Raushan Amanzholova

Managed Aquifer Recharge (MAR) has emerged as an effective approach for enhancing groundwater resources in water-scarce regions under increasing climate variability and growing water demand. However, most GIS-based MAR studies are limited to regional suitability mapping, whereas the hydrogeological feasibility of identified sites is rarely verified under field conditions. This study presents a multi-scale framework integrating regional GIS-based screening with detailed site-scale verification to improve the reliability of MAR suitability assessment in southeastern Kazakhstan. This research was conducted within the Zhetysu Alatau–Balkhash First-Order Hydrogeological Basin to develop and evaluate a multi-scale framework for identifying suitable MAR sites. Regional suitability assessment combined Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), Monte Carlo simulation, Fuzzy Gamma analysis, remote sensing, and regional hydrochemical data. Based on the regional assessment, the Byzhy River Basin was selected as a representative site for detailed hydrological, hydrogeological, hydrochemical, LiDAR-based, and field investigations to verify regional suitability under actual hydrogeological conditions. The regional suitability assessment identified 21.82% (58.41 km ² ) of the representative study area as highly suitable for MAR, while Monte Carlo simulation and Fuzzy Gamma analysis confirmed the robustness of the suitability model. Hydrological assessment indicated a positive long-term mean potential water balance of 27.69 mm·year ⁻¹ , suggesting sufficient water resources for MAR. Three double-ring infiltration tests yielded a mean field-saturated hydraulic conductivity of 1.92 m·day ⁻¹ (range: 1.86–1.96 m·day ⁻¹ ), indicating high infiltration capacity of the Quaternary alluvial deposits. Groundwater mineralization remained below 1 g·L ⁻¹ , and hydrogeological investigations confirmed a 50 m thick Quaternary alluvial aquifer suitable for MAR. Long-term daily river discharge records indicated a potential annual recharge volume of 17.44 million m ³ , representing only 2.33% of the effective aquifer storage capacity (746.8 million m ³ ), demonstrating substantial storage potential. The close agreement between the regional suitability assessment and field investigations demonstrates that the multi-scale framework substantially reduces uncertainty in GIS-based MAR site identification. By integrating regional suitability mapping with hydrological, hydrogeological, hydrochemical, LiDAR, and field investigations, the framework provides a transferable methodology for identifying and verifying MAR sites in arid and semi-arid regions, supporting groundwater management and long-term water resource planning.

Climate change is increasing the frequency of hydrological extremes such as floods, requiring more effective approaches to build community preparedness alongside traditional flood risk management. This study develops and evaluates a serious-game-based flood risk education intervention in Kazakhstan and examines whether post-intervention protective behavioral intention can be predicted from gameplay, hydrological-literacy, and governance-attitude data. A cross-sectional, post-intervention survey of 60 participants from flood-, GLOF-, and drought-exposed regions of Kazakhstan was analyzed using descriptive/inferential statistics and three machine-learning classifiers (logistic regression, random forest, multilayer perceptron) to predict protective behavioral intention and to identify its strongest correlates. The participants showed good knowledge about immediate responses in flood events: 91.7% were able to identify the appropriate evacuation procedures, and the mean hydrological-literacy score was 5.13 out of 7. There was relatively less knowledge about longer-term mitigation measures, especially the cost effectiveness of nature-based flood protection (56.7%). Most participants agreed with the introduction of measures for flood management using nature (58.6%) and mandatory disaster insurance (87.7%), and 81.0% indicated that they planned to take preparedness measures after playing. The predictive models with the highest discrimination were logistic regression and random forest, with an area under the receiver operating characteristic curve (AUC) of 0.81 each, while the multilayer perceptron (MLP) had an AUC of 0.75. Importantly, both machine-learning and conventional statistical analyses revealed that perceived scenario fidelity was the strongest and most consistent predictor of protective behavioral intention. These results suggest that serious-game-based flood risk education provides a useful context for examining hydrological knowledge and generating behavioral data for predictive modeling of preparedness. The framework combines experiential learning, flood-governance assessment, and machine-learning-based behavioral prediction, while the findings indicate an association between perceived scenario realism and preparedness-related behavioral intention. These results provide a basis for further investigation of data-driven disaster risk education in Kazakhstan and other settings facing similar hydrological hazard risks.

Kazakhstan faces growing water security and environmental management challenges and needs more practice-oriented environmental science education. This study examines two complementary Kazakhstan-based cases. Case Study 1 analyzes faculty and student surveys from the 2022 Science with a Purpose project, which delivered short courses in water quality, air quality, remediation, sampling methods, and scientific communication. Case Study 2 examines the 2025 pilot adaptation of the Nebraska-based Know Your Well (KYW) participatory-science model. An exploratory comparative two-case-study design drew on survey responses, project documentation, instructional materials, participant feedback, workflow analysis, and two illustrative laboratory water analyses. In Case Study 1, 22 of 25 faculty members responded (88.0%); student response rates were 14 of 18 (77.8%) before and 10 of 18 (55.6%) after the courses. Participants generally rated practice-oriented content positively. In the KYW pilot (N = 15), 14 participants (93.3%) reported that the training met expectations, and 14 (93.3%) considered the skills fully or partially applicable. The pilot demonstrated a feasible workflow linking preparation, field activity, sample handling, and laboratory-supported interpretation. Although the findings do not establish national representativeness, intervention effects, long-term learning outcomes, or large-scale monitoring impact, they support complementary capacity-building pathways for Kazakhstan.

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through a multi-criteria analysis using geographic information systems (GIS) and hydrogeological field exploration, water balance modelling. Remote sensing datasets and evapotranspiration (ET) analyses were conducted for the 2014–2024 period, while field investigations, infiltration tests, and hydrochemical sampling were performed during the 2025 field campaign. The suitability testing was preliminarily performed in the Google Earth Engine (GEE; Google LLC, Mountain View, CA, USA) environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified, out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could potentially be added to groundwater recharge is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge, which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that the potential MAR rates range between 174 and 5282 m³/day depending on local hydrogeological conditions. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies.

The hydrological prediction in transboundary river basins is difficult because of their heterogeneous data distribution, restrictions of data sovereignty, and the irregular nature of the discharge pattern. In this paper, Federated Learning (FL) with an LSTM neural network is proposed for next-day predictions of discharge in the Chu–Talas transboundary basin. The basin area belongs to both Kazakhstan and Kyrgyzstan. In an FL scenario, two hydrological stations from the basin were selected as client nodes, representing two different discharge regimes. Station 15125—the Chu main channel is characterized by the highest discharge regime among stations located in the basin, while Station 15233—Merke tributary represents a small catchment with an irregular regime. The federated LSTM model is compared against a centralized LSTM and a local-only LSTM baseline model. The training process is based on nearly three decades of daily discharge measurements. The preprocessing step includes synchronization, lag calculation, and windowing operation. The models are trained using three metrics: root mean square error, mean absolute error, and Nash–Sutcliffe Efficiency, as well as using Monte Carlo Dropout for estimation of the probabilistic uncertainty. The results demonstrate that the federated model demonstrates comparable performance with the centralized one for the Chu main channel. It also improves prediction accuracy for the smaller Merke tributary compared with both centralized and local-only models. These findings show that FL can work effectively with non-IID and heterogeneous hydrological data. The study makes three main contributions: (i) it implements the FedAvg algorithm on transboundary, heterogeneous hydrological data, proving that decentralized optimization can effectively capture autoregressive temporal hydrology without data centralization; (ii) it systematically compares federated, centralized, and local-only models, demonstrating that the federated approach eliminates the scale bias that traditionally neglects smaller, high-variance catchments; and (iii) it utilizes Monte Carlo Dropout to translate deterministic AI outputs into risk-aware probabilistic bounds. Ultimately, the results of this study demonstrate the practical and scientific usefulness of FL in operational water management, as the method presents a privacy-saving means of increasing predictive capacity and enabling risk-based decision-making in transboundary river basins.

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through the multi-criteria analysis using GIS and hydrogeological field exploration, water balance modelling. The suitability testing was preliminarily performed in the Google Earth Engine environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests, produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could be recharged into the suitable sites is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that there are recharge rates of between 174 and 5,282 m³/day, with a high degree of spatial variability which is caused by local hydrogeological circumstances. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies.

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.

The study is dedicated to analyzing the economic efficiency of Managed Aquifer Recharge (MAR) systems as a method of sustainable water supply under the conditions of climate change and water resource scarcity. The work explores the fundamental principles of MAR, its advantages in reducing water supply costs, minimizing water losses, improving resilience to extreme climatic conditions, and maintaining ecosystem balances. Special attention is given to the economic and social benefits that the use of MAR can provide to agriculture and local communities. The research methodology includes an analysis of existing practices, economic modeling, and an assessment of the impact of implementing MAR technologies at the regional level. The results demonstrate that MAR is an effective and economically advantageous strategy for water supply, promoting sustainable use of water resources and improving quality of life. The scientific novelty lies in justifying the economic feasibility of applying MAR systems in the context of climate change, while the practical significance of the study lies in the potential to implement the findings into real-world water resource management practices.

Agriculture is becoming increasingly demanding due to climate change challenges, necessitating continuous monitoring and changes, including soil assessment for precision agricultural requirements. Agricultural soils are heavily utilized by farmers through the application of pesticides and nitrate phosphates to enhance yield. The exacerbation of flood-drought conditions is resulting in soil irregularity, necessitating meticulous soil monitoring at each location. Soil monitoring is prohibitively costly for numerous farmers. To address this issue, the implementation of a compact, energy-efficient, low-cost mobile robotic platform equipped with various sensors for soil monitoring would be prudent. Farmers can remotely manage, analyze surface upper soil strata, and examine topography. The relevance to research activities and active recreation may result in a low cost for a series of behaviors that enhance comprehension of the examined environmental details. The specialized three-wheeled mobility platform is a novel apparatus engineered for autonomous navigation and task execution. The robot’s three-wheel design confers exceptional mobility and stability, enabling effective operation in restricted areas and across various terrains. It is outfitted with sensors and a control system that guarantees accurate navigational control and obstacle evasion. The programming and modification features enable the robot to be tailored for specialized functions, including data collecting, small load transfer, and environmental monitoring. The robot is applicable for educational, scientific, industrial, and domestic uses. Consequently, the three-wheeled mobile robot serves as a versatile and promising platform for the advancement of contemporary robotic systems.