Seasonal snow cover in mountainous regions represents a critical natural freshwater reserve for arid and semi-arid areas of Central Asia. This study evaluates the long-term (2000–2024) spatiotemporal dynamics of snow cover in the Arys River basin, located within the Western Tien Shan. The research utilizes daily satellite data from MODIS Terra and Aqua, along with data from the MODSNOW automated processing system. Terra-Aqua composite imagery was employed to minimize cloud cover effects. Satellite-derived estimates were validated against observational data from five meteorological stations of the Republican State Enterprise (RSE) “Kazhydromet”. The results indicate significant interannual variability in snow cover extent: the snow-covered area during the cold season ranged from 16.2% to 54.1%, with a mean value of 34.4%. Trend analysis revealed a weak negative trend, while Sen’s slope estimator showed an average annual reduction in snow cover area of 0.37% per year. The most pronounced decline in snow accumulation was observed in mid-elevation mountain zones. These findings suggest potential increased risks to seasonal water availability in the Arys River basin and, more broadly, across the Syr Darya basin under ongoing climate change conditions. The results provide a scientific basis for quantifying climate impacts and developing adaptation strategies for integrated water resources management in Central Asia.

This study analyzes the interannual dynamics of fallow land extent in the North Kazakhstan Region during 2021–2025 using Sentinel-2 data. Fallow fields were identified through a rule-based multi-temporal approach in which the adapted Plowed Land Index (PLI) was used as the principal indicator of mechanically processed soil surfaces, while the NDVI was applied as a supporting indicator to exclude actively vegetated fields and control for vegetation overgrowth. Annual fallow masks were generated within a unified seasonal observation window and subsequently analyzed at the district level. The results revealed pronounced interannual variability in fallow land extent, including both reductions and partial recovery over the study period, together with substantial spatial heterogeneity among districts. The largest fallow area was recorded in 2021, whereas the minimum was observed in 2024. Comparison with district-level hydrothermal coefficient (HTC) values showed that lower moisture availability generally corresponded to larger fallow areas, while relatively more favorable hydrothermal conditions were associated with their reduction. At the same time, district-specific trajectories indicate that interannual dynamics were controlled not only by agroclimatic variability but also by differences in land-use structure and agricultural management. The proposed approach confirms the applicability of Sentinel-2 time series for regional monitoring of fallow lands and demonstrates the methodological value of the adapted PLI for identifying mechanically processed fallow surfaces under the heterogeneous agricultural conditions of North Kazakhstan.

In the article features of temperature regime and heat resources in the North-Kazakhstan region are considered on the basis of long-term meteorological data. Spatial and temporal analysis of solar radiation, temperature regime, climate continentality, date of onset and duration of seasons of the year, climate favorability for recreation, heat availability of vegetation period, frosts and unfavorably hot days on the territory of North-Kazakhstan region has been carried out. On basis of heat resources the agricultural crops provided with heat are determined. The obtained results are of practical value for assessment of climatic risks, planning of agricultural activity and development of adaptation measures in conditions of changing climate of North-Kazakhstan region.

Ozone control and monitoring are very important because they provide the world’s population with a basis for making informed decisions and policies to protect life on earth. Since the thinning of the ozone layer is harmful to nature and human health, the study of the spatial and temporal dynamics of stratospheric ozone on the territory of Kazakhstan does not lose its significance. Based on aerological data obtained from observations of the world ozone and ultraviolet data center and the University of Wyoming, the total amount of ozone and its relationship with other meteorological parameters (air temperature above 20 km altitude, relative humidity and wind speed) at stations where ozone observations are carried out in Kazakhstan were investigated and the correlation between them was calculated. Correlation analysis of control series was used to study the relationships between values, hypotheses were made about possible mechanisms of their interaction, and the method of statistical processing was used as a research method. The average annual moves of the considered characteristics are given. As a result, it was found that each of the considered meteorological parameters affects the ozone concentration, especially in close connection with changes in air temperature. Under the influence of strong heating of the stratosphere, a positive relationship between the air temperature and the total ozone was revealed, while under normal conditions it has a negative relationship. Контроль озона и мониторинг его содержания очень важны, поскольку они дают населению мира основу для принятия обоснованных решений и мер в области политики, направленной на защиту жизни на земле. Поскольку истончение озонового слоя наносит вред природе, здоровью человека, изучение пространственно-временной динамики стратосферного озона на территории Казахстана не теряет своей значимости. На основании аэрологических данных, полученных в результате наблюдений Всемирного центра данных по озону и ультрафиолетовому излучению и Университета Вайоминга, исследовано общее количество озона и его связь с другими метеорологическими параметрами (температура воздуха выше высоты 20 км, относительная влажность и скорость ветра) по станциям, на которых проводятся наблюдения за озоном в Казахстане, и вычислена корреляционная связь между ними. С помощью корреляционного анализа контрольных рядов изучались отношения между величинами, высказывались гипотезы о возможных механизмах их взаимодействия, в качестве метода исследования использовался метод статистической обработки. Приведены среднегодовые ходы рассматриваемых характеристик. В результате было установлено, что каждый из рассмотренных метеорологических параметров влияет на концентрацию озона, особенно в тесной связи с изменениями температуры воздуха. Под действием сильного нагрева стратосферы выявлена положительная связь между температурой воздуха и ОСО, при этом она в нормальных условиях имеет отрицательную связь Озонды бақылау және оның құрамына мониторинг жүргізу өте маңызды, өйткені олар әлем халқына жер бетіндегі өмірді қорғауға бағытталған саясат саласында негізделген шешімдер мен шаралар қабылдауға негіз береді. Озон қабатының жұқаруы табиғатқа, адам денсаулығына зиян келтіретіндіктен, Қазақстан территориясындағы стратосфералық озонның кеңістіктік-уақыттық динамикасын зерттеу маңыздылығын жоғалтпайды. Озон және ультракүлгін сәуле жөніндегі дүниежүзілік деректер орталығы мен Вайоминг университетінің бақылаулары нәтижесінде алынған аэрологиялық мәліметтер негізінде Қазақстанда озонға бақылау жүргізілетін станциялар бойынша озонның жалпы мөлшері мен оның басқа да метеорологиялық параметрлермен (20 км биіктіктен жоғары ауа температурасы, салыстырмалы ылғалдылық және жел жылдамдығы) байланысы зерттелді және олардың арасындағы корреляциялық байланыс есептелді. Бақылау қатарларын корреляциялық талдау көмегімен шамалар арасындағы...

In data-sparse Central Asian watersheds, machine learning and Geographic Information Systems (GIS) are increasingly applied to hydrological hazard classification. This article compares Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM) models for daily elevated-discharge classification in the Koksu River basin, Zhetysu Region, Kazakhstan (1614 km², semi-arid, snowmelt- and glacier-influenced climate; 2005–2023), using verified discharge, precipitation, and temperature records from four monitoring stations. The elevated-discharge threshold (80th percentile, ~90.5 m³/s) was computed using only the training period (2005–2017) to avoid temporal data leakage into validation and test periods. Class imbalance, addressed via class weighting, was moderate under this threshold. All models were benchmarked against a naive persistence baseline. Contrary to expectation, the persistence baseline achieved the highest Critical Success Index (CSI = 0.821, 95% block-bootstrap CI [0.725, 0.891]), narrowly ahead of XGBoost (CSI = 0.813, [0.703, 0.897]), Random Forest (CSI = 0.809, [0.691, 0.897]), and LSTM (CSI = 0.763, [0.630, 0.873]); the confidence intervals overlap substantially, indicating no statistically distinguishable advantage of any trained model over simple persistence in this basin. Feature-importance and ablation analyses confirmed that lagged discharge, not precipitation, drove nearly all predictive skill: a discharge-lags-only model performed as well as or better than the complete pipeline, while a meteorology-and-seasonality-only model performed markedly worse (CSI ≈ 0.57–0.59). As a preliminary, exploratory illustration rather than a core result, Gumbel-derived return-period discharges (Q10, Q25, and Q100) were separately translated into illustrative, uncalibrated, DEM-proximity-based inundation extents, a candidate direction for future work rather than an engineering-grade flood-mapping contribution of this study. These results indicate that, in this basin and period, machine learning classifiers provide no clearly demonstrated advantage over simple discharge persistence, underscoring the necessity of routine persistence benchmarking before adopting machine learning approaches for early warning in Central Asian watersheds facing increasing flood risk under climate change.

Problems of modern climate change and assessment of their consequences IPCC (2021-2023) according to the latest assessment reports, there is an increase in dangerous weather phenomena that cause significant social and economic damage in various regions of the Earth. In this article, various climatic factors affecting the state of dust storm formation associated with modern climate change were studied. It was established that the longest (72 hours) duration of dust storms at the Aral Sea station in the southern region of Kazakhstan in the period from 2005 to 2022 was recorded in May 2018. The values of meteoparameters at that time, that is, its dependence on weather conditions, quantitative indicators of solar activity (Wolf number), bioclimatic indicators, as well as the conditions of macrocirculation formation, were considered, and it turned out that there was an important correlation relationship between the number of days when dust storms were most observed and the Wolf number (r=1.00), Wangenheim-GIRs circulation forms, including the e Form (r=0.49), El- Niño indices (r=0.47 as a result of the analysis, it was determined that the prolonged Obser ation of dust storms was associated with the north-western cold front.