Changes in precipitation patterns, a fundamental aspect of climate change, can significantly impact soil erosion processes. This article aims to evaluate the current state of soil erosion in the Talas area utilizing the Revised Universal Soil Loss Equation (RUSLE). Climate projections for the study were obtained through the CMIP6 Global Climate Model (GCM) and the climatic data were integrated into the RUSLE to simulate potential changes in soil erosion patterns. The mean annual soil erosion rate, observed over the research duration, ranges from 0 to 127 (t y⁻¹). Results indicate that 56.29% of the study area is characterized by a low susceptibility to soil erosion, with an additional 33.56% classified as at moderate risk and 7.36% deemed at high risk of erosion. Furthermore, the evaluation reveals an average increase in precipitation levels compared to the baseline. Models project a rise of 21.4%, 24.2%, and 26.4% by the years 2030, 2050, and 2070, respectively. Concurrently, the study observes a parallel increase in soil loss with precipitation, demonstrating a rise of 34%, 35.5%, and 38.9% for the corresponding time periods. Also, the spatially distributed results show that the southern part of the territory of the Talas region has been impacted by erosion over the past and will also be in the future period. These findings underscore the intricate interplay between climate-induced changes in precipitation and their significant impact on soil erosion. The results provide essential insights for developing targeted soil conservation strategies in the Talas area under evolving climatic conditions.

With the rapid shifts in environmental conditions, accurately predicting soil erosion has become crucial for the sustainable management of land resources. This study introduces a deep learning-based approach to forecast soil erosion risks in Western Kazakhstan up to 2030, focusing on the LS factor defined by the Universal Soil Loss Equation (USLE). High-resolution digital elevation models (DEMs) from ASTER GDEM and historical data on climate and land use were utilized to train a convolutional neural network (CNN), enabling projections of future LS-factor changes and the corresponding erosion risks. To further improve the accuracy of LS-factor calculations, the System for Automated Geoscientific Analyses (SAGA) was applied using a multiple-flow algorithm. The results forecast a significant rise in erosion risk by 2030, with areas having LS values between 8 and 24 expected to increase by 10%, and those with LS values above 24 by 0.05%, potentially affecting an additional 24,000 km². The model achieved a 92% accuracy rate, underscoring the effectiveness of deep learning in environmental risk analysis. The integration of SAGA results provides a more detailed understanding of the erosion processes, enhancing the precision of the predictions.

While rock glaciers (RGs) are widespread in the Zhetysu Alatau mountain range of Tien Shan (Kazakhstan), they have not yet been systematically investigated. In this study, we present the first rock glacier inventory of this region containing 256 rock glaciers with quantitative information about their locations, geomorphic parameters, and downslope velocities, as established using a method that combines SAR interferometry and optical images from Google Earth. Our inventory shows that most of the RGs are talus-derived (61%). The maximum downslope velocity of the active rock glaciers (ARGs) was 252 mm yr⁻¹. The average lower height of rock glaciers in this part of the Zhetysu Alatau was 3036 m above sea level (ASL). The largest area of rock glaciers was located between 2800 and 3400 m ASL and covered almost 86% of the total area. Most rock glaciers had a northern (northern, northeastern, and northwestern) orientation, which indicated the important role of solar insolation in their formation and preservation.

The study aims to evaluate the impact of climate change on pasture ecosystems in Central Kazakhstan, particularly within the Karaganda and Ulytau regions. The assessment combines remote sensing indicators (NDVI, LST) with long-term climatic datasets (CRU TS v4.09 and national meteorological records) for the period 2000–2024. Non-parametric statistical methods, including the Mann–Kendall trend test, Sen’s slope estimator, and Pettitt’s test, were applied to identify the direction, intensity, and structural shifts in temperature and precipitation trends. The results indicate significant regional warming, especially during summer and spring, alongside spatially inconsistent precipitation changes. The southern and southwestern areas (Zhezkazgan and Satpayev) show intensified aridization, manifested in rising land surface temperatures, decreasing rainfall, and declining vegetation productivity and exacerbated by anthropogenic pressures. Conversely, the eastern and northeastern regions exhibit stable or increasing NDVI values and moderate precipitation growth, suggesting potential for natural recovery. The study concludes that pasture degradation in Central Kazakhstan is driven by combined climatic and human factors, with pronounced spatial heterogeneity. The integrated approach enhances the reliability of climate impact assessments and provides a scientific basis for developing adaptive and region-specific strategies for sustainable pasture management under ongoing climate change.

Central Asia is well-known for its productive irrigated lands and pastures. However, the region is currently dealing with serious land degradation and soil salinization issues. This article discusses the problem of soil salinization in the Kazakh part of the Myrzashol massif (The Hungry Steppe), where irrigated agriculture is developed. The reclamation state of the soil cover in this massif was assessed using field and laboratory studies from 1995 to 2014 and from 2014 to 2020. In the study area, in the period from 2015 to 2020, several samples were continuously taken before the growing season, as well as after the growing season at 529 wells for sampling the level and mineral composition of groundwater and the chemical composition of the soil. If in 1995, out of 125.4 thousand hectares of irrigated land, 31.8 thousand hectares (25.4%) were moderately and heavily salted, then in 2020, out of 146.5 thousand hectares of land, 57 thousand hectares (38.7%) were subjected to secondary salinization. Over the last 25 years, the area of moderately and heavily saline lands on the irrigated Myrzashol massif has grown by 25.2 thousand hectares. Before the transformation of the Myrzashol zone into an irrigated one, the groundwater level was within 15-30 m. In 2020, the groundwater level on the irrigated area of 3622 ha was 0-1 m (in 1994 it was 105 ha), on 38,155 ha (in 1994 it was 7,792 ha) - 1-2 m. The results of the study showed that the increase in the level of salinity of the soil cover is associated with an increase in the groundwater level due to the poor level of irrigation systems, as well as the weak filtration capacity of soils.

An updated glacier inventory is important for understanding the current glacier dynamics in the conditions of actual accelerating glacier retreat observed around the world. Here, we present a detailed analysis of the glaciation areas of the Zhetysu Alatau Range (Tien Shan) for 1956–2016 using well-established semiautomatic methods based on the band ratios. The total glacier area decreased by 49 ± 2.8% or by 399 ± 11.2 km² from 813.6 ± 22.8 km² to 414.6 ± 11.6 km² during 1956–2016, while the number of glaciers increased from 985 to 813. Similar rates of area change characterized the periods 1956–2001, 2001–2012, 2012–2016, and 2001–2016: −296.2 ± 8.3 (−0.8% a⁻¹), −63.7± 1.8 (−1.1% a⁻¹), −39.1 ±1.1 (−2.2% a⁻¹) and −102.8 ± 2.9 (−1.3% a⁻¹) km², respectively. The mean glacier size decreased from 0.57 km² in 2001 to 0.51 km² in 2016. Most glaciation areas of the Zhetysu Alatau faced north (north, northwest, and northeast), covered 390.35 ± 11 km², and were located in altitudes between 3000 and 4000 m.a.s.l. With shrinkage rates of about −0.8% and −1.3% a⁻¹ for the periods of 1956–2001 and 2001–2016, our results show that study area has the highest shrinkage rate compared to other glacierized areas of Central Asian mountains, including Altai, Pamir, and even the inner ranges of Tien Shan. It was found that a significant increase in temperature (0.12 °C/10 years) plays a main role in the state of glaciers.