Statistical modelling of mountain permafrost distribution: Local calibration and incorporation of remotely sensed data

Field mapping of mountain permafrost is laborious and is generally based on interpolation between point information. A spatial model that is based on elevation and a parameterization of solar radiation during summer is presen ted here. It allows estimation of permafrost distribution and can be calibr ated locally, based on bottom temperature of snow (BTS) measurements or oth er indicators such as mapped features of permafrost creep. Local calibratio n makes this approach flexible and allows application in various mountain r anges. Model output consists of a continuous field of simulated BTS values that are subsequently divided into the classes 'permafrost likely','permafr ost possible' and 'no permafrost' following the rules of thumb established for BTS field measurements in the Alps. Additionally, the simulated BTS val ues can be interpreted as a crude proxy for ground temperature regime and s ensitivity to permafrost degradation. A map of vegetation abundance derived from atmospherically and topographically corrected satellite imagery was i ncorporated into this model to enhance the accuracy of the prediction. Base d on the same corrected satellite image, a map of albedo was derived and us ed to calculate net short-wave radiation, in an attempt to increase model a ccuracy. However, the statistical relationship with BTS did not improve. Th is is probably due to the correlation of short-wave solar radiation with sn ow-melt patterns or other factors of permafrost distribution which are bein g influenced differently by the introduction of albedo. Copyright (C) 2001 John Wiley & Sons, Ltd.