Volume 8 Issue 4
Oct.  2015
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Xin-e Tao, Hua Chen, Chong-yu Xu, Yu-kun Hou, Meng-xuan Jie. 2015: Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China. Water Science and Engineering, 8(4): 273-281. doi: 10.1016/j.wse.2015.11.002
Citation: Xin-e Tao, Hua Chen, Chong-yu Xu, Yu-kun Hou, Meng-xuan Jie. 2015: Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China. Water Science and Engineering, 8(4): 273-281. doi: 10.1016/j.wse.2015.11.002
shu

Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China

doi: 10.1016/j.wse.2015.11.002

  • a State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China

  • b Department of Geosciences, University of Oslo, Oslo N-0316, Norway

Funds:  This work was supported by the National Natural Science Foundation of China (Grants No. 51190094, 51339004, and 51279138).
More Information
  • Corresponding author: Hua Chen
  • Received Date: 2015-04-06
  • Rev Recd Date: 2015-09-09
    Abstract
  • Reference evapotranspiration (ET0) is often used to estimate actual evapotranspiration in water balance studies. In this study, the present and future spatial distributions and temporal trends of  in the Xiangjiang River Basin (XJRB) in China were analyzed.  during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith (FAO P-M) method, while  during the period from 2011 to 2100 was downscaled from the Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under two emission scenarios, representative concentration pathway 4.5 and representative concentration pathway 8.5 (RCP45 and RCP85), using the statistical downscaling model (SDSM). The spatial distribution and temporal trend of  were interpreted with the inverse distance weighted (IDW) method and Mann-Kendall test method, respectively. Results show that: (1) the mean annual  of the XJRB is 1 006.3 mm during the period from 1961 to 2010, and the lowest and highest values are found in the northeast and northwest parts due to the high latitude and spatial distribution of climatic factors, respectively; (2) the SDSM performs well in simulating the present  and can be used to predict the future  in the XJRB; and (3) CMIP5 predicts upward trends in annual  under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period (1961 to 1990),  increases by 9.8%, 12.6%, and 15.6% under the RCP45 scenario and 10.2%, 19.1%, and 27.3% under the RCP85 scenario during the periods from 2011 to 2040, from 2041 to 2070, and from 2071 to 2100, respectively. The predicted increasing  under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100.

     

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