Modeling the Role of the Cryosphere in the Definition of Ground Temperature in an Alpine Permafrost Area

The abundant existence of permafrost in the Alpine region provokes the need to understand permafrost characteristics in the area, given the context of rapid climate change and its potential impacts on permafrost and its containing environments. Understanding the processes involved in permafrost degradation and active layer thickening requires studying the governing energy regime. This is done by properly tuning and applying physically based models to simulate the occurring processes and model the parameters of interest such as the snow cover and surface soil temperature. This research work aims to provide a better understanding of the role of the surface energy balance in a permafrost-dominated area in Passo del Monte Moro, Macugnaga, in the Italian Alps. The main focus is to appropriately model the thermodynamical processes governing the evolution of the cryosphere, namely the snow layer, to investigate its role in defining the ground temperature in the presence of permafrost. The study is done at a point-scale by utilizing a state-of-the-art hydrological model (GEOtop), with a complete representation of the thermodynamical processes controlling the evolution of the snow layer. In its full configuration, GEOtop is a complete distributed hydrological model, with coupled water and energy budgets, while here it used in its 1D mode at a single point. The evaluation extends over the period from August 2012 till August 2018 and is done at an elevation of 2823 m s.l.m. Initial setting of the model was achieved by forcing accurate sky view factor and topographic aspects of the area. Studying the thermodynamic processes and setting the physical parameters in each module aided the proper tuning of the model. The model results are assessed against measured snow depth and surface ground temperature at Passo del Moro monitoring station, and they show good agreement for most years included. The initial realization of model results shows that the accuracy of meteorological input data is an integral part of a successful simulation of the snow depth. This was overcome by correcting the precipitation input data by different methods, using real snowfall and wind speed data, and lead to great improvement in the accuracy of model results. A sensitivity analysis is also done to determine the most important parameters affecting GEOtop model outcomes. The highlighted parameters that resulted are the threshold temperature for discriminating rain and snow, parameterization equation of incoming longwave radiation and snow correction factor, having important impacts on snow depth variations including the accumulation and melting trends.