Climate change effects, such as melting of glaciers and sea ice in response to rising temperatures, may lead to an increase in global water availability and thus in precipitation. In Central Yakutia, as one of the possible options for climate change, an increase in rainfall is possible, which makes up more than 60% of the annual precipitation. Rainfall is a highly variable meteorological parameter both spatially and temporally. In order to assess its effect on the ground temperature regime in Central Yakutia, we conducted manipulation and numerical experiments with increased rainfall. The manipulation experiment results suggest that a significant (three-fold) increase in rainfall can lower the mean annual ground temperatures locally. The long-term simulation predicts that a 50% increase in rainfall would have a warming effect on the ground thermal regime on a regional scale. For Central Yakutia, infiltration of increased precipitation has been shown to have both warming and cooling effect depending on the area affected.

Rapid permafrost degradation is observed in northern regions as a result of climate change and expanding economic development. Associated increases in active layer depth lead to thermokarst development, resulting in irregular surface topography. In Central Yakutia, significant areas of the land surface have been deteriorated by thermokarst; however, no mitigation or land rehabilitation efforts are undertaken. This paper presents the results of numerical modeling of the thermal response of permafrost to changes in the active layer hydrothermal regime using field data from the village of Amga, Republic of Sakha (Yakutia), and mathematical analysis. The results suggest that restoring a thick ice-enriched layer will require increasing the pre-winter soil moisture contents in order to increase the effective heat capacity of the active layer. Snow removal or compaction during the winter is recommended to maximize permafrost cooling. The thickness of the restored transition layer varies from 0.3 to 1.3 m depending on soil moisture contents in the active layer. The modeling results demonstrate that damaged lands can be restored through a set of measures to lower the subsurface temperatures. A combination of the insulating layer (forest vegetation) and the high heat capacity layer (transition layer) in the atmosphere–ground system would be more effective in providing stable geocryological conditions.

Pipelines are critical infrastructure for Yakutia, transporting vital supplies to communities in this vast northern region. The pipeline routes in central Yakutia traverse areas of ice-rich permafrost that is sensitive to temperature changes. This study examined the thermal state of permafrost in undisturbed and disturbed settings along the Lena River to Mundulakh Reservoir water pipeline and the Mastakh to Yakutsk gas pipeline and considered the effects of climatic fluctuations and surface disturbances (forest removal and wildfire) over the monitoring periods of 28 and 18 years, respectively. The geocryological conditions in the study area, as well as the methods of permafrost thermal monitoring, are described. The observation results demonstrated significant increases in the mean annual ground temperature in the upper permafrost layers, as well as in the active-layer thickness following fire and post-fire clearing. At some sites, progressive deepening of the active layer caused the formation of taliks and reached the top of massive ground ice, initiating permafrost degradation. The development of extreme ground temperatures in the layer of annual temperature fluctuations was analyzed according to the combination of seasonal and annual meteorological conditions and the type of anthropogenic impact. The research results can be extrapolated to similar anthropogenic landscapes.

The subject of the research is the functional dependence of the thermal conductivity coefficient of snow on its density. The object of the research was the linearization of the function expressed by a polynomial of arbitrary degree, characteristic of the quantitative dependence of the thermal conductivity coefficient on the density of snow. Special attention is paid to the analysis of errors arising from replacing a polynomial function with a linear one. Particular attention is also given to the analysis of existing functional dependencies of the thermal conductivity coefficient on snow density, which is an integral indicator of complex heat and mass transfer processes occurring during the metamorphism of the snow cover. The main formulas for predicting the thermal conductivity coefficient based on snow density have been analyzed. All dependencies are conditionally divided into two groups: linear and nonlinear (expressed by polynomials of the second, third, and fourth degrees). To find the point corresponding to the maximum value of the linearization error of the second group of methods, an appropriate target function has been constructed and studied in its most general form. When constructing the function defining the absolute error that arises from linearization, a generalized polynomial of derivative degree, which describes known experimental and theoretical dependencies of the thermal conductivity coefficient of snow on its density, was taken as the original formulas. The resulting function was analyzed for its maximum using the classical method of differentiating the original dependence with respect to the argument. The scientific novelty lies in the fact that for the first time a dependence has been obtained between the error arising from the linear and nonlinear representations of the experimental approximating dependencies of the thermal conductivity coefficient of snow and its density. It has been shown that when linearizing a quadratic dependence (Abel's, Kondratyeva's, Brecht's, Sturm's formulas, etc.), the maximum absolute error is found in the middle of the averaging interval. At this point, its value equals the value of the original function. As the degree of the polynomial increases, the maximum error shifts toward the upper boundary of the linearization interval and changes, for example, for the cubic polynomial (Van Dussen's formula) to a value equal to 0.58 of the linearization range. For the fourth-degree polynomial (Jansen's formula), it reaches 0.63 of the range. When the degree is reduced to less than two (Yen's, Schwander's formula), on the contrary, the maximum linearization error shifts from the middle of the interval to the lower boundary.