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.

The differentiation rule and driving mechanisms of collapse disasters in various regions are unclear, and the results from existing methods of research are not sufficiently scientific. To reveal the nature of collapse disasters, this study utilized data from the 1:50,000 geological disaster investigation results database, 1:50,000 topographic data, and TM images. Topography, human activity intensity, rock mass structure, hydrological conditions, vegetation status, and meteorological conditions were used as indicators in the DEA model to analyze their validity and to explore the differentiation law and driving mechanisms of the highway slope along the YaLu river, a location of frequent geological disasters in Changbai County. In the analysis process, each index was quantitatively graded, i.e., the number of disaster points corresponding to each index was used as an input index, and the number of disaster points and the scale and stability of disaster points corresponding to the graded quantitative index were used as the output indexes. The results of the analysis of the study area indicate that there are significant differences in geological disasters due to different regional characteristics. We carried out three evaluations and performed spatial superposition analysis of the indicators corresponding to the effective values and the regional collapse points. The driving factors of collapse disasters can be divided into three categories, namely the impact of human activities, rainfall, and gravity stress. The GIS analysis and mapping found that the collapse points located to the south of the Grand Canyon of Changbai County were primarily affected by rainfall. Additionally, the areas affected by activity intensity are mostly concentrated in county towns with concentrated populations and road slopes.

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.

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices.

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response.