/10.5194/egusphere-egu26-2995, 2026.

Floods are among the most destructive natural hazards worldwide, causing severe impacts on human health, ecosystems, cultural heritage and economies. Over the past decades, both developed and developing regions have experienced increasing flood-related losses, a trend that is expected to intensify under climate change due to shifts in precipitation patterns and the frequency of extreme events. In many large river basins, particularly in data-scarce regions, flood forecasting remains highly uncertain because of limited in situ observations and complex hydrological and hydraulic dynamics.

EO4FLOOD is an ESA-funded project aimed at demonstrating the added value of advanced Earth Observation (EO) data for improving flood forecasting at regional to continental scales. The project focuses on the integration of multi-mission satellite observations with hydrological and hydrodynamic modelling frameworks to support flood prediction up to seven days in advance, with an explicit treatment of uncertainty.

A key outcome of EO4FLOOD is the development of a comprehensive and openly available EO-based dataset designed to support flood modelling and forecasting studies. The dataset covers nine large and hydrologically complex river basins worldwide, selected to represent a wide range of climatic, physiographic and anthropogenic conditions, and characterized by limited or heterogeneous availability of ground-based observations. It integrates high-resolution satellite products from ESA and non-ESA missions, including precipitation, soil moisture, snow variables, flood extent, water levels and satellite-derived river discharge.

Within EO4FLOOD, these EO datasets are combined with hydrological and hydraulic models, enhanced by machine learning techniques, to improve flood prediction skill and to better quantify predictive uncertainty in data-scarce environments. The project also investigates the role of human interventions, such as reservoirs and land-use changes, in modulating flood dynamics across the selected basins.By making this multi-variable EO dataset publicly available, EO4FLOOD aims to support the broader hydrological community in testing, benchmarking and developing flood modelling and forecasting approaches in challenging large-basin settings. The project provides a unique opportunity to explore the potential and limitations of EO-driven flood forecasting and contributes to advancing the use of satellite observations for global flood risk assessment and management.

How to cite: Tarpanelli, A. and the EO4FLOOD Team: Advancing Flood Forecasting in Large River Basins Using Multi-Mission Satellite Data: the EO4FLOOD project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3018, https://doi.org/10.5194/egusphere-egu26-3018, 2026.

            Water security in the Chi River Basin is critical for the agricultural economy and ecosystem stability of Yasothon Province, Thailand. However, effective spatiotemporal monitoring of water surface dynamics is frequently hindered by persistent cloud cover during the monsoon season, limiting the utility of traditional optical remote sensing. This study addresses this challenge by developing a robust Multi-Sensor Deep Learning Fusion system that integrates Synthetic Aperture Radar (SAR) and optical satellite imagery to ensure continuous observation capabilities.

            We employ a U-Net convolutional neural network architecture, selected for its high boundary precision and efficiency with limited training datasets. The model is trained on a fused six-channel input configuration, combining Sentinel-1 SAR data (weather-independent) with Sentinel-2 optical bands (RGB), augmented by the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). This multi-modal approach enhances feature extraction, allowing for the accurate differentiation of open water from floating vegetation and flooded agricultural lands in complex transition zones.

            The study analyzes the hydrological cycle of 2022, capturing distinct drought, flood, and post-flood conditions. To ensure hydrological validity, the model’s segmentation outputs are not merely visually assessed but are quantitatively validated against ground-truth water level data from the E.20A gauge station in Kham Khuean Kaeo District. By establishing a precise Stage-Area Relationship, this research demonstrates a scalable, cost-effective framework for flood risk assessment and water capital estimation, offering a resilient solution for river basin management in cloud-prone tropical regions.

How to cite: Pruekthikanee, P.: Multi-Sensor Deep Learning Fusion for Spatiotemporal Water Surface Monitoring in the Yasothon Province's Chi River Basin, Thailand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4154, https://doi.org/10.5194/egusphere-egu26-4154, 2026.

Accurate water cycle representation in data-scarce and flood-prone regions like the Niger River Basin demands stronger integration between remote sensing and hydrological modelling. Spanning ten water-stressed nations, this basin faces critical challenges under climate change, requiring robust water-budget assessments to guide resilience strategies. We employ DHI’s Global Hydrological Model (DHI-GHM) to simulate key hydrological components of the regional water cycle. Model outputs for surface and root-zone soil moisture (SSM and R-ZSM) and terrestrial water storage (TWS) are systematically compared against satellite observations (GRACE/GRACE-FO and multiple soil moisture products) to identify discrepancies and enhance the understanding of regional hydrological behavior. A near real-time SSM data assimilation scheme is implemented to enhance spatiotemporal accuracy of surface and top-soil interactions, particularly beneficial in the flood-sensitive Inner Niger Delta. Post-assimilation hydrological outputs are coupled with the CaMa-Flood surface hydraulic model to simulate inundation dynamics, enabling improved flood prediction and supporting risk management. Finally, we pursue two-way coupling of hydrological and hydrodynamic models by integrating river flow–storage feedbacks to advance flood forecasting and sustainable water-resources planning. 

How to cite: Azimi, S., Murray, A., Chewning, C., Kittel, C., Madsen, H., Yang, F., Schumacher, M., and Forootan, E.: Satellite-Enhanced Flood Modelling for the Niger River Basin using a Synergy of Hydrological Modelling and Earth Observation Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5752, https://doi.org/10.5194/egusphere-egu26-5752, 2026.

Existing global wetland datasets and monitoring approaches emphasizepersistent inundation, while intermittent inundation and waterlogged states—especially where vegetation is present—are underrepresented or of lower accuracy. This leads to inaccurate estimates of greenhouse gas emissions from carbon-rich systems (e.g., peatlands). Meanwhile, the predominance of annual mapping limits the capture of intra-annual variability, further reinforcing these inaccuracies and obscuring sub-seasonal disturbances from human activities (e.g., shifts in rice-cropping intensity). This study presents an unsupervised, wetness-driven framework for improving global wetland monitoring that leverages earth observation data streams. For framework development, the OPtical TRApezoid Model is applied to Harmonized Landsat-Sentinel imagery to retrieve surface wetness, followed by wetland delineation using a scene-adaptive grid-based thresholding algorithm. This framework is applied to 824 globally distributed 0.1° grid cells encompassing 9,781 land-cover-labeled sites and 134 sites with daily wet–dry labels across 28 Ramsar wetlands, and validated for spatial delineation, thematic, and temporal accuracy. Comparative analysis employs Dynamic World, the first global 30 m wetland map with a fine classification system (GWL_FCS30), and the modified Dynamic Surface Water Extent algorithm (DSWE). Our framework achieved moderate spatial delineation accuracy with F1 of 0.64 (recall 0.75, precision 0.56), comparable in F1 to Dynamic World and with higher recall than DSWE and GWL_FCS30. It delivered the highest temporal accuracy (F1 0.72; precision 0.81; recall 0.64) and improved thematic accuracy for vegetated wetland, reducing omission with modest commission. The proposed wetland monitoring framework enables more accurate targeted policy interventions.

How to cite: Li, Y., Tsendbazar, N.-E., de Beurs, K., Päkkilä, L., and Kooistra, L.: Refining global wetland characterization using an unsupervised, wetness-based dynamic framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5862, https://doi.org/10.5194/egusphere-egu26-5862, 2026.

Flood extent mapping from satellite imagery plays a critical role in disaster response and flood risk management, particularly as flood events become more frequent and severe under a changing climate. At its core, the task involves classifying each pixel in an optical satellite image as flooded or non-flooded. Recent deep learning-based segmentation models have demonstrated strong performance at the global scale. However, despite their accuracy, most existing approaches provide deterministic predictions and offer limited information on the reliability of individual pixel-level outputs. This lack of uncertainty information constrains their operational applicability, especially in high-risk scenarios where models may exhibit overconfident but incorrect predictions.

To address this limitation, we extend a global flood extent segmentation framework by explicitly incorporating uncertainty quantification. Specifically, an Evidential Deep Learning (EDL) approach is integrated into a UNet++ architecture within the ml4floods framework, enabling simultaneous prediction of flood extent and associated pixel-wise uncertainty. Within the EDL formulation, network outputs are interpreted as evidence and parameterised using a Beta distribution, providing a principled estimate of predictive uncertainty. Furthermore, total uncertainty is decomposed into aleatoric and epistemic components, allowing clearer interpretation of whether uncertainty arises from data ambiguity or from limited model knowledge.

The proposed approach is evaluated using the extended WorldFloods global flood dataset. Preliminary results indicate that the EDL-enhanced model maintains promising segmentation performance while producing informative uncertainty maps. Elevated uncertainty is consistently observed in misclassified regions and along land-water boundaries, where optical signals are inherently ambiguous. These results demonstrate that uncertainty estimates offer valuable insight into model reliability and support operational decision-making by highlighting areas that require closer inspection. In practice, uncertainty-guided triage can help prioritise expert review and resource allocation, focusing attention on regions where decision risk is highest.

How to cite: Chen, C. and Wang, L.-P.: Evidential Deep Learning for Uncertainty-Aware Global Flood Extent Segmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6114, https://doi.org/10.5194/egusphere-egu26-6114, 2026.

Remote sensing technologies provide effective tools for monitoring and assessing the state of inland water bodies, enabling extraction of various hydrological parameters from satellite observation. Central Asian and some African countries are currently implementing practical programs aimed at mitigating water scarcity and improving the management of transboundary water resources. Rivers and their tributaries flowing across national boundaries require continuous monitoring to support early warning of droughts and floods at the basin scale.

Conventional ground-based hydrological stations are traditionally used to measure water level, estimate daily river discharge, and support hydrological forecasting. However, limitations related to accessibility, data-sharing restrictions, and the high cost of installation and maintenance often constrain their spatial coverage and long-term operation.  Virtual gauging station (VGS) represents a complementary remote-sensing approach, providing time series derived from the long-term satellite image archives. A VGS is defined as a free-shaped polygon on the map used to analyze data within the borders of this polygon and collect observations based on the requirements. Currently, VGS applications primarily rely on optical satellite imagery from Sentinel-2, Landsat-4, -5, -7, -8, -9 missions to estimate water surface area (WSA) using spectral water index (MNDWI, AWEI or AWEI_sh). Variations in WSA serves as a proxy for surface water availability and river dynamics. 

In addition, VGS can be used to enrich satellite altimetry-based water level (H) time series. For this purpose, the VGS polygon is calibrated using reference altimetric observations obtained from open-access data source (e.g. SDSS, DAHITI, Hydroweb). Calibration involves estimating the parameters of a regression model describing the functional relationship between water level and water surface area.  The resulting values can finally be integrated into hydrological models to support short-term river discharge forecasting. Thus, VGS provides continuous hydrological information independent of ground-based measurements, while optional validation against in-situ observations allows for the assessment of the model uncertainty.  Based on the experimental analysis, optimal placement of VGS polygons is recommended dynamically active river sections that account for annual riverbed displacement, as well as in river reaches located near satellite altimeter ground tracks to improve calibration accuracy.

The experiments demonstrated that correlation between ground truth and forecasted water level values is upper 0,85 and mean absolute error is lower than 0,3 m. The following result has been obtained using linear regression which shows that application of more complex forecasting models could significantly improve the results.

How to cite: Mukhamedjanov, I. and Umirzakov, G.:  The capabilities of virtual gauging stations in satellite monitoring of water bodies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6180, https://doi.org/10.5194/egusphere-egu26-6180, 2026.

Ephemeral pans and seasonal ponds in arid and semi-arid lands supply critical water for pastoral and ecological systems, yet are not routinely monitored due to their small size, highly dynamic and spectral confusion with vegetation and shadows. We present and evaluate a multisensor mapping approach to detect sub-0.5 ha surface water bodies and quantify their linkage to rainfall variability to inform decision making.

Our approach fuses Sentinel-1 SAR, Sentinel-2 optical indices and DEM derived covariates within an ensemble classifier (voting of Random Forest, Gradient Boosting, and Decision Tree models). Predictive uncertainty is mapped using ensemble agreement and class probabilities, and we compare SAR-only, optical-only, terrain-only, and fused configurations. Additionally, rain and ephemeral surface water dynamics are modelled using generalised additive models with CHIRPs  and local rain gauge observations to test the lagged relationships in monthly water area anomalies.

Results show the fused model achieves an overall accuracy of 85%, outperforming Sentinel-1, and Sentinel-2 (78% and 72%, respectively). Generalised additive models explain 62% of variance in monthly water area anomalies, with a strong response at 1-3 month lags. These results show multisensor fusion with  quantified uncertainty improves detection of ephemeral surface water and enables estimation of rainfall thresholds and lagged dynamics relevant to pastoral water planning and targeted anticipatory action interventions.

How to cite: Muthoka, J., Rowhani, P., Hopling, C., Memarian Sorkhabi, O., and Todd, M.: Multisensor Ensemble Mapping of Sub-hectare Ephemeral Surface Water in Kenyan ASALs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6408, https://doi.org/10.5194/egusphere-egu26-6408, 2026.

Accurate and timely flood delineation is a cornerstone of disaster response and hydrological risk management. Synthetic Aperture Radar (SAR) is uniquely suited to this task because it operates independently of cloud cover and illumination, yet its interpretation remains challenging due to speckle, terrain effects, vegetation scattering, and ambiguities between flooded and permanent water as well as shadows and smooth surfaces such as tarmac. While deep learning has substantially advanced SAR-based flood segmentation, most existing models are trained from scratch and often struggle to generalize across regions and flood regimes. Recently, geospatial foundation models (GFMs) pretrained on massive satellite archives have shown promise, but their benefits for SAR-based flood mapping remain insufficiently quantified. This paper presents a controlled, large-scale global scale evaluation and benchmarking of a vision-transformer based GFM (NASA IBM Prithvi) against two task-specific segmentation architectures, the SegFormer (hierarchical transformer) and the commonly used U-Net (convolutional neural network), including lightweight variants, for post-event SAR-based flood mapping. All models were trained and evaluated under a standardized pipeline that explicitly addresses extreme class imbalance via stratified negative sampling and weighted loss functions. Training and validation used the expert-annotated Kuro Siwo dataset (43 flood events, 67,490 Sentinel-1 VV/VH tiles), while generalization is assessed on both the in-distribution Kuro Siwo test set and the out-of-distribution Sen1Floods11 hand labelled benchmark dataset. Results show that stratified negative sampling (controlling how many background-only tiles are shown to the model in each training epoch) increases precision by approximately 6% and mean Intersection-over-Union (mIoU) by about 7% relative to no sampling, while stabilizing training loss dynamics. On the in-distribution data, all architectures reach similar performance (mIoU ≈ 0.82), indicating that well-designed task-specific models remain competitive with GFMs. However, under out-of-distribution conditions, the foundation model Prithvi (mIoU 0.768) closely matches the performance of the SegFormer (mIoU 0.772) and clearly outperforms the U-Net (mIoU 0.712), highlighting the robustness of transformer-based representations when transferring across datasets. Pretraining on optical imagery yields only modest gains for SAR (+3.4% mIoU), suggesting that architectural inductive biases and data handling matter more than cross-modal pretraining. Notably, lightweight GFM variants achieve comparable accuracy with up to 94% fewer parameters, demonstrating strong potential for operational deployment. Scene-level analysis reveals that CNNs suppress scattered false alarms due to the neighborhood contextualization but miss large, continuous floods, while transformers preserve spatial coherence yet overpredict along complex boundaries and scattered surface water ponding, especially near permanent water bodies. Findings demonstrate that while SAR-based flood mapping accuracy requires a combination of appropriate model architectures and class imbalance-aware training, rather than foundation-scale pretraining alone. However, for spatial and statistical transfer to out of distribution datasets, GFMs offer substantial advantages and provide above-average performance for unseen cases, even without localized fine-tuning.

How to cite: Dasgupta, A. and Zouaidi, M.: Do Geospatial Foundation Models Improve SAR-Based Flood Mapping? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6586, https://doi.org/10.5194/egusphere-egu26-6586, 2026.

How to cite: Wilhelm, P., Hosch, P., and Dasgupta, A.: SARFlood: A Web-Based, Cloud-Native Platform for Automated and Optimized ML-based SAR Flood Mapping   , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6617, https://doi.org/10.5194/egusphere-egu26-6617, 2026.

In the last decade rapid advancements in remote sensing have opened new frontiers in our ability to monitor freshwater bodies dynamics at the global scale. Most works have taken advantage of the long time series of Landsat constellations (30 m resolution) relying on spectral indices to identify water. Recently, much progress has also been made in the development and use of deep learning models capable of explicit semantic classification of river water, lake water and sediment bars, based on Sentinel-2 (S2) MSI imagery (10 m resolution). In this work, we present an approach that seeks to extend these existing, trained, fluvial landscape classification models to Landsat data in order to observe long-term water and morphological shifts in rivers and lakes. Rather than explicitly re-training the models with Landsat data and labour-intensive manual label data, we apply a domain transfer approach to generate synthetic S2 MSI imagery from Landsat inputs. This approach has the advantage that the training of deep learning domain transfer models only requires synchronous Landsat and Sentinel data and thus obviates the need for manual labels.

The results show that, when using these synthetic images, river water, lake water and sediment bars are classified with an F1 score of 0.8, 0.94, 0.65 respectively, which represents a decrease of ca. 10% for river water and 20% for sediment with respect to real S2 imagery. By adopting this integrated approach, we are therefore able to monitor, for the first time, lake water, river water and sediment bars at 10 m resolution, over a 40-year period, integrating both synthetic S2 and real S2 acquisitions through a single, fluvial landscape segmentation model. Classification obtained from median monthly images can then be aggregated at the yearly or multi-yearly scale to delineate river or lake water fluctuations, and active channels (river water plus sediment bars) trajectories, from specific freshwater bodies to the global scale.

How to cite: Vanzani, F., Carbonneau, P., Bizzi, S., Cecchetto, M., and Bozzolan, E.: Monitoring Freshwater Bodies over the Past 40 Years Using Synthetic Monthly Sentinel-2 MSI Imagery , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7132, https://doi.org/10.5194/egusphere-egu26-7132, 2026.

Flood and surface water mapping from satellite observations remains challenging due to the complementary yet heterogeneous characteristics
of optical and synthetic aperture radar (SAR) data. While deep learning has achieved promising results, existing studies are often evaluated on
isolated datasets or focus on a single modality, limiting their comparability and operational relevance. In this study, we conduct a large-scale and systematic evaluation of optical, SAR, and combined optical–SAR learning strategies for flood and surface water mapping across multiple public satellite benchmarks. Using a common training and evaluation protocol, we compare lightweight convolutional networks and large pretrained vision models under single-modality and multimodal settings. The analysis reveals that attention-based multimodal fusion consistently improves water delineation accuracy on most datasets, while model capacity and preprocessing choices play a critical role in balancing missed detections and false alarms. On global-scale benchmarks, moderately sized backbones coupled with dedicated fusion mechanisms achieve robust performance without relying on extremely large models.These findings provide practical guidance for selecting architectures and fusion strategies in operational flood mapping and establish a reproducible benchmark for future optical and SAR studies.

How to cite: xiao, J., li, Z., and tian, F.: Evaluating multimodal optical and SAR learning strategies for flood and surface water delineation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7320, https://doi.org/10.5194/egusphere-egu26-7320, 2026.

Floods are among the most deadly and destructive natural disasters. Improving our understanding of large-scale flood dynamics is crucial to mitigating their dramatic consequences. Unfortunately, systematic observation-based datasets—especially featuring flood depths—have been lacking.

This contribution presents advancements in developing an unprecedented catalogue of satellite-derived flood maps across Europe from 2015 onwards. Results are based on the systematic identification of floods in the entire Sentinel-1 archive at 20 m spatial resolution as provided by the Global Flood Monitoring component of the Copernicus Emergency Management Service. Using a novel algorithm that accounts for terrain topography, flood maps are enhanced and provided with water depth estimates—a critically important information for flood impact assessments.

The resulting dataset represents a significant step towards the creation of a global flood archive. It provides new tools for interpreting flood hazards on large scales, with substantial implications for flood risk reduction, urban development planning, and emergency response.

How to cite: Betterle, A. and Salamon, P.: Ten years of floods across Europe mapped from space with reconstructed water depths , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7998, https://doi.org/10.5194/egusphere-egu26-7998, 2026.

Coastal wetlands provide a wide range of ecosystem services, including shoreline protection, attenuation of storm surges and floods, water quality improvement, wildlife habitat and biodiversity conservation. These ecosystems have been observed to sequester atmospheric carbon dioxide at rates significantly higher than many other ecosystems, positioning them as promising nature-based solutions for climate change mitigation.  However, projections of coastal wetland conditions under sea-level rise (SLR) remain highly variable, owing to uncertainties in environmental factors as well as the necessary simplifications embedded within the wetland evolution modelling frameworks. Assessing wetland resilience to rising sea levels and the effect of anthropogenic activities is inherently complex, given the uncertain nature of key processes and external influences. To enable long-term simulations that span extensive temporal and spatial scales, models must rely on a range of assumptions and simplifications—some of which may significantly affect the interpretation of wetland resilience.

Here we present a novel eco-hydro-geomorphological modelling framework to predict wetland evolution under SLR. We explore how accretion and lateral migration processes influence the response of coastal wetlands to SLR, using a computational framework that integrates detailed hydrodynamic and sediment transport processes. This framework captures the interactions between physical processes, vegetation, and landscape dynamics, while remaining computationally efficient enough to support simulations over extended timeframes. We examine several common simplifications employed in models of coastal wetland evolution and attempt to quantify their influence on model outputs. We focus on simplifications related to hydrodynamics, sediment transport, and vegetation dynamics, particularly in terms of process representation, interactions between processes, and spatial and temporal discretisation. Special attention is given to identifying modelling approaches that strike a balance between computational efficiency and acceptable levels of accuracy. We will present recent model results to assess the resilience of coastal wetland to SLR on several sites around the world and will discuss new results to assess the effect of human interventions and infrastructure on wetland resilience.

How to cite: Saco, P., Jose, R., Angelo, B., Sandi, E., and Sandi, S.: Modelling wetland resilience to climate change and anthropogenic impacts., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8292, https://doi.org/10.5194/egusphere-egu26-8292, 2026.

Wetland inundation dynamics are key for understanding flood regulation, ecosystem functioning and greenhouse gas emissions. Synthetic Aperture Radar (SAR) can map water extent independent of cloud cover and can partly penetrate vegetation, particularly at L-band. Many SAR inundation products rely primarily on intensity thresholding and indicators such as specular reflection and double-bounce scattering. However, these approaches can underestimate inundation extent in densely vegetated wetlands where volume scattering can obscure the water signal. Here we demonstrate how L-band interferometric SAR (InSAR) can complement intensity-based inundation mapping under vegetation by exploiting phase differences between repeat SAR acquisitions. Using ALOS PALSAR-1 and PALSAR-2, together providing a nearly two-decade observational archive, we show that L-band InSAR can capture inundation dynamics in tropical floodplain wetlands, such as the Atrato floodplain (Colombia) and Amazon várzea floodplains (e.g., along the Río Pastaza). In the Atrato floodplain, the InSAR-derived flooded vegetation extent shows pronounced seasonal variability, ranging from ~500 to >1500 km² during 2007–2011. Comparison with existing L-band SAR inundation products yields ~70% overall agreement, while InSAR consistently detects broader inundated extents in densely vegetated floodplain areas where intensity-based thresholding underestimates inundation. This complementarity among methodologies is particularly relevant for inundation extent data products from the NASA–ISRO NISAR mission, which are expected to rely largely on SAR backscatter thresholding. Our results highlight the value of integrating InSAR-derived information to strengthen wetland inundation monitoring under vegetated canopies.

How to cite: Hübinger, C., Fluet-Chouinard, E., Escobar, D., and Jaramillo, F.: L-band InSAR to complement SAR inundation mapping under vegetation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9354, https://doi.org/10.5194/egusphere-egu26-9354, 2026.

How to cite: Groth, S., Wieland, M., Geiß, C., and Martinis, S.: Hydrologically-Informed DTM Super-Resolution for Rapid Flood Depth Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9758, https://doi.org/10.5194/egusphere-egu26-9758, 2026.

Flash flood disasters have increased by more than 50% in the first 20 years of the 21st century compared to the last 20 years of the 20th century. Monitoring and understanding flood events might lead to better mitigation of this natural hazard. Using SAR and SAR interferometry (InSAR) proved to be a useful tool for mapping flooded areas due to the lower backscatter or decorrelation of the SAR signal in an open-water environment. In Arid regiem, flash flood water is rapidly drained by evaporation or percolation, often before the satellite image is acquired. To overcome this challenge, we propose in this study to use the InSAR coherency loss, created by surface changes during a flash-flood, to map the runoff path and utilize it to quantify peak discharge (Q_max).

We focus on the Ze’elim alluvial fan along the western shore of the Dead Sea, Israel, an arid area affected by seasonal flash floods a few days a year. We use 34 interferograms of X-band (COSMO-SkyMed/TerraSAR-X) SAR data, covering 25 runoff events between 2017 and 2021, and upstream hydrological gauge data. To consider the natural decorrelation processes, we calculate a normalized coherence (ϒ_n) term, using the average coherence of the study area and the average coherence of a stable reference area, identified by differential LiDAR measurements.

We find a strong correlation between g_n and the logarithm of the peak discharge (Q_max). However, the method is limited by a minimal peak discharge—where energy is too low to change the surface—and maximal total water volume—where decorrelation is saturated. The method may provide tools for reconstructing runoff data in arid areas where historical SAR data is available, and for monitoring in difficult access areas or where hydrological stations are sparse or damaged.

How to cite: Nof, R.: Estimating Flash Flood Discharge in Arid Environments Using InSAR Coherence: A Case Study of the Ze’elim Fan, Dead Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11948, https://doi.org/10.5194/egusphere-egu26-11948, 2026.

Rivers in regions with cold winters can seasonally freeze up. River ice breakup and freeze-up processes can lead to river ice jams, which are a major contributor to flood risk in cold regions (across most of the high latitudes of the northern hemisphere). In Canada, satellite remote sensing is used across the country to provide timely information on the status of river ice. Methods and algorithms to classify various stages of river ice from the Radarsat Constellation Mission (RCM) are available, but the operational implementation of these, especially the integration into larger flood forecasting and early warning systems, requires specific expertise, software and computational resources, and comes with its own set of challenges. In collaboration with various agencies across Canada we have set up operational monitoring systems with the purpose of assisting the daily tasks of forecasters on duty. These have been used in practice over multiple ice breakup and freeze-up seasons, which has highlighted both their usefulness and shortcomings. We will focus on various aspects of such a system and share lessons learned on its design, setup and operational use, as well as a framework to analyse various factors relevant for operational monitoring purposes (e.g. spatiotemporal coverage and latency of the data, critical elements in the support of decision-making relating to floods). In this, we do not shy away from problems and pitfalls, so that others can learn from these. While various challenges remain, this work is a good example of the value in the joint engagement of applied science and end users.

How to cite: Haag, A., Bovenschen, T., Vandebroek, E., Tsiokanos, A., Balk, B., and van der Sanden, J.: Lessons Learned from Remote Sensing of River Ice for Flood Early Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12249, https://doi.org/10.5194/egusphere-egu26-12249, 2026.

The global degradation of river ecosystems and the growing impacts of flood hazards have highlighted limitations in current river management approaches. In Europe, the Water Framework and Flood Directives promote integrated, catchment-scale assessments of hydromorphological conditions and flood risk. Such integration is essential for sustainable management. Planform dynamics and river bed aggradation/incision, for example, can modify channel conveyance and compromise flood mitigation measures, whereas granting more space to rivers can both enhance ecological quality and reduce flood peaks.

In this context, the availability of long-term satellite archives and advances in computational and machine-learning methods enable large-scale, high spatiotemporal resolution monitoring of large and medium river systems. However, despite this potential, the operational adoption of satellite-based river monitoring remains limited due to data complexity, interdisciplinary requirements, and the lack of harmonised computational infrastructures.

Thanks to a collaboration between industry, public institutions and the university, we developed a methodology to systematically map monthly water channel, channel width, sediment bars and vegetation dynamics, testing the results on the full archive of Sentinel-2 (10 m resolution) for medium-large Italian rivers (active channel > 30m - i.e. 3 Sentinel-2 pixels). In this talk, I will outline the applied methodology, discuss its applicability at national scale with Sentinel-2 data, and show how the generated products can better inform river habitat mapping, river conservation practices, and flood risk assessments by supporting consistent national scale geomorphic trajectories identification.

How to cite: Bozzolan, E., Micotti, M., Matteligh, E., Piovesan, A., Vanzani, F., Carbonneau, P., and Bizzi, S.: Operational, national-scale monitoring of river trajectories using satellite imagery , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13343, https://doi.org/10.5194/egusphere-egu26-13343, 2026.

Flood inundation mapping has become increasingly critical as climate change intensifies the frequency and severity of flooding worldwide, amplifying risks to populations, infrastructure, and ecosystems. Recent advances in Earth Observation (EO) have shown unprecedented opportunities to monitor flood dynamics across large spatial scales.. However, significant challenges remain due to the limitations of single-sensor approaches. While multispectral imagery provides rich semantic information, it is frequently constrained by cloud cover during flood events. Conversely, Synthetic Aperture Radar (SAR) offers all-weather capability but suffers from signal ambiguity in complex terrains and urban environments. Effectively integrating these heterogeneous modalities therefore remains a challenge, particularly with limited labelled flood event data.

In this study, we propose a deep learning-based cross-modal fusion framework that leverages the representational capacity of Remote Sensing Foundation Models (RSFMs). High-level feature embeddings are extracted from Sentinel-1 and Sentinel-2 multispectral imagery by initializing modality-specific encoders with pretrained weights from state-of-the art multi-modal foundation models, providing a robust and semantically aligned feature space despite limited task-specific training data 

To integrate the multi-modal representations, we adopt a Gated Cross-Modal Attention mechanism, which adaptively modulates the information flow from each modality based on their observation reliability. Specifically, the model is trained to prioritise SAR features to ensure spatial continuity under cloud-obscured conditions, while simultaneously leveraging richer optical semantics to disambiguate SAR signals, correcting for example false detections caused by radar shadowing or smooth impervious surfaces. 

To assess the generalisation of the proposed framework across diverse regions and sensor conditions, we trained and evaluated our model using a comprehensive dataset compiled from publicly available benchmarks, including Kuro Siwo and WorldFloods. Our framework not only establishes a new benchmark for all-weather flood monitoring but also demonstrates the critical role of remote sensing foundation models in overcoming the limitations of traditional, data-hungry fusion approaches.

How to cite: Chen, Y. C. and Wang, L. P.: Integrating SAR and Multispectral Satellite Observations for Flood Inundation Mapping: A Cross-Modal Fusion Framework Leveraging Foundation Models and Gated Attention Mechanism, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13502, https://doi.org/10.5194/egusphere-egu26-13502, 2026.

Flood mapping plays a key role in understanding hazard impacts, supporting emergency response, and guiding long-term risk planning. Remote sensing is now widely used in flood studies because it offers low-cost data, avoids the need for dangerous field surveys, and provides rapid observations over large areas. Despite these advantages, comparative research remains limited, particularly with respect to differences among flood-mapping algorithms, such as machine-learning versus threshold-based approaches, and the performance of optical versus radar sensors. This research addresses these gaps by applying multiple flood-mapping methods to the same flood event in Pakistan, and then comparing their performance with respect to a validation benchmark to provide a clearer insight into how data selection and methodological design influence flood detection outcomes

This study evaluates four distinct methods for mapping floods using multi-sensor satellite data. To ensure a fair comparison, three unsupervised machine-learning approaches including a synergetic Sentinel-1 and Sentinel-2 workflow, a method integrating harmonized Landsat–Sentinel data with radar, and a daily MODIS imagery technique were tested alongside a traditional Otsu thresholding baseline. All four were tested on the same 2025 Pakistan flood event, characterized by intense monsoon rains and flash flooding across regions such as Sindh and Punjab in mid- to late-2025.  The flood maps were then validated against UNOSAT flood reports for this event, where UNOSAT’s flood extent closely matches the results produced by the Sentinel-1/Sentinel-2 workflow, which yields the most conservative flood extent among the tested methods.

 Larger flood extents from some methods, especially the Sentinel-1 Otsu thresholding approach, include areas not clearly flooded in optical images. This happens because SAR backscatter also responds to wet soil and saturated vegetation, which a simple threshold can misclassify as water, leading to flood overestimation.

Overall, the results show that flood maps are not just different versions of the same answer, they reflect different satellite data and the utilized algorithms detect flooding. Approaches that combine multiple data sources with machine-learning strike a better balance, producing flood extents that are both spatially consistent and physically realistic. This indicates that multi-sensor, machine-learning–based methods are better suited for operational flood monitoring than simple thresholding, which is too sensitive to surface noise and often overestimates flooding. 

How to cite: Mones, J., Mhanna, S., Halloran, L., and Brunner, P.: A Comparative Assessment of Threshold-Based and Machine Learning Methods for Flood Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13888, https://doi.org/10.5194/egusphere-egu26-13888, 2026.

Rivers play a central role in shaping the Earth's surface and ecosystems through physical, chemical, and biological interactions. The intensity and locations of these interactions change as rivers continuously migrate across the landscape. In recent decades, human activity and climate change have altered river hydrology and sediment fluxes, leading to changes in river position, or migration. However, a comprehensive perspective on and understanding of these recent changes in the rate of river position shifts is lacking. To address this knowledge gap, we created a continuous global dataset of yearly river positions and migration rates over the past four decades and analyzed trends. The global annual river positions were detected using Landsat-derived surface water datasets and processed in Google Earth Engine, a cloud-based parallel computation platform. The resulting river extents and centerlines reflect the yearly permanent position, corresponding to the rivers’ location during base flow. This approach improves the representation of position changes derived from geomorphological rather than hydrological processes. To robustly analyze river position changes across different patterns and complexities and at large scales, we developed and applied a global reach-based quantification method.

Results show that while alluvial rivers maintain stable positions in certain regions, others exhibit trends in the rates of position change. For instance, the Amazon Basin, which has experienced significant deforestation and hydrological modifications, has shown increased rates of river position change in recent decades, directly modifying active floodplains. In this presentation, we will discuss the advantages, limitations, and applications of the global yearly river position dataset, offer insights into the changing rates of river position, and highlight current and future impacts on one of Earth’s most vulnerable hydrologic systems.

How to cite: Dente, E., Gardner, J., Langhorst, T., and Yang, X.: Multidecadal Changes and Trends in Global River Positions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16468, https://doi.org/10.5194/egusphere-egu26-16468, 2026.

Satellite-based surface water monitoring is essential for traking the spatiotemporal dynamics of global water bodies. However, most existing systems rely on a single mission or sensor modality, constraining both accuracy and temporal coverage. To overcome these limitations, we propose a multi-mission data fusion framework that integrates SAR Sentinel-1 and optical Sentinel-2 observations. Two U-Net convolutional neural networks were trained independently on the S1S2-Water dataset: one using Sentinel-1 sigma-nought backscatter (VV/VH) and the other using Sentinel-2 RGB and NIR bands, with terrain slope incorporated as ancillary input in both models. Predictive uncertainty is quantified via Monte Carlo dropout embedded within the networks, modeling pixel-wise predictions as Gaussian distributions. These probabilistic outputs are subsequently fused using a Bayesian framework and refined through sensor-specific exclusion masks. Evaluation across 16 geographically diverse test sites demonstrates that the fused probabilistic predictions achieve an overall IoU of 89%, highlighting the synergistic benefits of uncertainty-aware, multi-sensor integration. Furthermore, we show that model evaluation restricted to cloud-free optical imagery introduces substantial bias, limiting applicability for near-real-time monitoring. The proposed framework improves temporal availability, robustness, and reliability, advancing multi-satellite approaches for global surface water monitoring.

How to cite: Hassaan, M., Festa, D., and Wagner, W.: SAR and optical imagery for dynamic global surface water monitoring: addressing sensor-specific uncertainty for data fusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17524, https://doi.org/10.5194/egusphere-egu26-17524, 2026.

The RESCUE_SAT project was launched as part of the “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) programme (Agreement no. 2025‑2‑HB.0), funded by the Italian Space Agency (ASI), with the goal of enhancing the performance of the RESCUE model through the integration of satellite data. RESCUE is a large‑scale inundation model that enables probabilistic flood‑hazard assessment over large areas by preserving computational efficiency while explicitly representing hydrologic-hydraulic processes along the full drainage network. Primarily based on digital terrain models (DTMs), RESCUE is a hybrid framework that combines a geomorphology-based representation of the river network with simplified hydrological and hydraulic formulations to estimate water levels and inundation extents. The central challenge of the RESCUE_SAT project is to deliver a flood‑modelling tool capable of providing a more reliable and detailed representation of both large‑scale hydrological behavior and local hydraulic processes, including flow interactions with structures such as levees, bridges and dams which are currently not explicitly represented in RESCUE. To this purpose, the Synthetic Aperture Radar (SAR) imagery acquired by the ASI’s COSMO-SkyMed constellation is processed using interferometric techniques to derive high-resolution digital elevation models (DEMs), reaching meter-scale resolution. Starting from high-resolution DEMs derived from COSMO-SkyMed satellite imagery, RESCUE_SAT enables the identification of the locations of structures that interacts with flow propagation, supporting their systematic mapping. Once the infrastructures have been identified and parameterized from the high-resolution DEM, the DEM is resampled and processed to a computationally advantageous coarser resolution, while the detected infrastructure elements are directly integrated into the hydrological–hydraulic model.

How to cite: Volpi, E., Cipollini, S., Pavesi, L., Gagliardi, V., Mwangi, R., Sanvitale, G., Pomarico, I., Fiori, A., Tapete, D., Virelli, M., Ursi, A., and Benedetto, A.: RESCUE_SAT project: Leveraging Satellite Data to Improve Large‑Scale Flood Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18308, https://doi.org/10.5194/egusphere-egu26-18308, 2026.

Flooding is a major natural hazard across the global tropics. Although flood occurrence is shaped by rainfall characteristics—including duration, frequency, and intensity—accurate prediction remains challenging. A key limitation is the lack of reliable, long-term flood databases that capture events across all spatial scales and durations, hindering a clear understanding of how rainfall variability translates into flood onset. This limitation is particularly critical in the Maritime Continent, where extreme rainfall is common and many small, short-lived, yet severe, floods remain undocumented. To address this limitation, we investigate whether a relatively new approach, global navigation satellite system reflectometry (GNSS-R), can help close this observational gap.

In this work, we assess whether data from the CYGNSS small-satellite constellation can be used to identify small- to regional-scale floods, including short-lived events. Our study focuses on Sumatra, an island within the Maritime Continent that is frequently affected by such hazards. A joint analysis of CYGNSS inundation estimates and two independent flood databases allowed us to evaluate how CYGNSS measurements can be used for flood detection. Three detailed case studies demonstrate that CYGNSS provides an unprecedented ability to monitor day-to-day changes in surface water extent, including floods at the urban scale. Specifically, we show that CYGNSS-derived inundation anomalies can clearly capture evolution of a flooding event, with the largest signature one day after known flood initiation. A systematic analysis of 555 flood events over a 21-month period enabled us to identify characteristic patterns in inundation anomalies that reliably distinguish flood events from non-flooding conditions, through the definition of an inundation-anomaly threshold and a maximum distance between CYGNSS detections and reported flood locations. We established that CYGNSS observations within 15 km not-only significantly differ from base-line conditions, but they allow tracking day-to-day flood dynamics as well.

The proposed methodology is transferable and can be applied to establish flood-inundation thresholds for any region within the global tropics, enabling automated detection of previously unreported flood events or the study of relationships between extreme precipitation and flood evolution. An example of its application is the automatic detection of flooding from CYGNSS data associated with subseasonal variability in tropical circulation: the passage of multiple convectively coupled Kelvin waves embedded within an active Madden–Julian Oscillation in July 2021. These waves propagated eastward across the Maritime Continent, triggering extreme rainfall and widespread flooding in equatorial Indonesia and East Malaysia. The day-to-day evolution of floods could be observed alongside the propagating waves, with the termination of the MJO coinciding with the cessation of the flood events.

Relying on low-cost small satellites, this approach shows strong potential for future scalability with larger constellations, ultimately improving flood monitoring and advancing our understanding of how rainfall patterns shape flood dynamics across global tropics.

How to cite: Bałdysz, Z., Baranowski, D. B., Flatau, P. J., Flatau, M. K., and Chew, C.: Automated Detection of Flood Events from CYGNSS: Observing Flood Evolution Along Propagating Tropical Waves , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18518, https://doi.org/10.5194/egusphere-egu26-18518, 2026.