Author(s): Hong Zhang, Jia Li, Linglei Zhang, Ruidong An, Yun Deng
Linked Author(s): Jia LI
Keywords: Yarlung Zangbo River, runoff, GLDAS, GRACE, applicability analysis
Abstract: Runoff change has a great influence on the pattern of global water cycle as well as the process of migration and transformation of biogenic matters in drainage basin. Yarlung Zangbo River is a typical lack-data basin, where the quantity of available runoff data is extremely limited and the spatial and temporal resolutions are very low. Combining total water storage variations from five data sources (i. e. , four models of Global Land Data Assimilation System (GLDAS), Gravity Recovery and Climate Experiment (GRACE) ), precipitation and evapotranspiration from China meteorological data service center, runoff of Yarlung Zangbo River Basin are estimated by using the water balance equation. The regular of distribution, the variation trend, the continuity and mutability are comprehensively analyzed. And then, four statistical parameters (i. e. , Correlation coefficient (R), Mean bias (BIAS), Standard deviation of differences (?d), and Ratio of standard deviations (?r/?obs) ) are calculated to compare correlation and error between five data sources and in-situ measurements. At last, the Brunke ranking method is applied to comprehensively evaluate the data quality and applicability of the five data sources in Yarlung Zangbo River. The results reveal that the runoff estimated from GRACE can represent the runoff of Yarlung Zangbo River Basin better than other four models of GLDAS with a total ranking score of 2. 00. This study carried out a helpful attempt on the hydrological study in lack-data basin by using several data source systems. In the matter of medium and long term, large and medium scale, the result is beneficial to deepen cognition and comprehend on the runoff characteristics of Yarlung Zangbo River, and settles foundation for setting up a data assimilation system, which is specifically aimed at Yarlung Zangbo River Basin, and then provides data, method and scientific reference for Yarlung Zangbo River Basin
Year: 2017