Background As a crucial hydropower base in China, changes in the runoff-sediment relationship within the Jinsha River Basin significantly influences the sustainable management of water resources in the region. However, pervious studies often rely on singular methodologies, which leading to considerable uncertainty in the findings. This study aims to adopt multi methods for the quantitative analysis of changes in runoff and sediment dynamics, thereby enhancing both the accuracy and scientific rigor of the findings.

Methods To identify the dominant driving factors of runoff and sediment variations in the mainstream of the Jinsha River basin, this study systematically investigates the characteristics of runoff and sediment as well as its influence factors based on long-term hydrological datasets during 1960—2022 from four hydrological stations: Zhimenda, Shigu, Panzhihua, and Pingshan. This study employed Mann-Kendall non-parametric test to conduct trend detection and attribution analysis to distinguish and quantify the respective contributions of climate change and human activities, employing double mass curve method, cumulated slope method, and hydrological method.

Results 1) Runoff variation within the Jinsha River basin exhibited significant spatial heterogeneity. Specifically, significantly increasing trends were observed at the Zhimenda and Panzhihua stations, with change rates of 255×10⁶ m³/a (P < 0.01) and 357×10⁶ m³/a (P < 0.001), respectively. In contrast, the total sediment load showed a significant decreasing trend, with a rate of −408×10⁶ t/a (P < 0.001). However, distinct variations were found among individual stations: sediment at Shigu Station increased significantly (251×10⁶ t/a), while at Pingshan Station, it decreased significantly (−408×10⁶ t/a), and changes at other stations were not significant. 2) There are certain differences in the abrupt points detected by different methods. Overall, The abrupt change of runoff generally occurred earlier than that in sediment load, with the timing of runoff transitions becoming progressively earlier from upstream to downstream. 3) Attribution analysis indicated that climate change was the dominant factor driving runoff variations in the Jinsha River basin (contribution rate > 60%), whereas human activities were the primary cause of sediment reduction (contribution rate > 84%). The dominant factors differ across different stations: increased runoff at Zhimenda was mainly attributed to climate change, but the rise in sediment load was primarily driven by human activities. At Shigu, both runoff and sediment changes are largely regulated by human activities (contribution rate > 65%).

Conclusions The three attribution analysis methods show generally consistent results in quantifying the contribution rates of driving factors. But the hydrological method tends to underestimate the contribution of climate change in runoff attribution analyses, typically by 35% to 45% on average. In contrast, attribution analyses of sediment load based on the double mass curve method exhibit a systematic bias ranging from 5% to 25%. Overall, the cumulative slope method demonstrates relatively better robustness.