The implementation of accurate water balance assessment in glacier basins is essential for the management and sustainable development of water resources in the basins. In this study, a hybrid modeling framework was constructed to enhance runoff prediction and water balance assessment in glacier basins. An improved physical hydrological model (SEGSWAT+) was combined with a machine learning model (ML) to capture the relationship between runoff residuals and water balance components through the Shapley additive explanations (SHAP) method. Based on the enhancement of the runoff fitting results of the existing model, the runoff residuals are decomposed and used to correct the hydrological process component values, thus improving the accuracy of the water balance results. We evaluated the performance and correction results of the method using various ML methods. We analyzed the results for two consecutive periods from 1959 to 2022 for the glacial sub-basins of three tributaries of the Upper Ili River Basin in central Asia. The results show that the hybrid framework based on extreme gradient boosting (XGBoost) with an average NSE value of 0.93 has the best performance, and the bias based on the evapotranspiration component and soil water content change component is reduced by 3.2–5%, proving the effectiveness of the water balance correction. This study advances the interpretation of ML models for hydrologic assessment of areas with complex hydrodynamic characteristics.

Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management.

Understanding the impacts of climate change and human activities on large endorheic lakes is crucial for sustainable water management, yet quantitative attribution remains a significant challenge. This study introduces the Hydrological Attribution and Analysis Framework (HAAF), a novel three-stage methodology, to provide a comprehensive explanation for the nearly-centennial (1931–2024) water balance dynamics of Lake Balkhash. The HAAF first establishes a high-fidelity hydrological reconstruction using a Physics-Informed Machine Learning (PIML) model, then employs the Budyko framework to attribute runoff changes, and finally links these catchment-scale drivers to the lake's terminal water balance. Our results confirm the robustness of the PIML model in simulating historical runoff (KGE > 0.75). The attribution analysis then reveals a complex interplay of competing forces. During the intensive intervention period (1970–1990), a substantial human-induced runoff reduction of -9.21 km³ completely masked a significant climate-driven wetting potential (+6.13 km³), triggering the lake's sharp decline. In the recent period (1991–2024), the basin's hydrology has been governed by a fragile stalemate in which a massive, climate-driven potential for increased runoff (+10.80 km³) was almost entirely neutralized by the persistent negative impact of human water use (-11.36 km³). At the lake level, this translated into an apparent stability sustained only by a favorable climatic subsidy. Future projections under various climate scenarios indicate that this climatic buffer is transient and unlikely to persist, exposing the lake to a high risk of rapid decline. We conclude that the recent stability of Lake Balkhash is not a sign of systemic recovery but a "masked vulnerability." This highlights the urgent need for sustainable and forward-looking water management strategies that account for these underlying, competing drivers.

Understanding the impacts of climate change and human activities on large endorheic lakes is crucial for sustainable water management, yet quantitative attribution remains a significant challenge. This study introduces the Hydrological Attribution and Analysis Framework (HAAF), a novel three-stage methodology, to provide a comprehensive explanation for the nearly-centennial (1931–2024) water balance dynamics of Lake Balkhash. The HAAF first establishes a high-fidelity hydrological reconstruction using a Physics-Informed Machine Learning (PIML) model, then employs the Budyko framework to attribute runoff changes, and finally links these catchment-scale drivers to the lake's terminal water balance. Our results confirm the robustness of the PIML model in simulating historical runoff (KGE > 0.75). The attribution analysis then reveals a complex interplay of competing forces. During the intensive intervention period (1970–1990), a substantial human-induced runoff reduction of -9.21 km³ completely masked a significant climate-driven wetting potential (+6.13 km³), triggering the lake's sharp decline. In the recent period (1991–2024), the basin's hydrology has been governed by a fragile stalemate in which a massive, climate-driven potential for increased runoff (+10.80 km³) was almost entirely neutralized by the persistent negative impact of human water use (-11.36 km³). At the lake level, this translated into an apparent stability sustained only by a favorable climatic subsidy. Future projections under various climate scenarios indicate that this climatic buffer is transient and unlikely to persist, exposing the lake to a high risk of rapid decline. We conclude that the recent stability of Lake Balkhash is not a sign of systemic recovery but a "masked vulnerability." This highlights the urgent need for sustainable and forward-looking water management strategies that account for these underlying, competing drivers.

Understanding the impacts of climate change and human activities on large endorheic lakes is crucial for sustainable water management, yet quantitative attribution remains a significant challenge. This study introduces the Hydrological Attribution and Analysis Framework (HAAF), a novel three-stage methodology, to provide a comprehensive explanation for the nearly-centennial (1931–2024) water balance dynamics of Lake Balkhash. The HAAF first establishes a high-fidelity hydrological reconstruction using a Physics-Informed Machine Learning (PIML) model, then employs the Budyko framework to attribute runoff changes, and finally links these catchment-scale drivers to the lake's terminal water balance. Our results confirm the robustness of the PIML model in simulating historical runoff (KGE > 0.75). The attribution analysis then reveals a complex interplay of competing forces. During the intensive intervention period (1970–1990), a substantial human-induced runoff reduction of -9.21 km³ completely masked a significant climate-driven wetting potential (+6.13 km³), triggering the lake's sharp decline. In the recent period (1991–2024), the basin's hydrology has been governed by a fragile stalemate in which a massive, climate-driven potential for increased runoff (+10.80 km³) was almost entirely neutralized by the persistent negative impact of human water use (-11.36 km³). At the lake level, this translated into an apparent stability sustained only by a favorable climatic subsidy. Future projections under various climate scenarios indicate that this climatic buffer is transient and unlikely to persist, exposing the lake to a high risk of rapid decline. We conclude that the recent stability of Lake Balkhash is not a sign of systemic recovery but a "masked vulnerability." This highlights the urgent need for sustainable and forward-looking water management strategies that account for these underlying, competing drivers.

Lake Balkhash, a vital endorheic system in Central Asia, faces increasing hydrological uncertainty driven by the interplay of climate change and anthropogenic disturbances. Disentangling the individual contributions of these concurrent drivers is essential for sustainable basin management but remains methodologically challenging due to data scarcity and complex feedback mechanisms. This study introduces the Hydrological Analysis and Disentanglement Framework (HADF) to quantitatively assess the drivers of the lake's centennial water balance (1931–2024). By integrating a hybrid hydrological model – coupling SEGSWAT+ with machine learning error correction – with the Budyko framework, we reconstructed historical naturalized and actual inflow with good agreement against observations (KGE > 0.75) and explicitly linked catchment-scale drivers to lake storage dynamics. Our results reveal a system defined by substantial, opposing forces. During the intensive intervention period (1970–1990), human water withdrawal was the dominant driver of inflow decline, reducing streamflow by 9.21 km ³ yr ⁻¹ and effectively masking a significant climate-driven wetting trend (+6.13 km ³ yr ⁻¹ ). In the recent period (1991–2024), the lake entered a state of “masked vulnerability”: a large climate-driven potential for recovery (+10.80 km ³ yr ⁻¹ ), fueled by increased precipitation and glacial melt, was almost entirely neutralized by sustained human consumption (−11.36 km ³ yr ⁻¹ ). Consequently, the recent stability of Lake Balkhash is not a sign of intrinsic resilience but a fragile equilibrium supported by a transient climatic subsidy. Future projections under Shared Socioeconomic Pathways (SSPs) indicate that as this climatic benefit wanes, the system risks a rapid decline, with water levels potentially dropping 2.5–4.0 m by 2100. These findings underscore the urgency of decoupling water allocation policies from temporary climatic bonuses to prevent irreversible ecological degradation.