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remote sensing
Article
Changing Pattern of Water Level Trends in Eurasian Endorheic
Lakes as a Response to the Recent Climate Variability
Xin Zhang 1,2 , Abilgazi Kurbaniyazov 3and Georgiy Kirillin 1, *
Citation: Zhang, X.; Kurbaniyazov, A.;
Kirillin, G. Changing Pattern of Water
Level Trends in Eurasian Endorheic
Lakes as a Response to the Recent
Climate Variability. Remote Sens. 2021,
13, 3705. https://doi.org/10.3390/
rs13183705
Academic Editor: Pavel Kishcha
Received: 29 July 2021
Accepted: 11 September 2021
Published: 16 September 2021
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4.0/).
1Department of Ecohydrology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries,
12587 Berlin, Germany; xzhang0828@mail.bnu.edu.cn
2College of Global Climate and Earth System Science, Beijing Normal University, Beijing 100875, China
3Institute of Continuing Education, Yassawi International Kazakh-Turkish University,
Turkestan 161200, Kazakhstan; abilgazy.kurbaniyazov@yu.edu.kz
*Correspondence: kirillin@igb-berlin.de; Tel.: +49-30-64181669
Abstract:
Lake level is a sensitive integral indicator of climate change on regional scales, especially
in enclosed endorheic basins. Eurasia contains the largest endorheic zone with several large terminal
lakes, whose water levels recently underwent remarkable variations. To address the patterns of these
variations and their links to the climate change, we investigated the variability of levels in 15 lakes
of three neighboring endorheic regions—Central Asia, Tibetan Plateau, and Mongolian Plateau.
Satellite altimetry revealed a heterogeneous pattern among the regions during 1992–2018: lake levels
increased significantly in Central Asia and the Tibetan Plateau but decreased on the Mongolian
Plateau. The shifts to the increasing trend were detected since 1997 in Central Asia, since 1998 in the
southern part of the Tibetan Plateau, and since 2005 in its northern part. The shift in air temperatures
around 1997 and the precipitation shifts around 1998 and 2004 contributed to the trend’s turning
points, with precipitation being the major contributor to the heterogeneous pattern of lake levels.
Our findings reveal the linkage of the heterogeneous pattern of lake levels to climatic factors in the
endorheic basins, providing a further understanding of the hydrological regime in the Eurasian
endorheic zone and its sensitivity to climate change.
Keywords: lake; water level; satellite altimetry; climate change; change point
1. Introduction
An endorheic basin is a closed or internal drainage system without an outflow into
an ocean or a sea. The closed character of the hydrological cycle makes endorheic basins
especially sensitive to basin-scale climate variations. Endorheic basins are inherent features
of intracontinental arid and semiarid regions. Surface runoff in endorheic basins typically
accumulates in large terminal lakes; the largest number of endorheic lakes worldwide is
concentrated on the continent of Eurasia, covering Central Asia (CA), the Tibetan Plateau
(TP), and the Mongolian Plateau (MP). The lake levels in those regions present “end
points”, accumulating multiple responses of the basin-scale water balance, and are therefore
considered to be one of the most sensitive indicators for regional response to climate change.
Endorheic lakes play an important role in maintaining biodiversity and providing valuable
water support for ecosystem services [
1
,
2
]. The endorheic lakes in Mongolia are the main
water resource for endangered species and migratory waterfowl [3,4].
The lakes in Central Asia are important for local agriculture, vegetation, and ecol-
ogy [
5
]. The pristine lakes of the Tibetan Plateau that remain generally undisturbed by an-
thropogenic activities have gained attention as “sentinels” of regional climate
change [6–9]
.
Several recent studies have indicated significant climate variations in CA, TP, and MP, such
as the warmer temperature [
10
], a weakened aridity in Central Asia [
11
], and a wetter
environment in the central part of the Tibetan Plateau [
12
]. The corresponding changes
in the large-scale hydrological cycle [
13
] affect the water levels of the terminal endorheic
Remote Sens. 2021,13, 3705. https://doi.org/10.3390/rs13183705 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2021,13, 3705 2 of 21
lakes as integrated indicators of the hydrological cycle respond to climate change [
14
]. The
Eurasian endorheic zone is affected by several global circulation patterns: Central Asia
and the Mongolian Plateau are influenced by westerlies, whereas the Tibetan Plateau is
affected by the intersection of the westerlies and the monsoonal system. Therefore, the
water levels of the endorheic lakes in the three large continental regions are expected to
have heterogeneous responses to global change. Several previous studies [
14
–
18
] have
investigated water level dynamics in terminal lakes with a projection on climate change.
However, no attempt has been made to date to compare the responses of neighboring
endorheic basins covering the largest continental area. Such a comparative analysis of
lake changes at the regional scale can hint at global tendencies in climate change. With
this purpose in mind, we selected five lakes in Central Asia, seven lakes in the Tibetan
Plateau, and three lakes in the Mongolia Plateau as objects in order to investigate changes
in their water levels and to reveal their links to the regional climatic patterns. In-situ
data on lake water levels and relevant meteorological records are extremely scarce in this
area compared to those in other parts of the world. The availability of meteorological
records in Central Asia were limited for the past 30 years, since many meteorological
stations were discontinued since the Soviet Union collapsed [
19
]. The gauge stations in
the Tibetan Plateau were also limited due to the harsh environment and high elevation.
The rapid development of remote sensing technology has provided an opportunity to
collect continuous data for lakes in recent decades, especially at a large regional scale.
In particular, satellite altimetry data has developed as an alternative tool for lake level
estimation. Several studies have applied time-series satellite altimetry data to detect lake
level change [
6
,
16
,
20
–
23
]. There are two major categories of satellite altimeter data: laser-
and radar-based. The traditional radar altimetry missions (TOPEX/Poseidon, Jason, ERS,
ENVISAT, etc.,) have been collecting lake level data since October 1992, with the revisiting
period of 10 days (for TOPEX/Poseidon, Jason-1, Jason-2) or 35 days (Envisat, ERS-2), and
the footprint diameters of 2–4 km. Compared with radar altimeters, the laser altimeter
missions (ICESat/ICESat-2 and CryoSat-2) have a longer revisit period, e.g., 91 days for
ICESat/ICESat-2, and a relatively denser ground track (0.07 km footprint diameter for
ICESat/ICESat-2), and has been collecting data since 2003.
The ‘dry-gets-drier’ and ‘wet-gets-wetter ’ pattern has been recognized across the
globe as a part of a global change [
24
,
25
]. Previous studies have reported a drying trend
in CA and a warming trend in MP [
26
], and a tendency for warmer and wetter climates
in western Kyrgyzstan zones and Tibetan Plateau [
27
,
28
], especially after 1997 [
15
]. Air
temperature and precipitation were found to be the main factors affecting the changes
in the water level of most lakes in TP [
16
], CA [
29
,
30
], and MP [
15
,
28
]. This temperature
increase over a lake basin could accelerate the melting rate of snow and glaciers, but
also lengthen the period of melting, providing lakes with more water. An increase in
precipitation supplies water to lakes directly as well as via surface and groundwater runoff.
To qualify the reasons for the heterogeneous patterns observed in the lake water levels, we
examined the relationship between the two major climatic factors—precipitation and air
temperature—and the water level of terminal lakes.
The combination of different altimetry satellites can increase the spatial coverage of
lakes and extend the temporal resolution span to nearly thirty years. There are several
global databases that are available to provide water level time series of inland water bodies
by merging different altimeter missions. In our study, we employed three global datasets,
namely the Hydroweb dataset, the Global Reservoir and Lake Monitor (G-REALM), and
the Database for Hydrological Time Series of Inland Waters (DAHITI), to (1) investigate the
interannual characteristics of lake level in Eurasian lakes, (2) compare the spatial patterns
in lake water levels in three adjacent regions, and (3) assess the relationship between lake
dynamics and climatic factors. Finally, we discuss the potential climate drivers for the
spatial heterogeneity of lake-level variations.
Remote Sens. 2021,13, 3705 3 of 21
2. Materials and Methods
2.1. Study Sites
In the following analysis, data from 12 terminal lakes were used, spotted over the
Eurasian endorheic zone. They include the four large terminal lakes of Central Asia: the
Aral Sea (having two virtually separated basins, i.e., the North Aral and the South Aral),
Lake Balkhash, Lake Issyk-Kul, and Lake Sarykamysh. The term “Central Asia” refers
here to the geographical region bounded by the Caspian Sea on the west, the Tian Shan
Mountains on the south, the Altai Mountains on the east, and by the basins of the Ural and
Ob Rivers on the north. The strongly continental arid climate of the region is characterized
by cold winters and hot dry summers. The largest lake of the region, the Caspian Sea,
was intentionally excluded from the analysis as having a unique hydrological regime
determined by complex interactions on its large catchment area (see [
18
] and citations
therein on the Caspian Sea water level change). Two more lakes, the Uvs and Hyargas
(Khyargas), are the largest terminal lakes of the “Great Lakes Depression” in the western
Mongolian Plateau (MP), bounded by the Altai Mountains on the west, the Khungai
Mountains on the east, the Tannu-Ola Mountains on the north, and the Gobi Desert to
the south. The remaining six lakes are located on the Tibetan Plateau (TP). Based on
Yao et al. [31]
, the Kunlun Mountains divide the TP in two parts with different dominant
atmospheric circulation patterns: the area to the south of 35
◦
N is dominated by the Indian
monsoon circulation, and the northern part of the Kunlun Mountains is dominated by
the mid-latitude westerlies [
31
,
32
]. Four of the investigated lakes—Namco, Ngangzco,
Silingco, and Zharinamco—are large saline terminal lakes in the southern TP of the Kunlun
Mountains, whereas Qinghai and Ayakkum are the largest terminal lakes located in the
northern TP. Additionally, one exoreic lake per each large region was included in the
analysis: Lake Zaysan (the largest freshwater lake in Central Asia), Lake Hovsgol (the
largest freshwater lake on Mongolian Plateau), and Lake Ngoring (the largest freshwater
lake on the TP). Herewith, the 15 lakes (Figure 1) provide a representative reference set
allowing for a comparative analysis of the common patterns and differences in the water
level variations of terminal lakes over the Eurasian endorheic zone, and their relation to
the level changes in the open exoreic lakes of the same climate. The basic characteristics of
the selected lake basins are summarized in Table 1.
Table 1. Detailed information about the selected lakes.
Region Lake Name Latitude
(◦N)
Longitude
(◦E)
Area
(km2)
Elevation
(m) Country
Central
Asia
Aral Sea 46.4 60.6 18,999 42 Kazakhstan
Uzbekistan
Sarykamysh
41.9 57.4 3852 5 Uzbekistan
Balkhash 46.1 74.2 16,683 349 Kazakhstan
Issyk-Kul 42.4 77.3 6148 1619 Kyrgyzstan
Zaysan 48.1 83.9 2913 379 Kazakhstan
Tibetan
Plateau
Qinghai 37 100.1 4312 3260 China
Ngoring 34.9 97.7 621 4292 China
Ayakkum 37.5 89.4 856 4161 China
Silingco 31.80 88.99 2222 4550 China
Namco 30.74 90.60 2021 4730 China
Zharinamco
30.92 85.61 1001 4292 China
Ngangzco 31.10 87.10 390 4680 China
Mongolian
Plateau
Uvs 50.3 92.7 3421 759 Mongolia
Hyargas 49.1 93.1 1362 1028 Mongolia
Hovsgol 55.1 100.5 2741 1645 Mongolia
Remote Sens. 2021,13, 3705 4 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 22
Plateau
Hyargas
49.1
93.1
1362
1028
Mongolia
Hovsgol
55.1
100.5
2741
1645
Mongolia
Figure 1. Locations of lakes selected in this study: The rectangles represent the spatial extent of
general endorheic regions in (A) Central Asia, (B) the western Mongolian Plateau, and (C) the Ti-
betan Plateau. The lakes in Central Asia include (1) the South Aral Sea, (2) the North Aral Sea, (3)
Sarykamysh, (4) Balkhash, (5) Issyk-Kul, and (6) Zaysan; The lakes in the Mongolian Plateau are (7)
Uvs, (8) Hyargas, and (9) Hovsgol; The lakes in the Tibetan Plateau are (10) Qinghai, (11) Ngoring,
(12) Ayakkum, (13) Namco, (14) Silingco, (15) Ngangzco, and (16) Zharinamco.
2.2. Lake Water Level Dataset from Satellite Altimetry Data
The water levels of the target lakes were obtained from three different satellite altim-
etry databases: the Hydroweb from Laboratoire d’Etudes en Géophysique et Océanogra-
phie Spatiales (http://hydroweb.theia-land.fr/, accessed on 14 September 2021) [33],
Global Reservoir and Lake Monitor (G-REALM, https://ipad.fas.usda.gov/cropex-
plorer/global_reservoir/, accessed on 14 September 2021) [34,35], and Database for Hydro-
logical Time Series of Inland Waters (DAHITI, https://dahiti.dgfi.tum.de/, accessed on 14
September 2021) [36].
In the Hydroweb database, the lake water level records were provided by combining
several altimetry data, including Topex/Poseidon (T/P), Jason-1, GFO, ERS-1 and ERS-2,
and Envisat satellites, and are available from 1992 to the present. The water level data
were generated by applying several corrections on each altimetry datum, such as iono-
spheric and tropospheric correction. The details on the processing procedures are de-
scribed in [33]. The mean lake level was computed by averaging the altimetry measure-
ments over time, and the lake level anomaly was calculated by subtracting the mean lake
level. The long-term time series of lake water levels since 1992 was generated on a monthly
basis by merging several altimetry data using T/P data as a reference during the overlap
period [33,37]. The lake level records from this dataset range in accuracy from a few cen-
timeters (e.g., 3–9 cm rms at Great Lakes, USA) to tens of centimeters (e.g., 29 cm rms at
Lake Chad, Africa), which is comparable to the gauge data accuracy [37,38].
In the G-REALM altimetry database, the water levels were utilized from T/P, Jason-
1 and Jason-2 and Envisat satellites and are available from 1992 to present. The relative
water level records were provided from G-REALM by merging T/P, Jason-1 and Jason-2
time-series at 10-day intervals. This time series has been smoothed with a median-type
Figure 1.
Locations of lakes selected in this study: The rectangles represent the spatial extent of
general endorheic regions in (
A
) Central Asia, (
B
) the western Mongolian Plateau, and (
C
) the
Tibetan Plateau. The lakes in Central Asia include (1) the South Aral Sea, (2) the North Aral Sea,
(3) Sarykamysh, (4) Balkhash, (5) Issyk-Kul, and (6) Zaysan; The lakes in the Mongolian Plateau are
(7) Uvs, (8) Hyargas, and (9) Hovsgol; The lakes in the Tibetan Plateau are (10) Qinghai, (11) Ngoring,
(12) Ayakkum, (13) Namco, (14) Silingco, (15) Ngangzco, and (16) Zharinamco.
2.2. Lake Water Level Dataset from Satellite Altimetry Data
The water levels of the target lakes were obtained from three different satellite altime-
try databases: the Hydroweb from Laboratoire d’Etudes en Géophysique et Océanographie
Spatiales (http://hydroweb.theia-land.fr/, accessed on 14 July 2021) [
33
], Global Reservoir
and Lake Monitor (G-REALM, https://ipad.fas.usda.gov/cropexplorer/global_reservoir/,
accessed on 14 July 2021) [
34
,
35
], and Database for Hydrological Time Series of Inland
Waters (DAHITI, https://dahiti.dgfi.tum.de/, accessed on 14 July 2021) [36].
In the Hydroweb database, the lake water level records were provided by combining
several altimetry data, including Topex/Poseidon (T/P), Jason-1, GFO, ERS-1 and ERS-2,
and Envisat satellites, and are available from 1992 to the present. The water level data were
generated by applying several corrections on each altimetry datum, such as ionospheric and
tropospheric correction. The details on the processing procedures are described in [
33
]. The
mean lake level was computed by averaging the altimetry measurements over time, and
the lake level anomaly was calculated by subtracting the mean lake level. The long-term
time series of lake water levels since 1992 was generated on a monthly basis by merging
several altimetry data using T/P data as a reference during the overlap period [
33
,
37
]. The
lake level records from this dataset range in accuracy from a few centimeters (e.g., 3–9 cm
rms at Great Lakes, USA) to tens of centimeters (e.g., 29 cm rms at Lake Chad, Africa),
which is comparable to the gauge data accuracy [37,38].
In the G-REALM altimetry database, the water levels were utilized from T/P, Jason-1
and Jason-2 and Envisat satellites and are available from 1992 to present. The relative
water level records were provided from G-REALM by merging T/P, Jason-1 and Jason-2
time-series at 10-day intervals. This time series has been smoothed with a median-type
filter to eliminate outliers and reduce high-frequency noise, using the mean value of the
Jason-2 water level as the reference [
39
]. The rms in the range of 6 cm to 34 cm and median
correlation higher than 0.90 were observed between G-REALM and gauge-based data on
18 lakes and reservoirs distributed across three continents [38,39].
Remote Sens. 2021,13, 3705 5 of 21
The DAHITI database also combined many altimetry satellite products, such as T/P,
Jason 1, Jason-2 Envisat, ERS-2, and SARAL/AltiKa. The processing strategy was based on
a Kalman filtering approach and extended outlier detection [
36
]. Compared with in-situ
data, the lake level data from DAHITI show the accuracies to be between 4 and 36 cm rms,
depending on the surface extent of the lake and climate conditions (i.e., ice coverage) [
36
].
These databases have been widely used in related studies on lake water levels because
of their fine temporal resolution and good validated accuracy [
6
,
29
,
33
]. The annual time
series of the water levels was calculated for each lake from three altimetry datasets. The
consistency and accuracy of the time series from the three products were verified by
correlation analysis and cross-evaluation, taking into account the different datum/reference
systems when combining the records from different satellite data. In our study, the bias
of the absolute water level value was not removed among the different products when
the consistency of water level records was significantly good to avoid unnecessary errors.
Thereafter, the statistical information for evaluating the water level variability, such as the
trends and mean values, was calculated from the Hydroweb database as the reference,
considering that it had the longest record for most target lakes.
2.3. Meteorological Dataset
The climate effect on lake level variation was traced with the regional precipitation and
temperature patterns. Due to possible large variation and uncertainty in single-point mea-
surements, precipitation records were selected from global gridded observation datasets
to evaluate the climatic pattern. We used three in-situ-based products: Global Precipita-
tion Climatology Centre products (GPCC, https://www.dwd.de/EN/ourservices/gpcc/
gpcc.html, accessed on 14 July 2021) [
40
,
41
], precipitation products from the University of
Delaware (UDEL, http://climate.geog.udel.edu/~climate/html_pages/download.html,
accessed on 14 July 2021) [
42
], and Climate Research Unit products (CRU, http://www.
cru.uea.ac.uk/data, accessed on 14 July 2021) [
43
]. The temperature dataset was adopted
from the CRU products.
The GPCC precipitation data was based on 85,000 meteorological stations spread
worldwide with a record duration of 10 years or longer. It collected and integrated many
observations from the national meteorological agencies (NMAs), which were the primary
data source, and the Food and Agriculture Organization (FAO), the Global Historical
Climate Network (GHCN2), and the World Meteorological Organization (WMO), among
others. The latest version of GPCC V7 [
41
] provided monthly precipitation data from
January 1901 to December 2016. The UDEL product was also compiled from several
updated sources, and the number of stations in this dataset ranges from 4100 to 22,000,
globally. The newest version of UDEL (V5.01) provided the monthly precipitation spanning
from January 1900 to December 2018. The CRU dataset comprised a suite of climate
variables, including precipitation and temperature. This dataset was obtained based on
more than 4000 meteorological stations through the NMAs, the WMO, the FAO, and
other sources. The latest version of CRU (TS 4.01) provided data at a monthly scale from
1901 to 2018.
These datasets were built based on a network of gauge observations and were widely
used as a “baseline” dataset for the validation of other model outputs and satellite prod-
ucts [
44
–
46
]. In our study, we adopted data from the newest version on a monthly basis
with a spatial resolution of 0.5
◦×
0.5
◦
and selected a comparable period of 1990–2016. The
precipitation and temperature records for each lake were extracted at the basin scale.
2.4. Data Integration
The precipitation datasets mentioned above have generally shown similar spatial
patterns and temporal variations [
47
,
48
]. The seasonal cycles of precipitation over the study
areas were demonstrated from three products in the three respective lake basins (Figure 2).
The three products showed a generally good agreement since they use many rain gauges
Remote Sens. 2021,13, 3705 6 of 21
in common. The spread showed a slight difference in summer, especially in mountainous
areas [49], possibly due to the differences in grid and interpolation approaches [50].
Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 22
(Figure 2). The three products showed a generally good agreement since they use many
rain gauges in common. The spread showed a slight difference in summer, especially in
mountainous areas [49], possibly due to the differences in grid and interpolation ap-
proaches [50].
Figure 2. The seasonal cycle of precipitation from different gridded datasets over three lakes from
three endorheic lake zones during the period of 1990–2016: (a) Lake Silingco, selected as the example
for the Tibetan Plateau (TP), (b) Lake Balkhash, from the Central Asia (CA), and (c) Lake Hovsgol,
from the Mongolian Plateau (MP).
The simple weight approach from [51] was employed to integrate different precipi-
tation datasets into one single data series with a minimum root mean square error (RMSE).
Specifically, the method was used as a weighted average of all the products, and the
weights were determined based on the error level. First, the error variance of each product
was calculated using the mean of the products as the truth. The weights were summed up
to 1 and were calculated as follows:
(1)
(2)
where is the weight for the product at the grid of ,
is the error variance of the
product , is the total number of products to merge, is the precipitation from the
product at the grid of ,
is the mean value of the product .
2.5. Trend Analysis
We adopted the non-parametric Mann–Kendall (MK) test [52,53] to detect the signif-
icance of trends in the time series of lake water level and climate-related variables. The
MK test was minimally affected by the un-normalized distribution of variables [54]. How-
ever, data should be assumed to be independent. According to Von Storch [55], autocor-
relation would lead to a rejection of the null hypothesis of no trend when the null hypoth-
esis was actually true. To eliminate this concern, the "trend-free pre-whitening” method,
based on Yue, et al. [56] and Yue and Wang (2004) was applied prior to the MK test to
preserve the magnitude of a trend. The combination can provide an accurate trend esti-
mate of the autocorrelation process and has been widely used in hydrological and mete-
orological time series [57–59]. The slope was estimated using Sen’s estimator [60], consid-
ering its robustness against outliers. In our study, we adopted significance levels of α =
0.01 and α = 0.05.
2.6. Cumulative Anomaly Analysis
The cumulative anomaly analysis can be used not only to identify the state of changes
in the time series anomaly, i.e., above or below the average condition, but also to evaluate
the accumulated effects of climate variables over a certain period and their long-term
tendencies. It has commonly been used to assess variations in hydrological and meteoro-
logical factors [61–63]. In our study, we calculated the cumulative anomaly time series of
annual precipitation and temperature in each lake basin. The cumulative anomaly
at the year of t can be expressed as:
Figure 2.
The seasonal cycle of precipitation from different gridded datasets over three lakes from
three endorheic lake zones during the period of 1990–2016: (
a
) Lake Silingco, selected as the example
for the Tibetan Plateau (TP), (
b
) Lake Balkhash, from the Central Asia (CA), and (
c
) Lake Hovsgol,
from the Mongolian Plateau (MP).
The simple weight approach from [
51
] was employed to integrate different precipita-
tion datasets into one single data series with a minimum root mean square error (RMSE).
Specifically, the method was used as a weighted average of all the products, and the
weights were determined based on the error level. First, the error variance of each product
was calculated using the mean of the products as the truth. The weights were summed up
to 1 and were calculated as follows:
wi,j=1
σ2
i,j,n
∑
j=1
1
σ2
j
(1)
σ2
i,j=vi,j−vj2(2)
where
wi
is the weight for the product
j
at the grid of
i
,
σ2
i,j
is the error variance of the
product
j
,
n
is the total number of products to merge,
vi,j
is the precipitation from the
product jat the grid of i,vjis the mean value of the product j.
2.5. Trend Analysis
We adopted the non-parametric Mann–Kendall (MK) test [
52
,
53
] to detect the signifi-
cance of trends in the time series of lake water level and climate-related variables. The MK
test was minimally affected by the un-normalized distribution of variables [
54
]. However,
data should be assumed to be independent. According to Von Storch [
55
], autocorrelation
would lead to a rejection of the null hypothesis of no trend when the null hypothesis was
actually true. To eliminate this concern, the “trend-free pre-whitening” method, based
on Yue et al. [
56
] and Yue and Wang (2004) was applied prior to the MK test to preserve
the magnitude of a trend. The combination can provide an accurate trend estimate of
the autocorrelation process and has been widely used in hydrological and meteorological
time series [
57
–
59
]. The slope was estimated using Sen’s estimator [
60
], considering its
robustness against outliers. In our study, we adopted significance levels of
α= 0.01
and
α= 0.05.
2.6. Cumulative Anomaly Analysis
The cumulative anomaly analysis can be used not only to identify the state of changes
in the time series anomaly, i.e., above or below the average condition, but also to evaluate
the accumulated effects of climate variables over a certain period and their long-term ten-
dencies. It has commonly been used to assess variations in hydrological and meteorological
factors [
61
–
63
]. In our study, we calculated the cumulative anomaly time series of annual
precipitation and temperature in each lake basin. The cumulative anomaly
CUMt
at the
year of t can be expressed as:
Remote Sens. 2021,13, 3705 7 of 21
CUMt=∑n
i=1xi−X(t=1, 2, 3, , , , n)(3)
X=1
n
n
∑
i=1
(xi)(4)
where
xi
is the yearly value of temperature or precipitation,
n
is the number of years of
data used.
2.7. Change Point Detection
The change point was defined as the time when the means become statistically dif-
ferent. This point was regarded as a possible starting point of the new regime. The Pettitt
test [
64
] and the Bayesian change point test [
65
,
66
] were applied to detect shifts in the
meteorological variables. The Pettitt test is a non-parametric trend test used to estimate
the occurrence of a change point and has been widely used to detect abrupt changes in
hydrological and climatic series [
62
,
67
]. The Bayesian change point test detects a change
point at an unknown time point and the amount of shift in the time series, operating under
the assumption that a change had occurred. This test detects changes in the mean, trend,
and/or variance by using a minimum segment length between two shifts. A change point
was selected only when the two methods detected the same change point; then, mean
values before and after the regime shift were calculated.
3. Results
3.1. Spatiotemporal Variations of Lake Level
The water level changes during the period of 1992–2016 for the selected lakes are
summarized in Figure 3(see also Supplementary Figures S5–S8). The time series of water
levels from the three altimetry datasets showed a high consistency in all the examined lakes,
as the correlation coefficients (R) were significant at the 95% confidence level, and the R
values for 14 lakes were higher than 0.9 (Table 2). The results were also in good agreement
in depicting intra-annual variations and abrupt changes in lake levels. For example, a
sudden turning point of the water level in Lake Qinghai occurred in 2005, followed by an
increasing trend from this year, which was captured by all the satellite altimetry data.
Table 2.
Trends of lake water levels from the Hydroweb dataset during the period of
1992–2018
and
Pearson correlation coefficients calculated between three altimetry datasets: The significance is shown
in bold font. The blank in the table is due to a lack of data in the corresponding altimetry databases.
Lake Name Trend
(cm/yr)
Pearson Correlation Coefficient
Hydroweb
GREALM
GREALM
DAHITI
Hydroweb
DAHITI
Central
Asia
Balkhash 5.762 0.996 0.930 1.000
Issyk-Kul 1.698 0.994 0.981 1.000
Zaysan 6.067 0.865 0.982 0.968
Aral Sea South −37.824 0.995 1.000 0.997
Aral Sea North 5.862 0.987 0.989 0.996
Sarykamysh 21.151 0.988 0.997 1.000
Tibetan
Plateau
Qinghai 10.461 0.996
Ngoring 8.651 0.983
Ayakkum 32.631 0.991
Zharinamco 14.763 0.980 0.742 0.953
Ngangzco 3.012 0.996 0.962 0.959
Namco 13.821 0.940
Silingco 52.451 0.999
Monglian
Plateau
Uvs −0.6172 0.936
Hovsgol −1.192 0.586 0.377 0.497
Hyargas −36.143 0.999
Remote Sens. 2021,13, 3705 8 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 22
Table 2. Trends of lake water levels from the Hydroweb dataset during the period of 1992–2018 and
Pearson correlation coefficients calculated between three altimetry datasets: The significance is
shown in bold font. The blank in the table is due to a lack of data in the corresponding altimetry
databases.
Lake Name
Trend
(cm/yr)
Pearson Correlation Coefficient
Hydroweb
GREALM
GREALM
DAHITI
Hydroweb
DAHITI
Central
Asia
Balkhash
5.762
0.996
0.930
1.000
Issyk-Kul
1.698
0.994
0.981
1.000
Zaysan
6.067
0.865
0.982
0.968
Aral Sea South
−37.824
0.995
1.000
0.997
Aral Sea North
5.862
0.987
0.989
0.996
Sarykamysh
21.151
0.988
0.997
1.000
Tibetan Plat-
eau
Qinghai
10.461
0.996
Ngoring
8.651
0.983
Ayakkum
32.631
0.991
Zharinamco
14.763
0.980
0.742
0.953
Ngangzco
3.012
0.996
0.962
0.959
Namco
13.821
0.940
Silingco
52.451
0.999
Monglian
Plateau
Uvs
−0.6172
0.936
Hovsgol
−1.192
0.586
0.377
0.497
Hyargas
−36.143
0.999
Figure 3. The annual change of water level in lakes located at Central Asia (a,b), Tibetan Plateau
(c,d), and Mongolian Plateau (e).
Figure 3.
The annual change of water level in lakes located at Central Asia (
a
,
b
), Tibetan Plateau (
c
,
d
),
and Mongolian Plateau (e).
All three regions revealed diverging spatial characteristics between the CA, the TP,
and the MP; the temporal patterns of water level variations in the 15 lakes were also
distinguished by different trends. In the CA region, the water levels showed a generally
increasing trend (Figure 3a,b), except for the south Aral Sea. The south Aral Sea (Figure 3a)
had a continuous decreasing trend since the beginning of the analyzed period (1992), which
was consistent with the Aral Sea desiccation starting in 1960 and described in previous
studies [
68
,
69
]. However, the lake level stopped decreasing in 2009 (Figure 3a). The water
levels of Lake Balkhash and Lake Issyk-Kul showed similar patterns during the whole
period, increasing by 0.058 cm/yr and 0.017 cm/yr, respectively. The lake water levels
grew until approximately 2008, followed by fluctuations with no obvious trends. The
fluctuations in water level were found in Lake Zaysan, where the initial level decrease was
replaced by an increasing trend since 2008. It is interesting to note that the latter increase in
the lake level in the open lake coincided with a simultaneous increase of the lake water
level in the endorheic lakes of the region.
In the TP, the water levels in the lakes showed an overall upward trend during the past
three decades, with a decrease in the early stage, followed by a strong increase (Figure 3and
Supplementary Figures S2–S4). These increasing trends were more evident from around
1997 onwards in the lakes located in the southern part of the TP and from approximately
2005 onwards in the northern part of the TP. In the southern TP region, based on the time
series from the Hydroweb altimetry dataset, the water levels of the four lakes since circa
1997 have increased by 17.815 cm/yr for Lake Namco, 12.882 cm/yr for Lake Zharinamco,
56.923 cm/yr for Lake Silingco, and 30.312 cm/yr for Lake Ngangzco (Figure 3d). Note-
worthy, the increasing trend of the water level in Lake Namco paused in approximately
2005 and was then followed by a fluctuation. From 1992 to 1997, the lake levels showed
a decreasing trend in Lake Zharinamco and Lake Ngangzco. This decreasing trend was
unclear in Lake Namco and Lake Silingco, as the records of the water levels in the two
lakes started in 1995. In the northern TP region, the lake water levels presented noticeably
Remote Sens. 2021,13, 3705 9 of 21
increasing trends of 9.214 cm/yr (Lake Qinghai), 8.576 cm/yr (Lake Ngoring), 57.023 cm/yr
(Lake Ayakkum), and 30.284 cm/yr (Lake Ngangzco). Notably, these increases were more
evident from 2005 onwards in Lake Qinghai and Lake Ngoring; before 2005, the water
levels in the two lakes were stable, with a slight decrease.
In the MP region, the lake water levels in all three lakes showed generally decreasing
trends. Lake Uvs and Lake Hyargas showed continuously decreasing water levels since 2002,
by
−
6.211 cm/yr and
−
36.102 cm/yr, respectively (Figure 3e,
Supplementary Figure S4)
.
The water level in open Lake Hovsgol decreased slightly by
−
1.183 cm/yr from 1992 to
2018, with an initial positive trend before 2004, followed by an obvious level decrease.
Notably, the lake levels showed clear decreases from 2002–2004 onwards in all the lakes of
the region, whereas the rate of change was different for open and closed lakes.
Generally, the lake water levels showed increasing trends both in the CA and in the
TP, while showed decreasing trends in the MP. However, the increasing patterns exhibited
different characteristics in CA and TP regions. There was a turning point in approximately
2005 at the northern part of the TP, and from 2005 onwards, the lake levels experienced
significant rapid increases. In turn, in the southern part of the TP, except Lake Silingco, the
turning point occurred in 1997, and the lake levels showed slow increases after 2005. In the
CA region, the lake levels continuously increased until approximately 2005 and showed
fluctuating behavior afterwards.
3.2. Climate Effects on the Lake Levels
Among the two major climatic factors—precipitation and air temperature—a similar
the air temperature pattern was found over both TP and CA: Air temperature decreased
from 1990 to 1997 and changed to an apparent increase afterwards. In contrast to the
CA, the air temperature increase slowed down from 1997 to 2005 in the TP. In the MP,
increasing trends of air temperature were found over the three lake basins before 2007,
followed by large fluctuations with an evident temperature drop in 2012 (Figure 4and
Supplementary Figure S8).
Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 22
showed obvious negative trends of water level over TP and showed a slightly positive
trend of water level over CA (except the Aral Sea South), but experienced different cli-
mate: a warmer and wetter climate in TP, but warmer and a slightly dryer climate in CA
(except Lake Issyk-Kul).
The different response of lake levels to the precipitation and air temperature can be
ascribed to the effect of additional glacier runoff. The surface runoff from precipitation
will have a direct impact on lake level variation, but the warmer air temperature will in-
crease the meltwater runoff from glacier and snow in glacier-fed lakes, and the surface
runoff from precipitation. For the non-glacier-fed lakes in CA, the fluctuation of lake level
is more consistent with the annual variation of precipitation than with air temperature,
indicating a direct impact of precipitation on water levels. In turn, the selected lakes in TP
with an apparent water level growth were glacier-fed and had a significantly positive cor-
relation between spring air temperature and lake level (Figure 4). For example, in Lake
Namco, the lake level showed a significant correlation of 0.626 with the April temperature
of 0.626, but was not correlated with precipitation, demonstrating the higher importance
of meltwater runoff compared with precipitation.
Figure 4. The correlation relationship map between water level variation and monthly precipitation
and air temperature over three lakes in the Mongolian Plateau: Asterisks mark significant correla-
tions. The circle size scales with the absolute correlation coefficient value.
Table 3. The summary of trends of lake level anomaly, precipitation anomaly and temperature
anomaly in the lake over CA, TP, and TP.
Lake Names
Glacier
Area/La
ke Area
Lake Level
Anomaly
Precipitation
Anomaly
Temperature
Anomaly
Central
Asia
Balkhash
Increase(+), then
fluctuate
Fluctuated
Slight increase
(+)
Zaysan
Decrease (−), then
increased (+)
Decrease (−),
then increased
(+)
Increase (+)
Issyk-Kul
0.08
Increase (+)
Increase (+)
Increase (+)
Sarykmysh
Increase (+)
Fluctuated
Increase (+)
Aral Sea
Fluctuated
Fluctuated
decrease (−)
Increase (+)
Tibetan
Plateau
(south)
Zharinamco
0.15
Increase (+),
Increase (+), then
fluctuated
Increase (+)
Namco
0.1
Increase (+),
Increase (+), then
fluctuated
Increase (+)
Silingco
0.13
Increase (+)
Increase (+)
Increase (+)
Ngangzco
0.02
Increase (+)
Increase (+)
Increase (+)
Figure 4.
The correlation relationship map between water level variation and monthly precipitation
and air temperature over three lakes in the Mongolian Plateau: Asterisks mark significant correlations.
The circle size scales with the absolute correlation coefficient value.
In CA, precipitation generally decreased during most of the study period, but found an
increase after 2014. Precipitation in the lake basins exhibited different patterns in the south-
ern and northern part of TP. The cumulative precipitation value reached a low point in ap-
proximately 2005 in the northern TP, and then started to increase
(Supplementary Figure S6)
.
In the southern TP, the precipitation had a large variability from 2005 onwards, with no obvi-
ous trend during this period (Figure 4, Supplementary Figure S7). Except for Lake Silingco,
the increase rates of the lake levels slowed down in this region
(Supplementary Figure S7)
.
In the MP, the cumulative precipitation showed a general decrease during the entire period
for three lakes (Supplementary Figure S8), which is consistent with the pattern of lake level
Remote Sens. 2021,13, 3705 10 of 21
variations. A warmer and drier environment, especially after 2009, when the temperature
increased significantly, probably mainly contributed to a lower water level in this region.
The different patterns of precipitation and temperature at the lake basin scale
(Table 3)
were reflected in the spatial heterogeneity of lake level variations. The selected lakes
showed obvious negative trends of water level over TP and showed a slightly positive
trend of water level over CA (except the Aral Sea South), but experienced different climate:
a warmer and wetter climate in TP, but warmer and a slightly dryer climate in CA (except
Lake Issyk-Kul).
Table 3.
The summary of trends of lake level anomaly, precipitation anomaly and temperature anomaly in the lake over CA,
TP, and TP.
Lake
Names
Glacier Area/
Lake Area
Lake Level
Anomaly
Precipitation
Anomaly
Temperature
Anomaly
Central
Asia
Balkhash Increase(+), then fluctuate Fluctuated Slight increase (+)
Zaysan Decrease (−), then increased (+) Decrease (−), then increased (+) Increase (+)
Issyk-Kul 0.08 Increase (+) Increase (+) Increase (+)
Sarykmysh Increase (+) Fluctuated Increase (+)
Aral Sea Fluctuated Fluctuated decrease (−) Increase (+)
Tibetan
Plateau
(south)
Zharinamco 0.15 Increase (+), Increase (+), then fluctuated Increase (+)
Namco 0.1 Increase (+), Increase (+), then fluctuated Increase (+)
Silingco 0.13 Increase (+) Increase (+) Increase (+)
Ngangzco 0.02 Increase (+) Increase (+) Increase (+)
Tibetan
Plateau
(north)
Qinghai 0.01 Increase (+) Increase (+) Increase (+)
Ngoring Increase (+) Increase (+) Increase (+)
Ayakkum 0.55 Increase (+) Increase (+) Increase (+)
Mongolian
Plateau
Uvs Decrease (−) Decrease (−) Fluctuated increase (+)
Hyargas Decrease (−) Decrease (−) Fluctuated increase (+)
Hovsgol Decrease (−) Decrease (−) Fluctuated increase (+)
The different response of lake levels to the precipitation and air temperature can be
ascribed to the effect of additional glacier runoff. The surface runoff from precipitation will
have a direct impact on lake level variation, but the warmer air temperature will increase
the meltwater runoff from glacier and snow in glacier-fed lakes, and the surface runoff
from precipitation. For the non-glacier-fed lakes in CA, the fluctuation of lake level is more
consistent with the annual variation of precipitation than with air temperature, indicating
a direct impact of precipitation on water levels. In turn, the selected lakes in TP with an
apparent water level growth were glacier-fed and had a significantly positive correlation
between spring air temperature and lake level (Figure 4). For example, in Lake Namco, the
lake level showed a significant correlation of 0.626 with the April temperature of 0.626, but
was not correlated with precipitation, demonstrating the higher importance of meltwater
runoff compared with precipitation.
3.3. Regime Shifts in Precipitation and Temperature
To explore possible factors leading to the turning trends in lake level, the regime shift
of annual time series of precipitation and air temperature were checked by the Pettitt test
and the Bayesian change point test (Table 4).
Two shifts were detected in the precipitation records in the CA region
(Figure 5)
,
dividing the period of 1990–2016 into three periods: 1990–1997, 1998–2008, and
2009–2016
.
Stepwise water level increases were found in the three periods in Lake Balkhash, Lake
Issyk-Kul, and Lake Zaysan, linking the lake level growth since 1997 to the regime shift
in air temperature in 1997. In the TP, the temperature shift occurred in 1997, similar to
that in the CA, and was associated with a corresponding turning point in the lake level
variation. The regime shifts in precipitation in the TP were different between the northern
and southern parts (Figures 6and 7): In the northern part of TP, a shift in 2004 was detected
Remote Sens. 2021,13, 3705 11 of 21
in Lake Qinghai and Lake Ngoring, and a shift in 2001 was found in Lake Ayakkum, with
an increasing trend of precipitation after the change point. In the southern TP, 1997 and
2004 were identified as shift years in Lake Namco and Zharinamco, respectively. The mean
value of precipitation increased in first phase and decreased in second phase
(Table 4)
. In
the MP region, the patterns of precipitation and temperature were different from those in
the other two regions, corresponding to the gradual decrease in the lake level variation
(Figure 8). The shift years of precipitation were found in 1994 and 2003. The precipitation
decreased in the first two phases and then increased in the last phase, while shifts in
temperature were found in 1997. The air temperature also showed a slight decrease from
2009 onwards.
Table 4.
The change points and the mean value of each regime in precipitation and temperature over
the lake basin located at Tibetan Plateau, Central Asia, and Mongolian Plateau.
Region Lake Names
Precipitation Temperature
The Timing of
Change Point
Mean Value of
Each Regime
(cm)
The Timing of
Change Point
Mean Value of
Each Regime
Tibetan
Plateau
Qinghai 2004 284,714
323,244
1997
2004
−7.677
−7.202
−6.990
Ngoring 2004 291,226
340,488
1997
2004
−10,490
−10,106
−9.663
Ayakkum 2001 59,604 1997 −9.910
70,768 −9.035
Silingco 1997 400,316 1997 −10,475
458,957 −9.532
Namco 1997
2004
447,550
1997 −7.680
−6.962
530,700
485,552
Zharinamco 1997
2004
656,850
1997
−10,549
792,739 −9.749
713,963
Ngangzco 1996 618,187 1997 −10,096
744,426 −9.191
Central
Asia
Balkhash 1997
2008
256,614
1997 −1.430
−0.436
276,950
302,053
Issyk-Kul 1997
2008
289,133
1997 −5.215
−4.012
344,317
359,559
Zaysan 1997
2008
348,753
1997 −3.430
−2.719
373,358
402,177
Sarykmysh 1997
2008
133,173
1997 7.073
7.897
88,694
112,688
Aral Sea 1997 4.084
5.081
Mongolian
Plateau
Uvs
1994 283,536 1997 −10,935
2004 237,685 2005 −10,188
258,436 −10,579
Hovsgol
1994 328,044 1997 −11,428
2004 271,891 2007 −10,656
302,621 −10,971
Hyargas
1994 241,836
2007
−9.371
2004 202,668 −8.737
220,327
Remote Sens. 2021,13, 3705 12 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 22
Figure 5. The significant regime shift in time series of precipitation (green line, left panel) and tem-
perature (red line, right panel) over five lakes in Central Asia: vertical lines represent regime shift
years and horizontal dash lines represent the mean value of each regime.
Figure 5.
The significant regime shift in time series of precipitation (green line,
left panel
) and
temperature (red line,
right panel
) over five lakes in Central Asia: vertical lines represent regime
shift years and horizontal dash lines represent the mean value of each regime.
Remote Sens. 2021,13, 3705 13 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 22
Figure 6. The significant regime shift in time series of precipitation (green line, left panel) and
temperature (red line, right panel) over three lakes in the northern part of the Tibetan Plateau:
vertical lines represent regime shift years and horizontal dash lines represent the mean value of each
regime.
Figure 6.
The significant regime shift in time series of precipitation (green line,
left panel
) and
temperature (red line,
right panel
) over three lakes in the northern part of the Tibetan Plateau:
vertical lines represent regime shift years and horizontal dash lines represent the mean value of
each regime.
Remote Sens. 2021,13, 3705 14 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 22
Figure 7. The significant regime shift in time series of precipitation (green line, left panel) and
temperature (red line, right panel) over four lakes in the southern part of the Tibetan Plateau:
vertical lines represent regime shift years and horizontal dash lines represent the mean value of each
regime.
Figure 7.
The significant regime shift in time series of precipitation (green line,
left panel
) and
temperature (red line,
right panel
) over four lakes in the southern part of the Tibetan Plateau: vertical
lines represent regime shift years and horizontal dash lines represent the mean value of each regime.
Remote Sens. 2021,13, 3705 15 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 22
Figure 8. The significant regime shift in time series of precipitation (green line) and temperature
(red line) over three lakes in the Mongolian Plateau: vertical lines represent regime shift years and
horizontal dash lines represent the mean value of each regime.
4. Discussion
The essence of the above analysis consisted in evaluating the potential of terminal
lakes as single-point indicators for multiple climate change stresses in large endorheic ba-
sins. In this way, the water level variations in endorheic lakes may provide a valuable
insight into both the regional hydrological regimes and global circulation changes. The
patterns revealed in the three regions under study suggest diverging trends in the Tibetan
Plateau, the Central Asia, and the Mongolian Plateau.
The rapidly increasing lake water levels in the TP agreed with previous studies
[6,14,15], whereas apparent differences between the northern and southern parts indi-
cated the different characteristics of the hydrological response in the monsoon-dominated
southern part of the plateau and the westerlies-dominated northern part. The lake levels
in the southern TP started to increase in 2003 but stabilized in approximately 2008, fol-
lowed by fluctuations without a significant trend. This pattern is also supported by results
from Zhang, Xie, Kang, Yi and Ackley [20], who used the ICESat altimetry data available
from 2003. On the other hand, the turning point in the northern TP was in approximately
2005, with continuous significant increase in lake levels occurring afterwards.
All lakes in the CA region have experienced a dramatic increase in lake water levels
since the 1990s. This consistent rise in lake water levels across all basins in the largest
endorheic region is a notable result of the present study. Numerous previous studies have
reported an alarming decrease in lake levels from the mid- to late 20th century. The grow-
Figure 8.
The significant regime shift in time series of precipitation (green line,
left panel
) and
temperature (red line,
right panel
) over three lakes in the Mongolian Plateau: vertical lines represent
regime shift years and horizontal dash lines represent the mean value of each regime.
4. Discussion
The essence of the above analysis consisted in evaluating the potential of terminal
lakes as single-point indicators for multiple climate change stresses in large endorheic
basins. In this way, the water level variations in endorheic lakes may provide a valuable
insight into both the regional hydrological regimes and global circulation changes. The
patterns revealed in the three regions under study suggest diverging trends in the Tibetan
Plateau, the Central Asia, and the Mongolian Plateau.
The rapidly increasing lake water levels in the TP agreed with previous studies
[6,14,15]
,
whereas apparent differences between the northern and southern parts indicated the
different characteristics of the hydrological response in the monsoon-dominated southern
part of the plateau and the westerlies-dominated northern part. The lake levels in the
southern TP started to increase in 2003 but stabilized in approximately 2008, followed by
fluctuations without a significant trend. This pattern is also supported by results from
Zhang, Xie, Kang, Yi and Ackley [
20
], who used the ICESat altimetry data available from
2003. On the other hand, the turning point in the northern TP was in approximately 2005,
with continuous significant increase in lake levels occurring afterwards.
All lakes in the CA region have experienced a dramatic increase in lake water levels
since the 1990s. This consistent rise in lake water levels across all basins in the largest
Remote Sens. 2021,13, 3705 16 of 21
endorheic region is a notable result of the present study. Numerous previous studies
have reported an alarming decrease in lake levels from the mid- to late 20th century. The
growing water use demand during this century was charged as being responsible for the
continuous drying of Lake Issyk-Kul [
70
,
71
], Lake Balkhash [
72
], Lake Zaysan [
73
], and
Lake Sarykamysh [
74
]. These lakes were threatening to share the fate of the infamous
Aral Sea, which had desiccated to 10% of its volume within 40 years between 1960 and
2000 [
69
,
70
]. The nearly simultaneous and consistent turn to the water level increase can,
in this case, be treated as a signature of a large-scale change in the hydrologic regime in the
arid zone of CA. The results agreed with recent findings: Bai et al. [
75
] investigated the lake
area variation in CA based on Landsat images from 1975 to 2007 and found that most of the
lake surface area had an increasing trend since 1997.
Propastin [76]
and
Imentai et al. [5]
also found that the water level in Lake Balkhash increased from 1993 onwards. We have
revealed a similar rise in water level in Sarykamysh, which was additionally affected by
regulation of the drained water.
In this regard, Sarykamysh and the two largest remaining water bodies of the Aral
Sea: The South Aral Sea and the North Aral Sea are the important representatives of the
strongly regulated waters, with different facets of human impact: the level of the North
Aral Sea is regulated by a dam, the South Aral Sea experiences a continuous water deficit
due to the agricultural water withdraw on its catchment, and Sarykamysh is mainly fed
by the runoff from the surrounding irrigated lands. A special case is represented by the
two largest remaining water bodies of the Aral Sea: the Aral Sea South and the Aral Sea
North. The latter is mainly fed by the inflow of the Syr-Daria River, and its water level
has been regulated since 2005 by the Dike Kokaral—a dam separating the North Aral Sea
from the rest of the former Aral Sea basin. As a result, the water level in the North Aral Sea
had quickly grown to the maximum value allowed by the construction of the dam and has
remained nearly constant after 2006, whereas the dam floodgates were kept open for most
parts of the year. Simultaneously, the water level in the Aral Sea south stabilized around a
constant value after decades of continuous decline. The main tributary of the South Aral
Sea, the Amu-Daria River, is strongly regulated and intensively used for irrigation and
generally does not reach the lake, partially draining to Sarykamysh Lake (Figure 1). Hence,
stabilization of the water level in the South Aral Sea may be interpreted as a result of the
same large-scale processes causing the water level increases in other lakes of CA.
Only one of the three regions in our study, the MP, revealed a gradual decrease
in lake water levels since 1997. The drying trends are apparent in both large terminal
lakes in the region and, to a lesser degree, in the exoreic Lake Hovsgol. This decreasing
trend is supported by previous studies which have reported a significant loss in lake
area and number, especially from the late 1990s to 2010 based on remote sensing image
analysis [
15
,
28
]. Our results exhibited decreasing rainfall and growing temperatures in this
region, which could further explain this shrinking pattern of lakes that were affected by a
drier and warmer climate.
The coherent pattern of the water level changes in the lakes in CA and the TP, covering
the largest intracontinental endorheic area, provides an important insight into the climate
change effects on the arid Eurasian regions. The turn of the water level trend in the terminal
lakes from the long-term decrease [
76
–
79
] to a consistent increase suggests the fundamental
changes in the regional atmospheric circulation and water balance. Our results have shown
that the spatial pattern of lake water levels was considerably related to climatic variables,
such as precipitation and air temperature. The “turning point” can be seen in the trend
of lake water levels, for example, in 1997 in CA, 1998 in the southern TP, and 2005 in
the northern TP. This phenomenon was considerably related to the climate regime shift:
more rainfall and a higher air temperature in the lake basins were found in CA and the TP,
especially since 1997. These two regions experienced a climate shift from a warm and dry to
a warm and humid environment, which has also been supported by several studies [
77
–
79
].
The variability in water level in Lake Zaysan (an open lake) was also highly correlated with
precipitation variation. Climate regime shift would also have a profound influence on the
Remote Sens. 2021,13, 3705 17 of 21
regional hydrological cycle, and terminal lakes demonstrated their efficiency as indicators
of climate shifts at basin spatial scales.
Our results suggest that precipitation was the dominant factor explaining the interan-
nual variability in lake water levels, in particular, in the TP and CA. Our findings were also
supported by previous studies [
6
,
7
] and validated using hydrological models [
80
,
81
]. It is
likely that glacial meltwater may also contribute to the water level increases in endorheic
lakes when the basins contain glaciers. This is especially important in the TP and CA, where
the meltwater from glaciers can be the major tributary to surface runoff, and most glaciers
have been experiencing melting over the past decades due to increasing air temperatures
since 1997 [
82
]; this provided further positive feedback on the evaporation–precipitation
balance. However, satellite data and glacier mass balance suggest that increased glacial
meltwater contributed only ~10% to lake expansion in the interior TP [
83
], and that the
glacial runoff into the lakes themselves should not increase the overall water volume mass
on the TP [
9
]. Additionally, according to hydrological modeling [
80
], the glacial meltwater
contribution to basin runoff played a less important role compared to precipitation in
nonglacial-fed land surfaces. The study on Lake Silingco [
84
] found that glacial meltwater
contributed to less than 10% of the water input to the lake basin. On the other hand, several
studies have pointed to the importance of glacial meltwater in recent lake variations [
20
,
85
].
Hence, the role of glacial meltwater needs further investigation with regard to lake level
variations. Furthermore, the different interactions between glacial runoff and atmospheric
circulation, when analyzed in more detail, may explain the differences found in the lake
responses, such as non-coincidental turning points in the water level trends between the
northern and southern parts of the TP.
5. Conclusions
Based on satellite altimetry databases and global gridded climate products, we ana-
lyzed the characteristics of the water levels in terminal lakes in Central Asia, the Tibetan
Plateau, and the Mongolian Plateau regions, where more than 50% of endorheic basins are
concentrated. The synergetic comparative analysis of magnitude and variability in lake
levels included cumulative analysis and change point tests and revealed their links with
climatic variables. The major outcomes of this study demonstrated that the water levels in
Mongolian lakes dramatically decreased during the past two decades, whereas the lake
levels showed generally increasing trends in the TP and CA. The lake level patterns in the
TP and CA had different interannual variabilities: the increasing trends were significant
until 2008 in CA, while the increasing trends were more noticeable from approximately
1997 onwards in the southern TP and from approximately 2005 onwards in the northern
TP. Precipitation was found to be the main climatic driver causing the differences in the
response of terminal lake levels to the global warming in the three neighboring regions.
While all three regions revealed similar patterns of air temperature with consistent warm-
ing after 1997, the precipitation were diverging: the TP and CA became wetter while the
MP became drier. The shift year of precipitation occurred in 1997 and 2008 in CA, in 2004
in the northern part of the TP, and in 1997 and 2008 in the southern part of the TP. The
decadal variability and distinctly different spatial patterns of lake water level variability in
the three adjacent lake zones demonstrated diverging responses to climate change within
the Eurasian endorheic zone.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/
10.3390/rs13183705/s1, Figure S1. Comparisons of annual water levels from three altimetry datasets
for five lakes in Central Asia between 1992 and 2018. The blue line is the data from Hydroweb, the
black line is from GREALM and the grey line is from DAHITI. Figure S2. Comparisons of water level
in four lakes located in the southern Tibetan Plateau. The annual time series are shown since 1995
in Lake Namco and Lake Silingco, and since 1992 in Lake Zharinamco and Lake Ngangzco. The
blue curve is the data from Hydroweb and the black is the data from GREALM and the grey is from
DAHITI. Figure S3. Comparisons of water level in three lakes located in the northern Tibetan Plateau.
The annual time series are shown since 1992 in Lake Ngoring and since 1995 in Lake Qinghai and
Remote Sens. 2021,13, 3705 18 of 21
Lake Ayakkum. The blue curve is the data from Hydroweb and the black is the data from GREALM
and the grey curve is from DAHITI. Figure S4. Comparisons of water level in three lakes located in
the Mongolian Plateau. The annual time series are shown since 1992 in Lake Hovsgol and since 2002
in Lake Uvs and Lake Hyargas. The blue curve is the data from Hydroweb and the black is the data
from GREALM and the grey curve is from DAHITI. Figure S5. The relationship between water level
variation and climatic factors over six lakes in Central Asia. The climatic factors include precipitation
and air temperature from 1992 to 2018. The left panel is the cumulative precipitation anomalies
(blue line), and the right panel is the cumulative temperature anomalies (orange line) at basin scale
compared with lake levels (gray line) shown on the right x-axis. Figure S6. The relationship between
water level variation and precipitation and air temperature from 1992 to 2018 over three lakes in
the northern Tibetan Plateau. The precipitation and air temperature are from 1992 to 2018. The left
panel is the cumulative precipitation anomalies (blue line), and the right panel is the cumulative
temperature anomalies (orange line) at basin scale compared with lake levels (gray line) shown on
the right x-axis. Figure S7. The relationship between water level variation and precipitation and air
temperature are from 1992 to 2018 over four lakes in the southern Tibetan Plateau. The left panel is
the cumulative precipitation anomalies (blue line), and the right panel is the cumulative temperature
anomalies (orange line) at basin scale compared with lake levels (gray line) shown on the right x-axis.
Figure S8. The relationship between water level variation and precipitation and air temperature from
1992 to 2018 over three lakes in the Mongolian Plateau. The left panel is the cumulative precipitation
anomalies (blue line), and the right panel is the cumulative temperature anomalies (orange line) at
basin scale compared with lake levels (gray line) shown on the right x-axis.
Author Contributions:
G.K. and X.Z. conceived the study; X.Z. developed the methodology; X.Z.
and A.K. prepared and processed the data; G.K. and X.Z. performed the final analysis, X.Z. wrote the
original draft manuscript; G.K., X.Z. and A.K. contributed to the final version. All authors have read
and agreed to the published version of the manuscript.
Funding:
This research was funded by the funded by the German Research Foundation (DFG grants
KI 853/16-1 and GR 1540/37-1), by the German Federal Ministry of Education and Research (BMBF
grant 01LP2006A), and by the Sino-German Center for Research Promotion (CDZ project GZ1259).
GK and AK were supported by the Ministry of Education and Science of the Republic of Kazakhstan
(Project ID: AP05134202).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The satellite altimetry data used in the study are available from
the Hydroweb (http://hydroweb.theia-land.fr/, accessed on 13 July 2021) Global Reservoir and
Lake Monitor (G-REALM, https://ipad.fas.usda.gov/cropexplorer/global_reservoir/, accessed
on 14 July 2021), and Database for Hydrological Time Series of Inland Waters (DAHITI, https:
//dahiti.dgfi.tum.de/, accessed on 13 July 2021). The precipitation data are available from the Global
Precipitation Climatology Centre products (GPCC, https://www.dwd.de/EN/ourservices/gpcc/
gpcc.html, accessed on 13 July 2021), precipitation products from the University of Delaware (UDEL,
http://climate.geog.udel.edu/~climate/html_pages/download.html, accessed on 13 July 2021). The
temperature and precipitation dataset was adopted from Climate Research Unit products (CRU,
http://www.cru.uea.ac.uk/data, accessed on 13 July 2021).
Acknowledgments:
We gratefully acknowledge the work of research teams behind the Hydroweb,
G-REALM, and DAHITI satellite altimetry products, who provided the data essential for the study.
The study was performed under financial support of BMBF, DFG, and CDZ (see Funding Section for
details), which is thankfully appreciated.
Conflicts of Interest: The authors declare no conflict of interest.
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