A comparison study of process complexity in permafrost dominated regions

The Qinghai-Tibet Plateau (QTP), also known as the “Water tower of Asia”, is threatened by climate warming. Climate warming leads to permafrost degradation, which in turn affects the natural and man-made environment. Permafrost is defined as ground where temperatures remain at or below 0°C for a minimum period of two consecutive years. Near-surface atmospheric processes give rise to seasonal thawing and freezing of permafrost. The thawing promotes groundwater movement because of the increase in liquid water content and hydraulic conductivity. The pore water phase change from ice to liquid also causes variation in the thermal parameters of the soil leading to non-linear coupled processes. Therefore, complex interactions exist between hydraulic and thermal surface and subsurface processes.

Numerical models are useful tools to study coupled processes. Model complexity arises as several physical processes need to be considered, especially due to the presence of permafrost. The amount of input data, parameters, boundary conditions and hence the difficulty increases as the number of physical processes increases. The main aim of this research work is to therefore conduct a comparison study of three modelling scenarios: (i) Coupled subsurface flow and energy transport with ice content, (ii) including coupled surface flow and surface energy balance to scenario (i), (iii) including snow component to scenario (ii). The Advanced Terrestrial Simulator (ATS) and Parameter ESTimation (PEST) codes were applied for simulation and calibration, respectively. The near-surface temperature and moisture measurements from a meteorological station at QTP were used for calibration. Results show that all three models have good agreement with the measurement dataset, however scenario (i) exhibited the best performance in terms of both matching the measured data and representative literature parameter values. Future work will focus on predicting permafrost behavior under various climate change scenarios.