Fenggui Liu

Snow disasters constitute a major natural hazard in Northwestern China, where heavy snowfall, blowing snow, and avalanches pose significant threats to infrastructure and socioeconomic activities. A scientific assessment of regional snow disaster risk is therefore critical for disaster prevention, spatial planning, and sustainable development. This study integrates multi-source remote sensing and geographic data to develop a comprehensive risk assessment method. By entropy weight method (EWM), we construct an assessment model that quantifies the combined hazard potential of heavy snowfall, blowing snow, and avalanches. The results reveal a significant spatial correlation among the three primary hazard types. The average potential hazard intensity of heavy snowfall is greater than that of blowing snow, which in turn exceeds that of avalanches. Spatially, the comprehensive snow disaster risk is most severe in the Altai Mountains, the Ili River valley, and the Tacheng region. A moderate-to-high risk level is distributed across the southwestern valleys and the foothills of the northeastern mountains. In contrast, the lowest risk areas are concentrated in certain interior valleys and the leeward slopes of the Junggar Basin. The resulting regional risk zoning was evaluated using receiver operating characteristic (ROC) curve analysis and disaster records. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.871 and an overall accuracy of 84.3%, indicating good spatial discrimination between high- and low-risk zones. These metrics support the application of the framework to regional snow disaster risk assessment.

The source area of the Yellow River (SAYR) is a critically important cryospheric region highly sensitive to climate warming. However, capturing fine-scale thawing–freezing dynamics remains challenging due to the region’s rugged topography and sparse observational networks. In this study, we developed a 1-km, 40-year (1981–2020) dataset of surface temperatures and thawing–freezing indices by applying month-specific topographic corrections to ERA5-Land reanalysis data. Validation against independent station observations yielded a root mean square error (RMSE) of 1.49°C, and uncertainty assessments demonstrate that the monthly-derived indices capture interannual trends with high consistency ( R ² > 0.97). Over the past four decades, regional freezing indices have significantly decreased, while thawing indices have increased, resulting in an extension of the average thawing duration by 1.17 days per decade. Furthermore, a regime shift detected in 2005 prolonged the annual thawing duration by 14.8 days, extending the unfrozen soil period and potentially enhancing moisture infiltration into the subsurface. Spatially, this thawing intensification is most pronounced in the southeastern valleys, whereas changes in the thermal regime over the northwestern and central highlands indicate intensified ground warming, which is likely associated with permafrost degradation and active-layer deepening. Overall, this high-resolution dataset offers an improved representation of surface thermal processes compared to traditional air-temperature-based proxies, providing valuable support for climate adaptation and infrastructure planning in this critical headwater region.

With the increasing volatility and extremity of global climate change, the frequency, intensity, and associated impacts of compound extreme climate events have escalated substantially. To investigate the temporal trends and characteristics of such events, we identified compound extreme climate events in the Huangshui River Basin, located in the northeastern Qinghai–Tibet Plateau, using daily mean temperature and precipitation records from eight meteorological stations. Compound warm–wet, warm–dry, cold–wet, and cold–dry events from 1960 to 2022 were detected based on cumulative distribution functions, and their long-term trends and intensity structures were examined. The results show that: (1) Warm–dry events dominate the basin, with an average annual frequency of 32.84 days per year, occurring frequently across all seasons; cold–dry events rank second (22.38 days per year) and are particularly frequent in winter. (2) Warm–dry events are highly concentrated in the river valley region (e.g., Minhe station), whereas cold–dry and warm–wet events mainly occur in the low-mountain areas (e.g., Huangyuan and Datong). (3) From 1960 to 2022, warm–dry and warm–wet events exhibit a highly significant increasing trend (p < 0.001), cold–dry events show a significant decreasing trend, and cold–wet events display no statistically significant trend. (4) In terms of intensity, all four types of compound events—warm–wet, warm–dry, cold–wet, and cold–dry—are dominated by weak to moderate grades. Overall, the basin is undergoing a compound-risk transition from historically “cold–dry dominated” conditions toward a regime characterized by “warm–dry predominance with emerging warm–wet events.” By identifying compound extreme climate events and analyzing their spatiotemporal variability and intensity characteristics, this study provides scientific support for disaster prevention, daily management, and risk mitigation in climate-sensitive regions. It also offers a useful reference for developing strategies to address compound extreme events induced by climate change and for implementing regional risk-prevention measures.

Recognized as the world’s “Third Pole”, the Qinghai–Xizang Plateau poses significant challenges to human health due to its harsh environment. With improved transportation and a tourism boom industry bringing over 90 million low-altitude residents to the plateau annually, hypoxia has become a critical concern. This study analyzes oxygen content data (2017–2022) together with environmental variables including elevation, temperature, precipitation, and vegetation cover, using the GeoDetector method to identify key drivers of near-surface oxygen distribution. Within the framework of disaster system theory, we evaluated the risk of hypoxia among short-term residents. Results show that the near-surface oxygen distribution across the plateau is primarily regulated by climatic and topographic factors. Interactions among environmental variables markedly enhance the explanatory power for spatial variation in oxygen content, with the coupled effects of humidity, atmospheric pressure, elevation, and temperature being especially pronounced. A high hypoxia hazard prevails across the plateau, particularly in the high-altitude western, northern, and central regions. The spatial distribution of hypoxia risk is strongly shaped by human activities, with high-risk zones clustering in densely populated towns, transportation corridors, and regions of intensive tourism. This results in a distinctive coexistence of “high hazard–low exposure” and “low hazard–high exposure” patterns. These findings provide scientific insights for tourism planning, health protection, and risk management in plateau regions.

Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.

Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.

Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.

Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.

Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.