Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.