To resolve a series of ecological and environmental problems over
the Loess Plateau, the “Grain for Green Program” (GFGP) was initiated at
the end of 1990s. Following the conversion of croplands and bare land on
hillslopes to forests, the Loess Plateau has displayed a significant
greening trend, which has resulted in soil erosion being reduced. However, the GFGP has also
affected the hydrology of the Loess Plateau, which has raised questions regarding
whether the GFGP should be continued in the future. We investigated the
impact of revegetation on the hydrology of the Loess Plateau using
relatively high-resolution simulations and multiple realizations with the
Weather Research and Forecasting (WRF) model. Results suggest that
revegetation since the launch of the GFGP has reduced runoff and soil
moisture due to enhanced evapotranspiration. Further revegetation associated
with the GFGP policy is likely to further increase evapotranspiration, and
thereby reduce runoff and soil moisture. The increase in evapotranspiration
is associated with biophysical changes, including deeper roots that deplete
deep soil moisture stores. However, despite the increase in
evapotranspiration, our results show no impact on rainfall. Our study
cautions against further revegetation over the Loess Plateau given the
reduction in water available for agriculture and human settlements and the lack of
any significant compensation from rainfall.
Introduction
The Loess Plateau is a highland region of north central China, covering
about 640 000 km2. The loess soils are well suited for agriculture, so
natural forests have been progressively converted to farmland to support the
growing population over the last 7000 years (Fu et al., 2017). However, the
loess is also prone to wind and water erosion; thus, the area's long history of
deforestation is associated with soil erosion, resulting in land
degradation, low agricultural productivity, and significant local poverty in
some farming communities (Bryan et al., 2018; Chen et al., 2015; Fu et al.,
2017). The soil erosion aggravates the flux of sediment into the Yellow
River (Fu et al., 2017; Miao et al., 2010; Peng et al., 2010), increasing the
risk of catastrophic flooding in some densely populated regions downstream
(Bryan et al., 2018; Chen et al., 2015; Fu et al., 2017).
To minimize soil erosion, mitigate flood risk, store carbon, and improve
livelihoods over the Loess Plateau, the “Grain for Green Program” (GFGP)
was initiated by reforesting hillslopes in the late 1990s (Bryan et al.,
2018; Fu et al., 2017; Liu et al., 2008). Consequently, the Loess Plateau
has displayed a significant “greening” trend (Chen et al., 2015; Fu et al.,
2017; Li et al., 2017). The large-scale vegetation restoration program has
also reduced soil erosion over the Loess Plateau and alleviated sediment
transport into the Yellow River (Fu et al., 2017; Liang et al., 2015; Miao
et al., 2010; Peng et al., 2010; Wang et al., 2016).
As a consequence of the beneficial outcomes of the GFGP, further investment
is planned with a commitment of around USD 33.9 billion from China through
to 2050 (Feng et al., 2016). However, further revegetation over the Loess
Plateau is controversial (Cao et al., 2011; Chen et al., 2015; Fu et al.,
2017) with evidence from field (Jia et al., 2017; Jin et al., 2011; Wang et
al., 2012) and satellite (Feng et al., 2017; Lv et al., 2019a; Xiao, 2014)
observations that revegetation has affected the hydrological balance of the
region. Compared with croplands or barren surfaces, the planted forests
enable higher evapotranspiration associated with a larger leaf area, higher
aerodynamic roughness, and deeper roots (Anderson et al., 2011; Bonan, 2008;
Bright et al., 2015). Consequently, revegetation tends to decrease soil
moisture and runoff with the associated risk of limiting water availability
for agriculture, human consumption, and industry (Cao et al., 2011; Chen et
al., 2015; Fu et al., 2017). Indeed, the present vegetation over the Loess
Plateau, which to some extent reflects decades of reafforestation, may
already exceed the limit that the local water supply can support; hence,
further revegetation may not be sustainable (Feng et al., 2016; S. L. Zhang et
al., 2018).
Despite the increasing observational evidence demonstrating that
revegetation tends to impair the hydrological balance of the Loess Plateau,
the response of rainfall to revegetation over this region has commonly been
overlooked. This is mainly due to the difficulty in detecting the impact of
revegetation on rainfall from observations. As an important component of
hydrological cycle of the Loess Plateau, rainfall not only controls the
terrestrial water budget but also influences soil erosion and the discharge
of sediment into the Yellow River (Liang et al., 2015; Miao et al., 2010;
Peng et al., 2010; Wang et al., 2016). Therefore, information on how rainfall responds to
revegetation is critical for a comprehensive assessment of the impact of
revegetation on the hydrology of the region. Indeed, if rainfall responds to
revegetation, this may influence national policies on whether to continue
large-scale vegetation restoration programs. Afforestation or deforestation
does have the potential to affect rainfall via changes in biogeophysical
processes, but any impact of afforestation or deforestation on rainfall
tends to be highly region specific (Findell et al., 2006; Lorenz et al.,
2016; Winckler et al., 2017).
In contrast with observations, modeling can help disentangle the impact of
revegetation on rainfall from the impact of other drivers. Cao et al. (2017)
and Li et al. (2018) performed numerical experiments over the whole of China
and demonstrated that the revegetation over the Loess Plateau can enhance
rainfall locally. Very recently, Lv et al. (2019b) and Cao et al. (2019)
performed simulations focused on the Loess Plateau in order to examine the impact of
revegetation or afforestation on rainfall. Lv et al. (2019b) reported a
significant increase in rainfall, while Cao et al. (2019) found spatially
divergent changes in rainfall. We also note some earlier studies
investigating the response of rainfall to land cover change across China
(e.g., Chen et al., 2017; Ma et al., 2013; Wang et al., 2014).
Unfortunately, these studies either focused less on the Loess Plateau (Ma et
al., 2013) or applied land cover changes unable to reflect the revegetation
of the Loess Plateau (Chen et al., 2017; Wang et al., 2014). Therefore,
large uncertainties remain in the response of rainfall to the revegetation of
the Loess Plateau owing to inconsistent conclusions derived from limited
studies. We note that Li et al. (2018) reported that the increased rainfall due
to revegetation over North China (covering but not limited to the Loess
Plateau) was large enough to compensate for the increase in
evapotranspiration and resulted in little impact on soil moisture. This
simulated negligible soil moisture change associated with revegetation is
contradicted by extensive studies based on observations (e.g., Feng et al.,
2017; Jia et al., 2017; Wang et al., 2012). Here, we note that it might be unfair
to directly compare the observational and modeling results because
observational results commonly incorporate multiple factors and modeling
results are subject to uncertainties in both land cover change and
biophysical parametrization schemes implemented in models (de
Noblet-Ducoudre et al., 2012; Pitman et al., 2009). These intrinsic
differences between observational and modeling results cannot fully account for the
disagreement on the runoff and soil moisture change due to revegetation over
the Loess Plateau. Thus, the impact of revegetation on the hydrology of the
Loess Plateau remains unclear and needs careful reevaluation.
In this study, we examine the impact of revegetation following the launch of
the GFGP on the hydrology of the Loess Plateau using relatively high-resolution simulations with the Weather Research and Forecasting model. We
also examine the impact of further revegetation on the hydrology of the
Loess Plateau with the goal of providing helpful information to
policymakers. As far as we know, there has been no study investigating how
the regional hydrology would be affected by further revegetation over the
Loess Plateau, which is an important factor for informing policymakers on the
mitigation and adaptation of climate change for this region. Additionally,
the vegetation over the Loess Plateau is fragile and highly dependent on
water availability (Fu et al., 2017). How the hydrology would be impacted by
further revegetation determines the water availability and, in turn, how much
more revegetation can be sustained over the Loess Plateau. Neglecting this
process risks errors in assessing the upper threshold for vegetation in the
Loess Plateau (Feng et al., 2016; S. L. Zhang et al., 2018). Given the importance
of revegetation over the Loess Plateau now and in the future, we examine the
impact of further revegetation on the hydrology of the Loess Plateau and pay
particular attention to the response of rainfall to revegetation.
MethodsModel configuration
The Weather Research and Forecasting (WRF, version 3.9.1.1, Skamarock et
al., 2008) model, a fully coupled land–atmosphere regional weather and climate
model, was used in our study. WRF has been shown to perform well in the dynamic
downscaling of regional climate over China (e.g., He et al., 2017; Sato and
Xue, 2013; Yu et al., 2015). Additionally, WRF has been used to study the
impact of land use and land cover change on the hydrological balance at
regional scales (Deng et al., 2015; L. J. Zhang et al., 2018). Thus, while WRF is
potentially suitable for evaluating the impact of revegetation on
the hydrology of the Loess Plateau, we undertake an evaluation of WRF in
simulating surface air temperature and rainfall for this region (see Sect. 3.1). To perform simulations at a high spatial resolution over the Loess
Plateau region, we applied two-way nested runs with two domains at
different grid resolutions running simultaneously. The ERA-Interim
reanalysis data (Dee et al., 2011; Table 1) provided the boundary conditions
for the larger and coarser-resolution (30 km) domain, and the larger domain
provided boundary conditions for the smaller and higher-resolution (10 km)
domain. The ERA-Interim reanalysis data also provided the initial conditions
for both domains. Using a Lambert projection, the larger domain was centered
at 100∘ E, 37∘ N, with 180 grid points in the west–east direction and
155 grid points in the south–north direction, covering most of China and some of the
surrounding regions (Fig. 1a). The inner domain covers the entire Loess
Plateau with 166 grid points in the west–east direction and 151 grid points in the
south–north direction (Fig. 1a, b). Both domains had 28 sigma levels in the
vertical direction with the top level set at 70 hPa. Figure 1b shows the
region analyzed in this paper.
Descriptions of datasets used in this study.
VariableDatasetTime span availableTemporalSpatialresolutionresolutionLand coverMCD12Q12001 to 2017Yearly500 mLAI/FPARMCD15A2H4 July 2002 to present8 d500 mLAI/FPARMOD15A2H8 February 2000 to present8 d500 mAlbedoGLASS (Global Land Surface Satellite)1981 to present8 d0.05∘Initial and boundaryERA-Interim1979 to present6 h0.75∘conditions for WRFSurface air temperatureNational Meteorological1961 to presentMonthly0.5∘Information CenterRainfallNational Meteorological1961 to presentMonthly0.5∘Information CenterSlopeSRTM (Shuttle Radar Topography Mission)––3 s (about 90 m)
(a) The larger domain (D01) and (b) the inner nested domain (D02) configured for the WRF model. The topography (meters above sea
level) is shown using colored shading. The Loess Plateau is enclosed by the
black border. The black rectangle covers the region analyzed in this
study.
The main physical parameterization schemes used in our study included the
WRF single-moment 6-class microphysics scheme (Hong and Lim, 2006), the
Dudhia shortwave radiation scheme (Dudhia, 1989), the Rapid Radiative
Transfer Model (RRTM, Mlawer et al., 1997) for longwave radiation, a revised
MM5 scheme (Jimenez et al., 2012) for the surface layer, the Noah land
surface model (Ek, 2003), the Yonsei University scheme (Hong et al., 2006)
for the planetary boundary layer, and the Kain–Fritsch scheme (Kain, 2004)
for cumulus convection. The Noah land surface model used the Unified
NCEP/NCAR/AFWA scheme with soil temperature and moisture in four layers
(the first layer from 0 to 10 cm, the second layer from 10 to 40 cm, the third layer from 40 to 100 cm, and the fourth layer from 100 to 200 cm), fractional snow cover, and frozen soil
physics. A sub-tiling option considering three land cover types within each
grid cell was applied to help improve the simulations of the land surface
fluxes and temperature (Li et al., 2013).
DataSatellite data
We used satellite-observed land cover type obtained from the Moderate
Resolution Imaging Spectroradiometer (MODIS) Land Cover Type product
(MCD12Q1, Version 6; Friedl and Sulla-Menashe, 2019; Table 1). This provides
land cover types based on the “International Geosphere-Biosphere Programme” (IGBP)
classification scheme (Table 2) globally at a spatial resolution of 500 m,
and at yearly intervals from 2001 to 2017. The MCD12Q1 Version 6 is improved
over previous versions via substantial improvements to algorithms,
classification schemes, and spatial resolution (Sulla-Menashe et al., 2019).
We changed the land cover type within the Loess Plateau while retaining the
default land cover type for other regions in our experiments (see details in
Sect. 2.3). Therefore, the MCD12Q1 data were reprojected to geographic
grid data with a resolution of 30 s (approximately 0.9 km) by the MODIS
Reprojection Tool to make them consistent with the default land cover map in
WRF.
The International Geosphere-Biosphere Programme (IGBP)
classification and class descriptions.
NameValueDescriptionEvergreen Needleleaf Forests1Dominated by evergreen conifer trees (canopy >2 m). Tree cover greater than 60 %.Evergreen Broadleaf Forests2Dominated by evergreen broadleaf and palmate trees (canopy >2 m). Tree cover greater than 60 %.Deciduous Needleleaf Forests3Dominated by deciduous needleleaf (larch) trees (canopy >2 m). Tree cover greater than 60 %.Deciduous Broadleaf Forests4Dominated by deciduous broadleaf trees (canopy >2 m). Tree cover greater than 60 %.Mixed Forests5Not dominated by deciduous nor evergreen (40 %–60 % of each) tree types (canopy >2 m). Tree cover greater than 60 %.Closed Shrublands6Dominated by woody perennials (1–2 m in height). Shrub cover greater than 60 %.Open Shrublands7Dominated by woody perennials (1–2 m in height). Shrub cover between 10 % and 60 %.Woody Savannas8Tree cover between 30 % and 60 % (canopy >2 m).Savannas9Tree cover between 10 % and 30 % (canopy >2 m).Grasslands10Dominated by herbaceous annuals (<2 m).Permanent Wetlands11Permanently inundated lands with between 30 % and 60 % water cover and greater than 10 % vegetated cover.Cropland12At least 60 % of the area is cultivated cropland.Urban and Built-up lands13At least 30 % impervious surface area, including building materials, asphalt, and vehicles.Cropland/Natural Vegetation Mosaics14Mosaics of small-scale cultivation (40 %–60 %) with natural tree, shrub, or herbaceous vegetation.Snow and Ice15At least 60 % of the area is covered by snow and ice for at least 10 months of the year.Barren16At least 60 % of the area is non-vegetated and barren (sand, rock, soil) with less than 10 % vegetation.Water Bodies17At least 60 % of the area is covered by permanent water bodies.
Key land surface biogeophysical parameters include the green vegetation
fraction (VEGFRA), snow free albedo (α), leaf area index (LAI), and the
background roughness length (Z0). The fraction of photosynthetically
active radiation (FPAR) can be used as a proxy for VEGFRA (Kumar et al., 2014; Liu et
al., 2006), enabling both VEGFRA and LAI data to be obtained from the MODIS
Terra+Aqua LAI/FPAR product (MCD15A2H, Version 6; Myneni et al., 2015a;
Table 1). This provides 8 d composite LAI and FPAR globally at a spatial
resolution of 500 m from 4 July 2002. The MODIS Terra LAI/FPAR
product (MOD15A2H, Version 6; Myneni et al., 2015b; Table 1) was also used
to provide observations prior to 2002, as it started on 8 February
2000. Although MOD15A2H covers a longer time span, MCD15A2H is generally
preferred. This is because only observations from the MODIS sensor on NASA's
Terra satellite are used to generate MOD15A2H, but observations from sensors
on both Terra and Aqua satellites are used for MCD15A2H. The MCD15A2H and
MOD15A2H sinusoidal tile grid data were reprojected before use. The 8 d
LAI and FPAR data were composited to monthly data to make them suitable for WRF.
As we only focus on the growing season (see Sect. 2.3.1), α can be
assumed to be equivalent to satellite-observed snow-free albedo. The
α data were derived from the blue sky albedo for shortwave provided
by the Global Land Surface Satellite (GLASS) product (Liang and Liu, 2012;
Table 1). This provides an 8 d composite albedo globally at a spatial
resolution of 0.05∘ from 1981 to present. Compared with the MODIS
albedo product, the GLASS albedo product has a higher temporal resolution
and more successfully captures the surface albedo variations (Liu et al., 2013). The
8 d α data were composited to monthly data.
The background roughness length (Z0) was calculated following Eq. (1):
Z0=Zmin+VEGFRA-VEGFRAminVEGFRAmax-VEGFRAmin×Zmax-Zmin,
where Zmax and Zmin are the land-cover-dependent maximum and minimum
background roughness lengths, respectively, provided by lookup tables.
VEGFRA, VEGFRAmax, and VEGFRAmin are the instantaneous, maximum, and minimum green
vegetation fractions, respectively, which were calculated from satellite-observed VEGFRA (equal
to FPAR) that would be implemented in WRF (see Sect. 2.3).
Observation data
To evaluate the WRF model performance with respect to simulating the surface air
temperature and rainfall over the Loess Plateau, we used a gridded
observation dataset developed by the National Meteorological Information
Center of the China Meteorological Administration (Zhao et al., 2014; Table 1). The dataset provides monthly surface air temperature and rainfall at a
spatial resolution of 0.5∘ from 1961 to present and was produced by
merging more than 2400 station observations across China using thin plate
spline interpolation. The dataset has been widely used to analyze the
surface air temperature and rainfall over the Loess Plateau (Sun et al.,
2015; Tang et al., 2018). To facilitate the comparison between simulations
and observations, the observation data were bilinearly interpolated to the
WRF inner domain grid.
Experiment designThe impact of revegetation since the launch of the GFGP
To examine the impact of revegetation on the hydrology of the Loess Plateau
since the launch of the GFGP, we conducted a control experiment (LC2001)
and a sensitivity experiment (LC2015; Table 3). For LC2001, satellite-observed land cover type, VEGFRA, LAI, and α in 2001 were used to approximate the
land cover type and land surface biogeophysical parameters before the launch
of the GFGP. There is a 1-year gap between the launch of the GFGP (end of
1999) and 2001, but any bias introduced by this gap is small compared with
the changes in land cover type and land surface biogeophysical parameters
between 1999 and present. Satellite-observed land cover type, VEGFRA, LAI, and
α in 2015, representing the current land cover type and land surface
biogeophysical status, were used for the LC2015. Model configurations
were identical for the LC2001 and LC2015 except for land cover
type and land surface biogeophysical parameters. Thus, comparing the LC2001
and LC2015 isolates the impact of revegetation since the
launch of the GFGP.
Description of the experimental design.
ExperimentLand coverVEGFRALAIαSimulation periodLC200120012001200120011 May to 30 September from 1996 to 2015LC201520152015201520151 May to 30 September from 1996 to 2015LCfutrArtificially constructed land cover and 1 May to 30 September from 1996 to 2015land surface biogeophysical parameters (see text) LCENS20012001200120012001From a varying initial time (from 21 April to 1 May)to 30 September 2001LCENS20152015201520152015From a varying initial time (from 21 April to 1 May)to 30 September 2001
We note that the difference between LC2001 and LC2015 should not
be regarded as equivalent to the impact of GFGP for two reasons. First,
actual changes in land cover type since the launch of the GFGP are highly
spatially heterogeneous due to various anthropogenic activities, including
GFGP, irrigation, and urbanization. MCD12Q1 suggests that most changes in
land cover type have occurred in the south Loess Plateau (SLP;
35–37∘ N, 105–111∘ E) and east Loess Plateau (ELP; 35–39∘ N, 111–114∘ E) (Fig. 2a, c, e, g). In addition to the gain of forests
(including evergreen needleleaf, evergreen broadleaf, deciduous needleleaf,
deciduous broadleaf, and mixed forests) and savannas (including woody
savannas and savannas), other changes in land cover type include the
expansion of croplands (including croplands and cropland/natural vegetation
mosaics) at the expense of grasslands and savannas (Fig. 2g). These
increased croplands revealed by the MODIS land cover product, which seem
unlikely, have been reported previously (Fan et al., 2015; Lv et al., 2019b),
and are likely associated with expanded irrigation activities along the
Yellow River (Fan et al., 2015; Zhai et al., 2015). Second, the observed
VEGFRA, LAI, and α changes also incorporate other factors including improved
agricultural management, climate variability, rising atmospheric CO2
concentration, and nitrogen deposition (Li et al., 2017; Fan et al., 2015;
Piao et al., 2015). As shown in Fig. 3a, c, e, and g, the biogeophysical
changes are not strictly limited to the regions undergoing changes in land
cover type. For example, the α decrease mostly occurs over
grasslands in the northwest (Fig. 3e), where the land cover type rarely changes
(Fig. 2c). This decreased α is attributed to increased precipitation
as well as the restoration of grasslands benefiting from the Returning
Rangeland to Grassland Program launched in 2003 over this region (Zhai et
al., 2015). In contrast, the α change is negligible in the SLP and
ELP owing to the combined effects of increased forests (Fig. 2a) and
croplands (Fig. 2d). However, overall, the MCD12Q1 demonstrates a significant
greening trend (increased VEGFRA, LAI, and Z0 and decreased α) over the
Loess Plateau since the launch of the GFGP (Fig. 3), which is spatially
consistent with previous studies (e.g., Cao et al., 2019; Xiao, 2014; Zhai
et al., 2015).
Land cover type changes (a, c, e, g) between LC2001
and LC2015 (LC2015–LC2001) and (b, d, f, h) between
LC2001 and LCfutr (LCfutr–LC2001). Green, brown, and
gray denote the gained, lost, and unchanged land cover types,
respectively, in the LC2015(a, c, e, g) and LCfutr(b, d, f, h) compared with the LC2001. Forests include evergreen needleleaf,
evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, and mixed
forests (see Table 2). Savannas include woody savannas and savannas.
Croplands include croplands and cropland/natural vegetation mosaics. The
south (35–37∘ N, 105–111∘ E) and east (35–39∘ N, 111–114∘ E)
Loess Plateau are enclosed by black rectangles and are labeled SLP and ELP,
respectively.
Changes in the June–July–August–September mean (a, b) green
vegetation fraction (%), (c, d) leaf area index (m3 m-3), (e, f) albedo, and (g, h) roughness length (m) between
LC2001 and LC2015 (LC2015–LC2001; a, c, e, and g) and
between the LC2001 and LCfutr (LCfutr–LC2001; b, d, f,
and h). The south (SLP) and east Loess Plateau (ELP) regions are defined in
Fig. 2.
Both LC2001 and LC2015 were run from 1 May to 30 September from 1996 to 2015 resulting in 20 realization
members for LC2001 and LC2015, respectively. We only run simulations for the growing
season; any impact of revegetation should be most apparent during the
growing season given that over 70 % of the annual rainfall occurs over the
Loess Plateau in this season (Sun et al., 2015; Tang et al., 2018).
The impact of further revegetation on the Loess Plateau
If the GFGP is continued in the future, further revegetation could impact
the hydrology of the Loess Plateau. Therefore, we conducted a third
experiment (LCfutr) in which the coverage of forests was assumed to be at a
maximum over the Loess Plateau following the policy of the GFGP (Table 3). To maximize
forests, we first assumed that all croplands and barren land on hillslopes were
converted to forests. We then assumed that savannas or forests with low
coverage (e.g., low VEGFRA) became dense forests. The land cover and land surface
biogeophysical parameters for the LCfutr were then constructed
following two steps:
All cropland, barren, and savanna pixels on hillslopes (>15∘) were replaced by forest pixels over the Loess Plateau based on
the land cover map from 2015. The slope was derived from the Shuttle Radar
Topography Mission (SRTM version 2.0; Table 1) digital elevation model at a
spatial resolution of 3 s (about 90 m). The pixel resolution of the
land cover type was 30 s; thus, every land cover type pixel covered 100
(10×10) slope values. To maximize the revegetation, land cover
type pixels with maximum slope values over 15∘ were regarded as
hillslopes. For a pixel to be changed, the forest class was determined by
the class of neighboring forest pixels, considering the adaptation of
planted trees to local climate. Using this strategy, forest pixels
increased by 164 % and croplands pixels decreased by nearly half in the
constructed land cover map compared with the land cover type in 2001, with
most conversions occurring in SLP (Fig. 2b, h).
We constructed the VEGFRA, LAI, and α map in line with the land cover
type constructed in step 1. For each forest class, we screened out
the “dense forests” pixels with VEGFRA over the 95th percentile among the
pixels labeled as the same forest class over the Loess Plateau. The
monthly values of VEGFRA, LAI, and α of the “dense forest” pixels were
calculated for each forest class. We then adjusted the monthly VEGFRA, LAI, and
α of other “non-dense forests” pixels to the values of the “dense
forests” pixels. Using this strategy, all forest pixels over the Loess
Plateau were changed to more dense forest. Consequently, the Loess Plateau
shows an amplified greening trend in LCfutr, especially in SLP (Fig. 3b, d, f, h).
The LCfutr was run from 1 May to 30 September
from 1996 to 2015. Therefore, comparing LC2001 and LCfutr isolates
the impact of further revegetation on the hydrology of the Loess Plateau.
Identification of the impact of revegetation
Model internal variability is defined as the difference between realization
members where the only differences are the initial conditions. These
differences result from nonlinearities in the model physics and dynamics
(Giorgi and Bi, 2000; Christensen et al., 2001). This means that some differences
between LC2001 and LC2015 (or LCfutr) will be caused by
internal variability in addition to revegetation (Lorenz et al., 2016; Ge et
al., 2019). To minimize the impact of internal model variability, we
performed multiple simulations for the year 2001 by changing initial
conditions. Specifically, we carried out a pair of experiments named
LCENS2001 and LCENS2015 (Table 3), which were the same as LC2001 and
LC2015 except that LCENS2001 and LCENS2015 were only run for
the year 2001 but initialized for each day between 21 and 30 April and ended on 30 September. This led to a total of 11
members (including the members with initial dates of 1 May in
LC2001 and LC2015) for LCENS2001 and LCENS2015,
respectively. Comparing LCENS2001 and LCENS2015, simulated changes
were likely robust if the impact from revegetation was large and consistent
relative to the differences caused by the change in the initial condition.
Results before 1 June was discarded as spin-up time in each
simulation. Our analysis focuses on June, July, August, and September (JJAS)
averages.
Local significance test
To test the statistical significance of the local impact of revegetation on
the hydrology we calculate a grid point by grid point Student's t test. This
tests the null hypothesis that the two groups of data are from independent
random samples from normal distributions with equal means and equal but
unknown variances. The local difference is regarded as statistically
significant when the p value of the two-tailed t test passes the significance
level of 95 %.
ResultsEvaluation of WRF's skill in simulating temperature and rainfall
We first evaluate WRF's simulation of surface 2 m air temperature (T2) and
rainfall (RAIN), the quantities with the most credible observations available
over the Loess Plateau, by comparing the averaged value of the 11
members in LCENS2001 with the observed values in 2001. After
topographic correction (Zhao et al., 2008), WRF simulates T2 over the Loess
Plateau mostly within 2 ∘C of the observations (Fig. 4a, c, e),
although there are small areas where WRF simulates warmer temperatures (by 4 ∘C) than
the observations. The model also performs well with respect to simulating
RAIN (Fig. 4b, d, f), including a region of higher observed rainfall from the
southwest to the central Loess Plateau. The RAIN bias between the WRF
simulations and the observations is below 0.5 mm d-1 for almost the entire
Loess Plateau (Fig. 4f). Larger RAIN biases mostly occur around the eastern and
southern borders of the Loess Plateau, most likely due to extremely complex
topography in these locations. As we focus on the impact of land cover
change on the hydrology of the region, the reasonable simulation of RAIN gives
us confidence in the results from WRF, particularly in SLP.
June–July–August–September (JJAS) mean (a) observed surface air
temperature (∘C), (b) observed rainfall (mm d-1), (c) simulated surface air temperature (∘C), (d) simulated rainfall
(mm d-1), (e) the differences between observed and
simulated surface air temperature (∘C; simulation minus observation),
and (f) the differences between observed and simulated rainfall
(mm d-1; simulation minus observation) over the Loess
Plateau in 2001. The observed surface air temperature and rainfall are from
the gridded observation dataset developed by the National Meteorological
Information Center of the China Meteorological Administration. The simulated
surface air temperature and rainfall are obtained by averaging the 11
members (with different initial conditions) of LCENS2001.
Impacts on surface fluxes
We first examine the change in the land surface radiation budget, energy, and
water fluxes, as these are directly impacted by changes in land cover type
and the surface biogeophysical parameters. Comparing LC2001 and
LC2015 (LC2015–LC2001), land surface net radiation
(Rnet), latent heat flux (QE) and sensible heat flux (QH)
changes mainly occur where the land cover type and land surface biogeophysical
parameters are changed, suggesting a strong local effect on Rnet,
QE, and QH. Rnet increases by around 5–20 W m-2
(Fig. 5a) over most of the region due to a reduction in α (Fig. 3e). While QE increases by 10–30 W m-2 (Fig. 5c),
QH reduces by around 10 W m-2 (Fig. 5e), mostly in SLP
and ELP as a result of increased VEGFRA, LAI, and Z0 (Fig. 3a, c, g). Changes
in Rnet and QE are statistically significant at a 95 % confidence
level over most of the region, but statistically significant changes in
QH are mostly limited to SLP and ELP (see the embedded subplots in each
panel, Fig. 5a, c, e). As a consequence of further revegetation
(LCfutr–LC2001), Rnet, QE, and QH changes are
intensified (Fig. 5b, d, f), especially in SLP where large areas of
croplands are converted to forest leading to large changes in land surface
biogeophysical parameters in LCfutr (Figs. 2, 3).
Changes in June–July–August–September mean (a, b) land surface
net radiation (W m-2), (c, d) latent heat flux
(W m-2), and (e, f) sensible heat flux (W m-2) between the LC2001 and LC2015 (LC2015–LC2001;
a, c, and e) and between the LC2001 and LCfutr
(LCfutr–LC2001; b, d, and f) over the Loess Plateau from 1996 to
2015. The south Loess Plateau (SLP) and east Loess Plateau (ELP) regions are defined in
Fig. 2. The map of the statistical significance test is shown in the inset
figure in the upper-left corner of each panel. Gray denotes that the local
change is statistically significant at a 95 % confidence level using a
two-tailed Student's t test.
Focusing on SLP, the increase in evapotranspiration (ET) is 0.49 mm d-1 between LC2001 and LC2015 (Fig. 6a). WRF simulates
further water loss (0.85 mm d-1) through ET if the
revegetation is continued in the future (Fig. 6c). For ELP, where relatively
fewer croplands or barren areas can be further converted to forests in
LCfutr, the future ET increase is still considerable (0.72 mm d-1; Fig. 6b, d). The values of the regional mean ET change among the
20 members of LC2015–LC2001 and LCfutr–LC2001 remain
consistently positive over SLP and ELP. This indicates that the simulated
higher ET is a consistent result from WRF as a consequence of revegetation
since the launch of the GFGP and is likely to be further strengthened by
continued revegetation over the Loess Plateau.
Box plot of changes in June–July–August–September mean
evapotranspiration (ET, mm d-1), rainfall (RAIN,
mm d-1), surface runoff (SFROFF, mm d-1), underground runoff (UDROFF, mm d-1), and soil
moisture (m3 m-3) of the first layer (SMOIS1, 0–10 cm),
the second layer (SMOIS2, 10–40 cm), the third layer (SMOIS3, 40–100 cm), and the fourth layer
(SMOIS4, 100–200 cm) averaged over (a, c) the south Loess Plateau and (b, d) the east Loess Plateau between LC2001 and LC2015
(LC2015–LC2001; a, b) and between LC2001 and LCfutr
(LCfutr–LC2001; c, d) from 1996 to 2015. The south Loess Plateau (SLP) and
east Loess Plateau (ELP) regions are defined in Fig. 2. The first and
second line members denote absolute and relative changes averaged by
20 members. The black asterisk denotes that the change is statistically
significant at a 95 % confidence level using a two-tailed Student's
t test.
Impacts on rainfall
Increased ET can contribute to the formation of clouds and rainfall; therefore, we examine whether this is the case for the Loess Plateau. The RAIN is
composed of convective rainfall (RAINC), calculated by the cumulus convection
scheme, and non-convective rainfall (RAINNC), calculated by microphysics scheme, in
WRF. Thus we separate RAINC and RAINNC changes in addition to the RAIN change in Fig. 7. As
for LC2015–LC2001, the change in RAIN is spatially heterogeneous, with
an increase of up to 1.2 mm d-1 in small parts of the
northeast and a decrease of around -1.0 mm d-1 along the
southeast border of the Loess Plateau (Fig. 7a). The RAIN change is divided
almost evenly between RAINC and RAINNC (Fig. 7c, e). However, most of the RAIN, RAINC, and
RAINNC changes are not statistically significant. In terms of
LCfutr–LC2001, RAIN, RAINC, and RAINNC are not significantly changed by further
revegetation (Fig. 7b, d, f). Moreover, the increased RAIN in the northeast
Loess Plateau occurring in LC2015–LC2001 dissipates when further
revegetation is implemented, while the changes in both land cover type and
biophysical parameters are relatively small over this region. This
increased RAIN should be maintained in LCfutr–LC2001 if the change in
RAIN is robust for LC2015–LC2001. We will analyze the increased RAIN of
the northeast Loess Plateau in LC2015–LC2001 in Sect. 3.6.
Same as in Fig. 5 but for (a, b) total rainfall (mm d-1), (c, d) convective rainfall (mm d-1), and (e, f) non-convective rainfall (mm d-1). The south Loess Plateau (SLP)
and east Loess Plateau (ELP) regions are defined in Fig. 2.
For both LC2015–LC2001 and LCfutr–LC2001, most
RAIN changes seem to be randomly scattered around the Loess Plateau instead of
being located coincident with SLP or ELP where land cover type, land surface
biogeophysical parameters, and land surface fluxes are most strongly modified
(Fig. 7a, b). In contrast, the RAIN change is negligible over SLP and ELP
for both LC2015–LC2001 and LCfutr–LC2001 (Figs. 6, 7). However, the RAIN change in individual realizations is not small, e.g.,
the RAIN change varies from -2.11 to 2.21 mm d-1 over the ELP
for LC2015–LC2001 (Fig. 6b). Thus, averaging the divergent RAIN changes
among the 20 members causes a negligible RAIN change overall. This large
variability in RAIN changes among the 20 members can be attributed to either
different boundary conditions (background climate), which cause the impact
of land cover change to diverge (Pitman et al., 2011), or model internal
variability. This will be further analyzed in Sect. 3.6.
Impacts on runoff
As a consequence of the significant ET increase and negligible and
statistically insignificant RAIN change, underground runoff (UDROFF) is reduced by up
to 1.5 mm d-1 locally for LC2015–LC2001 (Fig. 8c). Averaged over the SLP and ELP, the UDROFF decreases by 0.16 mm d-1 (-23 %) and 0.34 mm d-1 (-23 %) for SLP and
ELP, respectively (Fig. 6a, b). These UDROFF changes are not statistically
significant and vary strongly among the 20 members, suggesting a large
uncertainty in the UDROFF change. WRF simulated a larger UDROFF decrease due to further
revegetation (Fig. 8d), especially over SLP and ELP where the regional mean
UDROFF decreases by 0.38 mm d-1 (-54 %) and 0.63 (-42 %),
respectively (Fig. 6c, d). These UDROFF decreases are statistically
significant at a 95 % confidence level for both SLP and ELP. Moreover, the
upper quartile of UDROFF changes among the 20 members systematically shift
below 0 mm d-1 for both the SLP and ELP. These
results indicate a larger chance of a UDROFF decrease if revegetation is
continued over the SLP and ELP. Moreover, the spatial change in UDROFF is
consistent with that of the net budget of RAIN and ET (RAIN–ET) for both
LC2015–LC2001 and LCfutr–LC2001 (Fig. 8e, f),
suggesting that the UDROFF change can be mostly explained by the change in
RAIN–ET. We also note some UDROFF changes in adjacent regions of the Loess Plateau (Fig. 8c, d) associated with RAIN changes (Fig. 7a, b).
Same as Fig. 5 but for (a, b) surface runoff (mm d-1), (c, d) underground runoff (mm d-1), and (e, f) rainfall minus evapotranspiration (mm d-1). The
south Loess Plateau (SLP) and east Loess Plateau (ELP) regions are defined in Fig. 2.
Compared with the UDROFF change, the surface runoff (SUROFF) change is mostly small for
both LC2015–LC2001 and LCfutr–LC2001 (Fig. 8a, b).
However, the relative change of SUROFF is considerable, especially for
LCfutr–LC2001 in which SUROFF decreased by 21 % for SLP and
14 % for ELP (Fig. 6c, d). We also find that the upper
quartile of the SUROFF change systematically shifts below 0 mm d-1, although the SUROFF change is not statistically significant for
LCfutr–LC2001.
Impacts on soil moisture
In addition to the decline in runoff, the soil moisture (SMOIS) of each layer is
significantly reduced over the Loess Plateau for LC2015–LC2001
(Fig. 9a, c, e, g) with larger decreases in the middle two layers. The
regional mean SMOIS for the SLP decreases by 0.02 m m-3 (-8 %)
and 0.03 m m-3 (-12 %) for the second and third layers, respectively (Fig. 6a). WRF simulated further falls in soil moisture following further
revegetation, with a larger impact on deeper soil layer moisture (Fig. 9b,
d, f, h). For example, the decrease in the regional mean soil moisture of
the bottom layer for the SLP varies from -0.01 (or -5 %) in
LC2015–LC2001 (Fig. 6a) to -0.04 (or -17 %) in
LCfutr–LC2001 (Fig. 6c). Similar to the UDROFF change, the spatial
change in SMOIS for each layer is consistent with that of RAIN–ET for both
LC2015–LC2001 and LCfutr–LC2001 (Fig. 8e, f).
Same as Fig. 5 but for the soil moisture change
(m3 m-3) of (a, b) the first layer (0–10 cm), (c, d) the second layer (10–40 cm), (e, f) the third layer (40–100 cm), and (g, h) the fourth layer (100–200 cm). The south Loess Plateau (SLP) and east Loess Plateau (ELP)
regions are defined in Fig. 2.
Robust identification of rainfall change
We found a large variability in changes in RAIN among the 20 members over
the SLP and ELP for both LC2015–LC2001 and
LCfutr–LC2001. We next examine whether these can be attributed to
revegetation. We first show the RAIN change in individual members for
LC2015–LC2001 (Fig. 10). Large variability in RAIN changes among
the 20 members occurs throughout the study region. Even the increase in
RAIN over the northeast Loess Plateau (Fig. 7a), which is available by comparing
multiyear mean RAIN between LC2001 and LC2015, is not consistent for
every year. As for the northeast Loess Plateau, the RAIN shows an increase in 8 years (1997, 2001, 2003, 2004, 2007, 2010, 2012, and 2015), a decrease in 5 years (1996, 1999, 2006, 2009, and 2014), and negligible change in the other 7 years. This results in a net increase in RAIN over the 20 years, but a
different selection of years could show an overall decrease (the result is
similar for LCfutr–LC2001, not shown). Similarly, other
statistically significant RAIN changes occur in the study region (e.g.,
decreased RAIN to the southwest Loess Plateau shown in Fig. 7a), but these are
not consistent across the 20 years. As mentioned earlier, this large
variability in RAIN changes among the 20 members is possibly attributed to
different boundary conditions (background climate); we next examine
whether this is true over the Loess Plateau.
Changes in the June–July–August–September mean rainfall
(mm d-1) of each realization member (years) between the
LC2001 and LC2015 (LC2015–LC2001) over the Loess Plateau
from 1996 to 2015. The south Loess Plateau (SLP) and east Loess Plateau (ELP) regions are
defined in Fig. 2.
Changes in the June–July–August–September mean rainfall
(mm d-1) of each realization member (a–k) and ensemble
mean (l) between the LCENS2001 and LCENS2015
(LC2015–LC2001) over the Loess Plateau in 2001. The south Loess Plateau (SLP)
and east Loess Plateau (ELP) regions are defined in Fig. 2. The map of the
statistical significance test is shown in the inset figure in the upper-left
corner of panel (l). Gray denotes that the local change is statistically
significant at a 95 % confidence level using a two-tailed Student's
t test.
We note that the pattern of RAIN change in 2001 is very similar to the multiyear
averaged pattern, although with a larger magnitude (Figs. 7a, 10f). The RAIN increase
of the northeast Loess Plateau in only 2001 explains about 30 % of the
multiyear mean RAIN increase in the same region. Therefore, we show the RAIN change
in each realization for LCENS2015–LCENS2001 in Fig. 11. These
11 ensemble members share the same boundary conditions with small
differences in initial conditions. In contrast with the increased RAIN obtained
from setting the initial date to 1 May (Fig. 10f), the RAIN changes are
modified by an advance of 1 to 10 d in initial conditions. For example,
WRF cannot simulate the increased RAIN over the northeast Loess Plateau when using
an initial date of 22, 25, 27, or 30 April,
highlighting that the RAIN change is very sensitive to the initial conditions.
Thus, the RAIN increase in 2001 with an initial date of 1 May is likely
associated with internal variability rather than revegetation. In another
words, the RAIN change due to revegetation is negligible relative to the RAIN change
induced by internal variability. Thus, we conclude that the multiyear
averaged RAIN increase over northeast Loess Plateau for LC2015–LC2001
(Fig. 7a) cannot be robustly linked to revegetation.
How many members do we need to get a robust signal?
Model internal variability is inevitable when we use models to investigate
the impact of land cover change on climate. The model internal variability
can be minimized as the number of individual realizations is increased to
form a larger sample to calculate any average. Therefore, we examine the
relationship between the RAIN change and the number of realization members (Fig. 12). Focusing on the SLP and ELP, the range of RAIN change decreases as the
number of realizations increases. For example, the RAIN change over the ELP
varies from -0.97 to 1.07 mm d-1 when only three members
are included. The range of RAIN is narrowed to between -0.25 and 0.24 mm d-1 when 15 members are simulated. It is similar
for LCENS2015–LCENS2001: the range in the change in RAIN decreases as
the number of simulation members increases. The change in RAIN suggests an
increase of 0.48 and 0.40 mm d-1 for SLP and ELP,
respectively, when the simulation members are increased to 11.
The relationship between the changes in the
June–July–August–September mean rainfall (mm d-1) and the
number of members. The number of members ranges from 1 to 20 for (a, b) LC2015–LC2001 and (c, d) LCfutr–LC2001 and from 1 to
11 for (e, f) LCENS2015–LCENS2001. The mean rainfall change is
averaged over (a, c, e) the south Loess Plateau and (b, d, f) the east Loess
Plateau, respectively. The south Loess Plateau (SLP) and east Loess Plateau (ELP) regions
are defined in Fig. 2. For a given number of realizations, the rainfall is
averaged over these members. The gray area denotes the range of rainfall
changes from all possible combinations of a given number of members. The red
dashed line denotes the 5th and 95th percentile of the rainfall
changes from all possible combinations of a given number of members.
Discussion
Following the launch of the GFGP by China in the late 1990s, the Loess
Plateau has shown a significant greening trend, although with simultaneous
concerns about water security for agriculture and other human activities. We
investigated the impact of revegetation on the
hydrology of the Loess Plateau since the launch of the GFGP using WRF. Simulations show that the
revegetation of the plateau is associated with a decrease in runoff and soil
moisture as a consequence of higher evapotranspiration and little feedback
from rainfall. Our results on changes of evapotranspiration, soil moisture,
and runoff are broadly consistent with both field (Jia et al., 2017; Jian et
al., 2015; Jin et al., 2011) and satellite (Feng et al., 2017; Li et al.,
2016; Xiao, 2014) observations. For example, the spatial pattern of our
simulated soil moisture decline in the growing season is similar to
observations from the Advanced Microwave Scanning Radiometer on the Earth
Observing System by the Japanese Aerospace Exploration Agency (Feng et al.,
2017). Although the increased evapotranspiration due to revegetation of the
Loess Plateau has been examined before (e.g., Cao et al., 2017, 2019; Li et
al., 2018; Lv et al., 2019b), the reduction in runoff and soil moisture in
response to revegetation of the Loess Plateau, which is consistent with
observations, has rarely been reported in modeling results to date.
Moreover, our simulated weak response of rainfall to the revegetation of the
Loess Plateau, which is hard to determine from observations, is useful in
assessing the hydrometeorology of this region.
We also investigated the potential future impact on the hydrology of the
Loess Plateau if revegetation was continued, which has not been assessed
before but is important for both scientific communities and policymakers.
WRF suggests that further revegetation would exacerbate soil moisture and
runoff declines with particularly large effects on the underground runoff
and soil moisture in deeper layers. Our simulations suggested that the
potential revegetation that could still be achieved would have larger
consequences than those simulated since the launch of the GFGP. Our results
provide useful advances in our understanding of the impact of further
revegetation on the Loess Plateau. For example, both Feng et al. (2016) and
S. L. Zhang et al. (2018) estimated the current vegetation over the Loess Plateau
is approaching or may have exceeded the threshold of ecological equilibrium.
They omitted the potential response of rainfall to further revegetation over
the Loess Plateau when predicting future thresholds (Feng et al., 2016;
S. L. Zhang et al., 2018). Our result demonstrate that there is almost no feedback
of rainfall associated with further revegetation, supporting the approach of
Feng et al. (2016) and S. L. Zhang et al. (2018) in this specific region. That
said, our approach does not attempt to incorporate changes in climate over
the Loess Plateau; thus, the viability of large-scale reforestation in this
region is not something that we attempted to assess.
We focused on the response of rainfall to revegetation over the Loess
Plateau, which is probably the most uncertain of the hydrological
components. WRF shows little response of rainfall to revegetation since the
launch of the GFGP, which contradicts earlier results (Cao et al., 2017,
2019; Li et al., 2018; Lv et al., 2019b). Moreover, the rainfall is weakly
affected by further revegetation despite a large increase in
evapotranspiration. We also demonstrate that the rainfall change is strongly
affected by internal variability and a large number of realizations are
required before any impact of revegetation on rainfall might be robustly
identified. We suggest that some previous studies (Cao et al., 2017, 2019;
Lv et al., 2019b) based on model simulations may have exaggerated the impact
of revegetation on rainfall over the Loess Plateau due to the lack of
sufficient realizations. For example, Cao et al. (2017, 2019) and Lv et al. (2019b) used the same WRF to perform only three- or five-member simulations,
and concluded a significant change in rainfall caused by revegetation over
the Loess Plateau. More interestingly, Cao et al. (2017, 2019) obtained different conclusions on the rainfall change over the Loess
Plateau with same WRF model. They used a broadly similar experimental design
but a different spatial resolution (30 and 10 km, respectively) and
simulations from 2001 to 2002 with three ensembles and consecutive simulation
from 2000 to 2004, respectively. We could also demonstrate large changes in
rainfall over the plateau if we chose three to five members, but we could demonstrate
either large increases or large decreases in three- to five-member averages. Returning
to Fig. 6, ET shows a highly consistent increase in the response to revegetation
among the 20 years, suggesting that ET change is robustly linked to
revegetation. Although changes in runoff and soil moisture also show large
variability among the 20 years, the distribution of the runoff and soil
moisture changes are negatively biased. More importantly, the distribution of
the runoff and soil moisture changes systematically shift towards negative
values. This suggests that runoff and soil moisture changes are very likely linked
to revegetation. The large variability in runoff or soil moisture changes
is induced by the large variability of rainfall. Given the tight linkage
between rainfall and runoff or soil moisture, the changes in runoff or soil
moisture tend to be mistakenly represented if the rainfall change is not
robustly examined, and this requires internal model variability to be
thoroughly addressed.
Our studies are also subject to some caveats. First, observations of soil
moisture declines associated with revegetation can be alleviated once trees
mature (Jia et al., 2017; Jin et al., 2011). Our simulations only capture an
initial decline in runoff and soil moisture linked to the higher
evapotranspiration, and we note that the impact of revegetation on the
long-time trend (25–50 years) would be valuable. Second, we used current
boundary conditions (1996–2015) for WRF to predict the impact of further
revegetation on the hydrology, which means that the boundary conditions do not
change in the future in response to climate change. This suggests that we
might underestimate the impact of further revegetation in the future if the
future climate of the Loess Plateau suffers from large changes in response
to global warming. Third, uncertainties exist in the current land surface
model used to represent the response of vegetation to climate change in
future. While using satellite observations to construct the land surface
biogeophysical parameters helps overcome some land surface parameter
limitations, this approach is obviously limited looking forward in terms of
the status of future vegetation. Furthermore, we note that our results are
likely model dependent, as we only used one model. Although we performed
relatively high-resolution simulations (10 km for the nested domain), the cumulus
convection scheme remains necessary and is a further potential source of
uncertainty. These factors account for the discrepancy between our result
and another model-based study (Li et al., 2018). Li et al. (2018) found a
positive rainfall feedback to greening and, consequently, small changes in
runoff and soil moisture over north China using a global climate model. In
contrast, we demonstrate the rainfall change is too small to compensate for
the strongly enhanced evapotranspiration, causing a reduction of runoff and
soil moisture in response to revegetation over the Loess Plateau. A large
ensemble of models, each with a reasonable number of realizations, is needed
to build a model-independent assessment of the impact of revegetation; however,
this is clearly beyond the scope of this study. Last, we investigated the
impact of revegetation or greening, rather than GFGP, on the hydrology of
the Loess Plateau. Directly linking our results to the impact of GFGP on the
hydrology of the Loess Plateau should be avoided.
Overall, our results highlight how revegetation of the Loess Plateau led to
increased evapotranspiration and how, as a consequence, the runoff and soil
moisture declined. This is consistent with the understanding of land surface
processes and how they respond to land cover change (Bonan, 2008). Critical
in this impact of revegetation on the hydrology is what happens to rainfall.
If the higher evapotranspiration increases rainfall, revegetation has
the potential to increase soil moisture and runoff. It is very likely this
would be the consequences in some regions, such as Amazonia (Lawrence and
Vandecar, 2015; Perugini et al., 2017; Spracklen et al., 2018) and Sahel
(Kemena et al., 2018; Xue and Shukla, 1996; Yosef et al., 2018). However,
over the Loess Plateau we find no such result; thus, the higher
evapotranspiration simply leads to lower soil moisture and runoff.
Additionally, Bargues Tobella et al. (2014) reported a positive impact of trees on
soil hydraulic properties influencing groundwater recharge when termite
mounds are taken into account in Africa. However, termite mounds are rare over
the Loess Plateau suggesting that this positive impact of trees is unlikely to
occur. An implication of this result is that further revegetation, which
requires water to be sustained, may not be viable. We also recognize that
afforestation can help to sequester carbon, mitigate warming, and alleviate
soil erosion. Therefore, if and how to implement further revegetation
should be cautiously determined with the pros and cons of afforestation
being carefully weighted for the Loess Plateau.
Conclusions
We evaluated how the growing season hydrology of the Loess Plateau has been
impacted by revegetation since the launch of the “Grain for Green
Program” and by further revegetation in the future using the WRF model. We
used satellite observations to describe key biophysical parameters including
decreased albedo and increased leaf area index and the fraction of
photosynthetically active radiation. The observed greening trend increased
evapotranspiration, but because the impact on rainfall was negligible the
underground runoff and soil moisture both decreased. Further future
revegetation enhanced evapotranspiration but still had little impact on
rainfall. Thus, overall, revegetation over the Loess Plateau leads to
higher evapotranspiration and, as a consequence, lower water availability for
agriculture or other human demands. Considering the negative impact of
revegetation on runoff and soil moisture and the lack of benefits on
rainfall, we caution that further revegetation may threaten local water
security over the Loess Plateau.
Code and data availability
The MODIS land cover type product (MCD12Q1) and LAI/FPAR products
(MCD15A2H and MOD15A2H) are available from NASA's Land Processes Distributed
Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/data/ (last access: 1 September 2018; Friedl and Sulla-Menashe, 2019; Myneni et al., 2015a, b).
The GLASS albedo product is available from the Global Land Surface Satellite
(GLASS) products download and service: http://glass-product.bnu.edu.cn/ (last access: 1 September 2018; Liang and Liu, 2012). The ERA-Interim reanalysis data are
available from the European Centre for Medium-Range Weather Forecasts (ECMWF) data server: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim (Dee et al., 2011).
The gridded observation dataset is available from the National Meteorological
Information Center of the China Meteorological Administration: http://data.cma.cn/data/cdcindex.html (Zhao et al., 2014). The code for the Weather Research and
Forecasting model is available from http://www2.mmm.ucar.edu/wrf/users/ (last access: 1 September 2018; Skamarock et al., 2008).
Author contributions
CF, JG, and WG developed the scientific hypotheses and designed the
research. JG, AJP, and BZ analyzed the data and wrote the paper. All
authors contributed to the discussion of the results and to revising the
paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is supported by the Jiangsu Collaborative Innovation Center for Climate Change. The model simulations were conducted on the NCI (National Computational Infrastructure) at the Australian National University, Canberra. The authors gratefully acknowledge financial support from the China Scholarship Council. The authors also acknowledge Xing Yuan and two anonymous reviewers for their constructive comments on this work.
Financial support
This research has been supported by the Natural Science Foundation of China (grant nos. 41775075 and 41475063) and the Australian Research Council via the Centre of Excellence for Climate Extremes (grant no. CE170100023).
Review statement
This paper was edited by Xing Yuan and reviewed by two anonymous referees.
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