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Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (2): 104–118.doi: 10.3724/SP.J.1226.2020.00104.

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  • 收稿日期:2019-06-11 接受日期:2019-10-08 出版日期:2020-04-30 发布日期:2020-04-27

The heterogeneity of hydrometeorological changes during the period of 1961-2016 in the source region of the Yellow River, China

ZhiXiang Lu1,Qi Feng1(),SongBing Zou1,JiaLi Xie2,3,ZhenLiang Yin1,Fang Li1,3   

  1. 1.Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    3.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-06-11 Accepted:2019-10-08 Online:2020-04-30 Published:2020-04-27
  • Contact: Qi Feng E-mail:qifeng@lzb.ac.cn

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Abstract:

Runoff in the source region of a river makes up most of water resources in the whole basin in arid and semi-arid areas. It is very important for water resources management to timely master the latest dynamic changes of the runoff and quantitatively reveal its main driving factors. This paper aims to discover the variation heterogeneity of runoff and the impacts of climatic factors on this runoff in the source region of the Yellow River (SRYR) in China from 1961 to 2016. We divided SRYR into four sub-regions, and analyzed changes of their contributions to total runoff in SRYR. We also revealed the impacts of precipitation, temperature and potential evapotranspiration on runoff in each sub-region by constructing the regression relationships between them at multiple temporal scales. The changes of runoff in the four sub-regions and their contributions to the total runoff were not exactly consistent. The climatic variables’ changes also have heterogeneity, and runoff was mainly affected by precipitation compared to influences of temperature or potential evapotranspiration. Their impacts on runoff have spatiotemporal heterogeneity and can be reflected by very significant-linear regression equations. It provided a simple method to predict headwater runoff for better water management in the whole basin.

Key words: source region of the Yellow River, hydrometeorology, spatiotemporal variation, runoff contribution, heterogeneity

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RegionDEM (m)Mean altitude (m)Area (km2)Hydrological stationMeteorological station
D14,210-5,2454,50220,930HHYMaduo
D23,941-5,3544,42624,089HHY, JMMaduo, Dari
D33,406-5,3283,88141,029JM, MQDari, Hongyuan, Jiuzhi, Ruoergai
D42,680-6,2483,95335,924MQ, TNHGuoluo, Xinghai, Guinan

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ItemRegionEquationR2Significance FP-value of independent variables
R&P, TD1R=0.45P-0.29T0.220.00130.0007; 0.0254
D2R=0.71P-0.15T0.470.00000.0000; 0.1460
D3R=0.87P-0.37T0.810.00000.0000; 0.0000
D4R=0.73P-0.25T0.580.00000.0000; 0.0065
R&P, ET0D1R=0.45P-0.29ET00.260.00030.0019; 0.0056
D2R=0.69P-0.27ET00.520.00000.0000; 0.0073
D3R=0.8P-0.41ET00.840.00000.0000; 0.0000
D4R=0.62P-0.28ET00.590.00000.0000; 0.0041

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ItemRegionEquationR2Significance FP-value of independent variables
R&P, TD1R=0.46P-16.7T-171.760.160.7700.58; 0.51
D2R=0.22P-6.9T+30.640.080.8800.68; 0.65
D3R=0.84P-37.2T-288.250.940.0160.023; 0.016
D4R=1.28P-28T-405.310.650.2000.12; 0.20
R&P, ET0D1R=0.5P-0.22ET0+190.680.290.6000.44; 0.36
D2R=0.51P-0.19ET0+196.220.270.6200.40; 0.37
D3R=0.9P-0.48ET0+403.920.930.0180.02; 0.018
D4R=1.25P-0.47ET0+398.170.680.1800.11; 0.17

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ItemRegionEquationR2Significance FP-value of independent variables
R&P, TD1R=0.006P+0.085T+3.060.220.00020.74; 0.14
D2R=0.16P+0.33T+6.490.820.00000.0000; 0.03
D3R=0.27P-0.09T+5.240.720.00000.0000; 0.81
D4R=0.2P-0.0052T+5.840.740.00000.0000; 0.98
R&P, ET0D1R=0.01P+0.03ET0-10.270.00000.31; 0.001
D2R=0.19P+0.11ET0-8.30.840.00010.0000; 0.0001
D3R=0.24P-0.14ET0-11.180.740.00000.0000; 0.05
D4R=0.19P-0.05ET0-10.740.00000.0000; 0.11
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