Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.
Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.