Journal of Hydrology 426–427 (2012) 39–54

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

Effects of multiple environment stresses on evapotranspirationand runoff over eastern China

Mingliang Liu 1, Hanqin Tian ⇑, Chaoqun Lu, Xiaofeng Xu, Guangsheng Chen, Wei RenEcosystem Dynamics and Global Ecology (EDGE) Laboratory, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USAInternational Center for Climate and Global Change Research, Auburn University, Auburn, AL 36849, USA

a r t i c l e i n f o s u m m a r y

Article history:Received 24 April 2011Received in revised form 24 September2011Accepted 10 January 2012Available online 18 January 2012This manuscript was handled by AndrasBardossy, Editor-in-Chief, with theassistance of K.P. Sudheer, Associate Editor

Keywords:ChinaEvapotranspirationGlobal changeRunoffWater resources

0022-1694/$ - see front matter � 2012 Elsevier B.V. Adoi:10.1016/j.jhydrol.2012.01.009

⇑ Corresponding author at: Ecosystem DynamicsLaboratory, School of Forestry and Wildlife Sciences, A36849, USA. Tel.: +1 334 844 1059; fax: +1 334 844 1

E-mail address: tianhan@auburn.edu (H. Tian).1 Present address: Department of Civil and Environm

ton State University, Pullman, WA 99164, USA.

Little is known about how the terrestrial hydrological cycle responds to multiple environmental changesat large spatial scale and over long time period. Here, we applied a well calibrated and verified ecosystemmodel (the Dynamic Land Ecosystem Model, DLEM), in conjunction with newly developed data sets ofmultiple environmental factors including land use change, climate variability, elevated atmosphericcarbon dioxide (CO2), nitrogen deposition, ozone pollution, and nitrogen fertilizer application, to charac-terize the spatial and temporal patterns of evapotranspiration (ET) and runoff in eastern China during1961–2005 and further quantified the relative contributions of multiple environmental factors to thesepatterns at both basin and regional scales. The simulation results indicated that annual ET in the studyarea had a significantly increasing trend from 1961 to 2005. Yet there were no significant changing trendsfor estimated runoff and net water balance (precipitation minus ET). Substantial spatial heterogeneitiesin ET and runoff were observed. Annual ET increased in all basins except Yellow River Basin. Runoffincreased in the southern part of the study area (including Pearl River and the Southeast basin), butdecreased in northern part of the study area, particularly in Haihe and Huaihe river basins. Factorialanalysis showed that climate change was the dominant factor responsible for the interannual variationsin ET and runoff in the past 45 years. Land use change (including land conversions and land managementpractices) yielded minor effects on the interannual variations in ET and runoff but caused relatively largeeffects over long-term period. For the accumulated change in ET at basin scale, climate change was thedominant factor in the basins of Song-Liao, Pearl River, while land use change contributed the most inthe basin of Haihe, Huaihe, and Yellow River. Climate change was the dominant factor controlling runoffchange in all basins except Huaihe and Yangtze River basins. Our simulated results imply that it isimportant to quantify relative roles of natural and anthropogenic disturbances on water fluxes formaintaining water sustainability in a changing climate.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

The terrestrial hydrological cycle is essential for the functioningof land ecosystems; it plays crucial roles in maintaining the sus-tainability of the Earth system (Hutjes et al., 1998). Understandingthe variations of terrestrial hydrological cycle and their underlyingmechanisms are fundamental for predicting response of terrestrialecosystems to environmental changes. Previous studies have re-vealed the changes in hydrological fluxes over the past decades;for instance, both observational data and simulations indicate anon-going intensification of the hydrological cycle since the 20th

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and Global Ecology (EDGE)uburn University, Auburn, AL084.

ental Engineering, Washing-

Century (Huntington, 2006). Recent data-driven analyses detecteda declining trend of ET since 1998 at global scale (Jung et al., 2010);however, the underlying mechanisms for these changes are shortof investigation.

Many factors may contribute to the variations in ET at both tem-poral and spatial scales, such as climate variability (Arnell, 1999;Jackson et al., 2001; Milly et al., 2005; Vorosmarty and Sahagian,2000), elevated atmospheric CO2 (Field et al., 1995; Gedney et al.,2006), ozone (O3) pollution (McLaughlin et al., 2007b), nitrogen(N) inputs (Dickinson, 1991), and human activities, especially landuse/cover change (Bosch and Hewlett, 1982; Foley et al., 2005; Hut-jes et al., 1998; Liu et al., 2008; Sun et al., 2005; Vorosmarty andSahagian, 2000). The effects of these factors on the hydrological cy-cle vary for different regions and biomes. Yet the sparse observa-tions and field experiments prohibit accurately characterizing thespatial and temporal patterns of ET and runoff over large spatialscales (Sun et al., 2005). Therefore, a bottom-up modeling approach,

Fig. 1. The study area and boundary of river basins.

40 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

which simulates the hydrological processes at plant–soil–air inter-faces, can be used as a valuable tool to characterize and quantify theeffects of natural and anthropogenic factors on the water cycle onregional scales. In recent decades, process-based ecosystem modelshave been broadly applied to estimate the effects of naturalenvironmental change and anthropogenic activities on water andcarbon (C) fluxes (Cox et al., 2000; Felzer et al., 2004; Hansonet al., 2004; Houghton et al., 1983; Hurtt et al., 1998; Melilloet al., 1995; Mu et al., 2007, 2008; Sellers et al., 1997; Tian et al.,2011a,b, 1999; Xu and Singh, 2004). Although a number of studieshave been conducted to examine the spatiotemporal variations ofhydrological fluxes and their mechanisms at global scale (Jacksonet al., 2001; Jung et al., 2010; Milly et al., 2005); the regional studies,however, are still in its infancy. Given that the policy making pro-cesses are normally conducted on the basis of regional knowledgeof hydrological processes, it is highly urgent to study terrestrialhydrological processes at regional scale.

Over past decades, China has faced severe deficiency in waterresources due to increasing water demand and changing environ-ment (Brown and Halweil, 1998; Edmonds, 1999; Liu, 1998; Liuand Diamond, 2005; Xia and Chen, 2001). While China has vastfresh water resources as a nation, its water availability per capitais only one quarter of the global average, and the temporal and spa-tial dimensions of water resource distribution vary substantiallyacross the country (Xia and Chen, 2001). Consequently, the varia-tions of China’s water resources have substantial influences onits national food security (Brown and Halweil, 1998) and long-termeconomic growth (Gleick, 2000; Winpenny, 1994). Either runoff orthe balance between water supply (precipitation) and water de-mand (land evapotranspiration (ET)) can be used to quantify theregional availability of water resources. Given the scarcity oflong-term observations on runoff and ET in China, the spatiotem-poral variation of hydrological flux and their underlying mecha-nisms are short of investigation (Wei et al., 2005).

Several studies have discussed the effects of climate change onthe water cycle in China by using empirical model or watershed le-vel observations (Fu, 2003; Gao et al., 2007; Qian and Zhu, 2001;Shi and Zhang, 1995; Vorosmarty et al., 2000; Xue et al., 2005).The complex ecological responses of terrestrial ecosystems to cli-mate change, however, have rarely been considered in these esti-mations on ET and runoff. Catchment experiments and modelingapproaches have indicated the significant changes in water fluxesafter land use change (Bosch and Hewlett, 1982; Foley et al.,2005; Johnson and Kovner, 1954; Sun et al., 2006). However, mostof previous efforts were conducted at watershed scales and wereinappropriate to be extrapolated to national scales. In the mean-while, the previous studies primarily focused on single or a fewenvironmental factors, for example, variations on ET and runoffinduced by forest land conversions are the major topic in thesestudies (Fu, 2003; Sun et al., 2006; Wei et al., 2005). Given thecomplexity of land ecosystem and current global change factors(Heimann and Reichstein, 2008; Schimel et al., 1997), a study con-sidering multiple environmental factors is needed.

During the past several years, we developed an integrated pro-cess-based model – Dynamic Land Ecosystem Model (DLEM), toinvestigate the water and biogeochemical cycles of terrestrial eco-systems in the context of multifactor global change (Liu et al.,2008; Ren et al., 2007a; Tian et al., 2010a,b). As one of our earlierefforts, Liu et al. (2008) applied the DLEM to estimate effects ofland use change on ET and water yield in China during 1900–2000.

To quantify relative contributions of different factors to large-scale hydrological cycle, this study examines the spatial and tem-poral variations of ET and runoff in eastern China during1961–2005 driven by multiple environmental factors including cli-mate change, land use and land management, elevated atmo-spheric CO2, nitrogen deposition, and ozone pollution. The study

area includes seven river basins: Song-Liao, Haihe, Huaihe, YellowRiver, Yangtze River, Pearl River, and the Southeast (Fig. 1). Thisarea covers 55% of the national land area, supports 96% of nationalpopulation, and contains 75% of total water resources in China(Zhang, 1999).

2. Methodology

2.1. The DLEM model

DLEM couples major biogeochemical cycles, water cycle, andvegetation dynamics to make daily and spatially-explicit estimatesof storage and fluxes of water, carbon (C), and nitrogen (N) in terres-trial ecosystems (Fig. 2). It includes five core components: (1) bio-physics, (2) plant physiology, (3) soil biogeochemistry, (4)dynamic vegetation, and (5) disturbance, land use and management(Tian et al., 2010a). Briefly, the biophysics component simulates theinstantaneous fluxes of energy, water, and momentum within landecosystems and their exchanges with the surrounding environ-ment. The plant physiology component simulates major physiolog-ical processes, such as plant phenology, C and N assimilation,respiration, allocation, and turnover. The soil biogeochemistry com-ponent simulates the dynamics of C and nutrient compositions andmajor microbial processes. The biogeochemical processes, includingthe nitrogen mineralization/immobilization, nitrification/denitrifi-cation, decomposition, and methane production/oxidation are con-sidered in this component. The dynamic vegetation componentsimulates the structural dynamics of vegetation caused by naturaland human disturbances. Two processes are considered: the bioge-ography redistribution when climate change occurs, and the recov-ery and succession of vegetation after disturbances. Like mostdynamic global vegetation models, the DLEM builds on the conceptof plant functional types (PFT) to describe vegetation attributes. Thedisturbances, land use and management component simulatescropland conversion, reforestation after cropland abandonment,

Fig. 2. Structure of Dynamic Land Ecosystem Model (DLEM) (Tian et al., 2010a,b).

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 41

agricultural management practices such as harvest, irrigation,fertilizer application and rotation.

In this version of DLEM, the soil is treated as a simple bucketconsisting of two layers with depth of 0.5 m and 1.0 m, respec-tively. The top 20 cm of soil is considered as the evaporation layer.We divided land surface water pool into six boxes: the canopyintercepted snow (Wcan,snow) and intercepted water (Wcan,rain);the ground surface snow (Wsnow); the litter intercepted water(W0); the upper layer soil water (0–50 cm) (W1), and the lowerlayer soil water (50–150 mm) (W2) (Fig. A1) (Liu et al., 2008).The water content in each box is updated daily based on waterfluxes through the interfaces.

To account for irrigation effects on ET and runoff, we assume allpaddy crops will be irrigated. The irrigation date is identified as thetime point when soil moisture of the top layer drops below 30% ofthe maximum available water (i.e., field capacity minus wiltingpoint) during the growing season. Besides paddy field, the distribu-tions of other irrigated cropland are identified with the county le-vel agricultural census data (CITAS, http://citas.csde.washington.edu/data/chinaA/gswa.htm). The amount ofirrigated water is calculated as the deficit of soil moisture to fieldcapacity in the top soil layer. To account for the effect of land con-versions on water fluxes, the annual grid land-use data are used toparameterize the boundary conditions at the end of each year (Liuet al., 2008). The major equations of hydrological processes inDLEM are described in the Supplementary material.

2.2. Model input data

To drive the DLEM model, we developed gridded (10 km �10 km), geo-referenced, time-series data sets of climate, land use,N fertilizer application, N deposition, and tropospheric ozone con-centration data for China. The daily climate data sets (includingmaximum, minimum, and average temperature, precipitation(PPT), shortwave radiation, and relative air humidity) were

extrapolated from more than 700 weather stations from Chinamainland, Taiwan, and neighboring countries by using Thorntonet al. (1997) method (Liu et al., 2008). Ozone data was interpolatedfrom Felzer et al. (2005) global half degree data (Ren et al., 2007a).The annual N deposition data was developed by the combination offield observations with three-dimensional chemical transportmodel results (Lu and Tian, 2007). The historical land-use andland-cover data was reconstructed by using remotely sensed dataand historical survey data (Liu and Tian, 2010). For the agriculturalecosystems, 13 agricultural regions were identified in China tocharacterize the cropping system and the phenology (Ren et al.,2011). The atmospheric CO2 concentration data was from MaunaLoa Observatory and NOAA/ESRL (Keeling and Whorf, 2004).Additional details on input data have been published in Tian et al.(2011b).

2.3. Model initialization and simulation

The implementation of DLEM simulation includes three sequen-tial runs: (1) equilibrium run, (2) spinning-up run, and (3) transientrun. In order to include the legacy effects of environmental changes,especially land use change on the water fluxes, our simulationsstart from 1900 through 2005. We used the mean climate dataduring 1961–1990, the atmospheric composition data in 1900,and the land use data in 1900 as environmental factor to run themodel to reach the equilibrium state (i.e., the inter-annual varia-tions are less than 0.1 g C/m2 per year in total C stock, 0.1 g N/m2

per year in N stock, and 0.1 mm H2O per year in water stock). Themodel was then run for three spin-ups by using 30-year detrendedclimate data of 1961–1990; this step was conducted to reduce thebiases in the simulations toward transient run (Thornton andRosenbloom, 2005). Finally, the model was run in transient modewith a combination of various environment factors.

To quantify the individual and interactive effects of each envi-ronmental factor on water fluxes, we set up thirteen simulations

Table 1Simulation experiments.

Experiment Number CO2 Climate O3 Ndep Land usea Name

1 1901–2005b 1901–2005 1901–2005 1901–2005 1901–2005 Combination2 1900c 1901–2005 1901–2005 1901–2005 1901–2005 Combination without

CO2

3 1901–2005 Meand 1901–2005 1901–2005 1901–2005 Combination withoutclimate

4 1901–2005 1901–2005 1900 1901–2005 1901–2005 Combination withoutO3

5 1901–2005 1901–2005 1901–2005 1900 1901–2005 Combination withoutNdep

6 1901–2005 1901–2005 1901–2005 1901–2005 1900 Combination withoutLand use

7 1901–2005 Mean 1900 1900 1900 CO2 only8 1900 1901–2005 1900 1900 1900 Climate only9 1900 Mean 1901–2005 1900 1900 O3 only

10 1900 Mean 1900 1901–2005 1900 Ndep only11 1900 Mean 1900 1900 1901–2005 Land use only

12e 1901–2005 1901–2005 1901–2005 1901–2005 1901–2005 without irrigation,without fertilization

Combination without landmanagement

13f 1901–2005 1901–2005 1901–2005 1901–2005 1900 with changing management Combination without landconversion

a We consider the land-use map in 1900 as baseline of all simulations. All simulation experiments consider irrigation for cropland if there is irrigation facility (identified byirrigation map) except experiment 12. The experiment 12 needs a different equilibrium state from other simulations because it does not consider the irrigation on cropland.

b The period 1901–2005 means the transient input data during 1901–2005 is used to drive the model, i.e., this factor is changing over time.c 1900 means the status in the year 1900 is used for entire simulation period.d Mean climate is the mean climate data during 1901–1930.e In this simulation, no irrigation and fertilization is added to the cropland, including the paddy crops and rain-fed cropland, for entire period.f In this simulation, the LULC type do not change over time, i.e., the LULC in 1900 has been used during 1900–1995. However, the fertilization and the management

coefficients (including the changing harvest index and photosynthesis efficiency) changes over time.

42 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

(Table 1). Among these experiments, five simulations (i.e., experi-ment 7–11) were set up for estimating the individual effect of eachfactor and seven simulations (experiment 2–6, and experiment 12–13) were used for estimating their interactive effects. The overallcombination run (i.e., experiment 1) was set up to simulate theET, runoff, and water yield by considering the temporal and spatialvariations of all changing factors. The differences in simulation re-sults between the overall combination run (i.e., experiment 1) andthe simulations for estimating interactive effects can indicate eachfactor’s interactive contributions with other factors.

As we have no climate observational data before 1961, we usedthe 30-year detrended climate data twice to represent climate con-ditions during 1901–1960 (Tian et al., 2011b). This study will re-port the simulation results for the time period of 1961–2005.

2.4. Model calibration and verification

The model has been calibrated by using a number of field obser-vational data in China and other places for each functional biometypes (Chen et al., 2006; Ren et al., 2007a; Tian et al., 2010a,b,2011b). We firstly determined the reasonable range of key param-eters through comparing data from literature review, field observa-tions, and other model’s value (Bonan, 1996; Breuer et al., 2003;White et al., 2000). Then within these ranges, the parameter valuesin DLEM were optimized to fit the simulated result to observed C,water, and N fluxes and pool sizes. For calibrating water flux-re-lated parameters, if no ET observation is available, the average ra-tio of ET/PPT in the same functional type was used to estimate ETin the specific site. After the calibration, simulated ET should bewithin ±10% of the observations. Table 2 lists the sites used formodel calibrations in this study.

To evaluate model’s performance, we compared the simulationresults with independent observations and modeled data. Liu et al.(2008) reported the high consistency of simulated seasonal varia-tions of ET to observations at site level (e.g., Qianyanzhou station)and also compared the simulated ET with other empirical method

based ET estimations (Ahn and Tateishi, 1994) at regional level.However, no direct verifications on runoff have been conductedfor the DLEM. Here we try to evaluate the spatial patterns of sim-ulated runoff at grid and river basin level. Two independent datasets were used. One is the global runoff field data (GRF) from Fek-ete et al. (2002) which is based on both gauge observations and theWater Balance Model. Another is a multi-year mean runoff contourmap from China’s Geophysical Map Collections (CGMC), which hasbeen collected from multi-year field observations and been extrap-olated to the whole nation by researchers (Liao, 2001). GRF is agridded data set at a spatial resolution of 0.5� latitude by 0.5� lon-gitude. The simulated mean annual runoff during 1961–1990 fromthe overall combination run (i.e., experiment 1) was used to com-pare with the GRF and CGMC.

The comparisons indicated that the DLEM is able to reconstructthe spatial distribution of runoff that is derived from CGMC andGRF (Fig. 3). The comparison also indicated that our simulation re-sults are closer to CGMC than GRF at both river basin and grid lev-els (Fig. 4). The model slightly overestimated runoff in the basins ofHuaihe and Haihe, while underestimated runoff in the Yangtze Riv-er (Fig. 4A and B). One possible reason for our underestimationmight be that our model assumes paddy land could hold 20 cmwater volume over land surface. This assumption may lead to anoverestimation of soil water holding capacity and thus underesti-mate runoff in the Yangtze River basin where features large areaof paddy field. In the DLEM, we did not subtract the irrigated waterfrom local water pools; therefore, the decreasing water holdingcapacity due to increasing soil moisture in the irrigated field mightcause overestimation of runoff in the Haihe and Huaihe riverbasins.

CGMC and GRF had substantial differences across the studyarea. We agree with arguments from Niu et al. (2005) that GRFmay underestimate runoff because of its exclusion of dischargesfrom catchments that are less than 25,000 km2 (Fekete et al.,2000). In conclusion, the simulated runoff by the DLEM can capturethe spatial patterns and magnitudes on regional level.

Table 2Sites for the calibration of the Dynamic Land Ecosystem Model (DLEM).

Site Vegetation type Location References

Haibei Subalpine meadow 37.5�N,101.2�E

Jing and Yu (1999), Li et al. (2003), Wang et al. (1998), Zhang and Cao (1999), and Zhanget al. (2003)

Lhasa Subalpine meadow 37.67�N,91.08�E

Shi et al. (2006) and Xu (2006)

Changbaishan Boreal mixed forest; Tundra 42.4�N,128.1�E

Dai et al. (2002), Wu (2006), and Zhang et al. (2006)

Donglingshan Temperate broadleafdeciduous forest

39.9�N,115.5�E

Chen and Huang (1997), Du et al. (2004), and Sang et al. (2002)

Dinghushan Temperate broadleafevergreen forest

23.17�N,112.55�E

Mo et al. (2003), Zhang (1991, 2006), Zhou (2001), Zhou et al. (2005)

Qianyanzhou Temperate needleleafevergreen forest

26.73�N,115.06�E

Liu et al. (2006), Mo et al. (2003), Shen et al. (2003), Song et al. (2006), Wang (1994), Yuet al. (2008), and Yuan (1999)

Xishuangbanna Tropical broadleaf evergreenforest

21.9�N,101.2�E

Lv et al. (2006), Meng et al. (2001), Sha et al. (2002), and Zheng et al. (2000)

Shapotou Deciduous shrub 37.45�N,104.95�E

Zhang et al. (2006)

Xilinguole C3 grassland 43.63�N,116.7�E

Dong et al. (2000), Li and Li (1991), Liu et al. (2005), Xiao et al. (1996), Yang et al. (2005b),and Zhang et al. (1990)

Experimental RangeCentral Plains

C4 grassland 40.83� N,104.7�W

Mosier et al. (2002)

Sanjiang Herbaceous wetland 47.69�N,133.52�E

Song et al. (2009)

Yucheng Winter wheat; Summer corn 36.5�N,116.34�E

Huang et al. (2006) and Li et al. (2006), http://www.cerndata.ac.cn/

Taoyuan Rice paddy field 28.92�N,111.45�E

Wang et al. (2003), http://www.cerndata.ac.cn/

Fig. 3. The comparisons of simulated runoff with other results. (A) Runoff from China’s Geographical Map Collections (CGMC) (Liao, 2001); (B) Runoff from Global RunoffField (GRF) data (Fekete et al., 2002); (C) Estimated Runoff from this study (average during 1961–1990).

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 43

Liu et al. (2008) evaluated the temporal patterns of simulatedrunoff against the gauge observations in Huaihe and Song-Liao riv-er basins. These comparisons indicated that DLEM could simulatethe interannual variations of runoff in these two river basins (Liuet al., 2008). Here, we further evaluated the simulated river dis-charge against observations in both Yellow River and Yangtze Riv-er. The long-term river discharge data from lower reaches of theYellow River is reconstructed by Yu (2002). Fig. 5A indicated thatthe DLEM could capture the interannual and decadal trend of run-off during 1972–2000, but slightly overestimated the magnitude.The most likely reason for this overestimation is that the simulatedrunoff did not subtract the irrigated water use. For example, thesimulated water balance, which is calculated as annual total pre-cipitation minus total ET, is closer to the observed discharge than

the simulated runoff, especially in dry years (Fig. 5A). Another pos-sible reason for this overestimation in magnitude is that our modeldoes not consider dam effects on water resources. This may explainthat the simulated NWB was larger than observed discharge duringwet years (Fig. 5A), as suggested by Yu (2006).

The second new data for validation was a long-term river dis-charge observation in the Datong gauge station (lower reaches ofYangtze River) (UNESCO database, http://webworld.unesco.org/water/ihp/db/shiklomanov/). The interannual variations of simu-lated runoff matched well with observations (Fig. 5B) though sim-ulated runoff was slightly lower than observed runoff. Asmentioned earlier, the underestimation of runoff and thereforethe overestimation of ET might be caused by the optimal assump-tion of water holding capacity of paddy field.

Fig. 4. Comparisons of simulated runoff with CGMC and GRD runoff at river basin and grid levels. (A) simulated runoff vs. CGMC runoff at river basin level; (B) simulatedrunoff vs. GRF runoff at river basin level; (C) simulated runoff vs. CGMC runoff at grid level; (D) simulated runoff vs. GRF runoff at grid level.

44 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

3. Results

3.1. Environmental changes in eastern China

Annual precipitation had no significant trend during 1961–2005in eastern China although it has a slope of +7 mm per year per dec-ade (estimated Kendall’s tau is 0.12 calculated with Wessa (2008)’sonline software; p-value = 0.27) (Fig. 6). The slope of annual pre-cipitation shows large variations among different regions. Forexample, the river basins of Haihe, Yellow River, and the centralYangtze River had positive slope, while the Southeast of the east-ern China and the northern Song-Liao river basin had negativeslopes during the study period (Table 3, Fig. 7A).

Mean annual temperature (MAT) in eastern China significantlyincreased during 1961–2005 with a rate of +0.3 �C per decade(Kendall’s tau is 0.61; p-value <0.001) (Fig. 6). The increasing rateswere also unevenly distributed across the region. The highest in-creases in MAT were found in the north and southeast and the low-est located in the southwest of eastern China (Fig. 7B).

During 1961–2005, the atmospheric CO2, tropospheric O3, andN deposition considerably changed (Fig. 6). AOT40 (the accumu-lated hourly ozone concentrations above a threshold of 40 ppb)

increased from 4.1 ppb-hr/day in 1961 to 112.1 ppb-hr/day in2005. Atmospheric CO2 increased by 19.7% from 317.3 to379.8 ppm during 1961–2005 (Fig. 6). N deposition increased by70% from 1.69 gN/m2/year in 1961 to 2.87 g N/m2/year in 2005(Fig. 6). The amounts of deposited N largely varied over space rang-ing from <1.0 g N/m2/year in the north and northwest to >5.0 g N/m2/year in the east (Fig. 7E).

Eastern China experienced dramatic changes in land use and landcover over the 20th Century (Liu and Tian, 2010), which was charac-terized by the expansion of urban and forest area and the shrinkageof cropland area during 1961–2005. In sum, cropland area decreasedby 11.84 million ha (8.76%), while forest and urban area increasedby 2.87 million ha (1.45%) and 8.66 million ha (97.93%), respec-tively (Table 4). Huaihe and Yellow River basin had large increasein forest area by 27.1% and 10.8%, respectively, while forest area inSong-Liao basin had a decreased by 3.3% (Table 4). In addition toland use conversions, substantial changes in land managementpractices have also been observed during 1961–2005. For instance,N fertilizer application had been significantly increased, largelyvarying from an increase of 5 g N/m2/year in the northeast to an in-crease of 45 g N/m2/year in the east and southeast, consideringchemical N fertilizer level in 1961 is very low, close to zero (Fig. 7D).

Fig. 5. Estimated runoff and water balance vs. observations. (A) Yellow River, observations from Yu (2002); (B) Yangtze River, observation is from Datong station (http://webworld.unesco.org/water/ihp/db/shiklomanov/).

Fig. 6. Changes in temperature, precipitations, and atmospheric compositions (CO2, surface O3, and nitrogen depositions) in eastern China during 1961–2005.

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 45

Table 3Linear trends in annual precipitation and estimated water fluxes in each river basin during 1961–2005 (Units: mean and std.: mm/year; trend: mm/year/decade).

River Basin Name Precipitation ET Runoff Water balance

Mean Trend Std. Mean Trend Std. Mean Trend Std. Mean Trend Std.

Song-Liao 512 1.7 61 378 9 20 137 �6.4 45 135 �7.3 55Haihe 514 �16.9 110 451 9.5 35 133 �18.7 65 63 �26.5 94Huaihe 849 6.6 155 587 20.2 37 309 �9 113 261 �13.6 138Yellow River 441 �9.6 71 372 1.9 30 84 �9.4 31 69 �11.6 54Yangtze River 1040 14.6 81 598 11.2 23 444 3.5 63 442 3.4 69Pearl River 1559 20.7 194 780 11.3 27 782 11 172 779 9.4 182Southeast 1623 32 235 680 21 39 944 11.2 215 943 11.1 220Total eastern China 860 6.8 53 525 10 20 346 �1.8 41 335 �3.3 44

Fig. 7. The environmental change and distributions in eastern China A: linear trend of precipitations during 1961–2005 (unit: mm/decade); B: linear trend of temperatureduring 1961–2005 (unit: �C/decade); C: surface O3 exposure (AOT40) (unit: ppbv-hr/day); D: nitrogen fertilizer application in 2005 (unit: g N/m2-cropland/a); E: nitrogendeposition (unit: g N/m2/year) in 2005.

46 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

3.2. Spatial and temporal patterns of estimated ET

The estimated ET was 525 ± 20 mm per year in eastern Chinaduring 1961–2005 (Fig. 8). We found a significant increasing trendat a rate of 10 mm per year per decade (Kendall’s tau is 0.41; p-va-lue <0.001). ET was the highest during the period 2001–2005 withan average of 567 mm per year, which was 11% higher than that inthe 1960s. ET in the summer season (June–August) had the largestincrease at a rate of 22 mm per year per decade during 1961–2005.In the spring, autumn, and winter season, ET had a total increase of

13 mm, 8 mm, and 3 mm per year per decade, respectively, duringthe 45-year period.

ET showed an increasing trend, but its increasing rates variedamong different basins (Fig. 9A). Among the seven river basins, thegreatest increase in ET occurred in the Huaihe basin, with an averageincreasing rate of approximately 20 mm per year per decade, andover 50 mm per year per decade in some areas during 1961–2005;while, the ET in the Yellow River basin as a whole has the least overallchange (Table 3; Fig. 9A). However, in the central Yellow River basin,an obviously decreasing trend in ET was detected by the model

Table 4Changes of major land cover types from 1961 to 2005 (Units: million ha).

River Basin Name Cropland Forest Built-up area

In 2005 Change 1961–2005 % In 2005 Change 1961–2005 % In 2005 Change 1961–2005 %

Song-Liao 31.15 3.54 12.84 54.95 �1.88 �3.31 2.75 1.21 78.75Haihe 12.13 �2.14 �14.97 7.03 0.38 5.65 2.72 1.49 120.69Huaihe 17.52 �3.76 �17.68 7.33 1.56 27.07 4.96 2.28 85.09Yellow River 15.82 �4.39 �21.72 7.31 0.72 10.84 2.36 1.11 89.52Yangtze River 34.64 �3.91 �10.14 74.40 1.96 2.70 2.74 1.48 118.14Pearl River 9.03 �0.78 �7.96 34.60 0.03 0.10 1.50 0.78 109.72Southeast 3.08 �0.41 �11.76 15.77 0.11 0.67 0.48 0.30 167.32Total eastern China 123.38 �11.84 �8.76 201.40 2.87 1.45 17.51 8.66 97.93

Fig. 8. Interannual variations of precipitation, estimated ET, runoff, and water balance (annual precipitation minus annual ET) during 1961–2005.

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 47

(Fig. 9A). The decreasing ET in this region was likely related tothe decrease in annual precipitation as shown in Fig. 7A.

3.3. Spatial and temporal patterns of runoff

Overall, there is no significant changing trend in runoff over theeastern China during 1961–2005 (Kendall’s tau is �0.01; p-va-lue = 0.7) (Table 3; Fig. 8). However, our simulated results show asubstantial spatial heterogeneity in changing rate of runoff acrossthe study area (Fig. 9B). Runoff in the basin of Haihe and YellowRiver decreased by 18.7 mm per year per decade (P < 0.01) and9.4 mm per year per decade (P < 0.01), respectively (Table 3). Sig-nificant decrease was also shown in the center of the Yangtze Riverbasin. Fig. 9B demonstrated that increasing runoff occurred mainlyin the southeast of China and the west of the Yangtze River basin.

3.4. Spatial and temporal patterns of annual net water balance

The annual net water balance (NWB), i.e., the difference be-tween annual precipitation and ET, indicates the overall waterbudget in the study area. For natural ecosystems, multi-year meannet water balance is as same as runoff. For irrigated land, however,the modeled runoff is generally higher than the NWB due to thesupplemental water supply. Our simulation results showed thatannual NWB in eastern China had no significant changing trendduring 1961–2005 (Kendall’s tau is �0.04; p-value = 0.53). This re-sult implies that the increasing ET has no significant effects on thetotal water budget in the region as a whole. Similar with runoff, theestimated NWB had large spatial variations across the region. Thenorth of the study area, including Haihe and Yellow River basin,had a large decreasing rate (Table 3), while the south and thesoutheast showed an increasing trend in water balance.

The estimated NWB was consistent with previous studies thatthe northern China has a large water deficiency, especially in theHaihe, Huaihe and Yellow River basin, while the southern Chinahas more available water (Liu, 1998) (Fig. 9D). In the regions witha small positive or negative water balance, such as Haihe and Hua-ihe river basins, water demand had to be met with withdrawinggroundwater or water resources from other regions (Kirshenet al., 2005). Our results indicated that North China experienceda decline in available water resources over the last 45 years(Fig. 9C and D).

3.5. Relative contributions of environmental factors to water fluxes

We attributed the simulated variations in water fluxes to multi-ple global change factors including climate variability, elevatedatmospheric CO2 concentration, ozone pollution, N deposition,and land use/cover change. The simulated results indicated thatclimate change was the dominant factor controlling the interan-nual variations in ET and runoff. Over the study period, climatechange and its interaction with other environmental factors ac-counted for approximately 58% of the overall increases in ET; whileits accumulated effects on runoff and NWB were relatively small(Fig. S.2 and Fig. 11A).

According to the simulation results, elevated atmospheric CO2

caused a decrease in ET of 2.7% from 1961 to 2005, which is closeto the estimated global average decrease rate in ET due to CO2 fer-tilization effects (e.g., an estimated 6% decrease in ET with double-CO2 relative to pre-industrial level) (Bernacchi et al., 2007; Bettset al., 2007; Gedney et al., 2006; Morgan et al., 2004).

Changes in tropospheric ozone concentrations made a smallcontribution to variations in water fluxes according to our simula-tion results (Figs. 10 and S.2). In general, elevated O3 concentration

Fig. 9. Spatial patterns of estimated ET, runoff and net water balance. (A) Linear trend of ET during 1961–2005 (unit: mm/year/decade); (B) Linear trend of runoff during1961–2005 (unit: mm/year/decade); (C) Linear trend of net water balance during 1961–2005 (unit: mm/year/decade); (D) Mean annual net water balance during 1991–2005.

48 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

contributed 8% and 16%, respectively, to the overall increases in ETand decreases in runoff during the study period (Figs. 10 and S.2).Similar conclusion was made by McLaughlin et al. (2007b), whofound that increasing O3 concentration enhanced whole-tree waterusage and deplete soil water as observed in forest ecosystems inthe eastern Tennessee, USA.

Increased N deposition was estimated to increase ET and de-crease runoff and NWB in the study area. Overall, N deposition con-tributed 22% and 51%, respectively, to the total changes in ET andrunoff in eastern China (Figs. 10 and S.2). Our simulation results

indicated that land use change (including land conversions andland management) had small effect on the interannual variationsof water fluxes during 1961–2005 in the study area as a whole.However, its cumulative effects on the water fluxes over the studyperiod were relatively large (Figs. 10 and S.2).

The average irrigated water could reach up to 300 mm per yearin the northern plain such as the Yellow River basin (Fig. S.3B). Inour previous studies, we discussed spatial and temporal patterns ofland use and management during the last century for the entireChina (Liu et al., 2008) (Fig. S.3A). Our simulations showed that

Fig. 10. Relative contributions of each factor on the accumulated ET and runoff anomalies. (A) Accumulated ET anomaly; (B) accumulated runoff anomaly.

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 49

the contributions of land management practices (including irriga-tion and fertilization) to the changes in ET were 50% larger thanthose of land use conversions. Totally, land use change contributed35% and 50%, respectively, to the overall changes in ET and runoffin eastern China during the study period (Fig. 10).

3.6. Regional differences in the impacts of each environmental factoron water fluxes

The impacts of environmental factors on water fluxes variedamong different regions. Climate change caused an increasing ETin all river basins except for the Haihe and Yellow River basins(Fig. 10A). Climate change was the major controlling factor onthe trend of ET and runoff in the South of China over the study per-iod, primarily in the Pearl River, and Song-Liao basins (Fig. 10A).

Land use change increased ET in all river basins during 1961–2005 but the magnitudes were quite different. In the Huaihe, Hai-he, and Yellow River basins, land use change cumulatively led to anincrease in ET by 1511 mm, 1039 mm, and 252 mm, respectivelyduring the period (Fig. S.3A). The impacts of increasing CO2 con-centration on decreased ET have been detected in all river basins.Generally, the increased surface ozone concentration slightly en-hanced ET, showing small variations across basins. Effects of Ndeposition, on the contrary, varied greatly from the Yellow Riverbasin to the Huaihe basin (Fig. 10A).

In reality, all individual environmental factor influences theland surface water fluxes concurrently. Our simulated results showthat the total effects of all factors were less than the mathematical

summary of individual effects. For example, in the Haihe and Hua-ihe river basins, interactive effects among environmental factorsdecreased the sum of individual effects on ET by 30% (Fig. 10A).

In sum, climate change was the primary controlling factor forthe changes in ET during 1961–2005 in the basins of Song-Liao,Pearl River, and Southeast; land use change was the dominant fac-tor in the basins of Haihe, Huaihe, and Yellow River; the impacts ofthe other environmental factors were relatively small comparedwith those induced by climate change and land use change(Fig. 10A). Climate change was among the most contributing fac-tors for the changes in runoff in all other basins except Huaiheand Yangtze River basins (Fig. 10B).

4. Discussions

4.1. DLEM-estimated ET vs. other studies

Jung et al. (2010) reported a global estimation on ET changesduring 1982–2008 by using simulated results from multiple mod-els and model tree ensemble (MTE) data upscaled from eddycovariance observations (Jung et al., 2009). DLEM-based estima-tions on the trend of ET during 1982–2000 in eastern China(Fig. 11A) are consistent with MTE data for the same region. Bothapproaches detect a decreasing trend of ET in the Yellow River Ba-sin and some area in the Song-Liao basin (Fig. 11). During the per-iod of 1982–2000, neither of MTE and DLEM detected a significanttrend in ET in eastern China, while they all showed a positive slopewith 0.6 mm per year per year for the whole region in this period.

Fig. 11. Comparisons of DLEM estimations and model tree ensemble (MTE) data on ET trend during 1982–2000 (MTE data is from Jung et al. (2010)). (A) DLEM simulated ETtrend; (B) MTE estimated ET trend; (C) interannual variations of normalized ET (i.e., ET in each year/average ET during 1982–2000).

50 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

The interannual fluctuations in ET derived from these two methodsare also generally consistent (Fig. 11C). DLEM simulation resultshave larger interannual and spatial variations than MTE results.These differences may result from the different input data (e.g.,MTE use monthly climate data while DLEM use daily climate data;MTE use half degree while DLEM use 10 km resolution climatedata).

4.2. Environmental controls on water fluxes

4.2.1. CO2 effectsThe simulated effects of environmental factors on water fluxes

in this study are consistent with data from field experiments andmetadata analyses. The mechanisms used in the DLEM to quantifydirect and indirect effects of environmental factors on water fluxeshave been widely used in other large-scale ecosystem models.Increasing CO2 can affect ET through either radioactively forcingclimate change or ‘‘physiological forcing’’ by decreasing stomatalconductance, increasing leaf area index, and altering C allocations(Betts et al., 2007; Farquhar and Sharkey, 1982; Field et al., 1995;Gedney et al., 2006; Jackson et al., 2001; Sellers et al., 1997;

Waggoner and Zeilitch, 1965). Bernacchi et al. (2007) reportedthe decrease of 9–16% in ET for soybean at the ecosystem level un-der CO2 enrichment experiments (from 375 ppm to 550 ppm),which was closely related to a decrease in the stomatal conduc-tance of upper canopy leaves. Many uncertainties exist when scal-ing the CO2 effect from leaf to canopy or even larger scale (Fieldet al., 1995). By using the observational data and land surface mod-el, Gedney et al. (2006) found that increasing atmospheric CO2 con-tributed greatly to the increase of continental river runoff at aglobal scale. In their study, the irrigation process was ignored,which is a dominant component of water use in China (Brownand Halweil, 1998). Dai et al. (2009) also argued that Gedneyet al.’s results have large uncertainties in their data sets and model.We assume CO2 concentration is seasonal and spatially homoge-neous in China and this simplification might to some extent bringbias to our estimation of CO2 effects compared to real world.

4.2.2. Tropospheric ozone effectsIncreasing evidence has shown that air pollution, including tro-

pospheric O3, could directly, indirectly, or interactively affect bio-logical systems (Felzer et al., 2004; McLaughlin et al., 2007a,b;

M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54 51

Ollinger et al., 2002). However, current field observations andmodels have different results and explanations regarding the effectof ambient O3 exposure on the water use of natural vegetation andagricultural ecosystems (Grulke et al., 2002; McLaughlin et al.,2007b). Several field experiments and model simulations have re-vealed substantial reductions in C sequestration due to elevated O3

exposure in crops and grassland of China (Liu et al., 2009; Renet al., 2007b). No regional analysis has been conducted in Chinato estimate the regional contributions of ambient O3 to the hydro-logical processes.

4.2.3. Climate effectsSeveral previous studies also have displayed the impacts of cli-

mate change on ET and runoff in China. Ma et al. (2008) analyzedthe variation of stream flow during 1950–2005 in an inland riverbasin and found that climate variability, mainly precipitation, ac-counted for most of the change in streamflow. Wang et al. (2007)attributed the decreasing trend of runoff during 1950–2000 tothe ENSO events, rising temperature, and anthropogenic impactssuch as construction of dams and reservoirs. Liu and Zheng(2004) reported a decreasing trend of runoff based on gauge obser-vations during 1971–1998 in the Yellow River basin. They attrib-uted this pattern to climate change (decreased precipitation andincreased temperature), reforestation, and expanded irrigationarea in this region (Liu and Zheng, 2004). Our modeling work gavea comprehensive picture of the climate change effects on ET andrunoff in these basins and had close conclusions to previous stud-ies but considered both natural and human-related factors.

4.2.4. Interactive effectsInvestigating the effects of environmental factors on water pro-

cesses cannot ignore the interactions between C, water, and Nacross multiple scales (Burt and Pinay, 2005; Hutjes et al., 1998;Schimel et al., 1997; Schulze et al., 1994; Sellers et al., 1997). Atleaf scale, interactions between CO2, leaf N concentration, andwater flux take place in the inner leaf through stomata (Farquharand Sharkey, 1982; Sellers et al., 1997; Waggoner and Bravdo,1967). At plant or ecosystem scales, the structure and function ofecosystem regulates the distributions of water and N on canopy,land surface, and soil. The coupling of C, N, and water in a modelingsystem needs to be implemented on multiple scales from leaf toecosystem. The magnitude of interactions between C and water,as represented by water use efficiency, varied widely in differentplaces and biomes of China (Yu et al., 2008). C sequestrationsand water resources have always tradeoffs that must be consideredwhen proposing afforestation projects, particularly in arid regions(Farley et al., 2005; Jackson et al., 2001, 2005; Sun et al., 2006).Simulation results from this study indicate a positive effect ofincreasing N deposition on ET. Any factors that affect the C cyclemay also influence the water cycle. However, the mechanisms gov-erning the interactions between C, N, and water from the leaf scaleto the biome level are too complicated to be addressed. Therefore,more intensive field observations ought to be conducted to expandcurrent observation networks along environmental gradients, sothat abundant data sets can be obtained to support an integratedmodel that couples vegetation dynamics, biogeochemical cycles,hydrological cycles, and climate system at multiple scales.

4.3. Implications for policy makers and future studies

Our simulated results demonstrated a significant increasingtrend of ET in eastern China. Although the magnitude seems smallcomparing to the interannual variations of runoff, we need to iden-tify both the long-term and the short-term hydrological responsesof the terrestrial ecosystems to environmental changes, especiallyto land use change (Andreassian, 2004). Long-term ET trend can

reveal the condition of local water resources and this can be usedas an important index for managing water use and adapting toenvironmental changes.

With fast extension of urban area and speeding populationgrowth, eastern China is facing a great pressure from shortage offresh water resources. Yang et al. (2005a) attributed a decreasingtrend in discharge at Datong Hydrographical Station (downstreamof Yangtze River) during 1865–2004 to the increase of water con-sumption and reservoir construction. As simulated in this study,land use change substantially increased ET and decreased runofffor most areas. As discussed in Liu et al. (2008), land use manage-ment (including irrigation and nitrogen fertilizer application)enhanced water deficiency in North China through influencingboth quantity and quality. Increasing N fertilizer amounts on agri-cultural land are tended to increase N leaching to the surfacewater reservoirs, which has caused severe eutrophication in manyof China’s lakes (Ju et al., 2004). Urban sprawl increases runoff,decreases the response time and thus increases chemicals leach-ing to rivers and lakes. To maintain sufficient cropland area forfood security and reduce water use in China, it is clearly neededto have better land use planning. The grain-for-green policy is agood example that attempts to balance the needs for food andwater (Feng et al., 2005). China uses around 80% of its total waterconsumption for agriculture (Xia and Chen, 2001). At least 80% ofgrain crop in China is irrigated (Brown and Halweil, 1998). As con-cluded by Matson et al. (1997), continued increases in agriculturalproduction will require a sustained or increased irrigation watersupply.

Law et al. (2002) estimated that a one degree increase in surfacetemperature will cause an increase of 19.7 mm ET in Europe andNorth America. Our study suggests that available water resourcesare becoming more unevenly distributed due to climate changeand human activity, such as the northern of eastern China. Onthe global scale, the impact of climate change on irrigation waterrequirements for agriculture has been concluded to be very large(Tubiello et al., 2007). For instance, Doll and Siebert (2002) esti-mated an increase in irrigation requirements by 5–8% globally by2070, with an increase of 15% in Southeast Asia. Our simulation re-sults showed that climate change alone led to an increase in ET by9% in the cropland areas of eastern China during 2001–2005 com-pared with the 1960s. The severe drought condition has been pre-dicted over the second half of the 21th century in the southeast ofChina (Dai, 2010). To adapt to this drying condition in the future,the government and people need to use multiple strategies to im-prove water use efficiency, such as increasing water storage in res-ervoirs, cultivating crop types with higher water use efficiency, orpromoting a diet with low water consuming foods (Jackson et al.,2001; Rockstrom et al., 2007).

5. Conclusions

By using a well-calibrated and extensively-verified ecosystemmodel (DLEM), we characterized the spatial and temporal patternsof water fluxes in eastern China during 1961–2005 and furtherquantified the relative contributions from major environmentalfactors at regional and basin scales. The simulation results indi-cated that ET had a significant increasing trend in the study areaduring 1961–2005. The estimated runoff and net water balancedid not show a significant linear trend in the study area as a whole.However, there were large spatial variations in ET and runoffwhich are likely caused by spatial heterogeneity of environmentalfactors. ET increased in all major river basins except Yellow RiverBasin. Runoff had increased in the southern part of the study area(including Pearl River and the Southeast basin), but decreased inthe northern part, particularly in Haihe and Huaihe river basins.

52 M. Liu et al. / Journal of Hydrology 426–427 (2012) 39–54

Climate variability was the dominant factor governing interannualvariations of ET and runoff in the past 45 years. Land use change(including land conversions and land management) has minor ef-fects on interannual variations in water fluxes but had largelong-term effects.

By assessing effects of multiple environmental factors on thehydrological cycles in different regions, this study revealed that cli-mate change is the dominant factor controlling the long-termtrend of ET and runoff in Southern China, while the land-use andother environmental factors are the major players in Northernand Northeastern China during the period of 1961–2005. However,each factor always interacts with other factors in influencingregional water cycle. Therefore, we emphasize the needs ofsystems approach to understand and quantify the contributionsof environmental driving forces to regional water resources. Thesevariations in the relative importance of environmental factors incontrolling hydrological cycles in different basins suggest that itis important for the government and farmers to take multiplestrategies for maintaining water sustainability in eastern China.

Our study also indicates that the close coupling between hydro-logical process and biogeochemical processes across differentscales are necessary to quantify the overall long-term responsesof terrestrial ecosystems to environment changes. In addition,incorporating anthropogenic activities into the terrestrial modelwill be an important improvement toward predicting terrestrialhydrological cycles and providing insightful remarks for policymakers.

Acknowledgements

We would like to thank Xia Song, Chi Zhang, and Qichun Yangfor help in collecting the data and calibrating the model. This studyhas been supported by NASA Interdisciplinary Science Program(NNG04GM39C) and NASA Land Cover and Land Use Change Pro-gram (NNX08AL73G). Thanks also go to Dr. Martin Jung (MaxPlanck Institute for Biogeochemistry) for providing the model treeensemble (MTE) data and Dr. Aiguo Dai for providing the recon-structed historical river discharge data.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.jhydrol.2012.01.009.

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