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Arctic permafrost is rapidly degrading in response to global warming. Its thermodynamic evolution governs carbon emissions, hydrological shifts, and terrain stability, with critical consequences for both natural systems and built infrastructure. Accurate prediction of the thermodynamic behavior of permafrost remains elusive, hindered by limited observations and underdeveloped methodologies. Here, we introduce a digital twin framework that integrates differentiable modeling (DM) with high spatial resolution distributed temperature sensing (DTS) data to predict and infer key permafrost characteristics-ground temperature, unfrozen water content, thermal conductivity, and heat capacity. By leveraging a neural-network-based parameterization, our framework fuses observational data with physical heat transfer equations, enabling real-time calibration and updating of the spatiotemporally varying soil thermodynamic characteristics. Applied to permafrost beneath a road embankment in Utqia & gdot;vik, Alaska, the digital twin accurately reconstructs the spatiotemporal evolution of soil temperature fields and captures spatial variability in permafrost thermodynamic properties. The prediction results were further validated against shear-wave velocity distributions inferred from distributed acoustic sensing (DAS), temperature data obtained from borehole thermistors, and thermodynamic properties measured by laboratory testing, demonstrating the framework's robustness. This work advances the predictive understanding of permafrost dynamics under climate change and establishes a generalizable pathway for digital twin applications in Arctic science.

来源平台：JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE