Seasonal and interannual variability in surface energy partitioning in
Short grass steppe depending on the vegetation cover
Khishigbayar Jamiyansharav1 Dennis Ojima1 Roger A. Pielke2 Sr.,
Bill Parton1 and Jack Morgan3
1
Graduate Degree Program in Ecology, NREL and
2
Department of Atmospheric Science, CSU,
3
USDA-ARS Fort Collins, CO, 80523
Abstract
Latent and sensible heats play a significant role in the climate system. It is
important to model the partitioning of available energy between sensible and latent heat
and accurately simulate the diurnal, seasonal and longer term variations in these fluxes
since latent heat contributes water vapor to the atmosphere and tends towards increasing
cloudiness and precipitation, while increases in sensible heat tends to increase the air
temperature in the planetary boundary layer. At the earth’s surface, evapotranspiration is
the connecting link between the water and the energy budgets. To determine the surface
energy budgets for Short Grass Steppe and evaluate land-atmosphere energy interactions
we used Bowen Ratio Energy Balance method. The study site was located on the Central
Plains Experimental Range, 6200-ha research area, maintained by the USDA Agricultural
Research Service, approximately 61km northeast of Colorado State University. The main
objectives were to evaluate land-atmosphere energy interactions, including energy fluxes,
evapotranspiration rates and particularly the impact of grazing in dry and normal years to
obtain a clear understanding of surface energy budgets and evapotranspiration processes
at the SGS region. We had clear seasonal effects and interannual variability for most of
the parameters that we looked but not the expected grazing treatment effects. The
consistent changes in biomass and related energy fluxes with grazing intensity were not
observed for this study.
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Table of contents
Introduction
3
SGS case study (Objectives, Questions, Hypothesis)
3
Data sets and study site
4
Study site map 1. CPER. Map of the treatment plots
5
Research approach
5
Data quality check
6
Green biomass
7
Total biomass
8
Net radiation
9
Latent heat flux
11
Sensible heat flux
12
Bowen ratio
13
Latent heat flux versus green biomass
15
Soil heat flux
16
Soil water content
18
Heat contents
18
Effective temperature (Te) and air temperature (Tair):
19
Air temperature at 2 m
20
Accumulated latent heat flux
20
Seasonal effects
21
Surface energy partitioning scheme in dry and normal year
for different grazing treatments
21
Conclusions
22
Further analysis and recommendations
24
Acknowledgements
24
Literature cited
25
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Introduction
Latent and sensible heats play a significant role in the climate system. It is
important to model the partitioning of available energy between sensible and latent heat
and accurately simulate the diurnal, seasonal and longer term variations in these fluxes
since latent heat contributes water vapor to the atmosphere and tends towards increasing
cloudiness and precipitation, while increases in sensible heat tends to increase the air
temperature in the planetary boundary layer. Therefore, land surface processes are being
featured in climate systems modeling and represented in numerical models of weather
and climate (Kabat et al. 2004).
At the earth’s surface, evapotranspiration (ET) is the connecting link between the
water and the energy budgets. ET is the combination of two processes that lose water to
the atmosphere by evaporation from lake, reservoir, wetlands, soil and snow cover and
transpiration, in which water is lost from plant surfaces. Cell walls inside the leaf are
covered with a thin film of water and this water readily evaporates when the stomata
open to assimilate carbon for plant photosynthesis. During this trade-off the ratio of
moles of water lost for each mole of CO2 absorbed can reach 400 (Chapin et. al. 2002).
Direct solar radiation provides the energy that is required to change the state of the
molecules of water from liquid to vapor. The driving force to remove water vapor from
the evaporating surface is the difference between the water vapor pressure at the
evaporating surface and that of the surrounding atmosphere. As evaporation proceeds, the
surrounding air becomes gradually saturated and the ET rates will decline and eventually
ceases if the wet air is not transferred to the atmosphere. The replacement of the saturated
air with drier air depends greatly on wind speed. Hence, solar radiation, air temperature,
air humidity and wind speed are climatological parameters to consider when assessing the
evaporation process (Hanson 1991).
SGS case study
The objectives were to:
•
Evaluate land-atmosphere energy interactions, including energy fluxes, ET rates.
•
Evaluate the impact of grazing on the seasonal and interannual surface energy
fluxes in normal, and dry years to advance in the understanding of surface energy
budgets at the SGS.
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Questions:
•
How the Bowen ratio changes relative to humidity?
•
How does grazing influence surface energy partitioning and the heat content of
the air?
•
Is there difference in energy fluxes between wet and dry periods?
Hypothesis:
•
Net radiation increase would increase the ET rate; therefore, the Bowen ratio
(ratio of sensible to latent heat) would decrease after the rain event.
•
Bowen ratio would increase with grazing intensity due to decreasing transpiration
rate correlated with decreased biomass amount.
•
Green biomass would affect the latent and sensible heat flux partitioning after the
rain events or during the wet periods.
Data sets and study site
To evaluate land-atmosphere energy interactions we picked 2001 as a normal year,
which had total annual precipitation of 262 mm and 2002 as a dry year, which had 163
mm total annual precipitation and used Bowen Ratio Energy Balance method. We used
net radiation (Rn), latent heat flux (Le), sensible heat flux (H), soil heat flux (Go), Tair,
Tsoil, and soil water content data measured continuously and averaged over 20 minutes.
In addition to that we used green, brown and total biomass data measured once in a
month. Bowen ratio energy balance system descriptions and flux calculations can be
obtained from the instruction manual (Instruction manual 1998).
The study site was located on the Central Plains Experimental Range (CPER). The
CPER is a 6200-ha research area maintained by the USDA Agricultural Research Service
for rangelands research, located in the piedmont of north central Colorado approximately
61km northeast of Fort Collins and the campus of Colorado State University (lat.
40x49'N; long.104x46'W; elevation 1650 m). Data sets were provided by the Shortgrass
Steppe Long Term Ecological Research group, a partnership between Colorado State
University, United States Department of Agriculture, Agricultural Research Service, and
the U.S. Forest Service Pawnee National Grassland. Significant funding for these data
was provided by the National Science Foundation Long Term Ecological Research
program (NSF Grant Number DEB-0217631).
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Study site map 1. CPER. Map of the treatment plots
The diagonal fence was installed in 15NW in the spring to divide the Heavy (34 ha)
and Control (63 ha) treatments in 2001. The Moderate pasture was 10SW (61.5 ha). In
2001, there were 10 heifers in each of the heavy and moderate treatments from May 16 th
to Nov.01st. In 2002, there were 10 steers from May 16th to Aug.09th (dry year-163mm).
In 2000, both pastures were moderately stocked with heifers from May 17th to Aug.08th
(another dry year- 193mm). In 1999, 15NW was not grazed in preparation for the
CARBS (NASA) study and 10SW was moderately stocked. Prior to 1999 pastures were
moderately stocked for many, many years (personal communication with Ashby Mary).
Research approach
If the effects of photosynthesis and lateral advection can be neglected, the energy
budget is: Rn=Le + H + Go
[1]
where Rn is the specific flux of net incoming radiation, Le the specific flux of latent heat
into the atmosphere, H the specific flux of sensible heat into the atmosphere and Go is the
specific flux of heat conducted into the earth, expressed as W/m2.
In many situations it is practically impossible to deal with evaporation rate, without also
considering H in the analysis, or vice versa. The ratio of the two flux terms called the
Bowen ratio (BR) and expressed as: BR=H/Le
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[2] (Brutsaert 1982).
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BR range from less than 0.1 for tropical oceans to greater than 10 for deserts,
indicating that which flux can dominate the turbulent energy transfer from ecosystems to
atmosphere, depending on the nature of the ecosystem and the climate (Chapin et al.
2002).
Surface heat energy (He) or moist enthalpy is a better metric for measuring heat content
change (global warming) than surface temperature alone. It accounts not only for the
surface temperature but also the contribution of water vapor content to the heat of the air
(Pielke et. al. 2005) and is expressed as: He = CpT+Lvr
[3]
where, Cp is the specific heat content of the air at constant pressure (at 20oC
Cp=1.005J/goK), T is the observed air temperature at 2m height in Kelvin, Lv is the latent
heat of vaporization (at 20oC Lv = 2450 J/g), r is the mixing ratio, mass of water vapor to
the mass of dry air.
To express enthalpy in degrees for easy comparison to air temperature we use the term
effective temperature (Te) and it is described by the following formula:
Te = He/Cp = T + Lvr/Cp
[4]
where Te is the effective temperature that counts specific humidity of the air; therefore it
has contributions from both sensible and latent heat (Pielke et al. 2004 and 2005)
Data quality check: We were interested in daytime energy fluxes since the
nighttime and sunset and sunrise data can have large errors. Errors are common around
sunrise and sunset because when the Bowen ratio approaches -1, the calculated fluxes
approach infinity. It usually occurs during sunset and sunrise when the flux changes
direction and there is little available energy: Rn-G. We picked the daytime from 7am to
7pm. Since it is difficult to account for the daily variation of sunrise and sunset times we
decided to stay with the same time period and remove all the outliers that were related to
sunrise and sunset. We summed all the fluxes: sensible, latent and soil heat fluxes and
checked against the net radiation data (formula 1). Differences greater than 50 W/m2
were considered as outliers and were removed from the data file. From each treatment 4 12% of the data were counted as outliers and were removed. We averaged them over a
day except for the precipitation data, which we had a sum of the daily value.
We compared energy fluxes in three different grazing treatments: Heavy (HG),
Moderate (MG), and Ungrazed (UG) during the two growing seasons in 2001 and 2002.
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Green biomass: The green and brown biomass amount was measured 6 times
(one in each month) during the growing season (USDA data). We connected the 6
month’s biomass measurements linearly and found out the corresponding continuous
biomass amount for each day. The green biomass amount was higher than in MG and
lower in HG than UG in 2001 whereas in 2002 there was not significant change between
those treatments but early in the season MG had higher biomass and starting from June it
was lower than UG whereas HG and UG had almost same amount of green biomass
throughout the whole growing season (Fig.1a.b and Fig.2a.b). Holechek et al. (2006)
found that managed moderate grazing resulted in slightly higher grass production than
ungrazed treatment.
Fig.1. The comparison of the dry and normal year green biomass in different grazing
treatments.
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Total biomass: The total biomass amount was higher in grazed sites from the
beginning of the growing season until August and at the end of the season MG and UG
sites were almost same and eventually UG site had more total biomass amount than the
grazed sites in 2001 (Fig.2a). In contrast in 2002 UG site had slightly higher total
biomass amount then the grazed sites throughout the whole growing season except at the
beginning of May MG had slightly higher total biomass than UG site (Fig.2b). Overall
the total biomass difference between the grazing sites were not significantly different and
ranging from 0-42 g/m2 between MG and UG sites and 0-46 g/m2 between HG and UG
sites in normal year 2001. The dry year 2002 had expected pattern in total biomass even
though the differences were not significantly different ranging from 0-20 g/m2 between
MG and UG sites and 0-46 g/m2 between HG and UG sites (Fig.2a.b). Milchunas et al.
(1998) found that annual forage production was more sensitive to variations in
precipitation than to long-term differences in grazing practices. In fact, the short grass
steppe has long history of grazing therefore; grazing is the part of natural conditions of
SGS (Milchunas et al. 1988).
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Fig.2. The comparison of dry and normal year total biomass in different grazing
treatments.
Net radiation: Net radiation (Rn) is the balance between the inputs and outputs of
shortwave and longwave radiation. The amount of absorbed incoming radiation depends
on the albedo or shortwave reflectance of the ecosystem surface. The pastures were
adjacent so there shouldn’t be a big difference on net radiation unless albedo had
differences due to vegetation cover differences between the grazing treatments.
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Fig.3. The comparison of dry and normal year net radiation.
The net radiation in heavy grazed sites were consistently higher than UG sites for
both years but net radiation in MG site was higher in 2002 but lower in 2001, which
might be the result of higher green and total biomass amount in MG site for 2001
(Fig.3a.b). We don't have the actual albedo data to prove it for sure but we assumed that
green and dry dead vegetation cover would normally have the higher albedo in SGS: 0.26
and standing dead grass: 0.3 (Pielke Sr. and Avissar. 1990) than the bare soil: 0.05-0.13
except the dry light soil: 0.4 (Chapin et al.2002). Therefore, we would expect to have
lower net radiation in MG site due to higher reflectance of vegetation cover than bare
soil. The range of the net radiation differences were from 0-20.6 W/m2 between moderate
and heavy grazed sites and 0-22.8 W/m2 between heavy and ungrazed sites for 2001
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whereas the range of the net radiation differences were from 0.2-25.7 W/m2 between
moderate and heavy grazed sites and 0.1-23.4 W/m2 between heavy and ungrazed sites
for 2002.
Latent heat flux: Latent heat fluxes increased at rain events and then declined
during the dry period prior to the next major rain event (Fig.4). In normal year 2001
highest LH fluxes (ave. 150 W/m2) were observed during the first half of the growing
season and decreased thereafter (ave. 80 W/m2). Whereas in dry year 2002, latent heat
fluxes were so low (ave. 25 W/m2) in May due to the lack of rain events and further they
had similar patterns that rises after the rain event and decreases as it dries out and overall
there were no decreasing and increasing trend (ave. 50 W/m2).
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Fig.4. The comparison of dry and normal year latent heat fluxes.
The spatial variation of latent, sensible, and soil heat fluxes is very sensitive to
soil moisture and biomass distributions. High latent-heat fluxes are related to high
occurrences of green leaf area indices (LAIgreen), low brown leaf area indices (LAIbrown),
and high soil moisture contents, as well as to sufficient available energy (Song et al.
1997). For a prairie environment like the Konza Prairie with green leaf-area indices less
than 2 simulated latent-heat fluxes from the canopy are about two times greater than
those from the soil beneath the canopy. In our case we didn’t have LAI and there was no
chance to separate evaporation from the soil and transpiration from the vegetation cover
therefore the latent heat flux accounts only the combination of both evaporation and
transpiration.
Sensible heat flux: The sensible heat flux had increasing trend from May to
August and when the net radiation decreases in September and October it decreased
accordingly in 2001 (Fig.5a). In 2002 it has no significant trend but mostly were higher
(ave. 180) than the normal year 2001 (ave 140. Fig.5b). There was not any big difference
between grazing treatments in both years due to insufficient difference in biomass
amount. High sensible-heat fluxes often are coin and relatively dry soils (Song et
al.1997).
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Fig.5. The comparison of dry and normal year sensible heat fluxes.
Bowen ratio: BR was about 2 times higher in dry year 2002 than the normal year
2001. In 2001 BR had increasing trend over the growing season period due to decreasing
precipitation and green biomass amount. We also observed the expected pattern that is
decreased soon after the rain events and continually increased until the next rain event.
We had a very nice rain event in early May and sufficient big rain events during June and
early July. So the BR was always lower around 0-1 during this period. At the second half
of July, August and September the precipitation amount was decreased so the BR
increased. Even after the rain events BR didn’t go down as we seen in early season
because of dried soil and lack of sufficient big rain events. Therefore, the BR had an
increasing pattern from May to September (Fig. 6a).
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Fig.6. The comparison of dry and normal year BR during the growing season
In contrast 2002 year had very little amount of annual precipitation that is 163
mm, and early season rainfall events were insufficient, the most of the rain events
occurred at the end or middle of the season that doesn’t support the vegetation growth not
as high as an early season rain events.
At the beginning of the season in May there were only two rain events which are
very small 1-2 mm. so the vegetation cover was very low and sensible heat and BR was
so high. After the sufficient rain which is 30 mm rain event BR was lowered to less than
1 and again rapidly increased as the moisture is lost through time until the next big rain
event of two days of 30 mm rain. The soil and vegetation had enough moisture from
these two days precipitation event and as a result had low sensible heat fluxes therefore,
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the BR was lower until the next big rain event. At the last half of the growing season we
had pretty big rain events but the period of dryness were long enough that decrease the
amount of vegetation cover and so the BR increased. At the end of the season in
September we had sufficient rain events for 6 days and the soil were soaked enough with
moisture. Therefore, the sensible heat and BR were lower (Fig.6b).
Latent heat flux versus green biomass: We expected to have higher ET rate
from the site that has highest green vegetation after the rain events. According to Parton
et al. (1981) water loss is equal to potential evapotranspiration rate immediately after the
precipitation events, then decreases rapidly for three to four days. We picked the two to
three days after the rain events under conditions when net radiation exceeded 300 W/m2
to see how the latent heat and sensible heat fluxes relate to the green biomass amount.
We observed a clear pattern of higher latent heat flux with higher biomass, but no
significant treatment differences (Fig.7a.b). These results suggest a significant potential
impact of grazing on energy budgets if grazing treatments had a significant affect on live
biomass. The expected opposite pattern was observed for sensible heat flux. No
significant pattern were observed for soil heat flux and for net radiation except the small
increasing trend in net radiation with increasing green biomass in 2001 but the variances
were high.
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Fig.7. The green biomass versus the latent and sensible heat flux trends after the rain
events for 2001 and 2002. Note: 2002 had very low biomass, therefore the points are
clustered at the beginning of the X axes.
Soil heat flux: The results show that the moderately grazed site had higher SHF
than the heavy grazed and ungrazed treatments in both years. The average SHF were:
48.9, 60.8, 54 in 2001 and 55.8, 66.1, 60.5 (W/m2) in 2002 respectively in ungrazed,
moderate and heavy grazed treatments. Soil heat fluxes decreased during the wet periods
as we expected. The 2002 SHF data were more consistent with total biomass (the best
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indicator) data. It follows the right pattern (Fig.8b) but there wasn’t any consistent pattern
in biomass for 2001 (Fig.8a).
Fig.8. The comparison of dry and normal year soil heat fluxes.
The range of the soil heat flux differences were from 0.1-43.9 W/m2 between moderate
and ungrazed sites and 0.1-32.4 W/m2 between heavy and ungrazed sites for 2001
(Fig.8a) whereas the range of the soil heat flux differences were from 0.1-29.2 W/m2
between moderate and ungrazed sites and 0.06-35.4 W/m2 between heavy and ungrazed
sites for 2002 Fig.8b).
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Soil water content (SWC): Moderate treatment data looks the best responding to
rainfall events. But the other 2 treatments didn’t respond to rainfall for many events in
2002 (Fig.9a.b). Therefore, we neglected UG and HG treatment data for SWC. From the
following graphs we can see the normal and dry year difference in SWC related to the
precipitation amount. In normal year the growing season precipitation was 205 mm and
corresponding growing season SWC was 0.071% whereas in dry year 135 mm and
0.057% in MG treatments.
Fig.9. The comparison of dry and normal year soil moisture content
Heat contents: The surface heat content depends on air temperature and specific
humidity. A surface temperature increase may not correspond to a surface heat content
increase because it also depends on changes in humidity (Pielke 2001). High relative
humidity in hot weather makes us feel hotter than it really is by retarding the evaporation
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of perspiration (Ahrens 2003). Vegetation plays an important role in determining surface
heat content throughout a growing season as it removes moisture from the soil that is lost
through transpiration (Lu et al. 2001). During the day, transpiring vegetation converts
incoming solar energy into latent heat, thus reducing maximum air temperature. At night,
higher water vapor above vegetated areas increases minimum air temperatures
(Hanamean et al. 2003). Therefore we compared seasonal surface heat changes (moist
enthalpy changes) between the grazing treatments and between the two years.
There were no significant grazing effects on surface heat change between the
treatments due to relatively low biomass difference. The differences were ranging 0-60
kJ/kg, which is 0-20% of the actual value. But most of the time difference range was 0-20
kJ/kg, which is up to 7% of the actual value.
Effective temperature (Te) and air temperature (Tair):
Effective temperature is the term that has contributions both sensible and latent
heat and described by the formula [4]. During the winter time when the humidity is lower
there was not big difference between the T and Te, when the humidity is higher during
the growing season the differences were increased. The normal and dry year difference in
Te was observed in connection to precipitation amount difference. Dry year 2002 has
lower Te than the normal year 2001. But no significant treatment differences were found
(Fig.10) within the two years.
The time at which the Tair reaches a maximum and the time at which the Te
reaches a maximum often do not coincide due to boundary layer mixing in the afternoon.
Usually, the Te.max occurs before the Tair.Max. Typically, the hot days are characterized
by exceptionally low relative humidity in the late afternoon, which explains the drop in
Te (Pielke et. al. 2005). According to the five days average in July Te.Max occurred at
13:40 in both years whereas Tair.Max occurred at 15:00 in 2001 15:20 in 2002.
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Fig.10. Effective temperature and air temperature for 2001 and 2002.
Individual rain events (2 days before and 7 days after): We observed expected
patterns of surface fluxes but not the significant grazing treatment effects.
Max air and soil temperature trends after the rain events: We picked up 1-3 days after the
rain events with net radiation amount of 300 W/m2 or higher. There were higher
maximum soil temperatures over the maximum air temperature later in the season in both
years.
Air temperature at 2 m: The highest air temperature observed
MG>UG>HG vs. total biomass amount: MG>UG>HG for 2001
UG>MG>HG vs. total biomass amount: UG>MG>HG for 2002
The differences were not significantly different ranging from 0-0.5oC but
averages around 0.2oC. Cold (or warm) synoptic temperature advection and different
boundary layers significantly complicates the interpretation of temperatures as we have
no measurements of that.
Accumulated latent heat flux: The overall accumulated latent heat flux during
the growing season was highest in heavy grazed sites in both years, which we expected
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opposite that the highest would be in UG site. But the differences were too small to
validate.
Seasonal effects: Clear seasonal effect on energy fluxes, for example, higher
latent heat flux (ave. 170 W/m2) observed during May versus lower latent heat flux (ave.
50 W/m2) observed during August 2001. Early in the growing season we had sufficient
amount of rain events and higher net radiation so the evapotranspiration rate was higher
as we expected whereas late in the season rain events and the net radiation amount both
decreased so do the evapotranspiration rate. Same results obtained for 2002.
Surface energy partitioning scheme in dry and normal year for different
grazing treatments: Based on our data we got the following surface energy partitioning
scheme:
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Scheme 1. Growing season daytime surface energy flux schemes
Overall in dry year we had higher sensible heat (0.6Rn) and lower latent heat flux
(0.2Rn) whereas in normal year we had lower sensible (0.4Rn) and higher latent heat flux
(0.4Rn) as we expected. Soil heat fluxes didn’t change much. We didn’t find any
significant treatment affects but clear interannual differences.
Conclusions:
We evaluated the short grass steppe energy budgets based on the Bowen Ratio
Energy Balance method and analyzed the data for different grazing treatments.
We had clear seasonal interannual differences and expected patterns for most of
the parameters but we didn’t find any significant differences among grazing treatments.
The main results from our analysis are shown in a Fig.4. These results suggest a
significant potential impact of grazing on energy budgets if grazing treatments had a
significant affect on live biomass. The increased green biomass amount was correlated to
the Increasing latent heat flux and decreasing sensible heat flux.
There were clear seasonal and interannual variability in latent and sensible heat
fluxes as well as in Bowen ratio. For example: we found lower sensible and higher latent
heat fluxes at the beginning and middle of the growing season as it has lower net
radiation and higher precipitation amount. At the end of growing season when the Rn
increases and precipitation decreases we had lower Le and higher H. There were no big
difference in green and total biomass between the treatments and it could be the reason
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why we didn’t see the treatment effects clearly. We also observed the clear pattern of
energy flux responses to rain events. Treatment means of surface energy fluxes for the
growing seasons are shown in table 1.
Table 1. Treatment means of surface energy fluxes for 2001 and 2002.
Hypothesis 1 (Net radiation increase would increase the evapotranspiration rate;
therefore, the Bowen ratio would decrease after the rain event) was supported. Bowen
ratio decreased after the rain event and gradually increased as the net radiation increases
and when the soil gets drier over the time.
Hypothesis 2 (Bowen ratio would increase with grazing intensity due to
decreasing transpiration rate correlated with decreased biomass amount) was not
supported by this study. Possible main reason might be due to the insufficient biomass
difference between the treatments.
Hypothesis 3 (Green biomass would effect the latent and sensible heat flux
partitioning after the rain events or during the wet periods) was supported. This is the
main result from our analysis that show a significant potential impact of grazing on
energy budgets if the live biomass is greatly different for different grazing treatments.
The increased green biomass amount was correlated to the Increasing latent heat flux and
decreasing sensible heat flux.
“The energy balance will not close perfectly at every timescale all the time even
in the very best dataset from an ideal experimental site. The uncertainty inherent in every
measurement makes this the case” (Kabat et al. 2004). Therefore the unexpected results
could be due to the measurement uncertainty and especially the differences between the
treatments were not significant and small enough to count as an bias. In addition to that,
unconsidered site effects within this study might have influenced, for example; soil
specific characteristics, vegetation composition and different boundary layers. Also the
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biomass amount was measured once in a month therefore, the changes in between the
measurements were not accounted in these analyses.
Recently published Gu et.al. (2007) paper discussed the possible biochemical
energy storage in forest, which we haven’t accounted in our SGS study. According to
their results without biochemical energy storage, simulations had higher surface radiative
temperature during the day but lower radiative temperature at night.
The short grass steppe has long history of grazing therefore; grazing is the part of
natural conditions of SGS (Milchunas et al. 1988) and might not clearly responding to the
grazing treatments.
Further analysis and recommendations:
•
Albedo should be measured in further analysis to investigate the amount of
absorbed incoming radiation.
•
Latent heat flux should be calculated separately between evaporation and
transpiration.
•
Grazing treatments should have clear effects on surface energy budget if there
were significantly different green biomass amount between the treatments.
Testing would be recommended.
•
Leaf area index, vegetation greenness map using NDVI and remote sensing
technology would be helpful to study the surface energy budgets.
•
Species composition and vegetation cover, soil characteristics should be studied
to see clear treatment effects on SGS.
Acknowledgements: I would like to thank all of the people who contributed and
supported me to complete the SGS energy budget study and especially Jack Morgan and
David Smith for providing me with the data.
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Literature cited
Ahrens C.D. 2003 Meteorology Today: An Introduction to Weather, Climate, and the
Environment. Seventh edition. Pacific Grove, CA. Brooks/Cole-Thompson
Learningtm. p.544.
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