This study simulates future runoff in the Yalong River Basin by first identifying optimal NEX-GDDP-CMIP6 climate model data through Taylor diagrams and skill scores. Basin-specific meteorological datasets were derived for hydrological modeling. Using DEM, vegetation, and soil data, we parameterized and constructed a Variable Infiltration Capacity (VIC) model specifically calibrated for the basin. Daily and monthly runoff observations from Luning Station demonstrated model robustness during calibration and validation, yielding Nash-Sutcliffe Efficiency coefficients exceeding 0.8 and relative errors below 15%. The selected FGOALS-g3 historical climate data further validated the framework, achieving NSE > 0.75 and relative error < 15%. Coupling FGOALS-g3 with VIC yielded monthly runoff projections for 2025-2100. Analysis indicates a significant growth trend under both SSP245 and SSP585 emission scenarios, with intensification accelerating after 2069 under SSP585. Runoff remains seasonally concentrated (June-October ≈ 75%; winter ≈ 8%), consistent with historical patterns. Spectral analysis reveals distinct multidecadal periodicities: SSP245 shows 11 (5-10a), 17 (10-20a), and 7 (30-40a) wet-dry cycles; SSP585 exhibits 24 (5-10a) and 6 (40-50a) cycles. Primary oscillation modes occur at 5a, 13a, and 29a (SSP245) versus 5a, 20a, and 48a (SSP585). These findings provide critical insights for future hydrological planning and water resource management in the basin.
Feng et al. (Tue,) studied this question.