Mapping “cold spots” of potential hidden alpine permafrost using semi-supervised machine learning

Geophysical techniques revealed frozen ground within relict periglacial landforms in which the presence of ice was excluded by traditional geomorphological and topographic approaches. These unexpected frozen bodies, referred to here as cold spots, suggest that permafrost can exist outside traditionally mapped permafrost zones. Under climate change, with retreating glaciers and increasing snow variability, subsurface ice in periglacial landforms becomes a potentially important but overlooked water resource. However, its spatial distribution and climatic controls remain poorly understood.

Here, we develop a methodology to identify cold spots. We focus on the Southern Alps and we assume that cold spots are related to micro-climatic and topographic conditions that allow permafrost to persist. We use a limited set of sites investigated by geophysical surveys, including confirmed cold spots and geomorphologically similar control sites without permafrost. We analyze topographic and climatic remote-sensing data to derive relevant features and examine their relation to cold spots. We then use these features in semi-supervised machine learning classification models to identify areas with conditions similar to known cold spots. The resulting maps highlight potential cold-spot locations targeted for forthcoming geophysical field investigations and provide a practical framework for improving the detection of hidden permafrost.