This session invites contributions that explore the applications of Earth system digital twins, co-designed with stakeholders, ranging from extreme event prediction to long-term climate adaptation, from urban liveability to marine and hydrological systems. Building on the successful Digital Twin sessions at EGU in recent years, this session offers a forum for sharing user perspectives that will help shape Europe’s digital twin ecosystem and its global relevance.
Extreme heat is increasingly recognized as one of the most severe climate-related risks affecting urban populations, with disproportionate impacts on public health, energy systems, and vulnerable communities. As heatwaves intensify under climate change, cities require near-real time, high-resolution and actionable information to support early warning systems, preparedness, and long-term adaptation. Addressing this challenge at urban-local scale demands not only methodological innovation, but also robust digital infrastructures capable of delivering consistent and interoperable climate intelligence across regions.
Destination Earth (DestinE), a strategic initiative of the European Union, represents a transformative step in this direction by providing global, high-resolution climate and weather simulations, through Digital Twins of the Earth System. By coupling advanced numerical models, Earth Observation (EO) data, and high-performance computing, DestinE establishes a common backbone for next-generation climate services. However, translating these powerful datasets into locally relevant, operational products for cities remains a critical challenge.
DE_395-Urban Heat Health Forecasting (UHHF) project addresses this gap by demonstrating how DestinE Extremes Digital-Twin outputs can be transformed into urban-scale user-oriented heat-health indicators through the operational use of Machine Learning (ML). The project applies ML-based downscaling techniques to near-surface air temperature (T2m) and relative humidity (RH) forecasts, enhancing spatial resolution from kilometre-scale to approximately 200 m. These downscaled fields are subsequently used to derive human-biometeorological indicators such as the Universal Thermal Climate Index (UTCI) and Thermal Stress Duration (TSD), supporting health-oriented risk assessment.
The UHHF framework integrates DestinE atmospheric drivers with EO-derived and geospatial predictors describing urban form, land cover, vegetation, and topography, including Local Climate Zones. Quality-controlled crowdsourced observations from citizen weather stations are combined with WMO reference data to constrain and validate the ML models, ensuring robustness under both average and extreme conditions. The approach is being implemented across four climatically and socio-environmentally diverse Functional Urban Areas, e.g. Naples, Chicago, Santiago, and Cape Town, enabling a systematic evaluation of models across continents.
By building directly on DestinE and complementary European programmes led by ECMWF, ESA, and Copernicus, drawing on both their data assets and operational services, UHHF aims to illustrate how these can be leveraged to develop affordable, scalable, and reproducible urban-scale climate information and services. The project highlights the strategic importance of climate data platforms in bridging the gap between global simulations and local decision-making, contributing to the development of interoperable urban climate and health services aligned with European and international resilience frameworks.
How to cite: Girão, I., Paixão, J., Castro, M., Miranda, V., Souza Silva, F., Siemen, S., Di Napoli, C., and Oliveira, A. P.: Urban Heat Health Forecasting with Destination Earth: Leveraging Digital Twins and Machine Learning for Scalable Urban Climate Services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19009, https://doi.org/10.5194/egusphere-egu26-19009, 2026.
Urban mobility and air quality are tightly coupled in cities: travel demand, network performance, and the spatial distribution of activities shape transport-related emissions and accessibility, and therefore underpin policy instruments such as low-emission zones, speed regulation, network reconfiguration, and land-use adjustments. Urban transport systems, however, operate within a broader set of constraints that extend beyond traffic and emissions management. Flooding and other water-related disruptions are a recurrent and increasingly relevant challenge for cities, with the potential to severely affect the transport network functionality. These challenges are often addressed through separate analytical tools, reflecting isolated approaches to mobility, environmental quality, and climate resilience topics. This fragmentation limits the ability of decision-support systems to effectively evaluate interventions across interrelated domains, motivating the need for modular and extensible services capable of integrating multiple processes within a single analytical framework.
CityNexus Pro, an Advanced Application Service (AAS) onboarded on Destination Earth, addresses this need through an operational and modular architecture designed to support integrated and cross-cutting scenarios analysis. The service builds on CityNexus, initially developed to support scenario-based assessment of mobility patterns, transport-related emissions, and air quality, and extends this baseline by incorporating an interoperable module for modelling mobility disruptions caused by flooding.
Within CityNexus Pro, urban mobility dynamics are represented by coupling data-driven origin–destination estimation with a traffic simulation engine, generating high-resolution spatio-temporal traffic flows. These flows are translated into emission estimates and linked to air quality models to quantitatively assess pollutant concentrations under alternative urban scenarios. Model assumptions, data sources, and parameterisations are explicitly documented to ensure transparency and reproducibility. Flood modelling outputs derived from hydrodynamic simulations based on the SFINCS model are mapped onto road network elements and incorporated into the mobility simulation chain, enabling dynamic modification of traffic conditions through configurable speed-reduction and road-closure thresholds.
The service, which already supports a range of policy-relevant scenarios—including low-emission zones implementation, speed limit changes, partial or full road inaccessibility, land-use reallocations, and shifts in mobility demand—is further complemented by compound scenario analysis in which flood hazards and mobility-related policy interventions are evaluated jointly. Users can configure flood scenarios by adjusting parameters such as precipitation timing and duration, river and sea discharge, and the presence of flood defences. In addition, CityNexus Pro is designed to integrate forecast products from the DestinE Digital Twin for Weather-Induced Extremes, enabling the impact assessment of extreme rainfall and flooding on mobility patterns and accessibility.
CityNexus Pro is operational in Copenhagen, Seville, Bologna, and Aarhus, and is currently being deployed in Bucharest and Vitoria-Gasteiz. The service demonstrates how modular urban analytics on Destination Earth can be incrementally enhanced to address compound climate risks and support non-siloed, policy-relevant scenario analysis for urban resilience.
How to cite: Feliciotti, A., Lizama, A., Lemma, L., Ruess, A., Marconcini, M., Altenkirch, A., Stark, J., and Fratini, S.: Integrating Flood-Induced Mobility Disruption Modelling into CityNexus as a New Advanced Application Service on Destination Earth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19782, https://doi.org/10.5194/egusphere-egu26-19782, 2026.
Policy makers at local, national, and international levels are increasingly being required to make decisions that mitigate the effects of climate change on society and the economy. Earth Observations (EO) are already an important source of data to support such decisions, but this data represents only one aspect of the broader socio-technical systems that decision makers seek to influence. Policy effectiveness depends not only on environmental conditions, but also on household behaviour, technology adoption decisions, economic constraints, and feedbacks across scales. Capturing these dynamics requires modelling approaches that explicitly represent human decision-making alongside EO-derived inputs. This paper will present the results from the development of a scenario-planning digital twin (SPDT) designed to support decision-making processes related to local energy policies. The new SPDT will demonstrate how EO datasets can be integrated with multilevel agent-based models (MABMs) to enable specific scenarios to be used to support policy decisions.
The use case in this work enables policy makers to (i) model residential heating demand, (ii) test policy levers that might best encourage the uptake of low-carbon heating, and (iii) assess the implications for energy use and fuel poverty. Specifically, the MABM simulates hourly residential energy demand for space heating at the household level, accounting for building characteristics, policy levers, occupancy patterns, retail energy prices, and external ambient temperature. The MABM supports baseline demand estimation at fine spatial granularity (individual households), the assessment of new technologies (such as retrofit measures or heating controls/meters) and energy price variations, and counterfactual analysis (e.g., setpoint shifts, tariff changes, warm/cold snaps). Earth observation data is used to inform medium to long-term climate trends for the use case region. The project results presented in this paper have been developed to serve the city of Newcastle upon Tyne in the UK. We discuss results for Newcastle developed so far, and possible new future scenarios that could be developed using this type of methodology.
How to cite: Wagg, D. J., Malleson, N., Beltran, A., Tipuric, M., and Arribas-Bel, D.: Using Earth Observation Informed Agent-Based Models to build a Scenario Planning Digital Twin for Local Energy Policies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22325, https://doi.org/10.5194/egusphere-egu26-22325, 2026.
The AI4Clouds project develops a deep-learning-based enhanced short-term (up to 12 h) cloud fields for the Destination Earth (DestinE) Weather-Induced Extremes Digital Twin (Extremes DT). By fusing high-resolution Extremes DT simulations with EUMETSAT satellite observations (SEVIRI / FCI), the system learns to correct systematic model biases and provide enhanced cloud-related fields, such as cloud cover, optical depth, and top height fields, which are key variables for renewable-energy and weather applications.
AI4Clouds follows a multi-stage training strategy: it first pre-trains on ERA5 reanalysis data collocated with satellite datasets to capture large-scale dynamics, then fine-tunes on Extremes DT forecasts, also collocated with satellite datasets. It employs stretched-grid Graph Neural Network–Transformer architectures implemented within ECMWF’s Anemoi framework. Probabilistic forecasts are produced via an ensemble approach that quantifies aleatory and epistemic uncertainty. All data retrieval, preprocessing, training, and serving workflows are deployed on the DestinE Data Lake using its HDA, ISLET, and Stack services, ensuring reproducibility and operational integration through MLOps pipelines.
Validation relies on the open-source AQUA framework, extended with cloud-forecast and deep-learning diagnostics (e.g., RMSE, bias, CRPS, spectral metrics). An industrial partner from the solar-energy sector provides user-driven evaluation across several Iberian sites.
By integrating Earth-observation data, high-resolution numerical forecasts, and deep learning within DestinE’s infrastructure—a cloud-native environment—AI4Clouds demonstrates a scalable path toward building actionable applications for weather-sensitive sectors.
How to cite: Iglesias-Suarez, F., García, M., Heredia, I., Perez, A., Portilla, S., Sáinz-Pardo, J., and San-Martin Segura, D.: AI4Clouds: Enhancing Short-Term Cloud Fields in DestinE for the Solar-Energy Sector, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10081, https://doi.org/10.5194/egusphere-egu26-10081, 2026.
Spring-time frost events pose a significant risk for the agricultural sector. Vineyards, apricots and other crops might be damaged by freezing temperatures after vegetation season onset. Frost events occur commonly during cold outbreaks in April or May after prolonged periods of relatively high air temperatures in late-winter or early-spring. The damage can be significantly reduced by suitable measures, provided a reliable forecast is available. As frost intensity is often highly spatially variable, its forecast can benefit from hectometric-scale numerical atmospheric modelling. We present results related to frost events detection and impact modelling within the Destination Earth On-Demand Extremes (DEODE) project. The detection scheme uses the ECMWF ensemble forecasts of 2-m air temperature and cloud cover to identify regions of potential high-impact frost events. The Global Digital Twin 2-m air temperature sums since the start of the year are used to delimitate areas where vegetation is not active yet. These areas are masked from the risk assessment. Finally, the frost damage risk is expressed in 0–5 scale and may serve as guidance for hectometric-scale simulation domains. After a hectometric simulation is completed, the output is ingested into a downstream frost impact model. The impact model estimates current development phase of several crops and corresponding temperature thresholds for frost damage. These thresholds are compared with hectometric-scale forecasted temperatures to obtain the magnitude of excess of critical temperature for each crop. The impact model has been evaluated during several pilot frost events in the Czech Republic and Spain.
How to cite: Matějka, M., Hájková, L., Možný, M., Musilová, A., and Vlach, V.: Frost event detection and impact modelling within the Destination Earth On-Demand Extremes framework , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13552, https://doi.org/10.5194/egusphere-egu26-13552, 2026.
The GET-it project (Geohazards Early Digital Twin Component) is devoted to build a Digital Twin for geohazards in the framework of the ESA Early Digital Twin Component (DTC) programme. The Geohazard Digital Twin is transitioning from a demonstration prototype to a shareable, pre-operational Earth Observation (EO)-based service platform. This transition has been initiated through the integration of the GET-it scenario modules within the Geohazards TEP (https://geohazards-tep.eu), a long-standing operational platform developed under ESA, and will continue through their future integration and federation with the Destination Earth (DestinE) framework. The project explores how EO data can effectively drive DTCs for volcanic and seismic geohazards within the Destination Earth framework.
The Geohazard DTC developed in GET-it is designed as a modular and customizable environment, capable of integrating multi-sensor EO data, primarily from the Copernicus programme, with established physical models and EO-driven analysis tools. The adoption of standardized input and output formats ensures interoperability and facilitates the uptake of DTC products by diverse user communities with different operational needs. These characteristics enable repeatable, timely EO-driven simulations and facilitate the integration of the Geohazard DTC into downstream pre-operational workflows.
The current prototype includes several scenario modules operating at increasing levels of EO data exploitation: GEOMOD, for modelling EO-derived geodetic signals; FALL3D, for volcanic ash and SO₂ dispersion constrained by EO observations; GPUFLOW, for lava flow modelling based on EO-derived effusion rates and topography; and DAMSAT, for EO-based change and damage detection. Together, these modules act as building blocks for pre-operational services, empowering stakeholders to explore realistic emergency scenarios and assess potential mitigation and adaptation strategies.
A central aspect of GET-it is the systematic integration of stakeholder requirements into the DTC design. A structured engagement process, based on questionnaires and direct interactions, involved a broad range of public and private stakeholders, including civil protection authorities, aviation and transport stakeholders, infrastructure managers, insurance companies, energy providers, and decision-makers. All the scenario models were considered relevant, with FALL3D emerging as the most requested service. The collected feedback directly informed the definition of the scenario modules and the organization of a dedicated Demonstration Day, focused on the validation of EO-driven what-if scenarios.
The Geohazard DTC has been demonstrated through representative multi-hazard use cases, including the 2018 Mount Etna eruption, the 2021 La Palma eruption, and the 2016 Central Italy earthquake sequence. Based on these activities, a medium- to long-term roadmap is being defined, focusing on enhanced EO-driven simulations, advanced decision-support tools, interoperable visualization interfaces, scalable services, and extension to additional geohazards, in alignment with other DTC initiatives and community-building actions. This roadmap aims to consolidate the Geohazard DTC as a sustainable pre-operational platform, ready for future operational uptake.
How to cite: Ganci, G. and Stramondo, S. and the GET-IT team: Advancing the EO-based Geohazard Digital Twin from Prototype to Pre-operational Platform, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17698, https://doi.org/10.5194/egusphere-egu26-17698, 2026.
Desert locusts are known as the world’s most destructive migratory pest. A single swarm can count 80 million locusts, traveling up to 150 km daily and consuming the same amount of food as 35.000 people per day. They are cause of major mid-to-long-term impacts on the economy, quality of life and the environment. Climate change is amplifying the occurrence of such pests: the increase of extreme events such as cyclones creates ideal conditions for locust breeding.
The Desert Locust Monitoring Service (DLMS) is the new system for preventing upsurges of desert locusts across Eastern Africa, Southwest Asia and northwest of India. It leverages the power of satellite observations, model outputs, in-situ measurements and advanced AI techniques to monitor the locust threat, help mitigate crop damage, and safeguard essential food supplies.
Onboarded on the Destination Earth (DestinE) platform, the service consists of two layers: the Early-Stage Locust Appearance and the Locust Swarm Migration. The first layer relies on a customized Maxent model, a statistical approach widely used in species distribution modeling (SDM): it allows identifying areas with favorable environmental conditions for desert locust breeding. There are two available modes: the Safe Mode makes use of 50 days of climate data from ERA5-Land (soil water content, precipitation, and temperature) and normalized difference vegetation index (NDVI) from Sentinel-3 to generate daily probability forecasts; the Experimental Mode extends forecasts to two days thanks to the advanced projections of DestinE’s Weather Extremes Digital Twin. The variables used are: skin temperature, total precipitation and runoff.
The second layer forecasts adult locust swarm migration under biological and climatic conditions, accounting for their rapid and unpredictable long-distance movements. It takes as primary input the Early-Stage layer output, which provides initial location predictions under specific environmental conditions. Environmental variables, including the Leaf Area Index (LAI) from Copernicus, as well as wind velocity components and temperature from DestinE’s Climate Change Adaptation Digital Twin, inform the model of conditions that may trigger migration events. Swarm behavior is then represented using a stochastic model, which simulates an environment-biased random movement on a 2D lattice, generating batches of diverse potential scenarios. In this framework, locusts move based on environmental cues, including climate conditions and the availability of resources, such as vegetation. Finally, the model performs a statistical analysis across all generated scenarios to produce output maps estimating future locations of adult locusts and the size of their swarms.
Both Maxent and stochastic models were trained using a presence-only dataset provided by FAO’s Locust Watch. Prediction results have been validated with FAO data on desert locust activity and independent data provided by the International Center of Insect Physiology and Ecology (ICIPE).
Enabled by Destination Earth’s cutting-edge modeling and data infrastructure, the DLMS offers high-quality insights to anticipate risks, mitigate impacts and support the protection of crops, communities, and ecosystems in the most affected regions. Forecasting maps are available to all DestinE registered users, with some features reserved to users with upgraded access.
How to cite: Outmani, S., Grassi, A., Azami, W., Bellotto, K., and Houël, M.: The Desert Locust Monitoring Service: a new Destination Earth service for Environmental Pest prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22287, https://doi.org/10.5194/egusphere-egu26-22287, 2026.
Operating in the polar regions is growing increasingly important due to expanding research needs, emerging economic opportunities and efforts to protect national sovereignty.
Ships operating in the Arctic and Antarctic face heightened risks and more severe consequences in the event of an accident due to sea ice, icebergs, harsh sea states, low visibility and extreme temperatures. These hazards are compounded by sparse infrastructures and remoteness, with immediate assistances not readily available during emergencies.
While climate change is quickly reducing the amount of sea ice, this does not necessarily translate to a reduction in risk in the short-term. On the contrary, uncertainly due to changing sea ice conditions, and an increase in icebergs due to melting glaciers, can increase risk.
The operational sea ice community provides information to lessen risk to life, property and the environment and to improve operational efficiency. That information is used by ships to avoid or navigate through sea ice, by ship operators in planning polar voyages, and by policy makers to assess the impact of climate change on future decisions regarding polar operations. Beyond safety, these services also increase operational efficiency by enabling optimized routing, reduced fuel consumption, and shorter transit times.
The information provided by the operational sea ice community comes from in-situ measurements, satellite earth observation data, and sophisticated models of the atmosphere, oceans, sea ice, and icebergs. These data streams have historically been assembled and interpreted by highly trained human analysts. Given the rapid increase in the amount of data that needs to be analyzed and the constraints on workforces due to government fiscal reductions, their work is increasingly being assisted by artificial intelligence. Furthermore, because sea ice is highly dynamic and can change within a matter of hours, automation and AI are indispensable for providing real-time and forecast information.
The operational sea ice workflows have many of the attributes of an Earth System Digital Twin:
- Utilization of detailed digital models of relevant earth systems (atmosphere, ocean, sea ice),
- Continuous incorporation of near-real-time data from in-situ sensors and earth observation satellites,
- Use of artificial intelligence and machine learning,
- Generation of predictive models and forecasts, and
- Provision of advance analytics and decision support tools to allow end-users to optimize their choices.
In fact, much of the information both used and generated by the operational sea ice community is available in Destination Earth, the initiative of the European Commission to develop a digital twin of the Earth.
This presentation will examine the provision of operational sea ice information as an example of the application of digital twins to provide actionable insights on climate adaptation and disaster risk reduction. It will present current capabilities, AI-enabled workflows, and lessons learned from operational implementation, with a focus on supporting safe and sustainable polar shipping.
How to cite: Arthurs, D., Rabenstein, L., Dolezalova, T., Puestow, T., Rasmussen, T., and Korosov, A.: Operational Sea Ice Information as a Digital Twin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14321, https://doi.org/10.5194/egusphere-egu26-14321, 2026.
The Global Fish Tracking System (GFTS) and Pangeo-Fish integrate biologging data with high-resolution environmental data in a digital-twin framework to address key challenges in marine conservation and fisheries management. Linking fish movement models with climate projections from Europe’s Destination Earth (DestinE) Climate Change Adaptation digital twin yields an evidence-based tool for decision support in habitat conservation and fisheries management under climate change. The implementation is built on the open-source Pangeo ecosystem and deployed on the DestinE platform.
Pangeo-Fish is an open-source package that ingests multiple biologging data types—including archival tags, pop-up satellite archival tags (PSATs), and acoustic telemetry detections. It processes time series observed by fish (e.g., depth, temperature, and light) together with geolocation constraints derived from external sources such as acoustic receiver networks and tag-based positioning. These heterogeneous observations are harmonised in a cloud-native workflow to support scalable track reconstruction and downstream habitat-relevant products.
Tracks are reconstructed using a Hidden Markov Model (HMM) geolocation approach that combines tag-recorded time series (e.g., depth, temperature, and light) with external geolocation constraints (e.g., acoustic detections), together with priors such as bathymetry and release/recapture information. Processing leverages cloud-native tools (Jupyter, Dask, Xarray) and chunked cloud-optimised storage (Zarr) for scalable analysis. A key design choice is the use of HEALPix as the base spatial grid and indexing scheme from ingestion to visualisation, enabling efficient path-likelihood evaluation on an equal-area, iso-latitude grid while avoiding distortive resampling. Environmental reference fields are primarily sourced from the Copernicus Marine Service, but the workflow can also ingest user-defined datasets. In addition, in situ observations (e.g., Argo float temperature) can be incorporated to represent uncertainty in the ocean physics fields used by the geolocation model and better account for model–data discrepancies.
The Pangeo-Fish workflow yields most-probable tracks and daily presence-probability maps, while GFTS supports aggregation of distributions for management-relevant analyses. GFTS intersects these outputs with climate projections from the DestinE Climate Change Adaptation Digital Twin to assess potential habitat exposure under climate change. GFTS has so far been demonstrated for Atlantic applications, while Pangeo-Fish has been extended to the Pacific Ocean, enabled by portable cloud-native processing and the availability of cloud-accessible datasets. As an early DestinE platform use case, this work illustrates how Earth system digital twins can be operationalised for reproducible, scalable biologging analytics to inform marine conservation and sustainable fisheries management.
How to cite: Odaka, T. E., Cap, E., Mazouni, Q., Hue, C., Delouis, J.-M., Woillez, M., Fouilloux, A., Ragan-Kelley, B., and Wiesmann, D.: Pangeo-Fish and the Global Fish Tracking System: Scaling Biologging Analytics with Earth System Digital Twins for evidence-based policy support, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19040, https://doi.org/10.5194/egusphere-egu26-19040, 2026.
Mountain glaciers are critical elements of the Earth’s hydrological and climate systems. The rapid changes in glaciers due to climate change hinder our ability to monitor and address associated risks effectively. To address these challenges, we present the Digital Twin Component for Glaciers (DTC Glaciers), part of ESA’s Digital Twin Earth (DTE) programme. In this presentation, we will demonstrate the early prototype of the DTC Glaciers system, developed through close co-design with our stakeholders in the hydropower and water sectors. The demonstration will highlight current capabilities, including regional glacier mass-balance assessment, runoff estimation, and user-informed scenarios. We will also share key lessons learned from Phase 1, focussing on data assimilation of heterogeneous datasets, the practicalities of building adaptive architectures, and the challenges of meeting diverse user needs within a unified framework. Despite these challenges, DTC Glaciers offers a well defined test case to assess the transformative potential of digital twins in climate risk assessments.
How to cite: Maussion, F., Bizon, J., Gampierakis, N., Gourmelen, N., Jakob, L., Michael, C., Nagler, T., Nussbaumer, S., Schmitt, P., Schwaizer, G., and Zemp, M.: Lessons learned from the pilot phase of the Digital Twin Component for Glaciers project (DTC Glaciers), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8395, https://doi.org/10.5194/egusphere-egu26-8395, 2026.
Destination Earth has given rise to a new generation of global climate models capable of resolving kilometre-scale processes. These storm-resolving models are deployed operationally to produce multidecadal climate simulations within Climate Adaptation Digital Twin. OBSALL conceptualise and implemente an Earth observation-based system for monitoring the quality of the climate simulations. Technically, it is a one-pass algorithm in which observation models operate online on the state vector of the simulation model and generate a full-resolution trace in the observation space. This enables real-time monitoring of the simulation and posterior evaluation right after its completion, thus enhancing the overall resilience of the Climate DT workflow.
OBSALL has the projection and monitoring pipeline implemented for three observation modalities: surface variables with SYNOP weather stations, vertical profiles with TEMP radiosoundings, and satellite products with AMSU-A. OBSALL consumes climate model states in the form of stream of Generic State Vectors, projects the model states in to the observation space spanned by activated observation modalities, stores the projected states in Observation DataBase files and intermittently produces a suite of monitoring plots comparing the climate simulation to climatology of observations. All modalities share the same general workflow structure facilitating implementation of new modalities or features. Finally, OBSALL has been designed to allow deployment on a range of environments from personal computer to HPC infrastructures.
This functionality will be part of the EU’s Climate Adaptation Information Service and it is intended to benefit Users of the Service, although it also holds strong research appeal. From the User perspective, presenting the simulation in observation space is advantageous. Earth observations, especially the in-situ components, are intuitive and easy to interpret. Therefore, observation-space projections provide a concrete handle on adaptation information and enhance the User relevance of the DestinE Climate DT. These projections tend to lower the threshold for users to engage with the Information Service. Therefore, we see future development of user-oriented tools using the projection data as input as a promising strategy to attract new users. Extending the projection to other user-relevant observation types, such as long-term wind mast measurements, would be beneficial.
Here, we showcase the current capabilities of the climate model projection and monitoring software OBSALL from the view-points of runtime simulation monitoring through different observation types, and observation-based posterior validation, which offers a versatile way to validate process-level features in storm-resolving climate models.
How to cite: Bouvier, C., Tuppi, L., Räisänen, J., and Järvinen, H.: OBSALL: DestinE climate simulations in observation space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5404, https://doi.org/10.5194/egusphere-egu26-5404, 2026.
Developing operational Digital Twins for large-scale agro-hydrological systems presents significant challenges regarding data heterogeneity, computational efficiency, and the integration of Earth Observation (EO) with process-based modeling. This study presents the development and initial testing of DT-Agro, a spatially explicit Digital Twin of the Greek agro-hydro-system, designed to support sustainable water management and agricultural planning at the national scale.
DT-Agro integrates a high-resolution spatial database, a hybrid meteorological forcing scheme, and a distributed agro-hydrological model (AgroHydroLogos) recoded in C++ and Python for enhanced performance. A key innovation in the process simulation is the development of a novel, impervious-aware SCS-CN formulation. Unlike traditional lumped approaches, this method explicitly decomposes each grid cell into pervious and impervious fractions using high-resolution Copernicus Imperviousness Density data. This allows for a physically consistent representation of runoff generation in mixed landscapes, capturing the hydraulic response of small impervious patches that are often lost in standard gridded models.
Furthermore, to address the chronic fragmentation of ground-based monitoring networks, the system introduces a "virtual station" meteorological framework. Recognizing that raw global reanalysis products (e.g., AgERA5) often exhibit significant biases in Greece’s complex terrain, we developed a hybrid correction workflow. AgERA5 time series are sampled at the locations of historical stations and bias-corrected using station-specific regressions. This creates a network of "virtual stations" that provide continuous, homogenized daily records, filling temporal gaps while preserving local climatological characteristics. These records drive a dynamic spatial interpolation scheme that accounts for temperature and precipitation gradients, ensuring physically consistent meteorological forcing across the national domain.
We present results from the initial national-scale application of the system. The testing phase focused on quantifying irrigation water abstractions and their spatial-temporal drivers. Initial simulations estimate the long-term average national irrigation abstraction at approximately 6,600 hm³/year, with significant inter-annual variability (6,000–7,800 hm³) driven by climatic conditions. Validation against theoretical net irrigation requirements for major crops (maize, cotton, alfalfa) yielded consistent depths (380–420 mm), confirming the biophysical realism of the model core. These results demonstrate DT-Agro’s capability to provide a robust, evolving representation of the Greek agro-hydro-system for climate adaptation planning.
How to cite: Palli Gravani, S., Soulis, K., Soulis, X., and Kalivas, D.: DT-Agro, a digital twin of the Greek AgroHydroSystem, development and testing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1994, https://doi.org/10.5194/egusphere-egu26-1994, 2026.
This contribution presents the system architecture, data pipelines, and modelling logic at a conceptual level for a Digital Twin prototype named JÄÄTwin. The aim of JÄÄTwin is to integrate near-real-time observations from multiple heterogeneous data sources, with numerical models, and computational infrastructure into a live “river ice condition” digital twin. While large-scale initiatives focus on continental and global domains, digital twin implementations at the river scale remain limited, specifically for cryo-hydrological systems. JÄÄTwin integrates in-situ monitoring data, remote sensing products, and meteorological forecast inputs through a modular backend that supports data ingestion, preprocessing, model orchestration, and visualization. The emphasis of this contribution is on system architecture, data integration logic, and operational workflow design.
The Kiiminkijoki River in northern Finland is used as a pilot to demonstrate how a river-scale digital twin can be implemented using existing monitoring infrastructure. The presentation discusses design choices, integration challenges, and transferability considerations relevant to future Earth system digital twin developments and to emerging European digital twin initiatives.
How to cite: Jalali Shahrood, A.: River-Scale Digital Twin for Cryo-Hydrological Systems: Architecture and Integration Principles from the JÄÄTwin Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6018, https://doi.org/10.5194/egusphere-egu26-6018, 2026.
As Destination Earth (DestinE) matures, the capability to simulate not just natural phenomena but also the "related human activities" becomes critical for delivering actionable insights on sustainable development. This work presents TRANSITION, an operational Digital Twin application designed to model the complex socio-environmental dynamics of land-use change, renewable energy integration, and agricultural sustainability within the DestinE ecosystem.
While traditional Earth system digital twins excel at forecasting physical variables (e.g., crop yields or solar irradiance), they often lack the behavioral fidelity to predict how human actors will respond to these changes. TRANSITION bridges this gap by integrating Earth Observation (EO) data with a Multi-Level Agent-Based Modelling (ML-ABM) system driven by Reinforcement Learning (RL). In this framework, autonomous agents—representing farmers, landowners, and policymakers—make spatially explicit decisions based on environmental suitability, economic incentives, and social factors (PECS framework).
We demonstrate the application of this digital twin through three core stakeholder-co-designed use cases:
- Climate Change Adaptation Strategies: Simulating long-term land-use shifts under various CMIP climate scenarios to identify regions at risk of agricultural abandonment or suitable for crop diversification.
- Green Credit & Policy Simulation: allowing policymakers to "stress-test" interventions—such as subsidies for photovoltaics (PV) or green credits—in a risk-free virtual environment to assess adoption rates and potential conflicts between food and energy production.
- Renewable Energy Optimization: Utilizing Sentinel-derived analytics and high-resolution Digital Elevation Models (DEMs) to identify optimal deployment zones for renewable infrastructure while accounting for socio-economic acceptance.
To ensure these insights are scalable and actionable, the application is architected to eventually run on Destine’s Platform, with the potential to utilize High-Performance Computing (HPC) for heavy agent training and the DestinE Data Lake for seamless access to Sentinel and ERA5 datasets. By coupling high-precision physical modeling with realistic human behavior, TRANSITION offers a robust decision-support tool for evidence-based policymaking, directly contributing to the European Green Deal’s vision of a resilient and adaptive society.
How to cite: Paraskevas, C., Gousios, G., Mamouka, T., Vourlioti, P., Kasampalis, D., Kotsopoulos, S., and Vitolo, C.: Simulating the human dimension in Destination Earth. An EO-Informed digital twin application for climate-adaptive policy planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6871, https://doi.org/10.5194/egusphere-egu26-6871, 2026.
Climate resilience is a defining challenge of the 21st century, yet public health authorities continue to face difficulties in operationalising state-of-the-art geospatial and environmental science. In Portugal, as in Europe more broadly, extreme temperatures have already increased in frequency and severity, contributing to substantial excess mortality and morbidity. These impacts are often amplified by the simultaneous degradation of air quality. However, evidence has largely been event-specific, fragmented across case studies of individual heatwaves, cold waves, or air-quality episodes, limiting our ability to implement early-warning systems. The ESA-funded AIR4health project, developed under the Early Digital Twin Components initiative, addresses these gaps by designing innovative algorithms to predict human mortality and morbidity during compound extreme events. The project develops two Machine Learning (ML)–based AIR4health Risk Algorithms focusing on (1) Heat & Ozone and (2) Cold & Nitrogen Dioxide, using a two-decades-long, high-resolution healthcare database for mainland Portugal. These indicators will integrate EO data, in-situ air-quality records from the EEA, and CAMS/C3S model outputs. Satellite and model data are dynamically downscaled using approaches previously demonstrated for air-temperature modelling in Lisbon, enabling daily, spatially detailed (municipal-level) time series of compound extreme events. AIR4health advances beyond current country-level systems by implementing fully spatiotemporal exposure–response modelling. Its dynamic and continuous framework will deliver a prototype DTC capable of providing fine-scale early warning for combined climate and air-quality extremes. By benchmarking results against European-level datasets, AIR4health will support scalable pathways towards relevant practices in planetary health and climate-preparedness, while contributing to the broader European Digital Twin ecosystem.
How to cite: Villa de Brito, A., Oliveira, A., Marques, B., Fonteles, C., Pereira, É., Silva, F., Girão, I., Figueiredo, L., Lima, M., Cunha, R., Oliveira, M., Nogueira, P., Santos, B., Trancoso, R., and Teresa, V.: Climate and Environmental Digital Twins for Human Health: Leveraging Earth Observation for Compound Climate and Air Quality Extremes Early Warning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13491, https://doi.org/10.5194/egusphere-egu26-13491, 2026.
Meteorological imagery occupies a special role in Earth observation: its high temporal frequency and broad spectral coverage are indispensable for weather forecasting and climate modelling, while its spatial resolution remains limited. Technological advances, however, are driving a trend toward higher spatial detail and open new application domains beyond traditional meteorology.
The Flexible Combined Imager (FCI) aboard Meteosat-12 represents the latest generation of geostationary weather sensors and images the entire Earth disk at 10-minute intervals. Its 16 spectral channels span the visible to longwave infrared and offer native spatial resolutions ranging from 2000 m down to 500 m — a configuration well suited to super-resolution techniques.
The AIDE project develops a methodology to increase the spatial resolution of all FCI channels to up to 500 m while preserving the radiometric integrity of the data. The approach employs a purpose-built deep-learning model that is augmented with an estimate of its own predictive uncertainty, thereby enabling safe downstream use of the enhanced products in demanding applications and quantitative analyses. The method is implemented as a demonstrator within the Destination Earth (DestinE) Data Lake, demonstrating that appropriately designed machine-learning approaches can be deployed reliably in critical operational contexts.
This contribution presents the concept and development of the method, summarizes promising project results, and discusses the limitations and potential of super-resolution approaches in Earth observation.
How to cite: Fessel, A. and Krummrich, D.: AIDE: Trusted Deep-Learning Super-Resolution for MTG FCI within Destination Earth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18330, https://doi.org/10.5194/egusphere-egu26-18330, 2026.
The satellite-based monitoring solution operated by GEO4A B.V. and powered by GeoVille GmbH is commercially called HARVIC. This abbreviation stands for “Harvest in control”. HARVIC offers comprehensive in-season crop growth monitoring and evaluation at the field level, tailored specifically for potato crops in central Europe, with the potential for global expansion. This service provides a detailed, continuous assessment of potato growth dynamics throughout the growing season, using high-resolution satellite (Sentinel-1 & Sentinel-2) and meteorological data as input to the crop growth module.
The main benefit of the HARVIC service is an EO data-supported transition of stakeholders in the potato industry towards digitalisation. Critical decisions can be made based on objective and timely data. HARVIC therefore identifies and evaluates critical growth stages, including emergence, vegetative growth, maturity, and senescence, ensuring that users receive timely updates and insights into potatoes' unique development cycle. The service also provides yield forecasting and quality information for each growing stage to improve logistics and reduce costs, as well as unnecessary greenhouse gas emissions.
The HARVIC service has been selected as a high-priority agricultural service for onboarding and implementation within the European Commission’s Destination Earth (DestinE) initiative. The basic service is published, and registered users of the DestinE community are eligible to take advantage of this unique solution for the potato industry. Furthermore, the consortium of GeoVille GmbH and GEO4A B.V. is about to include monitoring services to track and trace regenerative agricultural practices, such as cover cropping and tillage. In addition, the latest climate data (ECMWF) will be used to enhance HARVIC, potentially delineating future growing regions for potato cultivation in the coming decades.
How to cite: Pichler, J., Kolitzus, D., and Santbergen, P.: Satellite-based monitoring solution for the potato industry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22126, https://doi.org/10.5194/egusphere-egu26-22126, 2026.
The UrbanSquare service within Destination Earth aims to deliver an operational digital twin of cities, enabling urban planners to explore and assess environmental processes and intervention scenarios. One key component of this digital twin is the representation of the urban heat island (UHI) effect. UrbanSquare relies on land surface temperature (LST) observations derived from thermal satellite imagery, primarily Landsat data resampled and distributed at 30 m spatial resolution.
However, actionable urban digital twins require both finer spatial detail and the ability to simulate “what-if” scenarios driven by land-cover change. In particular, UHI mitigation planning calls for high-resolution thermal information (well below 30 m) and dynamic coupling between land-cover configurations and surface temperature responses.
We present a three-stage framework for generating scenario-ready LST maps at 5-meter resolution.
In Stage 1, a Random Forest model upscales Landsat LST from 30 m to 5 m using Sentinel-2 spectral bands and indices (B2, B3, B4, B8, B11, B12, NDVI, albedo), solar geometry variables, and ERA5 meteorological predictors. Sentinel-2 data are harmonized to 30 m for model training, then applied at super-resolution (5 m) for inference. Cross-validation assesses predictive performance in the absence of in situ measurements.
Stage 2 applies pixel-wise linear regression between the super-resolved LST time series and synchronous ERA5 air temperature across a summer period. This normalization removes temporal and meteorological variability and enables LST generation for user-defined air-temperature scenarios, ensuring consistent thermal comparisons.
Stage 3 constructs a lookup table of thermal signatures for each land cover class. When users modify land cover, pixels are reassigned the corresponding thermal signature. A diffusion process accounts for lateral heat dispersion, producing delta-temperature maps, uncertainty layers, and decomposed contributions from different factors (vegetation, albedo increase, etc.).
Soon to be integrated into the UrbanSquare digital twin, this framework enables exploration, comparison, and quantification of UHI mitigation strategies, supporting evidence-based urban planning and climate adaptation decisions.
How to cite: Poupard, H., Fernandez, F., Gonzalez Fradejas, G., and Castel, F.: High-Resolution Thermal Mapping and Simulation Scenarios for Land Cover Intervention Planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22710, https://doi.org/10.5194/egusphere-egu26-22710, 2026.
CALIFE (Certification of LIfestyle and Environment) is an operational service designed to make Earth observation–derived environmental information directly accessible to citizens. The service delivers hyper-local “quality of life” reports for any address, synthesising complex Earth observation (EO), in-situ and model data into a clear, visual and actionable format. CALIFE indicators cover key dimensions of everyday living conditions, including air quality, climate and weather comfort, water and vegetation resources, biodiversity and access to green spaces, mobility and accessibility, and exposure to selected climate hazards. Results are presented through intuitive scores, maps and short recommendations, allowing users to understand how their local environment influences well-being.
A core ambition of CALIFE is to address the general public rather than professional or expert users. The service targets non-specialists with no background in EO, climate science or geospatial data. CALIFE explores how highly technical datasets—such as Copernicus services and Destination Earth digital twins—can be transformed into information that is meaningful at the scale of daily life. By lowering technical barriers and focusing on usability, CALIFE represents an attempt to put Earth observation “into the hands of citizens”, fostering environmental awareness, transparency and informed decision-making at neighbourhood level.
Beyond technical and scientific challenges, CALIFE also serves as a laboratory for experimenting with sustainable business models for citizen-oriented EO services. The long-term viability of such services remains a key open question. CALIFE initially explored a micro-payment model for individual users, but has since evolved towards a hybrid approach: free access for the general public with controlled usage (limited number of reports per user), combined with paid bulk report generation for public authorities and private actors requiring large-scale analyses. This contribution will discuss both the service design choices and the lessons learned regarding sustainability, scalability and citizen engagement when deploying EO-based services for non-expert audiences.
How to cite: Castel, F., Lavigne, C., Semlal, R., Cauneau, M., and Pialot, L.: CALIFE, a service to discover the quality of life in your neighbourhood, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22715, https://doi.org/10.5194/egusphere-egu26-22715, 2026.
Destination Earth (DestinE) is Europe’s flagship initiative for developing high-precision digital twins of the Earth system, enabling the simulation, monitoring, and prediction of natural phenomena and human activities. Coordinated by ECMWF, ESA, and EUMETSAT, DestinE brings together advanced Earth system models, comprehensive Earth observation data, Artificial Intelligence, and Europe’s leading supercomputing infrastructure to deliver actionable, decision-ready information for a wide range of users. Its overarching goal is to support climate adaptation, disaster risk reduction, and sustainable development by translating complex scientific outputs into practical tools.
DestinE is built around modular Digital Twin components representing the atmosphere, oceans, land, and human activities. These components provide high-resolution simulations, near-real-time monitoring, and predictive analytics that can be combined and tailored to specific user needs. AI-driven methods enhance forecast skill, detect emerging risks, and reveal cascading impacts across environmental and socio-economic systems, enabling users to better anticipate and manage complex challenges.
A core pillar of the DestinE Platform is its onboarding process, designed to ensure accessibility, usability, and long-term engagement. Onboarding supports users from first contact through to operational use, offering guided access to the platform, clear documentation, asynchronous support channels and videos to provide a flexible introduction to the onboarding integration process. User needs and levels of expertise are assessed early in the process, allowing stakeholders to be directed toward the most relevant Digital Twin components, data services, and interfaces. This structured approach enables users to progressively build capacity, moving from exploration and testing to confident use of DestinE services in real-world decision-making contexts.
The DestinE Platform provides a suite of integrated, user-oriented services, including:
- Real-time environmental monitoring, delivering continuous updates to support situational awareness and rapid hazard assessment.
- Scenario-based simulation services, allowing users to explore short- and long-term developments such as extreme weather events, floods, climate adaptation pathways, or the impacts of human activities.
- Predictive analytics and early warning tools, using AI to anticipate risks and support timely, informed responses.
- Interactive decision-support interfaces, enabling users to explore data, customize simulations, and test policy or management options in a virtual environment.
- Co-design and customisation services, closely linked to onboarding, which allow users to adapt DestinE capabilities to regional, sectoral, or organizational contexts, supported by ongoing expert guidance.
DestinE builds on and connects with complementary European initiatives, including ESA’s Digital Twin Earth programme, Horizon Europe projects, and national efforts, ensuring scientific robustness and operational relevance.
This abstract invites contributions that showcase user experiences with DestinE, including onboarding pathways, co-design approaches, and practical applications such as emergency planning, urban resilience, or marine and hydrological management. Emphasis is placed on how user engagement and onboarding enable the effective translation of advanced Earth system science into actionable insights.
By focusing on users, services, and onboarding, DestinE demonstrates how digital twins can empower stakeholders, support evidence-based decisions, and strengthen societal resilience in the face of environmental and climatic challenges.
How to cite: Lo Iacono, M., Tona, C., Cortese, M., and Scarda, B.: DestinE Platform: Transforming Earth System Science into Actionable Services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17401, https://doi.org/10.5194/egusphere-egu26-17401, 2026.
