Effects of the dendritic structural characteristics of water bodies on the spatiotemporal heterogeneity of stream water quality indicators

Mountain streams commonly exhibit dendritic river network structures, in which the spatiotemporal heterogeneity of water quality indicators is jointly influenced by seasonal variation and spatial morphology. In this study, a typical alpine dendritic stream in Laojun Mountain, Yunnan Province, was investigated based on one year of continuous monitoring across 11 sampling sites. We analyzed the spatiotemporal patterns of water quality indicators in the main stem, first-order tributaries, and second-order tributaries, and evaluated the effects of temporal and spatial factors using statistical analyses and multivariate ordination methods. The results showed that most water quality indicators exhibited significant temporal variation, with water temperature, dissolved oxygen, and several nitrogen-related variables differing significantly between seasons, whereas most indicators did not show significant differences among stream types. Linear mixed-effects models further indicated that seasonal effects were significant for only a subset of variables, while the influence of stream type was generally weak. In contrast, spatial structural metrics, such as downstream distance, significantly explained the variation of multiple nutrient variables. NMDS analysis revealed a high degree of overlap among stream types in the environmental space, indicating overall weak spatial differentiation. Overall, the variation of water quality indicators in this study was primarily driven by seasonal hydrological processes, while spatial variation was mainly expressed as continuous longitudinal gradients along the river network. By integrating time-series monitoring and multivariate statistical analyses, this study quantitatively disentangled the relative contributions of seasonal processes and network structure to water quality variation, and highlighted the importance of longitudinal hydrological gradients in regulating material transport. These findings provide a scientific basis for optimizing water quality monitoring and managing local anthropogenic pollution in mountain watershed systems.