Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS-based mapping method, centered on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement to the previously designed methodology, requiring a smaller amount of data, bringing new preliminary information on damming hazard management and risk reduction, and identifying the most critical area within the Central Asia regions.

The densely populated central part of the Ile Alatau mountains is one of the most mudflow-prone areas of Kazakhstan. Implementation of protection measures, early warning systems, and risk management plans is crucial to protect livelihoods and infrastructure from damage caused by mudflows. Increasing harm and damage from mudflows in recent decades—due to more frequent events, as well as increased economic development of the area—has made the establishment and implementation of a mudflow risk management system a priority. The effectiveness of such a system largely depends on the scientific validity of the management plan and hence is determined by the level of knowledge of the physical processes triggering such events. This knowledge is based on information that must be collected, analyzed, and systematized. However, such data are not easy to access; they are scattered over different archives and research institutes or simply missing. In recent years, scientific monographs, articles, and reports have been published that attempt to collect and systematize data on mudflow phenomena in general. These efforts provide a basis for further work but are often not readily available for use. This article presents the updateable, interactive, intelligent information system “Mudflow phenomena of the central part of the Ile Alatau” that links cartographic information with data on mudflow formation centers. This system concentrates and collates existing knowledge, making it accessible to stakeholders and decision-makers who can turn this knowledge into suitable applications for adaptive and sustainable risk management.

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems.

Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss, offers a representative case for studying climate-driven degradation. This study quantitatively assessed the influence of air temperature, precipitation, aridity index, and extreme climatic events on soil properties in the arid regions of western Kazakhstan (Atyrau and Mangystau). The analysis integrated long-term meteorological time series (1941–2023) with field and laboratory data (1967–2024) into a harmonised dataset of 1330 records. Principal component analysis (PCA) identified four degradation gradients explaining 73.6% of total variance, while Random Forest and SHAP algorithms quantified variable importance. Mean annual temperature, frequency of arid years, and aridity index were the strongest predictors of humus, salinity, pH, and CO₂ parameters, with climate factors accounting for up to 30% of soil variability. The findings demonstrate that climatic stressors are the main drivers of soil degradation in arid zones, with climate factors explaining up to 30% of the variability in key soil properties (humus, salinity, pH, and CO₂)—a substantial proportion that underscores their dominant role relative to local geochemical and anthropogenic influences. The proposed hybrid PCA—Random Forest/SHAP framework provides a robust tool for analysing climate–soil interactions and supports the design of adaptive land-use strategies to achieve Land Degradation Neutrality (LDN) in Kazakhstan and other arid regions.

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective was to reassess key morphometric parameters—surface area, depth, volume, and shoreline configuration—more than six decades after the only existing survey from 1962. High-density depth data were acquired with a Lowrance HDS-12 Live echo sounder, achieving vertical precision of ±0.17 m, and processed using ReefMaster and ArcGIS to produce a three-dimensional, hydrologically correct model of the lake basin. Compared with archival data, results show that while the surface area (455.365 ± 0.005 km²), length (38.304 ± 0.002 km), and width (19.138 ± 0.002 km) have remained stable, the maximum depth is lower (24.14 ± 0.17 m vs. 27 m), and the total water volume is slightly higher (6.667 ± 0.025 km³ vs. 6.37 km³). These differences highlight both the limitations of historical lead-line surveys and the enhanced accuracy of modern hydroacoustic and GIS-based methods. The workflow developed here is transferable to other remote alpine lakes, providing an invaluable baseline for limnological research, ecological assessment, hydrodynamic modeling, and long-term water resource management strategies in data-scarce mountain regions.

The active development of the foothill areas of the Almaty agglomeration (Southeastern Kazakhstan) requires reliable methods for landslide susceptibility assessment. This study aims to identify the spatial association between landslide occurrence and selected environmental and anthropogenic factors using the Frequency Ratio (FR) method and geographic information systems. The analysis is based on an inventory of 157 landslides, the SRTM digital elevation model, Landsat imagery, WorldClim climate data, geological maps, and OpenStreetMap data. Ten conditioning factors were analyzed: elevation, slope, aspect, precipitation, lithology, distance to faults, rivers and roads, the Normalized Difference Vegetation Index (NDVI), and land use. FR values were calculated for each factor and integrated to produce a landslide susceptibility map. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). The resulting susceptibility map identifies areas with different levels of landslide susceptibility and provides a scientific basis for sustainable land-use planning, engineering-geological investigations, safer infrastructure development, natural hazard assessment, and disaster risk reduction in rapidly developing foothill regions. By supporting risk-informed land-use decisions and targeted mitigation measures, the study contributes to the long-term environmental safety and resilience of the Almaty agglomeration.