climate-shift Land use and climate exposure, for risk and public policy
UmaTerra
UmaTerra The home page for all the platforms: what each one is for, who it helps, and how to support the project. Free and open source.
Camposcope Analyses one rural property at a time — its registered boundary (the CAR in Brazil, ParcelMap BC parcels in Canada) and the land around it: cover, forest loss, biomass and fire. Naturametrics Land-use history and landscape metrics around any point you click (Brazil: MapBiomas since 1985 and forest-inventory plots; Canada: crop inventory, forest age and forest change). Yvynation Land-cover change inside Brazil's Indigenous lands and conservation units, and Canada's protected areas, or inside areas you draw — with maps, charts and reports.
climate-shift You are here Climate exposure of the land around any point you click, in Brazil and Canada: today's land use read against CMIP6 projections of heat, drought and heavy rain — plus measured fire, terrain, rain and wind.
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Download data

Everything the console shows can be downloaded: the analysis of one point, or the whole climate grids it reads.

This point

No point analysed yet — click the map in the console, then come back here.

Whole grids (0.25°)

NumPy .npz: read with numpy.load; arrays are model × band × row × column (rows north to south), with bands, models, west, north and step alongside.

Every report carries the list of sources and a provenance hash (SHA-256) over the data versions used. Cite the sources too — see "How to cite".

How to cite

If climate-shift contributed to your work, cite it and also cite the data sources used.

Suggested citation

Biondo, L. M. (2026). climate-shift (muda-clima): probable climate exposure of land use in Brazil, Canada and the United States. University of British Columbia Okanagan. Available at: https://clima.umaterra.com.br

BibTeX

@software{mudaclima_2026,
  title        = {muda-clima: exposição climática provável do uso da terra no Brasil, no Canadá e nos Estados Unidos},
  note         = {English name: climate-shift},
  author       = {Biondo, Leandro Meneguelli},
  year         = {2026},
  organization = {University of British Columbia Okanagan},
  url          = {https://clima.umaterra.com.br}
}

Authors and institutions

Leandro Meneguelli Biondo
University of British Columbia Okanagan (UBC Okanagan), Canada

Data sources — cite these too

Each source has its own attribution requirements. When publishing figures or numbers obtained here, cite whichever ones were used (the report's Method tab lists them).

  • NASA NEX-GDDP-CMIP6
    Thrasher, B. et al. (2022). NASA Global Daily Downscaled Projections, CMIP6. Scientific Data 9, 262. doi:10.1038/s41597-022-01393-4. Temperature and rainfall projections (0.25°).
  • NASA NEX-GDDP-FWI
    Park, T. et al. NEX-GDDP-FWI: Downscaled 21st century global fire weather projections. Scientific Data (under review). Canadian Fire Weather Index from NEX-GDDP-CMIP6; provisional data.
  • Köppen-Geiger 1 km (Beck et al. 2023)
    Beck, H. E. et al. (2023). Scientific Data 10, 724. doi:10.1038/s41597-023-02549-6. Climate classes 1991–2020 and 2041–2070. Licence CC BY 4.0.
  • IPCC AR6 WGI — SPM, Tabela/Table SPM.1
    IPCC (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Global warming by scenario and period (the "+2 °C world").
  • MapBiomas — Coleção 11
    Souza, C. M. et al. (2020). Remote Sensing 12(17), 2735. doi:10.3390/rs12172735. Land use and cover in Brazil. Licence CC BY-SA 4.0.
  • MapBiomas Fogo — Coleção 5
    Alencar, A. A. C. et al. (2022). Remote Sensing 14(11), 2510. doi:10.3390/rs14112510. Annual burned area in Brazil, 30 m.
  • AAFC Annual Crop Inventory
    Agriculture and Agri-Food Canada. Agricultural land use in Canada. Open Government Licence – Canada.
  • Annual NLCD · NLCD Alaska (USGS)
    U.S. Geological Survey (2024). Annual National Land Cover Database (NLCD) Collection 1, 1985–; NLCD 2016 (Alaska: 2001, 2011, 2016). US land use and cover, 30 m. Public domain.
  • MTBS — Monitoring Trends in Burn Severity
    USDA Forest Service / USGS. Annual burn severity, 30 m, United States (1984–). Public domain.
  • USGS 3DEP
    U.S. Geological Survey, 3D Elevation Program, 1/3 arc-second (~10 m). Slope in the United States.
  • CHIRPS v2
    Funk, C. et al. (2015). Scientific Data 2, 150066. doi:10.1038/sdata.2015.66. Observed rainfall (Brazil).
  • TerraClimate
    Abatzoglou, J. T. et al. (2018). Scientific Data 5, 170191. doi:10.1038/sdata.2017.191. Observed water deficit and rainfall.
  • ERA5 / ERA5-Land
    Hersbach, H. et al. (2020). QJRMS 146, 1999–2049. doi:10.1002/qj.3803; Muñoz-Sabater, J. et al. (2021). ESSD 13, 4349–4383. doi:10.5194/essd-13-4349-2021. Wind gusts and rainfall (Canada).
  • INMET
    Brazilian National Institute of Meteorology — automatic stations, hourly gusts.
  • SRTM · Copernicus DEM GLO-30 (2024)
    Farr, T. G. et al. (2007). Reviews of Geophysics 45, RG2004. doi:10.1029/2005RG000183; Copernicus DEM, doi:10.5270/ESA-c5d3d65. Slope.
  • MERIT Hydro
    Yamazaki, D. et al. (2019). Water Resources Research 55, 5053–5073. doi:10.1029/2019WR024873. Height above drainage (low ground).
  • JRC Global Surface Water
    Pekel, J.-F. et al. (2016). Nature 540, 418–422. doi:10.1038/nature20584. Seasonal water.
  • MODIS MCD64A1 · MOD11A2 (v061)
    Giglio, L. et al. doi:10.5067/MODIS/MCD64A1.061 (burned area, Canada); Wan, Z. et al. doi:10.5067/MODIS/MOD11A2.061 (land-surface temperature).
  • NOAA PSL — ONI
    Oceanic Niño Index (El Niño / La Niña phase).

Example of use in text

"Probable climate exposure of the study area was read with climate-shift (Biondo, 2026), combining MapBiomas Collection 11 land use (Souza et al., 2020) with NEX-GDDP-CMIP6 projections (Thrasher et al., 2022)."

AI use disclosure

How this application was built, and with what help.

climate-shift's code was written with the assistance of Anthropic's AI models — Claude Opus and Claude Sonnet — under the author's supervision and review at every step. The choice of data, the thresholds and the wording of every conclusion the page draws are the author's, and are documented in the Method tab and in the code.

No AI model runs when you use the page: the story is assembled by fixed rules from the numbers shown, so the same point always gives the same text. The interface conventions come from the sibling UmaTerra platforms, built with the same process.

See for yourself

  • UmaTerra — The home page for all the platforms: what each one is for, who it helps, and how to support the project. Free and open source.
  • Camposcope — Analyses one rural property at a time — its registered boundary (the CAR in Brazil, ParcelMap BC parcels in Canada) and the land around it: cover, forest loss, biomass and fire.
  • Naturametrics — Land-use history and landscape metrics around any point you click (Brazil: MapBiomas since 1985 and forest-inventory plots; Canada: crop inventory, forest age and forest change).
  • Yvynation — Land-cover change inside Brazil's Indigenous lands and conservation units, and Canada's protected areas, or inside areas you draw — with maps, charts and reports.
Console Home About Sources Docs API
Click the map to analyse a point
Legend
  • Analysis circle
  • Climate model cell (0.25°)

How to use climate-shift

climate-shift answers one question: which land around this point is exposed to the climate change the models agree on?

Rather see it working? Open the step-by-step walkthrough on a real point →

  1. Pick a place. Search a CEP, a Canadian postal code, an address, a city or a coordinate (-12.54, -55.71 or 52.1, -106.6; pt-BR commas and Google Maps links work too), or use the Brazil / Canada buttons, or pan the map. A place found by search only frames the map — nothing is analysed until you click.
  2. Set the circle and the scenario. The radius (1–20 km) is the area whose land use is counted. The scenario chooses the emissions path of the climate projection.
  3. Click. The circle is drawn, and so is the dashed square of the climate model cell that supplies the numbers. Land use is read from MapBiomas in Brazil and the AAFC inventory in Canada (latest year); the climate from three CMIP6 models (NASA NEX-GDDP), 1985–2014 vs 2041–2070.
  4. Read the tabs. Story joins both and names the probable exposed area per hazard; Land use, Climate and Method show the numbers behind it.

What it is not. Not a forecast for a field, not a property assessment and not a compliance verdict. The climate signal is one value for a ~27 km square; inside it, only land use varies. For a property's own history go to Camposcope; for the land-use history since 1985 around a point, Naturametrics.

Every result can be shared: the address bar keeps the point, radius and scenario.