Pan-Arctic Permafrost Infrastructure Risk

• Pan-Arctic permafrost infrastructure risk is defined as the quantification and mapping of hazards resulting from thaw-induced ground destabilization affecting over $100 billion in assets.
• Hybrid physics–machine learning frameworks integrate high-resolution climate projections, spatiotemporal observations, and uncertainty quantification to produce spatially explicit risk maps.
• The approach enables adaptive infrastructure planning through improved geotechnical assessments, retrofitting strategies, and policy guidance under various climate scenarios.

Pan-Arctic permafrost infrastructure risk refers to the quantification, mapping, and forecasting of threats posed by permafrost degradation to critical infrastructure across the Arctic region. Accelerated Arctic warming leads to widespread permafrost thaw, inducing ground subsidence, foundation destabilization, and failure of transport, energy, and industrial assets, with more than $100 billion of infrastructure at risk. Accurately forecasting these hazards requires integration of high-resolution climate projections, physics-based modeling, spatiotemporal observations, and advanced machine learning with uncertainty quantification. Recent advances center around hybrid physics–ML frameworks leveraging millions of pan-Arctic observations for operational, spatially explicit infrastructure risk assessment (Kriuk, 2 Oct 2025, Pilyugina et al., 2023).

1. Governance of Permafrost Degradation and Infrastructure Exposure

Permafrost underlies approximately $25$\% of the Northern Hemisphere land surface ($\approx$27 million km²) and supports the daily activity of over five million residents and extensive industrial and municipal infrastructure. Climate-driven permafrost thaw manifests primarily as increased active-layer thickness (ALT) and increased mean annual ground temperature (MAGT), directly undermining ground stability. Transitions from stable permafrost (MAGT < –2 °C) toward or above $0\,^{\circ}$C, or rapid ALT increases ($>$50 cm), signal a shift to high subsidence risk, necessitating upgraded foundation engineering or asset decommissioning (Pilyugina et al., 2023).

The interaction between climate forcing (e.g., increasing mean annual air temperature, precipitation shifts), soil/vegetation conditions, and engineered structures creates highly heterogeneous, nonlinear risk patterns. Infrastructure exposure correlates strongly with the isotherm distribution of MAGT and observed or projected ΔPF (change in permafrost fraction), emphasizing the need for models that resolve regional–local gradients and quantify predictive uncertainty (Kriuk, 2 Oct 2025).

2. Data Sources and Feature Construction

Comprehensive pan-Arctic risk assessment frameworks integrate multi-scale, multi-modal datasets, including:

• Observational permafrost metrics: Over 2.9 million permafrost fraction (PF) samples (N = 2,917,285; 171,605 sites; 2005–2021) provide annual, spatially explicit records of permafrost presence (Kriuk, 2 Oct 2025). Additional sources include Circumpolar Active Layer Monitoring (CALM, 265 sites, 1969–2021; 121 points/km²) and GTNP TSP borehole records (MAGT, ZAA; 1901–2020) (Pilyugina et al., 2023).
• Forcing and predictor fields: Climate reanalysis and projections (CRU TS4.05, WorldClim 2.1, CMIP6 scenarios) supply mean annual/seasonal temperature, precipitation, humidity, wind, pressure, and solar radiation, generally regridded to 0.5° resolution.
• Static geospatial fields: IPA Permafrost Map (categorical extent), swampiness (% wetland), vegetation biotope, and permafrost type rasterizations.

Feature engineering yields approximately 38 predictor variables per site-year, incorporating instantaneous and lagged climatic metrics, physics-informed threshold indicators (e.g., above-freezing days, marginal zone flags), thawing/freezing degree-day proxies, energy-balance estimates (shortwave, dewpoint, wind shear), spatial trend and change metrics, and physics-based model outputs such as predicted ALT and MAGT for multiple soil types (Kriuk, 2 Oct 2025, Pilyugina et al., 2023).

3. Hybrid Physics–Machine Learning Frameworks

Model Architectures

A central innovation is the hybridization of machine learning with physics-based modeling to enhance both skill and structural plausibility under extrapolative (novel climate) regimes.

• Stacked ensemble architecture: The ML core uses stage-1 base learners—Random Forest (RF), Histogram-based Gradient Boosting (HGB), and Elastic Net regression—followed by stack ensembling with a Ridge meta-learner. Predictions are generated as:

$$\widehat{\mathrm{PF}}_{\mathrm{ML}}(x)=w_{\mathrm{RF}}\cdot\widehat{\mathrm{PF}}_{\mathrm{RF}}(x)+w_{\mathrm{HGB}}\cdot\widehat{\mathrm{PF}}_{\mathrm{HGB}}(x)+w_{\mathrm{EN}}\cdot\widehat{\mathrm{PF}}_{\mathrm{EN}}(x)$$

where $w_{\mathrm{RF}}$, $w_{\mathrm{HGB}}$, $w_{\mathrm{EN}}$ are meta-learned weights (Kriuk, 2 Oct 2025).

• Hybrid physics-ML scheme: To mitigate unphysical ML projections under out-of-sample warming, ensemble outputs are blended with a physics-based sensitivity model according to:

$$\Delta \mathrm{PF}_{\mathrm{hybrid}} = 0.6 \cdot \Delta \mathrm{PF}_{\mathrm{ML}} + 0.4 \cdot (\alpha \cdot \Delta T + \beta)$$

with $\alpha=-10\,\mathrm{pp}/^\circ\mathrm{C}$, $\beta=0$; the 60% ML / 40% physics weighting controls for both nonlinear pattern fidelity and thermodynamic validity (Kriuk, 2 Oct 2025). For ALT and MAGT, a CatBoost-based regressor ingests climate, static, and physics-based model features, with regularization targeting numerical stability and physical consistency (via implicit physics-informed loss) (Pilyugina et al., 2023).

Cross-Validation and Uncertainty Quantification

Rigorous five-fold spatial cross-validation prohibits any location from appearing in both train and test sets. Temporal splits restrict models to training on earlier years, disallowing information leakage from future climates. Uncertainty is estimated by Monte Carlo sampling for the hybrid coefficients and via ensemble prediction intervals, with high-variance regions flagged in output risk maps (Kriuk, 2 Oct 2025, Pilyugina et al., 2023).

Table 1 summarizes key model validation metrics for the principal frameworks:

Model / Task	RMSE	$R^2$	Coverage
PF hybrid (ensemble)	5.01 pp	0.980	2.9M obs. / Russia
ALT, physics-informed ML	25.53 cm	62%	Circumpolar
MAGT, physics-informed ML	1.08 °C	53%	Circumpolar

4. Scenario-Based Projections and Risk Stratification

Forecasts are produced for three canonical 10-year IPCC scenarios: RCP2.6 ($\sim$+3 °C Arctic), RCP4.5 ($\sim$+5 °C), and RCP8.5 ($\sim$+10 °C). Under RCP8.5 (+5 °C uniform Arctic perturbation over 10 y), the mean projected $\Delta\mathrm{PF}_{\mathrm{hybrid}}$ is –20.27 pp (median: –20.03 pp), with 51.5% of Arctic Russia sites experiencing $>$20 pp loss. Under RCP4.5, mean decline is –11.75 pp; under RCP2.6, –5.73 pp. Spatial distribution analysis identifies southern discontinuous zones as highest vulnerability (Kriuk, 2 Oct 2025).

Classification into infrastructure risk categories (anchored in permafrost physical status and projected decline) uses these thresholds:

Category	Current PF	MAT	$|\Delta$PF$|$
Low (60%)	$>85\%$	$<–5$°C	$<10$ pp
Medium (25%)	$50$–$85\%$	$–5$° to $–2$°C	$10$–$20$ pp
High (15%)	$<50\%$	$>–2$°C	$>20$ pp

High-risk zones are concentrated at 59.5–62.5°N, medium risk extends to ~68°N, and low risk is preserved above 74°N (Kriuk, 2 Oct 2025).

5. Operationalization, Uncertainty, and Geotechnical Decision-Making

Hybrid model outputs provide spatially explicit maps of permafrost state, projected change, risk classification, and 90% confidence intervals (±1 STD of ensemble predictions). Areas where local model disagreement exceeds 10 pp are flaggable for prioritized monitoring. Modelled risk stratification guides:

• Geotechnical site selection and foundation design adjustments (e.g., adaptive frost-heave allowances, piling with thermosyphons),
• Maintenance and retrofit prioritization (particularly in medium/high risk zones),
• Policy actions on asset lifetimes, zoning, and climate adaptation benchmarks,
• Integration with asset location/economic damage databases for regional studies (Kriuk, 2 Oct 2025, Pilyugina et al., 2023).

A recommended workflow overlays forecasts with infrastructure footprints to triage at-risk assets, adjusting safety factors and budget contingencies according to model uncertainty.

6. Open-Source Tools and Methodological Generalizability

Reproducibility and extensibility are enhanced by open-source code and data repositories (https://github.com/sparcus-technologies/Arctic25) and the Python package “permaML,” supporting probabilistic permafrost forecasts under user-specified warming trajectories using a high-level API. Outputs include per-pixel probability distributions of PF, $\Delta\mathrm{PF}_{\mathrm{hybrid}}$, risk category, and uncertainty bounds (Kriuk, 2 Oct 2025).

The hybrid methodology is readily adaptable to other circumpolar regions, North America, or the Third Pole, by retraining on locally available PF and climate data. CMIP6 scenario ensembles and region-specific forcing can be incorporated. The approach is integrable into national and regional infrastructure risk and climate adaptation studies (Kriuk, 2 Oct 2025).

7. Implications for Adaptation Planning and Policy

Physics-informed ML risk maps provide actionable input for engineering codes, land-use planning, and adaptation strategies. Suggested infrastructure responses include:

• Incorporating projected ALT/MAGT increases into foundation and maintenance design (e.g., +20–50 cm ALT allowance, passive cooling in moderate-risk zones, avoidance or robust elevation in high-risk),
• Expansion of monitoring infrastructure in high-uncertainty regions,
• Prioritization of settlement retrofits and emergency planning where permafrost loss is predicted by mid-century,
• Evaluation of co-benefits of emission reduction pathways on infrastructure resilience (Kriuk, 2 Oct 2025, Pilyugina et al., 2023).

This systematic integration of physics-based, data-driven, and uncertainty-aware forecasting constitutes the current state-of-the-art for operational pan-Arctic permafrost infrastructure risk assessment and decision support.