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Hydrologic and Climatic Responses to Global AnthropogenicGroundwater Extraction
YUJIN ZENG
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,
Institute of Atmospheric Physics, Chinese Academy of Sciences, and College of Earth Science,
University of Chinese Academy of Sciences, Beijing, China
ZHENGHUI XIE
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
JING ZOU
Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao, China
(Manuscript received 11 March 2016, in final form 28 July 2016)
ABSTRACT
In this study, a groundwater (GW) extraction scheme was incorporated into the Community Earth System
Model, version 1.2.0 (CESM1.2.0), to create a new version called CESM1.2_GW, which was used to in-
vestigate hydrologic and climatic responses to anthropogenic GW extraction on a global scale. An ensemble
of 41-yr simulationswith andwithoutGWextraction (estimated based on local water supply and demand)was
conducted and analyzed. The results revealed that GW extraction and water consumption caused drying in
deep soil layers but wetting in upper layers, along with a rapidly declining GW table in areas with the most
severe GW extraction, including the central United States, the north China plain, and northern India and
Pakistan. The atmosphere also responded to GW extraction, with cooling at the 850-hPa level over northern
India and Pakistan and a large area in northern China and central Russia. Increased precipitation occurred in
the north China plain due to increased evapotranspiration from irrigation. Decreased precipitation occurred
in northern India because the Indian monsoon and its transport of water vapor were weaker as a result of
cooling induced by GW use. Additionally, the background climate change may complicate the precipitation
responses to the GW use. Local terrestrial water storage was shown to be unsustainable at the current high
GWextraction rate. Thus, a balance between reducedGWwithdrawal and rapid economic developmentmust
be achieved in order to maintain a sustainable GW resource, especially in regions where GW is being
overexploited.
1. Introduction
With economic development and population growth,
water demands are rapidly increasing andwater resources
are becoming scarce inmany regions of the world (Rodell
and Famiglietti 2002; Rodell et al. 2009; Alvarez et al.
2012; Shi et al. 2013; Devic et al. 2014). Groundwater
(GW), because of its convenience for extraction and good
quality, is widely extracted to supplement human de-
mands of freshwater, especially in regions where surface
water (SW) supplies are limited (Liu et al. 2001). How-
ever, GW overextraction lowers GW tables, reduces total
terrestrial water storage, weakens hydraulic connections
between aquifers and rivers, and may even decrease lake
area (Coe and Foley 2001). Variations in a regional water
table can directly influence soil moisture content (Yuan
et al. 2008; Xie and Yuan 2010; Di et al. 2011; Xie et al.
2012). GW consumption, such as for irrigation, also has
been shown to increase local evapotranspiration and de-
crease the temperatures near the surface and in the lower
Corresponding author address: Dr. Zhenghui Xie, LASG, Institute
of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box
9804, Beijing 100029, China.
E-mail: [email protected]
Denotes Open Access content.
1 JANUARY 2017 ZENG ET AL . 71
DOI: 10.1175/JCLI-D-16-0209.1
� 2017 American Meteorological Society
troposphere by affecting soil moisture content (Yu et al.
2014; Zou et al. 2014). Increased water vapor resulting
from GW irrigation can induce local convection and fur-
ther alter atmospheric water balances (Haddeland et al.
2006; Lo and Famiglietti 2013). Irrigation using GW can
alter the carbon cycle and thewater cycle (Xie et al. 2014).
Therefore, continually overextracting a GW resource for
consumption not only reduces GW table levels but also
changes the regional, and even global, environment and
climate (Pokhrel et al. 2012b; Chen and Hu 2004).
Numerous studies have demonstrated the effects of
GW withdrawal and consumption on hydrological or
land surface processes. Most of these studies have used
hydrological models or land surface models to simulate
the effects of anthropogenic water management. For
example, Haddeland et al. (2006) incorporated an irri-
gation module into the Variable Infiltration Capacity
model and found that irrigation increases evapotranspi-
ration and decreases surface air temperature in the
Mekong and Colorado River basins. Hanasaki et al.
(2008) constructed a water resource model that consid-
ered GW withdrawal and anthropogenic reservoir oper-
ations. Ozdogan et al. (2010) incorporated crop
information from satellite remote sensing and an irriga-
tion scheme into a land surface model and found that
irrigation increases evapotranspiration by 12% and re-
duces surface sensible heat flux (SH) by a similar per-
centage in the growing season. Döll et al. (2012) applied awater use model [Water—A Global Assessment and
Prognosis (WaterGAP)] to study the impacts of GW
extraction on total terrestrial water storage; they found
that irrigation had a negative influence on local water
storage, especially in regions where the source of irriga-
tion water was dominated by GW. Pokhrel et al. (2012a)
incorporated an anthropogenic water management
scheme into the Minimal Advanced Treatments of Sur-
face Interaction and Runoff model and found that irri-
gation could cause a maximum increase of 50Wm22 in
latent heat flux (LH) in the summertime.Zou et al. (2015)
incorporated a GW extraction scheme into the Commu-
nity Land Model (CLM), version 3.5, to investigate the
effects of anthropogenic extraction of GW on land sur-
face processes in the Haihe River basin in northern
China; they found that GW extraction for human activi-
ties causes wetting and cooling at the land surface and
reduces GW storage. Pokhrel et al. (2015) presented an
integrated hydrologic model to explicitly simulate the
GWdynamics and pumping on a global scale and showed
that global GW withdrawal and depletion for year 2000
were 570 and 330km3yr21, respectively. However, these
studies only considered the hydrological effects of GW
use. They did not consider subsequent climatic effects of
GW pumping and consumption.
GW extraction and consumption can influence the at-
mosphere and climate through their effects on soil mois-
ture and surface heat fluxes (Chen and Hu 2004). In
regions with GW overextraction, soil moisture may de-
crease because of declining GW tables, even thoughmost
of the GW exploited is used for irrigation (Siebert et al.
2010), which should increase soil moisture. Until now,
only a few studies have investigated the climatic responses
to human GW management and all these studies have
been conducted on a regional scale. For example, Lo and
Famiglietti (2013) investigated the impacts of irrigation in
California’s Central Valley using a climate model driven
by a regional estimate of agricultural water use supplied
by surface and subsurface sources. Zou et al. (2014)
incorporated a GW extraction scheme into the Regional
ClimateModel, version 4 (RegCM4), for theHaihe River
basin in northern China; they revealed that GW extrac-
tion andwater consumption causedwetting and cooling in
the lower troposphere and increased precipitation.
In these regional investigations, nonlocal effects of
GW extraction were not fully considered. This study
aims to investigate both the hydrologic and climatic re-
sponses on a global scale to GWmanagement, including
GW pumping, GW irrigation, and other activities re-
lated to industrial and domestic GW use.
In section 2, we first describe the Earth system model
CESM1.2.0 and the GW extraction scheme we have
developed and incorporated into CESM1.2.0. The data
used to estimate the water supply and demand and the
model simulations are described in section 3. The results
from the simulations are discussed in section 4. Con-
clusions and discussion are presented in section 5.
2. Model development
a. The Community Earth SystemModel, version 1.2.0
The Community Earth System Model, version 1.2.0
(CESM1.2.0; Hurrell et al. 2013), consists of atmo-
sphere, land, ocean, and sea ice components linked by a
coupler that communicates among these components
and merges and regrinds fields as needed. The
CESM1.2.0 model was developed from the Community
Climate System Model, version 4 (CCSM4; Gent et al.
2011). The atmospheric component is the Community
Atmosphere Model, version 4 (CAM4; Neale et al.
2010), which applies the Lin–Rood finite-volume dy-
namical core (Lin 2004) with an atmospheric grid spac-
ing of 0.98 3 1.258 horizontally and 26 levels in the
vertical direction arranged in a hybrid pressure sigma
coordinate (Neale et al. 2013). The ocean component is
the Parallel Ocean Program (POP; Jones et al. 2005)
from the Los Alamos National Laboratory (LANL).
72 JOURNAL OF CL IMATE VOLUME 30
The sea ice component is an extension of the LANL sea
ice model (Hunke and Lipscomb 2008). The land com-
ponent is the Community Land Model, version 4.5
(CLM4.5; Oleson et al. 2013), which simulates bio-
geophysical exchange of radiation, LH, and SH; mo-
mentum between land and atmosphere as modified by
vegetation and soil; heat transfer in soil and snow; and
the hydrologic processes and snow dynamics (Lindsay
et al. 2014). To represent the heterogeneity within a
model grid, CLM4.5 applies a nested subgrid hierarchy
in which each grid cell is composed of multiple land
units, snow and soil columns, and plant function types
(PFTs). This allows different land covers, such as vari-
eties of vegetation and crops, as well as land unit types
(lake, urban, and glacier), to be treated differently
depending on their own biogeophysical and bio-
geochemical processes, even when they are coexist in
the same model grid box. More information about the
subgrid structure of CLM4.5 can be found in Oleson
et al. (2013).
b. Scheme for GW extraction and its implementationinto CESM1.2.0
The GW extraction and consumption scheme de-
veloped by Zou et al. (2014) was incorporated into
CESM1.2.0 by integrating it into the land component (i.e.,
the land surface model CLM4.5) of CESM1.2.0, as shown
in Fig. 1. The resultingmodel (called CESM1.2_GW)was
then used to investigate the effects of anthropogenic GW
withdrawal and use. As shown in Fig. 1, GW is pumped
FIG. 1. Framework of simulated human-induced water resource extraction and consumption
processes and their incorporation into the CLM4.5 and CESM1.2.0 models.
1 JANUARY 2017 ZENG ET AL . 73
from an aquifer and apportioned among agricultural, in-
dustrial and domestic uses. Water consumed by agriculture
is considered as ‘‘effective precipitation’’ reaching the soil
surface. The industrial and domestic consumptions have
two components: 1) the waste water produced by industry
and human daily life, which is treated as a discharge into
local rivers and 2) the net water consumption, which is
treated as evaporation to the atmosphere.
The aquifer recharge rate is updated by subtracting
the GW extraction rate from the original aquifer re-
charge rate, which can be expressed as
Qaqu_new
5Qaqu_ori
2Qg, (1)
where Qaqu_ori (mms21) is the original aquifer recharge
rate calculated by CLM4.5, Qg (mms21) is the GW ex-
traction rate (see section 2c), and Qaqu_new (mms21) is
the new aquifer recharge rate after considering anthro-
pogenic GW pumping, which is used by CLM4.5 for the
next groundwater table calculations.
The irrigation consumption supported by GW is
expressed in the model using
Qtopsoil_new
5Qtopsoil_ori
1 ragrQ
g, (2)
whereQtopsoil_ori (mms21) is the original net water input
into the soil surface simulated by CLM4.5, ragr is the
ratio of agricultural consumption rate to the total Qg
(see section 2c), andQtopsoil_new (mms21) is the new net
water input into the soil surface after consideration of
the GW irrigation effect, which is used by CLM4.5 in the
next surface runoff and infiltration calculation.
The waste water produced by industrial and domestic
water uses is described as
Qrunoff_new
5Qrunoff_ori
1a(rind
1 rdom
)Qg, (3)
where Qrunoff_ori (mms21) is the original surface runoff
calculated by CLM4.5, rind and rdom (see section 2c) are,
respectively, the ratios of industrial and domestic con-
sumption rates to the totalQg, a is the waste water ratio,
and Qrunoff_new (mms21) is the surface runoff revised to
include waste water.
Except for waste water, all other components of in-
dustrial and domestic GW consumption go into the at-
mosphere as described by
Qevp_new
5Qevp_ori
1 (12a)(rind
1 rdom
)Qg, (4)
where Qevp_ori (mms21) is the original total evapotrans-
piration flux, which is the sum of vegetation transpiration
and evaporation from soil, leaves, and stems, as simulated
by CLM4.5. The variable Qevp_new (mms21) is the new
total evapotranspiration flux increased by the industrial
and domestic net consumption;Qevp_new is transferred to
the coupler and then to CAM4, which calculates the ef-
fect of this variable on the atmospheric condition.
As shown in Fig. 1, based on the structure of coupling,
other variables in CLM4.5 are influenced by Qaqu_new
andQtopsoil_new, each of which is affected directly byGW
extraction and consumption. The atmospheric variables
in CAM4 are changed byQevp_new. In addition, changes
in both the land and atmosphere subsequently affect
each other through land–atmosphere coupling.
c. Estimation of GW withdrawal
Before starting a CESM1.2_GWmodel simulation, the
parameters Qg, ragr, rind, rdom, and a must be estimated
using historical GW withdrawal and consumption data.
Three sources of data were combined to estimate GW
withdrawal and application. The first data source was the
Food andAgricultureOrganization of theUnitedNations
(FAO) global water information system, developed by the
Land and Water Division of the FAO (http://www.fao.
org/nr/water/aquastat/main/index.stm). This dataset con-
tained regional water withdrawal data and expressed ag-
ricultural, industrial, and municipal water withdrawals as
percentages of the totalwithdrawal. TheFAOdataset also
gave the size of area equipped for irrigation in 1970, 1990,
and 2009 and it estimated the global water withdrawal by
agriculture (in the period around 2003) to be 2710km3yr21.
The second dataset was the Global Map of Irrigation
Areas, version 5.0 (GMIA5; Siebert et al. 2005, 2013),
which uses a raster format having a grid resolution of 50 toshow the size of area equipped for irrigation in each grid,
and the size of area that is irrigated usingGW in each grid.
The third dataset consisted of historical monthly soil
moisture and saturated soil moisture simulated by
CLM4.5 offline using the atmospheric forcing dataset
described in Qian et al. (2006) for years 1965–2000.
The methodology used to combine these data was as
follows. First, the total amount of global water with-
drawal by agriculture around 2003, provided by the
FAO dataset, was allocated to every model grid cell
(0.98 3 1.258) and weighted by the agricultural area of
each grid cell (as provided by GMIA5) as well as by the
soil water deficit (the difference between climatological
saturated soil moisture and historical soil moisture, both
of which were provided by the CLM4.5 offline simula-
tion), as described by
Qagr(i, j)5Q
agr_glob
Airr(i, j)W
def(i, j)
�i, jA
irr(i, j)W
def(i, j)
, (5)
where i and j are the indices for longitude and latitude,
respectively, of the model grid, Qagr(i, j) (m3) is the ag-
ricultural water withdrawal both from surface and
74 JOURNAL OF CL IMATE VOLUME 30
subsurface at the grid point (i, j) in 2003,Qagr_glob (m3) is
the global agricultural water withdrawal in 2003 provided
by FAO, Airr(i, j) (m2) is the size of area equipped for
irrigation on the grid point (i, j) provided byGMIA5, and
Wdef(i, j) (m3m23) is the soil water deficit calculated from
the CLM4.5 offline simulation. The soil water deficit here
indicates the irrigation intensity, which should be stron-
ger over drier soil and weaker over wetter soil. Then, the
agricultural water use supplied by the GW in each grid
point in 2003 [Qagr_gw(i, j) (m3)] was calculated using the
GW irrigation data provided by GMIA5 as
Qagr_gw
(i, j)5Qagr(i, j) 3 f
gw(i, j), (6)
where fgw(i, j) is the fraction of irrigated area fed by GW
at the grid point (i, j), which was estimated by GMIA5.
Next, using the regional dataset about municipal, in-
dustrial, and agricultural water consumption provided by
the FAO, the GW used by these three sectors, as well as
the totalGWwithdrawal (around 2003), were calculated as
Qtot_gw
(i, j)5Qagr_gw
(i, j)/fagr(i, j), (7)
Qind_gw
(i, j)5Qtot_gw
(i, j) 3 find(i, j), and (8)
Qlive_gw
(i, j)5Qtot_gw
(i, j) 3 flive
(i, j), (9)
where Qtot_gw(i, j) (m3), Qind_gw(i, j) (m3), and
Qlive_gw(i, j) (m3) are the total, industrial, and do-
mestic GW consumption at the grid point (i, j), re-
spectively, and fagr(i, j), find(i, j), and flive(i, j) are,
respectively, the fractions of the GW consumption by
agricultural, industrial, and domestic water use. These
fractions were calculated by combining the FAO-
provided fractions of the total water consumption (from
both GW and SW) for agricultural, industrial, and do-
mestic water use fagr0(i, j), find0(i, j), and flive
0(i, j), and the
global values evaluated by Döll et al. (2012) that GW
contributes 42%, 27%, and 36% of water used for irri-
gation, manufacturing, and households, respectively, as:
8>>>>>>>>>><>>>>>>>>>>:
fagr(i, j)5
0:42f 0agr(i, j)
0:42f 0agr(i, j)1 0:27f 0ind(i, j)1 0:36f 0live(i, j)
find(i, j)5
0:27f 0ind(i, j)0:42f 0agr(i, j)1 0:27f 0ind(i, j)1 0:36f 0live(i, j)
flive
(i, j)50:36f 0live(i, j)
0:42f 0agr(i, j)1 0:27f 0ind(i, j)1 0:36f 0live(i, j)
.
(10)
Figure 2a shows the resulting global spatial patterns of
Qtot_gw around year 2003. As shown in Fig. 2a, the three
largest contiguous areas with high GW extraction rates
are located in northern India and Pakistan, the north
China plain, and the central United States. Most areas in
these regions were suffering a GW withdrawal rate of
more than 90mmyr21 (around 2003). Besides, a large
number of scattered areas with severe GW extraction
(more than 90mmyr21) are seen in northern Italy, parts
of western Europe, parts of the Euphrates–Tigris basin
and southern Iran, northwestern China, eastern coastal
Australia, and the west coastal area of Florida in the
United States. In other areas across the world, such as
southern China, southern and eastern Brazil, eastern and
western Europe, South Africa, and across the entire
United States, GW extraction is significant, with a rate
of a few to tens of millimeters per year. These results are
corroborated by many recent studies (Khan et al. 2008;
Siebert et al. 2010; Döll et al. 2012; Leng et al. 2013;
Shi et al. 2013; Leng et al. 2014; Zou et al. 2014).
To estimate annual data, the amount of GW extrac-
tion was assumed to change linearly from 1970 to 1990
and from 1990 to 2009. The linear regression coefficients
were calculated for these two time periods using the
regional component of the FAO water use dataset,
which provides data only for 1970, 1990, and 2009. We
also extrapolated the groundwater withdrawal data
forward to 1965 using the same regression coefficient of
1970–90.With these coefficients and theGWwithdrawal
data for approximately 2003, the GW pumping rate in
every year from 1965 to 2005 was estimated using linear
interpolation. We recognized that the linear in-
terpolation may result in large uncertainties because the
water consumption and GW pumping rate in a certain
year depend on the water availability (such as pre-
cipitation) and the climate of that year. However, long-
term changes in annual GW pumping rates, which are
the focus of this study, aremore steadily increased by the
population growth and socioeconomic development
than by climatic variations, as demonstrated by many
previous studies (Llamas 1988; Liu et al. 2001; Omole
2013; Wu et al. 2014; Zou et al. 2015). Because annual
data for GW withdrawal were unavailable on a global
scale, the linear interpolation based on the scattered years
of available with GW withdrawal data provides a rea-
sonable and convenient way to represent the long-term
change inGWpumping rates from 1965 to 2005. Figure 2b
shows the time series of interpolated annualGWpumping
rate in the central United States, the north China plain,
and northern India and Pakistan, where GW extraction
was largest in the world (Fig. 2a). It is clear that the ex-
traction rate increases at a similar pace before and after
1990, further confirming the linearity of the change rate.
In reality, the seasonal allocation of annual water con-
sumption is very complex. Seasonal water consumption
depends on local climate, phenology of crops, economy,
1 JANUARY 2017 ZENG ET AL . 75
policy, and other natural and social conditions.Accurately
depicting seasonal water consumption on a global scale is
an extremely difficult task. Here, we will focus only on the
long-term annual-mean effects of GW extraction (rather
than the seasonal variations) to reduce the large un-
certainties from intra-annual allocation of the annual
water consumption. For this purpose, the GW consump-
tion for industrial and domestic water uses was simply
distributed evenly to every model time step, and the GW
consumption for agriculturewas evenly distributed only to
model time steps in the spring and summer (March–May
and June–August, respectively, for the Northern Hemi-
sphere, and September–November and December–
February, respectively, for the Southern Hemisphere)
when irrigation is dominated. Zou et al. (2014) showed
that on the north China plain the annual effects of GW
extraction were similar regardless of whether an even
allocation or a statistically based allocation was applied.
Thus, our simple allocation scheme should be acceptable
for studying the annual effects of GW withdrawal.
In this study, a (the waste water ratio) was set as 30%
based on Mao et al. (2000). The remaining 70% of in-
dustrial and domestic GW withdrawal includes not only
the amount of water actually consumed by factories and
residential communities, but also the amount of water
dissipated when waste water is discharged into streams.
3. Experimental design
To investigate the impacts of human GW extraction
on the land surface and climate, two sets of numerical
experiments were conducted using the original CESM1.2.0
(referred to as CTL) and using CESM1.2_GW coupled
with aGWwithdrawal and consumption scheme (referred
FIG. 2. (a) Global groundwater extraction rate around year 2003 and (b) the area-averaged ex-
traction rate in the central United States, the north China plain, and northern India and Pakistan
from1965 to 2005.As designated by the three boxes in (a), the centralUnited States is defined as the
region within 338–428N, 978–1058W; the north China plain is defined as the region within 348–408N,
1108–1208E; and northern India and Pakistan is defined as the region within 238–338N, 688–788E.
76 JOURNAL OF CL IMATE VOLUME 30
to as EXP). Thus, the CTL runs do not include the GW
extraction whereas the EXP runs do include it, and the
EXP-minus-CTL difference would provide a measure of
the impact of the GW extraction as simulated by the
CESM1.2_GW model. In these experiments, only the at-
mospheric (CAM4) and land (CLM4.5) components were
integrated, while the ocean surface conditions were pre-
scribed using monthly observations of sea surface tem-
peratures (SSTs) and sea ice concentration from the
HadISST dataset (Rayner et al. 2003). Each of the CTL
and EXP experiments contained six ensemble simulations
using different initial conditions. The ensemble averages
used here reduce the internal noise and enhance the forced
signal caused by the GW extraction. All simulations were
conducted with horizontal spacing of 0.98 3 1.258 and a
time step of 30min for both the CAM4 and CLM4.5. The
simulations were conducted for a 41-yr duration from 1965
to 2005 (using the period of 1965–69 for spinup), during
which time-varying data for GW extraction and other ex-
ternal drivers were used. We used the default external
forcing settings for CAM4 and CLM4.5 as described in
Neale et al. (2010) and Oleson et al. (2013), respectively.
These forcing data, such as greenhouse gas concentrations,
aerosol and ozone concentrations, solar irradiance, volca-
nic activity, and land use data were estimated from ob-
servations or remote sensing.
Because of internal variability, model results were
anticipated to be sensitive to initial conditions. One way
to reduce the sensitivity is to conduct ensemble simu-
lations using different initial conditions and then aver-
age the results over the ensemble of simulations (Koster
et al. 2002, 2006). In the current study, six individual
simulations, typically differing only in their initial at-
mospheric and land surface conditions, comprised each
ensemble (CTL or EXP). The method described by
Koster et al. (2006) was used to generate the six sets of
different ‘‘restart files,’’ which were obtained from a pre-
existing 70-yr 1850 control run (which was started from
another 500-yr 1850 control run by the CESM1.2.0)
with a separation time of 10 yr between the restart
conditions. Simulations with corresponding ensemble
members in EXP and CTL were started from the same
restart files; thus, EXP number 1 and CTL number 1
shared the same initial conditions. Ensemble averages of
the simulations for the years from 1970 to 2005 were
analyzed to study the effects of GW extraction.
4. Results
a. Validation of CESM1.2_GW
Key to validating the simulation ability of the
CESM1.2_GW model was verifying whether it could
reproduce observed long-term changes in the water
table induced by GW withdrawal. Unfortunately, the
worldwide data on GW table depth, especially time se-
ries data, were scarce. However, because there are only
three dominant contiguous areas in the world that are
experiencing severe GW extraction, we will focus on
validating the model’s ability to accurately represent the
GW table trends in these three areas (i.e., the north
China plain, northern India and Pakistan, and the cen-
tral United States; the outlined areas in Fig. 2a). The
north China plain has been studied by many researchers
because of its GW overextraction (Liu et al. 2010; Zou
et al. 2014). A dataset of GW table variations from 1991
to 2000 for 173 observation wells in the basin was pro-
vided by the Ministry of Water Resources and the In-
stitute forGeo-environmentalMonitoring of China. The
central United States has also suffered from over-
extraction of GW, as shown by recent studies (Döll et al.2012); water table data from this region were abundant,
and were provided by United States Geological Survey.
Data from 163 observation wells in the central United
States for 1965–2005 were used for validation purposes
here. Northern India and Pakistan experienced water
storage declines as shown by National Aeronautics and
Space Administration’s (NASA) Gravity Recovery and
Climate Experiment (GRACE) satellites (Tapley et al.
2004; Rodell et al. 2009; Tiwari et al. 2009). Neverthe-
less, GW table data in this region are unavailable. Thus,
we compared the model-simulated water storage with
the GRACE observations of total water storage varia-
tions for northern India and Pakistan. Figure 3 shows the
time series of simulated annual area-averagedGW table
depth against the observations in the central United
States and the north China plain, and the simulated
annual total water storage changes in northern India and
Pakistan against GRACE satellite observations.
Figure 3a indicates that although the long-term mean
values of the simulated GW table in the central United
States were significantly different from the observations,
the EXP ensemble of model simulations, which showed
downward trends of GW table with 0.32myr21, gener-
ally reproduced (although with a slight overestimation)
the observed downward trends (0.2myr21), while the
CTL ensemble did not shown any trends in the GW
table. Considering that the GW module in CESM is
relatively simple (it does not contain any expression of
GW lateral flow and bedrock depth distribution) and
that the aim of this study is to investigate the climate
change caused by GW extraction, it is not surprising to
see large mean biases in the simulated water table in
Fig. 3a. The overestimated GW table downward trends
of the EXP ensemble were also shown for the north
China plain (Fig. 3b), while the EXP ensemble showed
downward trends of 0.16myr21, which were significantly
1 JANUARY 2017 ZENG ET AL . 77
higher than the value of 0.09myr21 shown in observa-
tions. Figure 3c shows that the EXP simulations also
predicted the decreasing trend (20.62 cmyr21) in total
terrestrial water storage in northern India and Pakistan,
although the predictions were somewhat greater than
indicated by the GRACE satellite observations
(20.50 cmyr21). In the CTL ensemble, there was almost
no any trend for the terrestrial water storage
(only 20.02 cmyr21). The ability of the CESM1.2_GW
model in reproducing the observed declining trends of
the GW table and water storage in key geographic re-
gions provides confidence about the GW extraction
module for applying it to study the effects of GW ex-
traction on global climate.
b. Differences in spatial distribution of hydrologicvariables
Figure 4 shows the multiyear (1970–2005) mean spa-
tial distributions of the EXP-minus-CTL difference for
the GW table depth, top 10-cm soil moisture content,
runoff, total water storage, LH, and SH. The ensemble-
averaged results in Fig. 4a show that contiguous areas
with significant GW level declines were found in the
central United States, in parts of the north China plain,
and in northern India and Pakistan. Compared with
changes predicted by the CTL simulations, the GW ta-
bles in most areas declined more than 4m when aver-
aged from 1970 to 2005. These results correspond with
those of Zou et al. (2014) for the north China plain,
Sophocleous (2005) for the central United States, and
Kumar and Singh (2008) for northern India. Overall, the
water table depth pattern described by the difference in
EXP and CTL simulations was similar to the pattern of
GW extraction shown in Fig. 2a.
Figure 4b shows that the contiguous areas with sta-
tistically significant increases in surface soil moisture
were located in northern India along with Ganges River,
in theHuaiheRiver basin in the northChina plain, in the
Mississippi River basin in the central United States, and
in a large area of high-latitude regions in the Northern
Hemisphere. The first three areas correspond with the
distribution of GW extraction shown in Fig. 2a, implying
that the increased soil moisture at these three regions
was caused by GW irrigation, as shown in Fig. 1. How-
ever, the soil moisture increase in the central United
States was relatively weaker than in northern India and
China. This is because, according to the FAO’s statistics,
91% and 64% of GW extraction was consumed by ag-
riculture in India and China, respectively, whereas in the
United States only 38% of extracted GW was used by
agriculture. Areas that were extensively irrigated (north
of India and China) would be expected to have higher
soil moisture contents than areas with less irrigation
(central United States). There were large areas of high-
latitude regions in the Northern Hemisphere also
showing a high content of soil water even though GW
consumption by agriculture here could be negligible. This
might be caused by the mixed effects of atmospheric in-
ternal variability and land–atmosphere interactions (in-
ternal noise). Although the increases of 10-cm soil
moisture passed the significance tests over some area of
the north China plain, northern India and Pakistan, and
central United States, in fact the magnitude of changes
was relatively small (within 0.04m3m23 in China and
FIG. 3. Time series of simulated annual area-averaged groundwater table depth against observations in (a) the
central United States and (b) the north China plain, and (c) the simulated annual total water storage anomalies of
northern India and Pakistan against GRACE satellite observations.
78 JOURNAL OF CL IMATE VOLUME 30
FIG. 4. Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for
annual-mean (a) GW table depth (m), (b) 10-cm soil moisture (m3m23), (c) runoff (mmyr21), (d) terrestrial water
storage (cm), (e) latent heat flux (Wm22), and (f) sensible heat flux (Wm22). The stippled areas indicate the
regions where the difference passed the 95% confidence level in the Student’s t test.
1 JANUARY 2017 ZENG ET AL . 79
FIG. 5. Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for
annual-mean (a) 850-, (b) 500-, and (c) 200-hPa level temperature (8C) and (d) 850-, (e) 500-, and (f) 200-hPa level
geopotential height (m). The stippled areas indicate the regions where the difference passed the 95% confidence
level in the Student’s t test.
80 JOURNAL OF CL IMATE VOLUME 30
FIG. 6. Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for
annual-mean JJA (a) precipitation (mmday21); (b) 850-, (c)500-, and (d) 200-hPa level wind field (m s21); and
(e) horizontal vapor flux (kgm21 s21). The gray shading indicates the regions where the magnitude of the vector
difference passed the 95% confidence level in the Student’s t test.
1 JANUARY 2017 ZENG ET AL . 81
India and within 0.02m3m23 in the United States). Fur-
thermore, at a deeper range of soil, such as to 50- or 100-cm
depth, the increases in soil moisture will be further
weakened because the wetting effects of irrigation de-
crease rapidly with increasing soil depth (a relationship
that will be highlighted in section 4d).
Figure 4c shows the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL difference
for the annual runoff. Areas of the central United States
and the north China plain that were shown to be suf-
fering from severe GW extraction also showed increases
in runoff; however, runoff decreased significantly in
northern India even though the GW extraction was also
rigorous. The increased runoff in the United States and
China can be explained by Eqs. (2) and (3), in which the
waste water produced by industrial and domestic water
use was directly converted to runoff in the model,
whereas the irrigation water also added the runoff by
increasing the amount of water reaching the topsoil. The
decreased runoff in India may mainly be a consequence
of decreased precipitation due to regional GW with-
drawal and consumption (as discussed later in section 4c).
In other regions across the world, there were also a large
number of areas showing visible runoff differences due to
precipitation change, but most of themwere the results of
internal variability and failed to pass the Student’s t test.
Figure 4d shows the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL difference
for the total terrestrial water storage. A comparison of
Fig. 4d with Fig. 4a shows that changes in terrestrial
water storage had a similar pattern as changes in the
water table depth. The mechanism for this water storage
reduction is simple: GW extraction was a process of
transferring water from underground reserves to the
land surface. The water on the land surface was then
consumed by evapotranspiration and sent to the
FIG. 7. Vertical profiles of ensemble-averaged EXP-minus-CTL differences for annual-mean (a) soil moisture
(m3m23), (b) specific humidity (1021 g kg21), and (c) temperature (8C) in the centralUnited States, the northChina
plain, and northern India and Pakistan.
82 JOURNAL OF CL IMATE VOLUME 30
FIG. 8. Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation
years for annual-mean (a) latent heat flux (Wm22), (b) sensible heat flux (Wm22), and 850-hPa level (c) temperature
(8C) and (d) geopotential height (m). The stippled areas indicate the regions where the difference passed the 95%
confidence level in the Student’s t test.
1 JANUARY 2017 ZENG ET AL . 83
atmosphere. Through horizontal wind advection, part of
the lifted water vapor was transported to other places
and a local water deficit occurred. Figure 4d reveals that
the regions that were experiencing serious GW over-
extraction (especially the central United States, the
north China plain, and northern India) were indeed
suffering losses of water resources. These results are
supported by Rodell et al. (2009) and Döll et al. (2012).Figures 4e and 4f show the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL differences for
the LH and SH, respectively. The three contiguous over-
extraction areas (the central United States, the north China
plain, and northern India and Pakistan) were also identified
to be experiencing significant LH increases (5–20Wm22)
and SH decreases (2–10Wm22). According to Dirmeyer
et al. (2013), the evapotranspiration in these three places is
restricted more by water availability than by temperature;
thus, when a greater supply of water was available, more
evaporation and stronger LH would occur, and with more
energy taken away from land by LH, the SH would sub-
sequently decrease.
c. Differences in spatial distribution of atmosphericvariables
As noted earlier, the effects of GW withdrawal and
consumption on land can affect climate through flux
FIG. 9. Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation
years for annual-mean JJA (a) precipitation (mmday21), (b) 850-hPa level wind field (m s21), and (c) horizontal vapor
flux (kgm21 s21). The gray shading indicates the regions where the magnitude of the vector difference passed the 95%
confidence level in the Student’s t test.
84 JOURNAL OF CL IMATE VOLUME 30
exchange between the land surface and the atmosphere.
Figures 5a–c show the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL differences
for the air temperature at 850-, 500-, and 200-hPa levels.
From Fig. 5a, at the 850-hPa level, two contiguous areas
experiencing a significant cooling effect of approxi-
mately 0.38C were obvious. One of these areas was lo-
cated in northern India and Pakistan; the other area was
northern China plus a large area of central Russia. A
comparison of Figs. 5a and 2a indicates that the cooling
effect was caused by the GW irrigation. However, in
northern China and central Russia, the size of the area
experiencing lower temperature was much larger than
the area experiencing severe GW extraction. This situ-
ation was probably induced by the advection of air be-
cause in the irrigation seasons the prevailing wind of this
region at the 850-hPa level is from south to north (Atlas
et al. 1996; Young 1999). Thus, the prevailing wind takes
the cooling air farther north and reduces the ambient
temperature of the area north of the main sites of irriga-
tion. Such a process is a clear example of how irrigation
could affect the climate outside the local placewhere it was
used, a scenario that could not be simulated by an offline
model (Döll et al. 2012). This type of interaction stresses
the necessity of using a global scale land–atmosphere
coupling model to study GW extraction effects. There
was also a contiguous area, the Tibetan Plateau in eastern
China bordering northern India, experiencing obviously
higher temperature (more than 0.48C) at the 850-hPa level.This temperature effect might be caused by the irrigation-
induced thermal circulation and will be discussed later. At
the 500- and 200-hPa levels (Figs. 5b and 5c, respectively),
the cooling effects were almost invisible, indicating that the
impacts of GWuse on temperature weremore apparent in
the lower atmosphere than in the upper atmosphere.
Figures 5d–f show the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL differences
for the geopotential height at 850, 500, and 200 hPa.
FromFig. 5d, besides the internal noise in theArctic and
Antarctic, the Tibetan Plateau in China experienced
lower geopotential height at the 850-hPa level, which
passed the Student’s t test. This might be caused by the
cooling effects of irrigation in northern India (Fig. 5a),
which increased the air density and lifted the local ge-
opotential height. The added air in northern India
originated from its east side, so the geopotential height
in the Tibetan Plateau decreased, forming thermal cir-
culation between northern India and the Tibetan Plateau.
The thermal circulation transported the heat from
northern India to the Tibetan Plateau and increased the
temperature in theTibetanPlateau (Fig. 5a).At the 200-hPa
level of the upper atmosphere, the geopotential height
in the Tibetan Plateau increased, demonstrating the
existence of the irrigation-induced thermal circulation
(Fig. 5c). In other regions across the world, there was
no difference in geopotential height that was statisti-
cally significant according to the Student’s t test.
Figure 6a shows the multiyear (1970–2005) mean
spatial distributions of the EXP-minus-CTL differences
for June–August (JJA) precipitation. The pattern is in-
teresting. For GW overextracted regions of the north
China plain and northern India, the former experienced
increased precipitation while the precipitation in the
latter decreased. The contrasting effects of GW extrac-
tion on precipitation might come from the different
precipitation generating mechanisms of these two
regions. According to recent studies, northern China
belongs to areas that have a strong summer land–
atmosphere coupling interaction (Dirmeyer et al. 2013;
Zeng and Xie 2015), meaning that the precipitation
changes in the region are significantly affected by the
local soil moisture changes. Thus, with a wetter surface
soil (Fig. 4b), the north China plain received more pre-
cipitation. To understand the precipitation decrease in
northern India, we plotted the EXP-minus-CTL differ-
ences for the JJA wind field at the 850-, 500-, and
200-hPa levels in Figs. 6b–d. Figure 6b indicates that, at
the 850-hPa level, a wind anomaly from the Indian
subcontinent to the Arabian Sea is induced by GW ir-
rigation, the magnitude of which passed the Student’s t
test. In the upper level at 200hPa, the wind anomaly
reversed in direction from the sea to the continent, in-
dicating that a thermal circulation was produced by the
GW irrigation between the Arabian Sea and the Indian
subcontinent. Because most of the water vapor was lo-
cated in the lower atmosphere, the irrigation-induced
thermal circulation might have obstructed the water
vapor flowing from the Arabian Sea to the northern
Indian subcontinent in monsoon season. To demon-
strate this, we mapped the EXP-minus-CTL differences
for JJA horizontal vapor flux integrated from the 850- to
200-hPa level; the result is shown in Fig. 6e. The horizontal
vapor flux was calculated as:
DF5D
ðz2z1
qvdz , (11)
where F (kgm21 s21) is the flux of vertically accumu-
lated horizontal vapor; z1 (m) and z2 (m) are the heights
of the 850- and 200-hPa pressure surfaces, respectively;
q (kgm23) is the specific humidity in each layer; v (m s21)
is the wind vector in each layer; dz (m) is the thickness
of each layer; and D represents the difference between
the EXP and CTL ensemble simulations. As shown
in Fig. 6e, corresponding to the wind anomaly at the
850-hPa level, a water vapor flow anomaly was induced
1 JANUARY 2017 ZENG ET AL . 85
from the Indian subcontinent to the Arabian Sea in
summer, indicating that the vapor flow from sea to land
in the monsoon season was obstructed, and the JJA
precipitation in northern India was subsequently re-
duced. The vapor flow anomaly might be induced by the
cooling effects over northern India because the mon-
soon was forced by the land–ocean temperature differ-
ences. Besides, in India the vegetation also played an
important role. Lee et al. (2009) showed that the
anomalies in the normalized difference vegetation index
(NDVI) have increased over the Indian subcontinent as
the irrigated area has increased. Consequently the
evapotranspiration is increased and takes more heat
away from ground due to the irrigation and increased
NDVI. As a result, the surface temperature in July is
cooled, leading to a reduction of land–sea thermal con-
trast, which is a key factor driving the Indian monsoon.
Therefore, the monsoon circulation is weakened and
rainfall is reduced as both irrigation and the area cov-
ered by vegetation increased. It should be noticed that
the magnitude of precipitation change by GW use was
small although it could be statistically detected. Xie and
Arkin (1997) found that the precipitation rate of the
north China plain and northern India can exceed 500 and
1000mmyr21, respectively, while the magnitude of
change in our research is restricted to only 50mmyr21.
Our findings correspond with several previous studies
about the precipitation feedback to irrigation on a re-
gional scale (Barnston and Schickedanz 1984; DeAngelis
et al. 2010; Lo and Famiglietti 2013; Zou et al. 2015).
d. Differences in the vertical profiles
In addition to the mean spatial distributions, the mean
vertical profiles of EXP-minus-CTL differences for soil
moisture in the central United States, the north China
plain, and northern India and Pakistan (where GW ex-
traction was the most severe in the world) are shown in
Fig. 7a. As indicated in Fig. 7a, the effects of GW use on
soil moisture were different for the top of the soil profile
and deeper soil layers. The topsoil became wetter and
the deep soil became drier in all three regions. The
wetting effects on topsoil were caused by GW irrigation.
In contrast to the topsoil, the deep soil became drier
because of the declining GW table that results from
severe extraction. The vertical profiles of EXP-minus-
CTL differences for air temperature and specific hu-
midity of the three regions are shown in Figs. 7b and 7c,
respectively. As shown in Fig. 7b, the effects of GW
extraction on increased moisture in the lower tropo-
sphere appeared in all three regions. The moisture
effects in surface air were mainly induced by the
wetter topsoil in these areas compared to nonirrigated
areas. However, even in India where the world’s highest
consumption of GW by irrigation occurred, the en-
hanced water vapor content at the ground surface was
no more than 0.12 g kg21; compared with the climatol-
ogy value of nearly 70 g kg21, the effects were nearly
negligible. As shown in Fig. 7c, the cooling effects were
transmitted to the lower troposphere. The cooling ef-
fects decreased as height above the ground surface in-
creased. In the north China plain, where the cooling
effects were most significant, the decreased temperature
was nearly 0.38C.
e. Climatic responses to GW extraction excludingbackground climate change
The former runs of CTL and EXP ensembles used the
observed SSTs, atmospheric CO2 concentration, land
cover, and other sets of annually varying data from 1965
to 2000 as the external forcing of the simulations. Their
results reflected the effects of actual GW extraction.
However, on the other hand, the year-to-year varied
SSTs (as well as other forcing variables) complicated the
climatic response to GW extraction, making the results
not just related to GW forcing. To isolate the effects of
GW extraction, we conducted another two ensemble
simulations called CTL2 and EXP2with and without the
anthropogenic GWextraction, respectively. For the new
simulations, the SSTs, atmospheric CO2 concentration,
land cover, and other forcing datasets were fixed to the
climatological monthly values for year 2000, and the
GW extraction forcing values were fixed at 2003 levels.
As for the former CTL andEXP simulations, each of the
new ensembles contained six runs with different initial
conditions. All the runs were conducted for 15 yr (the
first 5 yr for spinup), cyclically using the forcing data.We
applied the ensemble-mean EXP2-minus-CTL2 differ-
ences averaged over the last 10 yr to investigate the
climatic responses to GW extraction excluding the year-
to-year background climate change.
Figure 8 shows the annual-mean spatial distributions
of the EXP2-minus-CTL2 differences for the land–
atmosphere LH, SH, and the air temperature and geo-
potential height at 850 hPa, which were shown in the
former CTL and EXP simulations to be significantly
modified by GW use. The effects of GW extraction on
LH and SH shown in Figs. 8a and 8b were the same as in
Figs. 4e and 4f, demonstrating that the background cli-
mate change did not greatly affect the irrigation-induced
LH and SH changes. However, Figs. 8c and 8d reveal
that the magnitudes of EXP2-minus-CTL2 differences
for temperature and geopotential height were much
higher than that they were in the EXP-minus-CTL dif-
ferences (Figs. 5a and 5d), although their spatial patterns
were similar. This result indicates that the mechanism
controlling how the GW use impacted the temperature
86 JOURNAL OF CL IMATE VOLUME 30
and geopotential height was the same in both cases
(including background climate change or not), but the
background climate change might weaken the magni-
tude of the impacts induced by GW extraction.
Figure 9 shows the annual-mean spatial distributions
of the EXP2-minus-CTL2 differences for the pre-
cipitation, 850-hPa level wind field and horizontal vapor
flux (all in JJA). Comparison of Fig. 9a with Fig. 6a
shows that northern India still experienced a pre-
cipitation decrease due toGWuse when the background
climate changes were removed. However, in the north
China plain, the expected precipitation increase (which
was shown in Fig. 6a) disappeared. The explanations for
this are shown in Figs. 9b,c. In the new runs, GW use
induced a cyclone anomaly at the 850-hPa level around
the north China plain, and the cyclone anomaly weak-
ened the water vapor flowing from the Yellow Sea and
East China Sea to the China mainland. This weaker
vapor flow offset the irrigation-increased evapotranspi-
ration, cancelling significant change demonstrated in
earlier runs for the JJA precipitation in the north China
plain. Additionally, in the new runs, the mechanism for
the decrease in JJA precipitation in northern India was
slightly different from that in the former CTL and EXP
simulations. In the new simulations, the vapor flow
anomaly was from the Indian subcontinent to the Bay of
Bengal (Fig. 9c), whereas in the former simulations the
flow was conveyed to the Arabian Sea. The comparison
between Figs. 9 and 6 indicates that the precipitation
responses to theGWusemight be strongly related to the
background climate change with large uncertainties.
The consumedGW seems to be easily conveyed into the
sea, and the precipitation of northern India in the
monsoon season was likely to be reduced by GW use.
5. Conclusions and discussion
In this study, a GW extraction and consumption
scheme was incorporated into the CESM1.2.0. Two en-
sembles of simulations with and without GW extraction,
respectively, were conducted to detect changes in the
global hydrological cycle and climate due to GW
extraction.
The main conclusions of this study are as follows. 1)
There were three contiguous areas in the world that
were experiencing high-level GWextraction rates (more
than 90mmyr21 around 2003); these were located in
northern India and Pakistan, the north China plain, and
the central United States. 2) The combined processes of
GW extraction and consumption caused significant de-
clines in GW tables in the central United States, parts of
the north China plain, and northern India and Pakistan.
The water table below the ground surface declinedmore
than 4m from 1970 to 2005 in these areas. With these
GW table declines, the total terrestrial water storage
was also depleted. In regions with intenseGW irrigation,
the upper soil became wetter while deeper soil became
drier as a consequence of irrigation and GW table de-
clines. The wet surface soils in turn increased LH by
5–20Wm22 and decreased SH by 2–10Wm22 in most
key areas of GW extraction. 3) The processes of GWuse
significantly cooled the air at 850 hPa, decreasing tem-
perature by approximately 0.38C in northern India and
northern China plus a large area of central Russia. A
thermal circulation was induced by the cooling effects
and decreased the geopotential height at 850hPa in the
Tibetan Plateau. The north China plain experienced an
increase of JJA precipitation, whereas the precipitation
in northern India decreased because the Indian mon-
soon and its transport of water vapor were weaker as a
result of cooling induced by GW use. 4) Background
climate change may complicate the climatic effects of
GW use, especially for the precipitation and wind field
responses.
The research demonstrated possible global hydrologic
and climatic responses to GW extraction. However, the
assumptions and limitations in the study should be
noted. Besides the uncertainties within the original
version of CESM1.2.0 (Gent et al. 2011), the scheme
developed in the current study to describe GW with-
drawal and consumption is highly simplified. In fact, not
all water consumed in agriculture is used for irrigation.
For example, the water used to supply livestock and fish
ponds is not counted in this research, although it is much
smaller in magnitude than the water consumed by irri-
gation (Siebert et al. 2010). In addition, the amount of
water consumed in industrial and domestic water use is
uncertain due to varieties of complex consumptive uses,
such as in cooling, heating, electricity generation,
washing, drinking, and catering. In the current study,
water consumption was divided only into net expense
and waste water. Moreover, the water withdrawal and
consumption data used as input in the model contained
many uncertainties. The ratios of water use for agricul-
tural, industrial, and domestic water use are archived by
the FAOon a regional basis, which was inconsistent with
the scale of the simulation grid cells. However, since
there are only several centers of severe GW extraction
worldwide, the regional data were probably adequate
for the study. Furthermore, the assumption that the
amount of GW extracted changed linearly from 1965 to
1990 and from 1990 to 2009 was a simple representation
that surely caused some deviations between the GW
extractions used in the model and actual extraction.
There are also some uncertainties within the GMIA5
data, which can be seen in Siebert et al. (2005). In
1 JANUARY 2017 ZENG ET AL . 87
addition, setting the waste water percentage of in-
dustrial water use and daily life to 30% for the whole
world is not accurate enough, and may produce some
uncertainties of the simulation results for the United
States and Europe, while in other regions the results
would be relatively unaffected since agriculture is the
main consumer of water in these areas. Another as-
pect of the uncertainties in this study relates to the
topographic heterogeneity and the spatial resolution
of the model. In reality, the interaction processes be-
tween land and atmosphere are tightly related to the
surface elevation at a much finer spatial scales than
our model resolution, as many previous studies have
shown (Avissar and Liu 1996; Avissar and Schmidt
1998). Although CAM4 and CLM4.5 include some
subgrid schemes that evaluate the standard deviation
of elevations within each model grid based on the
high-resolution data of topographic height and use the
standard deviation to calculate the fluxes between
land and atmosphere, the runoff processes, SW stor-
age, and snow dynamics to show the spatial hetero-
geneity (Neale et al. 2010, Oleson et al. 2013), these
simple parameterizations are relatively inaccurate
and thus produced more uncertainties in our results.
However, at this time, because the aim of this paper is
to make a first step toward showing the global impacts
of GW extraction and withdrawal on climate, we give
our results with a caution about the elevation-related
uncertainties. In the future, using an updated ver-
sion of CESM with its improved subgrid schemes for
the finescale processes, we will rerun our model to
see how the results are affected by topographic
heterogeneity.
The sustainable use of water resources must be
stressed. Although water reallocation by GW with-
drawal and consumption can temporally increase upper
soil moisture and runoff, all the results from this re-
search (i.e., GW table declines, reduced soil moisture in
deep layers of the soil profile, and decreases in total
terrestrial water storage) emphasize that available water
resources are unsustainable in regions that have high
GW pumping rates. In the future, water demand will
dramatically increase as the global population continues
to grow and economic development expands. To address
these difficulties, measures such as large-scale water
diversion, improvements in irrigation efficiency, and
water recycling should be implemented, instead of in-
tensifying GW extraction. Among those areas experi-
encing severe water resource problems due to GW
extraction, northern India may require the most urgent
interventions because the monsoon circulation is
weakened by GW use here, and both precipitation and
runoff are being reduced concurrently.
Future research related to this work is necessary in at
least two aspects. First, the climate model should be
further developed, especially in regard to the charac-
terizations of processes tightly connected to the GW
dynamic. A GW flow model that considers both vertical
and lateral GWflow has been tested at basin scale (Zeng
et al. 2016a,b) and will be incorporated into CESM that
better applies on the global scale in the future. The
coupling of a crop model with the CLM has also been
finished and tested in China (Chen and Xie 2012). These
two models will be applied into CESM1.2_GW and will
help improve the calculation of GW table levels and
water balances. Second, the scheme and data related to
GW extraction should be more specific and realistic to
more accurately reflect the variability of climate effects
at intra-annual and interannual scales. These improve-
ments will enable simulations that are more accurate in
assessing water sustainability and the hydrologic and
climatic effects of human activities with regard to water
use. As a consequence, the improved models will sub-
stantially benefit the management of water resources
and the adaptation to climate change with society
development.
Acknowledgments. This study was supported by the
National Natural Science Foundation of China
(Grants 91125016 and 41575096), and by the Chinese
Academy of Sciences Strategic Priority Research
Program under Grant XDA05110102. We thank Xing
Yuan, Xiangjun Tian, Shuang Liu, and Yan Yu for
their assistance with this work and helpful discussion.
We also thank Minghua Zhang and two other anony-
mous reviewers for the helpful comments that im-
proved the manuscript.
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