Introduction
There are 304 million lakes globally and they are of significant importance
in determining local weather and climate through complex physical,
biochemical and biological interactions (Cole et al., 2001; Downing et al.,
2006; Shao et al., 2015). Because of the substantial differences in
underlying surface characteristics between lake surface and its surrounding
land surface (i.e., albedo, roughness, heat capacity; O'Donnell et al.,
2010), the carbon and energy exchange processes over lakes are expected to respond different way
to climate change. Lakes react rapidly
to a change in the atmospheric parameters and are able to modify the
surrounding atmospheric circulation (Marie-Noëlle et al., 2012). Plenty
of studies on water–atmosphere carbon and energy exchange processes have been
reported over high-latitude water bodies (Nordbo et al., 2011; Huotari et
al., 2011; Mammarella et al., 2015). However, the characteristics of
water–atmosphere exchange processes differ for lake size, water depth, regional
climate and geographical location (Liu et al., 2009). High-altitude lakes
are exposed to more extreme meteorological conditions and are more sensitive to
variations in meteorological forcing (Rueda et al., 2007). Shallow lakes
respond more quickly to changes in the atmospheric forcing due to a smaller
heat capacity (Liu et al., 2012; Zhang and Liu, 2013). Understanding the
turbulent exchange processes between the lake surface and atmosphere and the
response to atmospheric properties is essential for improving numerical
weather prediction and climate models (Dutra et al., 2010; Nordbo et al.,
2011).
The change in atmospheric properties over a water surface can cause large
fluctuations in atmospheric forcing for lake–atmosphere interactions, and
subsequently affects the turbulent exchange processes (Lenters et al., 2005;
Liu et al., 2011; Huotari et al., 2011; Z. Li et al., 2015). The southeasterly
wind with warm moist air masses reduced and inverted the vertical temperature
difference between water surface and atmosphere to be negative over a large
high-latitude saline lake (Qinghai Lake) on the northeastern Qinghai–Tibetan
Plateau (QTP) in China (Li et al., 2016). The cold fronts and the
meteorological properties of the air masses behind cold fronts (e.g., windy,
cold and dry) significantly promoted turbulent exchange of sensible heat
(Hs) and latent heat (LE) through enhanced turbulent mixing
(thermally and mechanically), whereas southerly winds with warm and humid air
masses generally suppressed turbulent exchanges of Hs and LE over
a mid-latitude large reservoir in Mississippi (Liu et al., 2009, 2012). In
response to the changes in the weather conditions, the heat balance over a
large tropical reservoir in Brazil is substantially altered, and the heat
loss can be twice or 3-fold greater during cold-front days than that
during the non-cold-front days (Curtarelli et al., 2014). Consistent diurnal
peaks in LE flux during the afternoon were observed as a result of
strong, dry winds coinciding with peak water surface temperatures over a small
subtropical reservoir in Australia (McGloin et al., 2015). An increasing
sensible heat flux over the lake retarded the cooling of lower atmosphere
(below 500 m) and weakened the vertical potential temperature gradient over
the lake, while increasing wind speed and vertical wind shear further
facilitated the buoyancy flux to exert a higher heat convection efficiency when
cold air arrived over Ngoring Lake in the Tibetan Plateau (TP; Li et al.,
2017).
The CO2 emissions from lakes are traditionally measured by
non-continuous or indirect methods, e.g., floating chamber (Riera et al.,
1999) and boundary layer transfer techniques (Cole and Caraco, 1998). The
uncertainty in the floating chamber method is that the flux it measures only
represents a very small area and it produces biases because the disturbances
in the water–air surface (Vachon et al., 2010). The boundary layer method
estimates CO2 flux by the difference in CO2 concentration
between the water and atmosphere and the gas transfer velocity, which is
traditionally parameterized only by wind speed (Cole and Caraco, 1998).
However, it has been reported that different processes including convection,
microwave breaking and stratification could influence the gas transfer
velocity (Zappa et al., 2001; Eugster et al., 2003; Podgrajsek et al., 2014).
The eddy covariance technique (EC) could provide long-term continuous
measurements and the high-resolution data allows for examining the relation
between gas exchange velocity and other meteorological variables besides wind
speed (Mammarella et al., 2015). The changes in atmospheric properties could
affect the lake–air CO2 flux. Huotari et al. (2011) reported that the
CO2 efflux was enhanced under persistent extratropical cyclone
activities over high-latitude water bodies. The synoptic weather events
associating with extratropical cyclones produced larger CO2
effluxes by bringing the bottom rich CO2 water to the surface
through upwelling, internal wave-induced mixing and mixing by convection
(Liu et al., 2016). The windy and stormy days increased 16 % of the annual
CO2 effluxes over Ross Barnett reservoir in central Mississippi,
USA (Liu et al., 2016). A 15-year long study found that the
amount of precipitation had a large effect on dissolved organic carbon (DOC)
concentrations in rivers (Pumpanen et al., 2014). The waterside convection
was believed to cause the higher CO2 fluxes during night compared
to day (Podgrajsek et al., 2014).
Erhai Lake is a subtropical highland shallow lake on the southeast margin of
TP, which is influenced by both the South Asian and East Asian
summer monsoons. The summer monsoon induces an abrupt change in large-scale
atmospheric circulation and convective activity over Asia, and carries in air
mass with distinct atmospheric properties (i.e., air temperature, wind
direction, relative humidity; Li and Yanai, 1996; Lau and Yang, 1997; Zhou
et al., 2012). The seasonal reversals of atmospheric properties caused by
summer monsoon circulation play an important role in regulating
land–atmosphere heat and water exchange processes (Flohn, 1957; Hsu et al.,
1999). The land–atmosphere exchange processes are found to be closely related to the
onset and retreat of summer monsoon (Zhang et al., 2012). It is reported that
most of the available energy was transformed into Hs before the
arrival of monsoonal winds, whereas LE increased and exceeded Hs
after the onset of monsoonal wind (Xu et al., 2009; Mauder et al., 2007; Li
et al., 2016). Considering the substantial difference in atmospheric
properties during the monsoon and non-monsoon periods, the water–atmosphere
carbon and energy exchange processes are expected to display large
differences. However, few studies have reported the variation in heat and
carbon fluxes over lakes during different monsoonal periods, and the effect
of the monsoon on water–atmosphere heat and carbon is not clear.
Four years of continuous EC measurements from 2012 to 2015 have been
obtained over Erhai Lake. The summer monsoon generally bursts in May and
retreats in October. According to the activity of summer monsoon, three
monsoon periods are defined, including pre-monsoon (March–April), monsoon
(May–October) and post-monsoon (November–December) periods. We hypothesize
that the contrasting atmospheric properties during these three different monsoon
periods play an important role in modulating the turbulent exchange processes
over Erhai Lake. The objectives of this study are to investigate the energy
and CO2 exchange processes and their response to changes in
atmospheric properties during different monsoon periods.
Observation site and data process
Site description
Erhai Lake (25∘46′ N, 100∘10′ E) is located on
the southeast margin of TP, southwest China, (Fig. 1). The
altitude of the region is about 1972 m. The lake has a length of 42.6 km
from south to north, and a width ranging from 3.1 to 8.8 km from east to
west, with a total area of 256.5 km2. The water depth of the lake
varies from 10 to 20.7 m. The water depth around the tower is about 10 m.
The land surface of its surrounding area mainly consists of cropland and
towns. Because of the subtropical climate, no ice period occurs throughout the
whole year. More than 100 rivers and streams drain into the lake, with only
one outlet in the southwest (Xier River). The water level is artificially
regulated, ranging between 1971.1 and 1974.1 m. Due to the full mixing of
the water, only a short stratification period occurs in the middle of
the year (Feng et al., 2015).
(a) The location of Erhai Lake (Google) and the eddy covariance
measurement system (the red star denotes the flux tower); (b) Average
footprint source over Erhai Lake flux tower during the three different monsoon
periods (pre-monsoon period, monsoon period and post-monsoon period) from
2012 to 2015. The maximum radius of contour lines shows the source area
contributing to 95 % of flux.
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Erhai Lake has a subtropical highland monsoon climate, characterized by a
distinct wet and dry season. During the monsoon period (May–October), the area
is mainly controlled by both the southwest flow from tropical depression in
the Bay of Bengal and southeast flow from the subtropical Pacific high. The moist
marine air mass brings in abundant water vapor and intensive precipitation.
During the non-monsoon period (November–April), as a result of the southward
movement of the westerlies, the area is dominated by continental air mass
mainly from desert and arid area of Arabian countries, and characterized by a
warm and dry season. The average annual precipitation from 1981 to 2010 is
1055 mm. The majority of precipitation is concentrated in the monsoon period, with
an average of 895 mm. The average precipitation is only 64 mm
during the pre-monsoon period, and 42 mm during the post-monsoon period.
Observation
The EC instrument is mounted on a concrete platform at a height
of 2.5 m (Fig. 1). The turbulent fluxes (Hs and LE) and
CO2 flux are simultaneously measured with an ultrasonic anemometer
(CSAT3, Campbell Scientific, Logan, UT, USA) and an open-path infrared gas
analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA). The three components of
wind and virtual air temperature are measured with an ultrasonic anemometer.
The H2O and CO2 concentrations are acquired by an
infrared gas analyzer. The EC sensors are mounted on a pipe orienting to the
prevailing wind direction (southeast), which is shown in Fig. 2. A CR500 data
logger (CR500, Campbell Scientific) is applied to record the measurements
with a 10 Hz sampling frequency. Water temperature at eight depths (0.05,
0.2, 0.5, 1, 2, 4, 6 and 8 m below water surface) are measured with
temperature probes (model 109-L, Campbell Scientific, inc., USA) to
obtain the water temperature profile, which are tied to a buoy and can change
with the water level. The water surface temperature
(Ts) is calculated from longwave
radiation. Moreover, different micro-meteorological elements are also
measured at a height of 1.5 m above the platform. Air temperature and
relative humidity are also measured (HMP45C, Vaisala, Vantaa, Finland). The
radiation balance components, including upward and downward shortwave
radiation as well as upward and downward longwave radiation, are
measured with CNR1 (CNR1, Kipp & Zonen B.V., Delft, the
Netherlands). Meanwhile, the photosynthetic active radiation (PAR) is also
measured with an LI-190SB quantum sensor (Campbell Scientific inc., USA). The wind speed and wind direction is measured with a cup anemometer
(034B, Met One Instruments Inc., Grants Pass, OR, USA). The Dali National
Climatic Observatory, with a distance of 15 km from the flux tower, has
provided the precipitation data.
The wind rose of Erhai Lake during daytime (when downward shortwave
radiation is greater than 20 W m-2) and nighttime (when downward shortwave
radiation is less than 20 W m-2) from 2012 to 2015.
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Data processing
The raw data are checked and spikes as a result of physical
noise and instrument malfunction are discarded according to the procedures suggested by
Vickers and Mahrt (1997). The data values measured with the AGC (active gain
control) that are more than 40, which is recorded by the LI-7500, are also
filtered. The abnormal data points with a magnitude exceeding 3.5 times the average
standard deviations are also needed to be removed. The collected raw 10 Hz data are
processed with EddyPro software, version 4.2 (LI-COR Inc., 2013, USA). The double rotation method is applied to adjust the coordinate
system and tilt the vertical wind speed to be zero (Kaimal and Finnigan,
1994). The 30 min average turbulent fluxes are calculated with the block average
method. The time lags between anemometric variables and gas analyzer
measurements are compensated by the circular correlation procedure, which
determines the time lag that maximizes the covariance of two variables,
within a window of plausible time lags (Fan et al., 1990). Density
corrections for LE and CO2 flux are also applied with
the Webb–Pearman–Leuning (WPL) correction procedure (Webb et al., 1980). We
evaluate the uncertainty in the WPL correction on CO2 flux based on the
raw data from October of 2015. The daily average CO2 flux with and
without WPL correction is 0.91±1.95 and -0.25±2.69 g C m-2 d-1, respectively, indicating the large effect of
WPL correction. The high-pass filtering effect is also corrected to
compensate the flux losses at a high frequency (Moncrieff et al., 2004).
Quality checks for stability and integral turbulent characteristic tests
are applied to remove the low-quality fluxes (Foken et al., 2004). Data
quality is marked following the schemes of Mauder and Foken (2006), and the
high and moderate quality data are retained. After data quality control, the available
data for LE, Hs and CO2 flux account for 54 %, 66 %
and 55 %, respectively. A 3-month data gap from September to November
occurred in 2014 due to instrument failure. More detailed information about
measurements and post-processing procedures can be found in our previous
study (Liu et al., 2015).
The drag coefficient (the momentum bulk transfer coefficient, CD),
Dalton number (the heat bulk transfer coefficient, CH) and Stanton
number (the moisture bulk transfer coefficient, CE) are determined by
the bulk transfer relations, which are widely used for computing ocean–air
fluxes in numerical models (Fairall et al., 2003):
τ=ρaCDU2,Hs=ρaCaCHUTs-Ta,LE=ρaLVCEUqs-qa,
where τ is momentum flux (N m-2), ρa is air density
(kg m-3), U is wind speed, Ca is the specific heat of air
(1005 J kg-1 K-1), Ts is water surface temperature
(∘C), Ta is air temperature (∘C), LV is
latent heat of vaporization (J kg-1), qs is specific
humidity at saturation (kg kg-1) and qa is specific humidity
(kg kg-1).
The heat storage (ΔQ) in the lake is also calculated:
ΔQ=ρwcpΔTs‾Δtz,
where Ts is water temperature (∘C), ρw is
the density of water (kg m-3), Cp is the specific heat of water at
constant pressure (4192 J kg-1 K-1), ΔT‾sΔt is the depth-weighted time derivative of the water
column temperature (K s-1), and z is maximum depth of measured water
temperature profile (m). The ΔQ is defined as positive when it is
absorbed by the lake surface (heat is stored by the lake).
Because the flux site is close to the west bank of the lake, a footprint
model (Kormann and Meixner, 2001) is applied to analyze the distribution of
source area contributing to the flux. The 95 % of source area contributing
to flux ranges from 600 m in the southeast direction and 400 m in the west
direction during different periods (Fig. 1). Because the flux in the west
direction mainly originates from the land surface, the flux in the wind
direction (225 to 315∘) is excluded. The flux during the pre-monsoon period
is more affected by land surface compared to the other two
periods. Nearly 80 % flux originates from the lake surface during monsoon
and post-monsoon periods. Approximately 22 %, 15 % and 8 % of flux data
are filtered based on the footprint analysis.
The data recorded during rainfall is also discarded. According to the quality
control procedure presented above, around 34 % of the Hs,
46 % of LE and 45 % of CO2 fluxes are removed and the retained
data are analyzed in our study. Although the gap ratio is large, it is similar
to other studies over lakes (Nordbo et al., 2011; Goldbach and Kuttler, 2015;
Shao et al., 2015). A long, large gap between August and November exists in
2014 due to a malfunction. The times given in this study are in Beijing Time
(UTC+8).
Results and discussion
Atmospheric properties during different monsoon periods
The atmospheric properties show large differences during different monsoon
periods (Fig. 3; Table 1). There is a similar diurnal course for air
temperature (Ta) during
different monsoon periods, but with a large difference for the magnitudes.
The diurnal mean Ta is the largest during the monsoon period, second
largest during the pre-monsoon period and smallest during the post-monsoon period. The
difference for diurnal mean Ta between the monsoon and
pre-monsoon periods is smaller (3.4 ∘C) than that between the monsoon
and post-monsoon periods (8.1 ∘C). There is a large difference for diurnal
mean water surface temperature (Ts) between the monsoon period and
the other two periods (around 6∘C), but a small difference between
pre-monsoon and post-monsoon periods (around 0.2 ∘C). The difference
between water surface and air temperature (ΔT) remains negative
during most of the pre-monsoon period but positive during the post-monsoon period,
with an average values of -1.9 and 2.7 ∘C, respectively. The ΔT has a maximum around 08:00 and minimum around 18:00 (all times in this study are according to Beijing Time, UTC+8), which is opposite
with the diurnal pattern of Ta and Ts.
The average diurnal pattern of air temperature (Ta),
water surface temperature (Ts), difference between water surface
temperature and air temperature (ΔT), water–air vapor pressure
deficit (Δe) and wind speed (U) during pre-monsoon, monsoon and
post-monsoon periods from 2012 to 2015.
![]()
Daily average air temperature (Ta, ∘C), water surface
temperature (Ts, ∘C), difference between water surface temperature
and air temperature (ΔT, ∘C), vapor pressure difference (e, kPa) between
water surface (es, kPa) and the air (ea, kPa), wind speed
(U, m s-1), the downward and upward shortwave radiation flux (Rs_down and
Rs_up, W m-2), the downward and upward long wave radiation flux (Rl_down
and Rl_up, W m-2), and albedo during pre-monsoon, monsoon and post-monsoon
periods from 2012 to 2015.
Period
Year
Ta
Ts
T
e
U
Rs_down
Rs_up
Rl_down
Rl_up
Albedo
pre-monsoon
2012
16.0
14.2
-2.4
1.07
3.2
202
13.4
320
385
0.07
2013
16.9
15.4
-1.6
1.2
2.9
231
14.8
314
390
0.07
2014
17.1
15.0
-2.1
1.21
2.9
239
14.4
310
389
0.06
2015
17.0
15.3
-1.7
0.94
3.1
228
15.1
317
390
0.07
Average
16.8
15.0
-1.9
1.1
3.0
225
14.4
315
389
0.07
monsoon
2012
19.8
20.9
1.1
0.75
2.8
202
12.6
366
421
0.06
2013
19.5
20.9
1.4
0.74
2.7
202
12.4
368
421
0.06
2014
21.1
21.2
0.1
0.94
2.8
224
11.9
374
424
0.06
2015
20.2
21.1
1.2
0.98
3.2
212
13.5
366
422
0.07
Average
20.2
21.0
1.0
0.85
2.9
210
12.6
369
422
0.06
post-monsoon
2012
13.0
15.2
2.1
0.82
3.0
179
22.1
282
383
0.13
2013
12.1
14.8
3.1
0.65
2.3
174
19.6
280
381
0.11
2014
10.9
14.4
2.7
0.79
2.5
153
21.0
278
371
0.09
2015
12.1
14.7
2.7
0.9
2.8
154
18.7
294
382
0.12
Average
12.0
14.8
2.7
0.79
2.7
165
20.3
284
379
0.11
The water–air vapor pressure difference (Δe) has an opposite diurnal
pattern with ΔT, which has a maximum around 18:00 and the minimum
around 08:00. Overall, Δe is relatively high during the pre-monsoon period and low
during the post-monsoon period, with average values of 1.10 and
0.79 kPa, respectively. There is a larger difference for Δe between
pre-monsoon period and monsoon period during 2012 and 2013, but a larger
difference between post-monsoon period and monsoon period during 2014 and
2015, attributed to the annual variation in timing distribution of
precipitation. The wind speed (U) has a larger difference in daytime than
nighttime during different periods. The diurnal mean U is slightly higher
during the pre-monsoon period than the other two periods. In general, the
pre-monsoon period is characterized by higher U and e, while the post-monsoon
period is characterized by a lower U and Ta. The Ta
and Ts are the highest during the monsoon period. There is a large
difference for average Ts (around 6 ∘C) between monsoon
period and the other two periods, but a slight difference between pre-monsoon
and post-monsoon periods. The diurnal T remains negative during the pre-monsoon period
but positive during the post-monsoon period. A weak diurnal variation in
T is observed during the monsoon period.
The wind direction over Erhai Lake displays a typical diurnal pattern during
the three study periods (Fig. 4). Overall, the southeasterly wind and westerly wind
are dominant in daytime and nighttime, respectively, which represents the lake
breeze and land breeze. Generally, the wind direction shifts from west to
southeast in the morning (around 09:00), indicating the onset of lake breeze.
The lake breeze lasts until the afternoon. Then the wind direction shifts
abruptly from southeast to west around 17:00, which indicates that the lake
breeze transforms into land breeze. The numerical simulation of local
circulation over Erhai Lake also proved the development of lake breeze
circulation during daytime and land breeze circulation during nighttime (Xu
et al., 2018). The wind direction switches at the time of ΔT reaching
a maximum or minimum. The alternation between lake breeze and land breeze
is more obvious during pre-monsoon and post-monsoon periods, attributed to a
larger thermal difference between land and lake surface, compared to monsoon
period. Because the region is mainly dominated by a westerly belt during
non-monsoon season, a strong westerly wind is observed during nighttime during
pre-monsoon and post-monsoon periods. During the monsoon period, due to the
northward motion of a westerly belt, the westerly wind becomes weak during nighttime,
while the southwesterly and southeasterly flow, respectively, from tropical depression
in the Bay of Bengal and subtropical Pacific high, dominate the Dali region.
However, Cang Mountain, which is located close to the western
side of Erhai Lake, has obstructed the passing of southwesterly wind and makes
the southeasterly wind the prevailing wind over Erhai Lake during this period.
Because of the continuous southeasterly wind during nighttime, a weak circulation of
lake to land breeze occurs during the monsoon period.
The average diurnal pattern of frequency distribution of wind
direction during pre-monsoon, monsoon and post-monsoon periods from 2012 to
2015.
![]()
The characteristics of air masses from different wind directions are examined by
the bin-averaged wind speed (U), air temperature and relative humidity
against wind directions (Fig. 5). The U shows a large variability against
wind direction. The wind speed from the southeast is the highest than that from
other directions, which indicates that the lake breeze is stronger than land
breeze. The southeasterly wind is stronger during the monsoon period, whereas the
land breeze, which is from the west direction, is stronger during the pre-monsoon period.
The bin-averaged wind speed (U), air temperature (Ta)
and relative humidity (RH) along with wind direction (WD) during pre-monsoon,
monsoon and post-monsoon periods from 2012 to 2015. Every 22.5∘ was
bin averaged.
![]()
As a result of the control of the maritime atmospheric mass, the air mass
during the monsoon period is the warmest and wettest, which has a higher
Ta and relative humidity (RH) than that during the other two
periods. The characteristics of air masses also show variability in different
wind directions. The air mass from the southeast direction is warmer than
that from the west direction during both monsoon and post-monsoon periods,
while it is opposite during the pre-monsoon period. The land surface is warmed
faster during the early period and cooled faster during the latter period of
the whole year compared to the lake surface, which results in a warmer air
mass from the land surface during the pre-monsoon period, while a colder air mass during
the other two periods. The difference in RH between pre-monsoon and
post-monsoon periods is small in the southeast direction but large in the west
direction. The RH of air mass from west direction is higher during the post-monsoon period
than pre-monsoon period, which is attributed to the
intensive precipitation during the monsoon period.
The atmospheric stratification and bulk transfer coefficients for Erhai Lake
during different monsoon periods are also analyzed, as they are fundamental
parameters for computing sensible and latent heat fluxes between water
surface and air in numeric models (Fairall et al., 2003). The atmospheric
surface layer is mainly near neutral stratification during the three study
periods (Fig. 6). As Erhai Lake is located in a subtropical highland area,
the seasonal uniformly air temperature and the abundance of cloud have
contributed to the occurrence of neutral stratification. During the pre-monsoon period, the near neutral stratification accounts for as much as 85 % in
daytime and 92 % during nighttime, respectively. Compared to pre-monsoon
period, the near neutral stratification declined about 20 % in daytime and
10 % during nighttime during the other two periods, as a result of the increase
in weakly unstable and unstable stratification. The weakly unstable
stratification accounts for about 12 % during the monsoon period and
post-monsoon period, but only 3 % during the pre-monsoon period. Most of
the unstable stratification occurs during monsoon and post-monsoon periods,
and the percentage is much higher in daytime (20 %) than nighttime (about
5 %), while it is scarcely observed during the pre-monsoon period. On the
contrary, the stable stratification is hardly observed during the post-monsoon period.
The difference for the atmospheric stability during the three periods is
primarily caused by the variation in ΔT, which can be roughly used as
an indicator of atmosphere stability (Derecki, 1981; Croley, 1989). An
unstable stratification typically associates with a positive ΔT
(Ts>Ta). On diurnal scales, the Ts is
higher than Ta most of the time during the post-monsoon
period, while it is opposite during the pre-monsoon period (Fig. 3), which results in the
occurrence of unstable stratification during the post-monsoon period and stable
stratification during the pre-monsoon period, respectively. The stable
stratification was also observed in the spring and summer in high-latitude
lakes since the Ts increases much more slowly than the overlying
Ta (Oswald and Rouse, 2004). While in the fall and winter, the
air temperature decreases faster than the water surface, resulting in a
positive ΔT.
Frequency percent of stability classes in day (when the shortwave
radiation is >20 W m-2) and night (when the shortwave
radiation is ≤20 W m-2) during pre-monsoon, monsoon and
post-monsoon periods from 2012 to 2015. The stable classes are defined as
atmosphere stability (ζ=z/L, where z is the measurement height and L is
the Monin-Obukhov length): stable (ζ>0.1), weakly stable
(0.05<ζ<0.1), near neutral (-0.05<ζ<0.05), weakly unstable (-0.1<ζ<0.05) and unstable (ζ<-0.1).
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The relationship between bin (bin width 1 m s-1) averaged drag
coefficient (the momentum bulk transfer coefficient, CD), Dalton number
(the heat bulk transfer coefficient, CH), Stanton number (the
moisture bulk transfer coefficient, CE) and wind speed (U) during
pre-monsoon, monsoon and post-monsoon periods for the whole study period. The
error bars show the ±1 standard deviation of the average value.
![]()
The relationship between wind speed and bulk transfer coefficients during
different monsoon periods is shown in Fig. 7. The drag coefficient decreased
rapid with increasing wind speed when wind speed is lower than 8 m s-1.
When wind speed increased, the Stanton number first decreased and then
gradually increased. The Dalton number changed rapidly only under a very low
or high wind speed, and remained constant at most times. The negative
relationship between bulk transfer coefficients and wind speed under lower
wind speed is also found in other lake studies (Yusup and Liu, 2016; Xiao et
al., 2013; Verburg and Antenucci, 2010).
Although there is no obvious
tendency for the bulk transfer coefficients to increase with wind speed,
it is not contradictory to the bulk parameterization
scheme in COARE (Fairall et al., 2003), as the wind speed over Erhai Lake
corresponds to the transitional range (<10 m s-1). The observation
also indicates that a larger bias may be caused under weak wind conditions
when simulating water–air fluxes for shallow water regimes (Xiao et al.,
2013). The CD values during pre-monsoon and post-monsoon are close to each other
and both are larger than that during the monsoon period. There is a relatively larger
CH and lower CE during the pre-monsoon period compared to the other two
periods. The CD is larger than CH and CE during all periods,
which is consistent with another lake study (Nordbo et al., 2011).
Diurnal pattern of energy balance components and <inline-formula><mml:math id="M188" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> flux
during different monsoon periods
The diurnal pattern of net radiation (Rn) is larger during the pre-monsoon period, and lower during the post-monsoon period (Fig. 8). The difference for maximum diurnal Rn is around
60 W m-2 between pre-monsoon period and monsoon period, and around
76 W m-2 between the monsoon period and post-monsoon period, respectively.
The diurnal pattern of sensible heat flux (Hs) is consistent with
T, which reaches a maximum in the morning and the minimum in the afternoon.
The diurnal Hs is largest during the post-monsoon period, with a
maximum value of 28 W m-2 in 2013. The fluctuation of diurnal
Hs is very small during the monsoon period, with a difference between
a maximum and the minimum of about 10 W m-2, attributed to the weak
diurnal variation in T. The diurnal Hs remains positive during
monsoon and post-monsoon periods. The diurnal Hs has a greater
amplitude during the pre-monsoon period, with its magnitudes ranging from -24
to 5 W m-2. The diurnal Hs during the pre-monsoon period
remains negative for most times of the day, and changes to be positive for
a short time in the morning.
The average diurnal patterns of energy balance components (the net
radiation flux, Rn; the sensible heat flux, Hs; the
latent heat flux, LE; and the storage heat flux in the lake, ΔQ),
Bowen ratio (Bowen) and CO2 flux (Fc) during pre-monsoon, monsoon
and post-monsoon periods from 2012 to 2015.
![]()
The latent heat flux (LE) has an opposite diurnal pattern with
Hs, which reaches a maximum in the afternoon and the minimum in
the morning. The maximum diurnal LE values during the three study periods are close to
each other, with a value around 130 W m-2. Diurnal LE remains at a
relatively high level for most times of the day during the monsoon period
compared to the other two periods. The difference in LE between the three periods
is more evident during nighttime than daytime. The larger LE during the monsoon period than the other two periods is not consistent with the variation in e,
which is larger during the pre-monsoon period, indicating a weak relation between
e and LE. Since there is a higher Hs and lower LE during the post-monsoon period, the Bowen ratio is higher during this period than the
other two periods, with an average value of 0.16. There is a relatively small
difference in Bowen ratio between post-monsoon and monsoon periods during nighttime, and between pre-monsoon period and monsoon period in daytime.
The diurnal pattern of storage heat in the lake (Q) is similar with
Rn, with a maximum value occurring at noon. The diurnal Q
remains negative during nighttime and changes to be positive after sunrise,
indicating the heat is released to the atmosphere during nighttime and absorbed
to the lake in daytime. The diurnal mean Q values during pre-monsoon, monsoon and
post-monsoon periods are 28.9, 5.2 and -14.5 W m-2, respectively. The
lake absorbs more heat flux during daytime and releases less during nighttime during the pre-monsoon period in most years compared to the other two periods.
The diurnal pattern of LE and Hs are not in the same phase with
Rn, which was also reported in high-latitude and midlatitude water
bodies, and they were more closely related with other meteorological
variables (i.e., e, T, U; Assouline et al., 2008; Nordbo et al.,
2011). There is a smaller
difference for diurnal LE during the three different
periods, whereas there is a larger difference for diurnal Hs,
indicating a larger effect of monsoon on heat exchange processes over Erhai Lake.
The LE over Erhai Lake has a large diurnal variation compared to the
midlatitude reservoir (Liu et al., 2012).
Two peaks are observed for diurnal variation in CO2 fluxes during
the whole study period, one occurs in the early morning and the other one in
the evening. The CO2 fluxes could switch to be negative around
noon time during monsoon and post-monsoon periods, indicating the
CO2 flux uptake in the middle of the day during these two periods.
The observed maximum diurnal average CO2 flux was -0.53±1.66 µmol m-2 s-1 during the monsoon period (2014) and -1.62±1.52 µmol m-2 s-1 during the post-monsoon period (2013),
respectively. The CO2 flux uptake is believed to be caused by the
phytoplankton due to the eutrophication of Erhai Lake. It has been reported
that the shallow lake is more affected by rich phytoplankton (Huotari et
al., 2011; Shao et al., 2015). However, the CO2 uptake is weaker
during daytime during the pre-monsoon period compared to other periods. The seasonal
fluctuation of phytoplankton in Erhai Lake has been reported by some
researchers. Yu et al. (2014) observed that the concentration of Chl a and
phytoplankton in Erhai Lake were higher in mid-summer and fall and decreased from winter until April.
Daily and Monthly average of turbulent fluxes during different
monsoon periods
The daily average Hs during pre-monsoon is lower than that during
other two periods (Fig. 9). The average daily Hs during the post-monsoon period has a larger annual variation compared to other two
periods. The difference in daily average Hs among the 4 years
could be as large as 11 W m-2 during the post-monsoon period. The LE has a
small variation among different years compared to Hs. The daily
average LE is larger during the monsoon period, ranging from 102.5±39.2 to
114.8±29.6 W m-2. The ΔQ is observed to have a larger
annual variation. The daily average ΔQ remains positive during the pre-monsoon period and negative during the post-monsoon period.
The daily average
heat absorption during the pre-monsoon period is 29.8±13.4 W m-2 and
heat emission during the post-monsoon period is -13.8±7.8 W m-2.
Although the CO2 uptake is observed during midday, the daily
average CO2 fluxes remain positive during different periods. The
daily average CO2 flux is larger during the pre-monsoon period compared
to other two periods. The daily average CO2 flux is 0.55±0.23
and 0.19±0.08 g C m-2 d-1 during pre-monsoon and
post-monsoon periods, respectively.
Box plot of daily average sensible heat flux (Hs),
latent heat flux (LE), the storage heat flux in the lake (ΔQ) and
CO2 flux (Fc) during pre-monsoon, monsoon and post-monsoon periods
from 2012 to 2015. The upper and lower limits of the box represent the 75
and 25 percentiles; the horizontal line in each box represent the 1.5
inter-quartile range of the upper and lower quartile; the band inside the box
is the median; the squares inside the box represent the average value; the
cross-hatches represent the 1 and 99 percentiles; the end
whiskers represent maximum and minimum values.
![]()
The monthly average energy fluxes (the storage heat flux in the
lake, ΔQ; the net radiation flux, Rn; the latent heat
flux, LE; and the sensible heat flux, Hs) during pre-monsoon,
monsoon and post-monsoon periods from 2012 to 2015.
![]()
The monthly average energy balance components (Rn, ΔQ,
LE and Hs) show clear variations during the three different periods
from 2012 to 2015 (Fig. 10). Rn gradually increases from
pre-monsoon period to the early monsoon period and then decreases until
the post-monsoon period. The monthly average Rn during the post-monsoon period is lower than 40 % of the other two periods. LE remains at a higher
level during the three periods, with an average monthly value of 89.1, 103.9 and
82.2 W m-2 during pre-monsoon, monsoon and post-monsoon periods,
respectively. The average monthly LE/Rn ratio has a smaller annual
variation during the pre-monsoon period, which ranges from 0.50 to 0.82 for
4-year study period. LE/Rn ratio has a larger fluctuation during the monsoon period, which varies from 0.53 to 1 from 2012 to 2015. As a result of
the decrease in Rn, LE/Rn ratio exceeds 1 rapidly during the post-monsoon period,
with an average value of 1.68. LE plays a dominant
role in energy partitioning of the lake, while Hs dominated the
major proportion of Rn in the terrestrial land surface (Roth et
al., 2016). The monthly average Hs remains negative during the pre-monsoon period and the early monsoon period, and switches to be positive
in the middle monsoon period. The magnitude of monthly average Hs
remains at a very low level, with a value of less than 14 W m-2 during
the three periods. There is a negative monthly average
Hs/Rn ratio during pre-monsoon due to the negative monthly
Hs, indicating the Rn is consumed by heating the
water body. During the monsoon period, the Hs/Rn ratio is
still very low with an average value of 0.06, and the Rn is
primarily used for evaporation. Correspondingly, a positive monthly average
ΔQ is observed during pre-monsoon and early monsoon period, and a
negative monthly average ΔQ during the other periods, indicating that the
lake absorbs heat at first and releases it subsequently. The average monthly
ΔQ values during the pre-monsoon and post-monsoon periods are 28 and
-14 W m-2, respectively. The Hs/Rn ratio increases
to 0.20 and (Hs+LE)/Rn ratio reaches as high as 1.72 during the post-monsoon period. The excessive portion of the energy released from the
heat storage in the lake is transferred through the turbulent exchanges,
which is similar with other lakes (Rouse et al., 2005). A lower
Hs/Rn ratio with an annual average of 0.16 and a higher
LE/Rn ratio with an annual average of 0.81 were also observed in a
midlatitude reservoir (Liu et al., 2012). However, other lakes have been
reported to have a positive Hs on a monthly timescale over a whole year
(Liu et al., 2012; M. Li et al., 2015).
The difference between them and Erhai Lake is likely to be attributed to the positive T and unstable
stratification throughout the year in this midlatitude reservoir, whereas a
negative T during the pre-monsoon period and the major portion of near
neutral stratification over Erhai Lake.
Main drivers for <inline-formula><mml:math id="M307" display="inline"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> during different monsoon periods
The correlation coefficients between Hs and meteorological
variables (ΔT, U, Ta, Ts, Rn
and rain) on half-hourly, daily and monthly timescales are investigated during
the three different periods (Table 2). On half-hourly scale, the product of U
and ΔT (U⋅ΔT) is the main controlling factor
Hs, which could explain 44 % and 30 % variance in
Hs during monsoon and post-monsoon periods, respectively. However,
during the pre-monsoon period, both ΔT and the product of U and ΔT are found to have a major effect on half-hourly Hs. A close
relationship between half-hourly ΔT and Hs is also
observed during the monsoon period, with a Pearson correlation coefficient
between them of 0.56. On daily timescales, the ΔT is found to be most
closely related with Hs during all three different periods, with
a correlation coefficient ranging from 0.55 during the post-monsoon period to
0.78 during the monsoon period. Besides, Rn is also one of the major
drivers of Hs during the monsoon period; as the rain was mainly falling
during the monsoon period, the role of Rn on Hs becomes
noticeable. The product of U and ΔT is observed to have a main
effect on Hs only during the pre-monsoon period. The Ta
is also responsible for the variation in daily Hs during
pre-monsoon and monsoon periods, which explain about 30 % variance in daily
Hs. The factors controlling Hs during the monsoon period
are similar with those during the post-monsoon period on a monthly timescale. The
ΔT remains as the most significant factor controlling monthly
Hs, which could explain 85 % and 89 % variance in
Hs during the monsoon and post-monsoon periods, respectively.
The product of U and ΔT also shows close relationship with monthly
Hs during monsoon and post-monsoon periods. The similar
relationship between meteorological variables and monthly Hs is
not observed during the pre-monsoon period. Only the rain and Rn are
found to be closely correlated with monthly Hs during the pre-monsoon period.
The correlation between rain and Rn is also observed
during the monsoon period, indicating the large effect of Rn during
these two periods.
The Pearson correlation coefficients between 30Min, Daily and
Monthly sensible heat flux (Hs) and difference between water
surface and air temperature (ΔT), wind speed (U), the product of wind
speed and difference between water surface and air temperature (U⋅ΔT), air temperature (Ta), water surface temperature
(Ts), net radiation (Rn), and rain, during
pre-monsoon, monsoon and post-monsoon periods for the whole study period
(2012 to 2015). The significant levels of 0.01 and 0.05 are marked with
∗∗ and ∗, respectively. The correlation coefficients
between rain and Hs are estimated only at daily and monthly
timescales.
Variable
30Min
Daily
Monthly
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
T
0.42∗∗
0.56∗∗
0.40∗
0.61∗∗
0.78∗∗
0.55∗∗
0.28
0.92∗∗
0.95∗∗
U
-0.14∗∗
-0.02∗∗
-0.00
-0.13
-0.25∗∗
-0.17∗
-0.16
-0.59∗∗
-0.51
U⋅T
0.39∗∗
0.67∗∗
0.54∗∗
0.57∗∗
0.24∗∗
0.35∗∗
0.32
0.90∗∗
0.71
Ta
-0.38∗∗
-0.40∗∗
-0.31∗∗
-0.55∗∗
-0.53∗∗
-0.31∗∗
-0.24
-0.58∗∗
-0.61
Ts
-0.18∗∗
-0.07∗∗
-0.13∗∗
-0.20∗∗
0.04
-0.17∗
-0.01
0.26
-0.38
Rn
0.19∗∗
-0.05∗∗
0.19∗∗
-0.25∗∗
-0.55∗∗
-0.23∗∗
-0.47
-0.75∗∗
-0.35
rain
0.24∗∗
0.11∗
0.06
0.60
0.56∗∗
-0.30
In general, no significant relationship between U and Hs is
observed from half-hourly to monthly timescales during all three study periods.
The ΔT remains as the main factor controlling Hs on all
temporal scales during the three periods, and the effect of ΔT is
increased as the correlation coefficients between them rises when timescales
are extended from half-hourly to monthly. During the monsoon period, the role of
Rn on Hs becomes more important. The relationship
between meteorological factors and Hs is more complicated during the monsoon period, while there are similar main drivers for Hs
during the pre-monsoon period and post-monsoon period.
Main drivers for LE during different monsoon periods
The relationship between LE and meteorological variables during pre-monsoon,
monsoon and post-monsoon periods from half-hourly, daily and monthly timescales
from 2012 to 2015 are also analyzed (Table 3). Unlike the relationship
between U and Hs, a significant relationship between U and LE
is observed on all temporal scales during the three different periods, with a
higher correlation coefficient ranging from 0.51 to 0.80. However, the range
of the correlation coefficients between U and LE is similar on different
timescales, indicating that the effect of U does not increase as timescale
changes. The large effect of U on LE has been reported in small lakes. The
product of U and e is the second major factor controlling LE during the three
periods, especially on half-hourly and daily scales, with a correlation
coefficient ranging from 0.48 to 0.71. On monthly timescales, the significant
effect of the product of U and e on LE is only observed during the pre-monsoon period, which could explain 60 % variance in monthly LE. During the monsoon period, both Ta and Rn show a close
relationship with LE on a monthly timescale. The effect of Rn on LE has
also been reported in other studies. Because the variation in monthly
Ta is mainly determined by the magnitude of the available energy,
the close relationship between Ta and monthly LE reflects the
effect of Rn on LE during the monsoon period. During the monsoon period,
Rn is also found to be responsible for variation in LE on monthly
scale, which is similar with monthly Hs. The close relationship
between Rn and LE was also observed in a small boreal lake
(Nordbo et al., 2011; Goldbach and Kuttler, 2015).
The Pearson correlation coefficients between 30Min, Daily and
Monthly latent heat flux (LE) and vapor pressure difference (e) between water
surface (es) and the air (ea), wind speed (U), the product of wind speed and
vapor pressure difference (e) between water surface and the air (U⋅Δe), air temperature (Ta), water surface temperature
(Ts), relative humidity (RH), net radiation (Rn), and
rain, during pre-monsoon, monsoon and post-monsoon periods for the whole
study period (2012 to 2015). The significant levels of 0.01 and 0.05 are
marked with ∗∗ and ∗, respectively. The correlation
coefficients between rain and LE are estimated only at daily and monthly
timescales.
Variable
30Min
Daily
Monthly
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
e
0.29∗∗
0.22∗∗
0.41∗∗
0.21∗∗
0.11∗
0.29∗∗
0.12
0.02
-0.02
U
0.50∗∗
0.74∗∗
0.72∗∗
0.63∗∗
0.80∗∗
0.69∗∗
0.79∗
0.77∗∗
0.57
U⋅Δe
0.48∗∗
0.65∗∗
0.71∗∗
0.54∗∗
0.60∗∗
0.68∗∗
0.78∗
0.40
0.29
Ta
0.33∗∗
0.37∗∗
0.49∗∗
0.24∗∗
0.42∗∗
0.63∗∗
0.02
0.63∗∗
0.07
Ts
0.25∗∗
0.30∗∗
0.38∗∗
0.10
0.30∗∗
0.34∗∗
-0.02
0.42
0.00
RH
-0.24∗∗
-0.20∗∗
-0.49∗∗
-0.31∗∗
-0.11∗
-0.42∗∗
0.14
-0.04
-0.38
Rn
0.24∗∗
0.24∗∗
0.32∗∗
0.11
0.22∗∗
0.30∗∗
-0.09
0.50∗
-0.01
rain
-0.05
-0.08
0.09
-0.09
-0.35
0.28
Main drivers for <inline-formula><mml:math id="M518" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> flux during different monsoon
periods
The correlation coefficients between CO2 flux and meteorological
variables during the three different periods from half-hourly to monthly scales
are shown in Table 4. On half-hourly scale, there is a similar relationship
between meteorological variables and CO2 flux during pre-monsoon
and post-monsoon periods. Both Rn and photosynthetically active
radiation (PAR) are found to have a slightly higher correlation coefficient
with CO2 flux than other meteorological variables, which indicates
that the carbon exchange processes over Erhai Lake is affected by both physical
and biological processes. The U is found to have a significant relationship
with CO2 flux during the monsoon period. There is a relatively high
correlation coefficient between U and CO2 flux on all temporal
scales during the monsoon period, which increases from half-hourly (0.23) to
monthly scales (0.81). During the post-monsoon period, the U also has a large
impact on CO2 flux, mainly on longer temporal scale. The correlation
coefficient between them is also the highest on daily and monthly scales
during the post-monsoon period. The U could mediate the vertical transport of
gases by producing turbulent eddies across the air–water interface (Eugster
et al., 2003).
The Pearson correlation coefficients between 30Min, Daily and
Monthly CO2 flux and difference between water surface and air
temperature (ΔT), vapor pressure difference (e) between water surface
and the air, wind speed (U), air temperature (Ta), relative
humidity (RH), water surface temperature (Ts), photosynthetically
active radiation (PAR), net radiation (Rn), and rain, during
pre-monsoon, monsoon and post-monsoon periods for the whole study period
(2012 to 2015). The significant levels of 0.01 and 0.05 are marked with
∗∗ and ∗, respectively. The correlation coefficients
between rain and CO2 flux are estimated only at daily and monthly
timescales.
Variable
30Min
Daily
Monthly
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
pre-monsoon
monsoon
post-monsoon
T
0.04∗∗
0.12∗∗
0.08∗∗
-0.01
0.25∗∗
0.25∗∗
-0.60
0.52∗
-0.39
e
-0.05∗∗
-0.09∗∗
-0.03
0.18∗∗
-0.01
0.00
0.55
-0.39
0.47
U
-0.14∗∗
-0.23∗∗
-0.17∗∗
-0.31∗∗
-0.56∗∗
-0.34∗∗
-0.56
-0.81∗∗
0.61
Ta
-0.14∗∗
-0.18∗∗
-0.10∗∗
-0.09
-0.25∗∗
-0.12
0.06
-0.58∗∗
0.07
RH
0.01
0.08∗∗
0.02
-0.15∗
0.02
0.07
-0.68
0.31
-0.21
Ts
-0.20∗∗
-0.15∗∗
-0.09∗∗
-0.14∗
-0.08
0.01
-0.17
-0.20
-0.04
PAR
-0.23∗∗
-0.13∗∗
-0.23∗∗
0.32∗∗
0.20∗∗
0.02
0.78∗
0.27
-0.09
Rn
-0.26∗∗
-0.15∗∗
-0.28∗∗
-0.00
-0.06
0.08
0.17
-0.59∗∗
0.00
rain
-0.34∗∗
0.03
-0.14
-0.41
0.22
0.42
On monthly scale, the major drivers of CO2 flux vary greatly during
different periods. PAR is found to be the most significant driver for monthly
CO2 flux during the pre-monsoon period. During the monsoon period, the main
drivers for monthly CO2 flux (i.e., Rn, Ta
and U) are in good accordance with LE. The relationship between
Ts and CO2 flux is likely to be attributed to the fact that the
variation of Ts could influence the solubility in water (Shao
et al., 2015). The e also has a large effect on monthly CO2 flux
during both pre-monsoon and post-monsoon periods. A negative correlation
coefficient between rain and CO2 flux is observed on daily and
monthly scale during the pre-monsoon period. It has been reported that more
rain could bring more nutrients into the water body, which ultimately
promoted the photosynthesis of the phytoplankton (Shao et al., 2015).
However, a positive correlation coefficient between rain and CO2
flux is also observed on monthly scale during the post-monsoon period. The rain
could also promote CO2 emission by enhancing the transport of
carbon from land/catchment areas to the water system (lateral fluxes), which
enhanced the DOC and potentially the pCO2 in the water
(Pumpanen et al., 2014).
Overall, because the correlation coefficients between meteorological
variables and CO2 flux are comparatively lower on half-hourly
scale, large uncertainties exist for the main drivers controlling half-hourly
CO2 flux. The main drivers of CO2 flux are more
complicated on monthly scale during the three periods, indicating multiple
factors may govern CO2 exchange process, including biological and
physical processes (Vesala et al., 2006). More similar controlling factors for
CO2 flux are observed during pre-monsoon and post-monsoon periods,
compared with monsoon period, indicating the large impact of monsoon on
carbon exchange processes.
Conclusions
Erhai Lake is a subtropical shallow lake located in the key regions of
water-vapor transportation passages, which is influenced by both the South Asian
and East Asian summer monsoons. The contrasting atmospheric properties during
the monsoon and non-monsoon periods provide an excellent opportunity to examine
the effect of monsoon on turbulent exchange processes over the lake surface.
The pre-monsoon period is characterized by a higher U and e, with a
negative T, while the post-monsoon period is characterized by a lower U
and Ta, with a positive T. The air mass during the monsoon period
is the warmest and wettest. The monsoon period has a much higher
Ts than the other two periods. The southeasterly and westerly
winds are dominant during daytime and nighttime, respectively. The lake and land
breeze circulation is stronger during the pre-monsoon and post-monsoon periods.
The near neutral stratification occupies the major proportion during the
three study periods. The negative diurnal T during the pre-monsoon period has
contributed to the occurrence of stable stratification, while the positive
T has contributed to the occurrence of unstable stratification during
monsoon and post-monsoon periods. The monsoon also has an effect on bulk
transfer coefficients. The drag coefficient during the monsoon period is lower
compared to the other two periods. The Dalton number is larger but the
Stanton number is lower during the pre-monsoon period than that during other two
periods.
Due to the effect of cloud, the Rn during the monsoon period is lower
than pre-monsoon period. The albedo is higher during the post-monsoon period but
similar between pre-monsoon and monsoon periods. The diurnal pattern of
ΔQ is consistent with Rn, while diurnal Hs
and LE are out of phase of Rn, which are consistent with T and
e, respectively. The diurnal Hs during the pre-monsoon period
remains negative for most times of the day, and changes to be positive for
a short time in the morning. Diurnal LE remains relatively high for most times of
the day during the monsoon period. The higher Hs and lower LE
have resulted in a higher Bowen ratio during the post-monsoon period. LE dominates the
energy partitioning of the lake, and LE/Rn ratio exceeds 1 during the post-monsoon period due to the rapid decrease in Rn. The monthly
average Q is positive during pre-monsoon and early monsoon period, and
becomes negative during the other periods, indicating that the lake absorbs heat
at first and then releases it.
The ΔT remains as the main factor controlling Hs on all
temporal scales during the three periods, and the effect of ΔT is
increased when timescales are extended from half-hourly to monthly. The
factors controlling LE are more consistent from half-hourly to monthly timescales
compared to Hs. A significant relationship between U and LE is
observed on all temporal scales, and the product of U and e is the second
major factor controlling LE during the three periods. The U is also found to
have a strong relationship with CO2 flux during the monsoon period. The
high wind is expected to cause variations in the mechanical mixing and thus
modulate the water surface turbulent and CO2 exchange processes
(Zhang and Liu, 2013). During the monsoon period, the Rn plays an
important role on monthly variation in Hs and LE. Compared with
monsoon period, similar main drivers for Hs are found during the pre-monsoon period and post-monsoon period, which is also found for
CO2 flux, indicating the large impact of monsoon on heat and carbon
exchange processes over Erhai Lake. On a monthly timescale, the main drivers of
CO2 flux are more complicated during the three periods. In future, more
studies are needed to investigate the combing effect of biological and
physical process on carbon exchange processes over highland shallow lakes.