Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R²) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.

Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of the operational safety risk, this study proposes a comprehensive risk assessment framework that integrates risk probability and risk loss. The former is quantified using the Consequence Reverse Diffusion Method (CRDM), which systematically identifies and categorizes key factors of primary dike failure modes into four domains: hydrological characteristics, channel morphology, engineering structures, and operational management. The latter is assessed by integrating socioeconomic impacts, including population exposure, infrastructure investment, and industrial and agricultural production. A structured assessment framework is established through systematic indicator selection, justified weight assignment, and standardized scoring criteria. Application of the framework to Yangtze-to-Huaihe Water Diversion Project (Henan Reach) reveals that the risk probability across four segments falls within the (1, 3) range, indicating a generally low to moderate risk profile, while channel morphology shows greater spatial variability than hydrological, structural, and management indicators, driven by local differences in crossing structure density, sinuosity, and regime coefficients. Meanwhile, the segments along the Qingshui River face higher risk losses owing to their upstream location and large-scale water supply capacity, resulting in a relatively higher comprehensive risk level.