Data Sources
The foundation of our risk-resilience framework is the extensive collection of geospatial data layers engineered from first-party and third-party sources.
From satellite observations, climate models to socio-economic data and text-based reports, we monitor a growing list of high-quality data sources and curate the best available data onto our platform.
During this process, the data sources go through a stringent quality-control and curation process. Each layer is indexed onto our spatial grid at a governed resolution appropriate to the prepared source. Layers are not resampled at runtime merely to force them onto one shared scoring grid. Source-preparation steps may map pixels onto H3 cells, but do not create independent physical detail beyond what the underlying dataset can support. These curated layers help make AlphaGeo's results reliable and explainable across locations.
Database factsheet:
| Spatial coverage | Global |
| Climate projection coverage | 1950–2100 |
| Supported climate scenarios | SSP2-4.5, SSP3-7.0, SSP5-8.5 |
| Source resolution | Source-dependent, from approximately 25 m to 100 km |
| Runtime spatial resolution | Source-dependent H3 levels; each layer is read at its governed resolution |
| Review cycle | Quarterly, with source updates incorporated after quality and methodology review |
List of data sources:
When curating data sources, the target source must meet one or more criteria below to ensure their reliability. These are:
- Peer-reviewed: the data is published in academic journals that underwent a peer-review process. E.g. Downscaled CMIP6 ensemble models from HighResMIP.
- Well-documented: the data is published with clear explanations of its methodology and/or code repositories that allow third parties to reproduce the results. E.g. OpenStreetMap.
- Well-established: the data is published by reputable organizations and is widely used by the industry. E.g. the Aqueduct project by the World Resources Institute.
The table below presents a selection of source datasets and premium alternatives. WRI Aqueduct supplies the current CRRI flood-inundation inputs. JBA and Fathom are premium flood-data alternatives; their coverage and resolution depend on the selected product.
| Risk Datasets | Data Source | Temporal Coverage | Resolution |
|---|---|---|---|
| Global CMIP6 Climate Projections | NASA NEX-GDDP-CMIP6 | Daily values from 1950-2100 | 0.25 degrees (25km) |
| Global land reanalysis (current climate) | ECMWF ERA5-Land; Copernicus Climate Change Service | 2020–2025 average; primary climate-indicator input for the 2025 horizon | 0.1 degrees (approximately 9 km) |
| Aqueduct 4.0 water stress | World Resources Institute (WRI) | Baseline, 2030, 2050, 2080 | HydroBASINS level-6 sub-basins; read at H3 resolution 8 (approximately 460 m edge length) |
| Aqueduct Floods riverine and coastal inundation | World Resources Institute (WRI) | Baseline, 2030, 2050, 2080; 100- to 1,000-year return periods | 30 arcseconds (approximately 1 km); read at H3 resolution 8 |
| Global Flood Maps (premium alternative) | JBA Risk Management | Present day and climate-change scenarios; 100- to 1,000-year return periods | 30 m; 5 m in countries covered by JBA national maps |
| Flood hazard data (premium alternative) | Fathom | Product-specific coverage and horizons | Product-specific resolution |
| Global shoreline change projections | European Commission JRC LISCoAsT | To 2100 | Shoreline transects along the global coastline |
| Vertical land motion reconstruction | Oelsmann et al. (2024) | 1995–2020 | Coastal point estimates at tide-gauge and altimetry sites |
| Global Land Cover 100 m | Copernicus Land Monitoring Service | 2015–2019 | 100 m |
| Global Mean Sea Level Change | Intergovernmental Panel on Climate Change (IPCC) | 2021-2040, 2041-2060, 2081-2100 | 1 degree (100km) |
| Global Coastal Tropical Cyclone Wind Hazard | CHAZ/CLIMADA; Bloemendaal et al. | 1995–2014, 2041–2060 and 2081–2100 windows; 10- to 1,000-year return periods | Prepared at H3 resolution 6 (approximately 4 km) |
| Global Consensus Land Cover | EarthEnv | 2014 | 30 arcseconds (900 meters) |
| Global estimates of damaging hail hazard | PANGAEA | Daily Values from 1979 to 2015 | 0.7 degrees (70km) |
| Global Landslide Susceptibility | NASA LHASA | Static susceptibility model | Read at H3 resolution 8 (approximately 460 m edge length) |
| Global Seismic Hazard Model | GEM Foundation | 2018 model version | Read at H3 resolution 6 (approximately 4 km edge length) |
| Global Dam Watch | Global Dam Watch (Lehner et al. 2024) | Version 1.0 | Dam and reservoir polygons |
The 2025 climate-indicator baseline primarily uses ERA5-Land reanalysis, with nearby-cell filling and a NASA NEX-GDDP-CMIP6 SSP2-4.5 fallback where coverage remains unavailable. Other hazards retain their own source-specific baseline periods. See The 2025 horizon for processing and interpretation.
| Resilience dataset | Data source | Description |
|---|---|---|
| Points of interest | OpenStreetMap | Flood, fire and drought infrastructure used to represent local adaptation capacity. |
| Population | WorldPop | Gridded population estimates derived from approximately 100 m source products. |
| Age and sex structure | WorldPop | Local age-group composition used to estimate the share of the population that may be more vulnerable to climate shocks. |
| Human Development Index | Global Data Lab | Subnational health, education and income conditions used in the societal-resilience composite. |
| Gross fixed capital formation | World Bank | Gross fixed capital formation as a percentage of GDP, representing capacity to invest in and maintain physical infrastructure. |
| Global Consensus Land Cover | EarthEnv | Consensus prevalence of 12 land-cover classes at approximately 1 km source resolution. |
| Local Climate Zones | WUDAPT | Urban form and thermal context used in the Heat Stress methodology. |
Attribution
AlphaGeo uses these datasets under their published licences or under commercial licences and permissions held by AlphaGeo. Map and adaptation data © OpenStreetMap contributors, available under the Open Database Licence. Land-cover and climate reanalysis layers contain modified Copernicus Land Monitoring Service and Copernicus Climate Change Service information. Citations for each dataset are listed below.
References
(carbon)plan. “CMIP6 Downscaling,” n.d. https://carbonplan.org/.
Thrasher, B., Wang, W., Michaelis, A. et al. NASA Global Daily Downscaled Projections, CMIP6. Sci Data 9, 262 (2022). https://doi.org/10.1038/s41597-022-01393-4
Eyring, Veronika, Sandrine Bony, Gerald A. Meehl, Catherine A. Senior, Bjorn Stevens, Ronald J. Stouffer, and Karl E. Taylor. “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization.” Geoscientific Model Development 9, no. 5 (May 26, 2016): 1937–58. https://doi.org/10.5194/gmd-9-1937-2016.
Muñoz-Sabater, Joaquín, et al. “ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13, no. 9 (2021): 4349–4383. https://doi.org/10.5194/essd-13-4349-2021.
Bloemendaal, Nadia, et al. Global coastal tropical cyclone wind hazard. Scientific Data (2025). https://doi.org/10.1038/s41597-025-06452-0
Emanuel, Kerry. “Global Warming Effects on U.S. Hurricane Damage.” Weather, Climate, and Society 3, no. 4 (2011): 261–68. https://doi.org/10.1175/WCAS-D-11-00007.1
Eberenz, Samuel, Samuel Lüthi, and David N. Bresch. “Regional Tropical Cyclone Impact Functions for Globally Consistent Risk Assessments.” Natural Hazards and Earth System Sciences 21, no. 1 (2021): 393–415. https://doi.org/10.5194/nhess-21-393-2021.
Huizinga, Jan, Hans de Moel, and Wojciech Szewczyk. Global Flood Depth-Damage Functions: Methodology and the Database with Guidelines. JRC Technical Report JRC105688. Luxembourg: Publications Office of the European Union, 2017. https://doi.org/10.2760/16510
US Army Corps of Engineers. Economic Guidance Memorandum 01-03: Generic Depth-Damage Relationships. Washington, DC, 2000.
Federal Emergency Management Agency. Hazus Earthquake Model Technical Manual. Washington, DC: FEMA, 2020.
Pausas, Juli G., and Eloi Ribeiro. “The Global Fire–Productivity Relationship.” Global Ecology and Biogeography 22, no. 6 (2013): 728–36. https://doi.org/10.1111/geb.12043
Oelsmann, Julius, Marta Marcos, Marcello Passaro, et al. “Regional Variations in Relative Sea-Level Changes Influenced by Nonlinear Vertical Land Motion.” Nature Geoscience 17 (2024): 137–44. https://doi.org/10.1038/s41561-023-01357-2
Garner, Gregory G., et al. IPCC AR6 Sea Level Projections. Version 20210809. Zenodo, 2021. https://doi.org/10.5281/zenodo.6382554
Vousdoukas, Michalis I., et al. “Sandy Coastlines under Threat of Erosion.” Nature Climate Change 10 (2020): 260–63. https://doi.org/10.1038/s41558-020-0697-0
Demuzere, Matthias, et al. “A Global Map of Local Climate Zones to Support Earth System Modelling and Urban-Scale Environmental Science.” Earth System Science Data 14 (2022): 3835–73. https://doi.org/10.5194/essd-14-3835-2022
Lehner, Bernhard, et al. “The Global Dam Watch Database of River Barrier and Reservoir Information for Large-Scale Applications.” Scientific Data 11 (2024). https://doi.org/10.1038/s41597-024-03752-9
Pagani, Marco, et al. Global Earthquake Model (GEM) Seismic Hazard Map (Version 2018.1). GEM Foundation, 2018. https://doi.org/10.13117/GEM-GLOBAL-SEISMIC-HAZARD-MAP-2018.1
Kuzma, Samantha, Marc F. P. Bierkens, Shivani Lakshman, Tianyi Luo, Liz Saccoccia, Edwin H. Sutanudjaja, and Rens Van Beek. “Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators,” August 16, 2023. https://www.wri.org/research/aqueduct-40-updated-decision-relevant-global-water-risk-indicators.
O’Neill, Brian C., Elmar Kriegler, Keywan Riahi, Kristie L. Ebi, Stephane Hallegatte, Timothy R. Carter, Ritu Mathur, and Detlef P. van Vuuren. “A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways.” Climatic Change 122, no. 3 (February 1, 2014): 387–400. https://doi.org/10.1007/s10584-013-0905-2.
Tuanmu, Mao-Ning, and Walter Jetz. “A Global 1-Km Consensus Land-Cover Product for Biodiversity and Ecosystem Modelling.” Global Ecology and Biogeography 23, no. 9 (2014): 1031–45. https://doi.org/10.1111/geb.12182.
Prein, Andreas F; Holland, Greg (2018): Daily large hail probability on a global scale (1979 to 2015), Version 2, link to netCDF files [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.893160, Supplement to: Prein, AF; Holland, G (2018): Global estimates of damaging hail hazard. Weather and Climate Extremes, 22, 10-23, https://doi.org/10.1016/j.wace.2018.10.004
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879–1900. https://doi.org/10.1175/bams-d-11-00019.1
Aslam, A., & Rana, I. A. (2022). The use of local climate zones in the urban environment: A systematic review of data sources, methods, and themes. Urban Climate, 42, 101120. https://doi.org/10.1016/j.uclim.2022.101120
Bechtel, B., Demuzere, M., Mills, G., Zhan, W., Sismanidis, P., Small, C., & Voogt, J. (2019). SUHI analysis using Local Climate Zones—A comparison of 50 cities. Urban Climate, 28, 100451. https://doi.org/10.1016/j.uclim.2019.01.005
Verdonck, M., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J., Schneider, A., Dewulf, R., & Van Coillie, F. (2018). The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data. Landscape and Urban Planning, 178, 183–197. https://doi.org/10.1016/j.landurbplan.2018.06.004
Sohn, W., Kim, J., Li, M., Brown, R. D., & Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators, 118, 106774. https://doi.org/10.1016/j.ecolind.2020.106774
Blum, A. G., Ferraro, P. J., Archfield, S. A., & Ryberg, K. R. (2020). Causal effect of impervious cover on annual flood magnitude for the United States. Geophysical Research Letters, 47(5). https://doi.org/10.1029/2019gl086480
Yang, W., Yang, H., Yang, D., & Hou, A. (2021). Causal effects of dams and land cover changes on flood changes in mainland China. Hydrology and Earth System Sciences, 25(5), 2705–2720. https://doi.org/10.5194/hess-25-2705-2021
Dedekorkut-Howes, A., Torabi, E., & Howes, M. (2020). When the tide gets high: a review of adaptive responses to sea level rise and coastal flooding. Journal of Environmental Planning and Management, 63(12), 2102–2143. https://doi.org/10.1080/09640568.2019.1708709
Zhu, X., Linham, M. M., & Nicholls, R. J. (2010). Technologies for Climate Change Adaptation - Coastal Erosion and Flooding. Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi. TNA Guidebook Series
Nazarnia, H., Nazarnia, M., Sarmasti, H., & Wills, W. O. (2020). A Systematic review of civil and environmental infrastructures for coastal adaptation to sea level rise. Civil Engineering Journal, 6(7), 1375–1399. https://doi.org/10.28991/cej-2020-03091555
Tariq, M. a. U. R., Farooq, R., & Van De Giesen, N. (2020). A critical review of flood risk management and the selection of suitable measures. Applied Sciences, 10(23), 8752. https://doi.org/10.3390/app10238752
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