Long-Term Changes of Lake Level and Water Budget in the Nam Co

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JOURNAL OF HYDROMETEOROLOGY
VOLUME 15
Long-Term Changes of Lake Level and Water Budget in the Nam Co Lake Basin,
Central Tibetan Plateau
YANHONG WU
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences, Beijing, China
HONGXING ZHENG
CSIRO Land and Water, Canberra, Australian Capital Territory, Australia
BING ZHANG
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences, Beijing, China
DONGMEI CHEN
Department of Geography, Queen’s University, Kingston, Ontario, Canada
LIPING LEI
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital
Earth, Chinese Academy of Sciences, Beijing, China
(Manuscript received 22 May 2013, in final form 22 January 2014)
ABSTRACT
Long-term changes in the water budget of lakes in the Tibetan Plateau due to climate change are of great
interest not only for the importance of water management, but also for the critical challenge due to the lack of
observations. In this paper, the water budget of Nam Co Lake during 1980–2010 is simulated using a dynamical
monthly water balance model. The simulated lake level is in good agreement with field investigations and the
remotely sensed lake level. The long-term hydrological simulation shows that from 1980 to 2010, lake level rose
from 4718.34 to 4724.93 m, accompanied by an increase of lake water storage volume from 77.33 3 109 to 83.66
3 109 m3. For the net lake level rise (5.93 m) during the period 1980–2010, the proportional contributions of
rainfall–runoff, glacier melt, precipitation on the lake, lake percolation, and evaporation are 104.7%, 56.6%,
41.7%, 222.2%, and 280.9%, respectively. A positive but diminishing annual water surplus is found in Nam Co
Lake, implying a continuous but slowing rise in lake level as a hydrological consequence of climate change.
1. Introduction
In most regions of the world, climate change is
expected to significantly impact water resources. The
Denotes Open Access content.
Corresponding author address: Bing Zhang, Key Laboratory of
Digital Earth Science, Institute of Remote Sensing and Digital
Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South
Road, Haidian District, Beijing 100094, China.
E-mail: [email protected]
DOI: 10.1175/JHM-D-13-093.1
Ó 2014 American Meteorological Society
form and magnitude of the impact, however, varies
significantly from region to region (Solomon et al. 2007).
It has been reported that water resources in Asia could
be seriously affected by climate change, especially in the
Tibetan Plateau (TP; Barnett et al. 2005; Immerzeel
et al. 2010). The TP is the called ‘‘the Third Pole’’ of the
earth and ‘‘the Water Tower of Asia’’ (Qiu 2008). The
numerous inland lakes in the TP are important sources
of water and indicators of regional climate change. In
the literature, researchers have addressed the importance of water resource changes in the TP and the effects
of climate change on rivers and lakes inside and nearby
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WU ET AL.
1313
FIG. 1. Location of meteorological stations around Nam Co Lake.
the TP (e.g., Zheng et al. 2009; Kang et al. 2010). Because of the harsh physical conditions (remoteness, high
altitude, inclement weather, etc.) and lack of in situ
observations, water levels and water storage, not to
mention the water budget of lakes, in the TP are still
poorly known. Some studies have focused on monitoring
changes of lake surface area using remote sensing images
(e.g., Ye et al. 2007; Chu et al. 2008; Yang et al. 2008; Liu
et al. 2009), but only a few systematic water budget
analyses were conducted for the lakes (Ding and Liu 1995;
Zhang et al. 2003; Qi and Zheng 2006). To understand the
water cycle of the region, most recently, more research
efforts have been focused on investigating the bathymetry, surface area, water storage, and water budget of these
lakes (Wang et al. 2009; Wu and Zhu 2008; B. Zhang et al.
2011; Zhu et al. 2010) and also on simulating the long-term
water budget of the lakes (Krause et al. 2010).
For a lake basin with sparse or no gauge measurements, satellite remote sensing data are a valuable
source of information for the investigation and simulation of the lake water budget. The Ice, Cloud, and Land
Elevation Satellite/Geoscience Laser Altimeter System
(ICESat/GLAS) level 2 altimetry product (GLA14) is
one of the available datasets, which provides surface
elevation of land with laser footprint geolocation and
reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements (Zwally
et al. 2003). ICESat elevation data over water surface in
southern Egypt, the United States, East Africa, and
central Asia have been examined by numerous studies
and have shown accuracy of better than 10 cm (Urban
et al. 2008; Swenson and Wahr 2009). It has also been
confirmed as a reliable dataset for lake level interpretation in the TP (Phan et al. 2012; G. Q. Zhang et al.
2011). Based on the dataset, the satellite altimetry approach has been well developed for inland lake and
river monitoring (Berry et al. 2005; Medina et al. 2008;
Krop
acek et al. 2012).
The purpose of this paper is to investigate the longterm changes of lake level and water budget in the Nam
Co Lake, which is located in the central TP. A dynamic
monthly water balance model is developed for this purpose and is calibrated using lake level data derived from
the ICESat altimetry dataset. Based on the dynamic
simulation, long-term changes of lake water budget are
investigated with respect to climate changes in the TP.
2. Study area and data
a. Study area
Nam Co is a closed, semi-brackish lake (Wang et al.
2009) located in the central part of the TP (308300 –
308550 N, 908160 –918030 E) with a catchment area of
10 610 km2. It is the largest lake in the TP and one of the
highest lakes in the world (Fig. 1). The elevation of the
Nam Co Lake is 4718 m, with an area of about 1920 km2
measured in 1979 (Guan et al. 1984) and a maximum
depth over 90 m measured in 2005 (Wang et al. 2009).
The glacier fraction in the Nam Co Lake basin is about
0.015. Significant fluctuation of the lake area in past
decades has been detected by the interpretation of
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JOURNAL OF HYDROMETEOROLOGY
remote sensing images (Wu and Zhu 2008; Zhu et al.
2010; B. Zhang et al. 2011). The changes of the lake area
could be a result of global warming. However, as a solitary cold region, the lack of observed evidence makes it
difficult to understand the hydrological cycle of the region under climate change.
b. Data
No hydrometeorological observations are available
in the basin before 2005. Though some investigations
have been conducted to measure water depth, precipitation, and temperature of the basin recently (Wang
et al. 2009), data are limited for long-term water balance
research. In this case, in addition to a field investigation,
we try to conduct our research based on the remotely
sensed lake level data and long-term meteorological
records from observations near the basin.
1) BATHYMETRIC SURVEY OF THE LAKE
The water storage capacity of a lake relies on its
morphometry. To improve the estimation of water
storage in the lake, in September 2005, September 2006,
and August 2007 bathymetric surveys were conducted to
digitize the underwater morphology of the lake. From
the surveys, the available data points reached 305 721
with the accuracy of 0.01 m (Wang et al. 2009). The
dataset is then used to determine the relationship between lake depth and its storage capacity. The approximate function of the relationship can be depicted as
V 5 0:5156H 1 0:1574H 2 2 0:0006H 3
for
0 , H , 100,
(1)
where H (m) is lake level and V is water storage (109 m3).
The expression is statistically significant at the level of
1%, which enables reliable conversion between water
storage and lake level.
2) REMOTELY SENSED LAKE LEVEL
The ICESat launched in January 2003 was designed to
determine geocentric elevations of the earth’s surface
(Zwally et al. 2008). Fully calibrated ICESat data over
various surfaces achieve an absolute accuracy of 2–7 cm
and a precision of 2–3 cm (Urban et al. 2008). Though
the primary task of the ICESat is to measure elevation
changes of the polar ice sheets, the high-quality elevation measurements can be used to estimate lake level in
the TP as well (Urban et al. 2008; G. Q. Zhang et al.
2011; Phan et al. 2012). To obtain lake levels of Nam
Co, we use GLA14 (release 33) from the U.S. National
Snow and Ice Data Center (NSIDC).
The GLAS instrument worked from February 2003 to
October 2010. It measured the mean elevation of flat
VOLUME 15
TABLE 1. Meteorological stations used in this paper.
Station
No.
Station
1
Baingoin
908010
318230
4700
2
Damxung
918060
308290
4200
3
Nagqu
928040
318290
4507
4
Xainza
888380
308570
4672
5
Amdo
918060
328210
4800
6
Shigatse
888530
298150
3836
7
Lhasa
918080
298400
3648.7
8
Tsedang
918460
298150
3551.7
9
Gyantse
898360
288550
4040
10
Sokshan
938470
318530
4022.8
11
Lhari
938170
308400
4488.8
Lat (N) Lon (E)
Altitude
(m)
Record
period
Oct 1956–Dec
2010
Aug 1962–Dec
2010
Jul 1954–Dec
2010
Apr 1960–Dec
2010
Nov 1965–Dec
2010
Dec 1955–Dec
2010
Jan 1955–Dec
2010
Sep 1956–Dec
2010
Nov 1956–Dec
2010
Nov 1956–Dec
2010
Nov 1954–Dec
2010
surfaces at the 70-m footprint spaced 172 m apart along
the track. It collected elevations in 20 designated campaigns. Nam Co Lake was crossed by 43 ground tracks;
each track of ICESat intersecting with Nam Co Lake was
extracted for further data processing, which included
procedures like conversion of data formats, conversion of
spatial references (Bhang et al. 2007), and identification
and rejection of outliers. Afterward, lake levels of 27
months during the period 2003–09 were obtained for
subsequent calibration in hydrological simulation.
3) METEOROLOGICAL DATA
The routine observation records of 11 meteorological
stations around Nam Co Lake basin from 1971 to 2010
were used in this research (Table 1; Fig. 1). The dataset
included daily mean temperature, minimum temperature,
maximum temperature, rainfall, sunshine duration, wind
speed, and water vapor pressure. The kriging interpolation
and the thin-plate spline (TPS) method (Hutchinson 1998)
were adopted for spatial interpolation of temperature,
precipitation, and the calculated potential evapotranspiration with the consideration of the terrain effect.
3. Hydrological simulation
a. Monthly water balance model
Zhang et al. (2008) proposed a monthly water balance
(MWB) model based on the Budyko hypothesis (Budyko
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WU ET AL.
TABLE 2. Expressions of the monthly water balance model. Note
that a1 , a2 , Smax , and d are parameters to be calibrated, g is the
fraction of glacier, and t represents a time interval.
Variable
X: catchment
rainfall retention
X0 : demand
limit of X
Rd: direct
runoff
Expression
X0 (t)
, a1
X(t) 5 P(t)F
P(t)
X0 (t) 5 E0 (t) 1 Smax 2 S(t 2 1)
Rd (t) 5 P(t) 2 X(t)
W: water
W(t) 5 X(t) 1 S(t 2 1)
availability
E0 (t) 1 Smax
, a2
Y: evapotranspiration Y(t) 5 W(t)F
W(t)
opportunity
Re: recharge to
Re (t) 5 Y(t) 2 W(t)
groundwater
storage
E0 (t)
, a2
Ea: actual
Ea (t) 5 W(t)F
W(t)
evapotranspiration
S: soil water
S(t) 5 Y(t) 2 Ea (t) 1 SM(t)
storage
G: groundwater
G(t) 5 (1 2 d)G(t 2 1) 1 Re (t)
storage
Rb: base flow
Rb (t) 5 dG(t 2 1)
Rt: total runoff
Rt (t) 5 gRglacier 1 (1 2 g)(Rd 1 Rb 1 Rsnow )
1958), which assumes that actual evapotranspiration Ea
is a function of aridity index f as
Ea 5 P[F(f)] ,
(2)
where P is precipitation and the aridity index is defined as the ratio of the potential evapotranspiration
E0 over P, that is, f 5 E0 /P. The variable F(f) can be
presented as
F(x, a) 5 1 1 x 2 [1 1 x1/(12a) ]12a ,
(3)
where x represents the ratio between water demand and
water supply and a is a parameter. The variable x is
equivalent to f when E0 and P are assumed as water
demand and water supply, respectively. The principle
equations of the MWB model are listed in Table 2.
b. Snow and glacier
In the original framework of the model presented by
Zhang et al. (2008), the effect of snow and glacier on
water balance is not considered. For a frigid region like
Nam Co Lake, however, the effects of snow and glacier
in the process of the water cycle should not be neglected.
To estimate water balance of the basin, a simulation
module on mass balance of the snow and glacier is added
to the original rainfall–runoff model. As suggested by
Bengtsson (1980), if the evaporative losses over snow
and glacier are considered negligible, the balance of the
snowpack and glacier can be represented as
SN(t) 5 SN(t 2 1) 1 P(t) 3 f 2 SM(t) and
(4)
GC(t) 5 GC(t 2 1) 1 P(t) 3 f 2 GM(t) ,
(5)
where SN and GC are snowpack and glacier water
equivalence (i.e., the equivalent depth of liquid water),
respectively. The variables SM and GM are snow
meltwater and glacier meltwater, respectively. The snow
meltwater is assumed to enter into the soil layer, while
the glacier meltwater is assumed to contribute to runoff
directly. The variable P is the amount of precipitation,
f is the portion of precipitation in the form of snow, and
1 2 f is the portion of rain.
The estimation of the portion f is essential for modeling the mass balance of monthly snow cover (Rohrer
1989) and for accurate runoff model performance
(WMO 1986). The usual method for determining the
division of precipitation is to set a threshold ambient
temperature above which all precipitation is assumed
to be rain and below which is snow. Herein, the simple linear relationship between average monthly air
temperature Tair and the observed percentage of precipitation falling as snow proposed by Sevruk (1983) is
as follows:
8
< 0, Tair . 12:22
f 5 24:5Tair 1 55,
:
100, Tair , 210
210 # Tair # 12:22 .
(6)
The rate of snowmelt depends on the availability of
energy to the snowpack and is usually dominated by net
radiation (Marks and Dozier 1992). The snowmelt can
be simulated by an energy balance model, which requires reliable measurements like air temperature, incoming solar radiation, vapor pressure, and wind speed
and involves intensive calculation (Marshall and
Oglesby 1994). It is recognized that energy balance
snowmelt models are effective for short-term prediction
for small catchments. In the absence of detailed observation of energy fluxes and snow characteristics, to
estimate meltwater over larger spatial and temporal
scales, simpler approaches such as temperature indices
are better able to model the average conditions
(Kuusisto 1984; Ferguson and Morris 1987; Vehvil€
ainen
1992; Kuchment and Gelfan 1996). The snowmelt–runoff
models that incorporate a degree-day or temperature
index routine are commonly used in operational hydrology and have been successfully verified worldwide over
a range of catchment sizes, physical characteristics, and
climates (WMO 1986; Bergstr€
om 1992; Rango 1992). The
basic form of the degree-day approach is
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JOURNAL OF HYDROMETEOROLOGY
SM 5 Cs (Tair 2 Ts ) and
(7)
GM 5 Cg (Tair 2 Tg ) ,
(8)
where Cs and Cg are melt rate factors of snow cover and
glacier, respectively (mm 8C21 day21); Tair is the daily
air temperature; and Ts is the threshold melt temperature. The critical melt temperature is often set to 0.
The measured Cs averages in Finland are 2.4 and
3.5 mm 8C21 day21 for forested and open areas, respectively (Kuusisto 1984). A typical range for old
melting snow is 3.5–6 mm 8C21. Rain falling on snow is
assumed to percolate through the snowpack to the soil
surface.
c. Lake water balance
The original model proposed by Zhang et al. (2008)
with the snow and glacier module can be used to model
the water balance of the catchments and provide the
inflow of the lake. To simulate the water storage or water
level of the lake, an additional water balance module of
the lake is needed, which can be represented as
DH 5
Ac
Q 1 Pl 2 El 2 Qout 5 Rt /ra
Al in
1 Pl 2 El 2 Perc ,
(9)
where H is water level of the lake (mm), Pl and El are
precipitation (mm) and evaporation (mm) over the lake,
and Qin and Qout are inflow (mm) and outflow (mm) of
the lake. The Qin is equal to runoff from the catchment
Rt and consists of direct runoff Rd , base flow Rb , snow
melt flow Rsnow, and glacier melt flow Rglacier . The variables Ac and Al are area (km2) of catchment and Nam
Co Lake, respectively, while ra represents the area
percentage of the lake to the catchments and is set to
18.85% in this study according to the remote sensing
images. Because Nam Co is a closed lake without direct
runoff out of the lake, the only outflow loss is the percolation to the groundwater Perc, which is the net exchange between lake and groundwater. The percolation
loss can be assumed to be a function of water level H
(e.g., Kebede et al. 2006; Sene 1998). Herein, the relationship is represented by a linear model expressed as
Perc 5 kH ,
(10)
Organization (FAO; Allen et al. 1998), which is presented as
E0 5
900
U (e 2 ed )
T 1 273 2 s
,
D 1 g(1 1 0:34U2 )
0:408D(Rn 2 G) 1 g
e. Model inputs and calibration
The inputs of the monthly water balance described
above are monthly mean temperature (Tair), monthly
totals of precipitation, and potential evapotranspiration
(E0). The parameters of the model are a1 , a2 , Smax , and d
in the rainfall–runoff module; Cs and Cg in the snow and
glacier module; and k in the lake water balance module.
The seven parameters are calibrated using the global
optimization method particle swarm optimization (PSO)
to maximize the Nash–Sutcliffe efficiency (NSE) coefficient (Nash and Sutcliffe 1970):
NSE 5 1 2
n
å (Hr,t 2 Hs,t )
t51
The potential evapotranspiration for rainfall–runoff
simulation is estimated according to the Penman–Monteith
method recommended by the Food and Agriculture
(11)
where D represents the curve slope of saturated water
vapor pressure (kPa 8C21) at temperature T; Rn represents the solar net radiation at top layer (MJ m22 day21);
G represents soil-pass heat (MJ m22 day21); g is the dry–wet constant (kPa 8C21); T is the daily mean temperature
(8C); U2 is the wind speed at the height of 2 m (m s21); and
es and ed are the saturated and actual water vapor pressure
(kPa), respectively, at temperature T.
Equation (11) can be used to estimate potential
evapotranspiration in the land surface of the catchments. When it is applied to calculate potential evaporation of the lake, however, change of heat storage in the
lake has to be considered. Lake surface temperature is
required to estimate the heat change of a shallow lake
(e.g., Keijman 1974). For deep lakes, it is necessary to
conduct thermal surveys consisting of temperature profiles with depth, measured ideally at a sufficient number
of stations to produce a good average (e.g., Anderson
1954; Sturrock et al. 1992). Because the lake surface
temperature or the temperature profiles of the Nam Co
Lake are not available, E0 is used to represent evaporation of the lake in the nonfrozen months. In the frozen
months, the lake evaporation equals 0. The calculated
E0 is found to be in good agreement with evaporation
observed in 2008 using a pan with a diameter of 20 cm
(R2 5 0.84) and comparable with that observed via pan
E601-B (Ren et al. 2002).
where k is the percolation coefficient.
d. Evaporation
VOLUME 15
2
n
å (Hr,t 2 Hr )2 ,
(12)
t51
where Hr,t and Hs,t are the remotely sensed and simulated
water level, respectively; Hr is the arithmetic mean of the
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WU ET AL.
1317
FIG. 2. Comparison between remotely sensed and simulated monthly mean lake level of Nam
Co Lake (2003–09).
remotely sensed water level; and n is the sample number,
that is, the total number of months from 2003 to 2009.
4. Results
a. Model performance
As shown in Fig. 2, the simulated lake level matches
well with the remotely sensed one with NSE 5 0.889 and
R2 5 0.894. The absolute difference between the mean
simulated lake level and remotely sensed one is 1 mm. The
largest difference between the simulated and remotely
sensed lake level is on March 2006, when the simulated
lake level is 0.011% overestimated. In November 2004,
however, the simulated lake level is 0.006% underestimated. The simulation result also shows high consistency with field investigations conducted by the Chinese
Academy of Sciences (CAS) in July 1979, which concluded that the elevation of Nam Co is 4718 m (Guan et al.
1984). Compared with the simulated lake level in January
1980 (4718.512 m), the relative difference between the
investigated lake level and the simulated one is 0.01%.
b. Intra-annual variation of lake level and water
budget
As shown in Fig. 3, which displays the intra-annual
variations of lake level, the highest lake level of Nam Co
is 4722.97 m in September, while the lowest one is
4722.66 m in May, which suggests that the lake level rises
0.31 m from May to September. The result is very similar
to the study of Zhou et al. (2006), where it is reported
that lake level rose 0.36 m from May to mid-September,
according to their observations during ice-free periods
(May–October) in 2005. However, one should notice
that the variations of both lake level and water storage
are not concurrent with precipitation or inflow (Fig. 3).
The maximum monthly precipitation and inflow are
found to be in August. The lag of lake level rise could be
because of the catchment storage capacity, which depends on water storage in the catchment and gradual
release to the river and lake later in the dry months. It is
also evident that the lake level does not depend only on
the water gain, but it also relies on water surplus (gain
minus loss). For a negative water surplus, water level,
as well as water storage, would decrease, but positive
water surplus could result in the increase of water
level and water storage even in the month with rather
small inflow.
Figure 3 shows that the water gain of Nam Co Lake in
January is only 2.11 mm, which is mainly from the precipitation on the lake, but reaches to 253.96 mm in August. The lower water loss of the lake from winter to
early spring (January–March) mainly consists of percolation with little evaporation loss. Owing to lake evaporation loss, water loss from late spring to autumn
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VOLUME 15
FIG. 3. Intra-annual variations of lake level (H), lake water storage (V), precipitation (P),
potential evaporation (E0), total runoff (Rt), lake evaporation (El), temperature (T); lake water
gain (Gain), lake water loss (Loss), and surplus of lake water balance (Surplus).
(May–October) is much higher. The evaporation loss
accounts for 86.21% of the total water loss of the lake.
The lake water budget shows a deficit in October–May,
but surplus in June–September, with the highest net
water gain of 131.2 mm in August. Subsequently, water
storage increases from April to September then decreases until next March, leading to the observation that
the maximum and minimum lake water storages appear
in September (around 84.56 3 109 m3) and April (around
84.06 3 109 m3), respectively.
c. Interannual changes of lake level and water budget
As shown in Table 3, the long-term mean lake level is
4722.79 m for the period of 1980–2010, while mean water
storage of the lake is around 84.27 3 109 m3, which is
very close to 84.24 3 109 m3 estimated by B. Zhang et al.
(2011). A significant increasing trend of lake level is
detected by the nonparametric Mann–Kendall test
(Mann 1945; Kendall 1975), showing that the rising rates
of mean, minimum, and maximum annual lake levels are
all around about 0.180 meters per annum (m a21). In the
past 30 years, the lake level increased from 4719 m in
1980 to 4724.93 m in 2010, which is consistent with the
field investigation on Nam Co Lake conducted by Wang
et al. (2009) from September 2005 to September 2008.
The rising of lake level is also evident in the case that
the Tirangmubuduo peninsula and Gendaduo peninsula
are now submerged more than 2 m deep in the water.
The two peninsulas were above the lake surface in 1979
(Guan et al. 1984). A significant increasing trend is
found in water storage as well during the period of
1980–2010 (Fig. 4). The water storage of Nam Co
Lake increased from 77.33 3 109 in 1980 to 87.66 3
109 m3 in 2010, with an increasing rate of about 0.285 3
109 m3 a21. In different decades, however, the rising rate
of lake level and water storage are not the same. For the
three periods concerned, that is, 1980–90, 1991–2000,
and 2001–10, the mean lake levels are 4720.94, 4723.02,
and 4724.59 m, and the rising rates are 0.024, 0.009, and
0.014 m a21, respectively (Fig. 4; Table 3). Meanwhile, as
shown in Table 4, the mean water storage values are
81.35 3 109, 84.63 3 109, and 87.12 3 109 m3, with increasing rates of 0.45 3 109, 0.17 3 109, and 0.26 3
109 m3 a21, respectively.
For the annual water budget of the lake, according
to the simulation results, the annual-mean water gain,
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TABLE 3. Decadal variation of lake level and water storage.
Water storage (109 m3)
Lake level (m)
Period
Max
Min
Mean
DL (m)
Max
Min
Mean
Change rate (109 m3 a21)
1980–90
1991–2000
2001–10
4722.64
4723.89
4725.47
4718.51
4722.41
4723.60
4720.94
4723.02
4724.59
4.13
1.48
1.87
84.02
86.02
88.54
77.59
83.66
85.55
81.35
84.63
87.12
0.45
0.17
0.26
1980–2010
4725.28
4719.00
4722.79
6.28
88.23
78.33
84.27
0.29
water loss, and water surplus are 2.03 3 109, 1.66 3 109,
and 0.37 3 109 m3 a21, respectively (Table 4). The inflow
of the lake (i.e., the runoff from the basin to the lake)
accounts for 67.0% of the total water gain, outweighing
the amount of precipitation on the lake. The proportions
of surface runoff, base flow, and glacier melt flow to total
inflow are 97.18%, 2.82%, and 0.89%, respectively, indicating that the water gain of the lake depends greatly
on rainfall–runoff. As shown in Table 4, evaporation is
the dominant factor in water loss, accounting for 78.46%
of the total amount. Based on the budget during 1980–
2010, it is estimated that the total water gain could result
in a lake level increase of 12.04 m, among which rainfall–
runoff, glacier melt, and precipitation on the lake account for 51.58%, 27.89%, and 20.53%, respectively.
However, the water loss has led to a 6.11-m decrease of
lake level, where lake evaporation and percolation account for 78.46% and 21.54%, respectively. For the net
lake level rise (5.93 m), the proportional contributions of
runoff, glacier melt, Pl, Perc, and El are 104.7%, 56.6%,
41.7%, 222.2%, and 280.9%, respectively.
Figure 4 shows the long-term variation of water budget in the past decades. It is estimated that annual inflow
of 1991–2000 and 2001–10 is 13.46 and 1.23 mm smaller
FIG. 4. Interannual variations of lake level (H), lake water storage (V), precipitation (P),
potential evaporation (E0), catchment actual evapotranspiration (Ea), total runoff (Rt), glacier
melt runoff (Rglacier), lake evaporation (El), and air temperature (T).
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JOURNAL OF HYDROMETEOROLOGY
VOLUME 15
TABLE 4. Decadal variation of lake water budget in response to climate change.
Water gain
Water loss
Climate factors
Period
Pl
Rt
Total
El
Perc
Total
Water surplus
T (8C)
E0 (mm)
P (mm)
1980–90
1991–2000
2001–10
1980–2010
0.69
0.63
0.70
0.67
1.41
1.27
1.39
1.36
2.10
1.90
2.09
2.03
1.26
1.32
1.33
1.30
0.09
0.39
0.62
0.36
1.35
1.71
1.95
1.66
0.74
0.18
0.15
0.37
20.82
20.53
0.27
20.38
1085.10
1039.69
1061.81
1062.94
344.99
315.85
352.36
337.97
than the reference period (1980–90), respectively. Total
water gain (annual inflow plus annual precipitation on
the lake) in 1991–2000 is 100.53 mm smaller than the
reference period, but it is 0.86 mm higher in 2001–10.
Concurrently, the total water loss (annual percolation
plus annual evaporation on the lake) of 1991–2000
and 2001–10 is 179.10 and 295.50 mm higher than the
reference period (1980–90), respectively. The smaller
water gain in addition to the higher water loss of the
lake could result in the decrease of water surplus. Estimated water surpluses of the periods 1980–90, 1991–
2000, and 2001–10 are 0.74 3 109, 0.18 3 109, and 0.15 3
109 m3 a21, respectively, decreasing distinctly at a rate of
15.35 mm a21 (p , 0.0001) during 1980–2010. The positive but smaller surpluses indicate that water storage
as well as lake level should have been increasing, but
slowing down.
Moreover, the influences of global warming can be
amplified or attenuated by other environmental factors
such as precipitation, wind, etc. On the one hand, precipitation can be the direct water source of the lake (Pl),
but on the other hand, precipitation on the land in turn
becomes runoff to the lake as part of its water gain.
During 1980–2010, the fluctuations in both total inflow
and water gain of the lake are quite similar to that in
annual precipitation, showing significant correlation
between the Pl and water gain (R2 5 0.98) and between
P and inflow (R2 5 0.93) as well. In general, it can be
concluded that increasing temperature could result in
more water loss, while changes of water gain are mainly
due to precipitation change. To figure out the potential
response of lake level to climate change, however,
sensitivity analysis based on the model developed
would be helpful, which will be investigated in our future research.
5. Discussion
a. Impacts of climate change
b. Uncertainties
The long-term changes and variations of lake level
and water storage could be the consequence of climate
change in the TP. According to long-term meteorological records (1980–2010) of the stations around the Nam
Co Lake, the annual-mean temperature (T) showed significant increasing trend at the rate of 0.588C decade21
and higher rate in winter season (November–January).
Compared with the period 1980–90, the annual-mean
temperatures of 1991–2000 and 2001–10 are 0.298 and
1.098C higher, respectively. Owing to the increase of
temperature, it can be seen from the simulation that the
evaporation of the lake (El) during 1980–2010 increased at a rate of 2.12 mm a21. For 1991–2000 and
2001–10, the annual-mean El is 27.18 and 31.01 mm
more than that in 1980–90 (Table 4). The greater
evaporation means larger water loss and smaller water
surplus. Significant correlation (R2 5 0.58) between
temperature and water loss is found during 1980–2010.
However, it should be noted that relations between
global warming and lake level are far more complicated. Global warming could increase not only water
loss (e.g., higher evaporation) but also water gain (e.g.,
higher glacier melting rate).
The results shown above have been verified by some
field investigations in the literature; however, uncertainty exists because of the limited hydrological observation in the Nam Co Lake and intrinsic uncertainties of
the hydrological model. For instance, the freezing and
thaw process of permafrost could have an important role
in the hydrological cycle (Zhang et al. 2003; Liu et al.
2009; Zheng et al. 2009), but it is not represented in the
current version of the hydrological model. The model
can be further improved if the relations among water
level, water storage, and percolation are represented by
a more complicated model when more observations are
available for parameter calibration. With the limitations
of observation, however, a more complicated model
may not help to reduce the uncertainty. As mentioned in
the calculation of lake evaporation using the Penman–
Monteith method, the lake surface temperature or
temperature profile is required to estimate heat storage
change in the lake. Though the calculated evaporation
has been adjusted according to pan observation, the
neglect of the heat storage change could result at bias of
the lake evaporation. Uncertainty may also come from
the spatial interpolation of meteorological observations
JUNE 2014
1321
WU ET AL.
and the remotely sensed lake level. The meteorological
inputs such as precipitation and temperature used in this
study are all obtained via spatial interpolation, which
inherits uncertainties in the original observation records
and interpolation approaches.
6. Conclusions
The long-term change of lake water budget in the TP
responding to climate change is of great research interest. However, the harsh physical conditions as well as
the limitation of in situ observations has made it a challenge for researchers. In this paper, in addition to field
investigation, lake level derived from the ICESat/GLAS
dataset has been used to calibrate a monthly water balance
model to simulate the long-term water budget of Nam Co
Lake. The results show that the model performs well if
evaluated according to the remotely sensed lake level.
According to the long-term water budget simulation
using the monthly model, the annual-mean level and water
storage of Nam Co Lake are 4722.79 m and 84.27 3 109 m3,
respectively. During 1980–2010, the lake level rose from
4718.34 to 4724.93 m, accompanied by an increase in water
storage from 77.33 3 109 to 83.66 3 109 m3. It is found that
annual-mean water gain and loss of the lake are 2.03 3 109
and 1.66 3 109 m3 a21, respectively. The positive annual
water surplus is around 0.37 3 109 m3 a21, indicating
a possible increase in both lake level and water storage. The
annual water surplus remains positive but decreases in the
period 1980–2010, implying that the lake level of the Nam
Co Lake continues to rise, but at a slower rate. As a consequence of climate change and variation, the surplus of
water budget fluctuates with the variation of precipitation
and tends to decrease with the increasing temperature.
Acknowledgments. We thank all the members who
participated in the field investigations of Nam Co Lake
during 2005–08, and we thank all the staff of the Nam Co
Monitoring and Research Station for Multisphere Interactions, Institute of Tibetan Plateau Research, CAS.
We also want to thank the Climate Data Center, National Meteorological Information Center, China Meteorological Administration, for providing the long-time
meteorological data of the 11 field stations. We kindly
thank Dr. Ta-Yan Leong for carefully proofreading our
manuscript. This work was jointly supported by the National Natural Sciences Foundation of China (Grant
41371218), the Strategic Priority Research Program (B) of
the Chinese Academy of Sciences (Grant XDB03030406),
and the National Natural Sciences Foundation of China
(Grants 40901102 and 40901174). The comments and
suggestions of the three anonymous reviewers and the
editors are greatly appreciated.
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