2026/07/23 by Yifan Wu, Guojie Hu, Lin Zhao +11
Earth and Planetary Sciences · #Climate change and permafrost #Arctic and Antarctic ice dynamics #Cryospheric studies and observations
paper · pdf · doi:10.1016/j.earscirev.2026.105637
Extensive ground ice is a defining feature of permafrost regions. Climate warming degrades permafrost by thawing subsurface ice, altering water and heat transfer, and triggering ground subsidence, thermokarst development, and infrastructure instability. Accurately simulating ground ice dynamics and the resulting frost heave and thaw settlement is therefore a central challenge in predicting permafrost degradation and its environmental and hydrological consequences. Although numerical models have made substantial progress in representing coupled thermo-hydro-mechanical processes in frozen ground, considerable discrepancies persist in their accuracy and predictive performance. These discrepancies largely arise from the diverse and often over-simplified ways used to parameterize ground ice, including its initial abundance, phase-change behaviour, moisture redistribution, excess-ice thaw, segregated-ice formation, and contribution to surface deformation. This study presents a systematic review of model representations and parameterization schemes for ground ice processes, highlighting their importance for improving the performance and reliability of permafrost models. It first summarizes how ground ice processes are represented in numerical models, with particular emphasis on different treatments of ground ice formation and degradation mechanisms. It then synthesizes existing model-based simulations of surface deformation associated with ground ice changes. Finally, the review identifies key limitations in current approaches and outlines priorities for future model development, including improved parameterization of ground ice, integration of multi-source observational data, advances in parameter inversion techniques, enhanced thermo-hydro-mechanical coupling, and the application of artificial intelligence for automated parameter calibration. The overarching aim is to strengthen the theoretical foundation for understanding permafrost dynamics and to improve the reliability of model-based predictions.