<p>The variation law of streamflow processes in river channels is the key to flood risk evaluation and the regulation of large reservoirs in the mainstream. Under the combined impacts of climate change and human activities, the characteristics of streamflow processes have significantly changed. While the basic physical mechanisms and general variation patterns of daily streamflow temporal distribution are well understood, precise quantification of the altered dynamic patterns and their links to anthropogenic disturbances and extreme hydrological events remains challenging. A ranking method and clustering method were proposed to identify characteristics of daily discharge processes in the Lower Jinsha River Basin, China, from 1940 to 2020. The results indicate that large reservoir operations triggered a trend mutation in the streamflow process in 1998, with the average annual cumulative rank increasing from − 147.62 to -39.36, reflecting optimized streamflow dynamics. Intra-annual cumulative rank curves exhibit two distinct patterns: a three-stage pattern (initial decrease–plateau–second decrease) under general conditions and a linear declining pattern under extreme drought events. The difference between these patterns enables early forecasting of extreme drought events, while derivatives of the difference curve quantify flood peak distribution. Additionally, maximum flood discharge shows a positive non-linear correlation with the duration of the plateau stage, highlighting the latter as a key factor in flood risk evaluation. This study provides a novel quantification approach for characterizing streamflow processes and offers scientific support for flood risk management and reservoir regulation in the study area.</p>