The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin.

Floods are among the most frequent and destructive natural hazards in Kazakhstan, particularly in the Abai Region, Kazakhstan, where topographic, hydrological, and climatic factors strongly influence flood occurrence. This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region using a multi-criteria Geographic Information System (GIS) approach. The analysis integrates twelve flood-conditioning factors representing hydrological, topographic, environmental, and anthropogenic variables. The relative importance of these factors was determined using the Analytical Hierarchy Process (AHP). The results indicate that distance to rivers (20%) and precipitation (16%) are the most influential drivers of flood susceptibility, followed by Height Above Nearest Drainage (HAND) (11%) and drainage density (9%). The resulting flood susceptibility map classifies the study area into five susceptibility levels. Approximately 56.6% of the study area falls within the moderate susceptibility class, while 25.0% is categorized as high susceptibility, mainly concentrated in low-lying floodplains and foothill regions. Low-susceptibility areas account for 18.1% of the region, whereas the very high and very low susceptibility classes together represent less than 1% of the territory. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, yielding an Area Under the Curve (ROC–AUC) value of 0.893, indicating strong agreement between predicted susceptibility patterns and observed flood occurrences. Additional validation metrics derived from the confusion matrix show an overall accuracy of 83.3%, precision of 0.75, recall of 1.0, and a Kappa coefficient of 0.67, confirming reliable predictive performance. Sensitivity analysis with ±10% variation in factor weights further demonstrated the spatial stability of the susceptibility results. The resulting susceptibility map provides an important spatial basis for infrastructure planning, flood mitigation, and disaster preparedness in the Abai Region and offers a transferable framework for flood-susceptibility assessment in other semi-arid regions of Central Asia.

This study analyzed precipitation and air temperature in the Zhambyl, Almaty, Zhetysu, Abay, and East Kazakhstan regions of Kazakhstan using data from the national meteorological network of the RSE Kazhydromet. The purpose of the study was to reveal the climatic changes and their spatial distribution throughout the study area. A modified Mann–Kendall test and Sen’s Slope estimator were applied to analyze aridity conditions in combination with the drought indices SPEI and Selyaninov hydrothermal coefficient, enabling analysis of the magnitude and statistical significance of trend changes from April to September for the period 1981 to 2023. The magnitude of the observed trends of the mean growing-season temperature increased by 0.211 °C decade⁻¹, while precipitation declined by 2.074 mm decade⁻¹, which indicates a decrease in moisture availability for crops in the southeast and east of Kazakhstan. The results of this study may be of interest to agricultural specialists, ecologists, the Ministry of Emergency Situations, and hydrologists to develop activities aimed at preventing threats and mitigating the effects of climate change in Kazakhstan. The use of the above statistical methods in combination with drought indices is relevant in the context of climate change and worsening food security and can serve as a good indicator for determining when significant changes in climatic parameters occurred, which will be valuable information for making management decisions.

В статье представлена пространственно-временная оценка экологического состояния и уровня антропогенного воздействия в бассейне реки Терис (Жамбылская область) на основе многолетних данных дистанционного зондирования Земли за 2015-2025 гг. Информационной основой исследования послужили спутниковые данные Landsat 8 и 9 (Collection 2 Level 2), обработанные в среде Google Earth Engine с применением процедур атмосферной коррекции, маскирования облачности и нормализации спектральных показателей. Для количественной характеристики ключевых компонентов геосистем рассчитаны индексы NDVI (растительность), WET (влажностная компонента преобразования Tasseled Cap), LST (температура земной поверхности) и NDBSI (сухость и степень антропогенной трансформации). Интеграция стандартизированных индикаторов выполнена методом главных компонент с последующим формированием интегрального индекса экологического состояния RSEI. Полученные результаты свидетельствуют о выраженной пространственной дифференциации и высокой межгодовой вариабельности экологических условий. В структуре территории на протяжении всего периода исследования доминируют классы «ниже среднего» и «средний», совокупная доля которых в большинстве лет превышает 70 %. Наибольшая доля класса «средний» зафиксирована в 2016 г. (56,88 %), тогда как в 2025 г. отмечено существенное расширение класса «высокий» (34,29 %), отражающее локальное улучшение экологического состояния. Зоны с низкими значениями RSEI занимают ограниченные площади (до 1,34 %), однако пространственно приурочены к участкам интенсивного землепользования и деградированных земель. Результаты непараметрического анализа показали отсутствие статистически значимых долговременных трендов для большинства классов RSEI, что свидетельствует о преобладании межгодовой вариабельности экологических условий. Наиболее благоприятные экологические условия характерны для горных и пойменных ландшафтов, тогда как центральные и сельскохозяйственно освоенные районы демонстрируют пониженные интегральные показатели. Результаты исследования подтверждают методическую состоятельность RSEI как интегрального инструмента мониторинга динамики экологического состояния и оценки антропогенной нагрузки в пределах бассейновых геосистем полузасушливых регионов.

Water scarcity is one of the global risks in arid and semi-arid regions such as Central Asia (CA), including Kyrgyzstan-Kazakhstan. The main rivers of Central Asia are transboundary, such as the Chu-Talas River basin, which is located on the territory of Kyrgyzstan and Kazakhstan. Natural and man-made factors such as climate change can lead to water crises and conflicts due to reduced water supplies and deteriorating water quality. Unfortunately, in Central Asia, surface water resources are mainly studied, and groundwater resources are excluded from open analytical analysis. At the same time, groundwater is a vital component of the river basin ecosystem and should be studied simultaneously with surface water to obtain a more complete analysis of the sustainability of the integrated surface-groundwater system of the basin. Combined scientific research of surface and groundwater, forecasting the water balance of basins is associated with great difficulties in transboundary cooperation of specialists from meteorologists, hydrologists, hydrogeologists of bordering states. Basically, cross-border negotiations are dominated by departmental management, more administrative management, without system planning, analysis of adaptation and preparation for natural processes, without stimulating the involvement of the local population, students of schools, colleges and universities in studying, conducting field water research, modeling surface water balances -groundwater of swimming pools. Local residents of the villages of the basins of Kyrgyzstan and Kazakhstan need increased joint support for advanced training of the population, opportunities for adaptation to climate change in the school-college-industry-university chain. Joint hydrogeological studies of natural processes in Kyrgyzstan and Kazakhstan are poorly developed. Kyrgyzstan-Kazakhstan is missing out on opportunities for joint cooperation. In many countries there is now a “gold rush” of hydrogeological research and research on natural geological “white” hydrogen, including on the France-German border, where the world’s largest underground reserves of hydrogen have been discovered. It is important for Kyrgyzstan and Kazakhstan to strengthen cooperation with the creation of growth incentives, support for researchers, hydrogeologists, and strengthening of the Academies of Sciences of the two countries. This review presents opportunities for strengthening cooperation between Kyrgyzstan and Kazakhstan, including joint effective management of natural resources, studying the hydrogeology of Kyrgyzstan and Kazakhstan for the effective joint use of hydrogen and water resources of the two countries.

Endorheic lakes are sensitive indicators of climate variability and water-management pressure, yet their response in data-scarce Central Asian basins remains poorly quantified. This study examines long-term climate trends (1981–2020) and satellite-altimetry-based water-level dynamics (2002–2026) of the Alakol–Sasykkol lake system in eastern Kazakhstan. Meteorological data from five stations were analyzed using the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test. Significant warming of 0.025–0.034 °C year⁻¹ was found at four of five stations, while precipitation trends were spatially heterogeneous and potential evapotranspiration increased significantly only at Zhalanashkol. A catchment moisture deficit index ($\Delta =P-{ET}_0$) was derived. For Lake Sasykkol, the strongest lagged relationship was between $\Delta (t-1)$ and lake volume $V\left(t\right)$ ($r=0.602$, nominal $p=0.008$). This relationship was stable in bootstrap, permutation, and autoregressive checks, but it was not significant after correction for multiple comparisons and is therefore presented as an exploratory working hypothesis. Satellite altimetry for 2016–2026 showed that Lake Alakol declined at −14.07 cm year⁻¹ faster than Lake Sasykkol (−3.75 cm year⁻¹). Because both lakes experience similar climatic forcing, this difference suggests a probable additional anthropogenic contribution to Alakol’s decline, most plausibly irrigation water withdrawal, although quantitative attribution is not possible with the available data. DAHITI altimetry for Alakol was compared with in situ gauge records ($r=0.997, n=9, p<0.001);$ this supports the use of the product but does not conclusively validate it due to the small sampling number. The results highlight the need for integrated monitoring of tributary inflow, irrigation withdrawals, and groundwater to separate climatic and human drivers. The paper describes an integrated system for water resource monitoring based on station data, satellite data, and trend estimation under conditions of uncertainty. Results are compared with available measurements to guide sustainable management of the water resources. A summary of the main problems that need to be resolved is provided—inflow, groundwater level, water consumption, variations in lake levels—in a terminal sensitive basin subject to climate change. It is a valuable insight for providing knowledge to support the implementation of SDG 6, 13, and 15—Water Resource Management, Climate Resilience, and Wetland Protection.