Abstract
The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.
Evaluation of hydrological response under future climate change by revising meteorological data in the source region of the Yangtze River, China
