Andrei Viktorovich Koshurnikov

The subject of the research is the change in temperature and water-phase state of permafrost rocks in the technogenically disturbed area of the Norilsk industrial region until 2060. The initial state of 11 sections to a depth of 10–20 meters was determined based on the thermometry data from 2023. Below the observation interval, the temperature profiles were retrospectively extended to a depth of 125.36–126.03 meters. For 2024–2025, actual temperature and precipitation data from the Norilsk station were used. Starting from 2026, the repetition of climatology and three CMIP6 trajectories were considered. For the warmest trajectory SSP5-8.5, additional comparisons were made regarding snow clearance, natural and increased snow accumulation, as well as the removal, preservation, and development of the aboveground part of the vegetation cover. Temperature, the position of the roof and bottom of the permafrost, the thickness of the frozen layer, water reserve, and ice content were assessed. A coupled heat and moisture transfer scheme with daily updates of the water state was applied. Factors such as unfrozen water, infiltration, hydraulic redistribution, and parameterized migration-segregation of ice release were taken into account. Calculations were compared that differed only by the value of one factor. By 2060, under SSP5-8.5, relative to the state at the end of 2024, the temperature at depths of 2, 5, and 10 meters increased by 1.81 °C, 1.03 °C, and 0.62 °C, respectively. The phase roof deepened by 2.41 meters, and the thickness of the frozen layer in ten sections decreased by 2.09 meters, with the ice reserve reducing by 9.6%. Relative to natural snow accumulation, snow clearance lowered the temperature at 2 meters by 1.50 °C and raised the roof by 2.37 meters; increased snow accumulation enhanced warming. For the slag heap area, this factor-based assessment of climate, snow, and vegetation is conducted for the first time. Under principle I, the results characterize the stability of the frozen state and the role of snow accumulation; under principle II, they address the depth and rate of thawing and provide phase initial data for calculating deformations.

This paper presents the results of detailed monitoring of temperature fields and snowpack stratigraphy during the 2025/2026 winter-spring transition. The study was conducted at the Moscow State University Meteorological Observatory. Detailed stratigraphic descriptions of the snowpack, along with measurements of snow density and temperature, were also conducted. Changes in the thickness and thermal field of the snowpack at the Moscow State University Meteorological Observatory’s site were examined. Air and snowpack temperature measurements were conducted using an autonomous automatic system. The study aimed to determine characteristic diurnal and synoptic thermal rhythms, assess the insulating role of the snowpack, and describe the snowpack’s complex stratigraphy, including numerous ice crusts and deep hoar horizons. The feasibility of using an automatic measurement system to determine snowpack thickness based on its thermal field was tested. The study was conducted at the Moscow State University Meteorological Observatory. Using an original high-resolution measuring system (5 cm increments, 30 min–4 h time resolution), snow cover temperature was recorded. Detailed stratigraphic descriptions of the snowpack, along with measurements of snow density and temperature, were also conducted. It was shown that the winter season was characterized by an abnormally late onset of stable snow cover (December 22) and subsequent extreme snow accumulation, with an absolute snow depth record for 72 years of observations (86 cm on February 26, 2026). The study demonstrates the feasibility of measuring snow cover thickness by monitoring the thermal field at Moscow State University observation sites. Air temperature and snow cover measurements were conducted using autonomous automatic systems. The studies have shown the magnitude of snow thickness and identified thermal waves penetrating the snow layer at night, allowing us to estimate temperature gradients and thermal conductivity in the snow layer, as well as determine snow thickness based on the magnitude of daily temperature fluctuations and penetrating temperature waves.

This study examines the geocryological environment of a site near Norilsk’s Nickel Plant slag dump. The rectangular area spans 600 by 1000 meters. The goal is to assess permafrost properties within the geological section. The section is analyzed using geophysical methods to depths of 15 meters, with data validation through boreholes averaging 15 meters, one reaching 20 meters. Sparse and heterogeneous data necessitate interpolation for continuous models. Interpolation algorithms, including a three-dimensional Bayesian approach, were used with parameter tuning for search radius, neighbors, and covariance function type. This approach accounts for soil property variability and improves spatial model accuracy. The study adapts methods for reliable geocryological modeling. Analysis uses ArcGIS Pro, employing the empirical Bayesian method with validation through borehole and geomorphological data. Key conclusions include a methodology integrating geophysical investigations and statistical processing for permafrost modeling. The three-dimensional approach better captures environmental variability and enhances accuracy, confirmed by borehole data. For instance, the seasonally thawed layer’s thickness identified through geophysics aligns with geomorphological and lithological features. A three-dimensional method, Bayesian Kriging 3D, was adapted for permafrost conditions. Parameters like covariance function type, partitioning scale, and neighbors were studied. This is the first evaluation of empirical Kriging’s effectiveness in this area. The results support infrastructure planning and resource management, demonstrating advanced geostatistical techniques’ applicability for Arctic permafrost modeling.

The article presents the results of our seismic measurements carried out in the Earth Science Museum on the 32nd floor of the main building of MSU in 2020-2024, during which it was possible to record the period of natural vibrations of the main building, to assess its “health”, and to consider it as a tool for detecting the responses of distant earthquakes.

Using numerical modeling of a useful signal based on a characteristic geoelectric model of underwater permafrost and real recordings of a noise signal, we conducted a comparative analysis of noise suppression during transient sounding using a marine towed dipole-dipole array in accumulation modes with opposite-polar current pulses and pseudo-noise signals (PNS). For a series of current sequences in the form of PNS with different durations and numbers of pulses, as well as for a signal in the accumulation mode, with the superposition of an identical noise signal, transient sounding curves were obtained corresponding to the geoelectric model under consideration at a recording time of the order of 8-13 s per station. Based on the results of comparison of the obtained curves reconstructed from noisy synthetic data in the accumulation and PNS modes, it was established that in the PNS mode with pulses of duration 100 μs and 1 ms, the relative error in the PNS mode on average over the profile turns out to be significantly lower (up to 1.5 times) than a similar one error in the accumulation mode, and remains within an acceptable value (up to several percent) until later times.

Using numerical modeling of a useful signal based on a characteristic geoelectric model of underwater permafrost and real recordings of a noise signal, we

The study focuses on improving the accuracy of geophysical investigations of anthropogenic metallic pollution using digital elevation models (DEMs). The methodology was tested at the Norilsk site, located in close proximity to the slag heaps of the nickel plant, based on DEM data obtained through unmanned aerial vehicles (UAVs) to conduct a detailed geomorphological analysis of the area. Geophysical methods such as the natural field (NF) method and frequency sounding were also used to identify zones of potential metallic pollution, as well as the characteristics and spatial positions of predicted permafrost layers. The main emphasis is on the advantages of integrating geophysical methods with DEMs and derived cartographic materials to improve the accuracy and reliability of surface metallic pollution detection and mapping, considering the geocryological situation within the study area. Geophysical methods of natural field (NF) and frequency sounding were employed to determine the geoelectrical properties of the study area. Reconnaissance surveys and flight missions were conducted to create the digital elevation model and orthoimage, and a surface water runoff map was developed based on the generated rasters and their interpretation. Based on the obtained geophysical data, zones of possible metallic pollution spread from the slag heaps were identified, which were confirmed through DEM analysis by the geomorphology of the study site. The distribution of specific electrical resistance fields was also interpreted in consideration of the collected data and validated through borehole drilling. It is worth noting that the integrated approach helped reveal the need to revise the interpretation plan, as only through the analysis of the nature and intensity of metallic pollution could the abnormally low resistance values in areas far from thermokarst lakes, with relatively shallow permafrost depths, be explained. Additionally, it became possible to qualitatively assess the zones of dust material accumulation.