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\begin{document}

\title{Recent abnormal hydrologic behavior of Tibetan lakes inferred from
multi-mission altimetry and its driving force analyses}



\author[1]{Pengfei Zhan}%
\author[2]{Chunqiao Song}%
\author[3]{Jida Wang}%
\author[4]{Wenkai Li}%
\author[5]{Linghong Ke}%
\author[1]{Kai Liu}%
\author[1]{Tan Chen}%
\affil[1]{Nanjing Institute of Geography and Limnology Chinese Academy of Sciences}%
\affil[2]{Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences}%
\affil[3]{Kansas State University}%
\affil[4]{Nanjing University of Information Science and Technology}%
\affil[5]{Hohai University College of Hydrology and Water Resources}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The inland lakes in the Tibetan Plateau (TP), with closed catchments and
minimal human disturbance, are important indicators to climate change.
However, examination of the spatiotemporal patterns of the Tibetan lake
changes, especially for water level variation, was usually limited by
inadequate measurement accessibility. This obstacle has been remedied by
the developing satellite altimetry observations. The more recent studies
revealed the growth tendency of lakes in the central TP had been
decelerated or reversed during the period 2010-2016. It has not been
systematically investigated whether the deceleration or hiatus would
last in the following years thus far. This study aims to combine the
traditional and recently-advanced altimetry observations to update our
understanding of Tibetan lake changes in recent years. The results
reveal that water level changes of the 22 examined lakes showed abrupt
rises during the period 2016-2018, but the onsets and magnitudes of the
rises varied among the lakes. During the study period, the water level
of the nine lakes in the northern TP displayed a drastic rising trend
with an average rate of 0.82 m/a. In the central TP, the lake level
changes were generally divided into two categories. The water levels for
the lakes in the western CTP rose rapidly, while in the eastern CTP, the
lake water levels rose slowly with an average rising rate less than 0.40
m/a. The water levels for lakes in the northeastern TP and northwestern
TP kept a stably rising tendency. According to the results of climate
analysis, the spatial differences of the lake level rise rates were
primarily caused by the spatial and temporal changes of precipitation
over the TP, which may be related to the large-scale atmospheric
circulation affected by the El Ni\selectlanguage{ngerman}ño and La Niña events.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Abstract:}

The inland lakes in the Tibetan Plateau (TP), with closed catchments and
minimal human disturbance, are important indicators to climate change.
However, examination of the spatiotemporal patterns of the Tibetan lake
changes, especially for water level variation, was usually limited by
inadequate measurement accessibility. This obstacle has been remedied by
the developing satellite altimetry observations. The more recent studies
revealed the growth tendency of lakes in the central TP had been
decelerated or reversed during the period 2010-2016. It has not been
systematically investigated whether the deceleration or hiatus would
last in the following years thus far. This study aims to combine the
traditional and recently-advanced altimetry observations to update our
understanding of Tibetan lake changes in recent years. The results
reveal that water level changes of the 22 examined lakes showed abrupt
rises during the period 2016-2018, but the onsets and magnitudes of the
rises varied among the lakes. During the study period, the water level
of the nine lakes in the northern TP displayed a drastic rising trend
with an average rate of 0.82 m/a. In the central TP, the lake level
changes were generally divided into two categories. The water levels for
the lakes in the western CTP rose rapidly, while in the eastern CTP, the
lake water levels rose slowly with an average rising rate less than 0.40
m/a. The water levels for lakes in the northeastern TP and northwestern
TP kept a stably rising tendency. According to the results of climate
analysis, the spatial differences of the lake level rise rates were
primarily caused by the spatial and temporal changes of precipitation
over the TP, which may be related to the large-scale atmospheric
circulation affected by the El Ni\selectlanguage{ngerman}ño and La Niña events.

\textbf{Key words} : Tibetan Plateau, lake, water level, satellite
altimetry, climate change, precipitation

\subsection*{1 Introduction}

{\label{introduction}}

Lakes are an important part of global hydrological and biogeochemical
cycles, as well as an essential resource for human societies (Lehner and
Döll 2004; Palmer et al. 2015; Sheng et al. 2016). These inland
waterbodies are quite sensitive to climate change and human activities.
The last several decades have witnessed the widespread and tremendous
changes in lakes in the context of global changes at regional and global
scales (Shi et al. 2014; Smith et al. 2005; Song et al. 2013; Vörösmarty
2002; Wang et al. 2014). Lake changes have profound effects on regional
water balance, ecosystems, biogeochemical cycles, exchange of energy and
tracing gases with the atmosphere, and human water consumption.
Therefore, investigation of lake variability is critical for a wide
range of socioeconomic, political, and scientific interests. Several
recent efforts such as Pekel et al. (2016), Donchyts et al. (2016), Yao
et al. (2019), and Busker et al. (2019) elucidate lake dynamics at
global scale using multi-temporal satellite optical and altimetry
observations. The prominent characteristics revealed in these studies
suggest that many large lakes, such as Caspian Sea, Aral Sea, Urmia Lake
in endorheic basins of Eurasian hinterland, shrank remarkably in the
near decades(Wang et al. 2018). On the contrast, widespread lake growths
are observed on Earth's highest plateau, the Tibetan Plateau (TP).

The TP, known as ``Asian Water Tower'', accommodates vast distributions
of glaciers and lakes. There are more than 1200 lakes larger than 1
km\textsuperscript{2}, covering an inundation area of
\textasciitilde{}43,000 km\textsuperscript{2} (recorded around 2000)
(Zhang et al. 2014). The lack of historical in situ data in the remote
and sparsely populated TP has greatly limited the investigation of the
hydrological and climate responses of lakes to the changing climate.
However, this obstacle has been reduced by the rapidly emerging
satellite observations from various sensors. Many prior studies employed
optical satellite images to examine lake changes across space and time
in the TP (Huang et al. 2011; Lei et al. 2014; Lu et al. 2017; Mao et
al. 2018; Qiao et al. 2019; Song et al. 2014a; Song et al. 2014b; Wan et
al. 2016; Zhang et al. 2019; Zhu et al. 2010). However, the acquisition
of high-quality optical images is usually impeded by unfavorable weather
conditions, and the derived lake area variations cannot directly
indicate the water budget changes. Therefore, more and more satellite
altimetry measurements have been synergized for the investigation of the
lake dynamics across the TP.

Phan et al. (2012), Song et al. (2014b), Wang et al. (2013), and Zhang
et al. (2011) have examined the changes in the lake water levels in the
TP and found that most Tibetan lakes experienced rapid water level rises
during 2003--2009 by using the Ice, Cloud and land Elevation Satellite
(ICESat) laser altimetry data. However, the long revisit cycle and
sparse sampling of the ICESat mission is insufficient for tracing
long-term or abrupt changes (e.g. sudden shift) in water level,
resulting in an incomplete misunderstanding of the trends and
heterogeneous characteristics of lake changes in the TP. Favorably, the
height measurements of satellite radar altimeters that have been
launched continuously since the early 1990s can extend lake level
observations to a longer temporal coverage. The advantages are
applications that are not affected by weather and a reasonable high
frequency of revisit from monthly to ten-day time scales, so that we can
better solve the spatial and temporal patterns of water level changes
and driving mechanisms in the TP. The combination of multi-radar
altimetry missions, including TOPEX/POSEIDON, ERS-1/-2, Envisat,
Cryosat-2 and Jason-1/-2/-3, at the height accuracy ranging from
decimeters to centimeters, have been used to investigate the decadal
water level changes of lakes in the TP (Gao et al. 2013; Kleinherenbrink
et al. 2015; Song et al. 2015a). However, these radar altimeters are
only suitable for large water bodies and because of their large-size
footprint and along-track/cross-track spacing, many lakes are only
accessible by one or two satellites.

The recent advances in satellite altimeters, e.g. CryoSat-2 (2010\selectlanguage{english}-),
SARAL/AltiKa (2013\selectlanguage{english}-), Jason-3 (2016\selectlanguage{english}-) and Sentinel-3 (2016\selectlanguage{english}-), provide
alternative options of measuring relatively smaller water bodies. The
more recent studies benefiting from the longer and finer observations
revealed the growth tendency of lakes in the central TP had been
decelerated or reversed during the period 2010-2016 (Hwang et al. 2019;
Jiang et al. 2017; Lei et al. 2019; Song et al. 2015b). However, whether
the deceleration or hiatus would last in the following years remains
unclear. It has not been systematically investigated thus far. Thus,
this study aims to combine the traditional and recently-advanced radar
altimetry measurements to update our understanding of the changing
characteristics of Tibetan lakes in the near past several years.
Furthermore, we will explore the potential climate driving mechanism of
the recent varying tendency of lake changes in the TP.

\subsection*{2 Materials and methods}

{\label{materials-and-methods}}

\subsubsection*{2.1 Study region}

{\label{study-region}}

The TP is located in central Eurasia with a geographic area about 3
million km2 (Song et al. 2014b). It has a mean altitude of approximately
4,000 m. The climate in the TP is marked by low temperature and strong
solar radiation (Piao et al. 2011). The precipitation in the TP is
characterized by strong seasonality. Most of the precipitation (60\%
-90\% of annual total precipitation) occurs between June and September,
while the rainfall from November to February is less than 10\%. (Xu et
al. 2008). The TP and its surroundings are sometimes known as ``Earth's
Third Pole'' and ``Water Tower of Asia'' because of the the rich storage
of water resources. The geographical environment of this area is
complex, and the water system is developed. It is the birthplace of many
large rivers (Yangtze River, Yellow River, Mekong River, etc.) in China
and throughout Asia. In addition, there are many lakes in the TP, which
is one of the most densely distributed regions in China, and these lakes
are currently undergoing significant changes due to the impact of
climate changes (Ma et al. 2010). In this study, a total of 22 lakes
larger than 100 km2 in the TP were analyzed by combining the traditional
and recently-advanced radar altimetry measurements. The characteristics
of the studied 22 TP lakes covered by satellite altimetry are presented
in Fig. 1 and Table 2.

\textbf{{[}Insert Figure 1 here{]}}

\subsubsection*{2.2 Study data}

{\label{study-data}}

2.2.1 LEGOS Hydroweb and DAHITI altimetry data archives

{\label{legos-hydroweb-and-dahiti-altimetry-data-archives}}

Observation data provided by altimetry satellites has been gradually
increased since the early 1990s, including TOPEX/POSEIDON(T/P), ERS-1,
ERS-2, Jason-1/-2/-3, Envisat and Cryosat-2(Table 1). The combination of
different altimetry satellites can increase the spatial coverage of
altimetry measurements and extend the temporal span to nearly thirty
years. At present, the long-term lake level changes reconstructed by
many institutions is based on the altimetry satellite data of different
service years. As one data source of our analyzed water levels, we
obtained lake levels from the Laboratoire d Etudes en Geophysique et
Oceanographie Spatiales (LEGOS) Hydroweb altimetry data archive and the
Hydrological Time Series of Inland Waters (DAHITI) altimetry data
archive. The LEGOS Hydroweb altimetry data archive was derived from
merged Topex/Poseidon, Jason, Envisat and Sentinel data (Cretaux et al.
2009; Cretaux et al. 2011b). All background rectification have been used
to produce this data archive. Since different altimetry satellites are
used for the same lake, the water level calculation is mainly divided
into three steps. First, each satellite data was processed separately to
obtain the altimetry measurement data of different satellites, and then
Topex/Poseidon data was used as a reference standard to minimized
potential radar instrument deviations and geoid differences between
different satellites. Finally, lake levels from the different satellites
were merged. The DAHITI data archive was launched by the Deutsches
Geodatisches Forschungsinstitut (DGFI, now DGFI-TUM) in 2013. The
processing procedure of DAHITI database in detail can be referred to in
Schwatke et al. (Schwatke et al. 2015) which was based on an extended
outlier detection and a Kalman filtering of multi-source merged
altimetry measurements. In this paper, because the algorithms adopted by
the two data archives were different, we only used one of them for a
lake. When a lake was covered by the two data archives at the same time,
Hydroweb data archive was selected as the priority.

\textbf{{[}Insert Table 1 here{]}}

Both two data archives cover a certain number of lakes in the Tibetan
Plateau (Table 2). The long-term water level change information of 16
lakes was obtained based on Hydroweb and DAHITI data archives. However,
the number of lakes cannot fully meet the needs of our lake level
monitoring (refer back to Fig. 1). Therefore, in this study, SARAL and
Sentinel-3 altimetry data were used to supplement the missing
observations from the LEGOS Hydroweb and DAHITI data archives. We
interpreted and combined SARAL and Sentinel-3 altimetry data to obtain a
total of 12 lake level time series during the period 2013-2018. Six of
them, covering the same lakes as Hydroweb and DAHITI data archives, were
used to verify the reliability of SARAL and Sentinel-3 altimetry data.
Others were used as the data supplement.

2.2.2 Supplementary altimetry data

{\label{supplementary-altimetry-data}}

\subsubsection*{2.2.2.1 SARAL/AltiKa data}

{\label{saralaltika-data}}

SARAL is a common mission of the Indian Space Research Organization
(ISRO) and the France space agency (Project 2010). The mission, launched
on 25 February 2013, is a continuation of the ERS series and Envisat
missions. The orbit and revisit cycle of SARAL satellite were the same
as Envisat, but after July 2016, SARAL satellite was then transformed to
drifting ground track mode. Compared to the previous altimetry
satellites, the improvement of SARAL/AltiKa was that it was the first
altimeter to measure in the Ka band, with higher spatial resolution. In
addition, AltiKa's bandwidth (500 MHz) had been enhanced, which resulted
in a higher vertical resolution and higher pulse repetition frequency
(Project 2010). We downloaded the SARAL data (spanning from March 2013
to June 2016) from ftp://avisoftp.cnes.fr.

\subsubsection*{2.2.2.2 Sentinel-3/SRAL data}

{\label{sentinel-3sral-data}}

The Surface Topography Mission uses the Synthetic Aperture Radar
Altimeter (SRAL) instrument, which transmits pulses occasionally at Ku
band for altimeter range measurements, supplemented by a C-band
frequency to correct range delay errors. The SRAL instrument acquires
data in two measurement modes, the Low-Resolution Mode (LRM) which is
useful over homogeneous open-ocean surfaces, and the Synthetic Aperture
Radar (SAR) mode which achieves high along-track resolution over
relatively flat surfaces. Sentinel-3 satellite has multiple new
functions and modes with the dual-frequency SAR radar altimeter (SRAL)
(Donlon et al. 2012). Among them, the tracking mode is the most critical
characteristic(Jiang et al. 2019). We downloaded the needed data from
March 2016 to December 2018 through
https://sentinel.esa.int/web/sentinel/ sentinel-data-access.

\textbf{{[}Insert Table 2 here{]}}

2.2.3 Climate data

{\label{climate-data}}

To investigate the climate impact of lake water level changes, the
annual and monthly precipitation data for the period 1990-2018 from 10
stations in the TP was collected from the China Meteorological Network
(http://data. cma.cn). The locations of the ten stations are shown in
Fig. 1. In addition, we analyzed the changes of water vapor transport
modes over the TP and its surroundings based on ERA-interim reanalysis
data with a spatial resolution of 0.75degx0.75deg (Dee et al. 2011). The sea
surface temperature (SST) from the National Oceanic and Atmospheric
Administration was also applied to investigate the association of the TP
lake level changes and precipitation anomalies driven by El Nino and La
Nina events.

\subsubsection*{2.3 Methods}

{\label{methods}}

2.3.1 Altimetry water level processing

{\label{altimetry-water-level-processing}}

We retrieved important variables such as time, latitude, longitude,
water level elevation, and geoid from the collected raw data files.
Since the parameter information contained in each altimeter satellite is
not completely consistent, the formulas for calculating water level are
also not the same. Thus, this part of the work also needs to refer to
the data manual for each altimeter satellite. We calculated water levels
by the following the formula below (Hwang et al., 2016):

\(H_{\text{water}}=h_{\text{sat}}-R_{\text{alt}}-C\) (1)

where \(H_{\text{water}}\) refers to ellipsoidal height of the waterbody,
\(h_{\text{sat}}\) refers to ellipsoidal height of the satellite,
\(R_{\text{alt}}\) refers to distance between satellite and waterbody,
and \(C\) refers to the integration of all corrections.
Considered corrections include inverted barometer height correction
(\(C_{\text{inv}}\)), wet and dry tropospheric corrections
(\(C_{\text{wet}}\),\(C_{\text{dry}}\)), ionospheric correction
(\(C_{\text{iono}}\)), solid earth tide (\(C_{\text{solid}}\)), ocean earth
tide (\(C_{\text{ocean}}\)), poletide (\(C_{\text{pole}}\)), sea state
bias correction (\(C_{\text{stb}}\)), and high frequency fluctuations
correction (\(C_{\text{hf}}\)).

2.3.2 Removal of measurement outliers

{\label{removal-of-measurement-outliers}}

For all lakes, the original tracks of the satellite are observed as it
passes through the lake, then the track data falling within the lake
will be selected. As the observations contain outliers, it is necessary
to eliminate them for subsequent work. Outlier elimination for the
selected orbit data was divided into two steps.

Step 1: Apply a median filter with a six-month window size to smooth the
original time series during the study period, and remove the outliers
out of two standard deviations from the median for all the measurements.

Step 2: Remove the data other than twice the standard deviation for
single-day measurements.

2.3.3 Unifying the reference system of different altimetry sources

{\label{unifying-the-reference-system-of-different-altimetry-sources}}

In order to reduce the differences caused by different altimeter
instruments, all lake levels from multi-source altimetry satellite were
first converted to the same geographic reference system. The system
biases calculated by the data from different altimetry satellites at the
same period was subtracted from the elevation measurements for each lake
(Cretaux et al. 2011a; Jarihani et al. 2013).

\subsection*{3 Results and discussions}

{\label{results-and-discussions}}

\subsubsection*{3.1 Long-term lake level variations based on Hydroweb and
DAHITI data
archives}

{\label{long-term-lake-level-variations-based-on-hydroweb-and-dahiti-data-archives}}

The best way of altimetry data validation is to compare the
altimeter-derived lake level with the in situ data. At present, only a
few lakes such as Qinghai Lake and Nam Co have long-term continuous
water level observations in the TP which are extremely limited compared
with more than 1,200 lakes (\textgreater{}1km\textsuperscript{2}) across
the TP (Zhang et al. 2014). The altimetry data archives, such as
Hydroweb and DAHITI, play an critical role in verifying the lake level
changes obtained by other satellite altimetry data. In order to verify
the reliability of Hydroweb and DAHITI altimetry data archives, the
annual means of water level for Qinghai Lake was validated against the
in situ water levels measured by the hydrological station. As shown in
Fig. 2, the altimetry levels follow a robust linear correlation with in
situ measurement, with a R2 value of 0.90. This agreement indicates that
the lake level time series obtained from Hydroweb and DAHITI have a high
reliability, and therefore, were used as a reference for validating our
level time series processed from SARAL and Sentinel-3 altimeters.

\textbf{{[}Insert Figure 2 here{]}}

Fig. 3 presents the time series of water level for eight large TP lakes
from Hydroweb and DAHITI data archives from the 1990s to 2018. All of
the eight lakes showed rapid water level increase since around 2000. In
comparison, the abrupt changing points varied with different lakes. For
example, the lake level for Seling Co, Nam Co, Nganze Co and Tangra
YumCo began to rise rapidly before 2000, while Migriggyangzham Co and
Qinghai Lake began to grow rapidly after 2003 and 2005, respectively.
The most dramatic expansion occurred in Seling Co in Central Tibet. From
May 1998 to December 2010, the lake level for Seling Co rose from
4533.66 m to 4543.77 m, with an increasing rate exceeding 0.80 m/a.
However, for these lakes, the increasing trends became weaker or even
reversed after 2010. The findings were consistent with other previous
studies (Hwang et al. 2019; Jiang et al. 2017; Lei et al. 2019; Song et
al. 2015b; Yao et al. 2018). However, the lake levels for some lakes,
such as Qinghai Lake, Zhari NamCo, Tangra Yumco, Ngangze Co and
Migriggyangzham Co, began to increase significantly again after 2016.
Among them, the abrupt rise of Zhari NamCo was the most remarkable, with
a water level increase during 2016-2018 more than the total during the
earlier 16 years (2000-2016). In contrast, the lake level for Nam Co
showed inter-annual fluctuations without an evident trend during the
recent several years. Level time series of other lakes are provided in
the Supplementary Material (Fig. S1).

\textbf{{[}Insert Figure 3 here{]}}

\subsubsection*{3.2 Lake level variations based on SARAL and Sentinel-3
altimetry
data}

{\label{lake-level-variations-based-on-saral-and-sentinel-3-altimetry-data}}

In order to examine whether abrupt water level increase during 2016-2018
occurred similarly in other Tibetan lakes, we investigated the water
level changes of a total of 12 lakes from 2013 to 2018 by combining
SARAL and Sentinel-3 altimetry data. Six of them were aimed to
inter-compare with the Hydroweb and DAHITI altimetry data. Fig. 4
presents the comparisons of water-level time series from Hydroweb/DAHITI
data archives and SARAL/Sentinel-3 data between 2013 and 2018 for
Qinghai Lake, Nam Co, Ayakkum, Migriggyangzham Co, Zhari NamCo and
Tangra YumCo. The water level time series for all the lakes indicate
that not only are the general trends derived from two data sources
comparable but the abrupt changes and intra-annual variations of lake
level are overall consistent. For example, both data sources clearly
depict a sudden increase of about 1.0 m during the wet season
(July-September) of 2017 for Zhari NamCo. The characteristics of water
levels rises in wet seasons and declines in dry seasons are also in good
agreement for all the lakes, suggesting that SARAL and Sentinel-3 data
have reliable results.

Based on the above validation, the SARAL and Sentinel-3 data were used
to monitor the lakes larger than 100 km2 in the TP, and the water level
time series are shown in Fig. 5. The six lakes display an evident trend
of water level increase at varying rates during 2016-2018. For example,
the alpine lakes between the Tanggula and Kunlun Mountains, including
XijingUlan Lake, Mingjing Lake and UlanUla Lake, showed a significantly
rise (exceeding 1 m/a). In contrast, the lake level for Gyariog Co
showed an inter-annual fluctuation.

\textbf{{[}Insert Figure 4 here{]}}

\textbf{{[}Insert Figure 5 here{]}}

\subsubsection*{3.3 Spatial distribution of lake level changing rates
during
2016-2018}

{\label{spatial-distribution-of-lake-level-changing-rates-during-2016-2018}}

Our results of lake level changing rate are shown in Fig. 6 and Table 3.
Obviously, most lakes experienced a significant increase during the
period 2016-2018. To be specific, except the lake level of Nam Co
remained relatively stable with little fluctuations, the water level
time series of other lakes revealed the rising tendency with the mean
rate of 0.74 m/a. The three lakes in the northwestern TP, Jieze Caka,
LumajangdongCo, and Heishibei Lake showed significantly difference in
water level rising rates, and the level for Heishibei Lake rose fastest,
reaching 1.39 m/a. The northern part of the TP is located in the Hoh Xil
region, known for complex and harsh geographical environment. A large
number of endorheic lakes are distributed in this region. As listed in
Table 3, most of the lakes were expanding with an average level increase
of larger than 0.82 m/a. The level for XijingUlan Lake increased at the
greatest rate of 1.52 m/a. Ayakkum, the largest lake in this region,
also exhibited a significant increase of 0.83 m/a. The average
increasing rate was 0.66 m/a in the central part of the TP. The lake
levels for Dagze Co and Zhari NamCo experienced the rapid rising,
exceeding 1 m/a. In Qinghai Province, the level for Qinghai Lake
maintained a steady increasing trend of 0.47 m/a. Overall, most lakes in
the northern Tibetan Plateau experienced a relatively faster rates of
water level rise, compared to those in the NETP and eastern CTP.

\textbf{{[}Insert Figure 6 here{]}}

\textbf{{[}Insert Table 3 here{]}}

\textbf{3.4 Relationship between lake level changes and precipitation
variations}

Since the late 1990s, the expansion of Tibetan lakes has been mainly
affected by regional climate changes such as increased water vapor
content, potential evaporation reduction and significantly enhanced air
convection. (Yang et al. 2014; Yang et al. 2011; Lei et al. 2017; Lu et
al. 2015). Therefore, with the acceleration of climate warming and
humidification, precipitation could be also gradually increase (Yang et
al. 2014), which supports the observation of the rapid lake expansion in
the TP.

As shown in Fig. 3 and supplementary materials (Fig. S1), the trends of
lake level changes since 1990 are generally consistent with previous
studies. Our findings reveal that the drastic water level jumps in
Tibetan lakes since 2016, has been a result of continued precipitation
increase after a relatively drier period from 2012 to 2016. This
suggests that precipitation remains to be a first-order driver of
Tibetan lake dynamics. It was found that the drastic increase of lake
level in 2017-2018 was consistent with the increase of precipitation in
the near past several years, so the monthly precipitation data in recent
years was selected and compared with the lake level. According to the
study, the monthly precipitation and the lake water level during the
period 2013-2018 were selected to study the influence of specific
monthly precipitation on the lake water level. The most prominent
feature was the good correspondence between lake level increase and the
summer precipitation. In addition, we can find that the decrease of
water level in individual years (e.g. 2015) corresponded to less summer
precipitation. The amplitude of wet season lake level increases showed a
good relationship with precipitation amount. For example, the drastic
lake level rises in 2017 and 2018 in the TP corresponded to high
precipitation. This suggests that the excessive precipitation during the
wet season of 2017 and 2018 played a crucial role in the recent lake
level increase. Overall, precipitation appears to play a more dominant
role than temperature (and its induced glacier/permafrost melting). This
was implied by the fact warming-induced melting did not reverse the
slow-down or hiatus of lake expansion before 2016 (when precipitation
was less), and it would have to be the precipitation increase again that
eventually triggered another round of lake level increase. More details
on other lakes and meteorological stations can be found in the
Supplement (Fig. S2).

\textbf{{[}Insert Figure 7 here{]}}

\subsubsection*{3.5 Spatial linkage of increased precipitation to
enhanced water vapor
circulations}

{\label{spatial-linkage-of-increased-precipitation-to-enhanced-water-vapor-circulations}}

Previous studies have identified the main sources of moisture for
precipitation in the TP, including regions from the Indian sub-continent
to the Southern Hemisphere, the Bay of Bengal and the northwestern part
of TP. (Chen et al. 2012; Zheng et al. 2019). It is proven that these
circulation changes in the increase or decrease of different moisture
sources provide favorable water vapor conditions for rainfall on the TP.
Thus, the changes in moisture source contributed to different
precipitation changes across the entire TP during different periods. As
indicated in the preceding results, the lake water levels increased
rapidly during the period 2016-2018 relative to the previous stage. The
phenomenon was analyzed with the distribution of water vapor transport
on the TP.

The differences can be observed with the four years when the water vapor
is transported by abnormal northwesterly, southeast wind and south wind
in 2015, 2016, 2017 and 2018, respectively (Fig. 8B, Fig. 8C, Fig. 8D,
Fig. 8E). In comparison, the total water vapor increases with the most
significant anomaly in 2017-2018. The moisture transported to the
northwestern part of the TP has increased significantly in last several
years due to the strengthening of the westerlies. In this study,
precipitation in the NTP is more obviously influenced by the East Asian
monsoon and the Indian summer monsoon. The summer monsoon circulation
was enhanced, and the meridional water vapor transport from the south
plateau to the Bay of Bengal was significantly enhanced. Similarly,
precipitation in the CTP was more influenced by the Asian summer
monsoons. In addition, the water vapor flux from the Indian Ocean to the
plateau was enhanced. From Fig. 8D, we can also find that there is a
phenomenon of internal water vapor circulation and retention, which
might cause water vapor subsidence and significant rainfall over the
western CTP. In general, the change in water vapor transport during the
study period supports the changing characteristics of precipitation and
lake level for TP as analyzed above.

In this study, as shown in Fig. 6 and Table 3, we can find the rising
rates of lake levels at different parts of TP are different. The water
level rising rates for some typical lakes exceed 0.80 m/a in the
northern TP such as XijingUlan Lake, Mingjing Lake, Lexiewudan and
Ayakkum. There are some glaciers distributed in the northern TP, and
there is rich rainfall in the rainy season. Therefore, the rapid
increase of water level is affected by both rainfall and glacial
meltwater. The rising rate of lake level in central TP is mainly divided
into two categories. For example, at Zhari Namco and Dagze Co in the
western CTP, the rising rates are 1.34 m/a and 1.43 m/a respectively.
The rising rates of lakes in the central and eastern CTP are slower,
generally less than 0.40 m/a. The reason for this difference may be
because the phenomenon of internal water vapor circulation and retention
mainly occur in the western CTP. In NWTP which was mainly affected by
the westerlies, the distinction in water vapor transport distribution in
different years led to a significant difference in the water level
rising rates. As indicated in the preceding texts, the differences in
water level rise rate are caused by different water vapor transport
patterns and main sources in different regions.

\textbf{{[}Insert Figure 8 here{]}}

\subsubsection*{3.6 Associations of TP lake level and precipitation
changes with El Nino/La Nina
events}

{\label{associations-of-tp-lake-level-and-precipitation-changes-with-el-niuxf1ola-niuxf1a-events}}

We further found the potential associations of lake and precipitation
variations on the TP with El Nino and La Nina events, as illustrated in
Fig. 9, S3 and S4. In the CTP, considerable expansion can be found at
Zhari NamCo, Tangra YumCo and Seling Co during the 2016/2017 and
2017/2018 La Nina events, corresponding well with excessive
precipitation in these two years. On the NTP, dramatic expansions can
also be found at Dogai Coring Lake and Lexiewudan Lake during the
2016/2017 and 2017/2018 La Nina events (Fig. S4). The expansion of Zhari
NamCo was also remarkable due to the dramatic precipitation increase
likely driven by La Nina. Despite some exceptions, we can see that
drastic lake expansion in the TP is very consistent with historical La
Nina incidents, which indicates that La Nina events may exert evident
impact on the hydroclimate of the TP. In addition to ENSO, other climate
variabilities (e.g., the North Atlantic Oscillation, Arctic Oscillation,
and the Indian Ocean Dipole) may also promote the lake expansion in the
TP (Liu and Juarez 2001; Wang et al. 2008). In contrast, considerable
lake shrinkage (in 2011 and 2015) occurred in the TP during El Nino
events (Lei et al. 2019). We here found a phenomenon that is different
from the results of previous studies. In NTP, the lake level changes
have drastically increased relative to 2015 (El Nino event) during the
2016/2017 and 2017/2018 La Nina events (Fig. S4), whereas the
teleconnection between lake dynamics and La Nina appears weaker in NWTP.
The three selected lakes (LumajangdongCo, Jieze Caka and Heishibei Lake)
in NWTP expanded steadily even in 2015, but lake expansion was more
dramatic in 2016 and 2017 (Fig. 5 and Fig. S1). This continuous lake
expansion is dominated by lake dynamics in NWTP as reported in previous
study (Jiang et al. 2017; Kleinherenbrink et al. 2015; Lei et al. 2017).
Since glaciers are widely distributed in NWTP, the contribution of
glacier melt to lake level change may be greater than the impact of the
La Nina event. As indicated above, water level changes of different
lakes showed different spatial and temporal patterns due to the obvious
spatial heterogeneity of climate change. This anomaly rainfall change is
closely associated with El Nino/La Nina event or other atmospheric
circulation events.

\textbf{{[}Insert Figure 9 here{]}}

\subsection*{4 Conclusions}

{\label{conclusions}}

The more recent studies benefiting from the longer and finer
observations revealed the growth tendency of lakes in the central TP had
been decelerated or reversed during the period 2010-2016. However,
whether the deceleration or hiatus would last in the following years
remains unclear. It has not been systematically investigated thus far.
Thus, in this study, 22 large inland lakes were investigated to update
our understanding of the changing characteristics of Tibetan lakes
during the period 2016-2018 by combining the traditional and
recently-advanced radar altimetry measurements. Furthermore, we will
explore the potential climate driving mechanism of the recent varying
tendency of lake changes in the TP. For most lakes in the TP, lake
levels showed an abrupt rise during 2016-2018, compared with the earlier
stage during 2010-2016, but the onsets and magnitudes of water level
rise varied with subzones and lakes.

Our results reveal that the lake water levels in the NTP displayed a
sharply rise with an average rate of 0.90 m/a except Kusai Lake
(outburst of its upstream lake). In the central TP, the lake level
changes are divided into two categories during the period 2016-2018,
which is different from the dramatic increasing pattern for lakes in the
northern TP. The water level for Qinghai Lake in the northeastern TP
kept a stable growth tendency during the recent years, reaching 0.47m/a.
The water level rising rates for the three lakes in the northwestern TP
(LumajangdongCo, Jieze Caka and Heishibei Lake) are extremely different
from each other. The spatial differences of the lake level rise rates
were primarily caused by the changes of precipitation over the TP, which
may be related to the large-scale atmospheric circulation. The
atmospheric circulations such as ENSO and AO may contribute to the
anomalous precipitation by driving water vapor transport of TP, but vary
with different years. Although this study could be beneficial for our
understanding of the driving mechanism of the rapid water level rise of
Tibetan lakes and hydrological cycle under future climate change
conditions, more comprehensive investigations of precise physical
processes are required to clarify the climate-driven mechanism of
Tibetan lake level changes in the further
study.\textbf{Acknowledgments:}

This work was partly funded by the Second Tibetan Plateau Scientific
Expedition and Research (2019QZKK0202), the Strategic Priority Research
Program of the Chinese Academy of Sciences (Grant No. XDA23100102), the
National Key Research and Development Program of China (Grant No.
2019YFA0607101, 2018YFD0900804, 2018YFD1100101), the Thousand Young
Talents Program in China (Grant No. Y7QR011001), and the National
Natural Science Foundation of China (No. 41971403, 41801321). We are
also grateful to the Laboratoire d'Etudes en Geophysique et
Oceanographie Spatiales (LEGOS), the Hydrological Time Series of Inland
Waters (DAHITI), the NASA EOSDIS Physical Oceanography Distributed
Active Archive Center (PO.DAAC), Centre National d'Etudes Spatiales
(CNES), the China Meteorological Data Sharing Service System and the
European Centre for Medium-Range Weather Forecasts for providing
satellite altimetry data and meteorological data for this study.

\subsection*{Data Availability
Statement}

{\label{data-availability-statement}}

Hydroweb data and DAHITI data are downloaded from the Hydroweb service
(http://hydroweb.theia\selectlanguage{english}-land.fr) and the DAHITI service
(https://dahiti.dgfi.tum.de/en), respectively. We can download the SARAL
data from ftp://avisoftp.cnes.fr and Sentinel-3 data from
https://sentinel.esa.int/web/sentinel/sentinel-data-access. The
precipitation data and ERA-interim reanalysis data are collected from
the China Meteorological Network (http://data. cma.cn) and the NOAA
Physical Sciences Laboratory
(https://psl.noaa.gov/data/gridded/data.erainterim.html).

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\textbf{Hosted file}

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