Model simulation of flood season runoff in the headwaters of the yellow river basin using satellite-ground merged precipitation data

被引：0
|
作者：
Zhang A. [1 ]
Li T. [1 ,2 ,3 ]
Fu W. [1 ]
Wang Y. [2 ]
机构：
[1] State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing
[2] Department of Hydrology and Electric Power, Qinghai University, Xining
[3] Sanjiangyuan Collaborative Innovation Center, Xining
来源：
Li, Tiejian (litiejian@tsinghua.edu.cn) | 2017年 / Editorial Board of Journal of Basic Science and卷 / 25期
关键词：
Digital Yellow River integrated model; Flood season runoff simulation; Merged rainfall data; The headwaters of the Yellow River basin;
D O I：
10.16058/j.issn.1005-0930.2017.01.001
中图分类号：
学科分类号：
摘要：
The headwaters of Yellow River Basin (upstream Tangnaihai hydrological station), yielding an annual runoff of nearly 20 billion m3, contribute approximately 34% of the total runoff of the whole basin, and play a significant role in water resources and social impacts. Many studies have focused on the runoff change in this region. To get better understandings of the model simulation of flood season runoff in the headwaters of the Yellow River Basin, the Digital Yellow River Integrated Model (noted as DYRIM hereafter) was applied as a large-scale and high-resolution distributed hydrological model. The DYRIM was developed with the extraction of high-resolution drainage networks, as well as the double-layer parallel system. The accuracy of multisource rainfall data was compared and evaluated. Compared with the ground rainfall data and CMORPH satellite data, the historical rainfall data from the satellite-ground merged precipitation data have higher resolution and better precision. The merged rainfall data was used to complete the simulation of flood season runoff in the headwaters region. For the runoff simulation from 2008 to 2012, the results show that the Nash-Sutcliffe efficiency coefficients (NSEs) of daily runoff in both the calibration and validation period are good, and the total amounts of simulated runoff are satisfactory, which proves the feasibility of the DYRIM and its usefulness for the evaluation and management of water resources in the headwaters region. © 2017, The Editorial Board of Journal of Basic Science and Engineering. All right reserved.
引用
收藏
页码：1 / 16
页数：15
相关论文
共 24 条
  • [1] Zheng H., Zhang L., Liu C., Et al., Changes in stream flow regime in headwater catchments of the Yellow River basin since the 1950s, Hydrological Processes, 21, 7, pp. 886-893, (2007)
  • [2] Li C., Yang Z., Tendency and persistence of natural runoff in the Yellow River Basin, Hydrology, 1, pp. 12-17, (2005)
  • [3] She D., Xia J., Du H., Et al., Spatio-temporal analysis and multi-variable statistical models of extreme drought events in Yellow River Basin, China, Journal of Basic Science and Engineering, 20, pp. 15-29, (2012)
  • [4] Zheng H., Zhang L., Zhu R., Et al., Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin, Water Resources Research, 45, (2009)
  • [5] Zhang X., Srinivasan R., Debele B., Et al., Runoff simulation of the headwaters of the Yellow River using the SWAT model with three snowmelt algorithms, Journal of the American Water Resources Association, 44, 1, pp. 48-61, (2008)
  • [6] Li D., Tian Y., Liu C., Distributed Hydrological simulation of the source regions of the Yellow River under environment changes, Acta Geographica Sinica, 44, 1, pp. 48-61, (2008)
  • [7] Hao Z., Wang J., Li L., Et al., Impact of climate change of runoff in source region of Yellow River, Journal of Glaciology and Geocryology, 1, pp. 1-7, (2006)
  • [8] Tang F., Xu Z., Zuo D., Response of runoff to climate change in the Upper Yellow River Basin, Resources Science, 6, pp. 1079-1088, (2012)
  • [9] Xia J., Ye A., Qiao Y., Et al., An applied research on distributed time-variant gain hydrological model in Wuding River of Yellow River, Journal of Basic Science and Engineering, 15, 4, pp. 457-465, (2007)
  • [10] Joyce R.J., Janowiak J.E., Arkin P.A., Et al., CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, Journal of Hydrometeorology, 5, 3, pp. 487-503, (2004)
123