JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D22106, doi:10.1029/2004JD005179, 2004

Correction published 8 January 2005

Agrometeorological conditions of grassland vegetation incentral Mongolia and their impact for leaf area growth
Shin Miyazaki,1,2 Tetsuzo Yasunari,3,4 Tadashi Miyamoto,5 Ichirow Kaihotsu,6,7
Gambo Davaa,8 Dambarajaa Oyunbaatar,8 Luvsan Natsagdorj,8 and Taikan Oki9
Received 30 June 2004; revised 18 August 2004; accepted 16 September 2004; published 27 November 2004.

[1] The long-term observation of surface heat and water budget and hydrometeorologicalelements has been carried out over a grassland site at Arvaikheer (46.23N, 102.82E) incentral Mongolia as the framework of the GEWEX Asian Monsoon Experiment-Asian
Automatic Weather Station Network (GAME-AAN). The purpose of this study is toclarify the relationship between vegetation and climate using long-term data (1982–2000)
of satellite-derived leaf area index (LAI) and climatic data observed at Arvaikheer.
Furthermore, we aimed to reveal physical process by comparing soil moisture and heat
and water budgets in 1999 and 2000 as a case study of good and poor vegetation growth.
Significant positive correlations with 99% confidence levels were found for July
precipitation (P) and the LAI in July (0.538), August (0.826), and September (0.564).
Composite analysis for five highest (H5) and lowest (L5) LAI years showed the significant
positive anomalies of P in July and LAI in July and August for H5. In June and July 1999,
soil moisture and P values were higher than values in 2000; this pattern was reversedin August and September. The mean LAI during the 1999 growing season (1.0) was about
twice that of 2000 (0.6). In 1999 the ratio of evapotranspiration (ET) to P (ET/P) andchange of stored soil moisture (DW) to P (DW/P) were 0.79 and 0.15, respectively. In
2000, ET/P and DW/P were 0.94 and 0.0, respectively. These results suggest that the P and
INDEX
DW before July had the most influent on grass growth in central Mongolia.
TERMS: 1818 Hydrology: Evapotranspiration; 1866 Hydrology: Soil moisture; 3309 Meteorology andAtmospheric Dynamics: Climatology (1620); 3322 Meteorology and Atmospheric Dynamics: Land/
atmosphere interactions; 3360 Meteorology and Atmospheric Dynamics: Remote sensing; KEYWORDS:
Mongolia, grassland, leaf area index, soil moisture, rainfall, evapotranspiration
Citation: Miyazaki, S., T. Yasunari, T. Miyamoto, I. Kaihotsu, G. Davaa, D. Oyunbaatar, L. Natsagdorj, and T. Oki (2004),
Agrometeorological conditions of grassland vegetation in central Mongolia and their impact for leaf area growth, J. Geophys. Res.,109, D22106, doi:10.1029/2004JD005179.

1. Introduction[2] The global average surface temperature has increased
over the 20th century by about 0.6C [Intergovernmental
Panel on Climate Change (IPCC), 2001]. Most warming
has been observed over midlatitude and high-latitude
areas of Asia and parts of western Canada [IPCC, 2001].
1

Japan Science and Technology Agency, Kawaguchi, Japan.
Also at Institute of Industrial Science, University of Tokyo, Tokyo,
Japan.
3
Hydrospheric Atmospheric Research Center, Nagoya University,
Nagoya, Japan.
4
Also at Frontier Research Center for Global Change, Yokohama,Japan.
5
Nomura Research Institute, Yokohama, Japan.
6
Faculty of Integrated Arts and Sciences, Hiroshima University,
Hiroshima, Japan.
7
Also at Disaster Prevention Research Institute, Kyoto University, Uji,Japan.
8
Institute of Meteorology and Hydrology, Ulaanbaatar, Mongolia.
9Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
2

Copyright 2004 by the American Geophysical Union.
0148-0227/04/2004JD005179$09.00

Over a recent 40-year period (1951 – 1990), northern regionsof China and Mongolia have shown remarkable linear
increases in annual mean temperature [Yatagai andYasunari, 1994]. Warmer winter and spring temperatures
mainly account for these trends [Yatagai and Yasunari,1994]. Applying rotated empirical orthogonal functions(REOF) to a recent 40-year period of summer precipitation
data also showed a significant decreasing trend after 1955
from north China to central and southeastern Mongolia[Yatagai and Yasunari, 1995].
[3] Few studies have examined the relationship between
vegetation and climate in Mongolia. Kondoh and Kaihotsu[2003] found that Mongolian grasslands showed a clear
relationship between total summer precipitation and the
NDVI amplitude. Ni [2003] noted that grasses showed apositive relationship to precipitation and aridity index (meanannual precipitation divided by mean air temperature plus
10 degrees) in northeast China and southeast Mongolia.
Suzuki et al. [2003] pointed out that the Mongolia showed
11 weeks later start of green-up than Kazakh, while Mongolia and Kazakh located in almost same latitude. Theysuggested that Kazakh had more soil moisture from snow-

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Figure 1. Study site location. Contour lines show
the topography around the site. Closed circle shows theobservational site. Shaded rectangle shows the area of the
LAI data. The topographic data is derived from GTOPO30.

melt than its in Mongolia. However, neither study clarified
physical or physiological processes together with intersummer variation of precipitation and vegetation.
[4] The long-term observation of surface heat and water
budget in association with hydrometeorological elements
has been carried out over a grassland site at Arvaikheer(46.23N, 102.82E) in central Mongolia as the frameworkof the GAME-AAN (GEWEX Asian Monsoon ExperimentAsian Automatic Weather Station Network; Sugita et al.
[2003]). The purpose of this study is to clarify the relationship between vegetation and seasonal and interannual
variability of rainfall using long-term data (1982 – 2000)
of satellite-derived LAI and climate variables observed at
Arvaikheer. Furthermore, we aimed to reveal physicalprocess by comparing surface climate, soil moisture, andheat and water budgets of in 1999 and 2000 as a case studyof good and poor vegetation growth.

2. Data and Methods
2.1. Observational Site Description and Climatic Data
[5] Long-term observations of hydrometeorological elements and surface flux have been conducted by using
Automatic Weather Station (AWS) at the site in Arvaikheer
in central Mongolia (closed circle shown in Figure 1) since
17 September 1997. The area features grassland vegetation

D22106in summer and snow cover in winter and is classified as dry
steppe [Gunin et al., 1999]. Arvaikheer is located in the
alluvial fan of Ongi River which flows from Khangai
mountains to the Gobi desert. Annual mean air temperature
and annual precipitation (P) are 0.4C and 245 mm,
respectively. The study site is located at the southern edgeof Arvaikheer Airport, which has an unpaved, grass-covered
runway. The mobile observational result of soil moisturearound site shows the spatial distribution of soil moisture
was monotonous at the 5 – 10 cm depth in 10 km by 16 km
around the site except the area along Ongin River [Kaihotsu
et al., 2000]. All the vegetation at this site is C3-typegrasses, mainly in the Gramineae (e.g., Stipa Gobica),
Compositae (e.g., Artemisia adamsii), and Cyperaceae(e.g., Carex duriuscula) families. The soil consists of brown
humus and sand from the surface to 20 cm, brown soil withrock from 20– 70 cm, and brown silt below 70 cm. Winds
prevail from the northwest and homogeneous grasslands
extend more than 5 km around the site. Surface flux was
observed at 7.8 m height by Portable Automated Mesonet
III (PAM III; Militzer et al. [1995]) Automatic Weather
Station (AWS) developed by the National Center for
Atmospheric Research (NCAR) in the United States. The
observational height of surface flux to ensure sufficient
fetch since surface fluxes are considered 100 times greaterthan the measuring height (780 m; Horst and Weil [1994]).
The details about the observational system are described inAppendix A.
[6] Mongolia’s Institute of Meteorology and Hydrology(IMH) provided daily P data from 1999 to 2000 at Arvaikheer.
The IMH also provided long-term 10-day mean air
temperature and 10-day P data from 1982 to 2000. In
addition to station data provided by IMH, we used summarized global data on daily mean air temperature and daily
precipitation amounts. These data were obtained from the
Global Climate Observing System (GCOS) Surface Network (GSN), produced by the National Climate Data Center(NCDC), and are available via the NCDC web server(http://www.ncdc.noaa.gov/). The topographic data
(GTOPO30; Global 30 arc second elevation data) used for
Figure 1 was distributed by the Land Processes Distributed
Active Archive Center (LP DAAC), located at the U.S.
Geological Survey’s EROS Data Center http://LPDAAC.
usgs.gov.
2.2. Remote Sensing Data
[7] In this study, we used a global LAI data set derived
from global composites of maximum NDVI values. The
data are part of the National Oceanic and Atmospheric
Administration (NOAA) Pathfinder Advanced Very High
Resolution Radiometer (AVHRR) Land data set (PAL)
version 3. The global composites were created using analgorithm that incorporates results from a three-dimensionalradiative transfer model and a six-biome classification
scheme, as described in the work of Myneni et al. [1997].
The data contain monthly LAI values at a 16  16 km
spatial resolution from July 1981 to May 2001 and areavailable online at http://cybele.bu.edu.
[8] The NDVI data set used for deriving the LAI was
corrected in three stages, as further described in the work of
Nemani et al. [2003]. Buermann et al. [2002] examined fourkinds of LAI validation based on an algorithm Myneni et al.

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MIYAZAKI ET AL.: VEGETATION-RAINFALL RELATION IN MONGOLIA[1997] derived from NDVI values. The LAI data set usedhere is the same as that used in the work of Buermann et al.
[2002]. In their study, relative error in the LAI was about
10– 20% in dense biomes, which is approximately equal tothe estimated mean uncertainty in the AVHRR channel datafrom which the NDVI is computed. Satellite sensor-derived
LAI values were comparable to field observations at representative grass and needle forest biome sites in Buermann etal.’s [2002] analysis. Their LAI data showed good qualitative agreement with one other LAI data set, but wasconsistently larger than another LAI data set. Interannual
variation in their data set was meaningful, because of thespatial and temporal agreement between the satellite LAI
and station rainfall anomalies in tropical regions affected by
the ENSO phenomenon. Consequently, we considered theglobal data used in this study to be sufficient for discussing
interannual LAI variability. We used the data from the gridnear Arvaikheer (46.175 – 46.325N, 102.66 – 102.86E;
shaded area in Figure 1).

3. Relationships Between Local Climate and LAI[9] According to analysis of long-term monthly climatic
statistics using simple bioclimatic indices, water availability
most strongly limits vegetation growth over 40% of Earth’s
vegetated surface; temperature and radiation most limit
growth over another 33% and 27% of Earth’s vegetatedsurface, respectively [Nemani et al., 2003]. According to
Nemani et al. [2003], Mongolia is located near the boundaryof the temperature-limited and water-limited regions. The
monthly mean air temperature (T) at Arvaikheer exceeds
5C in May and the monthly mean T drops lower than 5Cin October. It is common that the monthly mean T with
5 degrees is the threshold for vegetation activity. Moreover,most precipitation (about 90% of annual precipitation) andgrowth of vegetation at Arvaikheer occurs from May to
September. Therefore we define the vegetation growing
season (GS) as May to September.
[10] To clarify the seasonal variation of T, P and LAI at
the site, we present the time series of monthly T, P and LAI
averaged from 1982 to 2000 with standard deviations (SD)in Figure 2. The seasonal maximum of both T (16.1C) andP (77.0 mm) appeared in July, while the seasonal minimumof both T (8.9C) and P (14.7 mm) appeared in September.
P had large SD from June to August with more than 30 mm,
while T had small SD from June to August with less than
1.5 degrees. LAI increased from May (0.4) to June (1.2)
rapidly then became nearly constant until September withslight maximum in August (1.3). From 1982 to 2000, T
during GS had significant increasing trend (0.1 degree/year)with 99% confidence level. In same period, P and LAI
during GS had no significant trend.
[11] To evaluate the relation between climate and vegetation, it is useful to calculate correlation between T, P and
LAI. No significant correlation showed for T and LAI.
Table 1 shows statistical correlations between P and the
LAI from 1982 to 2000. Nineteen data points were used forthis analysis. Thus the degree of freedom for correlation
analysis was 17, and the lower limits for significant correlation were 0.529 and 0.456 at 99% and 95% confidence
levels, respectively. Positive significant correlations at 99%
confidence levels were found for P in July and the LAI in

D22106

July (0.538), August (0.826) and September (0.564).
Positive significant correlations at 95% confidence levels
were found for P in May and the LAI in June, and for P in
June and the LAI in June. No correlation showed for
P in August and the LAI in August or September, or for
P in September and the LAI in September, implying thatafter August, P has less effect on grass growth.
[12] To reveal the actual phenomena guiding relationships
between P and the LAI, we created the composites of years
with the five highest (H5; in the years of 1984, 1985, 1990,
1997, and 1998) and five lowest (L5; in the years of 1982,
1986, 1988, 1995, and 2000) for mean LAI value during
GS. We present the anomalies of P and LAI for comparing
H5 and L5 composite in Figure 3. P of H5 in July had
significant (larger than SD) positive anomaly with 45.4 mm
while P of L5 had nearly significant negative anomaly with
44.4 mm. The LAI of H5 in July and August had thesignificant positive anomaly with 0.7 and 0.6, while the LAI
of L5 from July to September had the almost significant
negative anomaly with 0.5. In other months, no significant
difference showed for P and the LAI. These results support
findings of large, significant positive correlations for P in
July and the LAI from July to September. This suggests thatplenty of July rainfall is the most important factor for grass
growth.

4. Comparison of Summer Climate andVegetation in 1999 and 2000
4.1. Hydrometeorological Condition[13] Annual P in both years (150 mm) was about 60% ofthat in normal years (240 mm) while seasonal variability in
P differed in these years. We used these two years in a casestudy to examine results from the previous section further.
Figure 4 shows the time series of daily P from May to
September in 1999 and 2000. From May to July, 1999 had ahigher P frequency (43.5% of total days) than 2000 (33% of
total days). In 1999, P values exceeding 3 mm/day occurred
on 13 days from May to July; only 7 days in the same
period in 2000 exceeded 3 mm/day. From August to
September, both 1999 and 2000 showed the same frequencyof P. In 1999, no day had a P value greater than 10 mm/day;four such days were observed in 2000.
[14] The surface condition is the most important parameter
controlling heat and water budgets. Soil moisture is a usefulparameter for expressing the surface condition. To discuss
mechanisms of evaporation from the soil and rainfall infiltration into the soil, information on soil physical parameters
and a moisture characteristic curve are useful. Table 2 showslaboratory measurements of soil physical parameters at
Arvaikheer. It suggests that the near surface soil had a greater
water-holding capability than deeper soil. Figure 5 shows the
moisture-characteristic curve of soil at Arvaikheer. Values
were derived from laboratory experiments using the suction
plate assembly method. At the same volumetric soil watercontent, soil from 10-cm depths showed lower pressure headvalues than deeper soil. This implies that the soil at 10 cm
depth has stronger suction powers than the deeper soil layer.
Owing to the limitation of laboratory experiment, we could
not get the moisture characteristic curve of soil for thepressure head value higher than 450. For the soil at 10 cm
and 55 cm, it is not enough to describe the behavior of the

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Figure 2. Time series of (top) monthly mean air temperatures, (middle) monthly accumulated
precipitation, and (bottom) leaf area index averaged from 1982 to 2000. Error bars show the standard
deviation from 1982 to 2000.

soil drier than 0.2 of the volumetric soil water content (q). As
the wilting point and filed capacity might be less than 0.2, we
did not have the data of them for the soil at Arvaikheer. For
the reference, Erdenetsetseg [1997] showed the filed capacityof soil in steppe region for upper soil layer to 50 cm depth
was from 0.1 to 0.15.

[15] Figure 6 shows the time series of 5-day mean q at
depths from 5 to 20 cm, on average, during the growing
season. In both 1999 and 2000, q responded to P very well.
The q in 1999 was higher than in 2000 in June and July, andlower than in May and August. Erdenetsetseg [1997]
showed the soil moisture regime at typical sites with long-

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Table 1. Correlation Matrix of P and the LAIa
LAIP Month

5

6

7

8

9

5
6
7
8
9

0.245
—
—
—
—

0.482b
0.458b
—
—
—

0.074
0.445
0.538c
—
—

0.069
0.050
0.826c0.141
—

0.086
0.053
0.564c0.065
0.009a

The bold values are significant correlation coefficient.
The 95% significance level.
c
The 99% significance level.
bterm observation. The temporal variation of q in 1999
resembled with the long-term mean soil water content in
steppe region, while the temporal variation of q in 2000 was
similar to that in desert region shown in the work of
Erdenetsetseg [1997]. As shown in Figure 4, the frequencyof P in 1999 was about 1.5 times of its in 2000. This also
affected the different intraseasonal variation of q between in1999 and 2000. These results are consistent with resultspresented in the work of Knapp et al. [2002], in which themean soil moisture content was reduced when the rainfall
interval was extended.
[16] To consider the transpiration from the leaves ofgrass, deeper soil moisture is important. As shown in thework of Gunin et al. [1999], most of the root of grass was
located from several centimeters to one meter especiallymany roots was found below depths of 30 cm. Figure 7
shows the time series of 5-day mean volumetric soil watercontents at depths from 5 to 95 cm during the 1999 and2000 GSs. In mid-July of 1999, q at depths of 40 cm, 70 cm
and 95 cm abruptly increased from 0.09 to 0.15 (depth of
40 cm) followed the increase of q at depths of 5 cm and10 cm. In 2000, q at depth of 40 cm, 70 cm and 95 cm did
not show any change during GS. These results show that
there was no water infiltration to deeper layer in 2000. It is
likely that most of rainfall was evaporated as soon as
rainfall occurred in 2000 and did not infiltrate to deeper
soil layer, because the interval of P was large and amount of
P was small. In contrast, much water infiltrated to deep soil
layer below depths of 40 cm in 1999, which there was muchwater amount for the grass to suck the water from deeper
soil through their roots.
[17] Figure 8 shows a time series of the monthly LAI
derived from satellite-sensor NDVI observations overArvaikheer. The mean LAI during the 1999 GS (1.0) was
about twice that in 2000 (0.6). The annual maximum LAI
value reached 1.6 in June 1999 and 0.7 in June 2000. The
uncertainty in the LAI estimate may be order of 0.5 LAI[Myneni et al., 1997]. Therefore the difference of annual
maximum LAI is significant. The LAI peak in June 1999
likely related to the q in root zone deeper than 10 cm inJune. The q at 10 cm and 20 cm in June of 1999 were about
twice of its in June 2000 (Figure 7). In May and June 1999,
the frequency of P was about twice of its in May and June
2000 (Figure 4). The days with P more than 5 mm/day werefour times in middle and late May 1999 while there was noday with P more than 5 mm/day in May and June 2000
(Figure 4). In June and July 1999, q values were higher thanin 2000, while q values in August and September 2000 werehigher than in 1999. P in June and July 2000 wereD22106significant smaller than the average P from 1982 to 2000.
It implies that the small P caused low q in June and July
2000. These results point out that higher q in early 1999 GS
may have an impact on the LAI since the 1999 LAI was
significantly greater than the 2000 LAI.
4.2. Surface Heat Budget[18] The available energy (Net radiation (Rn) minus soil
heat flux (G); Ra = Rn  G) is the most important forcontrolling the heat budget. Figure 9 shows the time seriesof 5-day mean Ra during the GS in 1999 and 2000. From
May to early July in 1999, Ra was about 7 MJm2/day
smaller than in 2000 except some period. From mid-July to
September, Ra in 1999 and 2000 were almost same. The
lower values in 1999 correspond to successive rainy days.
[19] To test flux measurement performances, we checkedsurface heat balance closure by comparing the sum of the
turbulent fluxes (TF) with Ra. Regression slopes were0.9326 and 0.9043 in 1999 and 2000, respectively, suggesting that TF was about 10% lower than Ra. Such a TF
shortfall is often called an energy imbalance [Panin et al.,1998]. Many studies have examined the cause of energy
imbalances [e.g., Gu et al., 1999; Mahrt, 1998; Lee, 1998;
Tsukamoto et al., 1995; Twine et al., 2000; Tanaka et al.,
2001; Toda et al., 2002; Wilson et al., 2002]. Besides
measurement error, most studies have noted that an energy
imbalance results from the effects of nonzero mean verticalvelocity, nonstationary measured time series, and the development of horizontal energy advections in association withhorizontal inhomogeneity. The energy imbalance observedin this study was less than 10%, which is same as theprobable error of Ra for homogeneous sites showed by
Twine et al. [2000]. It is still difficult to evaluate the causesof energy imbalances that are less than 10% of Ra. Theenergy imbalance less than 10% is quite better than themean values observed in 22 sites of FLUXNET was in orderof 20% [Wilson et al., 2002]. Thus the turbulent fluxes
observed in this study may be considered accurate enoughfor quantitative analysis.
[20] To consider heat budget characteristics, we divided
available energy into sensible heat flux (H) and latent heat
flux (LE). Figure 10 shows a time series of 5-day meansensible heat flux and latent heat flux during the GS. From
May to June, H was the dominant component of the surface
heat budget with about 15 to 20 MJm2/day in both years;
during this same period, H reached its annual maximum of
20.2 MJm2/day and 22.3 MJm2/day in early June 1999
and mid-June 2000, respectively. In 1999, LE exceeded H inthree periods (mid-June, early August, and late September),in response to soil moisture increases after heavy rainfall. In
2000, LE surpassed H in mid-June and early August duringhigh soil moisture conditions. In 1999, LE reached its
annual maximum in mid-July at about 14.7 MJm2/day
that was about 60% of Rn, while LE in 2000 attained anannual maximum in early August at about 12.4 MJm2/day
that was about 50% of Rn. This result is different from theresults of Japanese grassland [Saigusa et al., 1998] which
the ratio of LE to Rn was same between wet and dry year.
This difference may imply that the Mongolian grasslandis more sensitive for the difference of climate than theJapanese grassland where there is enough precipitation for
the grass growth.

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Figure 3. Comparison of (top) P (bottom) LAI by composites of the five years with the highest LAI
values and 5 years with the lowest LAI values, which was chosen from mean LAI value during growing
season. Error bars show the standard deviation over 19 years (1982 – 200).

[21] The equilibrium latent heat flux (LEeq) is defined as
LEeq ¼

Dð Rn  GÞ;
Dþg

ð1Þ

where D and g are slope of the saturation water vapor
pressure curve and the psychrometric constant [Brutsaert,
1982]. The LE/LEeq is often called as Priesley-Taylor
coefficient. Typically, LE/LEeq < 1 represents a surface

where limitations in water supply are sufficient to reduce
LE below the equilibrium evaporation rate; in contrast, LE/
LEeq > 1 typifies surface where the water supply isunrestricted and available energy limits evaporation[Blanken et al., 1997]. Figure 11 shows the time series of
the 5-day mean LE/LEeq during the 1999 and 2000 growing
seasons. In 1999, LE/LEeq varied from 0.1 to 1.1; theaverage value during the GS was 0.38. Values ranged from
0.13 to 0.72, and the average value was 0.37 in 2000. The

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Figure 4. Comparison of daily precipitation from May to September in 1999 and 2000.
mean LE/LEeq during the first half of the GS (May to midJuly) in 1999 (0.34) was higher than that in 2000 (0.28); themean LE/LEeq during the latter half of the GS (mid-July to
September) in 1999 (0.42) was lower than that in 2000
(0.46). The difference of LE/LEeq between years in 1999
and 2000 is well corresponded to the difference of q
(Figure 6) in both years. The 1999 peak (1.1) at our studysite resembled results from a temperate grassland innorthern Canada (0.95) in a wet year (1998; Wever et al.
[2002]). However, the lowest LE/LEeq value in our study(0.1) was much smaller than that in the Canadian study(0.3). It is likely that water availability exerts a stronger
control at our site.
4.3. Surface Water Budget[22] As described in the previous section, most of the data
used in this analysis were daytime mean values from 0900
to 1600 LST. However, to examine the water budget, we
needed to use mean daily values. We estimated daily
evaporation using instantaneous or multiple observationsof evaporative fractions (EF = LE/(Rn  G)), under anassumption proposed by Sugita and Brutsaert [1991].
Sugita and Brutsaert [1991] determined daytime evaporation as
LEd ¼ EFd Qd ;

ð2Þwhere LEd is daytime evaporation, EFd is the daytime
evaporative fraction, and Qd is the daytime net radiation.
They argued that this equation would not apply to theestimation of daily evaporation since the assumption of
constant EF is not satisfied at nighttime. Thus for practical
purposes they proposed a simple correction procedure:
ETtd ¼ aETd ;ð3Þwhere ETtd is the total daily evapotranspiration, ETd is thedaytime evapotranspiration, and ‘‘a’’ is a locally calibrated
constant.

[23] In our study, daily evaporation is obtained by a =1.05. The correlation coefficient of between ETtd and ETd isquite high (r = 0.996); the slope through the origin and thestandard error of estimation are 0.9463 and 0.375 mm,
respectively.
[24] Figure 12 shows the time series of 5-day accumulated
evapotranspiration (ET) during the GS. In 1999, ET attained
an annual maximum of 1.74 mm/day in mid-July, while
ET reached an annual maximum of 1.48 mm/day in midAugust 2000. The ET in 1999 was 0.2 – 0.4 mm/day
higher than in 2000, except during late June and midAugust.
[25] In most applications, differences in lateral flow terms
and the rate of downward drainage are negligible whenevaporation is the only moisture depletion mechanism in thesoil profile [Brutsaert, 1982]. The Arvaikheer site has avery flat surface covered by naturally short grass. As
described in section 4.1, soil moisture was less than 20%.
Therefore we can assume that the downward drainage ofwater is negligible. Thus the water budget equation for soil
layer can be written as
P ¼ ET þ DW ;

ð4Þ

where P is the rate of precipitation (mm), ET isevapotranspiration (mm), and DW is the change of stored
moisture in the soil (mm). Change in observed soil watercontent was used to calculate DW . Figure 13 shows the time

Table 2. List of Soil Physical Parameters at the Observation Site

Depth,cm

Porosity,
cm3 cm3

Dry
Density,g cm3

10
35
55

0.475
0.298
0.335

1.35
1.79
1.69

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Saturated
Hydraulic
Conductivity,
cm s1

Soil Type2.5  102
1.83  1012.44  104

brown humus sandbrown soil with rockbrown silt

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Figure 5. Moisture characteristics curve of soil sampled at Arvaikheer and measured in a laboratory
using a suction plate assembly method.
series of 5-day cumulative values for each component (P,ET, and DW) of the water budget during the 1999 and 2000
growing seasons, respectively. The cumulative P before
July was 96.7 mm and 60.7 mm in 1999 and 2000,
respectively. The rain gauge using in Mongolia is Tretyakov
type with wind shield and measured at 2 m. The averagecatch ratios range from 81 to 97% for rain [Yang et al.,
1995], which corresponds from 4.5 to 28.5 mm whenannual precipitation was 150 mm. Therefore the differenceof cumulative P (36 mm) before July was significant. In
1999, cumulative DW gradually increased in May andabruptly increased from 4.7 to 20.7 mm following an

increase in cumulative P; cumulative DW remained nearlyzero until the end of July 2000. In mid-July 1999,
cumulative DW reached an annual maximum of 27.1 mm
when q reached its annual maximum (cf. Figure 6). In midAugust 2000, cumulative DW reached an annual maximumof 14.9 mm when q was at its annual maximum (cf.
Figure 6). In 1999, averaged ET/P and DW/P during the GS
were 0.79 and 0.15, respectively. In 2000, averaged ET/P
and DW/P during the GS were 0.94 and 0, respectively. The
water budget was thus not completely closed. The
remaining water, however, is only about 5% of P, whichis less than the uncertainty in evaluating evaporation and

Figure 6. Comparison of 5-day mean volumetric soil water content averaged from 5- to 20-cm depths
from May to September in 1999 and 2000.
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Figure 7. Time series of 5-day mean volumetric soil water content at 5, 10, 20, 40, 70, and 95 cm in(top) 1999 and (bottom) 2000.
measuring P. Results suggest that about 15% of precipitation
was stored in the soil layer, and approximately 80%
evaporated in 1999, which resembled results from the meanvalue of the classical estimation of evaporation near
Arvaikheer [Tuvedendorzh and Myagmarzhav, 1985], andSelenge river basin in Mongolia [Ma et al., 2003]. In
contrast, almost all P evaporated and little to no water wasstored in the soil in 2000, which is similar to the highest
value of Selenge river basin [Ma et al., 2003] and thecharacteristics of whole Mongolia [Natsagdorj, 2000].

5. Summary and Remarks[26] In this study, we investigated the impacts of seasonaland interannual variability on vegetation as well as soil
moisture and evapotranspiration over a grassland at
Arvaikheer, central Mongolia. Correlations were determinedfor 19 years (1982 – 2000) of monthly mean LAI andclimate data. The largest and significant correlation was

found for P in July with LAI in July, which continued until
September. Furthermore, significant correlation showed for
P in May and LAI in June, P in June and LAI in June. In thecomposite analysis of H5 and L5, the significant positive
anomalies of P (45.4 mm) in July and LAI in July (0.6) andAugust (0.6) were found, which was corresponded to thecorrelation analysis. It implies that the P in July had thelargest impact for grass growth.
[27] To study the physical processes of grass growth, we
conducted a case study using energy and water budget datawith hydrometeorological elements from 1999 and 2000. In
both 1999 and 2000, total precipitation during GS was
almost same about 130 mm. However, the intraseasonal
distribution of rainfall was different. P in 1999 before July
was about 70% of total P during GS while P in 2000 before
July was about 40%. These intraseasonal distribution of P
caused the difference of the intraseasonal variation of
surface q. The q in upper 20 cm in June and July 1999
was higher than in 2000. The LAI from June to September

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Figure 8. Comparison of the monthly LAI from May to September in 1999 and 2000.
in 1999 was always larger than in 2000. These differencesof surface conditions were closely related with the average
ET between 1999 (1.3 mm/day) and 2000 (0.7 mm/day). In
1999, averaged ET/P and DW/P during the growing season
were 0.79 and 0.15, respectively. In contrast, ET/P and DW/
P during the growing season were 0.94 and 0, respectively.
There was almost no stored water in the soil in 2000
because of low P frequency and the small P amounts inJune and July 2000.
[28] The results of this study suggest that grass growth incentral Mongolia is influenced by P and DW before July.
High amounts of P, provided after August, did not contribute to grass growth. Although this study focused on one site,
it is the first study to examine physical processes related toseasonal rainfall variation and its impact on grass growth incentral Mongolia. To generalize our results, we are conducting further analyses using LAI data and data from otherstations.

Appendix A: Details About the Observation
Done by AWS[29] Wind velocity and direction were observed at 9.8 m
with a wind-vane and anemometer (model: 09101, R.M.
Young). Soil heat flux was calculated from values recorded
by a heat plate (model: HFT3.1, REBS) buried 0.05 m
below the surface. Soil-temperature profiles were obtainedby a platinum thermometer (model: STP-1 Pt, REBS) at a0.05-m depth and a thermistor at 0.1, 0.2, 0.4, 0.7, and0.95 m depths (model: HOBO Temp, Onset). VolumetricFigure 9. Comparison of 5-day mean available energy values from May to September in 1999 and2000.
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Figure 10. Comparison of 5-day mean sensible heat flux and latent heat flux values from May to
September in 1999 and 2000.

soil water content was observed at the same depth as soil
temperature by a time domain reflectometry (TDR) sensor(model: TRIME-MUX6, Imko). We used a four-component
radiation system at 1.5 m to measure the radiation budget.
The system measured shortwave (model: PSP, Eppley) andlong-wave radiation (model: PIR, Eppley) in both downward and upward directions; an aspirating fan reduced theeffect of dome and case heating. The system also measured
net radiation using a net radiometer (model: Q7, REBs) andsolar radiation using a pyranometer (model: LI1200SA,
Licor). Net radiation was calculated using data obtained
from the four-component radiation system; long-wave radiation was corrected using pygeometer dome and casetemperatures and a dome coefficient (=3.0), derived from
Albrecht and Cox [1977].
[30] The PAM III station employs a 3-D sonic anemometer (model: R3A, GILL) and a hygrothermometer (model:
50Y, Vaisala) to determine surface turbulent fluxes ofmomentum and sensible heat flux by the eddy correlation
technique; latent heat flux is determined by the bandpass
covariance method, using the Environmental Variable Extractor (EVE; Horst and Oncley [1995] and Horst et al.
[1997]). The bandpass covariance method can serve as analternative to the eddy correlation method since it does not
require ideal sensor response covering a wide frequency
range. Rather, the method essentially uses direct measure-

Figure 11. Comparison of 5-day mean LE/LEeq from May to September in 1999 and 2000.
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Figure 12. Comparison of 5-day mean evapotranspiration values from May to September in 1999 and2000.

Figure 13. Time series of 5-day cumulative precipitation, evapotranspiration, and change of stored soil
moisture values from May to September in (top) 1999 and (bottom) 2000.
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ments [Watanabe et al., 2000]. Watanabe et al. [2000]
suggested that the bandpass covariance method is the bestsolution for scalar flux in a long-term continuous direct
measurement method. Toda et al. [2002] provide further
details of the bandpass covariance method used here.
[31] Although observations at the study site began in
1997, missing data present a problem until 1998. Therefore
we used data from 1999 and 2000. Owing to a lackof nighttime data, we mainly used daytime (i.e., 0900 –
1600 LST; GMT +8) mean values for this study. Problems
with EVE created gaps in the data set for 1999. To fill thesegaps we used data from other AWS operated at the samesite. AWS was made by AANDERA Instruments, Norway,
and have 3.6-m tower heights. They consist of a pyranometer (model: 2770, AANDERA), thermohygrometer (model:
HMP35D, Vaisala), anemometer (model: 2740, AANDERA), wind vane (model: 3590, AANDERA), barometer(model: 2810, AANDERA), and soil moisture sensor(model: CS615, Campbell). Meteorological sensors are
3.6 m high and soil moisture sensors are positioned at depths
of 5, 10, 20, 40, 70, and 95 cm. Time intervals for the
Aandera weather stations were 10 minutes or 30 minutes,
and the data collection period was from February 1999
to June 2000. Before gap filling, comparison between
AANDERA and PAM III data sets was carried out for aperiod without any gaps of PAM III data.
[32] Acknowledgments. The data used in this paper were obtained
from the GAME-AAN project supported by the Ministry of Education,
Sports, Culture, Science, and Technology of Japan. Parts of this study were
supported by Core Research for Evolutional Science and Technology(CREST), Japan Science and Technology Agency (JST). The authors
express special thanks to Zamba Batjargal, Mongolian Ambassador to
Japan and former Director General of the National Agency for Meteorology, Hydrology, and Environmental Monitoring, Mongolia, for his great
support of our research. The authors also express special thanks toDavasuren and Tserenyam of the Ubrkhangahi Aimag Environmental
Monitoring Center in Mongolia for maintenance of our AWS and for theirkind cooperation. We also thank Ganbaatar and Butafut of the Arvaikheer
Airport Authority in Mongolia for assisting with our AWS. The authors are
grateful to Hiroyuki Ohno, National Institute for Agro Environmental
Science of Japan, not only for providing data from his AWS but also for
his kind assistance. The authors express thanks to Ganbold and Erdenetsetseg, Institute of Meteorology and Hydrology of Mongolia for their kind
support to our observation. We also thank T. W. Horst, J. M. Militzer, K. S.
Semmer and M. C. Miichaelis, National Center for Atmospheric Research,
United States of America, for their great contribution of operating PAM III
station. The comments and suggestions of reviewers were greatly appreciated and helpful to improve the manuscript. The first author would like toexpress gratitude to all members of the Oki-Kanae Laboratory, Institute ofIndustrial Science, University of Tokyo, for their helpful discussion. The
first author received partial support for this research from the Japan Scienceand Technology Agency.

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G. Davaa, L. Natsagdorj, and D. Oyunbaatar, Institute of Meteorology
and Hydrology, Khudaldaany gudamj-5, Ulaanbaatar 46, Mongolia.
(meteoins@magicnet.mn)
I. Kaihotsu, Faculty of Integrated Arts and Sciences, Hiroshima
University, Kagamiyama 1-7-1, Higashi-hiroshima 739-8521, Japan.
(kaihotu@hiroshima-u.ac.jp)
T. Miyamoto, Nomura Research Institute, NRI Tower, 134 Godo-cho,
Hodogaya-ku, Yokohama 240-0005, Tokyo, Japan. (t-miyamoto@nri.co.jp)
S. Miyazaki and T. Oki, Institute of Industrial Science, University ofTokyo, 4-6-1 Komaba, Meguro-Ku, Tokyo 153-8505, Japan. (miyashin@
iis.u-tokyo.ac.jp; taikan@iis.u-tokyo.ac.jp)
T. Yasunari, Hydrospheric Atmospheric Research Center, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan. (yasunari@hyarc.nagoya-u.ac.jp)

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