1. Introduction
With economic development and population growth, water demands are rapidly increasing and water resources are becoming scarce in many 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 demands of freshwater, especially in regions where surface water (SW) supplies are limited (Liu et al. 2001). However, 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 decrease the temperatures near the surface and in the lower 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 further alter atmospheric water balances (Haddeland et al. 2006; Lo and Famiglietti 2013). Irrigation using GW can alter the carbon cycle and the water 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 irrigation module into the Variable Infiltration Capacity model and found that irrigation increases evapotranspiration and decreases surface air temperature in the Mekong and Colorado River basins. Hanasaki et al. (2008) constructed a water resource model that considered GW withdrawal and anthropogenic reservoir operations. Ozdogan et al. (2010) incorporated crop information from satellite remote sensing and an irrigation scheme into a land surface model and found that irrigation increases evapotranspiration by 12% and reduces surface sensible heat flux (SH) by a similar percentage in the growing season. Döll et al. (2012) applied a water 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 irrigation water was dominated by GW. Pokhrel et al. (2012a) incorporated an anthropogenic water management scheme into the Minimal Advanced Treatments of Surface Interaction and Runoff model and found that irrigation could cause a maximum increase of 50 W m−2 in latent heat flux (LH) in the summertime. Zou et al. (2015) incorporated a GW extraction scheme into the Community Land Model (CLM), version 3.5, to investigate the effects of anthropogenic extraction of GW on land surface processes in the Haihe River basin in northern China; they found that GW extraction for human activities 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 GW dynamics and pumping on a global scale and showed that global GW withdrawal and depletion for year 2000 were 570 and 330 km3 yr−1, 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 atmosphere and climate through their effects on soil moisture and surface heat fluxes (Chen and Hu 2004). In regions with GW overextraction, soil moisture may decrease because of declining GW tables, even though most 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 Climate Model, version 4 (RegCM4), for the Haihe River basin in northern China; they revealed that GW extraction and water consumption caused wetting 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 responses on a global scale to GW management, including GW pumping, GW irrigation, and other activities related 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. Conclusions and discussion are presented in section 5.
2. Model development
a. The Community Earth System Model, version 1.2.0
The Community Earth System Model, version 1.2.0 (CESM1.2.0; Hurrell et al. 2013), consists of atmosphere, 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 dynamical core (Lin 2004) with an atmospheric grid spacing of 0.9° × 1.25° 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). The sea ice component is an extension of the LANL sea ice model (Hunke and Lipscomb 2008). The land component is the Community Land Model, version 4.5 (CLM4.5; Oleson et al. 2013), which simulates biogeophysical exchange of radiation, LH, and SH; momentum 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 varieties of vegetation and crops, as well as land unit types (lake, urban, and glacier), to be treated differently depending on their own biogeophysical and biogeochemical 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 implementation into CESM1.2.0
The GW extraction and consumption scheme developed 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 resulting model (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 from an aquifer and apportioned among agricultural, industrial 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.
Framework of simulated human-induced water resource extraction and consumption processes and their incorporation into the CLM4.5 and CESM1.2.0 models.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Framework of simulated human-induced water resource extraction and consumption processes and their incorporation into the CLM4.5 and CESM1.2.0 models.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Framework of simulated human-induced water resource extraction and consumption processes and their incorporation into the CLM4.5 and CESM1.2.0 models.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
As shown in Fig. 1, based on the structure of coupling, other variables in CLM4.5 are influenced by Qaqu_new and Qtopsoil_new, each of which is affected directly by GW extraction and consumption. The atmospheric variables in CAM4 are changed by Qevp_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_GW model simulation, the parameters Qg, ragr, rind, rdom, and α 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 and Agriculture Organization of the United Nations (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 contained regional water withdrawal data and expressed agricultural, industrial, and municipal water withdrawals as percentages of the total withdrawal. The FAO dataset 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 2710 km3 yr−1. 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 5′ to show the size of area equipped for irrigation in each grid, and the size of area that is irrigated using GW 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.
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 90 mm yr−1 (around 2003). Besides, a large number of scattered areas with severe GW extraction (more than 90 mm yr−1) 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).
(a) Global groundwater extraction rate around year 2003 and (b) the area-averaged extraction rate in the central United States, the north China plain, and northern India and Pakistan from 1965 to 2005. As designated by the three boxes in (a), the central United States is defined as the region within 33°–42°N, 97°–105°W; the north China plain is defined as the region within 34°–40°N, 110°–120°E; and northern India and Pakistan is defined as the region within 23°–33°N, 68°–78°E.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
(a) Global groundwater extraction rate around year 2003 and (b) the area-averaged extraction rate in the central United States, the north China plain, and northern India and Pakistan from 1965 to 2005. As designated by the three boxes in (a), the central United States is defined as the region within 33°–42°N, 97°–105°W; the north China plain is defined as the region within 34°–40°N, 110°–120°E; and northern India and Pakistan is defined as the region within 23°–33°N, 68°–78°E.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
(a) Global groundwater extraction rate around year 2003 and (b) the area-averaged extraction rate in the central United States, the north China plain, and northern India and Pakistan from 1965 to 2005. As designated by the three boxes in (a), the central United States is defined as the region within 33°–42°N, 97°–105°W; the north China plain is defined as the region within 34°–40°N, 110°–120°E; and northern India and Pakistan is defined as the region within 23°–33°N, 68°–78°E.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
To estimate annual data, the amount of GW extraction 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 the GW withdrawal 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 interpolation may result in large uncertainties because the water consumption and GW pumping rate in a certain year depend on the water availability (such as precipitation) and the climate of that year. However, long-term changes in annual GW pumping rates, which are the focus of this study, are more 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 reasonable and convenient way to represent the long-term change in GW pumping rates from 1965 to 2005. Figure 2b shows the time series of interpolated annual GW pumping 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 extraction 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 consumption is very complex. Seasonal water consumption depends on local climate, phenology of crops, economy, 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 uncertainties from intra-annual allocation of the annual water consumption. For this purpose, the GW consumption for industrial and domestic water uses was simply distributed evenly to every model time step, and the GW consumption for agriculture was evenly distributed only to model time steps in the spring and summer (March–May and June–August, respectively, for the Northern Hemisphere, 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, α (the waste water ratio) was set as 30% based on Mao et al. (2000). The remaining 70% of industrial 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 a GW withdrawal and consumption scheme (referred 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 atmospheric (CAM4) and land (CLM4.5) components were integrated, while the ocean surface conditions were prescribed using monthly observations of sea surface temperatures (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 0.9° × 1.25° and a time step of 30 min 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 external 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, volcanic activity, and land use data were estimated from observations 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 simulations using different initial conditions and then average 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 atmospheric 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 series 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 central 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 provided by the Ministry of Water Resources and the Institute for Geo-environmental Monitoring of China. The central United States has also suffered from overextraction 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). Nevertheless, GW table data in this region are unavailable. Thus, we compared the model-simulated water storage with the GRACE observations of total water storage variations for northern India and Pakistan. Figure 3 shows the time series of simulated annual area-averaged GW 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 0.32 m yr−1, generally reproduced (although with a slight overestimation) the observed downward trends (0.2 m yr−1), 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 0.16 m yr−1, which were significantly higher than the value of 0.09 m yr−1 shown in observations. Figure 3c shows that the EXP simulations also predicted the decreasing trend (−0.62 cm yr−1) in total terrestrial water storage in northern India and Pakistan, although the predictions were somewhat greater than indicated by the GRACE satellite observations (−0.50 cm yr−1). In the CTL ensemble, there was almost no any trend for the terrestrial water storage (only −0.02 cm yr−1). The ability of the CESM1.2_GW model in reproducing the observed declining trends of the GW table and water storage in key geographic regions provides confidence about the GW extraction module for applying it to study the effects of GW extraction on global climate.
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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
b. Differences in spatial distribution of hydrologic variables
Figure 4 shows the multiyear (1970–2005) mean spatial 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 tables in most areas declined more than 4 m when averaged 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.
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 (m3 m−3), (c) runoff (mm yr−1), (d) terrestrial water storage (cm), (e) latent heat flux (W m−2), and (f) sensible heat flux (W m−2). The stippled areas indicate the regions where the difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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 (m3 m−3), (c) runoff (mm yr−1), (d) terrestrial water storage (cm), (e) latent heat flux (W m−2), and (f) sensible heat flux (W m−2). The stippled areas indicate the regions where the difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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 (m3 m−3), (c) runoff (mm yr−1), (d) terrestrial water storage (cm), (e) latent heat flux (W m−2), and (f) sensible heat flux (W m−2). The stippled areas indicate the regions where the difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Figure 4b shows that the contiguous areas with statistically significant increases in surface soil moisture were located in northern India along with Ganges River, in the Huaihe River basin in the north China 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. However, 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 agriculture 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 internal variability and land–atmosphere interactions (internal 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 0.04 m3 m−3 in China and India and within 0.02 m3 m−3 in the United States). Furthermore, 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 decrease 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 suffering 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 withdrawal 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 them were 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 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 overextraction (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 overextraction areas (the central United States, the north China plain, and northern India and Pakistan) were also identified to be experiencing significant LH increases (5–20 W m−2) and SH decreases (2–10 W m−2). 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 subsequently decrease.
c. Differences in spatial distribution of atmospheric variables
As noted earlier, the effects of GW withdrawal and consumption on land can affect climate through flux 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 approximately 0.3°C were obvious. One of these areas was located 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 situation was probably induced by the advection of air because 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 irrigation. Such a process is a clear example of how irrigation could affect the climate outside the local place where 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 0.4°C) 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 GW use on temperature were more apparent in the lower atmosphere than in the upper atmosphere.
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 (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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 (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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 (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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. From Fig. 5d, besides the internal noise in the Arctic 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 geopotential height. The added air in northern India originated from its east side, so the geopotential height in the Tibetan Plateau decreased, forming thermal circulation 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 the Tibetan Plateau (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 statistically significant according to the Student’s t test.
Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for annual-mean JJA (a) precipitation (mm day−1); (b) 850-, (c)500-, and (d) 200-hPa level wind field (m s−1); and (e) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for annual-mean JJA (a) precipitation (mm day−1); (b) 850-, (c)500-, and (d) 200-hPa level wind field (m s−1); and (e) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP-minus-CTL differences averaged over 1970–2005 for annual-mean JJA (a) precipitation (mm day−1); (b) 850-, (c)500-, and (d) 200-hPa level wind field (m s−1); and (e) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
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 extraction 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 humidity 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 troposphere 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 enhanced water vapor content at the ground surface was no more than 0.12 g kg−1; compared with the climatology value of nearly 70 g kg−1, the effects were nearly negligible. As shown in Fig. 7c, the cooling effects were transmitted to the lower troposphere. The cooling effects decreased as height above the ground surface increased. In the north China plain, where the cooling effects were most significant, the decreased temperature was nearly 0.3°C.
Vertical profiles of ensemble-averaged EXP-minus-CTL differences for annual-mean (a) soil moisture (m3 m−3), (b) specific humidity (10−1 g kg−1), and (c) temperature (°C) in the central United States, the north China plain, and northern India and Pakistan.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Vertical profiles of ensemble-averaged EXP-minus-CTL differences for annual-mean (a) soil moisture (m3 m−3), (b) specific humidity (10−1 g kg−1), and (c) temperature (°C) in the central United States, the north China plain, and northern India and Pakistan.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Vertical profiles of ensemble-averaged EXP-minus-CTL differences for annual-mean (a) soil moisture (m3 m−3), (b) specific humidity (10−1 g kg−1), and (c) temperature (°C) in the central United States, the north China plain, and northern India and Pakistan.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
e. Climatic responses to GW extraction excluding background 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 EXP2 with and without the anthropogenic GW extraction, 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 and EXP 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 differences 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 geopotential 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 climate 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 differences (Figs. 5a and 5d), although their spatial patterns were similar. This result indicates that the mechanism controlling how the GW use impacted the temperature and geopotential height was the same in both cases (including background climate change or not), but the background climate change might weaken the magnitude of the impacts induced by GW extraction.
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean (a) latent heat flux (W m−2), (b) sensible heat flux (W m−2), and 850-hPa level (c) temperature (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean (a) latent heat flux (W m−2), (b) sensible heat flux (W m−2), and 850-hPa level (c) temperature (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean (a) latent heat flux (W m−2), (b) sensible heat flux (W m−2), and 850-hPa level (c) temperature (°C) 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.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Figure 9 shows the annual-mean spatial distributions of the EXP2-minus-CTL2 differences for the precipitation, 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 precipitation decrease due to GW use 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 weakened the water vapor flowing from the Yellow Sea and East China Sea to the China mainland. This weaker vapor flow offset the irrigation-increased evapotranspiration, 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 the GW use might be strongly related to the background climate change with large uncertainties. The consumed GW 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.
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean JJA (a) precipitation (mm day−1), (b) 850-hPa level wind field (m s−1), and (c) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean JJA (a) precipitation (mm day−1), (b) 850-hPa level wind field (m s−1), and (c) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
Spatial distribution of ensemble-averaged EXP2-minus-CTL2 differences averaged over last 10 simulation years for annual-mean JJA (a) precipitation (mm day−1), (b) 850-hPa level wind field (m s−1), and (c) horizontal vapor flux (kg m−1 s−1). The gray shading indicates the regions where the magnitude of the vector difference passed the 95% confidence level in the Student’s t test.
Citation: Journal of Climate 30, 1; 10.1175/JCLI-D-16-0209.1
5. Conclusions and discussion
In this study, a GW extraction and consumption scheme was incorporated into the CESM1.2.0. Two ensembles 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 GW extraction rates (more than 90 mm yr−1 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 declines 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 declined more than 4 m from 1970 to 2005 in these areas. With these GW table declines, the total terrestrial water storage was also depleted. In regions with intense GW irrigation, the upper soil became wetter while deeper soil became drier as a consequence of irrigation and GW table declines. The wet surface soils in turn increased LH by 5–20 W m−2 and decreased SH by 2–10 W m−2 in most key areas of GW extraction. 3) The processes of GW use significantly cooled the air at 850 hPa, decreasing temperature by approximately 0.3°C 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 850 hPa in the Tibetan Plateau. The north China plain experienced an increase of JJA precipitation, whereas the precipitation in northern India decreased because the Indian monsoon 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 withdrawal 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 irrigation (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 agricultural, industrial, and domestic water use are archived by the FAO on 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 addition, setting the waste water percentage of industrial 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 aspect of the uncertainties in this study relates to the topographic heterogeneity and the spatial resolution of the model. In reality, the interaction processes between 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 storage, and snow dynamics to show the spatial heterogeneity (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 version 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 withdrawal and consumption can temporally increase upper soil moisture and runoff, all the results from this research (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 intensifying GW extraction. Among those areas experiencing 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 characterizations of processes tightly connected to the GW dynamic. A GW flow model that considers both vertical and lateral GW flow 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 improvements 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 substantially 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 anonymous reviewers for the helpful comments that improved the manuscript.
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