SCIENCE CHINA Earth Sciences, Volume 64 , Issue 6 : 920-931(2021) https://doi.org/10.1007/s11430-020-9751-8

Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China

More info
  • ReceivedOct 12, 2020
  • AcceptedMar 2, 2021
  • PublishedMay 6, 2021


Funded by

the National Key R&D Program of China(Grant,No.,2020YFA0608403)

the National Natural Science Foundation of China(Grant,Nos.,42022001,42041004,41877150,&,42001029)


This work was supported by the National Key R&D Program of China (Grant No. 2020YFA0608403) and the National Natural Science Foundation of China (Grant Nos. 42022001, 41877150, 42041004 & 42001029).


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  • Figure 1

    Spatiotemporal change of LAI ((a) and (d)), vegetation fraction ((b) and (e)), and albedo ((c) and (f)) in the Loess Plateau for the period 2000 to 2015. The dots in the top plots denote the statistical significance of the linear trend at the 95% confidence level. The dashed lines in the bottom plots denote linear trends in the time series. The rate of change (%) for each variable is also shown in the bottom plots, and the asterisk denotes the statistical significance of the linear trend at the 95% confidence level.

  • Figure 2

    Comparison of sensitivity tests for different categories of the parameterization schemes in the WRF model. (a) Microphysics schemes; (b) planetary boundary layer schemes; (c) cumulus schemes. The pentagram in (c) denotes the precipitation simulated by WRF with dynamic vegetation, while the purple dot denotes the precipitation simulated by WRF without dynamic vegetation.

  • Figure 3

    Comparison between WRF precipitation simulations (DYN and DEF) and CMFD observation (OBS). (a) Inter-annual variations; (b) intra-annual variations. The red line and bar represent WRF simulated precipitation with the dynamic vegetation scenario (DYN), the blue line and bar represent that with the default vegetation scenario (DEF). Meanwhile, the black line and bar represent the CMFD observations.

  • Figure 4

    The climatology (2000–2015) of annual precipitation (mm yr−1) in the two WRF precipitation simulations. (a) DYN and (b) CTL. The difference (DYN minus CTL) of annual precipitation climatology between DYN and CTL is shown in (c), and the dots in plot (c) denote statistical significance at the 95% confidence level.

  • Figure 5

    Spatial patterns of the linear trend (mm yr−2) in WRF simulated precipitation over the Loess Plateau during 2000–2015. WRF simulation under the dynamic vegetation scenario (a), and under the static vegetation scenario (b). (c) The difference in trends between the dynamic and static vegetation scenarios.

  • Figure 6

    Schematic diagram of the mechanism responsible for the positive effect of GfGP on precipitation. The plus sign, “+”, denotes a positive contribution (green line), and the minus sign, “−”, denotes a negative contribution (red line). All the values in the diagram represent differences between the climatological values of the default scenario and dynamic scenario. Percentages are differences relative to the default scenario, also shown as the corresponding absolute values (in brackets).

  • Table 1   Summary of various datasets used in this study



    Time period

    Temporal resolution

    Spatial resolution

    ERA-Interim Reanalysis Dataset

    Wind, specific humidity, temperature, etc.,at different pressure levels


    6 h


    ESA CCI Land Cover Dataset

    Land use/land cover



    0.3 km


    LAI, vegetation fraction, and surface albedo



    1 km


    Precipitation, and air temperature


    3 h



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