PROGRESS IN GEOGRAPHY ›› 2016, Vol. 35 ›› Issue (3): 339-348.doi: 10.18306/dlkxjz.2016.03.008

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Precision of data in three precipitation datasets of the Yarlung Zangbo River Basin

Xi HUANG1,2(), Zhonggen WANG1,*(), Yanfang SANG1, Moyuan YANG1,2, Xiaocong LIU1,2, Tongliang GONG3   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Water Resources Department of Tibet Autonomous Region, Lhasa 850000, China
  • Received:2015-07-01 Accepted:2015-11-01 Online:2016-03-25 Published:2016-03-25
  • Contact: Zhonggen WANG;
  • Supported by:
    Key Program of the National Natural Science Foundation of China, No.41330529;Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDB03030202;Program on Water Carrying Capacity of Tibet Autonomous Region


The Yarlung Zangbo River is a transboundary river with rich hydropower resources. Reliable precipitation data are important for water resource development planning of the region. Due to the high elevation, complex topography, and severe climate, especially in the western part of the basin, however, rainfall stations are sparse. Precipitation estimation from satellite data or assimilation provides potential alternatives for precipitation measurements in regions where conventional precipitation gauges are not readily available. In this study, the performance of the gridded Chinese ground precipitation dataset, the Climatic Research Unit (CRU) precipitation dataset, and the precipitation data of the Global Land Data Assimilation System (GLDAS) in 1973-2013 were evaluated for the Yarlung Zangbo River Basin using observations from 13 meteorological stations. The results show that the four precipitation datasets significantly differ. The annual gridded Chinese ground precipitation dataset is the closest to the observed data while CRU and GLDAS precipitation datasets should be calibrated before use due to their limited precision. The CRU precipitation data show strong correlation with the observed precipitation, which indicates that there is a relatively high consistency between the CRU precipitation dataset and observed precipitation although its mean relative error is large. Monthly data analysis shows that the gridded Chinese ground precipitation dataset can reflect the variation characteristics while the CRU precipitation dataset tends to overestimate in flood season. Different from these two datasets, the GLDAS precipitation dataset presents obvious smoothing effect during the year. Annual variation of precipitation in the gridded Chinese ground precipitation dataset is closer to that of the observed precipitation while the coefficients of variation of precipitation in the other two datasets are much smaller. The GLDAS dataset overestimates precipitation in drier areas and underestimate precipitation in areas where annual precipitation is high. All the three precipitation datasets are unable to reflect the extreme precipitation events according to the probability distribution. The probability distribution of the GLDAS dataset concentrates in the range of 300~500 mm while the probability distribution of CRU precipitation ranges from 200~500 mm, higher than the observed precipitation.

Key words: Yarlung Zangbo River, precipitation, data mining, spatiotemporal variation, probability distribution