Atmospheric Science, General Earth and Planetary Sciences, Aquatic Science, Global and Planetary Change
15
Scopus Publications
Scopus Publications
Transformation of River Runoff and Sensitivity of Hydrological Systems in the Arid Zone of Kazakhstan in the Context of Atmospheric Circulation Patterns Medeu Akhmetkal, Sayat Alimkulov, Lyazzat Makhmudova, Elmira Talipova, Lyazzat Birimbayeva, et al. Water Switzerland, 2026 This study investigates the transformation of river runoff and its sensitivity to changes in large-scale atmospheric circulation in the Zhaiyk–Caspian water management basin during the period of 1951–2023. The analysis is based on hydrometeorological observations data, the Vangengeim–Girs classification of macro-circulation patterns, and the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices. Correlation analysis, the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test were applied to identify trends, structural shifts, and the spatial coherence of hydroclimatic changes. The results show that interannual variability in river runoff is characterized by a degree of spatial coherence, with correlation coefficients between annual streamflow records at most gauging stations reaching up to 0.95. It is demonstrated that the most pronounced changes in the hydrological regime occur during the cold season and are expressed in a statistically significant increase in winter runoff, while no significant long-term trend in annual runoff is observed. Structural shifts in winter runoff are predominantly associated with the late 1990s, whereas changes in the temperature regime are detected earlier and exhibit spatial coherence. The findings indicate that the contemporary transformation of river runoff is primarily driven by rising air temperatures and the associated intra-annual redistribution of flow.
Compound Drivers of Extreme Spring Floods in a Changing Climate: The Esil River Case, Kazakhstan Lyazzat Makhmudova, Sayat Alimkulov, Ainur Mussina, Harris Vangelis, Elmira Talipova, et al. Earth Systems and Environment, 2026 There is a global increase in the frequency and intensity of extreme floods driven by climate change, enhanced hydrological variability, and transformations in seasonal moisture accumulation and runoff formation processes. In recent decades, shifts in the temporal and spatial characteristics of spring floods have led to more frequent exceedances of design water levels and increased damage to water management systems, infrastructure, and agriculture. Regions dominated by snow-fed rivers are particularly vulnerable, as imbalances between winter moisture accumulation and spring snowmelt create favorable conditions for extreme flooding. This study presents a comprehensive assessment of the factors controlling high spring floods using the Esil River basin as a case study. The analysis is based on long-term hydrometeorological observations combined with climate projections from the Coupled Model Intercomparison Projects Phase 6 ensemble. Key flood-forming drivers considered include autumn soil moisture conditions, soil freezing depth, snow accumulation, spring precipitation, and the synchronicity and intensity of snowmelt processes. To identify critical combinations of climatic and hydrological factors, statistical and factor analyses were applied to construct a matrix of factor interactions reflecting historical patterns of joint influence on extreme flood formation. The results indicate that the most hazardous floods occur when increased autumn soil moisture coincides with deep soil freezing, significant snow accumulation, and rapid spring warming accompanied by intense precipitation. Even under average snow storage conditions, the probability of extreme flooding remains high when soil waterlogging is present and heavy spring precipitation occurs, confirming the nonlinear and multivariate nature of spring flood generation. Based on the identified interaction patterns, a set of baseline flood scenarios was developed. Scenario analysis under the SSP3—7.0 climate pathway suggests an increase in spring air temperatures by 1.5—2.0 °C and precipitation by 10—15% by the mid-twenty-first century, leading to higher flood magnitude and frequency. The results highlight increasing climate variability and cyclical water availability, emphasizing the need to adapt water management systems, enhance early warning capabilities, and implement proactive flood risk mitigation measures in snow-fed river basins of northern and central Kazakhstan. Graphical Abstract The graphical abstract illustrates an integrated system for assessing and forecasting spring flood risks in the Esil River basin in conditions of increasing climate variability and growing extremes in hydrological processes. The approach presented is aimed at a comprehensive analysis of the factors contributing to flooding and is based on the combination of diverse data and methods, which improves the reliability of flood risk assessment and forecasting accuracy. The methodological basis of the study includes the use of long-term hydrometeorological observations, snow cover characteristics, temperature regimes during the cold and transitional seasons, data on atmospheric precipitation, as well as information on land use patterns and anthropogenic transformation of the basin territory. The abstract presents a comparison of historically recorded floods and modelled scenarios reflecting possible changes in the flood regime under different climatic conditions. The results obtained emphasise the multifactorial and nonlinear nature of extreme spring floods in basins dominated by snowmelt. The graphic abstract demonstrates the potential of modern monitoring, analysis and modelling methods for improving early warning systems, flood risk management and the development of climate change adaptation measures in river basins fed by meltwater.
Current Trends and Future Scenarios: Modeling Maximum River Discharge in the Zhaiyk–Caspian Basin (Kazakhstan) Under a Changing Climate Sayat Alimkulov, Lyazzat Makhmudova, Saken Davletgaliev, Elmira Talipova, Daniel Snow, et al. Hydrology, 2025 In the context of intensifying climate change, it is particularly important to assess the transformation of spring floods as a key phase of the hydrological regime of rivers. This study provides a comprehensive analysis of the characteristics of maximum runoff in the Zhaiyk–Caspian basin for the modern period and projected changes for 2030, 2040, and 2050 based on CMIP6 climate scenarios (SSP3-7.0 and SSP5-8.5). Analysis of observations at 34 hydrological stations showed a reduction in spring runoff by up to 35%, a decrease in the duration of high water and a reduction in maximum water discharge on some rivers by up to 45%. It has been established that those rising temperatures, more frequent thaws, and reduced autumn moisture lead to lower maximum water discharge and a redistribution of the seasonal flow regime. Scenario projections revealed significant spatial heterogeneity: some rivers are expected to experience an increase in maximum discharge of up to 72%, while others will see a steady decline in maximum discharge of up to 35%. The results obtained indicate the need to transition to an adaptive water management system focused on the regional characteristics of river basins and the sensitivity of small- and medium-sized watercourses to climate change.
Seasonally Contrasting Sensitivity of Minimal River Runoff to Future Climate Change in Western Kazakhstan: A CMIP6 Scenario Analysis Lyazzat Makhmudova, Sayat Alimkulov, Aisulu Tursunova, Lyazzat Birimbayeva, Elmira Talipova, et al. Water Switzerland, 2025 This study presents a scenario-based assessment of the future sensitivity of minimal low-water runoff to climate change in Western Kazakhstan. An ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), combined with dynamically downscaled projections for Central Asia, was applied to estimate minimal monthly runoff during the summer–autumn and winter low-water periods for the rivers of the Zhaiyk–Caspian water management basin. The analysis covers three future time horizons: 2040 (2031–2050), 2060 (2051–2070), and 2080 (2071–2090), under two greenhouse gas concentration scenarios: SSP3-7.0 (moderately high emissions) and SSP5-8.5 (high emissions). The results reveal a pronounced seasonal contrast in the projected hydrological response. During the winter low-water period, a steady increase in minimal runoff is projected for all rivers, with the most significant changes observed for the Or, Zhem, Temir, and Shagan rivers. This increase is primarily driven by higher winter precipitation, increased thaw frequency, and enhanced infiltration recharge. Conversely, despite modest increases in summer–autumn precipitation, minimal runoff during the summer–autumn low-water period is projected to decline significantly, particularly in the southern basins, due to elevated evapotranspiration rates and soil moisture deficits associated with rising air temperatures. These findings emphasize the importance of developing seasonally differentiated, climate-resilient water management strategies to mitigate low-flow risks and ensure water security under future climate conditions in arid and semi-arid regions.
Long-Term Water Level Projections for Lake Balkhash Using Scenario-Based Water Balance Modeling Under Climate and Socioeconomic Uncertainties Sayat Alimkulov, Lyazzat Makhmudova, Elmira Talipova, Gaukhar Baspakova, Akhan Myrzakhmetov, et al. Water Switzerland, 2025 The study presents a scenario analysis of the long-term dynamics of the water level of Lake Balkhash, one of the largest closed lakes in Central Asia, taking into account climate change according to CMIP6 scenarios (SSP2-4.5 and SSP5-8.5) and socio-economic factors of water use. Based on historical data (1947–2021) and a water balance model, the contribution of surface runoff, precipitation and evaporation to the formation of the lake’s hydrological regime was assessed. It was established that the main source of water resources for the lake is the flow of the Ile River, which feeds the western part of the reservoir. The eastern part is characterized by extremely limited water inflow, while evaporation remains the main element of water consumption, having increased significantly in recent decades due to rising air temperatures. Increasing intra-seasonal and interannual fluctuations in water levels have been recorded: The amplitude of short-term fluctuations reached 0.7–0.8 m, which exceeds previously characteristic values. The results of water balance modeling up to 2050 show a trend towards a 30% reduction in surface inflow and an increase in evaporation by 25% compared to the 1981–2010 climate norm, which highlights the high sensitivity of the lake’s hydrological regime to climatic and anthropogenic influences. The results obtained justify the need for the comprehensive and adaptive management of water resources in the Balkhash Lake basin, taking into account the transboundary nature of water use and changing climatic conditions.
SCENARIO FORECAST OF BALKASH LAKE LEVEL BASED ON CMIP6 GLOBAL MODELS Sayat Alimkulov, Lyazzat Makhmudova, Elmira Talipova, Gaukhar Baspakova, Akhan Myrzakhmetov International Multidisciplinary Scientific Geoconference Surveying Geology and Mining Ecology Management Sgem, 2025 In the presented scientific work, a scenario forecast for the long-term perspective of water level changes in Lake Balkhash, one of the largest drainless lakes in Central Asia, is carried out. The study takes into account the impact of projected climatic changes based on CMIP6 data and the impact of socio-economic factors, including water management activities. A water balance model was used as the main tool to quantify the role of surface inflow, precipitation and evaporation in shaping the hydrological regime of the reservoir. The results of modelling under scenarios SSP2-4.5 and SSP5-8.5 for 2030, 2040 and 2050 yy. indicate an expected decrease in surface runoff volumes as a result of the combined effects of climate change and economic activity. At the same time, evaporation is projected to increase against the background of rising air temperature. In the absence of effective mechanisms for transboundary water co-operation and climate adaptation, Lake Balkhash will be faces serious environmental risks � in particular, possible partial shallowing and fragmentation of the reservoir, especially in its eastern part. These results emphasize the need for a systematic and adaptive approach to water resources management in the Balkhash basin, taking into account natural non-stationarity, regional risks and socio-economic instability.
Transformation of seasonal distribution of river flow in the Zhaiyk–Caspian water basin under changing climate conditions Smagulov Zhanibek, Makhmudova Lyazzat, Sayat Alimkulov, Elmira Talipova, Zagidullina Alfiya, et al. Journal of Water and Climate Change, 2025 Under conditions of increasing climatic uncertainty, this scientific study analyzes multi-year trends of seasonal distribution of river flow in the Zhaiyk–Caspian water basin. In specific natural conditions of the territory under consideration, under scarce water resources availability in the vast territory, and exceptional variability of river flow in time, the problem of water supply is especially important. The paper considers multi-year dynamics of runoff-forming factors and flow parameters taking into account phases of different water availability. River flow estimation methods include analyses of average monthly maximum and minimum values of river flow. The assessment of homogeneity of river flow series was carried out on the basis of genetic analysis and statistical analysis, application of Mann–Kendall test, Student's t test, and Fisher criterion. The identified results indicate significant transformations in the river flow in the basin: the share of spring flow has decreased, while the share of low-water flow, especially in winter, has increased. These changes are associated with higher winter temperatures and an increase in the frequency of winter thaws. In the River Elek Basin, the share of spring flow decreased to 40% of the annual value, and in the River Or Basin, to 15%.
Modeling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application To the zhaiyk-caspian basin, Kazakhstan Sayat Alimkulov, Lyazzat Makhmudova, Bekzat Satenova, Aisulu Tursunova, Lyazzat Birimbayeva, et al. Earth Systems and Environment, 2025 The study presents a comparative assessment of eight machine learning (ML) algorithms - Random Forest (RF), Lasso Regression (LASSO), AdaBoost (ADB), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN) - for modeling daily river discharge at ten hydrological stations within the Zhaiyk - Caspian water management basin. Model performance was evaluated using mean absolute error (MAE), mean squared error (MSE), and symmetric mean absolute percentage error (SMAPE). The highest predictive accuracy (MAE ≈ 0.3) was achieved by ensemble tree-based methods (Random Forest, CatBoost, Gradient Boosting, LightGBM, XGBoost), while LASSO and AdaBoost exhibited the weakest performance (MAE ≈ 22). Identifying the most significant predictors enhanced both model interpretability and forecasting quality. The findings highlight the importance of tailoring ML approaches to the specific characteristics of river basins and suggest promising prospects for their integration with physically based hydrological models to improve river discharge forecasting and strengthen water resources management under climate change conditions. Graphical Abstract The Graphical Abstract Presents an Ensemble ML Framework for Modeling Daily River Discharge Under Climate Variability in the Zhayik–Caspian basin, Kazakhstan. Input Data Include Observed Daily runoff, Air temperature, and precipitation, Applied across Eight Machine Learning Algorithms: Random Forest, CatBoost, LightGBM, Gradient Boosting, KNN, LASSO, AdaBoost, and XGBoost. The graphical summary illustrates: the study area, the structure of the modeling framework, key predictors influencing runoff, and a comparative analysis of observed and simulated discharge. The results highlight the potential of ensemble machine learning methods for improving runoff prediction in arid regions and their application in water resource planning, flood and drought risk management, and climate change adaptation strategies.
