This study aims to evaluate the performances of two latest released GPM-era satellite precipitation final run products (IMERG-V06B and GSMaP-V07) and one TRMM-era product (TMPA-3B42-V07) at hourly, daily and monthly scale over the TP, with special focus on the performances at different rain intensities, sub-basins, and elevations at daily scales. Besides, hourly scale evaluations were carried out for one grid box value of IMERG-V06B and GSMaP-V07 versus 15 rain gauges on the rainy season, 2019. Results indicated that (1) GSMaP-V07 outperformed TMPA-3B42-V07 and IMERG-V06B with higher CC and lower RBias, MAE, and RMSE values at daily and monthly scale. (2) Satellite products showed overestimation during light rain and underestimation throughout moderate rain, heavy rain and rainstorm; (3) Satellite products have performed relatively better in lower elevation (4000 m). (4) Satellite precipitation products performed better in the source region of the Yellow River (SRYR), Yangtze Rivers (SRYZR), Lancang River (SRLCR), and Nujiang River (SRNR) basins, while the products greatly overestimated the precipitation in the source region of the Yarlung zangbo River (SRYLZB) basin. In arid climate regions (Qiangtang Basin and Qaidam Basin), GSMaP-V07 had better performances than other products; (5) Although GSMaP-V07 showed higher overestimation than IMERG-V06B product at hourly scale, it has better consistency with rain-gauge observations. For satellite precipitation data application within the TP, we recommend the GSMaP-V07 over the IMERG-V06B. Furthermore, improvement on the multi-satellite rainfall retrieval algorithm is required.

Medium- and long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was developed to further improve the accuracy and reliability of runoff forecasting fully considering of large-scale and local-scale climatic factors. First, with four large-scale atmospheric oscillations, sea surface temperature, precipitation, and temperature as the predictors, multiple linear regression (MLR), radial basis function neural network (RBFNN), and support vector regression (SVR) models were built. Next, a Bayesian model averaging (BMA)-based multimodel was developed using weighted MLR, RBFNN, and SVR models, and the performance of the BMA-based multimodel was compared to those of the MLR, RBFNN, and SVR models. Finally, the high-runoff performance of these four models was further analyzed to prove the effectiveness of each model. The BMA-based multimodel performed better than those of the other models, as well as high-runoff forecasting. The results also revealed that the performance of the forecasting models with multiple climatic factors were generally superior to that without climatic factors. The BMA-based multimodel with climatic factors not only provides a promising, reliable method for medium- and long-term runoff forecasting, but also facilitates uncertainty estimation under different confidence intervals.