Sensitivity and uncertainty analysis for streamflow prediction based on multiple optimization algorithms in Yalong River Basin of southwestern China

•Groundwater supply and effective hydraulic conductivity in channel had synergistic effects on streamflow with the highest weight among the parameter combination.•Baseflow recession factor was relatively at the center of parameter network, indicating wider relationships with other parameters.•The most commonly modified SCS-CN curve method related parameter was less applicable to YLRB with adequate available surface water.•SUFI-2 and PSO could achieve better fitting and uncertainty efficiency than GLUE algorithm. Semi-distributed model based on spatial information is effective for simulating hydrologic cycle. However, it cannot completely simulate all relevant natural processes. Uncertainty analysis is necessary for achieving high accuracy of hydrological modelling. In this study, optimization algorithms and weighted network analysis techniques were adopted to explore hydrological parameter combination characteristics as well as hydrological simulating uncertainties in Yalong River Basin (YLRB) of southwestern China based on Sequential Uncertainty Fitting version 2 (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE) and Particle Swarm Optimization (PSO). The results indicated that: a) groundwater recession factor, effective hydraulic conductivity in channel and saturated hydraulic conductivity could significantly affect streamflow in the studying basin, b) complex parameter combination responded diversely under aforementioned three optimization algorithms. Comparatively, GLUE brought out higher autocorrelation parameter network and c) SUFI-2 and PSO performed better in terms of effective uncertainty analysis and fitting values than those of GLUE. This work could provide references and insights for sensitive parameter modification and prediction uncertainty reduction of streamflow simulation, furthermore contributing to an optimal water resource management.