Attribution Analysis of Future Seasonal Runoff Variation and Their Uncertain Sources: Quantitative Assessment of Jinsha River, China

This study investigates the relative contributions of climate change and human activities to future seasonal runoff changes in the Jinsha River (JSR) Basin (western China), with particular emphasis on quantifying the influence of multiple sources of uncertainty on attribution results. An integrated framework combining global climate models (GCMs), shared socioeconomic pathways (SSPs), hydrological models (HMs), seasonal-scale Budyko models, and variance analysis (VAAN) is developed. The conclusions were as follows: (1) The mutation year of runoff in JSR was 1984. (2) Both the ABCD and dynamic water balance model with variable time scales (DWBM) hydrological models performed excellently in simulating historical runoff, with Nash–Sutcliffe efficiency (NSE) values exceeding 0.85 and relative errors within 10%. (3) Under the SSP119 scenario, human activities dominate runoff changes in spring, summer, and autumn, contributing 13.5 mm, 48.1 mm, and 30.5 mm, respectively, while climate change dominates in winter with a contribution of −10.7 mm. (4) Under the SSP245 and SSP585 scenarios, human activities remain the dominant factor for summer (46.5 mm and −47.4 mm) and autumn (29.1 mm and −30.8 mm), whereas climate change dominates in spring (−14.3 mm and −14.5 mm) and winter (−13.3 mm and −14.2 mm). (5) The interactions among the HMs, GCMs, and SSPs are the primary source of uncertainty, contributing 44.45% to 82.03% of the total variance in attribution results across different seasons.