Unraveling the long-term persistence of streamflow in China and its controlling factors

Mainland China. Investigating long-term persistence (LTP) in river flows is crucial for understanding hydrological variability and improving water resource management, yet systematic nationwide assessments remain limited, especially in China. In this study, 45 years of monthly runoff from 60 stations across China were analyzed to quantify basin-scale LTP and its seasonal variations using the Hurst coefficient ( H ) estimated by Least Squares Variance, Whittle Estimator, and Rescaled Range Analysis. Drivers of LTP were further examined using Spearman’s correlation, random forests (RF), and SHapley Additive exPlanations (SHAP). The national annual mean H was 0.710, with marked spatial and seasonal variability. Northern catchments exhibited stronger persistence than southern catchments, and winter showed the highest LTP (0.758), whereas spring and summer displayed weaker persistence (0.650). Catchment area, grassland, water bodies, unused land, and coarse- to medium-textured soils were generally positively correlated with H , indicating stronger hydrological persistence, whereas slope, climatic factors, forest land, and fine-textured soils tended to weaken it. RF and SHAP analyses revealed seasonal shifts in dominant controls, with forest land, soil texture, and catchment area exerting the greatest influence annually and in summer, while climatic factors gained prominence in other seasons. This study presents the first spatially and seasonally resolved assessment of LTP in Chinese rivers, offering new insights into streamflow predictability and catchment behavior. • China’s streamflow exhibits strong LTP, with a national mean H of 0.710. • Significant spatial and seasonal variability in H is observed. • Catchment slope, climate, forest land, and fine soils tend to suppress LTP. • Forest land, soil texture, and catchment area are the dominant controls at the annual scale. • Climatic factors become increasingly important in spring, autumn, and winter.