The Methodology

The Refugia habitability index is a composite raster built by classifying six environmental factors into three-class suitability masks (suitable, marginal, unsuitable) and combining them multiplicatively. A single failing factor drives the composite toward zero, ensuring that regions must satisfy all criteria simultaneously. Below, each factor is shown as its continuous input dataset alongside its reclassified index mask. The flooding factor combines projected sea-level rise with WRI Aqueduct 100-year riverine flood depth, and applies to all three epochs (current, 2100, 2300). Habitability is then crossed with two asset surfaces — gridded population and the Biodiversity Intactness Index — to produce a pair of risk rasters: Human Risk (people in declining habitability) and Biodiversity Risk (intact ecosystems becoming climate refugia).

1. Topography
Input — Elevation / Slope
Index — Suitability Mask
Input — GMTED2010 Slope
Index — Topography Suitability Mask
Justification
Approach

Slope shapes where humans can build, farm, and lay infrastructure. Steep terrain raises construction and transport costs, restricts mechanised agriculture, and limits population density. Although humans live across a wide range of topographies — from Andean valleys to Tibetan plateaus to Himalayan villages — there is a hard upper bound on slope above which sustained settlement and agriculture become prohibitive.

Slope is computed from the GMTED2010 1 km global mean elevation product (Danielson & Gesch 2011, USGS OFR 2011–1073) using Horn's algorithm (Horn 1981, Proc. IEEE 69:14) and aggregated as cell-mean to the 30 km analysis grid. The resulting continuous slope-in-degrees raster is reclassified following the FAO/UNESCO 3-class physiographic convention (Soils Bulletin 32; HWSD v1.2): cells with mean slope ≤ 8° are suitable ("level to gently undulating"), 8–16° are marginal ("rolling to hilly"), and ≥ 16° are unsuitable ("steeply dissected to mountainous"). The 8/16 thresholds are interpreted in degrees rather than percent grade (the FAO break points of 8% and 30% grade ≈ 4.6° and 16.7°) because cell-mean averaging at 30 km tends to mask the within-cell low-slope microsites where settlement and agriculture concentrate (Cohen & Small 1998, PNAS 95:14009). Topography is time-invariant across all epochs.

Methodology Source FAO/UNESCO Soils Bulletin 32 / HWSD v1.2 physiographic split; Cohen & Small (1998), PNAS 95:14009 on cell-mean topography.
2. Soil Suitability
Input — Soil Quality
Index — Suitability Mask
Input — HWSD v1.2 Soil Quality (SQ1)
Index — Soil Suitability Mask
Justification
Approach

Soil determines whether a cell can support agriculture and the food security of its population. Modern food systems redistribute calories globally, but sustained habitation still depends on local soil productivity, especially during disruption to long supply chains. Permafrost soils additionally shift role under warming — frozen permafrost is structurally unbuildable and biologically inert, while thawing permafrost transitions to waterlogged, methane-emitting, foundation-unstable conditions that remain hostile to settlement and agriculture.

Soil-quality classes from the FAO/IIASA Harmonized World Soil Database (HWSD v1.2, 1 km native, "soil quality 1 — nutrient availability" derivative) are reclassified into suitable (high fertility), marginal (moderate), and unsuitable (non-soil, inland water, permafrost). For 2100 and 2300 epochs, permafrost zones projected to thaw under CMIP6 SSP5-8.5 mean annual temperature are reclassified from unsuitable (frozen) to marginal (thawing). The HWSD v1.2 SQ1 product is held static across epochs for inherent fertility (which evolves on millennial timescales), with permafrost dynamics overlaid via the MAT-driven thaw rule.

Data Source FAO/IIASA/ISRIC/ISS-CAS/JRC (2012), Harmonized World Soil Database v1.2 (CC-BY-NC-SA 4.0); Nachtergaele et al. (2008–2012).
Methodology Source HWSD/GAEZ Soil Quality 1 (nutrient availability) class scheme; permafrost thaw rule from Chadburn et al. (2017), Nat. Clim. Change 7:340.
3. Water Availability
Input — Water Balance
Index — Suitability Mask
Input — Aqueduct Baseline Water Stress (BWSs)
Index — Water Suitability Mask
Justification
Approach

Without renewable water supply, no human community sustains itself. Water stress — the ratio of withdrawals to renewable supply — captures whether a catchment's hydrology can support continued use. Catchments under severe stress face declining reliability, salinisation, ecosystem collapse, and forced migration; arid catchments with low absolute supply face the same outcome regardless of demand level. While irrigation, desalination, and inter-basin transfer can defer the constraint, they do not eliminate it.

Water availability is encoded as the WRI Aqueduct Baseline Water Stress score (BWSs, 0–5), which is the ratio of total annual withdrawals to available renewable supply per catchment. Catchments with BWSs ≤ 3 (Low through Medium-High stress, <40% withdrawals) are suitable; 3–4 (High, 40–80% withdrawals) are marginal; > 4 (Extremely High, >80%, plus arid catchments with low water use) are unsuitable. The 40% withdrawal-to-availability boundary is the cross-framework consensus for "stressed but managed" vs "severely stressed" used by Raskin et al. 1997, UN SDG 6.4.2, and Wada et al. 2011 (Hydrol. Earth Syst. Sci. 15:3785). The baseline raster (Aqueduct Global Maps 2.1, 2010-vintage climatology) is held constant across all three epochs — this isolates climate-driven changes in other factors against a fixed hydrological baseline.

Methodology Source Wada et al. (2011), Hydrol. Earth Syst. Sci. 15:3785 (40% withdrawal/availability boundary); cross-checked against UN SDG 6.4.2.
4. Wet-Bulb Temperature
Input — Wet-Bulb Temp
Index — Suitability Mask
Input — Annual-Max Tw (CMIP6 + ERA5)
Index — Wet-Bulb Suitability Mask
Justification
Approach

Human thermoregulation depends on evaporative cooling. Wet-bulb temperature (Tw) combines heat and humidity into a single physiological-load metric — unlike dry-bulb temperature, it captures the actual cooling capacity available to the body. Above ~35 °C Tw, healthy adults cannot dissipate metabolic heat even at rest in shade with abundant water; sustained outdoor activity is impossible. Where annual peak Tw crosses this ceiling, an area becomes biologically uninhabitable regardless of cooling adaptation, since the same survivability limit applies indoors during power outages.

Wet-bulb temperature is computed from CMIP6 monthly maximum temperature and ERA5 monthly relative humidity using the Stull (2011) approximation; the raster reports the annual maximum Tw at each cell — the worst single hour of an average year. Thresholds are applied uniformly across all three epochs: cells with annual max Tw ≤ 30 °C are suitable, 30–35 °C are marginal, and > 35 °C are unsuitable. The 35 °C ceiling is the canonical uninhabitability threshold from Sherwood & Huber 2010 (PNAS 107:9552) — the thermodynamic survivability limit. The marginal regime (30–35 °C) corresponds to the "emerging extremes" documented by Raymond et al. 2020 (Sci. Adv. 6:eaaw1838): annual peaks above comfort but below survivability, requiring cooling, shelter, and schedule adaptation. Currently-populated humid metros (Mumbai, Houston, Lagos, Singapore) sit in this band today and into 2100. Lower thresholds in the physiological literature (e.g., Vecellio et al. 2022's 30 °C empirical compensability ceiling) describe sustained exposure and are not applied to annual-peak framing, where real-world heat is intermittent and people actively avoid worst-hour exposure.

Data Source CMIP6 tasmax via the IPCC AR6 Interactive Atlas (Santander Met Group) + ERA5 relative humidity from the Copernicus Climate Data Store; Tw via Stull (2011), JAMC 50:2267.
Methodology Source Sherwood & Huber (2010), PNAS 107:9552 (35 °C survivability ceiling); marginal band from Raymond, Matthews & Horton (2020), Sci. Adv. 6:eaaw1838.
5. Consecutive Dry Days
Input — CDD Count
Index — Suitability Mask
Input — CMIP6 CDD (annual max)
Index — CDD Suitability Mask
Justification
Approach

Drought duration determines whether rainfed agriculture, livestock, and natural ecosystems persist through dry seasons. Long unbroken dry spells exhaust soil moisture, kill perennial vegetation, and disrupt food production cycles. While irrigation can partially mitigate, climate-driven CDD lengthening is a leading driver of regional agricultural collapse and out-migration, especially in continental interiors and the subtropics.

The maximum number of consecutive dry days (CDD, days with precipitation < 1 mm) per year is sourced from the IPCC AR6 Interactive Atlas (Santander Meteorology Group) ensemble export of CMIP6 projections under SSP5-8.5: 1995–2014 historical mean for the current epoch, 2081–2100 mean for the 2100 epoch, with 2300 reusing the 2100 raster since no Aqueduct- or Atlas-equivalent extends past 2100. Regions with ≤ 63 CDD per year are suitable; 64–171 are marginal; > 171 are unsuitable — representing extreme drought stress incompatible with rainfed agriculture.

Data Source IPCC AR6 Interactive Atlas, CMIP6 SSP5-8.5 CDD index, prepared by the Santander Meteorology Group (Iturbide et al. 2021, ESSD 13:2959).
Methodology Source CDD as defined by the WMO Expert Team on Climate Change Detection and Indices (ETCCDI) 27-index suite.
6. Flooding (Sea-Level Rise + Riverine)
Input — DEM Elevation + Riverine Depth
Index — Suitability Mask
Input — GMTED2010 DEM + Aqueduct Floods RP100
Index — Flood Suitability Mask
Justification
Approach

Inundation directly displaces populations and destroys infrastructure. Coastal sea-level rise progressively claims low-elevation areas as permanently lost to the sea; riverine flooding adds episodic destruction to floodplains, even in catchments that are otherwise habitable. Together they bound the lowlands where cities, agriculture, and dense settlement can persist — and historically, mass population displacement in this century has come predominantly from these two hazards.

Two flood hazards are combined into a single 3-class suitability mask, taking the worst classification at each cell. Coastal sea-level rise is keyed to the GMTED2010 DEM. For the 2100 epoch (5 m SLR scenario): cells with elevation ≤ 1 m are unsuitable (deep inundation), 1–5 m are marginal (high-tide and storm-surge exposure), and > 5 m are suitable. For the 2300 epoch (65 m multi-millennial commitment ceiling: Antarctica ~58 m + Greenland ~7 m + other glaciers): ≤ 5 m are unsuitable, 5–65 m are marginal, and > 65 m are suitable. Riverine flooding uses the WRI Aqueduct 100-year return-period inundation depth: depth ≥ 1 m is unsuitable, 0.1–1 m is marginal, and < 0.1 m is suitable. The current epoch has no SLR component and applies only the riverine layer. Riverine depth at the future horizon is the per-pixel ensemble maximum across four CMIP5 GCMs (NorESM1-M, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR) for the RCP 8.5 / 2080 horizon; the historical (WATCH) baseline is used for the current epoch, and the 2080 horizon is reused as a proxy for 2300 since no published Aqueduct projections run past 2080.

Data Source DEM: GMTED2010 (Danielson & Gesch 2011). Riverine flood depth: WRI Aqueduct Floods v2 (Ward et al., RP100, CMIP5 RCP8.5/2080 ensemble).
Methodology Source Riverine: Ward et al. (2020), NHESS 20:1007. SLR commitment ceiling: IPCC AR6 WG1 Ch. 9 (Fox-Kemper et al. 2021).
Biodiversity (asset surface)
Input — BII
Input — BII v2.1.1 (PREDICTS, 2020 baseline)
Justification

Biodiversity is not a constraint on human habitability — humans live readily in biodiverse landscapes — but intact ecosystems are themselves an asset that climate-driven displacement threatens. As habitable land contracts under warming, remaining intact biomes face encroachment pressure from migrating populations, development, and agricultural expansion. The places that emerge as 21st-century climate refugia (boreal forests, montane regions, high-latitude wetlands) overlap heavily with the planet's last intact ecosystems. Modeling the spatial co-occurrence makes that conflict visible.

Approach

The Biodiversity Intactness Index (BII) from the PREDICTS database (Sanchez-Ortiz et al. 2019; Hill et al. 2022) measures how much of each cell's original biodiversity remains, as a fraction of its pre-industrial baseline. The 2020 vintage (BII v2.1.1, 5-arc-minute, CC-BY-NC-SA 4.0) is held constant across all three epochs since no published BII projection covers 2100 or 2300; future-projected BII is feasible via the De Palma BII tutorial × LUH2 land-use scenarios but is out of scope for the current pipeline. BII is not multiplied into the habitability composite — it feeds downstream as the asset surface in the Biodiversity Risk raster, multiplied by habitability to surface the places where future climate refugia overlap with remaining intact ecosystems.

Composite Habitability Index

The six factor masks are multiplied element-wise. Because each mask uses values of 0 (unsuitable), 1 (suitable), or 2 (marginal), the product encodes which combination of factors constrain each cell. A product of zero (any single unsuitable factor) maps to a habitability score of 0.0; all-suitable maps to 1.0. Intermediate products are mapped to a continuous red–yellow–green color ramp. Finally, ocean and inland water bodies are masked out using slope-derived water detection.

Habitability Index — Composite
Composite Habitability (Current → 2100 → 2300)

Risk

The composite habitability raster describes where the planet remains liveable, but not what stands to lose by its decline. We cross habitability with two static 2020 asset surfaces — gridded population and the Biodiversity Intactness Index — to produce a pair of risk rasters. Human Risk = (1 − habitability) × population, surfacing where many people live in places whose climate is becoming uninhabitable. Biodiversity Risk = habitability × BII, surfacing intact ecosystems whose climate is becoming a refugium for displaced humans, and which may therefore face encroachment. Both asset surfaces are held at their 2020 baseline; the evolution of risk through 2100 and 2300 is therefore driven entirely by climate-driven changes in habitability against fixed present-day human and ecological footprints.

Human Risk — (1 − Habitability) × Population
Human Risk (Current → 2100 → 2300)
Biodiversity Risk — Habitability × BII
Biodiversity Risk (Current → 2100 → 2300)