simulated water fluxes during the growing season in semiarid grassland ecosystems under severe...

Journal of Hydrology 512 (2014) 69–86

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Journal of Hydrology

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Simulated water fluxes during the growing season in semiarid grasslandecosystems under severe drought conditions

http://dx.doi.org/10.1016/j.jhydrol.2014.02.0560022-1694/� 2014 Published by Elsevier B.V.

⇑ Corresponding author. Address: College of Resources and Environment,University of Chinese Academy of Sciences, 19A Yu Quan Road, Shijingshan District,Beijing 100049, China. Tel.: +86 10 88256371; fax: +86 10 88256152.

E-mail address: [email protected] (N. Zhang).

Na Zhang ⇑, Chengyu LiuCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

a r t i c l e i n f o

Article history:Received 20 December 2012Received in revised form 16 February 2014Accepted 22 February 2014Available online 3 March 2014This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief

Keywords:Spatially explicit process-based modelTranspirationEvaporationEvapotranspirationWater deficitLand cover

s u m m a r y

To help improve understanding of how changes in climate and land cover affect water fluxes, waterbudgets, and the structure and function of regional grassland ecosystems, the Grassland LandscapeProductivity Model (GLPM) was used to simulate spatiotemporal variation in primary water fluxes. Thestudy area was a semiarid region in Inner Mongolia, China, in 2002, when severe drought was experienced.For Stipa grandis steppe, Leymus chinensis steppe, shrubland, and croplands, the modeled total, daily andmonthly averaged, and maximum evapotranspiration during the growing season and the modeled waterdeficits were similar to those measured in Inner Mongolia under similar precipitation conditions. Themodeled temporal variations in daily evaporation rate, transpiration rate, and evapotranspiration ratefor the typical steppes also agreed reasonably well with measured trends. The results demonstrate thatwater fluxes varied in response to spatiotemporal variations in environmental factors and associatedchanges in the phenological and physiological characteristics of plants. It was also found that transpirationand evapotranspiration (rather than precipitation) were the primary factors controlling differences inwater deficit among land cover types. The results also demonstrate that specific phenomena occur undersevere drought conditions; these phenomena are considerably different to those occurring under normalor well-watered conditions. The findings of the present study will be useful for evaluating day-scale waterfluxes and their relationships with climate change, hydrology, land cover, and vegetation dynamics.

� 2014 Published by Elsevier B.V.

1. Introduction

The availability of water has a critical influence on the structure,processes, and functions of arid and semiarid ecosystems. Waterfluxes (including inputs, storages, and losses of water) influencethe magnitude and dynamics of many environmental, physical,chemical, and biological processes, including energy partitioning,vegetation productivity, and local-to-regional water budgets.Moreover, changes in environmental and biological factors andtheir interactions may alter water fluxes dramatically at the landsurface, particularly for changes related to surface boundaryconditions, energy balance and partitioning, soil moistureconditions, and plant physiology and phenology (Baldocchi et al.,2004; Chen et al., 2009; Frank and Karn, 2005; Hunt et al., 2002;Miao et al., 2009; Wever et al., 2002; Wilske et al., 2010). Therefore,quantifying water fluxes in dry regions is important to improve

understanding of the factors that influence and control arid andsemiarid ecosystems.

Previously, quantitative studies have provided valuable infor-mation regarding water fluxes in semiarid grassland ecosystems;in particular, a number of studies have reported measured valuesof evapotranspiration, which is the most crucial water fluxcomponent (e.g., Chang and Zhu, 1989; Hao et al., 2007; Haoet al., 2008; Li et al., 2007; Miao et al., 2009; Miyazaki et al.,2004; Song, 1995, 1996; Wever et al., 2002; Yang and Zhou, 2011;Zhang et al., 2005). However, most of these studies were conductedat the local site scale. Accordingly, less attention has been devotedto investigating such processes for different land cover types overlarge areas (Miao et al., 2009). Consequently, our understandingof the spatiotemporal patterns of the water cycle and water budgetsat the regional and global scales remains limited.

In addition, some uncertainties persist in the measurementof evapotranspiration (Hudson and Oilman, 1993; Milly, 1994;Moehrlen et al., 1999; Parlange et al., 1995). It is difficult to measurecertain water flux components (Moehrlen et al., 1999; Wilske et al.,2010; Yang and Zhou, 2011), including the hydrological processesinvolved in precipitation (e.g., throughfall, precipitation droppingfrom canopy branches and leaves, and canopy interception and

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70 N. Zhang, C. Liu / Journal of Hydrology 512 (2014) 69–86

evaporation) and processes such as surface runoff, deep percola-tion, snowmelt, and snow sublimation. Partitioning the evaporationand transpiration contributions to water loss is also difficult(Lauenroth and Bradford, 2006). However, model simulations maybe able to address all of these problems (Domingo et al., 2001;Lauenroth and Bradford, 2006; Yang and Zhou, 2011). Simulationsprovide an effective approach to examining the different compo-nents of land-to-atmosphere water fluxes and their relationshipswith various characteristics and processes (e.g., climate change,hydrology, land cover, vegetation dynamics) at a broad spatiotem-poral scale (Fisher et al., 2008; Lauenroth and Bradford, 2006; Rey,1999; Wilske et al., 2010; Zhang et al., 2008).

Over the past decade, we have developed a spatially explicitprocess-based forest landscape productivity model (FLPM; previ-ously known as EPPML). The FLPM is based on principles of energytransfer, physiological regulation, the water cycle, and the carboncycle with reference to the Boreal Ecosystem Productivity Simula-tor (BEPS) (Liu et al., 1997), FOREST-BGC (BioGeochemical Cycles)(Running and Coughlan, 1988), and Biome-BGC (Running andHunt, 1993). The water cycle and carbon cycle submodels simulatehow climate, soil, and the compositional and physiological proper-ties of plants interact to affect water and carbon fluxes and theirbudgets in the soil–plant–atmosphere continuum. In the FLPM,spatial modeling, geographical information system (GIS), and re-mote sensing data and field-based observations are coupled effec-tively. The model has been used to simulate carbon and waterfluxes in forests in the cold temperate Changbai Mountain NatureReserve of northeastern China (Zhang et al., 2001, 2003a, 2003b,2004, 2007; Zhang and Yu, 2006) and an artificial masson pine for-est in the subtropical Qianyanzhou region of southern China(Wang and Zhang, 2010). The FLPM has also been modified to yieldthe Grassland Landscape Productivity Model (GLPM) for the simu-lation of carbon fluxes in the temperate semiarid grassland ecosys-tems of the Xilin River basin of the eastern Inner Mongolia Plateau,China (Zhang et al., 2009). The spatiotemporal variations in carbonor water fluxes in forests and grasslands simulated using the FLPMand the GLPM have been tested extensively and shown to exhibitsatisfactory agreement with experimentally observed values.

In the present study, the GLPM is used to simulate primarywater fluxes over a wide range of locations, climates, and ecosys-tems or land cover types in the Xilin River basin and an area imme-diately to its west during the growing season of 2002, which was asevere drought year. The Inner Mongolian grasslands, which arethe largest in China and represent typical Eurasian semiarid eco-systems, are particularly sensitive to climate change owing to thelimited availability of water. The climate of the study area wascharacterized based on 32-year meteorological records (1980–2011) from a national weather station in Xilinhot, which is the cap-ital of the Xilingol League and is located at 43�570N, 116�040E, inthe midstream reaches of the Xilin River basin. Based on the datafrom 1997 to 2011, 12 out of 15 years (80%) experienced midsum-mer and autumn precipitation that were at least 8.0% lower thanthe 32-year averages; conversely, for 1980–1996, only 6 out of17 years (35%) satisfied this criterion. Moreover, the growing sea-son mean air temperature increased significantly from 1980 to2011 (the p-value < 0.01, coefficient of determination r2 = 0.43).Thus, it can be concluded that this region has been experiencinga drying and warming trend during the growing season. This hasimportant implications for current and future soil water rechargeand ecosystem function. However, relatively few studies have beenconducted on the grassland ecosystems of Asia (Li et al., 2006),although this lack of information about the key processes affectingthe water budgets of semiarid grassland ecosystems and their re-sponse to climate change and soil water deficits has been recog-nized (Chen et al., 2009; Hao et al., 2007; Iijima et al., 2008;Lauenroth and Bradford, 2006). Additionally, many of the input

data and parameters required for the GLPM can be obtained froma number of studies conducted in this area over the past fortyyears; this availability of data provided further motivation for theselection of this study area.

To improve understanding of the sensitivity of semiarid grass-land ecosystems to climate change at the regional scale, the pres-ent study included the following specific objectives: (1) toquantify, test, and document how water fluxes and water deficitsvary temporally and spatially in different semiarid ecosystems ina drought year, and (2) to elucidate their dependence on key bio-logical and environmental factors under severe drought conditions.

2. Methods

2.1. Study area

The study area comprised the Xilin River basin and an areaimmediately to its west in Inner Mongolia, China (43�000–45�000N and 114�300–117�120E), covering an area of 47,199 km2.The soil and vegetation cover of the study area varies laterally,with moister meadows with chernozem soil in the southeast upri-ver region, grading into typical steppes with dark to typical chest-nut soil toward the northwest, and drier typical steppes with lightchestnut soil in the northwestern region itself. The entire studyarea experiences a semiarid continental temperate climate. Themean annual air temperature is 0.2 �C and the mean annual precip-itation is 350 mm, with 88% falling between May and September.Moreover, the climate of the study area becomes drier and warmergradually from the southeast (annual precipitation �400 mm) tothe northwest (annual precipitation �250 mm) (Bai et al., 2000;Li et al., 1988; Wang and Cai, 1988). The zonal native vegetationcover is typical steppe including Stipa grandis steppe and Leymuschinensis steppe, accounting for 50.74% and 15.08% of the entirearea, respectively. L. chinensis steppe is dominated by xero-meso-phytic grasses, with higher precipitation and biomass than S. gran-dis steppe, which is dominated by typical xeric grasses. The otherland cover types present include swamp and floodplain (9.24%),shrubland (8.66%), sand (6.47%), broadleaf forest (3.55%), salineland (2.89%), plain dry cropland (1.31%), water area (1.18%), barrenland (0.621%), urban land (0.242%), and hill dry cropland (0.023%)(Fig. 1). There are considerable horizontal variations in speciescomposition, diversity, and productivity from the southeast upri-ver region to the north central downriver region of the basin, cor-responding primarily to variations in climatic conditions(especially precipitation), altitude, and soil properties.

According to records from Xilinhot weather station, the annualprecipitation in 2002 (175.9 mm) was 82.8 mm (32.0%) lower thanthe 32-year average (1980–2011; 258.7 ± 80.3 mm, given asmean ± standard deviation). Precipitation in June was 23.4% higherthan the 32-year average, whereas precipitation in May, July, Au-gust, and September were 50.4%, 14.0%, 87.3%, and 76.5% lowerthan the 32-year averages, respectively. Based on data regardingactual drought impacts in Inner Mongolia (Hao, 1993), the regiondid not suffer severe drought in early summer (from mid-June toearly July), whereas severe drought occurred in spring (frommid-April to early June) and midsummer (from mid-July to mid-August) and extreme drought occurred in autumn (from late Au-gust to mid-September) in 2002.

2.2. Simulating water fluxes

2.2.1. Daily water budgetSurface water results primarily from precipitation (PPT), snow-

melt (Smelt), and capillary rise of deeper soil water toward the rootzone in the grassland of Inner Mongolia. In the present study,

Fig. 1. Land cover types of the study area in Inner Mongolia (from Zhang et al., 2009).

N. Zhang, C. Liu / Journal of Hydrology 512 (2014) 69–86 71

melted snow contributed part of the initial soil water in spring,and only water fluxes after snow had melted were simulated.Natural loss of soil water depends primarily on evaporation (Ev)from the soil surface (Sev) and canopy interception of precipita-tion (Cev), canopy transpiration (Tr), surface runoff (Rf), and deeppercolation in the grassland. Deep percolation is not a feature ofwater fluxes in semiarid areas, especially in drought years (Song,1995), because soil water is distributed predominantly in the top-soil layer and soil water content is below field capacity. Mostgrass roots are distributed in the top 30 cm of the soil (Huanget al., 1987); therefore, dominant species cannot easily take upwater from deeper layers. In general, a pedocal layer exists abovethe deeper layer of chestnut soil and retards water transportationbetween the soil surface and subsoil considerably (Li, 1988; Song,1995). Therefore, soil water changes due to deep percolation lossand uptake of water from deeper layers by grass roots can be ig-nored for the conditions that prevailed in 2002. In addition, Rf andsubsurface flow in or out of the basin is negligible owing to thelow gradient (0–4.4�) of the landscape and the dominance ofsmall rainfall events in the study area. Therefore, precipitationand evapotranspiration (ET) (including Tr and Ev) are the majorcomponents of the annual water budget in the study area (Chenand Wang, 2000). In the model, the change in soil water storagein the root zone on day i (DWi) during the growing season de-pends on the water inputs (Win,i) and outputs (Wout,i) of the eco-system for the same day:

DWi ¼Win;i �Wout;i ð1Þ

Win;i ¼ PPT ð2Þ

Wout;i ¼ ET þ Rf ¼ Ev þ Tr þ Rf ¼ Cev þ Sev þ Tr þ Rf ð3Þ

Wdi ¼ �DWi ð4Þ

where Wdi is the water deficit for day i (mm). All water input andoutput variables and water deficit are estimated or modeled in dailysteps. The average soil water content (SWC) in the root zone for dayi (SWCi) is expressed as follows:

SWCi ¼ SWCi�1 þ DWi ð5Þ

where SWCi�1 is SWC for day i � 1 (mm). When SWCi exceeds thefield capacity (hFC), it is assumed that SWCi equals hFC.

2.2.2. TranspirationIn the GLPM, the Penman–Monteith equation is used to esti-

mate Tr (Monteith, 1965):

ltrans ¼slope� aradþ cp � rho� VPD

ra

xlat � slopeþ pa�cp

xlat�EPS 1:0þ rwra

� �� ð6Þ

where ltrans is the leaf-level transpiration rate (mm s�1), slope is theslope of the saturation vapor pressure versus temperature curve(mbar K�1), arad is the net solar radiation available over sunlit orshaded leaves (W m�2), cp is the specific heat of air at constant pres-sure (1.008 � 103 J kg�1 K�1), rho is the mean air density at constantpressure (kg m�3), VPD is the vapor pressure deficit between thesaturation vapor pressure and the actual atmospheric vapor pres-sure (mbar), pa is the atmospheric pressure (mbar), xlat is the latentheat of vaporization (J kg�1), EPS is the ratio of the molecular weightof water vapor to dry air (0.622), ra is the aerodynamic resistance tovapor transfer over vegetative surfaces (s m�1), and rw is the leafresistance to vapor transfer (i.e., the resistance to latent heat trans-fer) (s m�1).

The aerodynamic resistance to radiative heat transfer throughair (rr) and the aerodynamic resistance to convective heat transfer(rh) were integrated to calculate ra for sunlit and shaded leaves(Zhang et al., 2001, 2004, 2007), and the leaf stomatal resistance


to vapor transfer (rs) and the leaf cuticular resistance to vaportransfer (rc) were integrated to calculate rw:

ra ¼rh � rr

rh þ rrð7Þ

rw ¼1

gs þ gc¼ 1

1=rs þ 1=rcð8Þ

where gc is the leaf cuticular conductance to vapor transfer (m s�1),estimated according to the drought tolerance of dominant plantsand soil water conditions (Table A.1). gs is the leaf stomatal conduc-tance to vapor transfer (m s�1), approximated as a function of envi-ronmental factors following Jarvis (1976), but some revisions weremade in the GLPM:

gs ¼ max½gs;max � f ðPPFDnÞ � f ðTÞ � f ðVPDÞ � f ðLWPÞ� f ðCO2Þ � f ðTmÞ � f ðSWCÞ � gcorr; gs;min� ð9Þ

where gs,max and gs,min are the species-dependent maximum andminimum stomatal conductance to vapor transfer (m s�1), respec-tively; PPFDn is the photosynthetic photon flux density at noon(lmol m�2 s�1), which differentiates sunlit leaves from shadedleaves; T is the mean daytime air temperature (�C); LWP is the leafwater potential (MPa); CO2 is the intercellular CO2 concentration(ppm); Tm is the freezing night minimum temperature (�C); gcorr

is a correction factor for gs that accounts for daytime air tempera-ture and atmospheric pressure.

When modeling transpiration, a canopy is stratified into sunlitand shaded leaves components. ltrans is scaled up to the canopy le-vel by integrating the leaf area index (LAI) for sunlit and shadedleaves (LAIsun and LAIshade):

Trr ¼ transsun þ transshade ¼ ltrans� Sd� ðLAIsun þ LAIshadeÞ ð10Þ

where Trr is the daily canopy transpiration rate (mm day�1),transsun and transshade are the daily transpiration rates for sunlitand shaded leaves (mm day�1), respectively, and Sd is the daily sun-shine duration (s).

2.2.3. Soil evaporationThe Penman–Monteith soil evaporation was also determined

according to the resistance concept. Most methods for estimatingvariables were unchanged, except that arad, rw, and ra in Eq. (6)were substituted with srad, srw, and sra:

srad ¼ irad0 � crad ð11Þ

where srad is the net solar radiation available over the soil surface(W m�2), irad0 is the total incoming solar radiation (W m�2), andcrad is the photosynthetically active radiation absorbed by the can-opy (W m�2).

srw ¼ 1=sgw ð12Þ

where srw is the resistance to vapor flow from beneath the soil sur-face (s m�1), and sgw is the soil conductance to vapor flow (m s�1),both of which are set as constants associated with relative soilmoisture (i.e., the ratio of SWC to available water capacity) (Waringand Running, 1998).

sra ¼ cra sun� LAIsun þ cra shade� LAIshade ð13Þ

where sra, cra_sun, and cra_shade are the aerodynamic resistances tovapor transfer over soil and over the surfaces of sunlit and shadedleaves (s m�1), respectively.

2.2.4. Canopy interception and evaporationThe actual value of Cev adopted depends on whether the poten-

tial evaporation from the canopy (i.e., canopy interception, Intp) or

the potential evaporation from the canopy assuming a radiationlimit (Rev) is smaller. This is similar to the criteria used in BEPS:

Cev ¼minðIntp;RevÞ ð14Þ

Rev ¼ irad=xlat0 ð15Þ

where irad is the daily total incoming solar radiation (MJ m�2 day�1)and xlat0 is the latent heat of vaporization of water (2450.0 MJ m�3).

When the daily minimum air temperature is higher than zero,canopy rain interception (Intprain) is estimated using the relation-ship between PPT and interception limited by the maximumthreshold PPT (PPT*) (He and Hong, 1999; Zhang et al., 2004). Thisinvolves two cases:

Intprain ¼ r� PPT ðPPT < PPT�Þ ð16Þ

Intprain ¼ r� PPT� ðPPT � PPT�Þ ð17Þ

PPT� ¼ a� LAI ð18Þ

where r is the fraction of total precipitation represented by canopyrain interception and a is the maximum water holding depth overthe leaf surface (m).

2.2.5. Surface runoffThe GLPM assumes that surface runoff occurs only if SWC is

higher than hFC:

Rf ¼ 0:5ðSWC� hFCÞ ðhFC < SWC < hSSÞ ð19Þ

Rf ¼ SWC� hSS ðSWC > hSSÞ ð20Þ

where hSS is the soil water capacity at saturation (mm).

2.3. Data processing and model parameterization

The GLPM requires input data to specify location, landform, soil,weather, vegetation and land cover, and initial soil water contentcharacteristics. Digitalized raster data describing latitude and lon-gitude, elevation, slope, aspect, soil type, soil texture, vegetationtype, and land cover type were obtained from the Data Center forResources and Environmental Sciences of the Chinese Academyof Sciences and the Land–Atmosphere Interaction Research Groupat Beijing Normal University.

Daily weather data (including maximum, minimum, and meanair temperatures (Tavg), PPT, relative humidity (Rh), wind speed(Ws), pa, and sunshine hours (Sh)) for each grid cell were obtainedby interpolating the daily observations from 20 surroundingweather stations using a thin-plate smoothing spline modeldependent on latitude, longitude, and elevation in the ANUSPLINsoftware (Hutchinson, 2004). The interpolated values werecross-validated. irad was estimated using MT-CLIM (Runninget al., 1987), taking topography (i.e., elevation, slope, and aspect)into account.

SWC values in the top 20 cm of the soil (SWC20cm) at the begin-ning of growing season in 2002 were inversed from MODIS far-infrared reflectance data and measured soil surface temperatureand precipitation values using a simplified thermal inertia modeldeveloped by Liu and Zhao (2006). These inversed SWC20cm valueswere input into the GLPM as the initial SWC in the root zone. Eight-day regional LAI values were inversed from MODIS reflectance datausing a reflectance model coupling radiative transfer and geomet-ric optical properties of the land cover type and the inversion arith-metic of Powell (Zhang and Zhao, 2009). The daily LAI values werederived by linearly interpolating the 8-day LAI values.

The values of 62 parameters were obtained or estimated pri-marily from observations in Inner Mongolia. Some of the parame-ters related to water and soil are presented in Table A.1.


All spatially explicit inputs were reprocessed to form a multi-layer GIS database at a spatial resolution of 1 � 1 km in the Albersprojection. All simulations were performed cell by cell with a day-long time step from April 15 to October 16 2002. The results arepresented as output for one day, which can be summed or aver-aged over any set time period, such as a month or the entire sim-ulation period (see Zhang et al. (2009) for more details).

2.4. Correlation between water fluxes and biological andenvironmental factors

The biological factors analyzed were LAI and gs, and the envi-ronmental factors analyzed included PPT, Tavg, Rh, VPD, Ws, irad,Sh, SWC, and soil sand fraction (SSF) and soil clay fraction (SCF)in the top 30 cm of the soil. Spearman correlation coefficients (R)were calculated between daily water fluxes and the daily averagevalues of these biological and environmental factors to examinetheir temporal relationships. All 44,968 grid cells over the studyarea were sampled, and spatial Spearman correlation coefficientswere calculated between the growing season total water fluxesand the total (i.e., PPT and irad) or average values of the biologicaland environmental factors with the same spatial locations toexamine their spatial relationships.

3. Validation of modeled water fluxes and water deficit

The modeled growing season ET values for the L. chinensissteppe and S. grandis steppe were within the measured ET range(210.4–378.8 mm) for the typical steppes in the Xilin River basin,and the modeled ET for shrubland was within the measured ETrange (150.9–207.5 mm) for the degraded or desert steppes in In-ner Mongolia. In general, the modeled growing season ET valueswere closer to those measured in Inner Mongolia under similarprecipitation conditions, higher than those for 30–90 mm less pre-cipitation, and lower or much lower than those for 100–250 mmmore precipitation (Table 1, Fig. 2a and b). The modeled growingseason Tr/ET ratio for the typical semiarid steppe (0.75) was closeto that measured in a drought year (e.g., Song, 1995) and was alsowithin the range of the simulated 39-year wet season average(0.6–0.8) for a semiarid shortgrass steppe in the US (Lauenrothand Bradford, 2006). This suggests that the partitioning of ET be-tween Tr and Ev is reasonable. The rankings of the modeled grow-ing season Ev, Tr, and ET by land cover type (such as L. chinensissteppe, S. grandis steppe, shrubland, and cropland) were similarto both measured values (e.g., Cui et al., 2001; Du et al., 2003; Miaoet al., 2009; Song, 1995) and other estimated values (e.g., Li andGao, 2004; Zhou et al., 2006) for Inner Mongolia.

The modeled temporal variations in daily evaporation rate (Evr),Trr, and daily evapotranspiration rate (ETr) and the occurrence ofthe maximum values for L. chinensis steppe followed the generaltrends of the measured values for typical steppes in Inner Mongoliareasonably well, regardless of precipitation (e.g., Chen and Wang,2000; Song, 1995; Wang et al., 2008; Zhou et al., 2006), althoughthe values were less than those under normal or higher precipita-tion. The modeled temporal variations in the Trr/ETr ratio for mostecosystems also followed the general trends (Allen et al., 1998). Themodeled mean daily ETr values for the L. chinensis steppe and S.grandis steppe (1.47 ± 0.25 mm day�1 and 1.27 ± 0.24 mm day�1,respectively) were at the lower end of the measured daily ETr range(1.4–2.1 mm day�1) for typical steppes in Inner Mongolia. Themodeled maximum daily ETr values for the L. chinensis steppe andS. grandis steppe (3.14 mm day�1 and 3.28 mm day�1, respectively)were within the measured maximum daily ETr range (2.8–4.0mm day�1) for typical steppes in Inner Mongolia (Table 1). The

modeled mean daily Trr for the L. chinensis steppe in July(1.64 ± 0.22 mm day�1) was extremely close to the measured val-ues (1.67 mm) under similarly arid conditions in the midstreamreaches of the Xilin River basin (e.g., Du and Yang, 1995).

The modeled growing season water deficits for the L. chinensissteppe, S. grandis steppe, shrubland, and cropland were close tothose measured in Inner Mongolia under similar precipitation con-ditions, lower than those under lower precipitation conditions, andmuch higher than those under normal or higher precipitation con-ditions with surplus water (Table 1). The modeled monthly varia-tions in water deficit followed the generally observed patterns(Chen and Wang, 2000). For the L. chinensis steppe, the modeledmonthly water deficit agreed well with that measured under sim-ilarly arid conditions (e.g., Song, 1995), but could be higher ormuch higher than that under normal precipitation conditions(e.g., Song et al., 2003) (Fig. 2c).

Water deficit can be examined through the ET/PPT ratio. In thepresent study, the modeled growing season ET/PPT ratios wereconsistent with observations for a semiarid typical steppe or drysteppe under arid conditions; these ratios were almost alwayshigher than those under normal or higher precipitation conditions(Table 1). However, the modeled growing season ET at Xilinhotweather station in 2002 (202.83 mm) was lower than the 32-year(1980–2011) growing season average PPT (234.41 mm) at thesame station. This suggests that soil water storage may be bestconsidered over longer timescales. Moreover, on an annual basis,ET may be nearly equal to or lower than PPT owing to winter snow-fall (Wang et al., 2008).

The eddy covariance (EC) technique has been used to measurethe fluxes of water vapor, CO2, and sensible heat over the past dec-ade. The modeled growing season total ET, mean daily ET, and max-imum daily ET were compared with these measurements for the L.chinensis community in Xilinhot and the midstream reaches of theXilin River basin in 2003–2007 (Table 1). However, a completecomparison of the day-to-day dynamics was not possible becausethe present study focused primarily on weather, water, and vege-tation conditions in 2002, whereas the EC measurements havebeen conducted since 2003. Moreover, the modeled daily ET valuefor the L. chinensis steppe was averaged over the entire study area,whereas the measurements were from a local site. These differ-ences in study year and spatial scale mean that validation can beconducted only at a coarse level. In addition, these comparisonscan only be used to validate ET values for the L. chinensis ecosys-tem; comparisons of other water flux processes (such as parti-tioned Tr and Ev) or other ecosystems cannot be made.

4. Results

4.1. Modeled total water fluxes and water deficit during the growingseason

4.1.1. PrecipitationThe interpolated PPT during the growing season over the entire

study area in 2002 averaged 190.86 ± 21.09 mm (mean and stan-dard deviation for 44,968 grid cells) and was mostly within therange 160–220 mm (84.4%), which is significantly lower than theaverage (325 mm) of the Xilin River basin. In the growing season,there were 42 rainy days, of which 31 experienced less than5 mm PPT and contributed approximately 27% of the total PPT.The L. chinensis steppe and dry cropland had the highest PPT (morethan 200 mm); barren land, the S. grandis steppe, and saline landexhibited the lowest PPT values (about 184 mm), and the remain-ing land cover types experienced about 193 mm (Figs. 3 and 4a).

Table 1Growing season evapotranspiration (ET) for different land cover types in Inner Mongolia.

Land cover type Quantifyingmethod

Location Climate Year Growingseasonprecipitation(mm)

Growingseason ET(mm)

Mean daily ET(mm day�1)

Maximumdaily ET(mm day�1)

Growingseason waterdeficit (mm)

ET/PPT Reference

Leymus chinensis steppe Simulatedby theGLPM

Xilin River basin(43�000–45�000N,114�300–117�120E)

Semiaridtemperateclimate

2002 212.39 ± 13.93 272.37 ± 46.15 1.47 ± 0.25 3.14 (July 20) 59.98 ± 43.58 1.28 ± 0.20 This study

Stipa grandis steppe Simulatedby theGLPM

Same as above Semiaridtemperateclimate

2002 182.93 ± 19.35 234.46 ± 44.72 1.27 ± 0.24 3.28 (July 20) 51.52 ± 46.70 1.29 ± 0.30 This study

Comparison with those under lower precipitation conditionFenced L. chinensis steppe Measure

(lysimeter)Midstream of XilinRiver basin(43�380N,116�420E)


1989 227.7 261.5 1.71 2.83 (June) 33.8 1.15 Song (1995)

Fenced steppe (L. chinensis, S. grandis,Achnatherum sibiricum, Agropyroncristatum)

Measure(ECa)

Xilinhot of XilinRiver basin(43�3300200N,116�4002000E)


2006 184.2 230.3 1.52 ± 0.06 3.5 (mid-June) 46.1 1.25 Miao et al. (2009)

Fenced steppe (same as above) Measure(EC)


2007 153.6 210.4 – 3.6 (mid-July) 56.8 1.37 Miao et al. (2009)

Fenced L. chinensis steppe Measure(EC)

Midstream of XilinRiver basin(43�320N,116�400E)


2005 126.0 214.2 1.4 3.3 (6 May) 88.2 1.70 Hao et al. (2008)

Fenced steppe (S. krylovii, Artemisiafrigida, A. cristatum, Cleistogenessquarrosa, L. chinensis)

Measure(EC)

Duolun, XilingolLeague(42�0204800N,116�1700100E)


2007 177.5 204.1 – 4.3 (earlyAugust)

26.6 1.15 Miao et al. (2009)

Northern temperate grassland(Agropyron dasystachyum, A. smithii)

Measure Lethbridge, Alta,Canada(49�2604800N,12�3303600W)

Moderatelycool andsemiaridclimate

2000 115 207 3.0 (mid-June) 92 1.80 Wever et al. (2002)

Northern temperate grassland (sameas above)

Measure Same as above Same asabove

1998 183 208 4.5 (early July) 25 1.14 Wever et al. (2002)

Northern temperate grassland (sameas above)

Measure Same as above Same asabove

1999 213 205 3.0 (late June) �8.0 0.96 Wever et al. (2002)

Comparison with those under normaland higher precipitation conditions

Fenced L. chinensis steppe Measure(lysimeter)



1996 317.0 299.0 – – �18.0 0.94 Song et al. (2003)




2004 344.7 263.0 2.1 4.0(July 25) �81.7 0.76 Hao et al. (2008)



2003 354.3 263.2 1.7 3.5 (early July) �91.1 0.74 Hao et al. (2007)

74N

.Zhang,C.Liu/Journal

ofH

ydrology512

(2014)69–

86

Fenced L. chinensis steppe Simulatedby SVATb

model



1990–2003

413.0 354.0 – – �59.0 0.86 Guo and Mo (2007)

Fenced L. chinensis steppe Measure(lysimeter)



1998 445.0 378.8 – – �66.2 0.85 Song et al. (2003)

Fenced steppe (S. krylovii, A. frigida,A. cristatum, C. squarrosa, L. chinensis)

Measure(EC)

Duolun(42�0204800N,116�1700100E)


2006 395.9 344.4 2.25 ± 0.10(0.20–5.69)

5.69 (earlyJuly)

�51.5 0.87 Miao et al. (2009)

Steppe MeasureEstimate

Globally – – – – – 3.0–5.54.2–6.2

– – Hunt et al. (2002),Kelliher et al. (1993),Meyers (2001), andWever et al. (2002)

Shrubland Simulatedby theGLPM

Xilin River basin Semiaridtemperateclimate

2002 196.66 ± 14.78 219.41 ± 30.11 1.19 ± 0.16 2.60(June 27) 22.75 ± 25.91 1.12 ± 0.13 This study

Degraded steppe (S. grandis, C.squarrosa)

Measure(EC)

Xilinhot(43�3301600N,116�4001700E)


2006 184.8 201.4 – 3.70 (mid-June)

16.6 1.09 Miao et al. (2009)

Degraded steppe (S. grandis, C.squarrosa)

Measure(EC)


2007 153.7 207.5 – 3.85 (mid-July)

53.8 1.35 Miao et al. (2009)

Desert steppe (S. Klemenzii, Alliumpolyrrhizum)

Measure(EC)

SunitezuoqiCounty, XilingolLeague (44�050N,113�340E)


2008 133.0 150.9 – – 17.9 1.13 Yang and Zhou (2011)

Desert grassland Estimatedby Bowenratio

US (32�360N,106�450W)

Temperateclimate

1996–2001

240.0 299.3 – 5.67 59.3 1.25 Mielnick et al. (2005)

Hilly dry cropland Simulatedby theGLPM


2002 200.19 ± 4.79 182.13 ± 9.06 0.98 ± 0.049 3.58 (July 21) �18.06 ± 4.59 0.91 ± 0.024 This study

Plain dry cropland Simulatedby theGLPM


2002 205.58 ± 21.80 197.42 ± 42.93 1.07 ± 0.23 4.03 (July 3) �8.16 ± 40.91 0.96 ± 0.23 This study

Cropland Measure(EC)

Duolun(42�0204400N,116�1604700E)


2007 178.0 190.5 – – 12.5 1.07 Miao et al. (2009)

Cropland Measure(EC)


2006 394.6 292.0 – – �102.6 0.74 Miao et al. (2009)

a EC: Eddy covariance.b SVAT: Soil–Vegetation�Atmosphere Transfer.

N.Zhang,C.Liu

/Journalof

Hydrology

512(2014)

69–86

75

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Fig. 2. Comparison of the modeled growing season monthly (a) precipitation, (b)evapotranspiration, and (c) water deficit for the Leymus chinensis steppe with thosemeasured in Inner Mongolia in both a drought year (1989) and a normalprecipitation year (1996). Standard deviation error bars are provided for themodeled values in 2002.


4.1.2. EvapotranspirationThe modeled ET in the growing season averaged

237.50 ± 50.72 mm, mostly in the range 170–270 mm (80.6%). TheET values decreased in the following order: L. chinensis steppe(272.37 ± 46.15); swamp and floodplain and the S. grandissteppe (about 234–255 mm); shrubland and sand (211–219 mm);

-150-100

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50100150200250300350

Leymus chinensis

steppe

Stipa grandis steppe

Swamp and

floodplain

Shrubland Hilly dry cropland

Plaincrop

Wat

er fl

ux a

nd d

efic

it (m

m)

Land cover ty

Precipitation Transpiration Evaporation Ee

Fig. 3. Modeled water budgets during the growing season of 2002 for different land covertypes.

saline land, plain dry cropland, and barren land (194–199 mm);hilly dry cropland (182.13 ± 9.06 mm) (Figs. 3 and 4b). Further-more, the results demonstrate that cultivation reduced the valueof ET by 10–33%.

4.1.3. Transpiration and evaporationThe modeled Tr in the growing season averaged 154.03 ± 60.22

mm and was mostly within the range 40–220 mm (88.2%). Evaveraged 83.46 ± 57.90 mm and was primarily within the range0–130 mm (79.3%). Tr and Ev accounted for 64.9% and 35.1% of ET,respectively. The L. chinensis steppe and S. grandis steppe exhibitedthe highest Tr and lowest Ev, accounting for about 75% and 25% ofET, respectively. In contrast, barren land, sand, and saline landexhibited the lowest Tr and highest Ev, accounting for about 28%and 72% of ET, respectively. Swamp and floodplain, shrubland,and dry cropland had intermediate Tr and Ev values, accountingfor about 53% and 47% of ET, respectively (Figs. 3, and 4c and d).In addition, the modeled Sev averaged 83.00 ± 57.95 mm, account-ing for 99.4% of Ev. The modeled Rf was close to zero. Thus, itappears that the contribution of Cev and Rf to the water budget isnegligible.

4.1.4. Water deficitThe modeled water deficit in the growing season averaged

46.64 ± 49.63 mm, mostly in the range 5–100 mm (80.1%). Waterdeficits varied considerably between land cover types. Swampand floodplain, the L. chinensis steppe, and S. grandis steppe exhib-ited the greatest mean water deficits (51–63 mm), whereas themean water deficits for shrubland, sand, barren land, and salineland were about 12–23 mm. Only dry croplands exhibited thesmall water surplus (8–18 mm) (Figs. 3 and 4e). The high ET/PPTratios (>1) found for all of the natural ecosystems suggests thatboth all of the precipitation during the growing season and somewater stored in the soil was lost as evapotranspiration.

4.2. Modeled temporal variations in water fluxes and water deficit

4.2.1. Modeled temporal variations in precipitation and soil water4.2.1.1. Precipitation. The interpolated PPT for the 2002 growingseason exhibited similar one-peak curves for all land cover types(Fig. 5a). PPT in June was 53.6% higher than the average value, with70.1 mm of PPT in 9 days, whereas PPT in July, August, and Sep-tember was 16.8%, 70.1%, and 53.2% lower than the average values,respectively. Substantial rainfall occurred on adjacent days fourtimes: 20.6 mm over May 5–14 (day of year or DOY 125–134),41.7 mm over June 9–12 (DOY 160–163), 26.0 mm over June 22–24 (DOY 173–175), and 54.1 mm over July 11–19 (DOY 192–200). The maximum daily PPT was 31.7 mm and occurred on July

dry land

Saline land

Sand Barren land

pe

vapotranspiration Water balance

Evapotranspiration

Evaporation

Transpiration

Water deficit

types in the study area. Standard deviation error bars are provided for all land cover

00.10.20.30.40.50.60.70.80.9

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PPT_LcPPT_PcropPPT_HcropPPT_ShrublandPPT_SandPPT_FlplainPPT_SaPPT_Saline landPPT_SgPPT_Barren land

00.10.20.30.40.50.60.70.80.9

1

0 30 60 90 120 150 180 210 240 270 300 330 360 390

Growing season evaporation (Ev) (mm)

Ev_SandEv_Barren landEv_Saline landEv_FlplainEv_ShrublandEv_PcropEv_SaEv_SgEv_LcEv_Hcrop

00.10.20.30.40.50.60.70.80.9

1

100 130 160 190 220 250 280 310 340 370 400 430 460 490

Growing season evapotranspiration (ET ) (mm)

ET_Lc ET_FlplainET_SaET_SgET_ShrublandET_SandET_Saline landET_PcropET_Barren landET_Hcrop

00.10.20.30.40.50.60.70.80.9

1

0 30 60 90 120 150 180 210 240 270 300 330 360 390

Growing season transpiration (Tr) (mm)

Tr_LcTr_SgTr_SaTr_FlplainTr_HcropTr_ShrublandTr_PcropTr_Saline landTr_SandTr_Barren land

00.10.20.30.40.50.60.70.80.9

1

-100 -50 0 50 100 150 200 250 300Growing season water deficit (Wd ) (mm)

Wd_HcropWd_PcropWd_Saline landWd_Barren landWd_SandWd_ShrubWd_SaWd_SgWd_LcWd_Flplain

a b

c d

e

Fig. 4. Cumulative relative frequency (CRF) of the grid cells with the modeled water budgets during the growing season of 2002 for different land cover types in the studyarea. Black and thick lines denote the CRF of water budgets for the entire study area. Dash-dotted lines denote the land cover type groups with the largest water budgets.Dashed or solid lines denote the groups with intermediate values. Dotted lines denote groups with the lowest values. For the same land cover type group, the thicker linesdenote the larger average values. The legends are given in the order of average values. Hilly dry cropland is a specific type with only 11 grid cells; thus, its CRF is unreliable. Sa:study area, Lc: Leymus chinensis steppe, Sg: Stipa grandis steppe, Flplain: swamp and floodplain, Hcrop: hilly dry cropland, and Pcrop: plain dry cropland.


18 (DOY 199). The longest rain-free period spanned 19 days inearly spring (DOY 135–153) (Fig. 9e).

4.2.1.2. Soil water. Generally, soil water tracked the amount ofrainfall received closely, especially when the 10-day total PPTexceeded 20 mm. Both soil water and rainfall experienced theirmaximum values on DOY 199, although there were time lags inthe soil–water dynamics, with high SWC values lasting severaldays after rain (Fig. 9e). SWC started out high (4.4–7.1%) at thebeginning of the growing season because of heavy snowfall inMarch and decreased slowly to 3.0–6.1% in early August. The highrainfall in June and mid-July caused an extended period with arelatively steady average SWC until early August. SWC decreasedrapidly to almost 2.6% in late September, before increasingslightly at the end of the growing season. However, for the dry

croplands, the harvest resulted in a reduction in water absorptionby plants; thus, an associated rapid increase in SWC occurredafter July (Fig. 5b).

Except for saline land and swamp and floodplain, SWC seldomexceeded hFC. Thus, little overland flow or deep percolationoccurred throughout the whole growing season. On average, on71.4% of growing season days, the soil in the root zone dried tobelow 4.5%. Spring contained the fewest days (45.6%) on whichSWC was equal to or below 4.5%, whereas SWC was equal to or lessthan 3% on all days in fall. Wilting point (hWP) was exceeded on46 days for the L. chinensis steppe (hWP = 6%) and on 59 days forthe S. grandis steppe (hWP = 5%), occurring most often aftersnowmelt in early growing season and during or shortly after thefour heavy rainfall events (Fig. 9e). Soil water loss to below hWP

occurred within 1–15 days after heavy rain.

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(LAI

) (m

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Month / DayShrubland SandSaline land Barren landSwamp and floodplain Hilly dry croplandPlain dry cropland Leymus chinensis steppeStipa grandis steppe Mean

a

b

c

Fig. 5. Monthly variations during the growing season in the estimated (a) meanprecipitation, (b) soil water content, and (c) leaf area index for different land covertypes in the study area in 2002. Standard deviation error bars are provided for theLeymus chinensis steppe and Stipa grandis steppe.


4.2.2. Modeled temporal variations in water flux rate4.2.2.1. Transpiration rate. The modeled peak Trr averaged over thestudy area occurred in early July (1.41 ± 0.094 mm day�1). The L.chinensis steppe always exhibited the highest or second highestTrr of all of the ecosystems. The modeled temporal variations inTrr for the major ecosystems (i.e., the L. chinensis steppe, S. grandissteppe, shrubland, and swamp and floodplain) were similar: a ra-pid increase with development of green vegetative tissues duringmid-April through mid-June, a steady decline with rapid senes-cence of leaves from mid-August, and a long peak duration frommid-June to mid-August. The modeled temporal variations in Trrfor sand, saline land, and barren land were also similar: a nearlylinear increase and decrease before July and after August, respec-tively, and a short peak duration from July to August. In contrastto the natural ecosystems, Trr declined sharply following the peakin June for dry croplands and approached zero after harvesting(Fig. 6a).

4.2.2.2. Evapotranspiration rate and evaporation rate. The modeleddaily ETr and Evr over the study area reached their maximum val-ues of 2.05 ± 0.26 mm day�1 and 0.76 ± 0.39 mm day�1, respec-tively, in mid-June. The L. chinensis steppe had the highest orsecond highest ETr values among all of the ecosystems duringthe active growth stage (June–August). For sand, saline land, barrenland, dry croplands, and shrubland, ETr increased nearly linearlybefore June. For the typical steppes and swamp and floodplain,ETr changed only slightly from April to May owing to the balancebetween the increasing Trr and decreasing Evr; from May to June,however, ETr increased rapidly to its maximum value. In July andAugust, although Trr values for the major ecosystems were verysimilar to those in June, ETr values were much lower than thosein June because of their lower Evr values. After September, ETr de-creased similarly to Trr for most ecosystems except for croplandsand sand, which consist primarily of bare ground and have weaktranspiration. In these ecosystems, ETr followed the trend of theincreasing Evr value (Fig. 6b and c).

The cumulative ET increased in an almost linear fashion for theentire study area and for the two typical steppes over the course ofthe growing season. The two steppes had nearly the same cumula-tive ET before DOY 172 (June 21). Then, the L. chinensis steppeexhibited more rapidly increasing ET values (Fig. 7b), which couldbe attributed to the more rapid growth of L. chinensis comparedwith that of S. grandis resulting from the precipitation after mid-June (Fig. 7a and c).

4.2.3. Modeled temporal variations in water deficit4.2.3.1. For the entire study area. There were obvious temporal vari-ations in water deficit over the entire study area, determined pre-dominantly by the two typical steppes (Fig. 6d). In June andOctober, water inputs and outputs were nearly equal. In April,May, and September, the water deficit was about 10–15 mm as aresult of less rainfall. The water deficit was greatest in August(about 22 mm), whereas the greatest water surplus was found inJuly for all ecosystems. The maximum daily water surplus (about30 mm) occurred on the same day as the maximum precipitation.The switch from the dry to the wet season (mid-June through mid-July) could result in such switches from water deficit to equilib-rium or surplus in these ecosystems.

4.2.3.2. For different ecosystems. The temporal variations in waterdeficits were significantly different among ecosystems (Fig. 6d).The L. chinensis steppe and S. grandis steppe had large water deficitsin the early (April–May) and late (August–September) growingmonths. Swamp and floodplain exhibited similar water deficits tothe typical steppes in early and late growing months, althoughthey exhibited a greater water deficit in June and smaller watersurplus in July than all of the other ecosystems studied. Dry crop-lands exhibited greater water deficits than the typical steppes inMay; however, after crops were harvested in late July, dry crop-lands had surplus water, particularly in August when most otherecosystems had the greatest water deficit. Shrubland reached awater balance in the growing months, except in May and August.Compared with the other natural land cover types, sand, salineland, and barren land had smaller water deficits in early and lategrowing months but a larger water deficit in June.

4.3. Modeled spatial patterns of water fluxes and water deficit

The modeled total PPT in the growing season decreased gradu-ally from the southeast to the northwest of the Xilin River basinand from the northeast to the southwest of the study area(Fig. 8a). The modeled Tr exhibited a spatial pattern similar to that

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4/15 4/30 5/15 5/30 6/14 6/29 7/14 7/29 8/13 8/28 9/12 9/27 10/12

a

b

c

d

Fig. 6. Temporal variations during the growing season in the modeled water budgets for different land cover types in the study area in 2002: (a) transpiration rate, (b)evaporation rate, (c) evapotranspiration rate, and (d) water deficit. Standard deviation error bars are provided for the Leymus chinensis steppe and Stipa grandis steppe.


of ET, decreasing in the following order: southeastern region (pre-dominantly L. chinensis steppe), central and northeastern regions(primarily typical steppes and swamp and floodplain), the north-western and northern regions (predominantly S. grandis steppe),and the southwestern region (mostly shrubland and sand)(Fig. 8b). Water deficit was influenced primarily by transpirationlosses, with higher Tr values leading to larger water deficits(Fig. 8c).

5. Discussion

5.1. Biological and environmental factors influencing temporalvariations in water fluxes

5.1.1. Biological factorsAt the daily or monthly scale, Trr, Evr, and ETr are inherently

connected to variations in certain biological and environmental

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a

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Fig. 7. Cumulative mean (a) precipitation, (b) evapotranspiration, and (c) transpi-ration during the growing season of 2002 in the study area (Sa), Leymus chinensissteppe (Lc), and Stipa grandis steppe (Sg). The vertical lines denote the first days ofthe year on which pronounced differences in the rates of increase of the waterfluxes were found for the two typical steppes.


factors (Song, 1996; Wever et al., 2002; Wilske et al., 2010; Yangand Zhou, 2011). The results of the present study demonstrate thatthe dynamics of both daily Trr and daily ETr were associated mostclosely with the phenological stage of green leaves and canopydevelopment, as represented by LAI over time (R = 0.80 and 0.66,respectively), whereas there was no significant correlation be-tween daily Evr and daily LAI (Figs. 6 and 9a). For the dry croplands,LAI and Trr were both high when the crops entered the rapid devel-opment stage from late May to mid-July; in contrast, LAI and Trrwere both low in early spring when the crops were small andsparse and after late July when the crops had been completely har-vested and there was no subsequent vegetation. For the naturalecosystems, Trr values were observed to be higher in June to Au-gust, when the leaves were well developed (Figs. 5c and 6a). In par-ticular, the maximum Trr for the L. chinensis steppe occurred at apeak LAI value (1.14 ± 0.30 m2 m�2) in July. As expected, therewere significant positive correlations between daily ETr, Trr, andEvr values and daily gs values due to the effects of climate and leafage on gs (Fig. 9a).

5.1.2. Environmental factorsThe major influences of environmental factors on daily ETr were

as follows, in order of decreasing importance: daily Tavg, irad, Rh,VPD, and SWC. Conversely, daily ETr values were not correlatedwith daily PPT, Ws, or Sh. The relationships between daily Trr andthese factors were exactly the opposite of those for daily Evr. DailyTavg, irad, VPD, and Sh exerted stronger positive effects on Trr andETr than on Evr, whereas daily SWC, Rh, PPT, and Ws exerted stron-ger positive effects on Evr than on Trr or ETr (Fig. 9). The influenceof Tavg, irad, Sh, VPD, and Rh on daily Evr was specific to a severedrought year and was the exact opposite of that seen under well-watered conditions. This demonstrates that severe drought hasstronger effects on evaporation than transpiration.

Snowmelt from early March to early April was responsible forthe increased initial SWC value (Fig. 9e), which might account forboth the higher Evr in mid- and late April (Fig. 6b) and the changesin the monthly water deficit (Fig. 6d) (Wilske et al., 2010). The areasuffered severe drought from mid-May to early June, with the lon-gest rain-free period lasting 19 days (Fig. 9e). Consequently, thetopsoil layer became extremely dry and less water was availablefor evaporation (Figs. 5b, 6b, and 9e). The capacity of the VPD toregulate Evr was largely limited by drought stress (Fig. 9c) (Liet al., 2007; Mott and Parkhurst, 1991; Wilske et al., 2010). How-ever, the severe drought did not retard the trend of leaf develop-ment; thus, Trr increased during the spring green-up stage,although the rate of increase of Trr was apparently reduced, likelyowing to the closure of some stomata in response to drought stress(Fig. 6a). The increase in VPD continued to regulate Trr, and tran-spiration remained the prime contributor to ETr, even under suchwater-stressed conditions. Again, Trr and Evr exhibited signifi-cantly different responses to severe drought conditions.

From May to June, Trr and Evr increased dramatically until Evrreached its peak values as both PPT and Tavg increased markedly(Figs. 6a and b, and 9b and e). ETr reached its peak values inmid-June, similar to years with higher PPT in June and earlier thanyears with higher PPT in July (e.g., the fenced and degraded typicalsteppes of Chen et al. (2009) and Miao et al. (2009)). From June toJuly, Trr reached or neared peak values; conversely, Evr declinedsharply in response to decreases in PPT, Rh, and Ws and increasesin Tavg, VPD, and Sh. From July to August, Evr decreased consis-tently as PPT dropped rapidly and the most severe soil water deficitoccurred, but Trr decreased slightly under still higher Tavg, VPD,irad, and Sh (Figs. 6a and b, and 9). In contrast to the meteorologicalconditions from June to July, Trr fell rapidly after mid-August withthe senescence of leaves and decreasing rainfall; this pattern issimilar to that seen in normal years (e.g., Chang and Zhu, 1989).Therefore, Evr changed more quickly as a function of PPT than Trrin the growing season.

5.1.3. Rainfall events and soil water controlsDaily Trr, Evr, and ETr were strongly affected by individual rain-

fall events (Fig. 10a). On rainy days, regardless of whether the rainwas light or heavy, Trr tended to decrease sharply owing to the clo-sure of most stomas. However, once rainfall ceased, Trr rose rapidlybecause of the increase of stomatal conductance 1–3 days after therain (Fig. 9a). Such changes in Trr were most pronounced duringthe heaviest continuous rain on July 11–19 (DOY 192–200), whenTrr decreased most rapidly; subsequently, Trr increased to almostits maximum value shortly after this rain event (Fig. 10a).

In the early and late growing season, the individual peaks in Evr(and hence in ETr) often occurred on the same days as small rainfallevents (e.g., on DOY 125, 237, and 270). This was likely because soilwater deposited by these small rainfall events remained in shallowlayers, which are affected most heavily by soil evaporation(Lauenroth and Bradford, 2006). In the peak growth stage,

a b

c

Fig. 8. The spatial pattern of the modeled growing season water budgets: (a) water input (precipitation), (b) water output (evapotranspiration), and (c) water deficit in thestudy area in 2002.


however, individual peaks in Evr and ETr (e.g., on DOY 163, 175,and 201) were observed 1–3 days following heavy rain, indicatingthat the effect of substantial rain on evaporation went beyond theday scale. The lag of peaks in Evr and ETr was due to the longertime required for the soil surface to dry following heavy rain thanfollowing light rain. Furthermore, VPD influenced Evr and ETr moststrongly after rainfall, when water intercepted by topsoil contrib-uted to evaporation (Wilske et al., 2010). These results are similarto those found previously for a temperate desert steppe (Yang andZhou, 2011) and a semiarid shrub steppe (Wilske et al., 2010) inInner Mongolia and a Mongolian dry steppe (Li et al., 2007).

Similarly to the response of daily Trr to concurrent PPT, daily Trrvalues were independent of the concurrent daily SWC values.However, they were observed to be correlated with the cumulativeSWC for the previous 2, 5, 10, 15, and 20 days to increasing de-grees. In contrast, daily Evr was independent of the cumulativeSWC for the previous 20 days but was correlated primarily withthe concurrent SWC (Fig. 10). This indicates that soil surface waterfollowed the rainfall event rather closely and affected Evr almost

immediately; these effects continued to decline gradually withdecreasing SWC until the next rainfall event. In contrast, a longertime lag was found in the response of soil water in the root zoneto the rainfall event and its effect on plant growth (Hunt et al.,2002; Li et al., 2007; Yang and Zhou, 2011).

5.2. Biological and environmental factors influencing spatial patternsof water fluxes

5.2.1. Biological factorsLAI is one of the most important indices related to plant growth

on a broad scale; thus, the relationships between water fluxes andLAI can help explain the effects of plant growth on the spatiotem-poral patterns of water fluxes (Miao et al., 2009; Song, 1995). Ahighly significant power law correlation between Tr and LAI ac-counted for 0.98 of the spatial variation in Tr (Fig. 11a), whichwas the primary reason that the Tr values were higher for theL. chinensis steppe than for the S. grandis steppe or swamp andfloodplain and can explain why sand, saline land, and barren land

Fig. 9. Temporal variations in biological and environmental factors including (a) leaf area index (LAI) and leaf stomatal conductance, (b) mean air temperature and windspeed, (c) relative humidity and vapor pressure deficit, (d) total incoming solar radiation and sunshine hour, and (e) soil water content and precipitation in the study area in2002.


0

5

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500

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1.5

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5

Precipitation(PPT) (m

m)

Soil water content (SW

C) (%

)

Wat

er fl

ux (m

m d

ay-1

)

Precipitation Soil water content

Spearman correlation coef f icient (R)Water f lux PPT SWCTrr -0.123 0.120Evr 0.195** 0.506**ETr 0.133 0.281**

0

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60

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800

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ulative soil water content last tw

o days (C

SWC

_L2D) (%

)

Wat

er fl

ux (m

m d

ay-1

)

CSWC_P2D

Spearman correlation coef f icient (R)Water f lux CSWC_P2DTrr 0.161*Evr 0.475**ETr 0.283*

0

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ulative soil water content last five days

(CSW

C_L5D

) (%)

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CSWC_P5D

Spearman correlation coef f icient (R)Water f lux CSWC_P5DTrr 0.236**Evr 0.396**ETr 0.310**

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ulative soil water content last ten days

(CSW

C_L10D

) (%)

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ux (m

m d

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CSWC_P10D

Spearman correlation coef f icient (R)Water f lux CSWC_P10DTrr 0.329**Evr 0.254**ETr 0.352**

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105 120 135 150 165 180 195 210 225 240 255 270 285

Cum

ulative soil water content last fifteen days

(CSW

C_L15D

) (%)W

ater

flux

(mm

day

-1)

Day of year Month / Day

Evapotranspiration rate (ETr) Transpiration (Trr),Evaporation (Evr)

CSWC_P15D

Spearman correlation coef f icient (R)Water f lux CSWC_P15DTrr 0.402**Evr 0.170*ETr 0.400**

4/15 4/30 5/15 5/30 6/14 6/29 7/14 7/29 8/13 8/28 9/12 9/27 10/12

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ulative soil water content last tw

enty days (C

SWC

_L20D) (%

)Wat

er fl

ux (m

m d

ay-1

)

Day of year Month /Day

Evapotranspiration rate (ETr) Transpiration (Trr)Evaporation (Evr)

CSWC_P20D

Spearman correlation coef f icient (R)Water f lux CSWC_P20DTrr 0.455**Evr 0.116ETr 0.448**

4/15 4/30 5/15 5/30 6/14 6/29 7/14 7/29 8/13 8/28 9/12 9/27 10/12

a b

c d

e f

Fig. 10. Relationships between daily variations in modeled water fluxes and (a) concurrent precipitation and soil water content, and cumulative soil water content for (b) theprevious two days (CSWC_P2D), (c) the previous five days (CSWC_P5D), (d) the previous ten days (CSWC_P10D), (e) the previous fifteen days (CSWC_P15D), and (f) theprevious twenty days (CSWC_P20D) in the study area in 2002. The larger symbols in (a) denote the large responses of evapotranspiration to rainfall events.


exhibited the lowest Tr values. The linear spatial correlation be-tween ET and LAI was dampened dramatically because of the sig-nificant reduction in Ev with increasing LAI (Fig. 11b and c). TheLAI of hilly dry cropland even exceeded that of the L. chinensissteppe from May to July; however, the total Tr and ET values ofthe hilly dry cropland were 1.5–1.6 times lower than those of theL. chinensis steppe owing to the shortened growing season of theharvested crops in comparison to that for the native plant species.This agrees with previous observations in Duolun, Inner Mongolia(Chen et al., 2009; Miao et al., 2009). Tr shifted from increasing al-most linearly to increasing slowly when LAI exceeded the thresh-old value of 0.8 m2 m�2 (Fig. 11a), likely because the increasedleaf and branch coverage greatly reduced incident radiation insidethe canopy. In contrast, Ev changed from decreasing almost line-arly to varying only slightly when LAI exceeded the threshold valueof 0.2 m2 m�2 (Fig. 11b), which implies the dominance of bare soilevaporation over canopy interception and evaporation. Waterdeficit increased linearly with increasing LAI (Fig. 11d), exhibiting

a pattern similar to that for ET. In addition, similar spatial correla-tions to those for LAI were found between gs and Tr, ET, and Ev(Table 2).

5.2.2. Environmental factorsThe horizontal gradient in the meteorological factors exerted

significant effects on the spatial patterns of Tr and ET. The L. chin-ensis steppe, which is located primarily in the southeast, exhibitedthe highest Tr and ET values in association with higher PPT, irad,and Ws and lower Tavg and VPD values than those in the other nat-ural ecosystems (Table 2). This pattern is similar to that found pre-viously for low SWC conditions (e.g., Chen et al., 2009). Of thenatural ecosystems, swamp and floodplain, the L. chinensis steppe,and the S. grandis steppe (which occur primarily in the eastern,northern, and central regions, respectively) exhibited the highestinitial SWC in early spring; however, no rainfall events that oc-curred during the growing season were sufficiently large to restoresoil water to its initial levels. In these ecosystems, more active

Table 2Spearman correlation coefficients for the spatial relationships between growing season total water budgets and the total or average values of the biological and environmentalfactors with the same spatial locations over the Xilin River basin and an area immediately to its west in Inner Mongolia in 2002.a

Water budget Environmental factor Biological factor

Precipitation Airtemperature

Vaporpressuredeficit

Windspeed

Total incomingsolar radiation

Soil watercontentb

Soil sandfraction

Soil clayfraction

Leaf areaindex

Leaf stomatalconductance

Transpiration 0.169** �0.279** �0.279** 0.279** 0.165** �0.530** �0.247** 0.241** 0.985** 0.834**

Evaporation 0.163** 0.151** 0.151** �0.151** 0.002 0.285** 0.247** �0.280** �0.627** �0.624**

Evapotranspiration 0.399** �0.204** �0.204** 0.204** 0.189** �0.428** �0.088** 0.049** 0.682** 0.509**

Water deficit �0.099** �0.142** �0.142** 0.142** 0.085** �0.639** �0.180** 0.185** 0.665** 0.396**

a Spearman correlation coefficients were computed based on all 44,968 grid cells.b Soil water content denotes that in the top 20 cm of the soil.

** Significance at the 99% (p < 0.01) confidence levels.

Tr = 274.92 LAI 0.81

r² = 0.98, P<0.01

0

50

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350

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Tran

spira

tion

(Tr )

(mm

)

Leaf area index (LAI) (m2 m-2)

n = 2801

Ev = 39.77 LAI -0.63

r2 = 0.43, P<0.01

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500

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Evap

orat

ion

(Ev)

(mm

)


n = 2801

ET = 183.59 + 107.66 LAIr2 = 0.25, P<0.01

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otra

nspi

ratio

n (E

T) (

mm

)


n = 2801

Wd = 0.85 + 91.52 LAI r² = 0.19, P<0.01

-200

-100

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Wat

er d

efic

it (W

d) (

mm

)


n = 2801

a b

c d

Fig. 11. Relationships between modeled growing season water budgets and mean leaf area index (LAI) in the study area in 2002: (a) transpiration (Tr)–LAI, (b) evaporation(Ev)–LAI, (c) evapotranspiration (ET)–LAI, and (d) water deficit (Wd)–LAI. The 2,801 grid cells were sampled every four cells in each row and column throughout the studyarea. The values were not considered when LAI = 0 or LAI > 3.0 m2 m�2.


plant growth typically leads to more transpiration, which almost orcompletely outweighs water input from precipitation. Accordingly,soil water becomes severely exhausted, resulting in the greatestwater deficits observed in the present study. The opposite situationwas found for the water budgets for sand, saline land, and barrenland, which occur mostly in the southwest (Figs. 3, 4, and 8c). Thisdemonstrates that the total feedback effects of plant growth on soilwater were stronger than those of soil water on plant growth un-der severe drought. These results suggest that vegetation charac-teristics strongly influence energy partitioning when SWC isbelow the threshold for soil water stress, which is consistent withideas proposed by Hao et al. (2007) and Wever et al. (2002).

Among the abiotic factors investigated, the spatial pattern of Evwas affected most by water conditions (especially SWC20cm) ratherthan temperature or radiation during severe drought (Table 2),implying that severe drought can limit the sensitivity of Ev totemperature and radiation. Ev decreased by a factor of 1.8–2.0 as

SWC decreased by a factor of 1.1–1.7. Thus, the two typical steppeswith lower SWCs exhibited the lowest Ev values and highest Tr/ETratios, whereas saline land, barren land, and sand (which had thehighest SWCs) exhibited the highest Ev values and lowest Tr/ETratios of all natural ecosystems. This is in agreement withmeasured results (e.g., Song, 1995, 1996). The higher Tr/ET ratioindicates more vigorous plant growth, further demonstrating thehigher transpiration depletion and greater water deficits of thetwo typical steppes.

Soil texture was the second most important environmental fac-tor affecting the spatial pattern of Ev (Table 2). Ev was significantlypositively correlated with SSF and negatively correlated with SCF,as verified by measurements under low soil moisture conditions,and opposite results have been found previously under high soilmoisture conditions (Song, 1995, 1996). For example, shrublandand sand exhibited much higher Ev values than the two typicalsteppes owing primarily to their significantly higher SSFs and


lower SCFs. In the severe drought year, soil moisture in the deeperlayers in sand significantly exceeded that in the steppes, with morestored water extracted by evaporation in sand than in the steppes.

6. Conclusions

The GLPM, a spatially explicit process-based model, has beenshown to be an effective approach for deriving information aboutthe temporal dynamics and spatial patterns of water fluxes fordifferent land cover types in the semiarid grassland of InnerMongolia.

The modeled results showed that water fluxes expressed interms of ET, Tr, and Ev for semiarid ecosystems were restrictedby water stress in the dry growing months. The modeled totalsand daily or monthly averages in the growing season agreed wellwith measurements made under similarly arid conditions insemiarid steppes. The data indicated that water fluxes varied inresponse to spatiotemporal variations in environmental factorsand associated changes in the phenological and physiologicalcharacteristics of plants.

The results of the present study demonstrate that there werelarge differences in the average ET, Tr, Ev, and water deficit duringthe growing season for different land cover types. Similarly, largedifferences were found in the partitioning of water budgets andtheir temporal and spatial patterns for different land cover types.Moreover, Tr and ET (rather than precipitation) were found to bethe primary factors controlling differences in water deficitsbetween land cover types within the same region and year. TheL. chinensis steppe, S. grandis steppe, and swamps and floodplainsplayed the dominant roles in water budgets in the study areabecause they had the highest LAIs and associated Tr values andwere distributed more extensively. These results provide ascientific basis for planning regional land use and managingresources based on water budgets.

The results presented here demonstrate that the responses ofdaily variations in Trr to daily variations in environmental and veg-etation conditions were almost the opposite of those found for Evr.For example, of the influencing factors analyzed, daily Trr valueswere correlated most strongly with daily LAI, yet no significanttemporal correlation existed between daily Evr and daily LAI. DailyEvr exhibited stronger responses to precipitation and drought thandaily Trr; that is, changes in daily heat-related conditions such asTavg, irad, VPD, and Sh typically exerted stronger positive effectson transpiration processes, whereas changes in daily water-relatedconditions such as SWC, Rh, and PPT exerted stronger positive ef-fects on evaporative processes. The responses of daily Trr and Evrto individual rainfall events and concurrent and cumulative SWCwere also significantly different. These findings will be particularlyuseful for evaluating the major components of land-to-atmospherewater fluxes in terms of climate change, hydrology, land cover, andvegetation dynamics at the day scale.

The results also demonstrate that specific phenomena occur un-der severe drought conditions that do not occur under normal orwell-watered conditions. For example, under severe drought con-ditions, the total ET, Tr, and Ev, mean daily ETr, Trr, and Evr, andthe maximum daily ETr of the typical steppes, shrubland, and crop-lands during the growing season were much lower. Moreover,water deficits for the typical steppes were slightly or much higher,and the ET/PPT ratios were higher than those under normal andhigh precipitation. The significantly negative effects of daily Sh,VPD, and Tavg, the significantly positive effects of daily Rh, andthe insignificant effects of daily irad on daily Evr are specific toyears in which severe drought was experienced. Similarly, thenegative correlation found between the spatial patterns of Tr andET and resulting soil moisture, and the stronger influence of plant

growth on soil water compared to that of soil water on plantgrowth, were also specific to the severe drought year, as werethe insensitivity of the spatial pattern of Ev to temperature andradiation and the correlations between the spatial pattern of Evand the soil sand/clay fraction. However, the shapes of curves oftemporal variations in water fluxes in the drought year were sim-ilar to those in the normal year.

Semiarid steppes exhibit very large asymmetric responses tointerannual variations in climate, particularly in terms of seasonal-ity, distribution, frequency, and magnitude of precipitation. Thus,the findings of the present study must be verified and assessedusing long-term (e.g., decadal) data. However, the results and con-clusions presented here can be considered representative of semi-arid grasslands in drought years.

Acknowledgements

This study was supported by the General Program of the Na-tional Natural Science Foundation of China (NSFC) (No.31270512, 30500076). I am grateful to the following organizationsfor the provision of data: the China Meteorological Administrationfor meteorological data; the Data Center for Resources and Envi-ronmental Sciences of CAS for elevation, land cover, and soil typedata; the Land-Atmosphere Interaction Research Group at BeijingNormal University for soil texture map; and the Inner MongoliaGrassland Ecosystem Research Station of CAS for field-based mea-surements. I also gratefully acknowledge the editor Konstantine P.Georgakakos and anonymous reviewers for their valuable com-ments and constructive suggestions. Finally, I would like to thankEditage, a language editing company, for providing editorialassistance.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.jhydrol.2014.02.056.

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http://dx.doi.org/10.1029/2004JD005179




































































http://dx.doi.org/10.1029/2007WR006563

http://dx.doi.org/10.1029/2007WR006563

http://dx.doi.org/10.1029/2004JD005474

http://dx.doi.org/10.1029/2004JD005474





simulated water fluxes during the growing season in semiarid grassland ecosystems under severe...

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