In alpine environments at middle latitudes, the spatial distribution of mountain permafrost is strongly controlled by local climatic conditions, and specifically by snow cover. We present a physical model, PERMACLIM, which uses as input data a Climatic DataBase (CDB) and Digital Elevation Model (DEM). The model calculates the Mean Annual Ground Surface Temperature (MAGST) for each point of the DEM including the snow buffering effect according to the heat conduction theory, which uses both the CDB and field measurements of snow thermal characteristics. PERMACLIM provides a permafrost map based on MAGST values, where permafrost is designated as absent, probable or present, respectively, with MAGST values higher than 0 Celsius, ranging from 0 to -2 C and lower than -2 C. PERMACLIM can be improved by incorporating the ground surface temperatures during the snow-free period when solar radiation provides energy to the ground surface (PERMACLIM B). Both models have been tested in the Passo del Foscagno area, Italian Central Alps, comparing the output with the known permafrost distribution (obtained with geophysical investigations and borehole data) and with the permafrost distribution derived by Bottom Temperature of winter Snow cover (BTS). The model prediction of the permafrost distribution gives a good result compared with the permafrost distribution detected with geophysical investigations. Work to include snow redistribution by wind, snow melting and vegetation influences should further improve these results.
PERMACLIM: a model for the distribution of mountain permafrost, based on climatic observations.
Aldighieri B;Testa B
2003
Abstract
In alpine environments at middle latitudes, the spatial distribution of mountain permafrost is strongly controlled by local climatic conditions, and specifically by snow cover. We present a physical model, PERMACLIM, which uses as input data a Climatic DataBase (CDB) and Digital Elevation Model (DEM). The model calculates the Mean Annual Ground Surface Temperature (MAGST) for each point of the DEM including the snow buffering effect according to the heat conduction theory, which uses both the CDB and field measurements of snow thermal characteristics. PERMACLIM provides a permafrost map based on MAGST values, where permafrost is designated as absent, probable or present, respectively, with MAGST values higher than 0 Celsius, ranging from 0 to -2 C and lower than -2 C. PERMACLIM can be improved by incorporating the ground surface temperatures during the snow-free period when solar radiation provides energy to the ground surface (PERMACLIM B). Both models have been tested in the Passo del Foscagno area, Italian Central Alps, comparing the output with the known permafrost distribution (obtained with geophysical investigations and borehole data) and with the permafrost distribution derived by Bottom Temperature of winter Snow cover (BTS). The model prediction of the permafrost distribution gives a good result compared with the permafrost distribution detected with geophysical investigations. Work to include snow redistribution by wind, snow melting and vegetation influences should further improve these results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
