Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

Semantic segmentation of high-resolution remote sensing images remains a challenging task due to the intricate spatial structures, scale variability, and semantic ambiguity among ground objects. Moreover, the reliable delineation of fine-grained boundaries continues to impose difficulties on existing CNN- and transformer-based models, particularly in heterogeneous urban and rural environments. In this study, we propose GPRNet, a novel geometry-aware segmentation framework that leverages geometric priors and cross-stage semantic alignment for more precise land-cover classification. Central to our approach is the Geometric Prior-Refined Block (GPRB), which learns directional derivative filters, initialized with Sobel-like operators, to generate edge-aware strength and orientation maps that explicitly encode structural cues. These maps are used to guide structure-aware attention modulation, enabling refined spatial localization. Additionally, we introduce the Mutual Calibrated Fusion Module (MCFM) to mitigate the semantic gap between encoder and decoder features by incorporating cross-stage geometric alignment and semantic enhancement mechanisms. Extensive experiments conducted on the ISPRS Potsdam and LoveDA datasets validate the effectiveness of the proposed method, with GPRNet achieving improvements of up to 1.7% mIoU on Potsdam and 1.3% mIoU on LoveDA over strong recent baselines. Furthermore, the model maintains competitive inference efficiency, suggesting a favorable balance between accuracy and computational cost. These results demonstrate the promising potential of geometric-prior integration and mutual calibration in advancing semantic segmentation in complex environments.

Reliable estimates of future precipitation are essential for adaptive water management in large river basins. This study presents a machine-learning approach that combines six CMIP6 models to examine precipitation changes in the Yangtze River Basin. ERA5 monthly precipitation for 1979–2025 served as the reanalysis reference. The random forest model incorporated individual model outputs, ensemble statistics, geographic variables, and monthly cyclic terms. It was trained with data from 1979–2009, evaluated for 2010–2014, and then applied to the period 2015–2099 under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Compared with the simple multi-model mean, the proposed method showed better agreement with ERA5 and generally smaller reconstruction errors during the validation period. Annual precipitation is projected to increase under all three pathways, with the largest increase under SSP5-8.5. Precipitation remains concentrated from May to August, while spring totals and intra-annual variability increase more clearly under high-emission conditions. Mean precipitation remains highest in the humid middle and lower reaches, while the magnitude and significance of future trends vary across the basin. Inter-model spread remains greater than the differences among emission pathways and reaches 85.92 mm under SSP5-8.5 during 2071–2099. These results represent uncertainty-aware climate estimates rather than verified forecasts. They can support flood-risk assessment, reservoir planning, and adaptive water management in the Yangtze River Basin.