Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH₃-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH₃-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH₃-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

Water pollution poses threats to urban environments and subsequently impacts the ecological health and sustainable development of urban areas. Identifying the spatiotemporal variation in non-point sources (NPS) pollution is a prerequisite for improving water quality. This paper aimed to assess the NPS pollution load and then recognized the spatiotemporal characteristics of the pollution sources in a typical urbanized area. A combination model based on land use type was used to simulate the NPS pollution load. The results showed the following: (1) ponds and farmlands had higher pollution production intensities than other land use types, but the intensity and magnitude of pollution emissions were generally greater in urban areas; (2) monthly and annual total nitrogen (TN) and total phosphorus (TP) emissions had the same pattern as rainfall, and TN and TP emissions accounted for 56.2% and 58.0%, respectively, of the total in summer; (3) TN pollution was more serious than TP pollution in the study area, especially in farmlands; (4) urban runoff (UR) and livestock and poultry breeding (LPB) were the main sources of NPS, TN and TP emissions in the study area. If these NPS pollutants cannot be removed from this area, a large amount of freshwater is needed to dilute the current rivers to meet the requirement of the fourth category of China national environmental quality standards for surface water. This problem is serious in the control of polluted rivers in many cities throughout China.

Urbanization has reduced river network connectivity, posing a great threat to water security. However, the mechanism through which changes in river network connectivity impact water security remains uncertain. River network connectivity and water security have been separately and thoroughly assessed by several researchers; however, few studies conducted a coupled assessment of these two aspects together, which may shed light on this mechanism. Based on assessment indicators developed in previous studies and key influence factors identified in the study area, this study proposed a comprehensive evaluation method to continually evaluate the adaptability between river network connectivity and water security in the Wuchengxiyu region, a highly urbanized area in Eastern China. The continuous evaluation was conducted for the period 2010–2019 based on the coupling coordination degree calculation formula. The results show that from 2010 to 2015, the coupling coordination degree between river network connectivity and water security followed a downward trend, due to the uncoordinated development between water systems and urbanization. After 2015, an increasing trend was observed with the implementation of environmental policies. In the past decade, the water surface rate and the coverage rate of suitable flow velocity decreased from 4.59% to 4.28% and from 54.1% to 30.9%, respectively, which may have negative effects on water quality and limit the improvement of regional flood control capacity. Moreover, the evaluation results also prove that policies such as the Ecological River–Lake Construction and the River Chief System have contributed to improve the quality of the water environment and regional flood control. The proposed assessment framework can be used as a guidance to evaluate the relationship between water network connectivity and water security; moreover, it provides new ideas for water network system protection and water quality maintenance in similar highly urbanized areas.

Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions.