Evaluating Simplifications of Subsurface Process Representations for Field-scale Permafrost Hydrology Models

Abstract. Permafrost degradation within a warming climate poses a significant environmental threat through both the permafrost carbon feedback and damage to human communities and infrastructure. Understanding this threat relies on better understanding and numerical representation of thermo-hydrological permafrost processes, and the subsequent accurate prediction of permafrost dynamics. All models include simplified assumptions, implying a tradeoff between model complexity and prediction accuracy. The main purpose of this work is to investigate this tradeoff when applying the following commonly made assumptions: (1) assuming equal density of ice and liquid water in frozen soil; (2) neglecting the effect of cryosuction in unsaturated freezing soil; and (3) neglecting advective heat transport during soil freezing and thaw. This study designed a set of 62 numerical experiments using the Advanced Terrestrial Simulator (ATS v1.2) to evaluate the effects of these choices on permafrost hydrological outputs, including both integrated and pointwise quantities. Simulations were conducted under different climate conditions and soil properties from three different sites in both column- and hillslope-scale configurations. Results showed that amongst the three physical assumptions, soil cryosuction is the most crucial yet commonly ignored process. Neglecting cryosuction, on average, can cause 10 % ~ 20 % error in predicting evaporation, 50 % ~ 60 % error in discharge, 10 % ~ 30 % error in thaw depth, and 10 % ~ 30 % error in soil temperature at 1 m beneath surface. The prediction error for subsurface temperature and water saturation is more obvious at hillslope scales due to the presence of lateral flux. By comparison, using equal ice-liquid density has a minor impact on most hydrological variables, but significantly affects soil water saturation with an averaged 5 % ~ 15 % error. Neglecting advective heat transport presents the least error, 5 % or even much lower, in most variables for a general Arctic tundra system, and can decrease the simulation time at hillslope scales by 40 % ~ 80 %. By challenging these commonly made assumptions, this work provides permafrost hydrology modelers important context for better choosing the appropriate process representation for a given modeling experiment.