ИСТИНА

The topic of climate change has been attracting increasing attention from scientists in recent decades. These studies cannot avoid the topic of permafrost degradation, which is a critically important component of the Earth's system. Permafrost, which covers significant areas in the north of Russia and other Arctic regions, plays a key role in regulating climate, geomorphological processes and biogeochemical cycles. However, as a result of climate change and anthropogenic activities, the permafrost is rapidly melting, which has serious consequences for the natural environment, society and the economy. Global climate warming causes an increase in the depth of seasonal thawing of the soil, which is accompanied by a decrease in the strength of permafrost rocks, has a significant impact on the formation of the landscape, the hydrological regime, which is manifested, among other things, in the intensification of erosion processes. The intensification of erosion caused by the melting of permafrost leads to significant changes in the relief, hydrological and hydrochemical regimes, as well as natural biocenoses. This leads to threats to the safety of the population and infrastructure, to deterioration of conditions for agriculture and disruption of the living space and way of life of local communities and indigenous peoples of the north. In addition, permafrost, being a storage of conserved carbon, can become a source of even greater warming with the release of greenhouse gases when the climate warms. In this study, it is proposed to carry out a systematic analysis of erosion processes in the context of permafrost degradation using modern scientific research methods. The main attention is supposed to be paid to the study of the mechanisms of erosion, numerical estimates of denudation and analysis of the patterns of the spread of erosion processes in the cryolithozone, their impact on economic activity. The main research tools are high-level programming languages for analyzing global databases of climatic, hydrological and geological information, machine learning methods for creating predictive models of the development of erosion processes, physical and mathematical hydrodynamic models of water flow movement.