Snow cover on the Qinghai-Tibet Plateau significantly impacts the climate, hydrology, and ecology of China and East Asia. Current studies mainly use snow cover days to describe its duration, overlooking the snow’s discontinuous nature. This study analyzes snow phenology and the spatiotemporal distribution of continuous snow cover events on the Qinghai-Tibet Plateau from 1961 to 2019. The findings indicate that continuous snow cover days better capture the temporal discontinuity of snow cover compared to snow cover days. The contribution and continuity are lower than regions like North America, Europe, Northeast and Xinjiang in China, indicating poorer snow cover continuity on the Qinghai-Tibet Plateau. Additionally, we found that temperature and precipitation, especially autumn temperatures and spring and winter precipitation, significantly impact various snow indices. Wind speed also significantly impacts snow cover, particularly in autumn. Atmospheric circulation indirectly affects the snow cover discontinuity by influencing temperature and precipitation.

In the context of global climate change, the Tibetan Plateau is particularly susceptible to meteorological disasters, including snow disasters. This study utilized daily temperature and precipitation data from 44 meteorological stations on the Tibetan Plateau spanning from 1960 to 2018 to construct a snow event dataset. Optimal marginal distribution and the copula function were chosen to calculate the joint return period and joint probability, which effectively assess the hazard of snow disasters in the region. Additionally, the study analyzed the comprehensive risk of snow disasters under various return periods by integrating social and economic data. The results indicate the following: (1) Based on the five different Archimedean copula functions, the joint return period of an error rate of each station was calculated to be less than 36%, which is significantly lower than the recurrence interval of univariate analysis; (2) High-hazard areas are predominantly concentrated in the northwest region of the Tanggula Mountains and the eastern foothills of the Bayankara Mountains. As the return period increases, the spatial distribution of snow disaster hazard probability shifts gradually from “double-core” to continuous distribution; and (3) the northwestern Karakorum Mountains and Bayankara Mountains are two distinct high-risk areas for snow disasters. The range of high-risk areas in the region expands with an increase in the return period.

The close relationship between land use and carbon stock is crucial for regional carbon balance, territorial and spatial planning, and the sustainable development of ecosystems. As a pioneer of Park Cities, Chengdu plays a vital role in Chinese cities. To investigate the impact of Park City construction on carbon stock, this study adopted a new perspective, the Park City perspective, using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to analyze the spatial and temporal differences in carbon stock. Additionally, we used Geographic Detector to analyze the driving factors of carbon stock in Chengdu. Based on the carbon peaking and carbon neutrality goals (peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060), we simulated the carbon stock in Chengdu for the years 2030 and 2060. Simultaneously, combining the Future Land Use Simulation (FLUS) model, we simulated the changing trends of carbon stock in Chengdu under three scenarios: the natural development scenario (NDS), cultivated land protection scenario (CLDS), and Park City scenario (PCS). The results show the following: (1) After the construction of the Park City, the quality of forest land improved, resulting in an increase in forest carbon stock by 1.19 × 10⁶ tons. (2) Compared to the scenario without Park City construction, the implementation of the Park City led to a total carbon stock increase of 3.75 × 10⁵ tons, with forest carbon stock increasing by 7.48 × 10⁵ tons. (3) The PCS is the most conducive to achieving the carbon peaking and carbon neutrality goals, with the highest carbon stock. (4) Carbon stock is mainly driven by socio-economic factors. Land use/land cover (LULC) has the greatest explanatory power, with a q value of 0.9. The Park City is of great significance for an increase in carbon stock in Chengdu.