Assessment of Future Water Stress on Surface Waters in the West Kazakhstan Region Caused by the Combined Impacts of Climate Change and Increased Anthropogenic Pressure

Abstract

The surface water resources of the West Kazakhstan Region (WKR) face escalating vulnerability due to the synergistic effects of a sharply continental climate, which intensifies climate change impacts, and rapidly increasing anthropogenic water demand. This study aims to quantify and project the future water stress on WKR’s surface waters, assessing the combined influence of climate change and socio-economic development over the critical period of 2030–2050. This study presents a comprehensive quantitative assessment and future projection of water stress on the region’s surface waters, integrating state-of-the-art CMIP6 climate scenarios (SSP2-4.5 and SSP5-8.5) with a novel model of water consumption growth accounting for regional demographic and economic developments. Employing advanced bias-corrected ensemble climate projections alongside physically interpretable water balance models, we estimate changes in river flows and lake volumes through mid-century. A newly developed Water Stress Index (WSI), supplemented by the Falkenmark Index, reveals an alarming increase in water stress, with projections indicating that over 70% of the WKR territory may face severe resource limitations by 2050. The analysis underscores significant spatial heterogeneity driven by climatic variability and socio-economic factors, emphasizing the urgent need for regionally tailored adaptation and water management strategies. These findings provide a robust scientific basis to guide policy decisions aimed at mitigating future water scarcity under evolving climate and development scenarios.

1. Introduction

Kazakhstan studies increasingly adopt a complex approach that combines the analysis of natural and anthropogenic factors [1]. This integrated perspective is essential to understand the multifaceted challenges facing water resource management in the region. For instance, forecasts extending to 2050 include not only consider projected changes in water availability but also the water demands from various economic sectors, which is critically important for assessing the risks of water deficits [2,3]. Without effective adaptation measures, the level of water availability in some districts may decline to as low as 900–1000 m³/person/year [4,5].

The subject of this research is the Western region, which comprises the Aktobe, Atyrau, Mangystau and West Kazakhstan regions. This region is notably characterized by diverse natural, climatic, and socio-economic conditions that determine its complex water resource dynamics (continue with original Introduction text from “Subject of Research”).

For example, CMIP-6 models are used in NASA GDDP programs and regional adaptation strategies in Central Asia, enabling the production of practically meaningful forecasts [6,7]. The application of ensemble modeling, bias correction, and rigorous statistical reliability assessment further enhances the credibility and practical utility of climate impact forecasts [8,9]. CMIP-6 climate models are actively used to analyze trends, assess drought and water resource risks, and predict extreme events [10]. In Kazakhstan, such studies [6] predicted an increase in precipitation and erosion processes. Climate models are also used to analyze the hydrological conditions of mountainous areas in north-eastern Kazakhstan [11].

Nevertheless, long-term forecasting of lake water levels remains a critical scientific and practical challenge worldwide. Understanding the intra-century variability of lake levels plays a pivotal role in supporting economic activities in lake catchment areas, including the safe operation of water transport, coastal protection infrastructure, and the formulation of adaptive measures to climate-induced hydrological changes expected through the 21st century [12,13,14]. Among the established approaches, autoregressive, intergroup, and numerical models have been successfully applied to major lakes in Europe and North America [15,16,17,18,19,20,21,22,23]. For example, the ESNAM4/OPYC3 models, integrated with IPCC climate scenarios, have facilitated projections of temperature increases between 2 and 3 °C, accompanied by increased evaporation and decreased water levels by mid-21st century in the Ladoga and Onega Lake basins. Likewise, combined observation-hydrodynamics-climate modeling systems such as GLCFS and AHPS have demonstrated effectiveness in the Great Lakes of America [20,21,22,23].

However, in conditions of sharp non-stationarity, such as in the Caspian Sea, traditional approaches have shown limited applicability. Probabilistic and stochastic models [24,25] did not reflect the real trend: since 1995, the sea level has been steadily declining. Climate-dependent forecasts based on GCM models have yielded contradictory results, ranging from stabilization to a 5–6 m decline in sea level by 2100 [26,27,28,29,30].

Forecasting water resources in a changing climate in the West Kazakhstan region is a pressing scientific task. To assess the river flow resources of the Zhayik-Caspian basin, scenario approaches and calculation methods are used that take into account the climatic trends and hydrological characteristics of the region [31]. The significant impact of climate change on the water regime of rivers in south-eastern Kazakhstan is highlighted in a number of studies [32], including in the context of assessing river flow based on the resources of the entire country [33] and taking into account the uncertainty of water resources [3].

Simulation modeling methods for hydrological processes have proven their effectiveness in the absence of comprehensive observations [34]. Studies also emphasize the role of atmospheric and anthropogenic factors in shaping the water regime of lowland rivers in Kazakhstan [35,36].

Water supply forecasting is an important element in the system of sustainable water resources management, especially in the context of climate change and increasing anthropogenic pressure. For the West Kazakhstan region, as well as for the arid territories of Central Asia in general, issues related to future water security are becoming particularly relevant.

Water supply forecasting thus represents a critical pillar of sustainable water resource management, particularly under the pressures of climate change and anthropogenic activity. For the West Kazakhstan region, and more broadly for the arid territories of Central Asia, these challenges underpin pressing concerns for future water security. Effective forecasting integrates both present and projected water availability data with population estimates derived from sources such as the United Nations, the Agency for Strategic Planning, and the National Statistics Bureau of the Republic of Kazakhstan. The application of trend and factor analyses of population size, alongside sectoral water consumption forecasts, further supports scenario development [37,38,39,40,41,42,43]. Advanced water management models like WEAP, combined with GIS spatial modeling—leveraging remote sensing and maps of population density, precipitation, infrastructure, and watershed characteristics—permit refined evaluation of urbanization impacts, economic growth, and water-saving technologies on water distribution and demand [44,45].

In particular, scenario modeling of runoff in the Ural basin, projecting trends until 2100, provides valuable input into regional water resource planning [46]. Compelling evidence has established that Kazakhstan is experiencing pronounced water stress—driven by increases in mean annual air temperature, intensified evaporation, changing precipitation patterns, shifts in hydrological regimes, reduced river discharge, and glacier retreat feeding major rivers. As a consequence, water availability for a substantial portion of Kazakhstan’s population is anticipated to fall below the internationally recognized critical threshold of 1700 m³ per person per year [47].

Given these interrelated dynamics, Kazakhstan studies are increasingly adopting comprehensive approaches that effectively combine the analysis of natural variability and anthropogenic influences. Forecasts extending to 2050 thus incorporate not only anticipated changes in water resources but also sectoral water demand projections, which are indispensable for robust assessment of water shortage risks. Without timely and effective adaptation, water supply levels may diminish to critically low 900–1000 m³/person/year in some vulnerable areas [48,49,50].

Novelty of the Research: Despite growing attention to Kazakhstan’s water security, there has been a lack of a comprehensive, spatially detailed prognostic assessment of surface water resources (covering both river runoff and lake water volumes) for the West Kazakhstan Region at the administrative district level, utilizing modern CMIP6 climate scenarios (SSP2-4.5 and SSP5-8.5). For the first time within this work, we employ regionally corrected CMIP6 ensemble modeling alongside a physically interpretable approach to estimate changes in lake water volume. This approach allows us not only to forecast the change in total water resources up to 2050 but also to conduct a granular water stress analysis using the Falkenmark Index for each district, which is critical for making targeted management decisions amid growing water scarcity and territorial heterogeneity.

2. Subject of the Study

The subject of the study is the West Kazakhstan region, encompassing the Aktobe, Atyrau, Mangistau, and West Kazakhstan administrative areas. Located in the western part of the Republic of Kazakhstan, this region shares borders with the Russian Federation and the Republic of Uzbekistan. It is hydrologically connected to these neighboring countries through the transboundary rivers Ural (Zhayik), Emba, and Or, which play a significant role in defining the region’s overall surface water regime (Figure 1) [51,52]. This region is characterized by significant natural, climatic and water management diversity, due to both its geographical location and climatic features. The spring river runoff of the lowland rivers in Western Kazakhstan is almost entirely formed by meltwater from seasonal snow, which determines both the magnitude and timing of the flood regime across the region. According to long-term observations, the duration of the snow cover period ranges from 138 to 170 days, with stable snow accumulation beginning in mid-November and complete melting occurring in the first decade of April. The annual water equivalent stored in the snowpack varies between 62 and 125 mm across different basins, and in extreme years may reach up to 150 mm. On average, 68–82% of the spring runoff is supplied by snowmelt, as indicated by flood hydrographs. The region covers a vast area, most of which is occupied by steppe, semi-desert and desert landscapes. The main sources of surface water are the Zhayik, Emba, Or and their tributaries, as well as a number of lakes, some of which are subject to seasonal and perennial fluctuations in water balance [51,53]. Groundwater is of particular importance in the region, playing a vital role in supplying water to settlements, industry and agriculture.

The West Kazakhstan region holds strategic importance in terms of water use, industry, and environmental management. It hosts major oil and gas fields, while the agricultural sector and transport infrastructure are actively developing [54]. However, the region is naturally characterized by an arid climate, which forms the fundamental environmental basis for the prevailing water resource deficit. This inherent dryness is further exacerbated by contemporary climate change processes, manifested through rising air temperatures, altered precipitation regimes, and intensified evaporation. These climatic shifts are superimposed on pre-existing natural constraints, thereby amplifying the regional water deficit. Consequently, the current and projected water scarcity is attributed to the combined effects of the region’s natural aridity, the additional impacts of climate change, and increasing water demand, compounded by low per capita water availability [55,56]. In addition, the Zhayik and Emba river basins are experiencing persistent droughts, water stress and degradation of aquatic ecosystems [57].

The region was selected for our study because of existing challenges on water use and available resources, which are expected to become even more pressing in the future. These aspects are particularly relevant in the context of the implementation of the Republic of Kazakhstan’s national strategy for water security and climate change adaptation [58]. Therefore, in the context of expected transformations of the natural environment, it is important to analyze both the current state of water resources and possible changes by 2050.

3. Materials and Methods

This study aims to assess and project the long-term dynamics of water resources in the Western Kazakhstan region for the period 2030–2050. The analysis encompasses both projected changes in river runoff and the dynamics of water storage in regional lakes. To address these objectives, an integrated methodological framework was applied, combining advanced climate modeling, hydrological computations, climate data processing techniques, and traditional methods based on regression analysis and water balance equations.

To accomplish the research objectives, the following data and techniques were utilized:

➢

Climate models from the CMIP6 suite, obtained via the NASA GDDP-CMIP6 platform, for forecasting the future conditions of the water resources;

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Stationary observational data from Kazhydromet (1974–2014) [59], used for verification purposes and bias correction;

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Runoff calculations based on established regression relationships linking precipitation (including snow water equivalent), air temperature, and river discharge;

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Water balance equations applied to lake systems for estimating changes in lake volumes.

The diagram below (Figure 2) illustrates the key approaches and methodological stages employed in the study):

The analysis of climatic deviations was conducted in accordance with the World Meteorological Organization (WMO) standards, which recommend averaging temperature, precipitation, and other climate variables over 30-year periods (CLINO) [60]. This study employed the baseline periods 1961–1990, 1981–2010, and 1991–2020, as recommended by the WMO and adopted in Kazakhstan’s national climate reports, including the Eighth National Communication [57,61,62,63,64,65].

To assess future water resource conditions in Kazakhstan, CMIP6 climate scenarios were applied. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), CMIP6 models demonstrate improved accuracy in simulating temperature, precipitation, and atmospheric circulation compared to previous generations of models [65]. The data were obtained from the NASA/GDDP-CMIP6 platform, based on 34 climate models, covering the historical period 1950–2014 and the projection period 2015–2099 [66].

The methodology for selecting climate models was based on three statistical indicators: the correlation coefficient (R), the Nash–Sutcliffe efficiency (NSE) coefficient, and the relative error. The correlation coefficient (R) reflects the degree of linear dependence between observed and simulated data; values of R above 0.7 indicate a high level of agreement. The NSE coefficient evaluates the model’s ability to reproduce observed values, with values exceeding 0.5 considered satisfactory. The relative error indicates the deviation between observed and simulated values, and an error level below 20% is regarded as acceptable.

To select the optimal models for constructing the CMIP6 ensemble, the following analysis was conducted:

Stage 1: A detailed analysis was conducted for the 34 models presented on the NASA/GDDP-CMIP platform, specifying the country of origin, examining the principles of model distribution, input parameters, as well as advantages and limitations.

Stage 2: Calculation and analysis of the obtained results for relative error, correlation coefficients, and NSE were performed, followed by the selection of models that meet the conditions of the calculated parameters.

To improve calculation accuracy and eliminate systematic errors in the output data of climate models, bias correction methods were applied:

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Delta change: This method is based on the assumption that relative changes in climate variables (e.g., temperature or precipitation) obtained from model projections can be applied to historical observational records. The difference (for temperature) or ratio (for precipitation) between the climate model outputs for the future and the baseline period is calculated. These adjustments are then applied to the observed data, enabling the creation of bias-corrected time series. The method is widely used due to its simplicity and transparency; however, it does not account for changes in variance, the sequence of extreme events, or seasonal characteristics.

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Linear scaling: In this approach, adjustment is performed by aligning the mean values of modeled climate variables with observed ones. For temperature series, this is implemented as the addition of a systematic difference (bias), while for precipitation it involves multiplication by a factor reflecting the ratio of observed to modeled totals. This method improves mean characteristics but often does not consider differences in value distributions and does not correct errors in reproducing extreme events.

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Quantile mapping is one of the most statistically robust methods, enabling correction not only of mean biases but also of differences in distributions between observed and modeled data. The method involves constructing empirical distribution functions for observations and model outputs over a calibration period and matching corresponding quantiles. Consequently, a corrective transformation is assigned to each modeled value, adjusting the distribution to that of the observations. This method effectively reduces errors in simulating extremes (heavy precipitation, heatwaves, cold spells) and is frequently used in hydrological modeling that requires high accuracy of input data. However, it is computationally more demanding and more sensitive to sample size than simpler methods.

The application of bias correction methods is a critical procedure when integrating climate projections into hydrological modeling. By ensuring consistency between simulated and observed parameters, such processing significantly reduces the risk of distortions in water flow assessments.

Two scenarios of socio-economic development and greenhouse gas emissions were selected for analysis: SSP2-4.5 and SSP5-8.5. Studies across Central Asia indicate that under SSP2-4.5, the mean annual temperature is projected to increase by 1.5–2.5 °C by the mid-21st century. Under SSP5-8.5, the warming could reach up to 4 °C, accompanied by substantial changes in the seasonal distribution of precipitation. These forecasts are based on numerous fundamental climate studies, including the latest CMIP6 model ensembles and IPCC assessments [67,68]. It is important to note that an increase in potential evapotranspiration does not imply unlimited water availability for evaporation, especially in arid climates where soil moisture is minimal. In West Kazakhstan, evaporation is driven not only by soil and vegetation but also largely by extensive surface water bodies, such as lakes, which occupy a significant area in the region. Notably, there are more than 140 lakes in the territory, approximately two-thirds of which are saline—among them the large lakes Shalkar, Rybnoye, and Kamysh-Samara, which together provide substantial surface area for evaporation [69,70,71,72]. Specifically, Lake Shalkar has a volume of about 1.4 billion m³ and an area of approximately 200 km². These lakes contribute to the overall dynamics of evaporation under changing climate conditions [67,68]. CMIP6 projections also suggest a possible increase in regional annual precipitation by up to 14% by the end of the 21st century under SSP5-8.5 [72], with West Kazakhstan expected to experience enhanced potential evaporation and decreased soil moisture content [73].

In the lowland rivers of Kazakhstan, snow cover plays a major role, contributing up to 80% of runoff during the spring flood period. To assess the influence of snow cover and its spatial distribution on river discharge, the following methods were applied:

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Hydrometeorological monitoring—analysis of data from 23 stations on snow depth, density, and water equivalent for the period 1971–2021. The average snow depth across the region ranges from 31 to 51 cm, while density varies between 0.36 and 0.44 g/cm³.

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Cartographic analysis—using ArcGIS 10.03, maps of monthly snow water equivalent distribution were developed, identifying zones of maximum snow accumulation (>120 mm) in the northeastern part of the region.

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Modeling—to estimate flood runoff, the following formula was applied:

$$Q_{snow}=\alpha S_{max}\cdot \rho$$

(1)

where $Q_{snow}$ is the runoff volume generated from snowmelt, $S_{max}$ is the maximum snow depth, $\rho$ is the mean snow density, and $\alpha$ is a correction coefficient accounting for evaporation and infiltration losses. For the region, the averaged parameters were Smax = 34–48 cm, $\rho$ = 0.35–0.43 g/cm³, α = 0.72.

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Statistical analysis—to identify interannual variability, the magnitude of extreme snow reserves, and their impact on the hydrological regime, correlation coefficients were calculated between the annual maximum snow water equivalent and spring flood peaks, ranging from R = 0.88 to 0.93.

Statistical approaches based on regression and correlation–autocorrelation relationships remain relevant under conditions of limited hydrological information [74,75]. The integration of CMIP6 data into hydrological models enables reliable projections for 2030, 2050, and 2100. In addition, bias correction and spatial refinement techniques are applied to improve the regional applicability of the data [76]. To enhance forecast accuracy, bias correction, downscaling, and regional hydrological models (SWAT, HBV, VIC, etc.) are used to transform climate data into hydrological variables [77,78]. Modern physically based models such as SWAT and HBV allow for the consideration of local natural and climatic features and the simulation of key components of the water balance [79,80,81].

Ensemble modeling is currently regarded as the most robust method for assessing uncertainties, as it provides a representative range of possible changes in temperature, precipitation, evaporation, and available water resources through the mid- and late 21st century [79,80,81].

In this study, projected changes in water levels were assessed. Contemporary approaches emphasize the necessity of integrating anthropogenic factors—such as population growth, urbanization, and the expansion of water use—into water balance models, as these factors increase pressure on resources and introduce additional risks [82]. A physically interpretable approach was employed, based on the water balance equation of water bodies and the regional moisture balance. The general circulation model of the atmosphere and ocean (GCM-AO), adapted by the Institute of Geography and Water Security [57], was applied. This model accounts for the influence of moisture transport from the Atlantic, precipitation, evaporation, and river runoff.

The mathematical framework was based on the long-term water balance equation in volumetric form [83,84]:

V_{surf.inf. 2030,2040,2050} + V_und.inf. + V_{precip. 2030,2040,2050} − V_outfl. − V_und.outfl − V_{evap. 2030,2040,2050} = ±ΔV

(2)

where V_{surf.inf. 2030,2040,2050} is the projected surface inflow into the lake; V_und.inf. is the underground inflow into the lake; V_{precip. 2030,2040,2050} the projected atmospheric precipitation on the water surface; V_outfl. thewater outflow from the lake via the river; V_und.outfl the underground outflow from the lake; V_{evap. 2030,2040,2050} the projected evaporation from the water surface; and ±ΔV is the accumulation or consumption of water in the lake basin.

For endorheic lakes, which are abundant in the western region, Vst = 0. For such lakes, the equation describing their long-term water balance consists of only three components:

V_{surf.inf. 2030,2040,2050} + V_{precip. 2030,2040,2050} = V_{evap. 2030,2040,2050},

(3)

The value of V_{evap. 2030,2040,2050} is obtained from the balance equation as its residual term and includes errors caused by any difference between V_und.inf. and V_und.outfl.

To assess the components of the water balance, data on runoff, precipitation, temperature, and evaporation are required. However, in the study region, such data are limited, particularly for water bodies located in remote and sparsely populated areas.

The level of water availability is determined using the Falkenmark index: values above 1700 m³ per capita per year indicate sufficient supply; 1000–1700 correspond to water stress; below 1000 indicate water scarcity; and below 500 reflect absolute scarcity [85].

A comprehensive assessment of the region’s water resources was conducted through the integration of climatic, hydrological, demographic, and spatial data, providing a scientifically grounded basis for future water resource management and decision-making.

4. Results

The current and projected status of water resources was evaluated through the integration of multiple modeling approaches and diverse datasets, enabling a spatially explicit quantification of water balance components and water demand. The analysis synthesized data on precipitation, snow water equivalent, river discharge, and lake water volumes, which were subsequently aggregated to estimate overall water availability at district and regional scales within Western Kazakhstan.

4.1. Projected Climate Parameters

Based on error assessments, models with the lowest reproduction errors were selected from a set of 34 models (

Table 1. Calculated error values and ensemble uncertainties of the selected CMIP6 models compared to observational data.

Climate Elements	Correlation Coefficient, R	NSE	Relative Error, %
Temperature	0.85	0.71	11
Precipitation	0.73	0.62	20

). For air temperature forecasting, 15 models were chosen: ACCESS-ESM1-5, CanESM5, CMCC-CM2-SR5, CNRM-ESM2-1, EC-Earth3-Veg-LR, GFDL-CM4, HadGEM3-GC31-LL, HadGEM3-GC31-MM, IITM-ESM, KACE-1-0-G, KIOST-ESM, MIROC6, MIROC-ES2L, NESM3, and TaiESM1. These models encompass a broad spectrum of climate systems and employ diverse modeling approaches, enabling a variety of projections of global temperature and changes there in in response to anthropogenic influences. The models integrate different methodologies, including alternate parameterizations for greenhouse gas effects, oceanic and atmospheric circulations, among other factors.

For precipitation forecasting, six models were selected: ACCESS-ESM1.5, CanESM5, GFDL-ESM4, INM-CM5-0, NESM3, and UKESM1-0-LL. Precipitation prediction requires accounting for multiple factors, such as atmospheric circulation, seasonal variability, and the influence of local and regional features including mountain ranges and ocean currents. Unlike temperature forecasts, precipitation generally exhibits higher spatial variability, necessitating more detailed models to achieve accurate predictions.

Using data from 51 meteorological stations operated by Kazhydromet, adjustment coefficients were derived and applied to generate prognostic time series spanning the period 2015–2055 under two scenarios: SSP2-4.5 and SSP5-8.5. The climate projections (Figure 3) indicate a steady increase in average annual temperature and a 15–20% rise in annual precipitation. However, this increase is offset by a significant rise in evaporation rates, particularly during summer months and in the southern parts of the study area, where evaporation may reach up to 1500 mm. The data obtained formed the basis for further calculations of hydrological characteristics, including forecasts of river flow and lake water volumes.

4.2. Verification of Hydrological Calculations

Hydrological calculations were verified using data from two base periods: 1986–2005 and 1974–2015. For each base (historical) period, relationships between runoff and precipitation and air temperature were obtained for each catchment. For all catchments, a strong relationship was observed between river runoff and atmospheric precipitation during the autumn-winter period (precipitation from October to February) and during the spring flood period (precipitation from March to May); with precipitation during the winter (November to March) and autumn periods (September, October); as well as with temperature in May (R > 0.6).

The deviation of the model runoff from the observed values for the validation periods ranged from −5% to +4% in the period 1974–2014 and from −3% to +2% in the period 1986–2005, which confirms the high accuracy and reliability of hydrological modeling. Statistical methods based on regression and correlation-autocorrelation relationships remain relevant when data are limited. The use of CMIP6 data in hydrological models has made it possible to reliably predict the state of water resources by 2030, 2040, and 2050 using techniques for correcting biases and increasing spatial resolution. Ensemble modeling is considered the most robust method for assessing uncertainties.

4.3. Cartographic Materials of Water Balance Components (Figure 4)

This section presents maps that clearly illustrate the key components of the water balance, including (a) the average annual river runoff volume, (b) the average annual atmospheric precipitation, and (c) the average annual evaporation from water surfaces. These spatial representations provide essential insights into the distribution and magnitude of hydrological processes across the study area, facilitating a comprehensive understanding of the regional water cycle dynamics.

Figure 4. Maps: (a) average annual river flow, (b) average annual atmospheric precipitation, (c) average annual evaporation from water surfaces.

Figure 4. Maps: (a) average annual river flow, (b) average annual atmospheric precipitation, (c) average annual evaporation from water surfaces.

Overall, the forecast values for natural (climatic) water resources for 2030 will be within the normal range for 1974–2021. The maximum decrease of up to 10% is expected by 2040 (Atyrau region).

4.4. Detailed Analysis of Changes in Surface Water Resources by Region (Table 2)

Based on data for 1974–2021 and forecasts up to 2050, a general trend of decreasing water resources with territorial heterogeneity is observed.

Table 2. Changes in surface water resources by region.

Region	Current Resources (1974–2021), Million m3	Forecast for 2030	Forecast for 2040–2050	Most Vulnerable/Growing Districts
Aktobe Region	3211	Moderate increase (+2%)	Decrease (−6%)	Decrease (13–15%): Martuksky, Kargalinsky, Kobdinsky, Alginsky, and Aktobe city. Increase (up to +14%): Khromtausky and Aytekebi.
Atyrau Region	191	Slight increase (+2%)	Decrease (−7% and −5%)	Decrease (up to 11% and 8%): Kyzylkoginsky, Indersky. Increase: Kurmangazinsky (+5%).
West Kazakhstan Region (Oblast)	3236	Stable	Moderate losses (−5%)	Maximum losses (30–31%): Bokeiordinsky and Zhanibeksky. Increase (8–13%): Karatobinsky and Syrymsky.
Mangystau Region	48.4	Moderate increase (+3%)	Decrease (−3% and −1%)	Remains the most water-deficient region.
West Kazakhstan Region (Total)	6685	Peak (6749, +1%)	Decrease (−6% and −6%)	The main contributors to the decrease are the Bokeiordinsky, Zhanibeksky, and Martuksky districts.

Table 2. Changes in surface water resources by region.

Region	Current Resources (1974–2021), Million m3	Forecast for 2030	Forecast for 2040–2050	Most Vulnerable/Growing Districts
Aktobe Region	3211	Moderate increase (+2%)	Decrease (−6%)	Decrease (13–15%): Martuksky, Kargalinsky, Kobdinsky, Alginsky, and Aktobe city. Increase (up to +14%): Khromtausky and Aytekebi.
Atyrau Region	191	Slight increase (+2%)	Decrease (−7% and −5%)	Decrease (up to 11% and 8%): Kyzylkoginsky, Indersky. Increase: Kurmangazinsky (+5%).
West Kazakhstan Region (Oblast)	3236	Stable	Moderate losses (−5%)	Maximum losses (30–31%): Bokeiordinsky and Zhanibeksky. Increase (8–13%): Karatobinsky and Syrymsky.
Mangystau Region	48.4	Moderate increase (+3%)	Decrease (−3% and −1%)	Remains the most water-deficient region.
West Kazakhstan Region (Total)	6685	Peak (6749, +1%)	Decrease (−6% and −6%)	The main contributors to the decrease are the Bokeiordinsky, Zhanibeksky, and Martuksky districts.

Table 2 presents the changes in surface water resources by region in Western Kazakhstan. It summarizes current water resources (1974–2021) and projects their trends for 2030 and 2040–2050. The table highlights both moderate short-term increases and mid-century decreases in different regions. For example, Aktobe Region shows a moderate increase of 2% by 2030 followed by a projected decline of 6% then more substantial decreases in some districts. The Atyrau Region, characterized by lower water availability, experiences a slight increase by 2030 but pronounced declines afterwards, particularly in already water-stressed districts. West Kazakhstan Region shows stability in the near term but moderate to significant losses by 2040–2050, with some districts showing notable vulnerability while others record increases. Mangystau Region remains the most water-deficient with only slight fluctuations forecasted. Overall, the table effectively captures spatial heterogeneity and identifies key districts both vulnerable and with growing water resources, providing a concise, clear overview of regional water resource dynamics and critical areas for future water management efforts.

4.5. Forecast of River Runoff and Lake Water Resources

Forecast of Natural Local River Runoff Resources

The forecast of river runoff and lake water resources provides important insights into the hydrological future of the West Kazakhstan region. For natural local river runoff, projected values for 2030 fall within the historical norms of 1974–2021. However, by 2040–2050, a modest decline in natural water resources of up to 6% is anticipated region-wide. Specifically, the total natural local runoff is expected to be approximately 3771 million cubic meters in 2030, decreasing to 3555 million cubic meters in 2040, and rebounding slightly to 3571 million cubic meters in 2050, against a long-term average of 3785 million cubic meters for the base period.

Forecast of Natural Lake Water Resources

In terms of natural lake water resources, the region is expected to experience a volume increase of 2.66% by 2030, followed by decreases of 5.16% and 5.56% in 2040 and 2050, respectively. Looking at regional differences between 2025 and 2034, Aktobe Region forecasts a net increase in lake water volume of 77.4 million cubic meters, with the most significant rises occurring in the Aytekebi district (+44.3 million cubic meters) and the greatest decreases in the Alginsky district (−0.36 million cubic meters or approximately 13%). Meanwhile, the Atyrau Region anticipates a minor increase of about 1.18% over the same period.

These projections highlight both temporal and spatial variability in water resource availability, which are critical for sustainable water management and planning under evolving climatic conditions.

4.6. Forecast of Specific Water Availability of the Territory (Table 2, Figure 4)

Indirect methods based on cartographic materials—average annual river runoff, precipitation and evaporation [77,78,86]—are mainly used to calculate water balance components. These were constructed by obtaining forecast values of climatic characteristics, which reduces the accuracy of estimates (Figure 4).

According to the forecasts, a slight decrease in natural local resources of up to 6% is expected across the entire West Kazakhstan region. Across the entire West Kazakhstan region, the projected natural local resources will amount to 3771 million m³ in 2030, 3555 million m³ in 2040, 3571 million m³ in 2050, while the long-term average for 1974–2021 is 3785 million m³ (Figure 5).

Below we present detailed results of the assessment of lake water resources for the future by administrative districts of the West Kazakhstan region (Figure 6).

The results of the calculations show that the total amount of water contained in the lakes of the Aktobe region is expected to be 1151 million m³ in the period 2025–2034. The maximum increase is expected in the Aitekebiy district, at 44.3 million m³. The maximum decrease in volume is expected in the Algin district, at 0.36 million m³, which is about 13% compared to the current period.

Overall, in the Aktobe region for the period 2025–2034, an increase in the volume of water contained in lakes of 77.4 million m³ is forecast.

In the Atyrau region, an increase in lake water volumes is expected in the Zhylyoi district by 2.13 million m³ for the period (2025–2034) 2030, which is about 5%. In other areas, a slight increase of just over 1% is also expected. Overall, in the Atyrau region for the period (2025–2034), a slight increase in lake water volumes is expected within 1.18%.

Overall, for the West Kazakhstan region for the period (2025–2034) 2030, an increase in water contained in lake basins of 2.66% is expected, for the period (2035–2044) 2040, a decrease in lake water resources of 5.16% is expected, and for the period (2045–2054) 2040, a decrease in lake water resources of 5.56% is also expected.

4.7. Forecast of Specific Water Availability in the Territory

Due to ongoing climate change and increasing anthropogenic pressure on water sources, it is important to analyze both the current state of water resources and their projected changes up to 2050 (

Table 3. Changes in the current and projected state of water resources in the West Kazakhstan region.

Region	District	W, mln. m3
		Surface Water Resources
		1974–2021	2030 (2025–2034)	2040 (2035–2044)	2050 (2045–2054)
Aktobe	Aktobe city district	83.6	71.2	72.7	71.6
	Aitekebiysky district	527.6	594.0	493.5	509.8
	Alginsky district	175.6	153.6	159.9	152.4
	Baiganinsky district	151.9	160.1	146.4	146.7
	Kargalinsky district	192.2	170.5	171.1	170.2
	Kobdinsky district	227.0	204.9	210.4	196.3
	Martuksky district	211.1	179.9	184.0	180.8
	Mugalzharsky district	484.6	507.7	460.2	474.6
	Uilsky district	94.3	97.1	86.8	89.4
	Temirsky district	226.8	228.3	205.1	209.2
	Khromtausky district	206.5	236.1	211.8	221.9
	Shalkarsky district	124.7	134.7	126.0	124.8
	Irgizsky district	504.9	531.8	491.7	465.3
	total for the region	3211	3270	3020	3012
Atyrau	Zhylyoi district	45.5	47.7	46.6	46.1
	Indersky district	99.0	100.1	91.0	93.9
	Isatai district	0.0	0.0	0.0	0.0
	Kurmangazinsky district	1.6	1.7	1.7	1.7
	Kyzylkoginsky district	44.2	44.5	39.2	40.0
	Makat district	0.3	0.3	0.3	0.3
	Makhambetsky district and Atyrau GA	0.1	0.1	0.1	0.1
	total for the region	191	194	179	182
West Kazakhstan	Akzhaik district	86.0	93.9	89.1	93.1
	Bokeyordinsky district	302.9	268.2	256.9	209.7
	Burlinsky district	166.3	168.9	158.6	163.9
	Zhangalinsky district	180.0	183.5	174.7	179.3
	Zhanybeksky district	109.4	96.8	93.6	76.4
	Baitereksky district and Uralsk g.a.	273.8	278.1	261.1	269.8
	Kaztalovsky district	264.9	257.0	245.3	235.0
	Karatobinsky district	80.3	90.3	86.2	90.8
	Syrymsky district	155.9	168.7	159.9	167.3
	Taskalinsky district	212.7	205.5	195.7	201.8
	Terektinsky district	1266.8	1283.4	1207.0	1245.4
	Chingirlausky district	136.5	140.0	131.7	136.4
	total for the region	3236	3235	3061	3068
Mangystau	Beineu	9.4	9.8	9.0	9.3
	Karakiyansky and Zhanaozen g.a.	21.5	22.0	20.7	21.2
	Mangistausky	9.9	10.2	9.8	10.1
	Munaylynsky and Aktau g.a.	7.5	7.7	7.2	7.4
	Tupkaragansky	0.0	0.0	0.0	0.0
	total for the region	48.4	49.8	46.8	48.1
West Kazakhstan region		6685	6749	6306	6310

).

The general trend indicates a decrease in specific water availability (expressed in million cubic meters per square kilometer) in most areas of the western regions of Kazakhstan compared to the baseline period of 1974–2021. Regional forecasts show variability with some fluctuations over time. For example, in Aktobe Region, values change from 10.7 (1974–2021) to 10.9 in 2030, then decrease to 10.1 in 2040 and 2050. Similarly, in Atyrau Region, there is a slight increase from 1.6 to 1.64 in 2030, followed by a decline to 1.51 and 1.54 in 2040 and 2050, respectively. West Kazakhstan Region remains relatively stable, with projections from 21.4 (baseline) to 21.4 in 2030, then decreasing to 20.2 in 2040 and a slight rise to 20.3 in 2050. Mangystau Region shows a small decrease from 0.3 (baseline) to 0.27 in 2040 with a minor recovery to 0.28 by 2050. Overall, specific water availability for West Kazakhstan Region decreases from 9.0 to 8.52 in 2040, with a slight increase to 8.53 in 2050.

Long-term analysis of snow water reserves shows a significant reduction in average snow cover thickness in central basins, decreasing from 50 to 56 cm in the 1970s and 1980s to 36–44 cm in subsequent decades. Likewise, snow reserves dropped from 115–142 mm to 68–95 mm, according to current estimates. The following hydroclimatic patterns were identified: the average multi-year snow cover height in main watersheds varies from 37 to 45 cm, with a density of 0.38–0.42 g/cm³. From 1971 to 2021, the minimum annual snow reserve was 62 mm, and the maximum was 147 mm. Notably, the largest floods in 1995, 2010, 2017, and 2021 coincided with record snow reserves, with spring runoff in those years exceeding long-term averages by 40–85%. The correlation between snow reserves and maximum river discharge was statistically significant. In 2017, northeastern tributaries accumulated snow reserves up to 134 mm, which caused flood intensity reaching 170% of the norm. The greatest snow reserves were recorded in northeastern basins (Kargalinka and Irgiz), and the least in the southwestern Ural basin. Seasonal peaks in snow reserves generally occur in the second half of March, while meltwater runoff peaks from April 5 to 18. In snowy winters, snow cover height ranges from 48 to 55 cm, density up to 0.44 g/cm³, and water reserves exceed 120 mm.

Based on data for the period 1974–2021 and forecasts up to 2050, a general trend of water resources decline can be observed, with varying degrees of severity across the region’s provinces and districts (Figure 7).

The Aktobe region is characterized by a relatively high level of water availability compared to other regions in the area. Currently, the total volume of surface water resources is approximately 3211 million cubic meters. In the short term (2030), a moderate increase in water resources of 2% is forecast. However, by 2040 and 2050, a decrease of 6% is forecast. This decrease may be due to a continued reduction in river water content, a decrease in snow cover area and an increase in evaporation.

There are significant differences at the district level. The most vulnerable districts in terms of water resources are Martuk, Kargaly, Kobd, and Algi, where a decline of 13–15% is forecast. The city of Aktobe is also losing up to 15% of its water resources, while positive dynamics are observed in the Kromtau and Aitekebiy districts (up to 14% growth). Moderate positive changes are also noted in the Uil, Irgiz, and Shalkar districts.

The Atyrau region is considered one of the most water-deficient regions in the country. The region’s water resources average 191 million cubic meters, which is dozens of times lower than in other regions. A slight increase in water resources of 2% is expected in 2030, but by 2040 and 2050, decreases of 7% and 5%, respectively, are forecast. Areas where water supply is already limited are particularly affected: in the Kyzylkoginsky district, the decline is up to 11%, and in the Indersky district, up to 8%. Areas with insignificant water resources, such as Makhambetsky and Isataysky, show no growth and a significant decline. The exception is the Kurmangazy district, where positive dynamics remain at 5%.

The West Kazakhstan region shows a relatively stable picture in terms of water resources, amounting to 3236 million m³ in the current period. No significant changes are forecast in the near future (2030), but moderate losses of 5% are possible by 2040 and 2050. However, there are significant differences between districts. The Bokeiordinsky and Zhanibeksky districts are losing up to 30–31%, which indicates serious climatic and anthropogenic challenges. At the same time, districts such as Karatobinsky and Syrymsky are showing increases of 8–13%. The Burlinsky, Baiterek, and Zhangalinsky districts remain relatively stable, with changes ranging from −5% to +2%. The most water-rich district in the region, Terekta, is losing up to 5%.

Despite belonging to the region, Mangystau Province remains the most deficient in terms of water supply: its water resources amount to only 48.4 million cubic meters. However, a moderate increase of up to +3% is expected in 2030, followed by a decrease of −3% in 2040 and a partial recovery to −1% in 2050.

An analysis by region shows that surface water resources amounted to 6685 million m³ in 1974–2021, peaking in 2030 (6749 million m³, +1%). However, by 2040, the volume will decrease to 6306 million m³ (−6%) and remain at approximately this level by 2050 (6310 million m³, also −6%). Thus, even with the existing territorial heterogeneity, general trends can be identified: a short-term improvement in the water balance by 2030. The main contribution to this decline comes from the Bokeyordinsky, Zhanibeksky, Martuksky and other arid regions, where the decline in water resources reaches 15–31%.

The forecast values for the specific water supply of the territory in Western Kazakhstan show significant differences between regions. (

Table 4. Specific water supply of the territory of the West Kazakhstan region.

Region	Specific Water Supply of the Territory, Million m3/km2
	1974–2021	2030 (2025–2034)	2040 (2035–2044)	2050 (2045–2054)
Aktobe	10.7	10.9	10.1	10.1
Atyrau	1.6	1.64	1.51	1.54
West Kazakhstan	21.4	21.4	20.2	20.3
Mangystau	0.3	0.29	0.27	0.28
West Kazakhstan region	9.0	9.12	8.52	8.53

, Figure 8).

Analysis of data on specific water availability in the territory shows that in most areas of the western regions of Kazakhstan, a decrease in available water resources is expected compared to the base period of 1974–2021. In the Aktobe region, the largest decline is forecast in the Kobda district—up to 16% by 2030, in the Baigan district—up to 15%, and in the Khromtau district—up to 14%, indicating a growing deficit. At the same time, in areas with extremely low water availability, such as Irgiz and Shalkar, a relative increase of up to 8–9% is expected. In the Atyrau region, the decline is particularly noticeable in the Kyzylkoginsky district (11% by 2040) and the Indersky district (8%). In the West Kazakhstan region, the largest decline is forecast in the Zhangalinsky district, where water availability will decrease by 30% by 2050, as well as in the Kaztalovsky (11%) and Karatobinsky (9%) districts. At the same time, a slight improvement in the situation is possible in the Akzhaik (12%) and Chingirla (7%) districts. In the Mangystau region, the changes are relatively moderate: a decrease of up to 5% by 2040 is expected in the Karakiyak and Zhanaozen districts, and up to 9% in the Tupkaragan district. Thus, at the regional level (including all four regions), a decrease in specific water availability is projected by an average of 6% by 2040 and 5% by 2050.

Based on the data presented on the specific water supply of the population with surface water resources (in thousand m³ per person) for the western regions of Kazakhstan for the period up to 2050, there is a steady negative trend in the decline of available water resources in most areas (

Table 5. Specific water supply to the population of the West Kazakhstan region.

Region	Specific Water Supply of the Population, Thousand m3/Person
	1974–2021	2030 (2025–2034)	2040 (2035–2044)	2050 (2045–2054)
Aktobe	3.54	3.14	2.61	2.31
Atyrau	0.28	0.25	0.20	0.18
West Kazakhstan	4.79	4.51	4.09	3.84
Mangystau	0.07	0.05	0.04	0.03
West Kazakhstan region	2.24	1.96	1.60	1.37

, Figure 9).

In the Aktobe region, the total decline will be 11% by 2030, 26% by 2040 and 35% by 2050. A particularly sharp decline is forecast in the Aktobe (46% by 2050), Irgiz (42%) and Aitekebiy (39%) districts. Significant declines will also affect the Algin (27%), Martuk (28%) and Kargaly (26%) districts. The situation is more stable in the Shalkar district, where the decline will be only 8% by 2050, and by 2030, a slight increase (2%) is even predicted.

In the Atyrau region, the overall level of water supply will decrease by 10% by 2030, 29% by 2040 and 38% by 2050, with a critical decline expected in a number of districts. The most vulnerable are the Makat (36%), Kyzylkoginsky (37%), and Inder (41%) districts. The Zhylyoi district will also lose up to 34% by 2050.

The West Kazakhstan region shows a relatively moderate decline across the region as a whole: 6% by 2030, 15% by 2040, and 20% by 2050. However, in some areas, the decline is significantly more pronounced. For example, a decline of up to 33% is expected in the Bokeiordinsky district, 32% in the Zhanibeksky district, and 23% in the Baiterek and Uralsk districts. At the same time, in areas with relatively stable water supply—Karatobinsky (13%), Syryms (8%), and Chingirlau (1%)—there may even be a slight increase.

The most serious reduction in water supply is forecast for the Mangystau region: 17% by 2030, 39% by 2040, and 51% by 2050. The most critical values are recorded for the Munayly district and Aktau (53%), Zhanaozen (49%), and Beineu (47%) districts. The Tupkaragan district will maintain zero water supply throughout the entire forecast period.

Overall, in the West Kazakhstan region, the specific water supply to the population will decrease from 2.24 thousand m³/person in 1974–2021 to 1.37 thousand m³/person by 2050, which corresponds to an overall decrease of −39%. This indicates growing water stress in the region and highlights the need to introduce water-saving technologies, rational water use and strategic planning to mitigate socio-economic and environmental risks.

An assessment of water availability for the population of the West Kazakhstan region using the Falkenmark water stress index (in m³/person per year) showed persistent negative trends, confirming the progressive increase in water scarcity (

Table 6. Falkenmark water stress index for the West Kazakhstan region.

Region	Falkenmark Water Stress Index, m3/Person
	1974–2021	2030 (2025–2034)	2040 (2035–2044)	2050 (2045–2054)
Aktobe	3543	3137	2614	2309
Atyrau	284	254	203	176
West Kazakhstan	4790	4513	4087	3843
Mangystau	66	55	40	33
West Kazakhstan region	2236	1964	1598	1366

, Figure 10).

The water stress index according to Falkenmark shows a steady downward trend in water availability in the West Kazakhstan region. On average for the region, it is decreasing from 2236 m³/person in 1974–2021 to 1366 m³/person by 2050 (39%). The Aktobe region shows a decrease from 3543 to 2309 m³/person (35%), with a decrease of 46% in the city of Aktobe. The situation is most critical in the Atyrau region: the average index is decreasing from 284 to 176 m³/person (38%). In the Mangystau region, the index has fallen from 66 to 33 m³/person (51%), indicating an extreme water shortage in all districts. Against this backdrop, the West Kazakhstan region retains the best conditions: a decrease from 4790 to 3843 m³/person (20%) does not take it beyond the limits of water stress.

5. Discussion

This study provides a critical and timely assessment of future water stress in the West Kazakhstan Region (WKR), offering an evidence-based foundation for regional water resource management. The applied methodological framework, as detailed in Figure 2 of the Methods section, integrates two principal drivers of water scarcity—climate change impacts, modeled through CMIP6 using RCP 4.5 and RCP 8.5 scenarios, and anthropogenic influences, represented by a dedicated water consumption growth model. This dual approach yields a comprehensive and robust understanding of the evolving water security landscape in WKR.

5.1. Comparative Analysis and Methodological Rigor

The Water Stress Index (WSI), calculated as the ratio of future consumption to available resources, serves as a highly relevant metric for policy-making. Our approach is validated by its alignment with international studies, which recognize the combined effects of reduced supply (Stage I) and increased demand (Stage II) as the primary cause of water scarcity in arid regions [7,17]. The use of the high-emission RCP 8.5 scenario ensures that the results represent a maximum-vulnerability scenario, essential for effective long-term adaptation planning.

The projection of a significant increase in WSI, affecting over 70% of the WKR territory (as stated in the Abstract), resonates strongly with broader regional parallels across Central Asia. Many river basins, including the Syr Darya and Amu Darya, are already experiencing severe water stress indices (WSI > 40%) due to glacier melt acceleration and inefficient water use practices in agriculture [21,25]. The vulnerability of the Ural, Emba, and Uil river basins in WKR confirms that climate-induced resource depletion is not merely a regional trend but a critical national security issue for Kazakhstan.

5.2. Implications for Conflict Mitigation and Policy

The anticipated augmentation of water stress necessitates strategic frameworks for conflict prevention. Elevated WSI values serve not only as ecological indicators but also as proxies for emerging socio-economic instability and transboundary tensions.

Intersectoral Conflicts: Scarcity will intensify competition between dominant water users, notably the energy and industrial sectors focused on oil and gas development and the agricultural sector dependent on irrigation. Resource allocation during drought episodes will require rigorous regulatory innovations, alongside possible shifts toward less water-intensive cropping practices.

Transboundary Diplomacy: Given that major WKR watercourses, such as the Ural River, traverse international borders, local resource stress will translate into heightened diplomatic engagement. The forecasted resource decline reinforces Kazakhstan’s strategic impetus to initiate and expedite bilateral negotiations geared toward equitable transboundary water sharing and coordinated management under constrained flow conditions.

Infrastructure and Adaptation: The study underscores an urgent need for investment in water-efficient infrastructure, including advanced irrigation systems, industrial water recycling technologies, and the establishment of strategic reservoirs. Spatially resolved WSI mappings provide policymakers with tools to channel adaptation investments toward the most vulnerable districts, enhancing resource use efficiency.

5.3. Comparison of Results with Previous Studies

The prognostic estimates of water resource change trends in the West Kazakhstan Region (WKR) obtained during the study are generally consistent with regional and global climate models, but possess higher spatiotemporal detail.

Climate Projections: The sustained rise in mean annual temperature, coupled with increasing precipitation, corroborates findings from the IPCC Sixth Assessment Report for Central Asia. Crucially, our analysis reveals that increased precipitation benefits are nullified by heightened evaporation, reaching up to 1500 mm in southern zones.

River Runoff: The forecasted decrease in natural local river runoff by approximately 6% by 2040–2050 aligns with prior research indicating runoff declines in precipitation-fed basins.

Lake Water Dynamics: The projected transient rise in lake water volumes by 2030, followed by declines, supports conclusions by Kulebayev et al. about the high sensitivity of WKR’s lakes to combined climatic and anthropogenic pressures. This study advances understanding through a quantitative evaluation grounded in SSP scenarios.

Thus, our research confirms the main climatic trends and contributes to the understanding of the region’s hydrological response, demonstrating that increased evaporation is a key factor in the formation of future water scarcity.

5.4. Consequences of Temporal Changes in Surface Waters in the Context of SDGs

The temporal changes in surface water resources in the WKR (resource reduction up to 6% and specific water availability by 2050), identified based on the analysis of the 1974–2021 period and forecasts, have critical implications for achieving the UN Sustainable Development Goals (SDGs) [87,88]:

SDG 6: Clean Water and Sanitation.

Threat: The projected reduction in natural water resources, especially in the most vulnerable districts (Bokeiordinsky and Zhanibeksky), directly threatens the achievement of Target 6.4 (substantially increasing water-use efficiency and ensuring sustainable withdrawals and supply of freshwater to address water scarcity). The intensification of water deficit will increase competition between sectors (agriculture, industry, domestic water use) and place a greater burden on existing water management infrastructure.

Recommendation: Adaptation strategies are necessary, including the introduction of water-saving technologies in agriculture, the restoration of irrigation systems, and increased efficiency in water resource management.

SDG 13: Climate Action.

Linkage: Since the primary cause of the projected deficit is climate change (rise in temperature and evaporation), the results of our study serve as a quantitative basis for developing regional plans for Target 13.2 (integrating climate change measures into national policies).

SDG 2, 8, 15: Agriculture, Economic Growth, and Ecosystems.

Impact: Reduced water availability jeopardizes Target 2.4 (sustainable food production systems), as the agricultural sector in the WKR heavily relies on surface waters. Water stress can also slow down regional economic growth (SDG 8) and negatively affect aquatic ecosystems and biodiversity (SDG 15), particularly in areas with maximum runoff losses.

Thus, the water stress index under consideration, as a key indicator, is confirmed by international scientific data, where the combined impact of declining water resources in nature and increasing consumption is recognized as the main cause of water scarcity in arid regions. In the context of the SDGs, emphasis is placed on the potential threats to achieving goals such as ensuring clean water and sanitation, combating climate change, sustainable agriculture, economic growth, and ecosystem conservation. The study points to the need for active implementation of adaptation measures and improved water management efficiency to help mitigate negative impacts and support national commitments.

Overall, the analysis shows that the study is a systematic and timely contribution to understanding water stress in the region, combining rigorous scientific approaches with practical relevance, which is important for effective policy-making and ensuring sustainable development in the West Kazakhstan region.

6. Conclusions

The analysis of surface water in the West Kazakhstan region for the period 1974–2021, with a forecast up to 2050, allows us to draw a number of key conclusions about the current state and trends in water supply in the region. Despite a short-term improvement in indicators by 2030, the downward trend in both absolute surface water volumes and specific water supply to the territory and population remains stable.

Snow cover variability is the main factor influencing water security and hydrological risks in Western Kazakhstan. The declining trend in snow storage observed since the 2000s has resulted in reduced spring floods and diminished river water supply—especially in 2002, 2012, 2019, and 2023, when the snow water equivalent did not exceed 70 mm and spring runoff was 32–49% below average. Snow storage distribution maps have revealed zones of intensive snowmelt that coincide with areas of elevated flood risk.

The projected trends for regions such as Atyrau and Mangystau raise particular concerns regarding future water availability, with a critical drop in the water stress index indicating a high degree of water scarcity. Even in relatively stable regions such as West Kazakhstan and Aktobe, local differences between districts reveal areas of high vulnerability, especially in arid and sparsely populated areas. Increasing water stress is caused by the combined effects of climate change, reduced snow cover, increased evaporation and increased anthropogenic pressure.

This study delivers a comprehensive and timely assessment of the emerging challenges related to future water stress in the West Kazakhstan Region (WKR). The projected temporal decline in surface water resources and specific water availability poses significant obstacles to achieving the United Nations Sustainable Development Goals (SDGs) within the region. The results obtained can be used in strategic planning, in the development of regional climate change adaptation programs, and in water resource monitoring systems. Further research should focus on clarifying interannual and seasonal variability in the water balance, as well as on quantitatively assessing the impact of various socio-economic development scenarios on the region’s water security.