Summary
The Climate–Health Adaptation through New Generation Earth Observations (CHANGE) is a three-year that uses Earth Observation (EO) data to better understand and address the health impacts of climate change.
The project integrates multi-source EO datasets with health information, to support research on how extreme events - such as floods, droughts, heatwaves, and ENSO-driven anomalies -affect vulnerable populations and health systems.
CHANGE contributes to the objectives of the Paris Agreement and helps close knowledge gaps identified by the IPCC, the Lancet Countdown, and WHO on climate–health interactions. Through six regional use cases across different continents, the project is delivering a Climate and Health Adaptation Roadmap, providing actionable guidance to strengthen resilience and inform evidence-based adaptation policies.
Project background
Climate change is a critical threat to global health, affecting human well-being, health systems, and population stability. International institutions - including WHO, IPCC, UNFCCC, and the G20 Health Working Group - emphasise that climate-related hazards are already driving premature mortality, disease outbreaks, food insecurity, mental health impacts, displacement, and disruption of essential services.
Policymakers urgently require spatially resolved, scientifically robust information to support adaptation planning, monitor risks, and strengthen climate-resilient health systems.
A significant policy challenge lies in supporting the UNFCCC Global Goal on Adaptation (GGA), particularly after the UAE Framework for Global Climate Resilience established health as a key thematic target. Nations need actionable, evidence-based guidance to assess and reduce vulnerability to climate hazards, yet the data and analytical frameworks to inform these decisions remain incomplete.
This project therefore seeks to close major knowledge gaps identified by the IPCC, WHO, the Lancet Countdown, and the European Commission regarding the climate–health nexus.
Scientifically, the project is responding to the limited integration of Earth Observation (EO) with health and socio-demographic data. While satellite-derived Essential Climate Variables (ECVs) developed under the ESA Climate Change Initiative provide long-term, global climate information, their potential for assessing climate-related health risks is underexploited. There is a need for rigorous methods to link climate hazards - especially heatwaves, floods, and droughts - with health outcomes and vulnerability dynamics.
Risk assessment is further constrained by incomplete understanding of compound and cascading events, which can amplify health impacts (e.g., flood-related disease outbreaks, drought-heat interactions). Additionally, existing studies often lack thorough uncertainty quantification and validation of models and data, which is essential for credible decision support.
A further scientific challenge is the quantification of the health burden attributable to climate change, comparing factual climatic conditions with counterfactual scenarios derived from climate projections (e.g., CMIP6). This requires accounting for social inequalities, demographic change, and adaptation mechanisms, which modulate both vulnerability and exposure.
Finally, policymakers need operational, evidence-based adaptation roadmaps showing how EO can inform planning, resource allocation, and long-term resilience of health systems. Generating such roadmaps demands the integration of scientific knowledge, climate projections, and satellite-based indicators of risk and feasibility.
Aim
CHANGE (Climate–Health Adaptation through New Generation Earth Observations) aims to show how Earth Observation (EO) can transform the identification, monitoring, and quantification of climate-related health risks to support resilient health systems and protecting vulnerable communities worldwide.
Key objectives
Integrate Data for Actionable Insights: Combine satellite observations, climate projections, epidemiological data, and socio-demographic information to anticipate health impacts and inform climate-smart decision-making.
Assess Climate–Health Linkages: Analyse how heatwaves, floods, droughts, wildfires, and climate variability affect human well-being, health systems, and population vulnerability, including identifying scientific gaps and necessary technical and methodological requirements for EO to better support climate-health research and policy development.
Develop EO-Enabled Risk Models: Use ESA CCI data records, Copernicus Sentinel mission observations, ERA5 reanalysis and other datasets to map exposure, vulnerability, to predict climnate-health threats at high resolution.
Quantify Climate-Attributable Health Burden: Compare current disease risks with counterfactual scenarios to measure the contribution of anthropogenic climate change to health outcomes such as infectious disease incidence, respiratory morbidity from fire emissions, waterborne contamination events, and healthcare system disruptions, while explicitly accounting for social inequalities, demographic trends, and adaptive capacity.
Deliver an EO-informed Climate & Health Adaptation Roadmap: Provide practical guidance aligned with WHO and UNFCCC frameworks to strengthen resilience, improve early warning systems, support extreme-event preparedness and support policy implementation. The roadmap will aim to include intuitive visual tools and satellite-based indicators.
Promote Open Science and Global Collaboration: Maintain transparency and long-term impact through open repositories, peer-reviewed publications, and partnerships with WHO, UNICEF, ICIPE, INPE (Brazil) and others the Environment Agency (UK).
Apply Knowledge in Real-World Cases: Conduct six case studies on climate-sensitive health challenges:
- E. coli contamination in UK bathing waters
- Leptospirosis outbreaks in Brazil
- Malaria transmission shifts in Sudan
- ENSO-driven dengue and leptospirosis in Sri Lanka
- Respiratory impacts from large wildfires
- Flood-related disruptions to health services in South Sudan
Develop EO-Enabled Risk Models: Use ESA CCI data records, Copernicus Sentinel mission observations, ERA5 reanalysis and other datasets to map exposure, vulnerability, to predict climnate-health threats at high resolution.
Quantify Climate-Attributable Health Burden: Compare current disease risks with counterfactual scenarios to measure the contribution of anthropogenic climate change to health outcomes such as infectious disease incidence, respiratory morbidity from fire emissions, waterborne contamination events, and healthcare system disruptions, while explicitly accounting for social inequalities, demographic trends, and adaptive capacity.
Deliver an EO-informed Climate & Health Adaptation Roadmap: Provide practical guidance aligned with WHO and UNFCCC frameworks to strengthen resilience, improve early warning systems, support extreme-event preparedness and support policy implementation. The roadmap will aim to include intuitive visual tools and satellite-based indicators.
Promote Open Science and Global Collaboration: Maintain transparency and long-term impact through open repositories, peer-reviewed publications, and partnerships with WHO, UNICEF, ICIPE, INPE (Brazil) and others the Environment Agency (UK).
Project plan
The CHANGE project is organised into the following 6 work packages:
- Project Management and stakeholder engagement with organisations such as WHO, UNICEF, ICIPE, EA, and INPE
- Climate & Health Assessment to establish the scientific foundation of the project
- Climate-Health Case Studies (six in total)
- Health Impact Attribution to provide the evidence base for future adaptation and policy planning
- Climate & Health Adaptation Roadmap - synthesising outputs of WP1-4
- Outreach, Communication & Open Repository
The project will provide a collection of EO, climate and epidemiological datasets designed to support research, policy analysis, and decision-making in the climate–health domain. All datasets, algorithms, and documentation will be made available through our two-official open-science communities:
These two platforms ensure full transparency, traceability, and long-term preservation of the project’s outputs. Each dataset published in Zenodo will follow a consistent metadata structure, enabling users to understand the spatial and temporal coverage, methodology, variables, and limitations of each product.
Each dataset will include a title in the format:
Title: Dataset name – Location, Date
(e.g., EO-based flooding maps – South Sudan, 2012–2024)
Followed by a structured description using the table below:
| Field Name | Example Description |
|---|---|
| Use Case Name | Name and focus area of the use case (e.g., Flooding and health care service disruption in South Sudan). |
| Dataset Name | Descriptive title (e.g., EO-based flooding maps). |
| Dataset Description | Detailed description of dataset content, purpose, and methodology (e.g., Dataset covering 2012–2024 providing flood maps derived from VIIRS 375 m/pixel water fraction products. Flood extent is downscaled to 90 m using a hydrologically conditioned DEM and HAND algorithm. Each layer represents a 5-day composite.) |
| Temporal Domain | Time range covered (e.g., 2012–2024). |
| Spatial Domain | Geographic area, resolution, and coordinate system (e.g., South Sudan, 90 m, WGS84). |
| Key Variables / Indicators | Main variables represented (e.g., surface water extent, flood frequency). |
| Data Format | Dataset file type (e.g., GeoTIFF, netCDF, CSV, Shapefile). |
| Source Data | Input datasets used (e.g., VIIRS, Sentinel-1, HydroDEM, ERA5). |
| Limitations / Assumptions | Important caveats (e.g., VIIRS resolution limits detection of small inundated areas; cloud cover may reduce accuracy; mixed pixels introduce uncertainty along flood boundaries.) |
Key contacts
- Science Leader: Prof. Rachel Lowe, Barcelona Supercomputing Center
- Deputy Science Leader: Prof. Joacim Rocklöv, Heidelberg University
- Project Manager: Dr Carlos Doménech, GMV
- ESA Technical Officer: Dr Clement Albergel, ESA
Project Prime
GMV
Project role: GMV leads the overall project coordination, technical and administrative management, implementation of the open repository, stakeholder engagement, and dissemination activities. GMV also leads the South Sudan use case on flooding impacts on health services and contributes to the EO integration strategy across all case studies
Project Partners
Barcelona Supercomputing Center
Project role: BSC serves as the Scientific Leader, ensuring scientific coherence and methodological excellence. BSC leads the health impact attribution work and contributes modelling and climate–health analytics across use cases. The team provides high-performance computing capabilities and validated climate–sensitive disease indicators
University of Heidelberg
Project role: Heidelberg leads the development of the Climate and Health Adaptation Roadmap, translating scientific results into policy-ready guidance. The university also contributes expertise on climate–health interactions, vector-borne disease dynamics, and adaptation planning, and leads the Sri Lanka use case on dengue and leptospirosis.
Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale" (IZS-AM)
Project role: IZS-AM leads the Sudan malaria use case, integrating EO, epidemiological modelling, and One Health approaches. The institute contributes expertise in vector-borne disease surveillance, EO-based risk modelling, and data processing pipelines for environmental health analytics.
Plymouth Marine Laboratory
Project role: PML leads the UK and Brazil water-quality use cases, using ocean colour and inland water EO products to model E. coli and leptospirosis risks. PML provides advanced EO processing capabilities, hydrological modelling expertise, and experience with waterborne disease risk mapping
Laboratory for Climate and Environmental Sciences (LSCE)
Project role: LSCE leads the wildfire–health use case, developing machine-learning models for burned area prediction, pollutant emission estimation, and respiratory health burden assessment. LSCE contributes climate modelling expertise, air-quality modelling, and multi-scenario projections.
Project Stakeholders
Instituto Nacional de Pesquisas Espaciais (INPE)
Project role: Provides in-situ water-quality data, regulatory context, and co-lead the use case “Climate-relate Leptospirosis health risks in Brazil”.
Environment Agency
Project role: Provides in-situ water-quality data, regulatory context, and operational insights for the UK E. coli risk-mapping use case.
