Precipitation serves as a crucial indicator of climate change and a vital part of the water cycle in mountainous regions. ERA5-Land, a cutting-edge global reanalysis dataset designed for land applications, has been extensively utilized in climate-related studies. In this research, we assessed the reliability of ERA5-Land monthly averaged reanalysis precipitation data in the Qilian Mountains (QLM). We did this by comparing it with the observations from 17 meteorological stations spanning from 1979 to 2017. The findings indicated that, overall, the ERA5-Land reanalysis precipitation data tended to overestimate the observed precipitation in the Qilian Mountains. The determination coefficient (R²) of the linear regression between ERA5-Land reanalysis precipitation and the observations was 0.97. This value implies that ERA5-Land reanalysis precipitation generally has good applicability in the Qilian Mountains. However, the annual-scale root mean square error (RMSE) was 3.97. This suggests that ERA5-Land reanalysis precipitation data cannot be directly applied to studies at a single meteorological station. The deviation between the ERA5-Land reanalysis precipitation data and the observed precipitation data can be ascribed to the altitude difference between meteorological stations and ERA5-Land grid points. Generally, as the altitude difference between meteorological stations and ERA5-Land grid points increases, the precipitation deviation also rises. This research can furnish a reference for the application of ERA5-Land reanalysis precipitation data in the Qilian Mountains.

Air temperature acts as a key indicator of climate change. In regions with high elevations and scarce meteorological stations, reanalysis temperature datasets are vital for estimating temperatures. However, due to the presence of biases in the observational data of these reanalysis datasets, it becomes necessary to perform bias correction to augment the accuracy of modeling and prediction. In the present study, a temperature lapse rate model was utilized to correct the ERA5 reanalysis temperature data within the Qilian Mountains (QLMs) in China from 1979 to 2017. The research results show that the constructed temperature lapse rate can effectively reflect the vertical temperature change characteristics in the Qilian Mountains. As the altitude increases, the absolute value of the temperature lapse rate on the northern slope decreases, while the absolute value of the temperature lapse rate on the southern slope increases. The accuracy of the corrected ERA5 temperature data is significantly improved, especially in winter. Among the 17 meteorological stations, 13 stations show a statistically significant improvement in accuracy after correction in winter, accounting for approximately 76.5% of the total stations. This study can provide a reliable data reference for climate research, ecological environment monitoring, and other fields in the Qilian Mountains area.

Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework consists of three sequential stages: (1) critical indicators for CGHs in basalt regions are identified, with iron-staining anomalies—a hallmark of such terrains—innovatively integrated as a slope stability metric; (2) a system dynamics (SD) model is developed in Vensim to quantify dynamic feedback mechanisms, focusing on the “rock weathering–rainfall triggering–slope instability” nexus, and time-varying parameters are introduced to enable monthly-scale risk prediction; and (3) a 500 m × 500 m grid system is established using ArcGIS 10.4, and a computer program is developed to achieve SD-GIS coupling and calculate grid parameters. The information value method is then employed to determine risk thresholds, thereby completing CGH risk assessment and prediction. The results indicate that over the next five years, high-risk areas will exhibit spatial agglomeration when monthly rainfall exceeds approximately 130 mm (July and August). Conversely, when monthly rainfall is below around 60 mm, the entire region will display low or no risk. Model simulations reveal that risks during the rainy season over the next five years will exhibit insignificant variability, prompting simplification of the resultant cartography. Field validation corroborates the robustness of the model. This research overcomes the primary limitations of conventional static assessment models by improving the dynamic predictability and the applicability to basalt terrains. The integrated SD-GIS framework presents a novel methodological paradigm for dynamic CGH risk analysis and offers support for the formulation of targeted disaster mitigation strategies.