The intensifying effects of climate change have led to increased flooding, even in desert regions, resulting in significant socio-economic and ecological impacts. This study analyzes the causes and consequences of flooding in the Zhem River basin using data from ground stations, including Kazhydromet, and satellite platforms such as USGS FEWS NET and Copernicus. Spatial analyses conducted in ArcGIS utilized classified raster data to map the dynamics of flooding, snow cover, vegetation, and soil conditions. This enabled a geoecological analysis of flood damage on the vital components of the local landscape. Results show that flooding in the Zhem River basin was driven by heavy winter precipitation, rapid snowmelt, and a sharp rise in spring temperatures. The flood damaged Kulsary city and also harmed the region’s soil, vegetation, and wildlife. In July 2024, the flooded sail area tripled compared to the same period in 2023. Additionally, the area of barren land or temporary water bodies (pools) formed three months after the water receded also tripled, increasing from 84.9 km² to 275.7 km². This study highlights the critical need for continued research on the long-term environmental effects of flooding and the development of adaptive management strategies for sustainable regional development.

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.

New hydrochemical measurements from the Syr Darya provide insights into factors affecting the composition and quality of a major freshwater source replenishing the Aral Sea. This river is heavily used for power and irrigation and crosses territories of four Central Asia republics. It is intensely managed, draining several major tributaries, many reservoirs, and numerous irrigation distribution systems and canals. Analysis of seasonal changes in dissolved ion concentrations using geochemical diagrams, elemental ratios, statistical correlation, and equilibrium modeling allowed the characterization of mineral formation processes that control the dissolved chemical composition. Measured water hydrochemistry and composition type differs substantially from previous reports from the upper reaches of the Syr Darya in Kyrgyzstan. Element ratios, statistical correlation, and the presence of NO3- and NO2- suggest that the downstream trend of increasing total dissolved solids (TDS) from Zhetysay city to the Aral Sea in June is controlled by evaporation–crystallization processes, which contribute to the river dissolved load through soil runoff and return irrigation waters following leaching of secondary salts. Downstream sample composition during the growing season shows significant changes in magnesium-to-calcium ratios in the river water. Elevated magnesium levels in Syr Darya waters may pose a problem to sustainable uses for irrigation.

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.

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.

As a consequence of global population growth and increasing demand for agricultural commodities, vast areas of cultivable land have been brought under cultivation, while natural ecosystems are being converted for agricultural use. This transformation has led to various forms of soil degradation, with excessive salinity emerging as a critical concern. Drought-prone ecosystems are particularly vulnerable to salinization processes, resulting in an annual reduction of arable land by approximately 1% to 2%. In the Kazakhstan, salt-affected soils account for approximately 43% of all agricultural land. The aim of this study is to assess the extent and spatial distribution of soil salinity in the Talas district of Kazakhstan and to identify the dominant chemical characteristics of soil salts across different land types. To achieve this, soil samples were collected from river valleys, desert pastures, irrigated lands, and sandy areas. The analysis focused on salinization processes, ion toxicity thresholds, and the chemical composition of soil salts. Results reveal that salt accumulation varies between the 30 cm and 80 cm soil layers in river valleys and desert pastures, whereas the highest concentrations are observed in the upper layers of irrigated soils. The greatest variation in average salt content by soil texture was ?0.81% in loamy soils and ?0.62% in silty loam soils. These findings highlight the urgent need for ecologically sound land management strategies to mitigate soil salinization, especially in irrigated areas. They also offer valuable insights for enhancing irrigation efficiency and preserving soil fertility in Talas and other comparable regions.