The paradigm of Essential Variables (EVs) - ECVs, EOVs, EBVs - provides a data-driven foundation for global environmental monitoring (GCOS, GEO, UN SDGs). Yet, their full potential is hampered by interoperability gaps, fragmented governance, and siloed infrastructures, limiting integrated use and translation into local action.
Conversely, local demand for actionable information is growing. Earth Observation data, often as Analysis-Ready Data (ARD), must be transformed into locally relevant, co-created Action-Ready Information (ARI) for climate solutions. This requires integrating global EVs with local data and knowledge.
This session bridges these fronts. We explore all aspects of climate service development from the co-creation of climate services that emphasize inclusive and novel methodologies and the integration of multiple knowledge systems, through to the development of usable, equitable and impactful solutions for multiple stakeholder groups a focus on the use of technical, infrastructural, and socio-technical advancements to evolve EVs into a truly interoperable, global common language and ensure their effective translation for local decision-making. We welcome contributions on:
- Interoperability Foundations: Semantic frameworks (iADOPT, SOSA/SSN), FAIR principles, and lessons from research infrastructures (ENVRI, CRDCs) aligning EVs across domains and global programmes.
- From ARD to ARI: Case studies on transforming EV-based products into local insights via co-creation, integrating satellite data with in-situ, citizen science, and indigenous knowledge.
- Cross-Scale Infrastructure: Architectures and platforms (e.g., digital twins) enabling seamless data flow from global systems to local applications.
- Policy and Capacity: How interoperable EVs strengthen global policy (IPCC, SDGs) and how local insights inform action, including funding, capacity building, and governance models.
We invite scientists, data engineers, social scientists, and policymakers to connect the "essential" with the "actionable", forging a coherent path from global observation to local solution.
Orals: Thu, 7 May, 16:15–18:00 | Room 2.24
The landscape of organizations which have functions along the value chain of climate data, is extremely complex. These functions include the collection, curation and exploitation of climate data, which ultimately lead to the production of climate information supporting decision making. The EU funded iClimateAction project supports three key organizations in this landscape GCOS, GEO, WMO in their common endeavours to strengthen the global system for standardised, open, accessible, usable, and interoperable observations of essential climate variables (ECVs). The project has for objective providing an assessment of the current Earth Observation value chain for ECVs and identify gaps, and shortcomings that limit full exploitation from observations to services. For that, it will realise: (1) a full assessment of in-situ ECV observation systems: Coverage, gaps, networks at risk, data centres, and best practices for data & metadata stewardship.; (2) A review of space-based ECV data availability: Limitations, continuity challenges, processing stream improvements, and better coordination among space agencies; (3) a systemic analysis of the global ECV observation. The iClimateAction project will foster EO data exploitation, and deliver a set of recommendations for a sustainable interorganization coordination to maximize the value and impact of the EO data chain for climate.
How to cite: Laj, P., Martin Miguez, B., Bombelli, A., Tassone, C., Clark, M., De Salvo, P., Chu, W., Bajwa, M., and Labrador, L.: Strengthening the global system for essential climate variables observations: the iClimateAction project , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13040, https://doi.org/10.5194/egusphere-egu26-13040, 2026.
African cities are experiencing rapid growth, with projections indicating that the majority of the continent’s population will become urban dwellers in the near future. However, this urban expansion is largely unplanned, often resulting in development on hazardous lands with limited regulatory controls and insufficient risk information. Consequently, cities are becoming increasingly vulnerable to the impacts of climate change, as climate risks manifest in more complex and multidimensional ways.
A critical challenge faced by African cities is the lack of baseline knowledge and digital data necessary for informed decision-making and effective management of climate-related risks. The fast-paced transformation of urban landscapes drives an urgent need for climate risk information that offers higher resolution, improved timeliness, and greater update frequency. Additionally, there is a need for data that better captures the interactions among socioeconomic factors, environmental conditions, and physical infrastructures.
To support informed and sustainable urban development, digital data production models and future climate services need to be transformative. Climate services, which are locally driven, contextually appropriate, possess low complexity and fit for purpose ensure that the data and decisions are reliable, locally owned, and actionable over time. For wider scalability and transfer, it is important that co-production models and data-driven climate service solutions can be adopted more widely in African cities.
Resilience Academy (RA) is a university-driven partnership model, which aims to improve climate resilience in urban Africa though co-creation of demand-driven, locally sustainable and scalable climate services operating in the nexus of the digital revolution, community engagement and local youth skills. RA an action-oriented and collaborative ecosystem, which thrives from open data, affordable technologies, skills development and inclusive participation of multiple actors. It builds particularly on the talent and commitment of young generation scientists and students, and local residents changing the ways cities are mapped, designed and managed for the future. Resilience Academy approach seeks to establish tangible co-benefits around co-created climate services by strengthening youths’ digital skills and future employment opportunities in cities.
Our presentation will discuss experiences of applying Resilience Academy approaches in mapping climate adaptation needs, collecting climate risk related digital data and co-creation of urban climate services to address communities’ adaptation to heat, pollution and flooding stressors in Dar es Salaam and Nairobi. In our presentation, we will share challenges, good practices and lessons learnt related to using low-cost digital tools and working with local communities and youths in vulnerable urban neighbourhoods. We will discuss opportunities and challenges related to wider adoption and scaling of RA -approaches for climate service provision across African cities.
How to cite: Käyhkö, N., Nying'uro, P., Aaltonen, V., Babere, N., and Mahonga, C.: Open data, disruptive technologies and community approaches in co-creation of climate services in urban Africa – The Resilience Academy approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19527, https://doi.org/10.5194/egusphere-egu26-19527, 2026.
The need to understand how climate is changing has never been greater, and we cannot understand what we do not observe.
This contribution will describe the origin and evolution of a set of essential climate variables (ECVs) that are managed by the Global Climate Observing System (GCOS) programme, through its three expert panels for the atmospheric, oceanic and terrestrial domain.
The ECVs constitute the minimum set of observations required to systematically observe the Earth’s changing climate across three domains: the ocean, land and atmosphere. ECVs have facilitated the implementation of the observing system through a user-driven process, guiding investment decisions, and mobilizing climate observing communities. The first set of Essential Climate Variables were developed by GCOS in the late 1990’s and since then the list has grown to the 55 current ECVs.
After 25 years, GCOS has started a process aimed at the rationalization of the ECV list. In this contribution, the main outputs of this rationalization process will be presented: (1) formalization of a governance process to adopt new ECVs; (2) revised definitions for ECVs and ECV quantities; (3) a proposal for an updated set of ECVs.
The connections between the ECV framework and other frameworks such as the Essential Ocean Variables framework will also be covered.
How to cite: Martin Miguez, B., Thorne, P., Bojinski, S., Buontempo, C., Connors, S., García Izquierdo, C., Gartner-Roer, I., Güntner, A., Herold, M., Kern, S., Schroeder, K., Trewin, B., Bombelli, A., and Tassone, C.: Origin, governance and evolution of the Essential Climate Variables framework managed by the Global Climate Observing System (GCOS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13413, https://doi.org/10.5194/egusphere-egu26-13413, 2026.
In the ENVRI-Hub-NEXT (EHN) project, environmental Research Infrastructures (RIs) collaborate within the European Open Science Cloud (EOSC) to improve access to observation datasets related to Essential Climate Variables (ECVs). The main goal is to enable users from any Virtual Research Environment (VRE) to process and analyse ECV-related data using ENVRI-Hub components. To support this, EHN provides a GUI-based Catalogue of Services (CoS) that describes RI services and datasets using an extension of DCAT (EPOS-DCAT-AP), complemented by a Catalogue of Data based on FAIR Data Points. Despite this, dataset discovery and federation remain challenging due to heterogeneous machine-to-machine services and differing vocabularies for observed variables.
To address these issues, an ECV Working Group was established within EHN to define an approach for matching ECVs as defined by the Global Climate Observing System (GCOS) to the diverse variables managed by RIs across multiple environmental domains. An ECV is defined as a physical, chemical or biological variable, or a group of linked variables, that is critical for characterising Earth’s climate. A key outcome was the publication of ECV concepts linked to the GCOS definitions, in a machine-readable vocabulary in the NERC Vocabulary Server (NVS). It enabled mappings to RI-specific vocabularies using the I-ADOPT approach, and the use of SPARQL queries to establish dynamic “ECV to observable properties” translations. Python notebooks were developed to interact with RI data access services, including a central notebook that translates a single ECV request into multiple RI-specific queries and data access requests. This work exposed limitations in the vocabularies used for observed parameters, as well as in the availability of direct and harmonised data access services.
In the next phase of EHN, several upgrades are planned to improve data accessibility and usability. All RIs will receive training on describing observational datasets using I-ADOPT-compliant vocabularies following recommended practices.
Because RI machine-to-machine services rely on different APIs and constraints, they cannot be queried uniformly. The ECV data access library developed earlier in the project translates a single ECV request into the multiple requests required to query relevant RI services, using I-ADOPT mappings to identify RI parameter sets. This library will be further optimised, while RIs work towards more harmonised and direct data access services.
Many RIs still lack direct data access and especially subsetting capabilities, instead offering file-based or aggregated access via metadata search. Experience gained through the notebooks will guide improved integration of available services into the CoS. All notebooks and scripts will be released as open source and integrated into the ENVRI-Hub Analytical Framework, including a JupyterLab extension. As analytical services require harmonised data chunks rather than heterogeneous files, the next stage will test subsetting solutions such as Beacon, ERDDAP and Zarr.
The presentation will highlight the implemented solutions and opportunities for broader uptake within the EOSC domain.
How to cite: Kokkinaki, A., Thijsse, P., Moncoiffe, G., Krijger, T., Gutierrez, M., Hellström, M., Dema, C., Turco, A., Dobler, D., Bundke, U., and Fiebig, M.: Enabling access to harmonised ECV-related observation datasets from environmental Research Infrastructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14538, https://doi.org/10.5194/egusphere-egu26-14538, 2026.
Systematic observations are essential for understanding the climate system and the changes that are rapidly unfolding. The ESA Climate Change Initiative (CCI) was established to meet the needs of the UNFCCC, supporting the development of long term data records of the Essential Climate Variables (ECVs) defined by GCOS that could most easily be addressed by satellite remote sensing.
Since 2009 the CCI programme has built-up European expertise by supporting more than 30 projects that are addressing ECVs, each of which produces multiple data products with detailed documentation to meet the needs of the climate research community and support countries’ goals under the Paris Agreement. Much of this research has been taken up by climate services for the operational production of data, most notably via the Copernicus Climate Change Service (C3S).
Co-developed with C3S, ESA CCI has pioneered common data standards, SI traceability, uncertainty characterisation, validation and evaluation processes and detailed product documentation. Furthermore, the metadata requirements from the World Climate Research Programme’s obs4MIPs effort are met by projects on a case-by-case basis, ensuring suitability for climate model evaluation. To date, interoperability and consistency between ECV data records have been more difficult issues to address, but are the target of the next phase of the programme (2026-2029), informed by recent cross-ECV project work.
This presentation highlights lessons learnt and future advancements in the CCI programme, with specific examples of how the programme’s integration with strategic partners is supporting improvements for data users, and how the ECV projects are directly working with reporting agencies and contributing to policy need. With the expansion of the Copernicus Sentinel missions up to 2030, and an increasingly diversified landscape of climate data providers, ESA aims to expand its role as custodian and developer of satellite-based ECVs, ensuring European expertise in this area is leveraged to support policy needs for understanding climate change, and tracking mitigation and adaptation action.
How to cite: Hebden, S., Connors, S., Pinnock, S., Pechorro, E., Campbell, A., Trofaier, A., Muir, F., Eisinger, M., Fisher, P., Albergel, C., Mecklenburg, S., Gunnarsson, K., MacIntosh, C., and O'Rourke, E.: Advances of the ESA Climate Change Initiative (CCI): Progress, Integration, and Future Directions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18690, https://doi.org/10.5194/egusphere-egu26-18690, 2026.
Essential Ocean Variables (EOVs) in ocean chemistry such as temperature, salinity, chlorophyll, nutrients and dissolved oxygen are critical for ocean monitoring and policy, particularly for assessing eutrophication and ocean acidification. These topics are recognized as priorities in global and regional frameworks, including the Sustainable Development Goals (SDG) and the Marine Strategy Framework Directive (MSFD). Despite their importance, implementation remains fragmented across infrastructures like EMODnet, Copernicus, and the World Ocean Database (WOD). The resulting datasets are large, vary in metadata standards, and are processed using diverse methodologies, thus creating significant challenges for interoperability and effective reuse.
Blue-Cloud addresses these challenges by acting as an open science platform for collaborative marine research, contributing to the European Digital Twin of the Ocean (EDITO) and serving as a marine science node of the European Open Science Cloud (EOSC). Built on the D4Science e-Infrastructure, it provides seamless access to services for storing, managing, analyzing, and reusing research data across disciplines. Within the goals of the Blue-Cloud 2026 project is to develop, validate, and document analytical big data workbenches to produce harmonized and validated data collections for selected EOVs in physics, chemistry, and biology.
These workbenches harmonize, integrate, validate and qualify large, heterogeneous in situ data collections from major European and global infrastructures and expose cloud-based workflows in their virtual research environments (VRE). Precisely, the workbench for eutrophication integrates Copernicus, WOD and EMODnet Chemistry's validated datasets with Beacon, a high-performance data-lake solution that enables rapid sub-setting and harmonized delivery of multi-source data, and employs webODV for exploration, initial validation and subset extraction, to support quality control and product generation. A crucial step after merging the data is the identification and management of duplicate records. When merging datasets from multiple sources, duplicates can arise due to overlapping sampling campaigns, repeated submissions, or variations in metadata. To address this, the Clone Wars tool has been developed to systematically detect, flag and handle duplicates. It applies advanced matching algorithms to compare the metadata ensuring that duplicate records are found and removed without loss of information.
Together, these services enable scalable, semantically harmonized workflows that deliver reproducible analytics and high-quality products, supporting policy-driven monitoring (MSFD, SDG 14) and global initiatives such as EDITO as EMODnet, EDITO and Copernicus.
How to cite: Reyes Suarez, N. C., Kooyman, R., Moncoiffe, G., Mieruch, S., Leroy, D., Giorgetti, A., Gatti, J., Iona, A. (., Racape, V., Fyrberg, L., French, M. A., Wesslander, K., and Vernet, M.: Blue-Cloud 2026 workbenches for Essential Ocean Variables: advancing harmonization and big-data workflows for eutrophication in marine science., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20251, https://doi.org/10.5194/egusphere-egu26-20251, 2026.
Many promising initiatives are advancing FAIR data in hydrology, yet a substantial semantic and technical interoperability gap remains between water data used in operational services and in research, from in situ observations to model-derived products. At present, national hydrological services and the Earth system science community often develop data structures, vocabularies and workflows in parallel, which hampers seamless reuse of water information across mandates and scales. Addressing this fragmentation requires a collaborative effort to co-design shared semantic and ontological standards that can underpin interoperable data exchange for both operational water management and scientific analysis. This includes the FAIR Water community, including OGC Hydrology Domain Working Group (OGC HydroDWG), WMO, UN bodies (UNEP, UNESCO IGRAC and ICWRGC), DANUBIUS, eLTER RIs, TERENO and the Water4all partnership amongst others.
This contribution presents the state-of-the-art in and a conceptual and practical framework for connecting the Earth and Space Science Informatics community with the implementation of an emerging international hydrological data exchange standard that serves both operational hydrology and Earth system science. It aligns the objectives of the WMO Plan of Action for Hydrology – in particular the ambitions “high-quality data supports science” and “science provides a sound basis for operational hydrology” – with the development of WIS2-based hydrological data exchange under the WMO Task Team on WIS2 for Hydrology (TT‑W2FH), which is responsible for defining hydrology-specific topic hierarchies, metadata, KPIs and implementation guidance for the WMO Hydrological Observing System “WHOS” within the WMO Information System “WIS”. The contribution supports also the strategy process of the water program of the UNESCO “IHP‑IX” and deals with output 3.3 on validating open-access data on water quantity, quality and use, with a focus on workflows, quality control, and governance arrangements required to make such data reliably reusable in transboundary and global assessments.
The presentation discusses concrete pathways to embed FAIR digital object concepts, interoperable metadata and federated workflows from the ESSI community into the WMO and UNESCO implementation processes, thereby fostering cultural change towards open, standards-based data sharing. The definition of how to reach a high FAIRness level within the water community in the light of the existing international standards and best practices (OGC, W3C, INSPIRE, RDA) with the target to produce FAIR Implementation Profiles (FIP). By explicitly linking EGU ESSI user-centric research data infrastructures developments with WMO and UNESCO programmes, the contribution aims to strengthen international collaboration and to co-develop sustainable, community-driven practices for a hydrological data exchange standard that equally supports real-time operations, long-term water resources assessment and integrated Earth system modelling.
How to cite: Dietrich, S., Saile, P., and Grellet, S.: EGU ESSI–WMO–UNESCO Synergies for Interoperable Hydrological Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21411, https://doi.org/10.5194/egusphere-egu26-21411, 2026.
The Intergovernmental Panel on Climate Change (IPCC) provides comprehensive information on the physical science of climate change in Working Group I (WGI), as well as on climate impacts, adaptation, and vulnerability in Working Group II (WGII). The breadth of information in the latest IPCC assessment report (AR6) can be difficult to navigate, in particular for end users looking for tailored outputs directly linking physical climate changes to the resulting risks for natural and human systems. While efforts have been made to facilitate the assessment of climate impacts and risks, prominent and systematically applied cross-Working Group products are still missing.
To address this gap, we have developed a climate impact taxonomy that pairs the 35 Climatic Impact-Drivers (CIDs) assessed in AR6 WGI with the eight Representative Key Risks (RKRs) identified in AR6 WGII. CIDs represent physical climate conditions that directly affect societal and ecological systems, while RKRs are clusters of key climate-related risks projected to become severe in a warming climate. Each RKR–CID combination is enriched with structured metadata describing spatial scale, type of change, temporal character, and the IPCC assessment of relevant subsystems. Additionally, the metadata include examples of identified research needs, adaptation linkages outlining illustrative responses by risk component and associated relevant targets aligned with the United Nations Framework Convention on Climate Change (UNFCCC) Global Goal on Adaptation, mitigation linkages, and critical global warming levels. References to relevant WGI and WGII chapters of IPCC AR6 and approved chapters for AR7 guide users toward the appropriate sources for further information.
By translating abstract physical climate indicators into actionable information, the climate impact taxonomy prototype—implemented as a machine-readable lookup table—supports end users, such as adaptation planners and policymakers, with more holistic impact and risk assessments.
How to cite: Werning, M., Byers, E., Andrijevic, M., Schleussner, C.-F., Monteith, S., Aldrete Lopez, L., Lemaire, V., Matsumae, E., Thomas, A., and Nauels, A.: A climate impact taxonomy operationalizing IPCC physical driver and risk concepts , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9398, https://doi.org/10.5194/egusphere-egu26-9398, 2026.
EarthCODE (https://earthcode.esa.int) is a strategic ESA EO initiative to support the implementation of the Open Science and Innovation Vision included in ESA’s EO Science Strategy (2024).
Collaboration and federation are at the heart of EarthCODE. First, EarthCODE integrates a wide range of available EO cloud computing platforms and services, including engineering support; second, it catalogs and manages the FAIR and open data, code, and documentation from ESA Earth System science studies and experiments so they can be discovered, reused, and adapted to new contexts; third, it builds a community of practice of Open Science in Earth Observation science, supported by targeted community trainings, especially with the ESA Science Clusters - and by providing an open forum for discussion and co-creation. The initiative helps scientists discover, visualize, explore, reuse, modify, and build upon the research of others in a fair and safe way, as well as to create end-to-end reproducible workflows on EO cloud platforms – aiming to maximize the utilization of data products and workflows for Earth Action and to systematically transform scientific data into actionable information usable in downstream applications for decision making.
EarthCODE actively supports initiatives across the Earth system sciences by providing practical development, code and data management tools, and an overall open science framework. One such example is the creation of analysis-ready data (ARD) cubes for the Antarctica Insync initiative. This resulted in FAIRification process to make complex data readily available for modelling (e.g. https://discourse-earthcode.eox.at/t/antartica-insync-data-cubes/107) and visualization (e.g. https://esa-earthcode.github.io/polar-science-cluster-dashboard/). By pre-integrating these diverse datasets, EarthCODE removes the burden of complex data engineering (such as reprojection and resampling), allowing downstream users to immediately apply these inputs to environmental monitoring and decision-making systems. Other examples of this support from EarthCODE can be seen with published datasets such as WAPOSAL and SMART-CH4 and others, enabling research outputs to be translated into actionable, accessible, relevant datasets. On top of that, to facilitate the bank of examples was developed to demonstrate good data management practices and encourage collaboration across scientific teams (https://esa-earthcode.github.io/documentation/Community%20and%20Best%20Practices/).
FAIR data collections such as the one above and many more value-added geophysical products are made available by EarthCODE through its Open Science Catalog (https://opensciencedata.esa.int) which provides harmonized access to wide range of products across all Earth system science domains. While many catalogues prioritise openness and access, EarthCODE goes beyond by focusing on FAIRness. EarthCODE leverages open-source geospatial technologies like stac-browser, pycsw, PySTAC, OpenLayers and others - while also contributing back to these projects in terms of software and standardization. The osc python library complements the OSC by providing a programmatic interface to search for and access catalogued research data for analysis.
How to cite: Samardzhiev, D., Samardzhiev, K., and Dobrowolska, E.: EarthCODE: Transforming Earth Observation Research into Action-Ready Information through Open Science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14938, https://doi.org/10.5194/egusphere-egu26-14938, 2026.
Argo is an international observation array of approximately 4 000 autonomous profiling floats measuring oceanic Essential Climate Variables (ECV) consisting of physical (pressure, temperature and salinity) and biogeochemical (dissolved oxygen, pH, nitrate, chlorophyll-a, downwelling irradiance and suspended particles) variables, from 2000-meter depth (or from 6000-meter depth for the deep floats) to the surface every 10 days, all over the ocean. More than 3 millions of vertical profiles have been collected in 25 years.
The Argo array is unique as it samples the global ocean, even in regions or seasons when vessels cannot operate, and depths that satellite sensors cannot probe. Argo is tightly connected to other observation arrays, in calibration or cross-validation efforts, such as with the accurate measurements performed onboard research cruises, essential for Argo to achieve the accuracy required for climate studies, or satellite data.
Argo contributes to monitor and understand climate change for several key climate change phenomena, including increase of the ocean heat content (and sea level rise), deoxygenation phenomenon, ocean acidification and carbon cycle. Because of its importance in science studies, including carbon cycle, the presentation will focus on the interoperability of the Argo dissolved oxygen data.
To facilitate science studies and support for public policies based on ECVs, FAIR share of both data and metadata is essential. The Argo international program has been continuously improving the findability, accessibility, interoperability and reusability of its dataset since its inception. Recently, Argo has increased its interoperability by exposing its vocabulary on the web, specifically on the NERC Vocabulary Server applying the I-ADOPT framework and thus facilitating the mapping of Argo vocabulary with the vocabulary of other research infrastructures. Argo metadata and data access services have also been improved to match evolving users’ needs. They have been made more accessible by being exposed under federative platforms, such as the ENVRI-Hub, currently developed under the ENVRI-Hub NEXT EU project, or on Galaxy Europe for a biogeochemical calibration collaborative workflow that has been developed under the FAIR-Ease EU project. Interoperability improvement activities are also currently undertaken within the AMRIT EU project for European marine research infrastructures datasets, including oxygen.
FAIRness challenges faced by the Argo program are multiple, for instance the necessity to simplify the dataset for a given audience, which is done through the development of products (e.g. easyOneArgo) or the necessity to increase the interoperability of the measures’ conditions, including methods and uncertainties.
Indeed, uncertainties are key information to climate change analyses: foreseeing that the sea level will rise by 10 meters +/- 5 cm has not the same meaning as foreseeing that the sea level will rise by 10 meters +/- 10 meters for a decision-maker. Argo uncertainties have the same dimension as the dataset itself (i.e., an error value is associated with each observation point), which means that uncertainties are to be considered as data. For the interoperability of essential climate variables, in science studies in general, and in predictive studies in particular, the FAIR share of uncertainties associated with ECVs is crucial.
How to cite: Dobler, D., Carval, T., Gourcuff, C., and De Roeck, Y.-H.: Interoperability of Argo Essential Climate Variables, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20638, https://doi.org/10.5194/egusphere-egu26-20638, 2026.
Posters on site: Thu, 7 May, 14:00–15:45 | Hall X4
Essential Ocean Variables (EOVs) play a central role in global ocean observation frameworks. They support the monitoring of biogeochemical processes, ecosystem dynamics, and long-term environmental change. Among them, nitrate is a key biogeochemical EOV, closely linked to primary production and phytoplankton dynamics. And transforming raw observations into reliable, interoperable, and reusable EOV products remains a major operational challenge.
The global Argo programme enables unprecedented global monitoring of ocean biogeochemistry through autonomous profiling floats sampling the ocean from 2000 m depth to the surface; at the same time, the sensitivity of biogeochemical sensors to drift, biofouling, and instrumental issues necessitates expert-driven Qualification, Calibration, and Validation (QCV), which operates within a fragmented ecosystem of tools, data formats, execution environments, and methodological practices.
This contribution presents a complete nitrate QCV workflow, illustrating in concrete terms how validated EOV products are obtained from raw Argo observations. The workflow integrates global Argo data access, data harmonisation, preparation for visual inspection, expert-driven qualification using Ocean Data View, tracking of manual decisions, nitrate calibration, and delayed-mode data production. Each step is documented, connected, and explicitly handled to ensure traceability of both automated processing and human interventions.
The service implementation relies on the Galaxy platform, which provides an open, web-based, and FAIR-oriented environment to orchestrate independent domain tools together with expert-defined QCV procedures into complete, reusable, and transparent workflows. These workflows are accessible to expert users without advanced programming skills. Rather than replacing existing tools, the approach aims to make them work together in a coherent, unified, traceable, and reproducible way, through fixed processing chains covering the full QCV process.
The QCV service will be deployed within the European Open Science Cloud (EOSC), building on thematic infrastructures coordinated by ENVRI, on platform services provided by NFDI, and on operational deployment ensured by Data Terra, in order to guarantee accessibility, interoperability, and long-term reuse.
Regardless of the selected presentation format, this contribution will introduce the EOV framework and the challenges associated with biogeochemical Argo data, before providing a concrete illustration of a complete nitrate QCV workflow. It will then detail the service implementation through interoperable workflows on the Galaxy platform and its deployment within the European Open Science Cloud (EOSC).
How to cite: Detoc, J., Racapé, V., Jossé, M., Weber, C., Dobler, D., Schmechtig, C., Sizun, A., and Carval, T.: Operationalising essential ocean variables through robust and trusted QCV Workflows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16592, https://doi.org/10.5194/egusphere-egu26-16592, 2026.
Accurate estimation of Origin-Destination (O-D) matrices is fundamental to effective transportation planning. Conventional approaches based on the four-step travel demand model are often time-consuming, data-intensive, and costly, primarily due to their reliance on extensive demographic and socio-economic data. Integrating Remote Sensing (RS), Geographic Information Systems (GIS), and the Global Positioning System (GPS) will be a more efficient and spatially explicit framework for travel demand analysis. This study presents an approach for estimating O-D matrices by establishing a relationship between land-use characteristics and traffic demand. High-resolution CARTOSAT-1 satellite imagery was used to generate updated ward-wise land-use maps for Tiruchirappalli city, Tamil Nadu, India in the absence of recent land-use data. Using GIS-based spatial analysis, land-use categories were quantified and linked to trip generation and trip attraction patterns across sixty wards. Trip production and attraction were estimated based on residential and non-residential land-use proportions, and these estimates were incorporated into a base-year O-D matrix to derive an updated matrix. The resulting O-D matrix was validated through link-level traffic volume comparisons on selected critical road segments. The findings demonstrate that wards with higher residential land-use exhibit greater trip production, while wards dominated by commercial, educational, industrial and public land uses show higher trip attraction. The study highlights the effectiveness of integrating 3S technology in simplifying O-D matrix estimation, reducing data requirements, and supporting cost-effective and reliable urban transportation planning.
Keywords: Land use; Travel demand modelling; O-D matrix; Trip generation
How to cite: Rema, A.: Integration of Remote Sensing and GIS for Origin–Destination matrix estimation in urban areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2687, https://doi.org/10.5194/egusphere-egu26-2687, 2026.
How to cite: Cohrs, J.-C., Brasseur, G., Choi, S., Hilbert, R., Klien, E., Kocon, K., Kumar, M., Mariscal, N., Matějka, J., Moors, E., Moravcová, K., Podsztavek, O., Samakinwa, E., Simonis, I., Sipina, S., Tewes, T., Würz, H. M., and Rechid, D.: FOCAL Urban Pilot: Efficient exploration of climate data locally for data-driven decision-support in urban climate adaptation planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14450, https://doi.org/10.5194/egusphere-egu26-14450, 2026.
Climate change is having an accelerating impact globally, and across Africa through the increased frequency, magnitude and duration of droughts, fires, floods and other extreme climatic events. Our ability to address this crisis requires policy makers, private enterprise, scientists and society at large to converge and co-create the solutions needed to understand, adapt and mitigate climate impacts. Research infrastructures (RIs) underpin our ability to develop appropriate climate services that address these issues, and through the scientific evidence they deliver, aligned with societal priorities they will reduce vulnerability to climate change and promote sustainable development across Africa.
The Horizon Europe funded KADI project (Knowledge and climate services from an African observation and Data research Infrastructure) has developed a conceptual framework for a pan-African RI that will deliver the science-based climate services required to reduce societal and economic costs of climate change, help to address national, regional and international political agendas and contribute to achieving the UN Sustainable Development Goals. The KADI-RI is driven, supporting successful co-creation and delivery of climate services that are sector relevant and user specific; transdisciplinary in nature integrating academic, non-academic and societal areas; scalable in space and time, producing interoperable and accessible data products; and sustainable in scope, through the incorporation of financial, organisational, technological, social and epistemological longevity.
This presentation will discuss the development of the KADI-RI blueprint using systems mapping approaches in the co-creation of climate services and how these outputs can be used to identify the diverse research networks and interoperable data systems that are essential for understanding climate trends and their associated impacts. KADI pilot studies have demonstrated; 1) how the use of low-cost sensors and citizen science engagement can address issues of air pollution and heat stress in urban environments; 2) how long-term African Union (AU) and European Union (EU) collaborative networks can provide insight into the benefits of long-term meteorological measurements to inform sensor and data analytical requirements, 3) explored how such exchanges can consolidate African networks measuring ocean biogeochemistry and integrate these into global RIs, and 4) examined the interactions between diverse observation networks and the development of earth system modelling and remote sensing capacity. Knowledge exchange activities have been central to the development of the KADI-RI blueprint, facilitating the mobility of scientists across Africa and the EU to attend stakeholder workshops, training courses and to develop communities of practice that will ensure all stakeholders work together to design solutions that reflect regional priorities.
Key recommendations of the KADI project include the need to minimise observational gaps to ensure better data coverage; combine in situ, remotely sensed and modelled data to enhance analytical capabilities; invest in infrastructure and skills; improve access to data products through open data policies and engage and include all communities in data collection and climate service design. This work provides the link between the science-based concept design and the policy cooperation required to develop a functional and collaborative RI that will provide long-term sustainable support to develop local ownership and integration of African climate-services into global observation systems.
How to cite: Saunders, M., Salmon, E., Bilola, T., Käyhkö, N., Omar, A., Bornman, T., Klausen, J., Garland, R., Feig, G., Merbold, L., Nying'uro, P., Mahonga, C., Guillaume Ossohou, M., and Kutsch, W.: Co-creating a pan-African Research and Knowledge Infrastructure for societal benefit through climate action: The KADI project., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11976, https://doi.org/10.5194/egusphere-egu26-11976, 2026.
The International Initiative on Water Quality (IIWQ), established in 2012 by the Intergovernmental Hydrological Programme (IHP) of UNESCO, addresses global water quality issues and combats the degradation of freshwater resources which endangers human health and ecosystems. The IIWQ provides a collaborative network of scientists, practitioners and policymakers for joint research and knowledge exchange on water quality monitoring and management.
During the previous two IHP phases (VII, 2008–2013 and VIII, 2014–2021), the IIWQ has contributed to basin-level water quality assessments, for example in the Kharaa and Selenge River Basins in Mongolia and Russia. The IIWQ also investigated the effects of emerging pollutants on freshwater resources, and published the open-access book “Emerging pollutants: protecting water quality for the health of people and the environment”. Additionally, the IIWQ developed lake-level and global remote sensing water quality portals.
During the ongoing ninth phase of the IHP (2022-2029) “Science for a Water Secure World”, the IIWQ is now co-led by the International Centre for Water Resources and Global Change (ICWRGC) and the UNESCO Chairs on Sustainable Water Security and Water, Energy and Disaster Management for Sustainable Development (WENDI).
In order to support the implementation of the IHP-IX strategy, the IIWQ focuses on the following outputs:
- Enhanced mobilization of remote sensing technologies by water quality management authorities.
- Simplified planning and implementation of water quality monitoring programmes and water management plans.
- Increased awareness and predictability of the effects of emerging pollutants and hydrological extremes on water quality.
- Amplified visibility for the importance of water quality and its relation to the UN system and SDGs.
This conference contribution provides a more detailed introduction to the IIWQ, focusing on activities during the current IHP-IX phase and highlighting associated engagement opportunities.
How to cite: Heinle, M., Saile, P., Dietrich, S., and Bharati, L.: Building bridges for sustainable water management - the UNESCO International Initiative on Water Quality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16912, https://doi.org/10.5194/egusphere-egu26-16912, 2026.
Deprived urban areas (e.g., slums and informal settlements), are characterized by substandard housing, inadequate services, and insecure tenure. They represent the physical manifestation of socioeconomic inequalities across rapidly urbanizing in Low- and Middle-Income Countries (LMICs). Populations residing in these areas face compounding challenges including elevated exposure to climate and environmental hazards (e.g., extreme heat). Yet, these communities are often underrepresented in official censuses, limiting efforts to identify and reach those most in need. Earth Observation (EO) and Machine Learning (ML) offer potential to address this gap, yet current mapping approaches produce mostly binary slum/non-slum classifications that obscure the continuous, multidimensional nature of deprivation.
This research develops a morphology-based framework for characterising urban deprivation in LMICs, using Zambia as a primary case study. Rather than training supervised models on binary slum boundaries, we leverage EO-derived urban elements including building footprints, heights, street network characteristics, and spatial arrangement patterns to compute a set of morphometrics at fine spatial resolution. Applying unsupervised ML techniques, we identify distinct morphological signatures across urban areas. To assess whether and how these signatures relate to deprivation, we integrate household-level data from accurately (~3m) geo-coded urban household surveys in Zambia in 2023 with EO imagery to examine associations between physical urban form and non-physical dimensions of deprivation, such as service access and socioeconomic status. Preliminary results will highlight which morphometrics demonstrate robust associations with socioeconomic indicators and how these relationships may vary across different urban contexts, as well as the rural-urban continuum.
The framework responds to the challenge of transforming globally available EO data into locally actionable information. By producing human-interpretable morphological characterizations rather than abstract deep learning features, the approach offers greater transferability across diverse urban settings and facilitates co-creation with local stakeholders who can validate whether outputs align with their understanding of deprivation patterns on the ground.
How to cite: Luo, E. and Tuholske, C.: Characterising Urban Deprivation through Earth Observation: Linking Physical Urban Form to Socioeconomic Conditions in Zambia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5768, https://doi.org/10.5194/egusphere-egu26-5768, 2026.
Climate services play a critical role in bridging scientific knowledge and societal needs, enabling informed decision-making for climate adaptation and mitigation. However, their effectiveness depends not only on scientific robustness, but also on inclusive co-creation processes, shared standards, and strong communities of practice that connect researchers, practitioners, policy makers, and users. This contribution presents the experience of Climateurope2, a European project coordinated by the Barcelona Supercomputing Center, as a case study demonstrating how community-driven approaches can advance trustworthy, accessible, and impactful climate services.
Climateurope2 aims to strengthen and expand the European climate services ecosystem by developing recommendations and standardisation procedures while fostering uptake of quality-assured climate services. Central to the project is the deliberate cultivation of an open and diverse climate services community, built through a wide range of participatory activities that prioritize bottom-up engagement and transdisciplinary exchange. In particular, the project has organized a series of interactive webstivals and festivals designed as co-creation spaces, where researchers, service providers, policy makers, private sector actors, and local stakeholders collaboratively explore needs, methodologies, tools, and future directions for climate services.
These events have facilitated knowledge integration across multiple domains, including Earth observation data, climate modelling, socio-economic analysis, and local knowledge systems, contributing to the development of more user-relevant and context-sensitive climate services. They also address common challenges in the field, such as fragmented data accessibility, limited dialogue between disciplines, and difficulties in scaling services across sectors and regions.
A distinctive feature of Climateurope2 is its strong emphasis on communication as an enabling mechanism for co-creation and societal impact. The project has invested in innovative communication formats and inclusive language to make climate science and services more accessible to policy makers, practitioners, and wider audiences. This effort includes a Traveling Climate Action Roadshow across the Southeast Europe that promotes climate services through the integration of art and science; two dedicated art–science calls designed to foster dialogue between artists and the scientific community, resulting in the creation of artistic works addressing key project themes and translating complex climate service concepts into accessible narratives for wider audiences; and the production of the “Climate at your Service” podcast, which offers an engaging entry point to understanding the role of climate services and their standardisation in supporting climate adaptation and informed decision-making.
By reflecting on lessons learned from community-building, co-creation practices, and communication strategies, this contribution highlights how transdisciplinary collaboration and shared standards can empower a broad range of stakeholders. The Climateurope2 experience offers transferable insights for advancing climate services that are not only scientifically sound, but also socially robust, scalable, and transformative across diverse socio-ecological contexts.
How to cite: Calderaro, C., Taddeo, S., Acierno, A., Slavković, L., Brkić, M., Terrado Casanovas, M., and Martin del Real, I.: Towards Transformative Climate Services: Community Building, Co-Creation, and Communication. Lessons learned from Climateurope2 project., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12348, https://doi.org/10.5194/egusphere-egu26-12348, 2026.
Methane emissions are a major driver of near-term climate warming across multiple sectors, and the European Union Methane Emissions Regulation (EUMER) represents a critical and timely policy instrument to address methane emissions from fossil fuels produced in the EU as well as those supplied to the EU. As EUMER enters its phased implementation, the operationalization of its wide-ranging and technically complex regulatory requirements necessitates the development of new data workflows, coordination mechanisms, and cross-disciplinary approaches to support actionable and accessible knowledge for a broad set of stakeholders beyond public authorities.
This contribution frames the implementation of EUMER as a data-driven process that requires bringing together distinct perspectives from industry, regulators, and local communities. Drawing on experiences from civil society initiatives that establish networks of organizations assessing and tracking implementation progress across the EU, we examine how empirically based data tools are being used to increase transparency and support effective mitigation. We further analyse emerging institutional configurations and collaborative practices that enable stakeholder engagement and regulatory oversight. We discuss key challenges related to data accessibility, transparency, comparability, and communication in the context of methane reporting and mitigation requirements, including issues arising from diverse emission sources, supply chains, and institutional responsibilities. Particular attention is given to the integration of multiple data streams - including Earth observation products, facility-level reporting, and international datasets such as those developed by the International Methane Emissions Observatory (IMEO) - to support the design and evaluation of affordable and accessible monitoring tools.
Our work will illustrate how this data-driven, multi-stakeholder implementation framework for methane mitigation can serve as a blueprint for similar approaches for emissions from other sectors and gases.
How to cite: Di Biase, V., Zavala-Araiza, D., and Pilsner, L.: Implementing the EU Methane Emissions Regulation through policy-relevant emissions data and a collaborative approach., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14063, https://doi.org/10.5194/egusphere-egu26-14063, 2026.
The increasing availability of EO data products from a growing number of Commercial EO data providers within the New Space domain represents a great opportunity towards the implementation of a concept that has been discussed at CEOS-level for several years: a Global Earth Observation System of Systems (GEOSS).
A key element towards the implementation of a GEOSS is undoubtedly data interoperability, by all means. This calls for new frameworks and tools to enable data quality and interoperability assessments, allowing a clear, standardized, process and output presentation.
ESA-side, the development of a quality and suitability assessment framework has started within the Earthnet Third Party Missions (TPM) realm, where candidate missions are assessed by the Earthnet Data Assessment Project (EDAP) team prior to integration, with a view to checking whether the mission stated requirements are met. The EDAP team has developed a successful reference set of guidelines, further instantiated by domain (Optical, SAR, DEM, Atmospheric Composition and others), with a view to harmonizing and standardizing the data quality assessments on a per-domain basis.
The EDAP Cal/Val Maturity Matrix and framework have been presented through international forums and conferences, having great success.
NASA-side, data quality assessments are carried out to support integration of commercial satellite data into Earth science research and applications at NASA. The NASA’s Commercial Satellite Data Acquisition (CSDA) Program’s commercial data evaluation process provides critical benefits by ensuring that all acquired datasets meet rigorous scientific standards for accuracy, reliability, and interoperability. Through comprehensive assessments of radiometric and geometric quality, validation against trusted reference data, and transparent documentation requirements, NASA ensures that commercial data can be confidently integrated into research and applications. This approach builds trust in commercial partnerships, accelerates scientific progress, reduces duplication of effort, and promotes cost efficiency by leveraging existing high-quality data. Continuous monitoring further supports long-term integrity and fosters innovation within the Earth observation community
Within the frame of the ESA-NASA International Cooperation and Collaboration through Joint Groups and International Workshops attendance (mainly JACIE and VH-RODA), agreement on joint development and maintenance of the EDAP framework has been reached, officially framing the activity and the framework as an official ESA-NASA Framework. A 1st official signature of the ESA-NASA guidelines for the SAR domain took place in 2024, and further signatures of guidelines covering the other domains are planned within the next years.
The aim of the ESA-NASA guidelines is to maintain an official, transparent and public framework dedicated to data quality assessments of candidate missions to both the TPM and CSDA programmes. At ESA, the guidelines are also used to carry out an operational assessment of missions within the Copernicus Contributing Missions scheme.
The Presentation will focus on the joint guidelines, its usage and main output, namely the Cal/Val Maturity Matrix, and future evolutions.
How to cite: Martin, M., Ostrenga, D., De Laurentiis, L., Fischer, P., and Goryl, P.: The ESA-NASA Joint EO Mission Quality Assessment Framework – Towards a standardized data quality assessment process, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3523, https://doi.org/10.5194/egusphere-egu26-3523, 2026.
SeaDataNet is a pan-European infrastructure that manages and provides access to marine datasets collected by European organisations through research cruises and observational activities in coastal waters, regional seas, and the global ocean. It was founded by National Oceanographic Data Centres (NODCs) and major marine research institutes. The network has expanded through successive EU-funded RTD projects and by contributing to major European initiatives such as EMODnet, Copernicus Marine Service, ENVRI, and the European Open Science Cloud (EOSC).
SeaDataNet develops and promotes widely adopted standards, vocabularies, software tools, and services that support FAIR marine data management. Its core service, the CDI (Common Data Index), provides unified online discovery and access to in situ marine observation data managed by more than 115 data centres in 34 countries. The service currently offers access to over 3 million datasets from more than 1,000 European organisations, covering physical, chemical, biological, geological, and geophysical data from European waters and the global ocean. The use of standard metadata, formats, and controlled vocabularies ensures rich, highly FAIR datasets.
SeaDataNet also delivers core data services for EMODnet Chemistry, Bathymetry, and Physics, harmonising large volumes of marine data that support the production of thematic data products, including Essential Climate Variable (ECV) and Essential Ocean Variable (EOV) datasets.
Environmental science increasingly relies on large, heterogeneous, and rapidly growing data collections that must be efficiently accessed, subsetted, and harmonised for use in models, digital twins, AI workflows, and Virtual Research Environments (VREs). The fully open-source Beacon software, developed by MARIS https://beacon.maris.nl/, addresses these challenges by enabling cloud-native, high-performance data lakes that are fast to deploy and access. Beacon supports parameter harmonisation using metadata annotations based on NERC vocabularies, ECV vocabularies, and the I-ADOPT methodology adopted in ENVRI-HUB Next.
To improve the ease of access to subsets of the SeaDataNet CDI data collection, a Beacon instance containing all the open SeaDataNet data was set-up. This now allows users to obtain real-time access to data subsets in multiple data formats (NetCDF, Parquet, Zarr) and flexible querying from Jupyter Notebooks or a newly developed Beacon studio (user interface) for non-technical users. Within ENVRI-HUB Next, this SeaDataNet instance enables on-the-fly access to ECV-related subsets from millions of files via Jupyter Notebooks, ready for use in the Analytical Framework.
The presentation focuses on this use case, the technical solution, and its potential applicability for other Research Infrastructures supporting EOSC use cases.
How to cite: Thijsse, P., Schaap, D., Krijger, T., Kooyman, R., and Weerheim, P.: Direct and harmonised access to Essential Climate Variable related in-situ observation data from SeaDataNet, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10771, https://doi.org/10.5194/egusphere-egu26-10771, 2026.
Sustainable roofs, including green roofs (GR) and photovoltaic (PV) roofs, are increasingly used as essential components of urban green infrastructure and building-scale renewable energy systems that support climate resilience and environmental quality in high-density cities. However, large-scale, spatially explicit analyses of sustainable roofs in urban core areas remain limited due to data scarcity and the difficulty of reliably distinguishing roof types. Recent advances in deep learning (DL)-based remote sensing have enabled automatic mapping of sustainable roofs at the city scale, but empirical applications remain scarce in Chinese megacities, and systematic comparisons within and across cities are still rare. To address this gap, we adopted a DL-based framework for sustainable roof identification and applied it to eight representative central business districts (CBDs) in two major cities (Guangzhou and Shenzhen, China). High-resolution satellite imagery was used to automatically detect GR and PV roofs, and spatial statistical analyses were conducted to examine their distribution patterns, compositional characteristics, and differences both within and between cities. The results reveal significant variations in the spatial configuration and composition of sustainable roofs across CBDs, reflecting disparities in development intensity, functional structure, and architectural form. This study highlights intra- and inter-city differences in sustainable roof deployment in high-density urban cores and provides empirical evidence to support context-appropriate planning and implementation strategies for sustainable roofs amid rapid urbanization.
How to cite: Liu, H., Wang, M., and Liu, K.: Deep learning–based mapping and spatial patterns of sustainable roofs in high-density urban CBDs: Evidence from Guangzhou and Shenzhen, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15413, https://doi.org/10.5194/egusphere-egu26-15413, 2026.
Cloud cover trends are highly relevant for the energy and health sectors, as clouds affect the radiation balance and thereby influence parameters such as air temperature and UV index. In particular, during heat waves, cloud-induced reductions of nocturnal cooling rates are of considerable interest. For effective climate adaptation and mitigation, cloud cover trends must be assessed at fine spatial scales and with sufficient temporal resolution to distinguish at least between day- and nighttime conditions. Within the Bavarian state-funded EO4CAM (Earth Observation Laboratory for Climate Adaptation and Mitigation) project, which aims to leverage spaceborne Earth observation and model data to support climate change adaptation and mitigation, we derive spatially resolved cloud cover trends over Bavaria from spaceborne observations between 2004 and 2019 for three-hourly time slots at monthly resolution.
The analysis is based on data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation (MSG). We apply a Mann-Kendall trend analysis to the Optimal Cloud Analysis Climate Data Record [1], which provides a homogeneous long-term record of cloud properties. The dataset has a temporal resolution of 15 minutes and a spatial resolution of 6x6 km² over Bavaria.
A generalization of cloud cover trends is precluded by their strong spatial, seasonal, and diurnal dependence. On the one hand, however, cloud fraction typically increases during daytime due to enhanced convective activity. On the other hand, the temporal evolution over the years within a given calendar month is similar across different daytime hours.
As an example, cloud cover over Bavaria at noon in August typically ranges between 60 and 80%, exceeding 80% in the Alpine region. Between 2004 and 2019, trends are predominantly negative across Bavaria, reaching values of up to -1.5 percentage points per year, with the strongest statistical significance observed in northern Bavaria. In contrast, cloud cover trends in the Alpine region remain largely neutral. A more detailed classification shows an increase in the number of days with low (<15%) and medium (15–85%) cloud fractions throughout Bavaria, accompanied by a decrease in days with high (>85%) cloud fraction. These changes are most pronounced in northern Bavaria.
[1] EUMETSAT. Optimal Cloud Analysis Climate Data Record (Release 1): MSG, 0°. 2022. doi: 10.15770/EUM_SEC_CLM_0049. url: https://user.eumetsat.int/catalogue/EO:EUM:DAT:0617/access.
How to cite: Schirmacher, I., Popp, T., Ullmann, T., and Kraus, T.: Satellite-Derived Trends in Cloud Cover over Bavaria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16642, https://doi.org/10.5194/egusphere-egu26-16642, 2026.
Climate is a key determinant of tourism patterns and destination viability, and climate services offer a promising pathway to support climate-resilient tourism planning. This contribution presents a set of co-created climate indicators designed to assess the spatio-temporal climate suitability of key tourist activities in Costa Daurada and Terres de l’Ebre, two major coastal destinations in Catalonia that are highly exposed to climate variability and change. Building on previous participatory workshops with tourism stakeholders, the study selects priority activities – such as beach tourism, hiking, and cultural gastronomy – and links them to relevant climate variables, including temperature, precipitation, wind, significant wave height, and sunshine duration – among others.
Using reanalysis climate data and activity-specific thresholds, the indicators are computed for present climate conditions to characterise favourable, acceptable and unfavourable periods and locations for each activity. The results provide a detailed picture of the region's tourism climate potential, highlighting both current strengths and emerging vulnerabilities related to heat stress and changing rainfall patterns. The co-created indicators translate complex climate information into decision-relevant metrics that can be directly used by destination managers, policymakers and tourism businesses to adjust products, marketing and infrastructure, and to design adaptation pathways for coastal tourism. Beyond the case study, the work illustrates how co-created climate indicators can strengthen climate services for tourism, contributing to the implementation of climate-resilient strategies and to broader sustainability agendas at regional and international levels. The results also aim to contribute to the Catalan strategy of climate change adaptation (ESCACC30).
How to cite: Boqué Ciurana, A. and Aguilar, E.: Co-created climate indicators for assessing the spatio-temporal suitability of key tourist activities in Costa Daurada and Terres de l’Ebre, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16680, https://doi.org/10.5194/egusphere-egu26-16680, 2026.
Extreme weather in Florida can result compound impacts, crop damage, prolonged waterlogging, and inundation, that disrupt farm activities and complicate field scale assessment. Following an event, extension agents and growers typically need information on short timelines related to crop damage assessment to prioritize scouting, report impacts, and support recovery decisions, and flood-prone area information to anticipate where standing water and access constraints will persist and where follow-up interventions should be targeted. However, producing these products from Earth observation (EO) analysis-ready data (ARD) often requires fragmented geospatial tools, intensive preprocessing, and repeated iterations that delay action.
We present GAIA Bot, a conversational AI-based geospatial assistant piloted in Florida with extension agents and growers to convert EO ARD into action-ready information (ARI) for post-event decision support. In the Florida pilot workflow, users can interact with GAIA Bot through natural-language questions (e.g., “Which fields show likely damage since the storm?”; “Where are the flood-prone low areas that may remain saturated?”; “How does my field compare to the same period in prior year?”). GAIA Bot translates each request into an executable sequence that integrates publicly available spaceborne (e.g. Sentinel-2) observations with contextual geospatial layers (e.g., terrain and drainage proxies) and AI classifiers to generate field-scale damage indicators and priority scouting hotspots, flood-prone area maps that inform access and recovery planning, along with concise explanations for stakeholder communication.
Operational testing with end growers and extension agents indicates significant time savings relative to traditional multi tool approaches, enabling faster product generation and more frequent updates as new satellite observations become available. To support trustworthy decisions, we also explore a reasoning mechanism that produces structured evidence trails.
How to cite: Tziolas, N., Golmohammadi, G., and Kritharoula, A.: GeoGPT for action ready flood and disaster risk geo-intelligence in Florida, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17602, https://doi.org/10.5194/egusphere-egu26-17602, 2026.
Data Terra is the French national research infrastructure dedicated to the observation, understanding and monitoring of the Earth system, with the explicit objective of transforming qualified Earth observation data into knowledge, services and indicators supporting scientific research, Earth system digital twins and public decision-making. It federates several long-standing thematic data and service hubs covering atmosphere, ocean, continental surfaces, solid Earth, biodiversity and provides high-resolution Earth observation. Together, these thematic poles curate, qualify and disseminate reference datasets, often based on long, homogeneous time series, essential to address major scientific challenges related to climate change, environmental dynamics and natural hazards.
A core objective of Data Terra is to foster interdisciplinarity through enhanced interoperability across disciplines, data types and scientific communities, a prerequisite for integrated Earth system science. This approach is strongly aligned with international frameworks of Essential Variables (Climate, Ocean, Land and Biodiversity EV), providing a shared scientific backbone for data production, qualification and reuse. The thematic poles structure their datasets, services and indicators around these Essential Variables, ensuring scientific consistency while enabling cross-domain analyses.
Data Terra relies on a federated, interoperable and scalable model, designed to be deployed and reused at different organisational and geographical scales. Its architecture and governance enable interoperability between national thematic poles, as well as integration with European and international initiatives, notably as a potential thematic or national node within the European Open Science Cloud (EOSC). This multi-scale design allows data, services and workflows developed within Data Terra to be exposed, combined and reused in broader research infrastructures without duplication or loss of semantic coherence.
Beyond core scientific use, Data Terra explicitly targets downstream applications such as Earth system digital twins, environmental services and decision-support tools for public policies. To strengthen the connection between scientific production, territorial needs and decision-makers, Data Terra has established regional and thematic coordination mechanisms (ART – Animations Régionales Thématiques). These ARTs act as interfaces between researchers, public authorities, private stakeholders and end-users, supporting the co-construction of indicators, dashboards and operational products adapted to policy and territorial contexts.
To support the full data-to-decision chain, Data Terra implements a coherent set of technical and semantic solutions. Semantic and machine-actionable interoperability is addressed through a pivot metadata model based on DCAT, combined with a shared repository of semantic artefacts, including controlled vocabularies, concept schemes and mappings. This enables automated discovery, cross-domain navigation and integration across platforms and infrastructures.
Technical interoperability relies on widely adopted standards and protocols, including OGC APIs for data discovery and access, S3-compatible object storage and cloud-optimised data formats such as ARCO. Emphasis is placed on the portability and reproducibility of data processing workflows, enabling execution across heterogeneous and federated computing environments.
Finally, Data Terra simplifies user interaction with complex and heterogeneous datasets to maximise scientific and societal impact. This is achieved through integrated resource catalogues linking datasets with example notebooks and documented use cases, advanced data preview and code generation capabilities, the federation of computing resources, and the development of dashboards and indicators.
How to cite: Bodéré, E., Rizzo, A., Ramage, K., and Detoc, J.: Data Terra: a federated research infrastructure transforming Earth system data into knowledge and services for science and public decision-making, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20567, https://doi.org/10.5194/egusphere-egu26-20567, 2026.
The Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard, with a dedicated Data Management System (DMS) Working Group organising data management activities. A core service of SIOS is its data catalogue, which aims to be the entry point to data discovery, visualisation and integration in the Svalbard region. This is only possible with a strong data management organization across partners and harmonisation of information from participating data centers. The central node in the SIOS DMS is harvesting information from these partner repositories through well established and standardized machine readable endpoints. SIOS, as an aggregator of metadata assets, is working on semantic interoperability and metadata enrichment to achieve a consistent and harmonized catalogue that can be used not only by researchers and decision-making bodies, but also integrated in data-driven arctic, polar, european and global initiatives (e.g. SAON Data Portal, WMO GCW, POLARIN, Arctic PASSION, ENVRI, EOSC).
A dedicated effort has been put to establish a list of essential Earth System Science (ESS) variables relevant to determine environmental change in the Arctic, through the SIOS Core Data (SCD) initiative - time series of data with at least a 5 year commitment. SCD are long lasting observing capabilities by SIOS partners.
Through the support of data publication guidelines, brokering activities, FAIR data and vocabularies and consistent semantic relations, SIOS is aiming to continuously improve interoperability within and across relevant domains.
How to cite: Ferrighi, L., Godøy, Ø., Mardsen, L., Brasseur, Z., and Kivits, D.: Data integration and semantic interoperability framework for the Svalbard Integrated Arctic Earth Observing System., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21539, https://doi.org/10.5194/egusphere-egu26-21539, 2026.
The effective development and use of climate services depend on specialists who possess not only
scientific knowledge but also clearly defined, practice-oriented competencies that enable the
transformation of climate data into actionable information for decision-making. In Ukraine,
climate services remain at an early stage of institutional development, and a persistent skills gap
exists between climate information providers and users, particularly in climate-sensitive economic
sectors and public administration.
The Erasmus+ project “Multilevel Local, Nation- and Regionwide Education and Training in
Climate Services, Climate Change Adaptation and Mitigation” (ClimEd; 2020–2026;
http://climed.network) addresses this challenge by implementing a competency-based approach to
climate education across multiple levels of learning. Rather than focusing on isolated training
activities, the project establishes an integrated education pathway that links postgraduate
education, professional development, and public climate literacy.
At the academic level, ClimEd has developed PhD and Master’s programmes in Climate Services,
alongside a Master’s programme in Climate Change Adaptation and Mitigation. These
programmes emphasise competencies related to climate data management, climate model
interpretation, climate product development, sectoral application of climate information, and
climate communication. In parallel, targeted professional development programmes support
decision-makers and practitioners in sectors such as agriculture, healthcare, urban management,
water resources, energy, and construction. Massive open online courses further extend climate
literacy to broader audiences.
Course content and competency profiles are informed by a structured needs assessment involving
297 stakeholders from climate-dependent sectors and 48 climate service providers, ensuring that
identified skills gaps are translated into concrete learning outcomes and assessment criteria.
Teaching and learning approaches prioritise applied learning through project-based, case-based,
inquiry-based, and experiential methods, supported by blended and online delivery formats.
Common quality principles ensure consistency, accessibility, and alignment between
competencies, learning activities, and assessment across institutions
By systematically embedding required competencies into curricula and training programmes at
different qualification levels, ClimEd provides a concrete mechanism for reducing the climate
services skills gap in Ukraine. The project demonstrates how competency-based education can
strengthen human capacity, improve the usability of climate information, and enhance the
integration of climate services into sectoral decision-making, offering a model applicable beyond
the Ukrainian context.
How to cite: Lappalainen, H., Tyuryakov, S., Aguilar, E., Olano Pozo, J. X., Mahura, A., Khomenko, I., Dyman, T., Malovanyy, M., Ovcharuk, V., Talalaiev, K., Tkachenko, T., and Vergeles, Y.: Closing the climate services skills gap in Ukraine through competency-based education , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22942, https://doi.org/10.5194/egusphere-egu26-22942, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot A
EGU26-14570 | Posters virtual | VPS31
How does the growing availability of novel data interact with the uses of data by policymakers in city-regions on their journey to net zero?Mon, 04 May, 14:12–14:15 (CEST) vPoster spot A
The net zero (NZ) agenda is one of the foremost planetary challenges facing urban policymakers - presenting both localised impacts, along with transnational co-operation and governance challenges. Data is fundamental to measuring policy success and failure and taking informed intervention decisions, and global Earth Observation data has enhanced evidence bases in terms of where local actions are needed. In the face of evolving national politics, it is common for city-regions to lead on NZ policy. However, the distribution of multi-level powers and resources fundamentally shapes what urban leaders can do and who they need to work with to respond to the climate emergency. Given this complex policy architecture, progress towards NZ is dependent on the effective use of data. Many intermediate city-regions need support to build capacity and marshal data effectively, and questions about which data sources to deploy at specific contexts can be difficult to resolve. This leads to the possibility for a gap between the sophistication of data which may be able to support policymakers – increasingly available from breakthrough techniques and modelling – and capability, governance and communication issues in subnational policymakers’ ability to act. Starting with the end users of data at city-region level, we explore the need for better understanding between the policy and data/science communities.
How to cite: Allan, G., Oda, T., and Waite, D.: How does the growing availability of novel data interact with the uses of data by policymakers in city-regions on their journey to net zero?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14570, https://doi.org/10.5194/egusphere-egu26-14570, 2026.
EGU26-3515 | ECS | Posters virtual | VPS31
EcoScapes: LLM-Powered Advice for Crafting Sustainable CitiesMon, 04 May, 14:15–14:18 (CEST) vPoster spot A
Climate adaptation is critical for the functionality and quality of life in urban areas under more frequent and severe extreme weather events, such as heatwaves, droughts, and floods. Smaller towns, however, may struggle to adapt because of funding issues, administrative burdens and difficulties using environmental data. This study presents EcoScapes, a decision-support framework to enhance LLM advisory with local Earth observation data. EcoScapes integrates three key components: automated acquisition and preprocessing of Sentinel-2 imagery; Vision Language Models (VLMs) for structured interpretation of satellite-derived representations; and a downstream knowledge-based advisory workflow inspired by prior work.
Given a user-provided town or city name, EcoScapes geocodes the location and retrieves Sentinel-2 imagery for a 5 km bounding box around the urban center. To minimize cloud interference, we use a 1% cloud cover filter, which enables usability but might bias towards drier conditions and miss seasonal water bodies. EcoScapes processes satellite data rendered by the Sentinel-2 API, which includes RGB, water, and moisture views. The system uses a modular analytical pipeline, with an RGB analysis module employing a VLM to describe urban structures, like built-up areas, green spaces, and roads, via focused prompts. This approach reduces hallucinations and ensures more accurate analyses. Separate water and moisture modules analyze the outputs. Water analysis removes small, likely irrelevant features before an RGB-based step connects identified water bodies to their environment and infrastructure. Moisture analysis is used to find heat islands. Finally, a local small language model combines outputs into a single “Climate Report”. This report is subsequently used as context for a ChatClimate-style [1] system that is grounded in the IPCC AR6. This enables a comparison between a baseline advisory system relying on the knowledge base alone and the same system augmented with EcoScapes’ local report.
Since EcoScapes generates varied text outputs, we qualitatively assess its performance using two contrasting case studies: Roßtal, a small rural community of 10,000 people, and Erlangen, a medium-sized city with a population exceeding 100,000. The results indicate EcoScapes can provide useful local context where pre-existing model knowledge is limited. EcoScapes’ report made Roßtal’s adaptation recommendations more relevant and usable, correcting geographically inaccurate suggestions in the baseline. However, EcoScapes’ own inconsistencies and occasional hallucinations remain a limiting factor. The downstream recommendations were affected by errors in interpreting water data in Erlangen, relative to the baseline system, which was more familiar with the city because of its training data. EcoScapes demonstrates Sentinel-2 data’s potential to improve climate advice in smaller towns. Achieving generalization will require improved multimodal reasoning and higher resolution images, while broader evaluation is necessary to determine whether such generalization holds.
More information can be found at our GitHub repository (https://github.com/Photon-GitHub/EcoScapes) and the corresponding paper on arXiv (https://arxiv.org/abs/2512.14373).
References
[1] S. Vaghefi et al., “Chatclimate: Grounding conversational ai in climate science,” Communications Earth & Environment, vol. 4, no. 1, pp. 480, 2023
How to cite: Röhn, M., Gourmelon, N., and Christlein, V.: EcoScapes: LLM-Powered Advice for Crafting Sustainable Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3515, https://doi.org/10.5194/egusphere-egu26-3515, 2026.
