Research ArticleATMOSPHERIC SCIENCE

Divergent hydrological response to large-scale afforestation and vegetation greening in China

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Science Advances  09 May 2018:
Vol. 4, no. 5, eaar4182
DOI: 10.1126/sciadv.aar4182
  • Fig. 1 Spatial patterns of the linear trend in vegetation changes over China during the past 30 years.

    (A) Trend in changes of the satellite-observed growing season (from April to October) leaf area index (LAI). AVHRR, Advanced Very High Resolution Radiometer. (B) Trend in changes of the forest cover fraction (%) reconstructed from maps of plant functional type (PFT), which incorporate spatial information from the 1:1,000,000 Chinese vegetation map and statistical information from the provincial forest inventories (see Materials and Methods). The shaded area indicates the statistical significance of the linear trend at the 95% confidence level.

  • Fig. 2 Temporal changes of the anomalies in annual hydrological variables over China from 1982 to 2011.

    (A) ET, (B) precipitation (Pre), and (C) relative soil moisture (SM). The black line represents the ensemble (15 members) mean from the experiment (“SCE”), and the red line denotes that from the control (“CTL”) (Materials and Methods). The solid magenta (gray) and dashed green lines denote the observations or observation-based data sets, that is, LAI from Global Inventory Modeling and Mapping Studies (GIMMS) (51); observation-based ET products from Jung et al. (59) and Zeng et al. (60); observed precipitation from data sets of the Climate Research Unit (CRU) (61) and the Global Precipitation Climatology Centre (GPCC) (62) and precipitation output from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) (65) and National Centers for Environmental Prediction (NCEP) reanalysis data set (66); observation-based soil moisture data from the output of the GLEAM (64) presented by surface soil moisture (GLEAM_surf) and root-zone soil moisture (GLEAM_root). The correlation coefficients and P values are shown for the relations between simulations and observations.

  • Fig. 3 Linear trend of vegetation-induced changes (that is, SCE − CTL) in annual hydrological variables (including Pre, ET, Pre − ET, and total SM) and the trend in observed LAI and forest cover (Fc).

    (A) The whole country, (B) North China, (C) Southeast China, (D) Southwest China, and (E) Northeast China. The spatial extent of each region is shown on the map above each subplot. One asterisk indicates statistical significance at the 90% confidence level, and two indicate statistical significance at the 95% confidence level.

  • Fig. 4 Schematic diagram of the vegetation biophysical feedback to ET, precipitation, and low-level (that is, 700 hPa) tropospheric circulation.

    (A) Spring (March to May), (B) summer (June to August), and (C) fall (September to October). The base map shows the trend in changes of the satellite-observed LAI during the corresponding season from 1982 to 2011. Blue solid arrows denote the climatological wind fields in the low-level troposphere. The red dashed arrows are the significant trend of the low-level troposphere composited wind fields caused by vegetation changes (that is, experiment − control: SCE − CTL), which was summarized from the spatial pattern of significant change in circulation such as fig. S10. Arrows denoting vegetation-induced changes in ET and Pre were drawn only when the trend was significant in at least 1 month of the corresponding season according to fig. S8 (B and C).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/5/eaar4182/DC1

    section S1. Optimization of vegetation physiology–related parameters

    section S2. Model evaluation

    section S3. Reconstructed map of the forest cover fraction and attribution of the greening

    section S4. Relationship between soil moisture and other water cycle fluxes

    fig. S1. Schematic diagram of the LMDZOR coupled model, zoomed over China.

    fig. S2. The spatial patterns of climatological precipitation, ET, and soil moisture.

    fig. S3. Seasonal distributions of climatological precipitation, ET, and soil moisture.

    fig. S4. Spatial patterns of the climatological precipitation, overlaid by the composited 850-hPa winds at the annual time scale and during summer.

    fig. S5. Evaluation of the EASM and changes in EASM caused by vegetation dynamics.

    fig. S6. Spatial extent of the four different climate regions with the main changes in forest cover in China.

    fig. S7. Temporal change in anomalies in annual hydrological variables from 1982 to 2011 over different climate regions.

    fig. S8. Linear trend of the differences in monthly LAI, ET, precipitation, total runoff, and total soil moisture between the experiment (SCE) and control (CTL) over different climate regions.

    fig. S9. Linear trend of the differences in monthly roughness, surface shortwave albedo, latent heat flux, sensible heat flux, and CAPE between the experiment (SCE) and control (CTL) over different climate regions.

    fig. S10. The trend in mechanism-related variables affected by vegetation (experiment − control: SCE − CTL) from June to August.

    fig. S11. Linear trend in annual precipitation (Pre) derived from observation-based data sets and our model simulations (that is, SCE and CTL) from 1982 to 2011.

    table S1. Changes in the LAI, forest cover fraction (fforest, %), and LAI over forest (LAIforest) and nonforest (LAInonforest) regions between 1982 and 2011 (that is, 2011 minus 1982) over different climate regions in China and in the country as a whole.

    table S2. A list of interannual trend (trend, ±1 SE), correlation coefficient (R), relative bias (bias, %) computed from observed and simulated ET, precipitation (Pre), and relative soil moisture (SM) at the country and regional scales.

    table S3. Comparisons of trend (units: mm year−2) in ET and WY (precipitation − ET) between the Liu et al. (17) offline model and our coupled simulations during the time period 2000–2011.

    table S4. Trends in the soil moisture of drainage basins within North China from 1979 to 2010, derived from the supporting information in the study of Chen et al. (44).

  • Supplementary Materials

    This PDF file includes:

    • section S1. Optimization of vegetation physiology–related parameters
    • section S2. Model evaluation
    • section S3. Reconstructed map of the forest cover fraction and attribution of the greening
    • section S4. Relationship between soil moisture and other water cycle fluxes
    • fig. S1. Schematic diagram of the LMDZOR coupled model, zoomed over China.
    • fig. S2. The spatial patterns of climatological precipitation, ET, and soil moisture.
    • fig. S3. Seasonal distributions of climatological precipitation, ET, and soil moisture.
    • fig. S4. Spatial patterns of the climatological precipitation, overlaid by the composited 850-hPa winds at the annual time scale and during summer.
    • fig. S5. Evaluation of the EASM and changes in EASM caused by vegetation dynamics.
    • fig. S6. Spatial extent of the four different climate regions with the main changes in forest cover in China.
    • fig. S7. Temporal change in anomalies in annual hydrological variables from 1982 to 2011 over different climate regions.
    • fig. S8. Linear trend of the differences in monthly LAI, ET, precipitation, total runoff, and total soil moisture between the experiment (SCE) and control (CTL) over different climate regions.
    • fig. S9. Linear trend of the differences in monthly roughness, surface shortwave albedo, latent heat flux, sensible heat flux, and CAPE between the experiment (SCE) and control (CTL) over different climate regions.
    • fig. S10. The trend in mechanism-related variables affected by vegetation (experiment − control: SCE − CTL) from June to August.
    • fig. S11. Linear trend in annual precipitation (Pre) derived from observation-based data sets and our model simulations (that is, SCE and CTL) from 1982 to 2011.
    • table S1. Changes in the LAI, forest cover fraction (fforest, %), and LAI over forest (LAIforest) and nonforest (LAInonforest) regions between 1982 and 2011 (that is, 2011 minus 1982) over different climate regions in China and in the country as a whole.
    • table S2. A list of interannual trend (trend, ±1 SE), correlation coefficient (R), relative bias (bias, %) computed from observed and simulated ET, precipitation (Pre), and relative soil moisture (SM) at the country and regional scales.
    • table S3. Comparisons of trend (units: mm year−2) in ET and WY (precipitation − ET) between the Liu et al. (17) offline model and our coupled simulations during the time period 2000–2011.
    • table S4. Trends in the soil moisture of drainage basins within North China from 1979 to 2010, derived from the supporting information in the study of Chen et al. (44).

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