Climate and Hydrological Change Characteristics and Applicability of GLDAS Data in the Yarlung Zangbo River Basin, China

• Main Authors: Hong Zhang, Ling Lei Zhang, Jia Li, Rui Dong An, Yun Deng
• Format: Article
• Language: English
• Published: MDPI AG 2018-03-01
• Series: Water
• Subjects: the Yarlung Zangbo River; climate; runoff; GLDAS; applicability
• Online Access: http://www.mdpi.com/2073-4441/10/3/254

The hydrological cycle is particularly sensitive to and is greatly affected by global climate change. In addition, runoff change has a strong influence on the hydrological cycle and migration of biogenic substances. The Yarlung Zangbo River basin in China is a typical basin for which climate and hydrological data are lacking. Land surface models can provide data for studying land surface substance and energy circulation, which are meaningful to face climate change. The midstream region of the Yarlung Zangbo River basin, which is strongly affected by climate change, was selected as the study area. First, the observed mean temperature, precipitation and runoff characteristics were analysed. Second, after combining the Global Land Data Assimilation System (GLDAS) and the water balance equation, we simulated climate and hydrological processes for the same time period. Finally, the correlation and error between GLDAS and observed data were analysed to verify applicability of the GLDAS data, and the impacts of climate factors on runoff were discussed. The results revealed that under the background of global warming, precipitation, temperature, and runoff changed significantly and showed strong consistency during the research period. Mean monthly precipitation, temperature and runoff exhibited clear cyclical fluctuations of approximately 12 months, and they all tended to increase. GLDAS is not a good system to describe the land surface conditions of the Yarlung Zangbo River basin all the time. However, within a certain time period, GLDAS data have a good applicability in the basin. Thereinto, the GLDAS mean monthly precipitation was moderately correlated with observed precipitation, with a correlation coefficient of 0.75. GLDAS mean monthly temperature was highly correlated with observed data, with a correlation coefficient of 0.94. Based on the Brunke ranking method, it indicates that GLDAS-Noah-based runoff data were closer to observed runoff data than the three other GLDAS models. Correlation coefficients between precipitation and runoff for the three time scales were higher than those between temperature and runoff. This means that rainfall was the main factor affecting natural runoff change, as opposed to temperature, and it can control the evolution of the river to some degree. This paper indicates the impacts of climate change on runoff and the application of GLDAS for data-limited basins. The results provide a deeper understanding of the Yarlung Zangbo River basin characteristics and can provide a scientific basis for the management of water resources and policy implementation for this basin.