Ecohydrological processes are undergoing significant reorganization under environmental stress, complicating process-based attribution of biological responses to hydrological and water quality conditions. Focusing on the Dahei River (a semi-arid Yellow River tributary), this study investigates the coupled dynamics of hydrology, water quality, and aquatic communities along the longitudinal gradient. The MDM was applied to partition the explanatory power of hydrological, water quality, and biotic variables, while characterizing longitudinal connectivity using Cumulative Dispersal Volume (CDV) and Relative Dispersal Potential (RDPI). Quantile regression estimated key niche parameters, including environmental optima, niche breadths, and interspecific interaction coefficients. Results indicate that planktonic communities exhibit broader niche breadths and higher adaptability than zoobenthos, which show greater sensitivity to environmental disturbances. Model performance was higher for plankton than zoobenthos, reflecting their contrasting associations with hydrological variability. A longitudinal "upstream output-downstream input" pattern was captured, with zooplankton demonstrating the highest spatial exchange potential. Attribution analysis indicates that biotic interactions account for substantial explanatory power, while associations with hydrological and water quality variables vary among groups: planktonic communities show higher sensitivity to water quality, whereas zoobenthos respond more strongly to hydrological alterations. Hydrological influence intensifies downstream, while water quality effects are more pronounced midstream, reflecting heterogeneous ecohydrological patterns. These findings deepen the understanding of multi-dimensional coupling among hydrology, water quality, and aquatic communities, demonstrate the robustness of the MDM framework in process-based attribution, and offer a process-oriented perspective for resolving ecohydrological complexity in water-stressed river systems.