PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (8): 1033-1039.doi: 10.18306/dlkxjz.2017.08.012

• Orginal Article • Previous Articles     Next Articles

Comparison of applications of different reanalyzed precipitation data in the Lhasa River Basin based on HIMS model

Yuhan GUO1,2(), Zhonggen WANG1,*(), Yuliang WU3   

  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. Jinan University, Jinan 250000, China
  • Online:2017-08-31 Published:2017-08-28
  • Contact: Zhonggen WANG E-mail:guoyh.16b@igsnrr.ac.cn;wangzg@igsnrr.ac.cn
  • 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, No.Y3M30170AL

Abstract:

Hydrological simulation in ungauged basins is a challenging topic in hydrology and water resource fields internationally. With the fast development of remote sensing technology, it is possible to utilize remote sensing derived precipitation data in hydrological fields to accelerate the progress of research in the PUB (predictions in ungauged basins) plan. This study compared the applications of different reanalyzed precipitation data—the grid and forcing precipitation data—in hydrological simulation in the Lhasa River Basin. The study built a distributed hydrological model using the HIMS model for the basin. The process started with inputting the daily precipitation data from the National Meteorological Center, then based on the measured flow in the Lhasa hydrological control station to calibrate and verify the hydrological model. After this, two types of remote sensing reanalyzed precipitation data in HIMS model were imported for runoff simulation, and the results were compared with simulation results of the measured weather station daily precipitation data. Subsequently, the applicability of the two types of remote sensing reanalyzed precipitation data in the Lhasa River Basin was analyzed. On the whole, the Nash-Sutcliffe efficiency coefficient of the runoff simulation based on daily precipitation data is 0.86 (daily process) and 0.93 (monthly process), and the correlation coefficient is above 0.9. The Nash-Sutcliffe efficiency coefficient of the rainfall-runoff simulations based on the two reanalyzed precipitation datasets are both above 0.7 in the daily scale process and over 0.8 in the monthly scale process, and the correlation coefficient are both around 0.9. The results show that the measured weather station daily precipitation data resulted in the best simulation outcomes and both the grid precipitation data and forcing precipitation data generate satisfactory runoff simulation results in the Lhasa River Basin. This indicates that daily precipitation data are useful although the number of rainfall stations in this area is limited. The remote sensing reanalyzed precipitation datasets can be well used in ungauged areas such as the Lhasa River Basin and it may become a reliable source when analyzing the relationship between rainfall and runoff in ungauged basins. Using rainfall-runoff model to examine the impact of multi-sources reanalyzed precipitation datasets on the accuracy of runoff simulation is essential for evaluating the quality of such datasets.

Key words: distributed hydrological model, multi-source reanalyzed precipitation data, runoff simulation, comparitive analysis, Lhasa River Basin