Effect of grazing intensity on evapotranspiration in the semiarid grasslands of Inner Mongolia, China

Journal of Arid Environments 83 (2012) 15e24

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Effect of grazing intensity on evapotranspiration in the semiarid grasslands of Inner Mongolia, China L. Wang a, H. Liu a, *, B. Ketzer b, R. Horn c, C. Bernhofer b a

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China Institute of Hydrology and Meteorology, Chair of Meteorology, Technische Universität Dresden, 01737 Tharandt, Germany c Institute of Plant Nutrition and Soil Science, Christian-Albrechts-University Kiel, Olshausenstr. 40, 24118 Kiel, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 January 2011 Received in revised form 23 March 2012 Accepted 3 April 2012 Available online 3 May 2012

The eddy covariance technique was used to measure evapotranspiration (ET) at four different grazing intensity sites to investigate the grazing effects on ET in the semiarid steppe ecosystems of Inner Mongolia. By reducing available energy, and decreasing soil water content (SWC), grazing decreased ET on a seasonal scale compared with the site ungrazed since 1979 (UG79). The most important climatic factor controlling ET on daily scale shifted from SWC to Net radiation (Rn) when grazing intensity increased. SWC, Rn and air temperature (or vapor pressure deficit) can explain 59%e71% of the variation in daily ET. On the other hand, leaf area index (LAI) affected ET slightly at UG79 under the commonly limited soil water conditions. Even no effect of LAI at the heavily grazed site was detected. This suggests that the direct effect of grazing reducing LAI on ET is not significant in this semiarid steppe ecosystem. Soil evaporation compensates for most of the loss in transpiration due to reduced LAI. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Eddy covariance Leymus chinensis PriestleyeTaylor model

1. Introduction Grazing by domestic animals is one of the most extensive forms of land use on the planet (Asner et al., 2004). Grazing removes green leaf area and changes the microclimate of the surface (Li et al., 2000; Svejcar and Christiansen, 1987). This may affect ET (the sum of evaporation from soil surface and transpiration from plant), which is the major form of water loss in grasslands (Aires et al., 2008; Gu et al., 2008; Hao et al., 2007; Wever et al., 2002). For instance, season-long cumulative ET was shown to be reduced by 6.1% due to the spring grazing of cattle at a tall grass site (Bremer et al., 2001). Bremer et al. (2001) also reported that grazing could reduce daily ET by up to 40% when the difference in green LAI was large and the soil surface was dry. Similarly, the three-year average ET at a grazed prairie was 7% less than at an ungrazed prairie in the semiarid grasslands of the Northern Great Plains of the US (Frank, 2003). However, another study reported that heavy grazing by prairie dogs increased ET compared with a lightly grazed site (Day and Detling, 1994). Additionally, in the first International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), the ET over the grazed and ungrazed sites was similar when the difference in leaf area index (LAI) was large and soil moisture was

* Corresponding author. Tel.: þ86 10 82083809; fax: þ86 10 62041393. E-mail address: [email protected] (H. Liu). 0140-1963/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2012.04.005

non-limiting (Stewart and Verma, 1992). Until now, the effects of grazing on ET have relation with vegetation cover, energy availability and soil water conditions. However, uncertainties about these effects still exist. Grazing has a long history and is still one of the main land management methods used in the semiarid grasslands of Inner Mongolia. Since the late 1940s, due to increases in population and the transition from nomadism to the settlement of herdsman, herd size and grazing intensity have increased greatly, especially around settlements (Cui et al., 2005). Since 1980s, northern China has suffered drought conditions, because of a long-term decrease in precipitation and increasing temperature (Ma and Fu, 2006). Degradation of the Inner Mongolian grassland ecosystem has dramatically increased due to improper land use methods. In 1989, Inner Mongolia had 86.7 million ha of grassland. But only 43.9% were considered to be in good condition (Chuluun and Ojima, 2002). Li et al. (2000) reported that increased albedo by grazing is an important indicator of grassland desertification. It was found that ET was controlled by soil water content and the phenology of the vegetation in this particular semiarid grassland (Hao et al., 2007, 2008), while vegetation development is affected by available precipitation (Schaffrath et al., 2010). Miao et al. (2009) discussed the effect of grazing on ET in this region. However, they did not explain why grazing had different effects on ET between different years. Based on the field observations using eddy covariance technique over UG79 (a site that has remained ungrazed since 1979) and three

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L. Wang et al. / Journal of Arid Environments 83 (2012) 15e24

different grazing intensity sites, this study focuses on the effect of grazing on ET in the semiarid grasslands of Inner Mongolia, as part of the SinoeGermany cooperation project (MAGIM, Matter fluxes in grasslands of Inner Mongolia as influenced by stocking rate). The objectives of this study were (i) to compare the surface characteristics and ET between UG79 and the three grazed sites, (ii) to quantify the relative importance of climatic and biological controls on ET at differently grazed sites and (iii) to investigate the climatic and biological controls on energy partitioning for the dynamic of grazing effects on ET. 2. Materials and methods 2.1. Site information The observation sites are located at the Inner Mongolian 0 0 Grassland Ecosystem Research Station (IMGERS, 43 33 N, 116 40 E, 1200 m a.s.l.). Based on the data obtained from the meteorology station at Xilinhot, the climate average (1971e2000) for annual temperature is 2.6  C and the mean precipitation is 286.6 mm, 88% of which occurs during the growing season (MayeSeptember). The experiment was conducted at four Leymus chinensis steppes with four different grazing intensities. These were: a site ungrazed since 1979 (UG79), a winter grazed site (WG, grazed from midOctober to mid-February by 0.5 sheep units per ha), a continuously grazed site (CG, grazed by 1.2 sheep units per ha) and a heavily grazed site (HG, grazed by at least 2 sheep units per ha for many years) (Ketzer et al., 2008). Before 1979, all the four sites were lightly grazed. WG, CG and HG were moderately grazed from 1979 to 1999 and then fenced in 1999. UG79, WG and CG are relatively close to each other, being only a few hundred meters apart. HG is about 3 km to the north of the other sites. The UG79, WG and CG sites have a gentle slope of less than 3 , whereas the HG site is dominated by flat terrain. The L. chinensis grasslands are dominated by L. chinensis, Stipa grandis, Koeleria cristata and Agropyron cristatum (Xiao et al., 1997). The soil type in the study area is dark chestnut (Mollisol) without a distinct CaCO3 layer. The soil depth is 100e150 cm (Wang and Cai, 1988). Detailed plant and soil characteristics are shown in Table 1 (Zhao et al., 2011). These data are not available at CG. The surface of CG is almost similar to that of WG. 2.2. Measurements A fixed meteorological tower and one portable tower, with the same system design, were used for the simultaneous measurements. During the 2005 and 2006 growing seasons, the fixed tower was located at UG79 and the portable tower was used to take measurements at the three other grazing treatment sites. The portable tower was moved between the grazing treatment sites around every six Table 1 Plant and soil characteristics at the UG79, WG and HG sites (values in parentheses denote the standard deviation). Characteristics

UG79

WG

HG

Maximum canopy height 2006 (cm) Soil cover 2006 (%) Living green plants Litter þ standing dead plants Bare soil Soil texture, 010 cm Sand (%) Silt (%) Clay (%) Bulk density, 04 cm (g cm3) Organic C content, 04 cm (g kg1)

30.6

18.8

7.5

54.0 37.9 8.1

33.9 37.4 28.7

28.4 4.0 67.7

62.0(2.6) 22.2(5.4) 15.8(3.0) 1.14(0.02) 3.10(0.55)

53.7(4.0) 27.2(5.6) 19.1(1.6) 1.18(0.02) 2.59(0.45)

67.9(3.5) 20.6(2.9) 11.5(1.0) 1.30(0.02) 1.70(0.42)

weeks. Measurements continued at the fixed tower until September 2009, but the grazing regime and measurement scheme changed from 2007 onwards. Therefore this study only addresses the effects of grazing during the 2005 and 2006 growing seasons. Table 2 gives detailed information about the measurement scheme. The eddy covariance system was composed of a three dimensional sonic anemometer (CSAT3, Campbell Scientific, USA) for the measurement of air temperature and wind speed in three dimensions, and an Open Path CO2/H2O Gas Analyzer (LI-7500, LICOR, USA) to measure the fluctuations in H2O and CO2 concentrations. The LI-7500 head sensor was installed at a slight angle from the vertical (about 15 ) to prevent water droplets remaining on the windows during and after rainfall. Both of the instruments were placed at 2 m height above the ground, about 0.1 m apart (Fig. 1). The measurement frequency of the EC system was 20 Hz. Net radiation was measured by a net radiometer (CNR1, KIPP&ZONEN, Netherlands) at a height of 1.5 m. The soil heat flux was measured with two heat flux plates installed about 2 cm below the ground (HFP01, Hukseflux, Netherlands) with the distance between them not more than 1.5 m. Air temperature and relative humidity were measured with temperature and relative humidity probes located near the EC system (HMP45C, Campbell Scientific, USA). In addition, at the UG79 site, precipitation was measured (52202 Tipping Bucket Rain Gauge, RM Young, USA) at a height of 1 m. At both stations, a CR5000 data logger (Campbell Scientific, USA) was used to record the data. At UG79, WG and HG, soil moisture at 5 cm depth was measured using horizontally inserted Theta-probes (Type ML2x, Delta-T Devices Ltd, Cambridge, UK) and data were recorded by automatic Data Logger (DL2e Data Logger, Delta-T Devices Ltd, Cambridge, UK) (Zhao et al., 2011). Leaf area index (LAI) was sampled at each site by clipping 5e8 representative 0.08 m2 leaf quadrants. Leaf area was measured using a portable area meter (LI-3000A) in conjunction with a transparent belt conveyer (LI-3050A, LI-COR, USA) (Fan et al., 2009). Canopy height (h) was determined by averaging 10-15 single point measurements for each plot, and then the zero displacement, d, was calculated from d ¼ 2/3h (Oke, 1987). 2.3. Date analysis EdiRe software was used to calculate half-hour energy fluxes (http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/). The energy fluxes were calculated using the following equations:

H ¼ ra cp w0 T 0

(1)

LE ¼ Lw0 r0v

(2)

where H is the sensible heat flux; ra is the air density; cp is the specific heat capacity of air at constant pressure; w0 T 0 is the covariance of vertical wind w and air temperature; LE is the latent heat flux, L is the latent heat of vaporization and w0 rv is the covariance of vertical wind w and the water vapor concentration rv . To obtain accurate energy fluxes, several correction procedures were conducted. After coordinate rotation (double rotation after Kaimal and Finnigan (1994)), correction of spectral loss (Moore, 1986) was necessary for H and LE. Then, the Schotanus correction for H (Schotanus et al., 1983) and the Webb correction (Webb et al., 1980) for LE were performed to arrive at the corrected energy fluxes. Further detailed information can be found in Mauder et al. (2006). Energy balance closure is an independent method for evaluating the reliability of eddy covariance measurements (Wilson et al., 2002). The energy balance equation over short vegetation can be written as:

L. Wang et al. / Journal of Arid Environments 83 (2012) 15e24

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Table 2 Average of albedo, surface temperature (Ts), LE/LEeq and surface conductance (gs) and sum of net radiation (Rn), soil heat flux (G), sensible heat flux (H), latent heat flux (LE) and evapotranspiration (ET) at the UG79, WG, CG and HG sites for each measurement period in 2005 and 2006. Values in parentheses denote the standard deviation. Year

2005

Date

15 Maye9 July

Site

UG79

CG

UG79

HG

UG79

WG

UG79

HG

UG79

WG

UG79

HG

Albedo (%) TS ( C) Rn (MJ m2 d) G (MJ m2 d) H (MJ m2 d) LE (MJ m2 d) LE/LEeq gs (mm s1) Total ET (mm)

18.9(1.8) 18.5(4.5) 11.2(3.1) 1.35(0.91) 4.38(1.97) 3.68(1.22) 0.53(0.14) 3.23(1.31) 91.5(4.1)

19.1(1.8) 19.9(4.6) 9.2(2.9) 0.82(0.63) 4.0(1.83) 2.49(1.10) 0.40(0.13) 2.10(0.89) 61.9(3.7)

14.9(0.7) 22.1(4.4) 11.0(3.2) 0.89(0.95) 4.80(2.34) 3.87(1.32) 0.54(0.17) 2.42(1.31) 70.5(3.8)

16.5(0.9) 23.9(4.4) 9.0(2.7) 0.73(0.88) 3.90(1.88) 2.85(1.06) 0.46(0.15) 1.71(0.79) 52.0(3.0)

16.0(1.0) 16.8(4.7) 7.8(2.8) 0.50(0.67) 4.58(1.87) 1.41(0.80) 0.27(0.14) 1.92(0.50) 23.2(2.1)

17.9(1.6) 17.3(4.7) 5.7(2.4) 0.36(0.46) 3.99(1.58) 0.63(0.39) 0.16(0.08) 0.66(0.32) 10.4(1.0)

16.9(1.4) 18.0(4.1) 10.8(3.4) 1.16(1.14) 3.33(1.95) 5.56(1.50) 0.81(0.18) 5.22(2.09) 81.6(3.8)

18.9(2.7) 19.2(4.4) 8.8(3.0) 0.79(1.07) 3.07(1.52) 3.37(1.29) 0.58(0.20) 3.79(1.58) 49.5(3.3)

15.7(0.7) 21.1(2.8) 11.3(3.2) 0.86(0.78) 4.49(2.04) 4.94(2.35) 0.63(0.23) 3.61(2.32) 92.3(6.7)

15.4(0.9) 22.3(3.2) 10.1(3.1) 0.75(0.66) 4.22(1.35) 3.31(1.67) 0.46(0.18) 2.11(1.44) 61.8(4.8)

19.7(14.5) 16.6(6.1) 8.6(3.0) 0.23(0.84) 4.08(2.45) 3.07(1.26) 0.56(0.22) 2.62(1.59) 57.3(3.6)

23.3(14.7) 17.2(7.1) 6.5(2.6) 0.30(0.81) 2.62(1.97) 2.65(1.38) 0.64(0.29) 4.21(2.11) 49.5(3.9)

Rn  G ¼ H þ LE

2006 10 Julye19 August

20 Auguste25 September

(3)

where Rn and G are net radiation and soil heat flux, respectively. Energy balance closure was examined by the regression of turbulent energy fluxes and the energy balance ratio (EBR). In the first method, linear regression was performed between the energy fluxes (H þ LE) and the available energy (Rn  G) for half-hour fluxes, as shown in the following equation:

H þ LE ¼ aðRn  GÞ þ b

(4)

The second method was to sum H þ LE and Rn  G over specific periods and then calculate the energy balance ratio (EBR) as shown below (Gu et al., 1999):

P ðH þ LEÞ EBR ¼ P ðRn  GÞ

(5)

The half-hour albedo was calculated from the shortwave radiation:

Sout albedo ¼ Sin

(6)

where Sin is the solar radiation and Sout is the outgoing shortwave radiation. The daily mean albedo was calculated by averaging the

29 Maye30 June

1 Julye11 August

12 Auguste22 September

half-hourly albedo from 10:00 to 14:00 (Beijing local time), to reduce the influence of solar angle on albedo. The surface temperature was derived from the long wave radiation. It was calculated as follows:

Ts ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 Lout  ð1  εÞLin ε$s

(7)

where Ts is the surface temperature; Lout is the outgoing long wave radiation and Lin is the incoming long wave radiation. The emissivity, ε, equals 0.95 for grassland (Yang et al., 2008) and s is the StefaneBoltzmann constant (5.669  108 Wm2 K4). Surface conductance (gs) was calculated using the inverted PenmaneMonteith equation (Monteith and Unsworth, 1990):

DH rcp VPD gLE  1 1 ¼ þ gLE gs ga

(8)

where r is air density; cp is the specific heat of air at constant pressure; VPD is the vapor pressure deficit; g is the psychrometric constant; D is the slope of the saturation vapor pressure curve at the mean wet-bulb temperature of the air; gs is the surface conductance and the air conductance ga is calculated by the following equation (Monteith and Unsworth, 1990):

Fig. 1. Pictures of the sites: (a) the tower at UG79, (b) the tower at WG and (c) the surface at HG (this photo is approximately 1.5 m across).

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L. Wang et al. / Journal of Arid Environments 83 (2012) 15e24

Fig. 2. Seasonal variation of meteorological conditions of the four sites during the growing seasons of 2005 and 2006: (a) daily sum of solar radiation (Sin); (b) daily mean air temperature; (c) daily mean vapor pressure deficit (VPD); (d) daily mean soil water content (SWC); (e) daily sum of precipitation at UG79.

1 u ¼ *2 þ 6:2u*0:67 ga u

(9)

where u* is friction velocity and u is wind speed. The equilibrium LE (LEeq) was calculated as follows (Priestley and Taylor, 1972):

LEeq ¼

DðRn  GÞ Dþg

2.5. Statistical analysis

(10)

The potential ET (PET) was calculated from the equilibrium ET (ETeq) (Priestley and Taylor, 1972):

PET ¼ 1:26ETeq

heat flux over the whole measurement period. For periods less than 2 h, linear interpolation was used to fill the gaps. For more than 2 h, the mean diurnal variation method (MDV) was used (Falge et al., 2001). 14-day windows of independent fluxes were applied on the MDV for gap filling.

(11)

2.4. Quality control and gap filling Two filters were applied to remove half-hour fluxes due to malfunction of the sensors. Firstly, energy fluxes were excluded when the detected spikes or absolute limit violations were more than 10% of the total measurement records of any three dimensional wind speeds, temperatures or H2O concentrations. Secondly, water fluxes were discarded if the half-hour H2O concentration measured by LICOR-7500 differed more than 30% from that measured by HMP45C. At the same time, the stationary test and integral turbulence characteristics test were applied, following the procedure proposed by Foken and Wichura (1996). Finally, energy flux data were also excluded if the friction velocity was below 0.1 m s1 during the night due to the non-developed well turbulence. The percentage of gaps in the data due to missing and discarded data points was 23% for the sensible heat flux and 28% for the latent

The data set was non-independent due to simultaneous measurements of each variable for UG79 and another grazed site. Therefore, the paired-sample T test was performed to evaluate the difference of the analyzed parameters between treatments (SPSS 12.0 for Windows; SPSS Inc., Chicago, USA). In addition, parallel measurements of the sensors used at different sites were performed at the same tower site (UG79) for at least three days in 2005 and 2006 before the field experiments. From the comparison, no significant differences between the instruments were found. This ensured that the differences detected were due to difference between sites rather than systematic errors of the instruments (Ketzer et al., 2008).

Table 3 Significance (P value) of the paired-sample T test of air temperature (Ta), soil water content (SWC), leaf area index (LAI), albedo, surface temperature (Ts), net radiation (Rn), LE/LEeq, evapotranspiration (ET) and surface conductance (gs) between UG79 and another grazed site (WG, CG or HG). Site to site

SWC

LAI

Albedo

Ts

Rn

LE/LEeq

ET

gs

UG79-WG UG79-CG UG79-HG

0.0 e 0.0

0.533 e 0.003

0.0 0.015 0.0

0.0 0.0 0.0

0.0 0.0 0.0

0.0 0.0 0.013

0.0 0.0 0.0

0.0 0.0 0.004

L. Wang et al. / Journal of Arid Environments 83 (2012) 15e24

19

Fig. 3. LAI of ungrazed site (UG79), winter grazed site (WG) and heavily grazed site (HG) in the growing seasons of 2005 and 2006.

3. Results 3.1. Meteorological conditions The meteorological conditions during the 2005 and 2006 growing seasons are shown in Fig. 2. The solar radiation (Sin) of UG79 and the grazed sites was similar, as the sites are quite close to each other. The solar radiation showed large temporal variability ranging between 15 and 25 MJ m2 d1. The air temperature reached its maximum value in July. The lower air temperature was found at UG79 for most of time. The climatical average of the precipitation (1971e2000) during the growing season (MayeSeptember) was 251.1 mm and the standard deviation among years was 78.5 mm. The 2006 growing season had a slightly lower precipitation (228.4 mm) compared with the climatical average, while precipitation for the 2005 growing season was 146.9 mm, which was almost half of the climatical average. The precipitation was fairly unevenly distributed throughout the growing season. In particular, only 5.7 mm of detectable rainfall was recorded over 31 days starting on 16 August, 2005, and only 3.6 mm rainfall was measured over 20 days starting on 31 July, 2006. The SWC at UG79 was significantly higher than at the grazed sites (Fig. 2, Table 3). 3.2. Leaf area index The leaf area index (LAI) decreased with grazing intensity increasing (Fig. 3). However, the difference in LAI between UG79 and WG was not significant (Table 3) because WG was not grazed during the growing seasons. Therefore, this study focused on the difference between UG79 and HG. At the beginning of the growing season, LAI increased rapidly both at UG79 and HG. However, grazing slowed the increase in LAI at HG. And then LAI reached their maximum values in August. Subsequently, LAI declined rapidly with senescence later in the growing season. Additionally, it is likely that higher precipitation caused the larger peak in LAI at UG79 in 2006 compared with the LAI peak recorded in 2005. The peak LAI at UG79 was 0.63 m2 m2 in 2005 and 0.83 m2 m2 in 2006. However, the situation at HG was exactly opposite. This indicates that there was a higher grazing intensity at HG in 2006. 3.3. Energy balance closure The regression slope was <1 for all sites, which indicates a lack of closure in the surface energy balance. The intercepts for all cases

were positive, which resulted in a larger EBR compared with the regression slope (Table 4). This underestimation of energy fluxes has occurred in many field experiments (Oncley et al., 2007; Wilson et al., 2002) and the cause is described in detail by Oncley et al. (2007). The difference in EBR between the four sites was not large. This suggests that, on a seasonal scale, the ET comparisons were not significantly affected by the energy balance. 3.4. Albedo and surface temperature Albedo at UG79 was relatively high early in the growing season because of the low LAI and a higher proportion of dead grass and soil. Albedo decreased steadily as green grass started to cover the surface. Later in the growing season albedo at UG79 rose again as plant senescence increased. The largest difference between sites was found between UG79 and HG. Table 2 indicates that HG had significantly higher albedo over all three measurement periods (Table 3). However, albedo may not reflect grazing intensity to any great degree at CG or WG. For example this conclusion is suggested by the small difference between UG79 and CG early in the 2005 growing season and between UG79 and WG at the peak of growth season in 2006 (Fig. 4a, Table 2). Surface temperature was also affected by grazing. Surface temperatures were significantly higher at grazed sites compared with UG79 throughout the measurement period (Fig. 4b, Table 2 and 3). A larger proportion of exposed soil at the grazed sites was the main contributor to the higher surface temperature. These results suggest that surface temperature is a good indicator of grazing intensity. 3.5. Energy partitioning and evapotranspiration Fig. 5a shows the seasonal variation in daily Rn. The seasonal pattern of Rn was always the same as the variation in Sin (Fig. 2a). Table 4 Energy balance assessments for the four differently grazed sites for half-hour measurements. Site

UG79 WG CG HG

LE þ H ¼ a (Rn  G) þ b

EBR

a

b

R2

0.82 0.69 0.64 0.66

5.95 16.85 14.96 16.42

0.92 0.93 0.92 0.90

0.86 0.86 0.79 0.83

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Fig. 4. Seasonal variation of (a) albedo and (b) surface temperature of the four sites during the growing seasons of 2005 and 2006.

Higher albedo and surface temperature, on average reduced Rn by 18.6% at the grazed sites compared with that at UG79 (Table 3). The LE/LEeq is often used to measure energy partitioning into LE because this ratio normalizes LE values with equilibrium energy (Baldocchi et al., 2004; Wilson and Baldocchi, 2000). The average values of the PriestleyeTaylor coefficient (a) at UG79 were 0.46 and 0.59 for 2005 and 2006, respectively. The lowest a (<0.2) occurred during periodic drought in September 2005. The highest a (>1) occurred during a period of high SWC in June 2006. The seasonal variation in a was associated with the distribution of precipitation. High values of a always occurred after a rain event. In addition, a at UG79 was larger than at the grazed sites except in September 2006 (Fig. 5, Table 2 and 3). During the fast-growth stage (JuneeJuly), ET at UG79 reached a maximum of 3.06 mm in 2005 and 4.85 mm in 2006. However, ET

was much smaller than PET due to the limited soil water supply in this semiarid steppe ecosystem. ET was depressed by the soil water deficits even when the maximum values of LAI occurred in August (Fig. 5c). On a daily scale, grazing caused a decrease in ET most of the time (Fig. 5c, Table 3). 3.6. Surface conductance The surface conductance (gs) was examined to assess the physiological control of ET at the leaf level. High gs was correlated with high soil moisture (Fig. 6a). The average values of gs at UG79 were 2.47  1.41 and 3.49  2.11 mm s1 for 2005 and 2006, respectively. The surface conductance reached its maximum value of 9.53 mm s1 in July 2006. Table 2 shows that the surface conductance was larger at UG79 than at the grazed sites for most of

Fig. 5. Seasonal variation of (a) daily net radiation (Rn), (b) LE/LEeq, (c) ET and PET of the four sites during the growing seasons of 2005 and 2006.

L. Wang et al. / Journal of Arid Environments 83 (2012) 15e24

21

Fig. 6. (a) Seasonal variation of daily mean surface conductance (gs) of the four sites of 2005 and 2006, and the size distribution of gs for (b) UG79 and (c) the three grazed sites during the growing seasons.

the time (Table 3). The frequency of daily gs < 3 mm s1 was 54% at UG79 (Fig. 6b), while the frequency at the three grazed sites was 74% (Fig. 6c). In contrast, HG had larger gs after the snow event in September 2006, which was similar to the PriestleyeTaylor coefficient. 4. Discussion 4.1. Controls of climatic and biological variables on ET ET is controlled by the complicated interaction between climatic and biological variables (Li et al., 2007; Wever et al., 2002; Wilson and Baldocchi, 2000). On a daily scale, linear regression was performed between LE and climatic variables. SWC had the largest influence on LE. Rn also significantly influenced LE (Fig. 7), while Ta and VPD only influenced LE slightly (data not shown). The linear regression equation, describing the relationship between LE and Rn, was good because the exponential equation only slightly improved the fit (coefficient R2 increased from 0.35 to 0.39). Therefore, a multiple linear regression analysis between LE and SWC, Rn, Ta, VPD and WS could be used for each site.

To examine the absolute effect of a single variable on ET, a stepwise method was used to choose climatic variables with entry and removal probabilities of 0.05 when applying the multiple linear regressions. The reason was that climatic variables were significantly correlated with each other and the correlations among climatic variables needed to be removed. For example, high SWC correlates with low Rn (r ¼ 0.65) and high VPD correlates with low Ta (r ¼ 0.92) in Inner Mongolia. As the soil moisture measurements did not cover the whole period (Fig. 2), the regression analysis was not conducted for the grazed sites in 2005. The analysis results are shown in Table 5. SWC, Rn and Ta are the major factors controlling LE at UG79 and WG. They can together explain 65%e71% of the observed daily mean LE. Part R revealed that SWC was the most important factor. This was consistent with other reports for semiarid Inner Mongolia and Mongolian steppe (Hao et al., 2007; Li et al., 2007). In particular, the best fit at HG was between LE and SWC, and Rn, and VPD where R2 ¼ 0.59. The major factor was Rn at HG. As discussed above, the daily mean LE was primarily related to variations in SWC, Rn and Ta (or VPD at HG). However, how available energy was partitioned into LE was not clear. The PriestleyeTaylor

Fig. 7. Relationships between daily mean latent heat flux and (a) SWC ¼ soil water content; (b) Rn ¼ net radiation.

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Table 5 R2 and part R of the multiple linear regressions between LE and climatic variables at UG79, WG and HG. Site

Year

R2

UG79 WG

2005 2006 2006

0.71 0.65 0.71

HG

2006

0.59

Part R SWC 0.48 0.49 0.64 SWC 0.18

Rn 0.29 0.40 0.26 Rn 0.32

Ta 0.18 0.33 0.19 VPD 0.25

depressed ET at the site of this study even when LAI was relatively large. This weakened the effect of LAI (Fig. 8b). Data from HG in 2006 showed that although SWC still influenced a (data not shown), correlations between a and Rn (Fig. 9a) and VPD (Fig. 9b) were more significant. VPD drove coefficient a when the soil was dry, which was associated with low gs. However, Rn was the major factor when the soil was relatively wet. Unlike at UG79, coefficient a was no longer correlated with LAI at HG; instead, it was only influenced by climatic variables.

Fig. 8. Relationship between the PriestleyeTaylor coefficient a and (a) SWC, (b) LAI at the UG79 site in 2005.

model computes ET as follows (Priestley and Taylor, 1972): LE ¼ aLEeq, where LEeq is the equilibrium evapotranspiration and a is a coefficient. Priestley and Taylor recommended a ¼ 1.26 for well watered conditions. However, due to the effects of soil moisture and canopy, coefficient a is much smaller than 1.26 in semiarid areas (Wever et al., 2002; Wilson and Baldocchi, 2000). To investigate how energy partitioning was affected, coefficient a was plotted against SWC and LAI using the 2005 data from UG79 (Fig. 8). Fig. 8a shows that coefficient a declined substantially when SWC dropped below 12%. This meant coefficient a was affected in different ways depending on whether SWC was above or below 12%. The critical value of 12% of SWC agrees well with that reported for a tussock grassland in New Zealand (Hunt et al., 2002). Moreover, Baldocchi et al. (2004) reported a similar threshold of 13% for semiarid grassland in California. Fig. 8a shows that SWC can explain 61% of coefficient a when the soil was dry (SWC < 0.12 m3 m3). Fig. 8b shows that LAI can describe coefficient a better than SWC (R2 increased from 0.01 using SWC to 0.34 using LAI), when the soil was relatively wet (SWC > 0.12 m3 m3). This was consistent with the previous result (Li et al., 2007). However, the low SWC

4.2. Effects of grazing intensity on ET Based on the results, grazing intensity changed the steppe land surface temperature, albedo and SWC. Firstly, higher surface temperatures were found at all three of the grazed sites. This was because the soil has smaller heat capacity than the vegetation and there was a larger proportion of exposed soil instead of grass at the grazed sites (Ketzer et al., 2008). Secondly, grazing exposed more soil surface and this increased albedo to a certain degree. Higher albedo also accompanies an increased grazing intensity (Li et al., 2000). However, these results are the opposite of those reported by Bremer et al. (2001). The reason for this was that there were different physical properties between soil and canopy. The soil reflected more shortwave radiation than the canopy at this study’s sites. The largest difference occurred between UG79 and HG, while WG and CG seem to have a similar albedo with UG79. Thirdly, grazing reduced SWC in the surface soil (0e5 cm) (Fig. 2d). UG79 can retain soil moisture longer due to the presence of vegetation, whereas lower root baffle and lower interception by plant and litter at the grazed sites increased the infiltration velocity of water (Tong

Fig. 9. Relationship between the PriestleyeTaylor coefficient a and (a) VPD, (b) Rn at the HG site in 2006.

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et al., 2000). Bremer et al. (2001) conversely suggested grazing could increase SWC by increasing water-holding capacity. As mentioned above, higher surface temperature and albedo at the grazed sites caused significantly lower Rn. Grazing reduced SWC and decreased the proportion of energy partitioned into LE. Therefore, all three levels of grazing intensities reduced ET significantly during each measurement period in 2005 and 2006 (Table 2, 3). Bremer et al. (2001) reported similar results, but the reason was different. Similar Rn and larger soil heat fluxes decreased available energy (AE) and canopy size difference (but not SWC) meant that less energy was partitioned into LE at the grazed site. In contrast, heavy grazing by prairie dogs increased ET compared with a lightly grazed site (Day and Detling, 1994). Heavy grazing led to a warmer canopy microclimate with higher evaporative demand, and greater soil water availability increased ET at the grazed site. In summary, grazing may affect surface characteristics and ET in different ways at different sites. Even at the same semiarid grassland site in Inner Mongolia, the effects of grazing on ET varied between years. Miao et al. (2009) reported that grazing reduced ET by 13% on average during the 2006 growing season, while ET between the sites was similar in 2007. Since Rn was reduced both in 2006 and 2007, the different effects of grazing on energy partitioning between years was responsible for the different effects on ET. 5. Conclusion In the semiarid grasslands of Inner Mongolia, the low ET/PET ratio indicates that water supply limits ET under the commonly dry soil water conditions. The climatic factors, including SWC, Rn and Ta (or VPD) can together explain 59%e71% of the variation in daily ET. On the other hand, the biological control was weaker. LAI had a slight influence on ET at UG79, and almost no LAI effect was detected at HG. By removal of leaf area, grazing increased the surface albedo and the surface temperature, and reduced the available energy. Grazing also decreased SWC and this led to a decrease in the energy proportion partitioned into LE. Therefore, grazing decreased ET significantly on a seasonal scale. Moreover, SWC was the major factor that limited ET at UG79 and WG. But Rn became the most important climatic factor at HG. Acknowledgments The work presented is supported by P6 of the MAGIM project in cooperation with IMGERS, and the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA05110102. Use of data collected by IMGERS (additional meteorological data) and by Yingzhi Gao (P2 of MAGIM; LAI data) is greatly appreciated. The authors also want to thank Uwe Eichelmann, David Schaffrath, Feng Jianwu and Du Qun for their support. References Asner, G.P., Elmore, A.J., Olander, L.P., Martin, R.E., Harris, A.T., 2004. Grazing systems, ecosystem responses, and global change. Environment and Resources 29, 261e299. Aires, L.M., Pio, C.A., Pereira, J.S., 2008. The effect of drought on energy and water vapour exchange above a mediterranean C3/C4 grassland in Southern Portugal. Agricultural and Forest Meteorology 148, 565e579. Baldocchi, D., Xu, L., Kiang, N., 2004. How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oakegrass savanna and an annual grassland. Agricultural and Forest Meteorology 123, 13e39. Bremer, D.J., Auen, L.M., Ham, J.M., Owensby, C.E., 2001. Evapotranspiration in a Prairie ecosystem: effects of grazing by cattle. Agronomy Journal 93, 338e348. Chuluun, T., Ojima, D., 2002. Land use change and carbon cycle in arid and semi-arid lands of East and Central Asia. Science in China (C) 45 (Supp.), 48e54. Cui, X., Wang, Y., Niu, H., Wu, J., Wang, S., Schnug, E., Rogasik, J., Fleckenstein, J., Tang, Y., 2005. Effect of long-term grazing on soil organic carbon content in semiarid steppes in Inner Mongolia. Ecological Research 20, 519e527.

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