The related dynamic change in meteorological and hydrological parameters is critical for available water resources, development management options, and making informed decisions. In this study, to enhance the resolution of the predicted meteorological and hydrological parameters under climate change, the statistical downscaling method (SDSM), the generalized regression neural network (GRNN) model, the Soil and Water Assessment Tool (SWAT) model, and the improved Tennant method were integrated

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... to a framework. The available water resources were assessed in the Huangshui watershed of the Qinghai–Tibet Plateau, which has the highest average elevation in the world. The meteorological parameters were obtained by the SDSM model and the GRNN model. The SWAT model used the meteorological parameters to simulate the hydrological data under climate change scenarios. Considering the meteorological conditions and the high sediment content in the basin, the available water resources are evaluated by the improved Tennant method. The meteorological data of the Xining station from 1958 to 2011 were used to analyze the dynamic changes and mutation trends in the data. The results indicated that the precipitation would have a great increase during the wet season from May to September, and the flows and available water resources would decrease with increasing carbon emissions under different representative concentration pathways (RCPs).