Hindawi Advances in Meteorology Volume 2018, Article ID 6280737, 15 pages https://doi.org/10.1155/2018/6280737

Research Article Impacts of Water Consumption in the Haihe Plain on the Climate of the ,

Jing Zou ,1 Chesheng Zhan ,2 Ruxin Zhao,3 Peihua Qin ,4 Tong Hu,1 and Feiyu Wang5

1Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266001, China 2Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, China 3Beijing Normal University, Beijing 100875, China 4Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 5School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China

Correspondence should be addressed to Chesheng Zhan; [email protected]

Received 2 August 2018; Revised 9 October 2018; Accepted 29 October 2018; Published 13 November 2018

Academic Editor: #eodore Karacostas

Copyright © 2018 Jing Zou et al. #is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this study, the RegCM4 regional climate model was employed to investigate the impacts of water consumption in the Haihe Plain on the local climate in the nearby Taihang Mountains. Four simulation tests of twelve years’ duration were conducted with various schemes of water consumption by residents, industries, and agriculture. #e results indicate that water exploitation and consumption in the Haihe Plain causes wetting and cooling of the local land surface and rapid increases in the depth of the groundwater table. #ese wetting and cooling effects increase atmospheric moisture, which is transported to surrounding areas, including the Taihang Mountains to the west. In a simulation where water consumption in the Haihe Plain was doubled, the wetting and cooling effects in the Taihang Mountains were enhanced but at less than double the amount, because a cooler land surface does not enhance atmospheric convective activities. #e impacts of water consumption activities in the Haihe Plain were more obvious during the irrigation seasons (primarily spring and summer). In addition, the land surface variables in the Taihang Mountains, e.g., sensible and latent heat fluxes, were less sensitive to the climatic impacts due to the water consumption activities in the Haihe Plain because they were strongly affected by local surface energy balance.

1. Introduction to decrease by 10.2 mm per decade, which is slightly less than the trend observed on the piedmont plain of these moun- #e Taihang Mountains, which extend over 400 km from tains, of 12.5 mm per decade [5]. north to south in North China, form a geographic boundary Numerous studies have investigated climatic changes between the Loess Plateau (to the west) and the North China in North China and have mainly attributed these changes Plain (to the east). Mountains and intermontane valleys lie to human activity [6–9]. For example, Jia et al. attributed to the west and north, and plains lie to the east and south the observed changes in water resources from 1961 to (Figure 1). A number of reservoirs in the Taihang Mountains 2000 to various factors and demonstrated that local are important water sources for North China. However, due human activity accounted for about 60% of the observed to long-term human exploitation and climatic changes, the changes [10]. Taihang Mountains suffer from soil erosion and vegetation For the Taihang Mountains, the most intense human degradation [1, 2]. activities occur in its eastern plains area, Haihe Plain, which During the past fifty years, significant drying and is in the northern part of the . #e Haihe warming trends have been detected in North China [3, 4]. Plain is an important agricultural area in China and supports #e mean precipitation in the Taihang Mountains was found rapidly developing industries. According to the Urban 2 Advances in Meteorology

Topography m the eastern Haihe Plain affects the climate of the western Taihang Mountains. 1800 45N #erefore, in this study, a series of water exploitation and 1600 consumption simulations were conducted by the regional 1400 climate model RegCM4, using data from 1996 to 2007. 1200 40N #rough the comparison of these simulations, we in- Taihang Mountains 1000 vestigated the effect of water consumption processes in the Haihe Plain 800 Haihe Plain on the spatiotemporal variability of the local 35N 600 climate of the Taihang Mountains. 400 200 2. Methodology 30N 0 2.1. Model Description. #e regional climate model used in 110E 120E this study, RegCM4, was developed by the International Figure 1: Topography of the simulation domain. #e Taihang Center for #eoretical Physics (ICTP) in Italy [27]. RegCM4 Mountains and Haihe Plain are outlined in black. is a hydrostatic climate model with a sigma vertical co- ordinate. #ree convective precipitation schemes, Kuo, Grell and Emanuel, and a large-scale precipitation scheme, Statistical Yearbook of China 2000, the population density SUBEX, were available as simulation options. #e land and gross domestic product (GDP) in 2000 in the Taihang surface models BATS (biosphere-atmosphere transfer 4 Mountains were 322 people per km and 239 × 10 scheme) and CLM (community land model) were available −2 yuan·km , respectively. #is compares with 687 people for use as land modules in RegCM4. 4 −2 per km and 530 × 10 yuan·km in the Haihe Plain [11]. #e In this study, version 3.5 of CLM was used. #e CLM dense population and industry in the Haihe Plain have model divides grid cells into multiple land units (glacier, induced a serious imbalance between water demands and wetland, vegetation, etc.). Vegetation units can be further surface water supplies [12–14]. Large amounts of ground- divided into 17 plant function types (PFTs) [28]. #e hy- water have been exploited for crop cultivation and industrial drological processes in CLM3.5 were obtained from a runoff development, leading to a rapidly dropping groundwater parameterization scheme with a simple groundwater model table and a series of eco-environmental crises [15–17]. developed by Niu et al. [29, 30]. Water consumption activities have been shown to en- #e RegCM4 model has been implemented around the hance evapotranspiration and reduce local temperature, and globe and demonstrated excellent climate simulation abil- moreover, increasing local water vapor from land may lead ities. For example, Wang et al. evaluated the monthly to more convection and further changes in atmospheric precipitation simulation of RegCM4 over the Tibetan Pla- circulation [18–22]. Numerous studies have investigated the teau using the station observation and TRMM (Tropical regional impacts of agricultural irrigation, which constitutes Rainfall Measurement Mission) data and found RegCM4/ a major proportion of water consumption. For example, CLM3.5 performed better than RegCM4/BATS in the sta- Saeed et al. applied the Max Planck Institute’s Regional tistical indices [31]. Ashfaq et al. used RegCM4 to dy- Model (REMO) and found that the irrigation over India namically downscale the historical and near-term future caused increased evapotranspiration and less westerlies outputs from 11 global climate models, in order to present entering into land from the Arabian Sea [23]. DeAngelis high-resolution ensemble projections of climatic changes et al. analyzed the precipitation observations in the Great over the continental United States [32]. Mbienda et al. Plains of the United States and found the increased compared the simulations performed by different convective evapotranspiration due to irrigation contributed to more schemes of RegCM4 over Central Africa and found the downwind precipitation [24]. Emanuel-MIT convective scheme showed better indices in For the Haihe Plain, Chen and Xie investigated the temperature and the Grell scheme with Arakawa–Schubert climatic effects of large-scale irrigation due to interbasin closure assumption was better to downscale precipitation water transfer and demonstrated that such irrigation causes and surface wind [33]. increased local precipitation and decreased temperature at the land surface [25]. Leng et al. revealed that the changes in topsoil moisture and subsurface water flux induced by 2.2. Water Exploitation Scheme Description. #e scheme of groundwater irrigation exceeded the potential change that water exploitation and consumption used in this study was could be attributed to various climate projections for the developed based on the CLM3.5 approach used in our pre- Haihe Plain [26]. #ese studies have made valuable progress vious studies [34, 35]. #e scheme relies on preset water in investigating the relationship between local climate demand and is composed of exploitation and consumption changes and water consumption activities. However, the components. As shown in Figure 2, the total water demand Dt extent of the effect of water consumption within the Haihe is supplied by water resources pumped from rivers (Qs) and Plain on its western water source area, the Taihang wells (Qg). #e total water demand Dt is consumed by three Mountains, remains unclear. Further discussion should consumption sections—irrigation consumption (Da), do- address the way in which high-level water consumption in mestic consumption (Dd), and industrial consumption (Di). Advances in Meteorology 3

Water consumption Evaporation Domestic D consumption ( d) Irrigation and industrial consumption (D ) D a consumption ( i)

Waste Effective water D Total water demand ( t) precipitation

Surface runoff Surface water pumped (Q ) Groundwater s Q pumped ( g)

Subsurface runoff

Well Rivers Aquifer Figure 2: Framework of the water exploitation and consumption scheme.

#e irrigation water is treated as effective rainfall reaching the 22 to August 28 (summer maize planting and harvesting topsoil, as per the approach in previous studies times, respectively) [39–42]. #e evaporation rate of do- [23, 25, 36, 37]. Furthermore, according to the descriptions of mestic and industrial consumption ε was set as 0.26 based on urban water use by Shiklomanov and the approach of specific the statistical value of water consumption rate for industrial hydrological modeling studies, the water for domestic and and urban domestic consumption in the Water Resource industrial consumption is highly simplified in the scheme to Bulletin of China for the year 2000 (http://www.mwr.gov. partly increase local evaporation and partly return to river cn/sj/tjgb/szygb/201612/t20161222_776035.html). channels [36–38]. #e scheme is based on the balance of water demand and supply, which can be described as 2.3. Water Demand Estimation. Due to the lack of spatial distribution data, the water demand in each grid cell was D � Q + Q . ( ) t s g 1 indirectly estimated from relevant eco-social data, as other studies have done [36]. In this study, the total demand D #e surface water supply Q is composed of the total t s was divided into three components—domestic, industrial, runoff R and stream discharge R in each model grid cell, t str and irrigation demand—which were estimated based on data and it has a higher priority than the groundwater supply Q g of population, GDP, and irrigated cropland areas in China for satisfying demand D . When the surface water supply t for the year 2000. #e estimation equation can be expressed cannot meet the total demand, the groundwater supply Q is g as subtracted from the groundwater storage and can be cal- culated as Dt,2000 � Dd,2000 + Di,2000 + Da,2000 ( ) −1 −1 3 Qg � max� Dt − Rt − Rstr�, 0�. (2) � ρλ S �c1Apop + βc2AGDP + αc3Aagr�,

In the consumption section, the effective rainfall in the where Dt,2000 is the total water demand in the year 2000 −2 −1 3 −3 model increases by Da due to irrigation, which will further (kg·m ·s ); ρ is water density (1 × 10 kg·m ); λ is the participate in the processes of runoff generation and in- number of seconds in a year (31,536,000 s·yr−1); S is the grid 2 filtration. #e increased evaporation in the model is defined cell area (m ); c1 is per capita annual domestic water 3 −1 −1 as ε(Dd + Di) and the remaining water of domestic and consumption (m ·person ·yr ); Apop is the population in industrial consumption, (1 − ε)(Dd + Di), is regarded as each grid cell (persons); β is the conversion ratio between wastewater recharge returning to river channels. GDP and industrial output; AGDP is GDP in each grid cell In this study, domestic and industrial water consump- (yuan); c2 is industrial water consumption for unit industrial tion occurred year-round, while irrigation consumption output (m3·yuan−1·yr−1); α is the ratio between the irrigated only occurred during the main crop growing periods. In and total cropland areas; c3 is annual irrigation water 3 −1 −1 North China, the main crops are winter wheat and summer consumption per hectare (m ·ha ·yr ); and Aagr is the maize. According to their known phenology, irrigation first cropland area in each grid cell (ha). occurred from March 10 (when wheat turns green) to June Population and GDP data for the year 2000 were ob- 18 (wheat harvest time). Irrigation also occurred from June tained from the Data Center for Resources and Environment 4 Advances in Meteorology of Sciences, Chinese Academy of Sciences (http://www.resdc. surface module of CLM3.5. Before the simulations were cn). Cropland area data for 2000 were obtained from the conducted, a test simulation using the original RegCM4 with China land cover classification dataset available at the Science ERA40 reanalysis data from 1961 to 1991 was conducted to Data Center for Cold and Arid Regions (http://westdc.westgis. obtain the balanced groundwater depth. A spin-up simu- ac.cn) [43]. #e three eco-social datasets had a resolution of lation from 1992 to 1995 was then conducted using the 1 km × 1 km and were then interpolated to suit the RegCM4 original RegCM4 without exploitation. Based on the final model’s resolution of 20 km × 20 km for the subsequent status of the spin-up simulation, four simulation tests were simulation tests. #e parameters of water consumption were conducted from 1996 to 2007. A control test (CTL) con- collected from the Water Resource Bulletin of China for the tinued to simulate the natural state without exploitation; year 2000, where the provincial mean values were available. Exploitation Test 1 (T1) used estimated water demand data #e other two parameters, β and α, were collected from the for the Haihe Plain, and Exploitation Test 2 (T2) used double China Statistical Yearbook 2000 and were also provincial this demand. In addition, Exploitation Test 3 (T3) used mean values [44]. combined demand data of the Taihang Mountains and #e spatial distribution of estimated water demand Haihe Plain. By comparing differences between the ex- Dt,2000 per unit area is shown in Figure 3(a), and the de- ploitation and control tests, this study aimed to investigate mands for the Taihang Mountains (Figure 3(b)) and Haihe the effects of water consumption activities in the Haihe Plain Plain (Figure 3(c)) were then cropped from Figure 3(a). As on local climate changes in the Taihang Mountains. shown in Figure 3(b), more water is consumed in the eastern piedmont areas and south-central valleys of the Taihang 3. Result Mountains. For the Haihe Plain (Figure 3(c)), and areas near Beijing (116.5°E, 40.0°N) and (117.0°E, 39.0°N), there 3.1. Validation of the Control Test. Station observed pre- were higher water demands due to intense industrial activity cipitation, and 2 m air temperature data during the simu- and high population densities. #e central areas of the plain lation period was derived from the China National are the main agricultural areas, and water demands there are Meteorological Information Center (http://data.cma.cn/en) also high. and then was interpolated as gridded data with a resolution Table 1 provides a comparison of the estimated and of 0.5° × 0.5°, by using the inverse distance square weighting actual total water demands in 2000 for China and the Haihe method as preformed in previous studies [45, 46]. #e spatial River Basin. #e estimation and actual values are basically distributions of annual mean precipitation and 2 m air consistent with each other, although they do not strictly temperature from 1996 to 2007 are shown in Figure 4. #e coincide due to the use of different data sources. For the CTL test run in RegCM4 simulated the regional climatology whole of China, the estimated irrigation demand provides over the study domain well. Specifically, the simulated most of the error, because of inconsistencies in cropland area precipitation and temperature increased from northwest to between the remote sensing data and China Statistical southeast, which is consistent with observations. However, Yearbook data used in this study. In addition, a comparison slight low biases were detected in the Taihang Mountains, of for the Haihe River Basin is also presented, because the −25.1 mm/year for precipitation and −0.64 K for 2 m air whole Haihe Plain and most areas of the Taihang Mountains temperature. Also, a dry and warm bias, of −115.3 mm/year lie in the basin, and few data for the Taihang Mountains are for precipitation and 0.88 K for 2 m air temperature, was available in water resource bulletins. detected in the Haihe Plain. Additionally, the statistical To obtain the demands during the simulation period indices of monthly series in the Taihang Mountains and (1996–2007), the mean annual water use for the Haihe River Haihe Plain are listed in Table 3, including the temporal Basin was derived from the Water Resource Bulletin of Haihe correlation coefficient, root-mean-square error, and stan- River Basin (http://www.hwcc.gov.cn/hwcc/wwgj/xxgb/). dard deviation. #ese statistics indicate that the simulated Using these statistics, the estimated water demands in 2000 series corresponds with observations; hence, the RegCM4 for the Taihang Mountains and Haihe Plain were scaled up model is suitable for simulating temporal variations in the or down, keeping the three water-consuming sectors un- local climate. changed, to approximate interannual variations. #e esti- mated annual demands from 1996 to 2007 are shown in Table 2. 3.2. Spatial Distribution of Climatic Differences. Figure 5 shows the spatial distribution of differences between the three exploitation tests and control test for groundwater 2.4. Experimental Design. #e simulation domain of depth, 10 cm depth soil moisture, and precipitation. Re- RegCM4 is shown in Figure 1, where the domains of Taihang garding the groundwater withdrawn continuously in the Mountains and Haihe Plain are outlined by black lines. #e exploitation tests, Figures 5(a)–5(c) show the differences in central projection of RegCM4 was located at 116°E, 38°N, groundwater depth in December 2007 (final status) and and it used a spatial resolution of 20 × 20 km. #e Grell January 1996 (initial status). #e other two climatic variables scheme was used as the convective precipitation scheme, and in Figures 5(d)–5(i) use the 12-year mean differences from ERA-Interim reanalysis data from 1992 to 2007 were used as 1996 to 2007. the lateral boundary forcing. #e time steps were 30 seconds As shown in Figure 5(a), the 12-year exploitation pro- for the atmosphere module and 30 minutes for the land cess in the Haihe Plain led to a significant drop of local Advances in Meteorology 5

(m/year)

0.3

0.27

0.24

0.21 40N 0.18

0.15

0.12

0.09

0.06 20N 0.03

90E 120E (a) (m/year) (m/year)

0.3 0.3 0.27 0.27 0.24 0.24 40N 0.21 40N 0.21 0.18 0.18 0.15 0.15 0.12 0.12 0.09 0.09 0.06 0.06 35N 0.03 35N 0.03

110E 115E 120E 110E 115E 120E (b) (c)

Figure 3: Spatial distributions of estimated water demand per unit area in 2000 over (a) all of China, (b) the Taihang Mountains, and (c) Haihe Plain.

Table 1: Estimated water demands and actual statistical values in differences in the areas outside of the Haihe Plain were 2000 (unit: 108 m3/year). almost negligible. #e groundwater depth only rose by a mean of 0.06 m in the Taihang Mountains, indicating that Data Domain Domestic Industrial Irrigation Total source the local processes of water exploitation and consumption in the Haihe Plain do not cause obvious changes to ground- Actual 574.9 1139.1 3783.6 5497.6 China Estimated 611.1 1035.7 3366.6 5013.4 water resources in the Taihang Mountains. For the T2 test (Figure 5(b)), when the water demands in the Haihe Plain Haihe river Actual 51.8 65.7 280.9 398.4 basin Estimated 50.8 80.6 247.7 379.1 were assumed to be double those of the T1 test, the groundwater depth dropped by more than 20 m in most areas of the plain, with a mean of 25.49 m. Also, the groundwater table, of a mean of 13.86 m. #e groundwater groundwater table in the Taihang Mountains rose by 0.10 m table dropped in the areas near Beijing (116.5°E, 40.0°N), due to slightly increased precipitation in the Haihe Plain. For Tianjin (117.0°E, 39.0°N), and (114.5°E, 38.0°N), the T3 test (Figure 5(c)), which considers the total demands corresponding with their high water demands. #e in the Taihang Mountains and Haihe Plain, the mean 6 Advances in Meteorology

Table 2: Actual statistical water use over the Haihe River Basin and As shown in Figure 5(g), increased precipitation was de- estimated water demands over the Taihang Mountains and Haihe tected in most areas of the Haihe Plain, with a mean value of 8 3 Plain from 1996 to 2007 (unit: 10 m /year). 0.046 mm/d. #e mean precipitation also increased by Year Haihe river basin Taihang mountains Haihe Plain 0.020 mm/d in the Taihang Mountains, and the spatial dis- 1996 413.1 140.1 244.1 tribution of increased precipitation was in accordance with 1997 432.8 146.8 255.8 the distribution of soil moisture (Figure 5(d)). #e distri- 1998 432.0 146.5 255.3 bution of precipitation differences in Figure 5(h) is similar to 1999 431.5 146.4 255.0 that in Figure 5(g) but with approximately double values, 2000 398.4 135.1 235.5 specifically, 0.030 mm/d in the Taihang Mountains and 2001 392.0 133.0 231.7 0.089 mm/d in the Haihe Plain. For the T3 test (Figure 5(i)), 2002 399.83 135.6 236.3 when the water consumption processes in the Taihang 2003 377.0 127.9 222.8 Mountains were considered in conjunction with the processes 2004 368.01 124.8 217.5 in the Haihe Plain, the precipitation in the Haihe Plain was 2005 379.1 128.6 224.1 slightly enhanced to 0.050 mm/day due to more land water 2006 391.0 132.6 231.1 2007 384.48 130.4 227.2 over a larger scale being transferred into the atmosphere. In addition, precipitation increased by 0.028 mm/day in the Taihang Mountains, which is slightly higher than the mean difference in the T1 test, due to local increased evaporation. groundwater depth difference in the Haihe Plain was Accompanying the regional wetting effects, the water 13.82 m, which is approximately the same as the difference consumption processes also led to cooling effects via changes observed in the T1 test. Due to local groundwater exploi- in the heat fluxes emitted from the land surface. As shown in tation, the groundwater table in the Taihang Mountains Figure 6(a), the mean 2 m air temperature decreased by dropped by 3.78 m on average. #e greatest drops appeared 0.613 K in the Haihe Plain due to the local water con- in the eastern piedmont areas and the south-central valleys, sumption of the T1 test. Weak cooling effects were also corresponding with the higher water demand there, as detected in the Taihang Mountains, with a mean tempera- shown in Figure 3(b). ture decrease of 0.049 K. For the T2 test with doubled de- #e processes of water exploitation and consumption mand (Figure 6(b)), the changes in temperature were slightly withdraw groundwater and consume it at the surface, which less than double the changes observed in the T1 test. #ere leads to increased upper soil moisture and humidity of the were decreases of 1.121 K in the Haihe Plain and 0.089 K in lower atmosphere. Increased moisture at the land surface the Taihang Mountains. For the T3 test (Figure 6(c)), the may further enhance regional wetting effects via vertical cooling effects were slightly enhanced in the Haihe Plain. convective activity and horizontal transport in the upper #e mean temperature decrease was 0.646 K in the Haihe troposphere. Plain and 0.201 K in the Taihang Mountains. #e differences in soil moisture at 10 cm depth be- In CLM, changes in the upward heat fluxes emitted from tween the exploitation and control tests are shown in Fig- the land surface are reliant on differences in temperature and ures 5(d)–5(f). #e changes in soil moisture at 10 cm depth humidity between the land surface and the lower atmo- were highly dependent on changes in precipitation and sphere. #us, locally wetter and cooler air, due to water irrigation water reaching the soil surface. As shown in Fig- consumption in the Haihe Plain, was transferred to the ure 5(d), the soil moisture increased by a mean of 0.015 m3/m3 Taihang Mountains via atmospheric activity. Subsequently, in the Haihe Plain, and a large increase in soil moisture changes in land-atmosphere differences in temperature and appeared in the central areas, where irrigation demands humidity further affected the heat fluxes in the Taihang occupy a high proportion of the total demand. Wetting effects Mountains. #erefore, the changes in the heat fluxes of the in the Haihe Plain also led to an increase in soil moisture in Taihang Mountains, which were indirectly affected by the the Taihang Mountains; however, this increase was only wetter and cooler atmosphere of the Haihe Plain, were far 0.002 m3/m3, because the increase was entirely from increased less than the changes in the Haihe Plain. For the T1 test local precipitation. For the T2 test with doubled water de- (Figure 6(d)), the sensible heat flux decreased by mand (Figure 5(e)), soil moisture increased by 0.040 m3/m3 in 7.682 W/m2, on average, in the Haihe Plain, yet only de- the Haihe Plain and 0.003 m3/m3 in the Taihang Mountains. creased by 0.098 W/m2 in the Taihang Mountains. For the T2 Although the water consumption processes in the Haihe Plain test (Figure 6(e)), the mean decrease was 13.884 W/m2 in the led to increased soil moisture in the Taihang Mountains, the Haihe Plain and 0.168 W/m2 in the Taihang Mountains. If extent was weaker than that of the wetting effects caused by local water consumption is considered, the heat fluxes will be local water consumption processes. Considering the local affected significantly due to the increased evaporation as- water consumption in the Taihang Mountains (Figure 5(f)), sociated with water consumption processes. #us, the the soil moisture increased by 0.016 m3/m3 in the Haihe Plain sensible heat flux in the T3 test (Figure 6(f)) decreased by and 0.008 m3/m3 in the Taihang Mountains. 2.837 W/m2 in the Taihang Mountains and by 7.793 W/m2 in Local wetting changes at the land surface in the Haihe the Haihe Plain. Plain led to changes in precipitation, not only in the local #e changes of latent heat flux shown in Figures 6(g)–6(i) plain areas, but also in the surrounding areas (including were similar to the changes of sensible heat flux except for the Taihang Mountains) via atmospheric moisture transfer. their larger magnitudes. In the Haihe Plain, the latent heat Advances in Meteorology 7

(mm/year) (mm/year) 45N 45N 1100 1100 1000 1000 900 900 40N 800 40N 800 700 700 600 600 500 500 35N 35N 400 400 300 300 200 200

110E 115E 120E 110E 115E 120E (a) (b) (K) (K) 45N 45N

287 287

285 285 40N 40N 283 283

281 281

279 279 35N 35N 277 277

275 275

110E 115E 120E 110E 115E 120E (c) (d)

Figure 4: Spatial distributions of mean (a) observed precipitation, (b) precipitation simulated by the control test, (c) observed 2 m air temperature, and (d) 2 m air temperature simulated by the control test.

Table 3: Statistics of control test in the Taihang Mountains and Haihe Plain. STD Domain Variable COR∗ RMSE Observation Simulation Precipitation 0.931 0.519 mm/day 1.380 mm/day 1.418 mm/day Taihang mountains Temperature 0.998 1.032 K 10.163 K 9.935 K Precipitation 0.936 0.732 mm/day 1.691 mm/day 1.312 mm/day Haihe Plain Temperature 0.997 1.186 K 10.296 K 10.640 K ���������������������������� ∗ n n 2 n 2 COR: correlation coefficient, COR � (�i�1(xmi − xm)(xoi − xo))/( �i�1(xmi − xm) · �i�1(xoi − xo) ), xmi and xoi are the simulated������������������� and observed n 2 monthly mean precipitation or 2 m���������������� air temperature in the month i, and n is the number of months; RMSE: root-mean-square error, RMSE � (1/n)�i�1(xmi − xoi) ; n 2 STD: standard deviation, STD � (1/n)�i�1(xi − x) .

flux increased by 11.683 W/m2, 21.412 W/m2, and 3.3. Vertical Profiles in Soil and Atmosphere Layers. To in- 11.994 W/m2 for the T1, T2, and T3 tests, respectively. In the vestigate changes in vertical profiles, Figure 7 provides Taihang Mountains, the fluxes increased by 0.413 W/m2, profiles of mean soil moisture and temperature differences in 0.673 W/m2, and 4.644 W/m2, respectively. #e sum of sen- the Taihang Mountains. #e profiles of soil moisture dif- sible and latent heat flux was positive in most areas of the ferences (Figure 7(a)) indicate that soil moisture does not Taihang Mountains, indicating that the total heat flux emitted change greatly with depth, except for the wetter upper layers into the atmosphere from the land surface increased and the in the T1 and T2 tests, because changes in soil moisture in temperature of the soil layers would subsequently change. the Taihang Mountains are reliant on increased precipitation 8 Advances in Meteorology

(m) (m) (m) 45N 45N 45N 20 20 20 16 16 16 12 12 12 40N 8 40N 8 40N 8 4 4 4 0 0 0 –4 –4 –4 35N –8 35N –8 35N –8 –12 –12 –12 –16 –16 –16 –20 –20 –20

110E 115E 120E 110E 115E 120E 110E 115E 120E (a) (b) (c)

(m3/m3) (m3/m3) (m3/m3) 45N 45N 45N 0.02 0.02 0.02 0.016 0.016 0.016 0.012 0.012 0.012 40N 0.008 40N 0.008 40N 0.008 0.004 0.004 0.004 0 0 0 –0.004 –0.004 –0.004 35N –0.008 35N –0.008 35N –0.008 –0.012 –0.012 –0.012 –0.016 –0.016 –0.016 –0.02 –0.02 –0.02

110E 115E 120E 110E 115E 120E 110E 115E 120E (d) (e) (f) (mm/day) (mm/day) (mm/day) 45N 45N 45N 0.1 0.1 0.1 0.08 0.08 0.08 0.06 0.06 0.06 40N 0.04 40N 0.04 40N 0.04 0.02 0.02 0.02 0 0 0 –0.02 –0.02 –0.02 35N –0.04 35N –0.04 35N –0.04 –0.06 –0.06 –0.06 –0.08 –0.08 –0.08 –0.1 –0.1 –0.1

110E 115E 120E 110E 115E 120E 110E 115E 120E (g) (h) (i)

Figure 5: Spatial distributions of groundwater depth difference between tests (a) T1−CTL, (b) T2−CTL, and (c) T3−CTL. Mean soil moisture difference at 10 cm depth for (d) T1−CTL, (e) T2−CTL, and (f) T3−CTL. Mean precipitation difference for (g) T1−CTL, (h) T2−CTL, and (i) T3−CTL. reaching the topsoil layer. #e differences between the T1 temperature decrease in the upper soil layers was greater and T2 tests in each soil layer basically remain constant. For than that in the lower layers, and the decrease tends to be the T3 test, which considered local water exploitation and constant with depth. consumption, soil moisture increased significantly in the While domain-averaged profile curves are provided in upper layers due to increased water fluxes (e.g., irrigation Figure 7, latitude-averaged contours of atmospheric tem- and precipitation) into the top layer. In CLM, drainage from perature and humidity between 35°N and 40°N are provided soil layers to aquifers is reliant on a water potential gradient in Figure 8 to investigate the transfer of cooling and wetting between the soil layers and aquifers. #is drainage will effects into the atmosphere. As shown in Figure 8(a), in- gradually approach the maximum infiltration capacity with creased air humidity over the Haihe Plain (approximately increasing groundwater table depth. #erefore, in the lower 114.5°E−119°E) below a pressure of 800 hPa was detected, soil layers, soil moisture was detected to decrease with depth. and at a pressure of around 600 hPa, a center of dry values Unlike the characterization of soil moisture, the soil was detected because the convergent flow from the lower bottom is regarded as being heat-insulated in CLM, and soil atmosphere becomes divergent at 600 hPa. For the T2 test temperature is only dependent on the energy balance at the (Figure 8(b)), the wetting effects in the lower atmosphere surface. As shown in Figure 7(b), soil temperature differ- were enhanced, and more moisture was transferred to the ences basically remained constant with depth in the T1 and atmosphere over the Taihang Mountains (approximately T2 tests, and the differences in the T2 test were slightly less 111°E−114.5°E). #e wetting effects in the south-central than double the differences in the T1 test. For the T3 test, due valleys of the Taihang Mountains (about 112.5°E) were to the continuous water consumption process, the even stronger in the T3 test (Figure 8(c)) than the wetting Advances in Meteorology 9

(K) (K) (K) 45N 45N 45N 1 1 1 0.8 0.8 0.8 0.6 0.6 0.6 40N 0.4 40N 0.4 40N 0.4 0.2 0.2 0.2 0 0 0 –0.2 –0.2 –0.2 35N –0.4 35N –0.4 35N –0.4 –0.6 –0.6 –0.6 –0.8 –0.8 –0.8 –1 –1 –1

110E 115E 120E 110E 115E 120E 110E 115E 120E (a) (b) (c)

(W/m2) (W/m2) (W/m2) 45N 45N 45N 10 10 10 8 8 8 6 6 6 40N 4 40N 4 40N 4 2 2 2 0 0 0 –2 –2 –2 35N –4 35N –4 35N –4 –6 –6 –6 –8 –8 –8 –10 –10 –10

110E 115E 120E 110E 115E 120E 110E 115E 120E (d) (e) (f)

(W/m2) (W/m2) (W/m2) 45N 45N 45N 20 20 20 16 16 16 12 12 12 40N 8 40N 8 40N 8 4 4 4 0 0 0 –4 –4 –4 35N –8 35N –8 35N –8 –12 –12 –12 –16 –16 –16 –20 –20 –20

110E 115E 120E 110E 115E 120E 110E 115E 120E (g) (h) (i)

Figure 6: Spatial distributions of mean 2 m air temperature difference between tests (a) T1−CTL, (b) T2−CTL, and (c) T3−CTL. Sensible heat flux difference for (d) T1−CTL, (e) T2−CTL, and (f) T3−CTL. Latent heat flux difference for (g) T1−CTL, (h) T2−CTL, and (i) T3−CTL. effects in the Haihe Plain. When compared with the changes Mountains. #e series in Figure 9(a) indicates that the in- in the T1 test (Figure 8(a)), the atmospheric wetting effects creased groundwater depths in the T1 and T2 tests were were enhanced over the Haihe Plain, and this slight en- almost negligible and the groundwater depth dropped hancement was in accordance with the mean differences in continuously due to local groundwater exploitation in the T3 climatic variables shown in Figures 5 and 6. test. For the other series of variables, the differences between #e profiles of air temperature differences (Figures 8(d)– the T1 and T2 tests indicate that doubled water consumption 8(f)) were similar to the air humidity profiles; that is, water in the Haihe Plain did not cause doubled changes in the consumption processes in the Haihe Plain lead to regional Taihang Mountains in most years. A negative feedback cooling effects in the atmosphere; not only over the Haihe mechanism exists between increased water demand and Plain, but also over its surrounding areas, including the western subsequent climatic changes. Taihang Mountains. #e cooling effects were basically below Meanwhile, the differences between the T1 and T3 tests the 800 hPa level and were enhanced as the water volume indicate that changes in land surface variables due to water consumed at the surface increased. Also, the cooling effects consumption are usually far greater when the water is over the Haihe Plain were enhanced slightly due to water consumed locally rather than in neighboring areas. #e consumption over a larger-scale domain. differences between the T1 and T3 tests also differed between variables. For the atmospheric variables, these differences were the lowest, because cooling and wetting effects oc- 3.4. Annual and Seasonal Variability. Figure 9 shows the curring in the Haihe Plain can be transferred to neighboring mean annual series of differences for the Taihang areas via atmospheric activity, including to the Taihang 10 Advances in Meteorology

0.50 0.50

1.00 1.00

1.50 1.50 Depth (m) Depth (m)

2.00 2.00

2.50 2.50

–0.30.0 0.3 0.6 0.9 1.2 1.5 –0.35 –0.30 –0.25 –0.20 –0.15 –0.10 Moisture (0.01m3/m3) Temperature (K) T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (a) (b)

Figure 7: Mean profiles of (a) soil moisture difference and (b) soil temperature difference in the Taihang Mountains.

(g/kg) (g/kg) (g/kg) 100 100 100

300 300 300 400 400 400 500 500 500

Pressure (hPa) 700 Pressure (hPa) 700 Pressure (hPa) 700 850 850 850 925 925 925 1000 1000 1000 110E 115E 120E 110E 115E 120E 110E 115E 120E

–0.16 –0.08 0 0.08 0.16 –0.16 –0.08 0 0.08 0.16 –0.16 –0.08 0 0.08 0.16 (a) (b) (c) (K) (K) (K) 100 100 100

300 300 300 400 400 400 500 500 500

Pressure (hPa) 700 Pressure (hPa) 700 Pressure (hPa) 700 850 850 850 925 925 925 1000 1000 1000 110E 115E 120E 110E 115E 120E 110E 115E 120E

–0.16 –0.08 0 0.08 0.16 –0.16 –0.08 0 0.08 0.16 –0.16 –0.08 0 0.08 0.16 (d) (e) (f)

Figure 8: #e distributions of longitude vs pressure for mean air humidity difference (a) T1−CTL, (b) T2−CTL, (c) T3−CTL; and air temperature difference (d) T1−CTL, (e) T2−CTL, (f) T3−CTL averaged between 35°N and 40°N. Advances in Meteorology 11

–1.0 0.010 ) 0.0 3 0.0080 /m

3 0.0060 1.0 0.0040 2.0 0.0020 3.0 Water depth (m)

Moisture (m 0.0000 4.0 –0.0020 1996 1998 2000 2002 2004 2006 1996 1998 2000 2002 2004 2006 Year Year T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (a) (b) 0.00 0.09 –0.05 0.06 –0.10 0.03 –0.15

0.00 –0.20

Temperature (K) –0.25

Precipitation (mm/d) –0.03 –0.30 1996 1998 2000 2002 2004 2006 1996 1998 2000 2002 2004 2006 Year Year T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (c) (d) 1.0 6.0

) ) 5.0 2 0.0 2 4.0 –1.0 3.0 –2.0 2.0 1.0 –3.0 Heat flux (W/m Heat flux (W/m 0.0 –4.0 –1.0 1996 1998 2000 2002 2004 2006 1996 1998 2000 2002 2004 2006 Year Year T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (e) (f)

Figure 9: Annual series of (a) groundwater depth differences, (b) soil moisture differences at 10 cm depth, (c) precipitation differences, (d) 2 m air temperature differences, (e) sensible heat flux differences, (f) latent heat flux differences in the Taihang Mountains.

Mountains. For example, the precipitation data series of the were nearly negligible, and the series of the two variables in T1 test (Figure 9(c)) was approximately 35% lower than the the T1 test were nearly 90–95% lower in magnitude than T3 series. #e differences between the T1 and T3 tests were those of the T3 test. higher for the land surface variables, which were further #e mean monthly differences between the three ex- affected by the cooler and wetter atmosphere. For example, ploitation tests and the control test in the Taihang Moun- the T1 series of 10 cm soil moisture (Figure 9(b)) and 2 m air tains are shown in Figure 10. #e monthly differences in temperature (Figure 9(d)) were nearly 20–25% of the T3 groundwater depth are not shown because their changes in series in magnitude. For the heat fluxes emitted from the the T1 and T2 tests were tiny, and the monthly changes in the land surface, sensible heat flux (Figure 9(e)), and latent heat T3 test had no obvious seasonal variations due to continuous flux (Figure 9(f)) were strongly affected by changes in exploitation. As shown in Figure 10(a), the monthly dif- evaporation associated with local water consumption pro- ferences of 10 cm soil moisture in the T1 and T2 tests did not cesses. #e indirect effects from the neighboring Haihe Plain vary greatly. #e increases in soil moisture were slightly 12 Advances in Meteorology

0.15 0.015

) 0.12 3 0.012 /m 3 0.009 0.09

0.006 0.06

0.003 0.03 Moisture (m Precipitation (mm/d) 0.000 0.00 Jul Jul Jan Jan Jun Jun Sep Sep Feb Feb Oct Oct Dec Apr Dec Apr Aug Aug Mar Mar Nov Nov May May

T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (a) (b)

0.00 0.0 )

2 –1.0 –0.20 –2.0

–3.0 –0.40 Temperature (K) Heat flux (W/m –4.0

–0.60 –5.0 Jul Jul Jan Jan Jun Jun Sep Sep Feb Feb Oct Oct Dec Dec Apr Apr Aug Aug Mar Mar Nov Nov May May

T3-CTL T3-CTL T2-CTL T2-CTL T1-CTL T1-CTL (c) (d)

10 )

2 8

6

4

Heat flux (W/m 2

0 Jul Jan Jun Sep Feb Oct Dec Apr Aug Mar Nov May

T3-CTL T2-CTL T1-CTL (e)

Figure 10: Mean monthly (a) soil moisture differences at 10 cm depth, (b) precipitation differences, (c) 2 m air temperature differences, (d) sensible heat flux differences, and (e) latent heat flux differences in the Taihang Mountains.

greater during the irrigation seasons (primarily spring and consumption processes, 10 cm soil moisture was detected to summer) due to increased precipitation, and during the increase clearly due to local irrigation. nonirrigation seasons (primarily autumn and winter), the For the other four variables, most differences occurred increased soil moisture reduced gradually due to the water- during the irrigation seasons, regardless of whether local holding capacity of soil. When considering local water water consumption processes were considered. During Advances in Meteorology 13 nonirrigation seasons, the wetter and cooler differences still atmosphere interactions. (4) #e cooling and wetting ef- existed in the T3 test, because increased evaporation due to fects of water consumption activities mainly occur during domestic and industrial consumption can also lead to wetter irrigation periods, because irrigation comprises most of the and cooler differences to a certain level. However, the dif- total water demand in the Haihe Plain and Taihang ferences in the T1 and T2 tests were almost negligible during Mountains. the nonirrigation seasons because the cooling and wetting #is study simulated the impacts of water consumption differences were weak, even in the Haihe Plain, and low activities in the Haihe Plain on the local climate in the temperatures in nonirrigation seasons also suppress con- Taihang Mountains, located immediately to the west. vection and moisture transfer from the Haihe Plain to the However, many aspects of the tests, including data esti- neighboring Taihang Mountains. mation, scheme design, and structural defects in RegCM4/CLM3.5, introduced uncertainties to the simula- 4. Conclusion and Discussion tions and are discussed next. Although the resolution of the water demand data used In this study, the regional climate model RegCM4 was in- in this study was higher than the one used in our previous corporated with a scheme of human-induced water ex- studies, there was potential underestimation of the total ploitation and consumption to investigate the impact of demand according to Table 1. #e largest uncertainty water consumption activities in the Haihe Plain on the local remains the estimation of irrigation water use. In this climate in the Taihang Mountains. Four simulation tests study, the ratio between the irrigated and total cropland (i.e., three exploitation tests and one control test) were areas, α, was constant derived from the China Statistical conducted. One Exploitation Test (T1) considered water Yearbook 2000. Future work should address the acquisi- consumption activities in the Haihe Plain and another (T2) tion of more reasonable distributions of α by using remote doubled these water demands. #e third Exploitation Test sensing data. #e Water Resource Bulletin of China further (T3) considered the combined water demands of the Haihe indicates that the per capita water use differs greatly be- Plain and Taihang Mountains, and a control test (CTL) tween urban and rural regions. #e lack of consideration simulated a case without any water exploitation or con- about the rural-urban difference may lead to excessive sumption. By comparing the differences between these three domestic water demand estimation over rural regions; exploitation tests and the control test, we were able to an- thus, a further division between rural and urban domestic alyze changes to climatic variables in the Taihang Mountains demand should be addressed to differentiate the allocation due to water consumption activities in the neighboring proportion of domestic water consumption. #e scheme of Haihe Plain. water exploitation and consumption used in this study was #e main conclusions are as follows. (1) #e processes of highly simplified, and more detailed data should be ob- water exploitation and consumption in the Haihe Plain tained to improve the spatial heterogeneity of water de- cause rapid increase of groundwater depth and local cooling mand estimation and the allocation process of water and wetting effects at the land surface. #is causes a cooler consumption. and wetter atmosphere that transfers to surrounding areas, Structural defects in the RegCM4/CLM3.5 models also including the Taihang Mountains, leading to decreases in introduced uncertainties. For example, water in lakes and temperature and increases in surface moisture. #e cooling reservoirs was not included in the water cycle process of the and wetting changes at the land surface of the Taihang model. Although significant improvements were made by Mountains affect the local energy balance, increasing latent introducing the lake depth dataset, more accurate lake pa- heat flux, and decreasing sensible heat flux emitted from the rameterization, and groundwater recharge processes, the land surface. (2) #ese wetting and cooling effects are water volume in lakes and reservoirs was still maintained positively related to the volume of water consumed. How- constant even in the newer versions of the CLM model ever, the cooling effects do not enhance the development of [47–49]. #us, the surface water supply was only withdrawn lower atmospheric convection and moisture transfer. For the from river channels, leading to overexploited groundwater T2 test with double the water demands of the T1 test, the and deeper groundwater table in the simulation tests. A wetting and cooling changes were slightly suppressed and mechanism of reservoir regulation based on the accurate were less than double the changes estimated in the T1 test. description of the reservoir water volume should be included (3) In the simulations, water consumption activities in the in the river transport model component of CLM. #e Haihe Plain-induced wetting and cooling effects in the processes of groundwater lateral flow were also not included Taihang Mountains via atmospheric transfer. However, in the model, and the groundwater table depth was only these wetting and cooling effects were rather weak at the land dependent on water input/output in the vertical direction. surface and caused less change than that caused by local #e lack of groundwater lateral flow data input may lead to water exploitation and consumption in the Taihang groundwater grid cells being isolated from each other and to Mountains. For the T1 and T2 tests, changes at the land spatial distributions of simulated groundwater depths dif- surface in the Taihang Mountains were entirely caused by fering from actual conditions, especially in mountainous changes in atmospheric variables. For comparison, the areas. Future efforts should be made to incorporate 3-D conditions of the T3 test were the opposite—local water groundwater flow into the CLM model. consumption activities first led to changes of surface vari- Furthermore, the time frame of the study is relatively old. ables, then atmospheric variables changed due to land- #e year 2000 is used as the benchmark year and the 14 Advances in Meteorology simulation tests are conducted for the years 1996–2007. As Mountain, China,” Journal of Food, Agriculture & Environ- there are significant demographic changes in the areas under ment, vol. 8, no. 3, pp. 985–990, 2010. study during the past decade, it would definitely be of in- [3] S. Piao, P. Ciais, Y. Huang et al., “#e impacts of climate terest to explore the climatic effects of water exploitation and change on water resources and agriculture in China,” Nature, consumption using the updated numbers of population, vol. 467, no. 7311, pp. 43–51, 2010. GDP, and water use. #is could be the focus of a future [4] S. Wang and Z. Zhang, “Effects of climate change on water resources in China,” Climate Research, vol. 47, no. 1, study; however, here we discuss a few preliminary aspects. In pp. 77–82, 2011. fact, the water use over China in general and the Haihe Plain [5] J. Liu, “#e temporal–spatial variation characteristics of in particular did not increase significantly from the year 2000 precipitation in Beijing–Tianjin–Heibei during 1961–2012,” onwards. #e rapid increase in water use that took place in Climate Change Research Letters, vol. 3, no. 3, pp. 146–153, the previous decades (mainly the 1980s and 1990s) was 2014, in Chinese. discontinued upon the beginning of the new millennium. [6] Z. Bao, J. Zhang, G. Wang et al., “Attribution for decreasing According to the Water Resource Bulletin of China over the streamflow of the Haihe River basin, northern China: climate years, the total water volume used all over China was 556.6 variability or human activities,” Journal of Hydrology, billion m3 in 1997 as opposed to 604 billion m3 in 2016. vol. 460-461, pp. 117–129, 2012. #erefore, and despite the significant increase of the Chinese [7] Z. Li, X. Deng, F. Yin, and C. Yang, “Analysis of climate and GDP after the year 2000, the total water use was charac- land use changes impacts on land degradation in the North China Plain,” Advances in Meteorology, vol. 2015, Article ID terized by a relatively small raise. And it is widely known 976370, 11 pages, 2015. from previous studies that the climatic impacts of water [8] J. Liu, Q. Zhang, V. Singh, and P. Shi, “Contribution of consumption rely heavily on the volume of the water multiple climatic variables and human activities to streamflow consumed [18, 19, 23, 25]. Moreover, our study uses mul- changes across China,” Journal of Hydrology, vol. 545, tiyear mean differences of the simulation tests to discuss the pp. 145–162, 2017. mechanism and magnitude of the climatic impacts of water [9] L. Ren, M. Wang, C. Li, and W. Zhang, “Impacts of human consumption, which are not expected to change significantly activity on river runoff in the northern area of China,” Journal if the climate forcing data of RegCM4 is replaced with of Hydrology, vol. 261, no. 1–4, pp. 204–217, 2002. a recent dataset. #erefore, based on the aforementioned [10] Y. Jia, X. Ding, H. Wang et al., “Attribution of water resources features, it would be expected that the results of our sim- evolution in the highly water-stressed Basin of China,” ulation tests would not change significantly if more recent Water Resources Research, vol. 48, no. 2, article W02513, 2012. data was to be used. [11] Department of Urban & Social Economic Survey, National Bureau of Statistics of China, Urban Statistical Yearbook of China 2000, China Statistics Press, Beijing, China, 2001, in Chinese. Data Availability [12] S. Foster, H. Garduno, R. Evans et al., “Quaternary aquifer of the North China Plain−assessing and achieving groundwater #e data used to support the findings of this study are resource sustainability,” Hydrogeology Journal, vol. 12, no. 1, available from the corresponding author upon request. pp. 81–93, 2004. [13] G. Cao, C. Zheng, B. R. Scanlon et al., “Use of flow modeling Conflicts of Interest to assess sustainability of groundwater resources in the North China Plain,” Water Resources Research, vol. 49, no. 1, #e authors declare that they have no conflicts of interest. pp. 159–175, 2013. [14] J. Liu, C. Zheng, and Y. Lei, “Ground water sustainability: Acknowledgments methodology and application to the North China Plain,” Ground Water, vol. 46, no. 6, pp. 897–909, 2008. #e authors would like to thank Prof. #eodore Karacostas [15] W. Feng, M. Zhong, J. M. L. Lemoine et al., “Evaluation of and the reviewers for the comments and suggestions on this groundwater depletion in North China using the Gravity paper. #is study was partially supported by the National Key Recovery and Climate Experiment (GRACE) data and Research and Development Program of China (grant ground-based measurements,” Water Resources Research, 2017YFA0603702), the National Basic Research Program of vol. 49, no. 4, pp. 2110–2118, 2013. [16] C. Davidsen, S. Liu, X. Mo, D. Rosbjerg, and P. Bauer- China (973 Program) (grant 2015CB452701), the Natural Gottwein, “#e cost of ending groundwater overdraft on the Science Foundation of China (grants 41705046, 41571019, and North China Plain,” Hydrology and Earth System Sciences, 41606112;), the Key Research and Development Program of vol. 20, no. 2, pp. 771–785, 2016. Shandong Province of China (grant 2016JMRH0538), and the [17] D. Han, G. Wang, B. Xue, T. Liu, A. Yinglan, and X. Xu, Natural Science Foundation of Shandong Province of China “Evaluation of semiarid grassland degradation in North China (grant ZR2016DB32). from multiple perspectives,” Ecological Engineering, vol. 112, pp. 41–50, 2018. References [18] K. J. Harding and P. K. Snyder, “Modeling the atmospheric response to irrigation in the Great Plains. Part I: general [1] X. Li, B. Wu, H. Wang, and J. Zhang, “Regional soil erosion impacts on precipitation and the energy budget,” Journal of risk assessment in Hai Basin,” Journal of Remote Sensing, Hydrometeorology, vol. 13, no. 6, pp. 1667–1686, 2012. vol. 15, no. 2, pp. 372–387, 2011. [19] E. M. Douglas, A. Beltran-Przekurat, D. Niyogi, A. Pielke Sr., [2] X. Liu, W. Zhang, Z. Liu, F. Qu, and W. Song, “Impacts of land and C. J. V¨or¨osmarty, “#e impact of agricultural in- cover changes on soil chemical properties in Taihang tensification and irrigation on land-atmosphere interactions Advances in Meteorology 15

and Indian monsoon precipitation—a mesoscale modeling [35] J. Zou, C. Zhan, Z. Xie, P. Qin, and S. Jiang, “Climatic impacts perspective,” Global and Planetary Change, vol. 67, no. 1-2, of the middle route of the south-to-North water transfer pp. 117–128, 2009. project over the Haihe River basin in North China simulated [20] M. J. Puma and B. I. Cook, “Effects of irrigation on global by a regional climate model,” Journal of Geophysical Research: climate during the 20th century,” Journal of Geophysical Atmospheres, vol. 121, no. 15, pp. 8983–8999, 2016. Research, vol. 115, no. D16, article D16120, 2010. [36] Y. Mao, A. Ye, X. Liu, F. Ma, X. Deng, and Z. Zhou, “High- [21] L. M. Kueppers and M. A. Snyder, “Influence of irrigated resolution simulation of the spatial pattern of water use in agricultural on diurnal surface energy and water fluxes, continental China,” Hydrological Sciences Journal, vol. 61, surface climate, and atmospheric circulation in California,” no. 14, pp. 2626–2638, 2016. Climate Dynamics, vol. 38, no. 5-6, pp. 1017–1029, 2012. [37] N. Hanasaki, S. Kanae, T. Oki et al., “An integrated model for [22] Y. Qian, M. Huang, B. Yang, and L. K. Berg, “A modeling the assessment of global water resources−part 1: model de- study of irrigation effects on surface fluxes and land-air-cloud scription and input meteorological forcing,” Hydrology and interactions in the Southern Great Plains,” Journal of Hy- Earth System Sciences, vol. 12, no. 4, pp. 1007–1025, 2008. drometeorology, vol. 14, no. 3, pp. 700–712, 2013. [38] I. A. Shiklomanov, “Appraisal and assessment of world water [23] F. Saeed, S. Hagemann, and D. Jacob, “Impact of irrigation on resources,” Water International, vol. 25, no. 1, pp. 11–32, 2000. the South Asian summer monsoon,” Geophysical Research [39] F. Zhang, D. Wang, and B. Qiu, China Agricultural Phenology Letters, vol. 36, no. 20, article L20711, 2009. Atlas, Science Press, Beijing, China, 1987, in Chinese. [24] A. DeAngelis, F. Dominuez, Y. Fan, A. Robock, M. Deniz [40] D. Xiao, F. Tao, Y. Liu et al., “Observed changes in winter Kustu, and D. Robinson, “Evidence of enhanced precipitation wheat phenology in the North China Plain for 1981–2009,” due to irrigation over the Great Plains of the United States,” International Journal of Biometeorology, vol. 57, no. 2, Journal of Geophysical Research, vol. 115, no. D15, article pp. 275–285, 2013. D15115, 2010. [41] F. Tao, S. Zhang, Z. Zhang, and R. Rotter, “Maize growing [25] F. Chen and Z. Xie, “Effects of interbasin water transfer on duration was prolonged across China in the past three decades regional climate: a case study of the Middle Route of the under the combined effects of temperature, agronomic South-to-North Water Transfer Project in China,” Journal of management, and cultivar shift,” Global Change Biology, Geophysical Research, vol. 115, no. D11, article D11112, 2010. vol. 20, no. 12, pp. 3686–3699, 2014. [26] G. Leng, Q. Tang, M. Huang, and L. R. Leung, “A comparative [42] Y. Liu, R. Xie, P. Hou et al., “Phenological responses of maize analysis of the impacts of climate change and irrigation on to changes in environment when grown at different latitudes land surface and subsurface hydrology in the North China in China,” Field Crops Research, vol. 144, pp. 192–199, 2013. Plain,” Regional Environmental Change, vol. 15, no. 2, [43] Y. Ran, X. Li, L. Lu, and Z. Li, “Large-scale land cover pp. 251–263, 2015. mapping with the integration of multi-source information [27] F. Giorgi, E. Coppola, F. Solmon et al., “RegCM4: model based on the Dempster-Shafer theory,” International Journal description and preliminary tests over multiple CORDEX of Geographical Information Science, vol. 26, no. 1, pp. 169– domains,” Climate Research, vol. 52, pp. 7–29, 2012. 191, 2010. [28] K. W. Oleson, G. Niu, Z. Yang et al., “Improvements to the [44] National Bureau of Statistics of China, China Statistical Community Land Model and their impact on the hydrological Yearbook 2000, China Statistics Press, Beijing, China, 2001, in cycle,” Journal of Geophysical Research, vol. 113, no. G1, article Chinese. G01021, 2008. [45] Z. Xie, F. Yuan, Q. Duan, J. Zheng, M. Liang, and F. Chen, [29] G. Niu, Z. Yang, R. E. Dickinson, and L. E. Gulden, “A simple “Regional parameter estimation of the VIC land surface TOPMODEL-based runoff parameterization (SIMTOP) for model: methodology and application to river basins in China,” use in global climate models,” Journal of Geophysical Research, Journal of Hydrometeorology, vol. 8, no. 3, pp. 447–468, 2007. vol. 110, no. D21, article D21106, 2005. [46] J. Xie, Y. Zeng, M. Zhang, and Z. Xie, “Detection and at- [30] G. Y. Niu, Z. L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su, tribution of the influence of climate change and human ac- “Development of a simple groundwater model for use in tivity on hydrological cycle in China’s eastern monsoon area,” climate models and evaluation with Gravity Recovery and Climatic and Environmental Research, vol. 21, no. 1, pp. 87– Climate Experiment data,” Journal of Geophysical Research, 98, 2016, in Chinese. vol. 112, no. D7, article D07103, 2007. [47] E. Kourzeneva, H. Asensio, E. Martin, and S. Faroux, “Global [31] X. Wang, M. Yang, and G. Pang, “Influences of two land- gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modeling,” Tellus surface schemes on RegCM4 precipitation simulations over A: Dynamic Meteorology and Oceanography, vol. 64, no. 1, the Tibetan Plateau,” Advances in Meteorology, vol. 2015, p. 15640, 2012. Article ID 106891, 12 pages, 2015. [48] Z. M. Subin, W. J. Riley, and D. Mironov, “An improved lake [32] M. Ashfaq, D. Rastogi, R. Mei et al., “High-resolution en- model for climate simulations: model structure, evaluation, semble projections of near-term regional climate over the and sensitivity analyses in CESM1,” Journal of Advances in continental United States,” Journal of Geophysical Research, Modeling Earth Systems, vol. 4, article M02001, 2012. vol. 121, no. 17, pp. 9943–9963, 2016. [49] M. Zampieri, E. Serpetzoglou, E. N. Anagnostou et al., [33] A. J. K. Mbienda, C. Tchawoua, D. A. Vondou, P. Choumbou, “Improving the representation of river-groundwater in- C. Kenfack Sadem, and S. Dey, “Sensitivity experiments of teractions in land surface modeling at the regional scale: RegCM4 simulations to different convective schemes over observational evidence and parameterization applied in the Central Africa,” International Journal of Climatology, vol. 37, Community Land Model,” Journal of Hydrology, vol. 420-421, no. 1, pp. 328–342, 2017. pp. 72–86, 2012. [34] J. Zou, Z. Xie, C. Zhan et al., “Effects of anthropogenic groundwater exploitation on land surface processes: a case study of the Haihe River Basin, northern China,” Journal of Hydrology, vol. 524, pp. 625–641, 2015. 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