The use of envelope structures is crucial in enhancing the safety and stability of foundation pits. However, excavation activities in the foundation pit can result in deformations affecting the envelope structure. To investigate the impact of the excavation process on the envelope structure and surrounding soil, the Midas GTS NX 2022R1 (64-bit) finite element software was employed to model the excavation process of Nantong East Railway Station. The settlement of the soil around the envelope structure and the horizontal displacement of the underground diaphragm wall (UDW) were compared with field measurements. The analysis demonstrated that the numerical analysis results exhibited a similar trend to the data obtained from field monitoring. This comparison provided valuable insights for selecting an optimal excavation method for the foundation pit and offered guidance for addressing similar challenges in the future. Additionally, a new method using metamaterials as a frame to improve the stability of continuous walls is proposed. The method relies on the excellent mechanical properties of metamaterials, improves the integrity and stability of the underground diaphragm wall, and provides a new way to solve the stability problem of underground diaphragm walls.

Downscaling is a critical step to bridge the gap between large-scale climate information and local-scale impact assessments. However, steep and complex mountainous terrain induces highly heterogeneous local-scale meteorological conditions, posing significant challenges for accurately downscaling meteorological variables, particularly daily precipitation, which exhibits strong nonlinearity. To improve the daily precipitation downscaling skills in mountainous regions, this study presents a novel deep learning approach: Deep Convolutional Residual Network (DCRN). This approach was constructed based on a deep convolutional neural network with residual blocks and Gamma-distribution-based two-stage loss function combining a classifier for rainfall occurrence and a regression for rainfall amount. The illustrative cases of downscaling daily precipitation from coarse resolutions (2.8° × 2.8°) to fine resolution (0.5° × 0.5°) in mountainous catchments in the Dadu River and Yalong River basins demonstrate that the improved DCRN has enhanced downscaling accuracy and captured the spatial and temporal patterns remarkably well. Furthermore, the downscaled future precipitation projections indicate that the study basins are likely to experience wetter conditions over the next 80 years. The results provide improved high-resolution precipitation information, offering valuable support for policy-making in water resource management and adaptive operation and planning of water diversion projects under future climate change.

Ecological risk assessment in arid inland river basins is challenging because ecological degradation is jointly controlled by water scarcity, surface exposure, thermal stress, energy exchange, and vegetation buffering. Existing remote sensing indices primarily characterize ecological quality and provide limited representation of risk formation and transmission. This study developed a Dryland Ecological Risk Index (DERI) within the source–pathway–sink framework to assess ecological risks in the Shiyang River Basin from 2000 to 2025. The Bare Soil Index and Temperature Vegetation Dryness Index represented risk sources, land surface temperature and albedo characterized transmission pathways, and the Normalized Difference Vegetation Index and wetness index represented ecological sinks. Principal component analysis, correlation analysis, high-resolution imagery, and comparison with the Remote Sensing Ecological Index were used for model validation, while transition matrices characterized changes among risk classes. The first principal component explained 93.97–97.51% of the total variance, and the directions of indicator loadings were ecologically consistent. DERI was most strongly correlated with drought stress (TVDI, r = 0.58) and effectively identified fine-scale degradation in deserts, bare land, oasis margins, and oasis–desert transition zones. Ecological risk exhibited a pronounced south–north gradient, with lower risks in the Qilian Mountains, oases, and river corridors and higher risks in the northern deserts. Basin-wide risk intensified during 2000–2010, underwent substantial spatial restructuring during 2010–2015, and gradually declined after 2015. The proportion of class V areas decreased from 32.94% in 2000 to 15.35% in 2025; nevertheless, classes IV–V still accounted for 52.40% of the basin in 2025. These findings demonstrate that DERI provides a process-oriented and spatially sensitive tool for identifying ecological risk hotspots and supporting differentiated ecological management in water-limited watersheds.