Pasture degradation under increasing climatic aridity threatens sustainable land use, livestock production, and ecosystem stability in southern Kazakhstan. This study assessed drought-related pasture degradation in Zhambyl District, Almaty Region, using Sentinel-2 imagery, climatic data, and GIS analysis to support sustainable pasture management and climate-adaptive land-use planning. The study area was subdivided into four zones: semi-desert, arid foothill, typical foothill, and mountain. Zone-specific seasonal windows were defined to account for differences in relief, aridity, and vegetation phenology. Pasture conditions were evaluated using NDVI, NDMI, and BSI, while precipitation, air temperature, and the hydrothermal coefficient (HTC) were used to characterize drought conditions for 2021–2025. Multi-year anomalies, repeatability of unfavorable signals, and an integrated degradation indicator were used for GIS-based mapping of drought-related pasture degradation signals. The results showed pronounced spatial differentiation of degradation processes. The semi-desert and arid foothill zones contained the largest total mapped areas of degradation, whereas the typical foothill zone showed a relatively higher share of moderate degradation. Weak and moderate degradation predominated in all zones, while severe degradation was confined to small local areas. In the semi-desert zone, weak and moderate degradation occupied 891,169 and 95,414 ha, respectively. In the arid foothill zone, these classes covered 303,283 and 43,185 ha; in the typical foothill zone, 134,966 and 67,163 ha; and in the mountain zone, 66,376 and 17,286 ha. HTC showed a more consistent correlation pattern with pasture conditions than precipitation alone. The proposed approach can support sustainable pasture management by identifying drought-sensitive areas, prioritizing zones for monitoring and restoration, and informing adaptive grazing and land-use decisions under increasing climatic aridity. Because formal accuracy assessment was limited by the available field data, the resulting maps should be interpreted as exploratory maps of drought-related pasture degradation signals rather than as a fully validated categorical classification of pasture degradation.

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

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.