This study aims to comprehensively examine diverse uncertainties/multiplicities (e.g., performance indicators, bias-correction methods, hydrologic models, bias-correction schemes, predictor combinations, watersheds, streamflow magnitudes, and temporal scales) in bias-corrected hydrologic simulations (BCHS). The focus is placed on the variations of BCHS accuracies (representing climatic impacts on runoffs) with every uncertainty, as well as their interactions with the other uncertainties. To achieve this, an integrated bias-corrected hydro-modeling uncertainty analysis approach (IBCHMUA) is developed based on one advanced hydro-modeling method, i.e., discrete principal-monotonicity inference (DiPMI), and two hydrologic models, i.e., Xin’anjiang and HyMOD. IBCHMUA is applied to two representative watersheds (Xiangxi and Zhongzhou) in southern China. Many findings are revealed. For instance, it is necessary to apply multiple performance indicators and DiPMI is effective in correcting hydro-model biases. Every uncertainty poses significant impacts on BCHS, and the significance of the impacts further varies with all or part of the other uncertainties. BCHS accuracies (or the estimated climatic impacts on runoffs in southern China) increase from daily to monthly scales, from Xiangxi to Zhongzhou Watersheds, from the highest through the lowest to the overall runoff magnitudes, from Xin’anjiang to HyMOD models, and from original to bias-corrected hydrologic simulations. Meanwhile, the impacts of the uncertainties in BCHS decrease from bias-correction schemes, temporal scales, streamflow magnitudes, hydrologic models or predictor combinations, to watersheds. These findings are helpful for reducing the complexity and enhancing the reliability of BCHS under diverse uncertainties, and point out the importance of taking into account the interactions of the uncertainties in BCHS studies.

The temporal and spatial distribution of water resources over China has changed and may continue changing in the future under ongoing global warming. Scientific water resources management requires reliable forecasting of the change. Meanwhile, the performance of deep learning in achieving it has not been comprehensively explored. To fill this gap, deep learning, i.e., multilayer perceptron (MLP) in this study, is used to study the change of streamflow over China under climate changes. MLP is compared with other machine learning methods for investigating its strengths, and three river basins (i.e., Xiangxi, Jinghe and Zhongzhou) in central, northwestern and southeastern China, respectively are selected to represent hydrologic regimes over China. Four regional climate models are used to drive MLP for forecasting streamflow from 2021 to 2050 under two greenhouse-gas emission scenarios (i.e., RCPs 4.5 and 8.5). Modeling results show that MLP is more accurate than the other methods, especially in terms of peak streamflow volumes. Annual average temperature in the three basins will increase, while precipitation shows different changing trends. The simulation accuracies among the regional climate models (RCMs) are slightly different. Correspondingly, streamflow will increase, and the increments decrease from Jinghe, through Xiangxi, to Zhongzhou River Basins. Due to climate changes, flooding will become more frequent in Jinghe and Xiangxi River Basins, Jinghe River Basin will experience no runoff in winter, and the timing of peak runoffs in Zhongzhou River Basin will move forward. Compared with the RCP 4.5 scenario, the above trends are more obvious under the RCP 8.5 scenario.