The Liujiaxia-Manglaxia segment of the upper Yellow River, located at the northeastern margin of the Tibetan Plateau, has experienced significant clustering of giant landslides since the Holocene, acting as a critical surface process shaping the regional geomorphology. While tectonic activity and climate change are recognized as primary drivers, the coupling mechanisms triggering landslide clustering during climate transitions remain unclear. This study compiled an inventory of 14 giant Holocene landslides in the upper Yellow River by integrating literature review, field investigation, and remote sensing. We calculated the distance between landslides and faults using ArcGIS, and inferred triggering factors based on deposit characteristics and mechanical deformation modes. Finally, by integrating high-resolution paleoclimate records, we investigated the coupled tectonic-climatic driving mechanisms behind landslide clustering. Since the Holocene, giant landslide clusters in the Liujiaxia-Manglaxia section of the Upper Yellow River have exhibited pronounced spatiotemporal clustering. Temporally, these events occurred primarily across four distinct phases: 10–8 ka B.P., 5.0–4.5 ka B.P., 4–3.7 ka B.P., and the modern era, with the 5.0–4.5 ka B.P. interval representing the most intensive period. Spatially, the landslides are predominantly distributed within the Jishixia Canyon and along the right bank (looking downstream) and concave banks of the Yellow River. The transitional phase from arid to humid climate conditions is identified as the critical stage for these giant landslide clusters. During this period, intensified precipitation served as a primary driver, while the lag effect of vegetation response and cumulative slope damage induced by earthquakes during preceding arid intervals also played significant roles in the clustering phenomenon. The transition from a dry to a wet climate promoted the clustering of giant landslides through the combined effects of increased precipitation, the lag effect of vegetation change, and tectonic activity.

Understanding how the internal structure of precipitation events evolves and responds to antecedent thermal conditions is essential for revealing the mechanisms of extreme precipitation in plateau-margin mountainous regions. Using hourly precipitation and air temperature data from 14 national reference meteorological stations in the Hehuang Valley during the warm seasons (May-September) of 2015-2024, this study constructed an event-based precipitation database and introduced the inter-event maximum temperature (T-max_inter) as an indicator of antecedent thermal accumulation. The Theil-Sen slope estimator, Mann-Kendall trend test, K-means clustering, and binary logistic regression were applied to examine changes in precipitation-event structure and their nonlinear response to antecedent high temperature. Results show that warm-season precipitation was characterized by fluctuating frequency but increasing intensity. Precipitation events were classified into three types-uniform, front-peaked, and rear-peaked-with the proportion of uniform events decreasing and the proportions of front-peaked and rear-peaked events increasing. T-max_inter was significantly positively associated with extreme precipitation occurrence: for every 1 degrees C increase in T-max_inter, the odds of extreme precipitation increased by 13.4% (OR = 1.134, 95% CI: 1.10-1.17, p < 0.001). These findings provide a reference for extreme precipitation risk identification and disaster prevention in plateau-margin mountainous areas.

In the context of global warming, extreme precipitation on the Qinghai-Tibet Plateau has intensified significantly. Understanding the internal structure of precipitation events and their response to rising temperatures is crucial for elucidating these intensification mechanisms. Focusing on the Hehuang Valley, this study constructed an event-scale dataset using hourly observations from 15 meteorological stations (2015–2024), introducing “Inter-event Maximum Temperature” as a key thermal driver. By integrating clustering, trend tests, and logistic regression, we analyzed the spatiotemporal evolution of precipitation extremes. Results indicate that: (1) regional precipitation exhibits a pattern of fluctuating frequency but increasing intensity; (2) the proportion of uniform precipitation has decreased while non-uniform types, especially rear-peak events, have increased significantly; (3) spatial heterogeneity is strongly influenced by topography, with extreme precipitation concentrated on windward slopes and in valley contractions;(4) Inter-event maximum temperature exerts a significant non-linear positive effect, where a 1°C increase raises the odds ratio for extreme precipitation occurrence by approximately 13.4%. These results confirm that antecedent thermal accumulation enhances extremes by increasing atmospheric water-holding capacity and convective instability. While decadal-scale uncertainties remain due to the limited 10-year data span, these findings provide a scientific basis for disaster prevention and water resource management in high-altitude basins.

To reveal the spatiotemporal variations in near-surface oxygen content during the growing season across different altitudinal gradients in Qinghai Province and to deepen the understanding of oxygen cycling in plateau ecosystems, this study analyzed daily observations from 12 monitoring stations spanning three elevation ranges (1500–2500 m, 2500–3500 m, and 3500–4500 m) during the 2022–2023 growing seasons (March–July). The Mann–Kendall test was employed to detect temporal trends, variability indices such as standard deviation and coefficient of variation were used to quantify fluctuation intensity, kernel density estimation (KDE) was applied to characterize distributional features, and the Kruskal–Wallis test was conducted to assess statistical significance. The results indicate that: (1) oxygen content showed a significant increasing trend at all three altitudinal gradients, with the strongest rise at low elevations and the weakest at high elevations; (2) fluctuation intensity exhibited clear spatial heterogeneity, with the most pronounced variability in summer at low elevations, a distinct peak in June at mid-elevations, and overall stability at high elevations; and (3) KDE analysis revealed a broader distribution and higher frequency of extreme oxygen values at low elevations, while mid- and high-elevations displayed more concentrated distributions. Both the Kruskal–Wallis test and post hoc comparisons confirmed highly significant differences among the three elevation ranges. These findings demonstrate that elevation is a key factor influencing the spatiotemporal distribution of near-surface oxygen content during the growing season in Qinghai Province. Differences are not only evident in absolute oxygen levels but also in fluctuation intensity and distributional characteristics. This study provides empirical evidence for understanding oxygen variability mechanisms on the plateau and offers theoretical and practical references for ecological management and health risk prevention in high-altitude regions.

Under a warming–humidifying climate, precipitation patterns on the Qinghai–Tibet Plateau have significantly shifted due to a water imbalance in its solid–liquid structure. Using monthly precipitation data (1961–2023), we analyzed the spatial distribution and dynamics of 200 mm and 400 mm isohyets through climate propensity rates and centroid center migration. The results show: (1) precipitation increased significantly (4.17 mm/decade), decreasing spatially from southeast to northwest. Regionally, it increased in areas like the southern Qinghai Plateau region, but declined in the southern Himalayas and central–southern Altyn−Tagh Mountains. (2) The 200 mm line migrated northward in southern Qiangtang, shrank around Qaidam Basin, with an overall northeastward shift; the 400 mm line moved westward in eastern Qiangtang and Hehuang Valley, northward in southern Qinghai, trending northwest. (3) From 1961 to 1990 and 1991 to 2023, the 200 mm isohyet’s centroid shifted 49 km north and 17 km east, while the 400 mm isohyet moved 22 km north and 19 km west. (4) Vertically, the 200 mm isohyet ascended by 7.11 m/decade, while the 400 mm line rose more slowly (2.61 m/decade). These changes indicate a significant shift in precipitation distribution, impacting regional hydrological processes.

Grassland fires represent a significant natural disaster affecting global grassland ecosystems, posing serious threats to ecological safety, livestock production, and the lives and property of herders. This study focuses on Qinghai Province, utilizing historical grassland fire data to pre-screen the factors influencing both natural and anthropogenic grassland fires. By applying the maximum entropy model and analyzing data from 150 fire incidents, this research predicts the spatial distribution of grassland fire susceptibility within the study area. The outcomes were as follows. (1) A maximum entropy–Kvamme gain statistical model was developed and validated for reliability. (2) The natural grassland fire-prone areas are predominantly located in southern Qinghai, covering a vast portion of the Guoluo Tibetan Autonomous Prefecture, as well as Zeku County, Henan Mongolian Autonomous County, Yushu City, and Mado County in the Yushu Tibetan Autonomous Prefecture. These regions are distinguished by their proximity to rivers and the presence of abundant vegetation. In contrast, grassland areas prone to anthropogenic fires are mainly concentrated in eastern Qinghai Province, where human activities are more intensive and population centers are located nearby. (3) The prediction results identify dominant grassland fire factors and their thresholds. (4) Natural grassland fires in Qinghai Province primarily result from spontaneous combustion, while anthropogenic grassland fires are primarily caused by electrical faults from high-voltage power lines, heating activities related to grazing, and the use of firecrackers. This study presents a disaster prediction model to support grassland management and fire prevention in Qinghai Province, providing a scientific basis for effective fire control strategies.

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.

Global warming has intensified freeze–thaw activity in high-latitude and high-altitude regions; along the western sector of the farming–pastoral ecotone in northern China, pronounced seasonal freeze–thaw cycles now pose a severe threat to land resources. This study aims to quantitatively reveal the spatial differentiation patterns of freeze–thaw erosion in the western segment and its influencing factors. This study begins with the fundamental concepts of freeze–thaw erosion, grounded in soil mechanical fragmentation and gravitational migration. Critical slope is used as the identification criterion to delineate freeze–thaw erosion zones. Building upon this foundation, a Random Forest model is employed to calculate the weighting factors influencing freeze–thaw erosion in the western segment of the northern agro-pastoral transition zone, thereby constructing a graded evaluation model for freeze–thaw erosion intensity. Results indicate the following: (1) Freeze–thaw erosion exhibits no discernible distribution pattern in the western segment, appearing scattered, while non-freeze–thaw erosion is primarily concentrated in the northern region. (2) Freeze–thaw erosion intensity ranges from 1.48 to 4.58 in the western segment. The total area of the study region is 151,000 km2, the affected area spans 122,400 km2, accounting for 81.11% of the total regional area. (3) Regionally, the Hehuang Valley exhibits predominantly strong and severe erosion, while the northern Loess Plateau shows mostly slight erosion. The southern Loess Plateau features light and moderate erosion with scattered instances of severe erosion. (4) Vegetation coverage and soil moisture are the primary contributing factors to freeze–thaw erosion. This study proposes, for the first time, a method that couples annual freeze–thaw day cycles with a critical slope threshold to delineate freeze–thaw erosion zones, demonstrating broad applicability. It systematically uncovers the spatial heterogeneity of freeze–thaw erosion in the western sector, substantially advancing scientific understanding of the process and providing a theoretical basis for its targeted management.

Freezing and thawing have important effects on soil erosion, especially in the western part of the agricultural and pastoral intertwined belt in northern China where seasonal freezing and thawing are significant. In this study, the effects of water content and the number of freeze-thaw cycles on the shear strength characteristics of soil were investigated by freeze-thaw cycle test and straight shear test using loessial soil, sierozem, and chernozem in this region. The results indicate that: (1) The soil shear strength decreases first and then stabilizes with the increase of freeze-thaw cycles and decreases with the increase of water content. After 10 freeze-thaw cycles, the shear strength of loessial soil, sierozem, and chernozem with 10% water content decreases by an average of 7.92%, 8.23%, and 12.24%, respectively. (2) Freeze-thaw cycles cause the soil's cohesion to decrease initially and then stabilize, while the increase in water content weakens this trend. After 10 freeze-thaw cycles, the cohesion of loessial soil, sierozem, and chernozem with 10% water content decreases by an average of 33.33%, 31.25%, and 16.39%, respectively. (3) Freeze-thaw cycles cause varying changes in the internal friction angle of the three soils, while an increase in water content reduces the internal friction angle. When the water content increases from 10 to 30%, the internal friction angle of loessial soil, sierozem, and chernozem decreases by an average of 7.14%, 8.79%, and 10.99%, respectively. These findings are of significant importance for deepening the understanding of the mechanisms underlying soil erosion and formulating targeted soil conservation measures.

The intensifying global warming and the increasing frequency of extreme weather events have created an urgent need for targeted resilience building in mountainous villages. This study focuses on three typical villages in the Hengduan Mountains region. From the perspective of individual villagers, a disaster resilience evaluation index system was constructed, encompassing four dimensions: disaster prevention capacity, disaster resistance capacity, disaster relief capacity, and recovery capacity. Using the entropy method and a village disaster resilience assessment model, the disaster resilience levels of each village were quantitatively evaluated. The results indicate the following: (1) Disaster resistance capacity is the key factor constraining the disaster resilience level of mountain villages. (2) The overall disaster resilience of mountain villages is at a medium level, with minor differences among villages. (3) Significant disparities exist in capacity dimensions across villages: Qina Village demonstrates the strongest disaster resistance capacity, while Xiamachang Village excels in disaster prevention capacity but shows relative weakness in recovery capacity. (4) Household material endowment has a significant positive impact on disaster prevention, resistance, relief, and recovery capacities, while individual self-rescue capability and individual–government collaboration capacity also significantly enhance disaster prevention, resistance, and relief capacities. We propose the following: Leveraging the rural revitalization strategy as a pivotal point, this approach promotes the diversified development of the village economy. It facilitates the increase in villagers’ income through the implementation of employment skill training programs, thereby strengthening household material foundations to enhance individual disaster resilience. By relying on the mass monitoring and mass prevention mechanism and a disaster information sharing platform, real-time exchange of disaster situation information is achieved, which enhances communication and collaboration between villagers and the government, consequently improving the synergistic efficiency between individuals and governmental bodies. Simultaneously, a villager-centered disaster prevention system is constructed. Through measures such as disaster prevention publicity and practical disaster response drills, villagers’ awareness of disasters and their capabilities for self and mutual rescue are elevated, ultimately strengthening the overall disaster resilience of rural areas in the Hengduan Mountains region.

This study aims to identify the key factors contributing to the destabilization of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley and provide a robust scientific basis for proactive disaster prevention, management of disaster chains, and mitigation of multi-hazard clusters in unstable regions. The research focuses on the Yellow River–Huangshui River Valley, evaluating the stability of its geohazard disaster-pregnant environment. The disaster-pregnant environment is classified into static and dynamic categories. The static disaster-pregnant environment encompasses factors such as lithology, fracture density, topography, slope, river network density, and vegetation cover. The dynamic disaster-pregnant environment incorporates variables such as extreme rainfall, consecutive rainy days, annual rainfall averages, monthly high temperatures, monthly maximum temperature variations, average annual air temperatures, and human activities. A random forest model was employed to quantitatively assess the stability of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley. The findings indicated that (1) extreme indicators were the primary contributors to the destabilization of the disaster-pregnant environment, with very heavy rainfall contributing 28% and consecutive rainy days contributing 27%. Human activities ranked next, accounting for 15%. (2) Unstable regions for static, dynamic, and integrated disaster-pregnant environments accounted for 44%, 45%, and 44% of the study area, respectively, with all unstable areas concentrated in river valley regions. (3) The overall trend of stability in the disaster-pregnant environment was characterized by widespread instability. Extremely unstable areas were predominantly located in river valley regions, largely influenced by human activities. Conversely, only 0.1% of the region exhibited signs of stability, and 2.1% showed a tendency toward extreme stability.

Frequent grassland fires have severely affected regional ecosystems as well as the production and living conditions of local residents. Grassland fire prevention capabilities constitute an integral part of the disaster prevention and mitigation system and play an important role in improving grassroots governance. To gain a deeper understanding of the practical foundation and influencing mechanisms of grassland fire prevention capabilities, establish an evaluation index system for prevention capabilities covering the four dimensions of disaster prevention, disaster resistance, disaster relief, and recovery. Combining micro-level survey data, a quantile regression model is used to analyze the influencing factors. The research findings indicate that (1) disaster resistance (0.49) plays a prominent role in grassland fire prevention capabilities, with economic foundations and individual disaster relief capabilities being particularly critical for overall improvement. Although residents have strong fire prevention awareness, their organizational collaboration capabilities are relatively weak, and there are significant differences in prevention capabilities across regions, necessitating tailored, precise enhancements. (2) There are significant differences in prevention capabilities among residents of different agricultural and pastoral production types, with semi-agricultural and semi-pastoral areas having the strongest comprehensive capabilities and pastoral areas relatively weaker. (3) A significant analysis of factors influencing grassland fire prevention capabilities: effective and diverse risk communication is a prerequisite for enhancing residents’ prevention capabilities; the level of panic regarding grassland fires and road infrastructure are important influencing factors, but residents’ understanding of climate change and grassroots organizations’ capacity for mechanism construction have insignificant impacts. Therefore, in future grassland fire disaster prevention and mitigation efforts, it is essential to strengthen risk communication, improve infrastructure, monitor environmental changes and the spatiotemporal patterns of grassland fires, enhance residents’ understanding of climate change, reinforce the emergency response capabilities of grassroots organizations, and stimulate public participation awareness to collectively build a multi-tiered grassland fire prevention system.

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.

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km2, highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions.

The Qinghai Plateau has a complex geographical environment and vast amounts of land with a sparse population, dispersed settlements, and a low traffic density. In the face of major disasters, the rational layout of emergency material reserve warehouses is crucial for reducing disaster losses, ensuring regional stability, and quickly restoring production and life. This paper starts by considering the rationality and timeliness of the location selection of provincial emergency material reserve warehouses, considering the distance costs of emergency material transportation on the Qinghai Plateau. By using a traffic accessibility analysis model combined with a location–allocation model and an L-A maximum coverage model, this study optimizes the location selection of emergency material reserve warehouses on the Qinghai Plateau. The research results show the following: (1) On the basis of the existing Golmud Depot and Chengxi Depot in Qinghai Province, it is necessary to add four more depots, i.e., the Yushu Depot, Gande Depot, Ping’an Depot, and Tongde Depot, to achieve the timely and efficient supply of emergency materials. (2) After the optimization, the layout of the six provincial emergency material reserve warehouses can achieve full coverage of Qinghai Province within 8 h in the event of major disasters, increasing the coverage by 20% compared to the original layout; the new plan allows for emergency material transportation to cover 87% of Qinghai Province within 4 h, an increase of 28% compared to before. (3) The optimized location selection plan for emergency material reserve warehouses saves 139 min of time costs, and the transportation efficiency is increased by 46% compared to the previous plan. The optimized location selection plan for emergency material reserve warehouses is instructive for the construction of emergency material reserve warehouses on the Qinghai–Tibet Plateau.

The Qinghai–Tibet Plateau is one of the most geologically active regions in the world, characterized by significant geomorphic variation and a wide range of geological hazards. The multifactorial coupling of tectonic movements, geomorphological evolution, climate variability, and lithological characteristics contributes to the pronounced spatial heterogeneity of the disaster-inducing environment. Identifying key controlling factors and their driving mechanisms is crucial for effective regional disaster prevention and mitigation. This study adopts a systematic framework based on regional disaster systems theory, integrating tectonic activity, engineering geology, topography, and precipitation to construct a multi-factor zoning system. Using the Random Forest model, we quantify factor contributions and delineate eight distinct disaster-inducing environment zones. Zones I–III (Himalayas–Hengduan Mountains–Qilian Mountains) are characterized by a dominant coupling mechanism of “tectonic fragmentation—topographic relief—precipitation erosion” and account for the majority of large-scale disasters. In contrast, Zones IV–VIII, primarily located in the central–western Plateau basins, are constrained by limited material sources, resulting in lower disaster densities. The findings indicate that geological structures and lithological fragmentation provide the material foundation for hazard occurrence, while topographic potential and hydrodynamic forces serve as critical triggering conditions. This nonlinear coupling of factors shapes a disaster geographic pattern characterized by “dense in the east and sparse in the west”. Based on these results, the targeted recommendations proposed offer valuable theoretical insights and methodological guidance for disaster mitigation and region-specific management across the Qinghai–Tibet Plateau.

Grassland fire risk perception constitutes a fundamental element of fire risk assessment and underpins the evaluation of response capacities in grassland regions. This study examines Qinghai Province, the fourth-largest pastoral region in China, as a case study to develop an evaluation index system for assessing residents’ perceptions of grassland fire risk. Using micro-level survey data, the study quantifies these perceptions and applies a quantile regression model to investigate influencing factors. The results indicate that: (1) the average grassland fire risk perception index among residents in Qinghai Province’s grassland areas is 0.509, with response behaviors contributing the most and response attitudes contributing the least; (2) Residents in agricultural areas perceive higher risks than those in semi-agricultural/semi-pastoral or purely pastoral areas, and individuals in regions with moderate dependency ratios and moderate fire-susceptibility conditions demonstrate the highest performance, whereas those in pastoral and high-susceptibility zones exhibit signs of “risk desensitization”; (3) risk communication and information dissemination are the primary drivers of enhanced perception, followed by climate variables, whereas individual characteristics of residents attributes exert no significant effect. It is recommended to monitor the impacts of climate change on fire risk patterns, update risk information dynamically, address deficits in residents’ cognition and capabilities, strengthen behavioral guidance and capacity-building initiatives, and foster a transition from passive acceptance to active engagement, thereby enhancing both cognitive and behavioral responses to grassland fires.

The accelerated urbanization process drives urban spatial expansion, making it essential to clarify the driving factors of this expansion and the corresponding ecosystem service value (ESV) response for effective regional urban planning. We selected Xining City, a typical plateau valley city with a spatial layout constrained by mountainous and riverine topography, as our study area. We analyzed land changes in Xining City over the past 20 years using the land transfer matrix and the PLUS model, and predicted land use changes under different scenarios for 2030. By combining these analyses with the improved unit area equivalent factor method, we quantitatively assessed the drivers of urban expansion in the main urban area of Xining City from 2000 to 2030 and estimated the ESV. The results showed that: (1) Over the past two decades, Xining City has experienced notable urban expansion, particularly along the Huangshui River, Beichuan River, and Nanchuan River. The urban construction land is mainly converted from cropland and grassland, and the simulation results under different scenarios in the future of 2030 show that the area of cropland and grassland continues to decrease. (2) The spatial expansion of urban areas in Xining City is primarily influenced by topographical factors, with urban transportation planning exerting a secondary influence. Distinct levels of roads exhibit varying degrees of impact on the expansion of constructed areas, with tertiary roads demonstrating the most widespread and substantial influence. Conversely, factors such as population density, GDP, and proximity to government have lesser influence on urban expansion. (3) The ecosystem service value (ESV) of Xining City exhibits a general decline from 2000 to 2030, marked by a significant loss of arable land and grassland and substantial shifts in ESV. Simulated ESV outcomes under diverse scenarios for 2030 consistently underscore that, irrespective of policy orientations, urban expansion and development, despite guarantees for urban, cropland, and ecological preservation, inevitably engender diminishing ecosystem service functionalities.

Livestock snow disaster is one of the most serious natural disasters in pastoral areas during cold seasons, leading to substantial livestock losses over the past few decades. Accurate assessment of livestock exposure is crucial for reducing the risk of livestock snow disaster, while much less is known about the livestock exposure due to the lack of available gridded livestock density data with long time series. In this study, the gridded density datasets of large livestock, sheep and total livestock at a spatial resolution of 1 km across the Qinghai Plateau (QP) during 1983-2018 were generated by developing livestock density models and disaggregating livestock census data. Then, the spatiotemporal patterns of livestock exposure to snow hazards in the QP were explored and the relative contributions of influencing factors (snow hazard, livestock density and prevention capacity) were quantified. There was a significant negative trend (p < 0.10) of -1.16 x 10(6) standard sheep units (SSUs) decade(-1) in annual total livestock number and -4.67 x 10(7) SSUs-day decade(-1) (p < 0.05) in annual total livestock exposure across the QP from 1983 to 2018. Spatially, snow hazard, livestock density and prevention capacity dominated the total livestock exposure changes in 38.11 %, 22.70 % and 34.95 % of pastoral areas, respectively. However, the reduction in total livestock exposure across the QP was primarily contributed by prevention capacity (56.13 %). This study provides a comprehensive understanding and scientific decision-making basis for livestock snow disaster mitigation and risk management, contributing to the high-quality development of pastoral areas.

Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.