The Tibetan Plateau (TP), known as Asia's water tower, is
quite sensitive to climate change, which is reflected by changes in hydrologic state
variables such as lake water storage. Given the extremely limited ground
observations on the TP due to the harsh environment and complex terrain, we
exploited multiple altimetric missions and Landsat satellite data to create
high-temporal-resolution lake water level and storage change time series at
weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km2 and 2 lakes larger than 100 km2) on the TP during 2000–2017. The data sets are available online at 10.1594/PANGAEA.898411 (Li et al., 2019). With
Landsat archives and altimetry data, we developed water levels from lake
shoreline positions (i.e., Landsat-derived water levels) that cover the
study period and serve as an ideal reference for merging multisource lake
water levels with systematic biases being removed. To validate the
Landsat-derived water levels, field experiments were carried out in two
typical lakes, and theoretical uncertainty analysis was performed based on
high-resolution optical images (0.8 m) as well. The RMSE of the
Landsat-derived water levels is 0.11 m compared with the in situ
measurements, consistent with the magnitude from theoretical analysis
(0.1–0.2 m). The accuracy of the Landsat-derived water levels that can be
derived in relatively small lakes is comparable with most altimetry data.
The resulting merged Landsat-derived and altimetric lake water levels can
provide accurate information on multiyear and short-term monitoring of lake
water levels and storage changes on the TP, and critical information on lake overflow flood monitoring and prediction as the expansion of some TP lakes
becomes a serious threat to surrounding residents and infrastructure.
Introduction
The Tibetan Plateau (TP), providing vital water resources for more than a
billion population in Asia, is a sensitive region undergoing rapid climate
change (Field et al., 2014). There are more than 1200 alpine lakes
larger than 1 km2 on the TP, where glaciers and permafrost are also
widely distributed. With little disturbance by human activity in this area,
lake storage changes may serve as an important indicator that reflects
changes in regional hydrologic processes and responses to climate change.
Wang et al. (2018) showed that global endorheic basins are experiencing
a decline in water storage, whereas the endorheic basin on the TP is an
exception. Given the fact that TP lakes have been expanding for more than 20 years (Pekel et al., 2016), quality data sets on lake water level and/or
storage could be the basis for investigating its causes (e.g., climate
change/variability) and interactions with the water/energy cycles and human
society (e.g., increasing risks of inundation and overflow floods).
As an important component of the hydrosphere, terrestrial water cycle, and
global water balance, millions of inland water bodies such as lakes,
wetlands, and reservoirs have been investigated globally, and their total
storage was estimated to be 181.9×103 km3 based on
statistical models (Lehner and Döll, 2004; Messager et al., 2016;
Pekel et al., 2016). Lake storage changes that play an important role in the
regional water balance can be derived from changes in lake water level and
area (Frappart et al., 2005). Lake water levels and areas are mostly
derived from satellite remote sensing due to the scarcity of in situ data
across the TP, where the harsh environment and complex terrain make in situ
measurements difficult to perform and costly (Crétaux et al., 2016;
Song et al., 2013; Yao et al., 2018b; Zhang et al., 2017a). Lake water
levels can be monitored using satellite altimeters initially designed for
sea surface topography or ice sheet/sea ice freeboard height measurements.
Satellite altimeters determine the range between the nadir point and
satellite by analyzing the waveforms of reflected electromagnetic pulses.
There are two major categories of satellite altimeters, i.e., laser and
radar. Laser altimeters, e.g., the Ice, Cloud, and land Elevation Satellite (ICESat), operating in the near-infrared band have smaller
footprints and generally higher accuracy than radar altimeters, facilitating
applications in glacier/ice mass balance studies (Neckel et al.,
2014; Sørensen et al., 2011). Radar altimeters, operating in the
microwave band, have larger footprints and are more likely to be
contaminated by a signal from complex terrain when applied to inland water
bodies. Nevertheless, it is possible to remove these impacts with waveform
retracking algorithms (Guo et al., 2009; Huang et al., 2018;
Jiang et al., 2017). Zhang et al. (2011) mapped water level changes in
111 TP lakes for the 2003–2009 period using ICESat data that have a
temporal resolution of 91 days. ICESat data have relatively denser ground
tracks but a lower temporal resolution than most of other altimetric
missions. This means that ICESat covers more lakes but provides few water
levels for each lake. After ICESat was decommissioned in 2010, CryoSat-2
data starting from 2010 were adopted in related studies (Jiang
et al., 2017), due to its similar dense ground tracks and competitive
precision compared to ICESat. Other altimetric missions, such as TOPEX/Poseidon
(T/P), Jason-1/2/3, the European Remote Sensing (ERS-1/2) satellite, and
Envisat, also have some but relatively limited applications in monitoring
changes in lake water level on the TP due to sparse ground tracks. In this
study, multisource altimetry data (i.e., Jason-1/2/3, Envisat, ICESat, and
CryoSat-2) were combined if available for lakes in this study, with the
Landsat-derived water levels developed in this study as a critical reference
to increase the water level observations and merging data from multiple
altimetric missions.
Changes in lake area can be captured by optical or synthetic aperture radar
(SAR) images from medium- or high-spatial-resolution remote sensing data,
such as Landsat and Sentinel series. Extraction of lake water bodies can be
manually (Wan et al., 2016) or automatically (Zhang et al.,
2017b) achieved. Automatic water extraction methods based on the
water index and auto-thresholding are more efficient in dealing with a mass
of remote sensing images. Even so, acquisition and preprocessing of such a
large amount of historical data (∼ 10 TB) covering TP lakes are
still intractable for researchers with limited computational resources. With
the help of cloud-based platforms, such as the Google Earth Engine (GEE)
that significantly reduces data downloading and preprocessing time, tens of
thousands of images may be processed online in days instead of months
(Gorelick et al., 2017). In this study, more than 20 000
Landsat images were processed online using GEE to extract lake water bodies
based on the water index (McFeeters, 1996) and Otsu algorithm (Otsu,
1979).
There have been studies focusing on changes in lake water storage on the TP
over recent decades; e.g., Zhang et al. (2017a) examined
changes in water storage for ∼ 70 lakes from the 1970s to 2015
with ICESat altimetry data and Landsat archives. An individual lake area
data set from the 1970s and annual area data after 1989 were used. Due to
the short time span of ICESat, they used the hypsometric method to convert
lake areas into water levels. Yao et al. (2018b) used digital elevation models (DEMs) and
optical images to develop hypsometric curves for lakes on the central TP
and estimated annual changes in water storage for 871 lakes from 2002 to
2015. These studies have a wide spatial coverage of lakes but relatively
lower temporal resolution and little spaceborne altimetric information,
which may limit the accuracy of trends in lake water level/storage in some
cases and short-term monitoring of lake overflow floods. The Laboratoire
d'Etudes en Géophysique et Océanographie Spatiales (LEGOS) Hydroweb
provides a lake data set, including multisource altimetry-based changes in
lake water level and storage as well as hypsometric curves for 22 TP lakes
(Crétaux et al., 2016, 2011b). The data set
incorporates more spaceborne altimetric information and has a higher
temporal resolution. However, there may be a remaining bias when different
sources of altimetric data are merged, due to the lack of some important
reference that can be derived from optical remote sensing to be shown in
this study. We term the reference data the “Landsat-derived water level”
to be introduced in Sect. 3.2. Here, we list recent studies and data sets
(Table 1) to provide a concise summary on remote sensing monitoring of water
levels and storage changes over lakes on the TP.
Recent studies and data sets on TP lakes. H, A, and V in the table
denote lake water level, area, and volume, respectively.
ReferenceNo. of lakesData typeTime spanTemporal resolutionSource dataSong et al.(2013)30H, A, V, andhypsometric curve4 records for the1970s, 1990, 2000,and 2011DecadalAltimetry data: ICESat Optical data: Landsat 5/7 TM/ETM+Crétaux etal. (2016)22H, A, V, andhypsometric curve1995–2015 relative bias partially removed(only for altimeters with overlapping period)Submonthly for lakeswith T/P and Jason-1/2 data, and ∼ monthly for lakes without Jason-1/2 or T/P dataAltimetry data: T/P, ERS-2, GFO, Envisat,Jason-1/2, SARAL, ICESat,and CryoSat-2 Optical data: Landsat 5/7/8 TM/ETM+/OLI and MODISJiang et al.(2017)70H2003–2015 relative biasbetween ICESatand CryoSat-2unremoved∼ MonthlyAltimetry data: ICESat and CryoSat-2Zhang et al. (2017a)60–70H, A, V, andhypsometric curveOne record for the1970s, and annualdata for 1989–2015AnnualAltimetry data: ICESat Optical data: Landsat 5/7/8 TM/ETM+/OLILi et al.(2017b)167H2002–2012∼ MonthlyAltimetry data: ICESat and EnvisatYao et al.(2018b)871H, A, V, andhypsometric curve2002–2015AnnualOptical data: Landsat 5/7/8 TM/ETM+/OLI and HJ-1A/1B DEM data: SRTM and ASTERHwang etal. (2019)59H2003–2016 relative bias partially removed(only for lakes withJason data/in situdata)Submonthly for lakeswith Jason-2 data, and ∼ monthly for lakeswithout Jason-2 dataAltimetry data: Jason-2/3,SARAL, ICESat, andCryoSat-2 (Jason-3 data for validation)Our study52H, A, V, andhypsometric curve2000–2017 all relative biasesremovedSubmonthly for mostlakesAltimetry data: Jason-1/2/3,Envisat, ICESat, and CryoSat-2 Optical data: Landsat 5/7/8 TM/ETM+/OLI
The overall objective of this study was to examine multiyear and short-term
changes in water level and storage across 52 lakes with surface areas larger
than 150 km2 on the TP by merging multisource altimetry and optical
remote sensing images to generate more coherent high-temporal-resolution
lake water level and storage change data sets ranging from weekly to monthly
timescales during 2000–2017 and the hypsometric curve (i.e., the lake-area–water-level relationship) for each lake. To investigate changes in lake
storage, lake water levels and areas need to be derived from multisource
remote sensing.
Spatial (the number of lakes covered) and temporal coverage and
their overlap periods of multiple satellite altimetric missions used in this
study, including Jason-1/2/3, Envisat, ICESat, and CryoSat-2.
First, water levels from various satellite altimeters (Fig. 1) for each
lake as well as lake shoreline positions and lake areas from optical remote
sensing images (i.e., Landsat) were derived. Second, systematic biases
between different altimetry data were removed by either comparing the mean
water levels during the overlap period (Fig. 1) or comparing the two water
level time series with lake shoreline positions, depending on the length of
the overlap period (details can be found in Sect. 3.1). Lake-shoreline-position-derived water levels, termed the Landsat-derived water levels in
this study, can serve as a unique source of information reflecting water
levels as well as a data merging reference. We will show that after deriving
two or three regression parameters, lake shoreline positions can well
reflect lake water levels with comparable accuracy to altimetry-derived
water levels. Third, with information on lake water levels and areas derived
from altimetry data and optical remote sensing images, the hypsometric curve
that describes the relationship between the lake water level and storage
changes can be derived. Fourth, the integral of the hypsometric curve was
performed to convert lake water levels into storage changes.
Results of this study provide a comprehensive and detailed assessment of
changes in lake level and storage on the TP for the recent 2 decades and
short-term monitoring of lake overflow floods for some lakes. This study
could largely benefit more detailed investigations into lakes, lake basins,
and regional climate change, because the generated data sets have the
highest temporal resolution during the study period with systematic biases
well removed. To ensure the data quality, field experiments were carried out
and in situ data were collected to examine the uncertainty in the
Landsat-derived water levels. Users are free to access the data set
described in this paper at 10.1594/PANGAEA.898411 (Li et al., 2019).
Study area and dataStudy area
The TP can be generally divided into 12 major basins (Wan et al., 2016;
Zhang et al., 2013), among which the inner/central TP (CP) is the only
endorheic basin and home to most TP lakes, including ∼ 300 TP
lakes larger than 10 km2. Therefore, it was chosen as the main study
area. The endorheic basin covers an area of ∼ 710 000 km2
(∼ 28 % of total TP) with a mean elevation of
∼ 4900 m and has a semiarid plateau climate with annual
precipitation ranging from 96 to 295 mm (Li et al., 2017c). Most lakes in
the endorheic basin were expanding under the influence of climate
change/variability as opposed to other areas in the TP, e.g., Selin Co
exceeded Nam Co in area and consequently became the largest lake in the
endorheic basin between 2011 and 2012 and expanded by 26 % over the past 40 years (Zhou et al., 2015), whereas Yamzhog Yumco (also known as Yamdrok Lake; outside the
endorheic basin, 350 km to the southeast of Selin Co) shrunk by
∼ 11 % during 2002–2014 according to Wan et al. (2016).
Located in the southeast endorheic basin, the Nam Co basin covering about
10 800 km2, with 19 % of the basin lake water area and a mean lake
elevation of ∼ 4730 m, was chosen as a field experiment spot.
The mean annual temperature and precipitation of Nam Co are 1.3∘
and 486 mm, respectively (Li et al., 2017a). The other
experiment spot was Yamzhog Yumco, which has a mean lake elevation of
∼ 4440 m. Subject to steep mountainous terrain, the lake has
a narrow-strip shape with complex shorelines. The basin of Yamzhog Yumco
covers ∼ 6100 km2, with mean annual temperature and
precipitation of 2.8∘ and ∼ 360 mm, respectively
(Yu et al., 2011). An overall map of experiment lakes is given in
Fig. 2.
Experiment locations: Nam Co and Yamzhog Yumco. Nam Co is located
in the endorheic basin of the TP, while Yamzhog Yumco is located in the
Yarlung Zangbo river basin (the upper Brahmaputra River). Both lakes are
close to Lhasa city.
S-GDR stands for Sensor Geophysical Data Record;
GDR stands for Geophysical Data Record;
GLAH 14 stands for GLAS/ICESat L2 Global Land Surface Altimetry Data
(HDF5), version 34;
CNES stands for Centre National d'Etudes Spatiales;
Aviso stands for Archiving, Validation and Interpretation of Satellite
Oceanographic data;
Aviso+ data set is available via FTP at http://ftp-access.aviso.altimetry.fr with a registered username and password (last
access: 18 August 2019);
ESA Envisat products are available via FTP at http://ra2-ftp-ds.eo.esa.int with a registered username and password (last access: 18 August 2019);
ESA CryoSat-2 products are available via FTP at http://calval-pds.cryosat.esa.int with a registered username and password (last
access: 18 August 2019);
NASA ICESat products are available at https://nsidc.org/data/icesat/data.html (last access:
18 August 2019).
Data
Multisource altimetry data were used in this study as shown in
Table 2. The earliest record dates back to 2002
(i.e., Envisat and Jason-1) and the latest record ends in 2017 (i.e.,
Jason-3 and CryoSat-2, Fig. 1). Most of the 52 lakes examined in this
study were covered by ICESat, Envisat, and CryoSat-2 data. ICESat data
provided by the National Aeronautics and Space Administration (NASA) were
available on 42 lakes in this study. Envisat and CryoSat-2 data provided by
the European Space Agency (ESA) were available on 35 and 51 lakes in this
study. Jason-1/2/3 data provided by the Centre National d'Etudes Spatiales
(CNES) were available only on 12 lakes in this study due to the relatively
sparse ground tracks or data quality issues. Note that Jason-2 inherited the
orbit of Jason-1 after its launch in 2008, whereas Jason-1 was shifted into
an interleaved orbit and continued functioning until 2013, thereby
increasing the spatial coverage of Jason altimetry series to some degree,
e.g., Jason-1 data in Lake Qinghai, the largest lake on the TP, were only
available after 2008 due to the orbit shift. ICESat and CryoSat-2 data have
the largest spatial coverage but relatively long repeat cycles of 91
and 369 days, respectively (Bouzinac, 2012; Zhang et al., 2011).
The Envisat mission has a lower orbit than Jason-1/2/3 but higher than
ICESat, resulting in a moderate spatial coverage and a temporal resolution
of 35 days (Benveniste et al., 2002). To determine if the altimetry
data fall into the lakes, a lake shape data set generated by Wan et al. (2016) was used. An example of using the lake shape data set to determine
altimetry data falling into the lake boundaries is given in Fig. 3a,
showing that data from all altimeters are available in Zhari Namco.
It should be noted that different altimeters vary with wavelengths of
electromagnetic radiation and mechanisms. For instance, Jason-1/2/3 using
the Ku and C bands and Envisat/RA-2 using the Ku and S bands work in the low-resolution mode (LRM). These dual-frequency radar altimeters can provide
more accurate range corrections due to the ionospheric effect (Tournadre,
2004). The LRM is typical for the early version of satellite altimeters such
as TOPEX/Poseidon. There are more advanced modes, such as SAR and
interferometric synthetic aperture radar (InSAR), for recent radar
altimeters, which generally have smaller footprints than the LRM mode.
CryoSat-2/SIRAL working at a single Ku band has three modes, including LRM,
SAR, and InSAR, which were designed to have an increasing resolution in turn
and work in different zones. The InSAR mode uses interference phenomena so
that the shift of the nadir point across the track can be detected, improving
the altimeter's performance on ice sheets with slopes (Bouzinac,
2012). The Geoscience Laser Altimeter System (GLAS) is the laser altimeter
carried by ICESat working in the near-infrared band.
We used Landsat 5 TM (2000–2011), Landsat 7 ETM+ (2000–2017), and Landsat
8 OLI (2013–2017) surface reflectance data sets provided by GEE to generate
information on lake shoreline positions and lake areas. Landsat 7 ETM+ was
subject to sensor failure, and all the Landsat 7 ETM+ images contain gaps
after 2003 (Markham et al., 2004). There were more than 20 000 images
processed, and half of them were excluded from the final results due to
cloud contamination or gaps. We collected daily in situ water level
measurements in Yamzhog Yumco for validation purposes with a pressure-type
water level sensor. The in situ water level measurements spanned half a year
from May to October 2018. We also performed unmanned aerial vehicle (UAV or
drone) imaging over a 1 km lake bank in Yamzhog Yumco and Nam Co for
obtaining better knowledge on the experimental environment.
In addition, GaoFen-2 (GF-2, the China High-Resolution Earth Observation
System mission) images were used to perform a rigorous statistical
analysis of uncertainty in the Landsat-derived water levels by taking the
GF-2-derived lake shoreline positions as the ground truth to analyze the
subpixel water area ratios of Landsat image pixels (see Sect. 4). GF-2
images have a spatial resolution of 0.8 m for the panchromatic band, and
preprocessing including orthorectification and radiometric calibration was
performed. Before analysis, we performed an image-to-image registration with
manually selected tie points between GF-2 and corresponding Landsat 8 OLI
images until the coregistration error reduced to ∼ 2 m.
MethodSatellite altimetry water level
The first step of deriving satellite altimetry water levels is to select
correct ground tracks and valid footprints falling on the lakes. Because
there is a random ground track shift at ∼ 1 km in different
cycles for most altimetry missions, it is uncertain whether valid lake
footprints can be obtained for each cycle, even though the nominal ground
track seems to cross the lake. This problem can be addressed by comparing
geographic coordinates of the footprints with a lake shape data set (Wan
et al., 2016). After picking out the valid footprints, the lake surface
height can be calculated for each footprint. All radar altimetry data share
a relationship:
LSH=Alt-(Range+cor),
where LSH represents the lake surface height with respect to the geoid; Alt
represents the altimeter height with respect to the reference ellipsoid;
Range represents the distance between the altimeter and lake surface; and cor
represents several range corrections due to atmospheric effects, sensor
design defects, or geophysical effects. Radar altimeters and laser
altimeters need different corrections, given that they are working in
different wavelengths and have different designs. For instance, corrections
for radar altimeters include waveform retracking correction, wet/dry
troposphere correction, ionosphere correction, pole/solid tide correction,
and geoid correction. Laser altimeters also need atmospheric delay
correction, geoid/pole tide correction, and geoid correction. Unlike radar
altimeters, saturation correction instead of waveform retracking correction
is more important to laser altimeters.
The retracking correction plays an important role in removing the
contamination of land signal when radar altimetry data are applied to inland
water bodies. In this study, Jason-1/2/3 data were retracked using a
classical waveform retracking algorithm, i.e., the improved threshold method
(ITR), whereas CryoSat-2 data were retracked using the narrow primary peak
threshold (NTTP) method (Birkett and Beckley, 2010; Cheng et al., 2010;
Jain et al., 2015). Retracking corrections were not performed for Envisat
and ICESat data, because the Envisat/RA-2 product already provided a
retracked range using the ICE-1 method, and the ICESat GLAH14 product already
included several corrections (such as saturation correction) that are
sufficient for most applications including studies on inland water bodies
(Zhang et al., 2011). The original idea of the NTTP, ICE-1, and ITR is
quite similar. All of them adopt a threshold defined as the percentage of
the waveform peak to determine the retracking gate and then to convert the
difference between the retracking gate and the nominal gate into range
correction by multiplying the gate range (cΔt/2, where c is the speed of light and Δt is the time duration of a gate). The differences lie in the choice of thresholds as well as the calculation of waveform peaks. For instance, ICE-1 uses a 30 % threshold, whereas ITR uses a 50 % threshold.
(a) Ground tracks of multiple altimetric missions over Zhari Namco
and (b) the merged altimetry water levels for Zhari Namco. LSH stands for lake surface height.
For each cycle of an altimeter, it is common that more than one footprint
fall on a lake, thereby providing several lake surface height (LSH)
observations on the same day. After removing outliers with the three-sigma
rule, frequency distributions of the LSHs from the same cycle were
calculated. The mean value of the histogram bin with the highest frequency
was selected to represent the LSH for the cycle. Meanwhile, the frequency of
the chosen histogram bin was reserved to evaluate the data quality for the
cycle; e.g., a cycle was marked as high quality if the frequency was higher
than 0.8, moderate quality if it was only higher than 0.5, and poor quality
if the frequency was lower than 0.5. The LSH from each cycle constituted the
water level time series for a lake. LSHs that were marked as poor quality
and obviously deviated from the moving average were removed from the
altimetry-based lake water level time series.
It is not uncommon that systematic biases exist in different altimetry data
sets due to variations in orbit, the discrepancy between correction models,
errors associated with sensors, and even the choice of the reference datum.
After deriving lake water level time series for each altimeter, we first
merged the Envisat and ICESat water levels if both were available for a
lake, because they have the longest overlap period (Fig. 1). We chose
Envisat-derived water levels as the baseline and removed the difference of
the mean values of the two products during the overlap period from the
ICESat data, because Envisat data are generally denser and longer than
ICESat data. A similar process was applied to Jason-1/2/3, as there are two
overlap periods connecting the three altimeters together. Figure 3b shows
a result of merged altimetry data when all sensors are available. There are
tradeoffs between CryoSat-2 and Jason-2/3 data in terms of spatial coverage
and time span. CryoSat-2 data are available for all lakes in this study but
they only have an overlap period with Jason-2/3 data, whereas Jason-2/3 data
are only available for 12 lakes. For most lakes without Jason-2/3 data, we
merged CryoSat-2 data with either ICESat or Envisat using Landsat-derived
water levels spanning from 2000 to 2017, because there is no overlap period
between these altimetry water levels (Fig. 1). Details on how
Landsat-derived water levels aid in merging the altimetry water levels are
shown in Sect. 3.2.
Landsat-derived water level
For most lake basins, it is possible to find a relatively flat portion of
lake banks with an average slope of 1/30 or even smaller, where obvious
interannual or intra-annual changes in lake shoreline positions can be
detected using Landsat images (30 m). These locations can be found by
comparing lake images from the first year and the last year of the study
period if the lake shows a clear expanding/shrinking trend. Otherwise, we
can compare images acquired in early summer when the LSH is at its lowest level with
those acquired in late autumn when the lake expands to its limit. In this
study, we assumed that the selected lake bank was flat enough such that the
relationship between the lake water level and shoreline position can be
depicted in a linear or quasilinear (parabolic) way. Thus, we can transform
the lake shoreline positions into Landsat-derived water levels by fitting
with altimetry water levels. The validity of this assumption can be
evaluated with the coefficient of determination (R2) for each lake as
shown in Table 3. For most lakes, the goodness of
fit is higher than 0.7, suggesting the generally good fitting relationship
between the lake water levels and shoreline positions.
Summary of regression analyses and hypsometric function by lake.
Though there were ∼ 500 Landsat images obtained for the
selected lake banks during the study period, many of them were largely
affected by cloud or cloud shadow. All the images were processed online
using the GEE application programming interface (API). Preprocessing such as
radiometric calibration, atmospheric correction, and geometric
correction was already performed in the production of the data sets. In
addition, the failure of the Landsat 7 sensor SLC left all the Landsat 7 ETM+
images with gaps after 2003 (Markham et al., 2004), making the available
images even fewer. We managed to make use of some images with gaps in
generating lake shoreline positions. By choosing the region of interest
(ROI) that is parallel to the image gaps, we made most of the Landsat 7
ETM+ images useable. However, the width of ROI must be reduced to avoid
shifting gaps as shown in Fig. 4b. The gaps may vary with time but are more like vibration around the midpoint. The ROI did not fill the interval of gaps, because the wider the ROI, the higher possibility of shifting gaps cross it.
Lake shoreline positions were characterized by water area ratios detected in
the ROI. To automatically extract water areas from a mass of Landsat
archives on GEE, the water index and Otsu threshold method were jointly
used. We calculated the normalized difference water index (NDWI) and the
modified normalized difference water index (MNDWI) of the images and
compared their performance in different seasons. It was found that the MNDWI
tends to be more sensitive to shallow water in summer but is less effective
than the NDWI when the lake bank is covered by snow in the cold season as
shown in Fig. 5c. Therefore, the two water indices were
jointly used by applying the MNDWI to images acquired during May to October
and applying the NDWI to the remaining months. The NDWI and MNDWI can be
calculated as follows (McFeeters, 1996; Xu, 2005):
2NDWI=Bgreen-BNIRBgreen+BNIR,3MNDWI=Bgreen-BSIRBgreen+BSIR,
where Bgreen, BNIR, and BSIR refer to surface reflectance of bands 2, 4, and 5 for Landsat 5/7 TM/ETM+ images and bands 3, 5, and 6 for
Landsat 8 OLI images.
(a) Yamzhog Yumco and its surroundings. The DEM was extracted from
the SRTM Global 90 m DEM product (Jarvis et al., 2008). (b) Region of
interest (ROI, yellow area) selected from a Landsat 7 ETM+ image for
detecting changes in lake shoreline (black areas represent gaps in the
image). (c) Linear regression of lake shoreline positions that are
represented by water area ratios in the ROI and altimetry water levels for
Yamzhog Yumco. (d) Landsat-derived water levels and altimetry water
levels for Yamzhog Yumco.
(a) A Landsat 7 ETM+ image of Lake Aqqikkol acquired in
summer in 2001; (b) water area extractions using the modified normalized
difference water index (MNDWI) and the normalized difference water index
(NDWI), showing that the MNDWI performs better in detecting shallow water;
(c) a Landsat 8 OLI image of Nam Co acquired in winter in 2015; (d) water
area extraction using the NDWI, showing good performance in distinguishing
water from snow; and (e) water area extraction using the MNDWI, showing some confusion of water and snow.
After calculating the water index, the grayscale image was binarized using
the Otsu method. If the selected ROI comprises ∼ 50 % water
and ∼ 50 % land, the performance of the method is good, as
the distribution of digital numbers of the grayscale image is close to the
assumption of the bimodal histogram implicit in the Otsu algorithm
(Kittler and Illingworth, 1985; Otsu, 1979). The binarized images were
further processed to provide the water area ratio in the ROI, which
represents the lake shoreline position. The lake shoreline position time
series were then converted into Landsat-derived water levels using linear
regression or second-order polynomial fit with altimetry-derived water
levels (Fig. 4c–d). For most cases, we only used linear
regression, and we performed the
second-order polynomial fit only for 2 lakes with Jason-1/2/3 data, because a higher-order regression requires more
input information to ensure the reliability. However, cloud, cloud shadow,
and shifting gaps may contaminate the ROI and cause errors in the
Landsat-derived water levels. Therefore, the QA band of the Landsat surface
reflectance product was used to filter the images. Data were excluded if the
fraction of the cloud or cloud-shadow-covered area in the ROI was higher
than 5 %. For every Landsat 7 ETM+ image acquired after 2003, the pixel
number of the ROI was counted and compared with those acquired before 2003.
If the loss of pixels exceeded 2 %, the ROI was considered to be affected
by a gap and the data were consequently excluded from the subsequent analysis.
A critical function of Landsat-derived water levels is to aid in merging
altimetry water levels when there was no overlap period between altimeters
or the overlap period was too short. For lakes without Jason-1/2/3 data,
lake shoreline positions were firstly translated into Landsat-derived water
levels by fitting with CryoSat-2 data functioning as extrapolation of
CryoSat-2 to 1–2 years. Then, we applied the same method of merging
Jason-1/2/3 to merge the extrapolated CryoSat-2 data with either Envisat or
ICESat data. In doing so, we were able to remove all systematic biases
between multisource altimetry water levels. After merging the altimetry
water levels, we performed regression analysis for the second time between
the Landsat-derived water levels and merged altimetry water levels to check
if the linear relationship is stable during the entire study period and at
different elevations. If the linear relationship was stable, the
Landsat-derived water levels would be merged with the altimetry water levels
using the linear fitting parameters from the second regression analysis.
Otherwise, there may have been a change in the lake bank slope, and, therefore, the extrapolation of CryoSat-2 data with Landsat-derived water levels was
not suited. In this case, we reselected the ROI to extract lake shoreline
positions and redid altimetry data merging until the Landsat-derived water
levels and merged altimetry water levels agreed well with one another in the
second linear regression. Detailed analysis about the potential
extrapolation issue can be found in the Supplement.
In summary, the basic idea of removing systematic biases of different
altimetry water levels is to calculate the means of two altimetry water
level time series during the overlap period. The difference between the
means is removed from one altimetry water level time series to make both
altimetry water level time series consistent and to form a longer time
series. This process was subsequently applied to all water level time series
with overlap periods to merge them into a single time series for each lake.
However, the overlap period may not be long enough, such as Envisat and
CryoSat-2 (e.g., there are limited data points (e.g., 1–2) during the
overlap period), or does not exist at all, such as ICESat and CryoSat-2. On
these cases, Landsat-derived water levels are used to extend or create an
overlap period that links two altimetry water level time series.
We derived the hypsometric curve for each lake by polynomial fitting of the
lake area and level time series. The lake area comprises two parts: the
inner invariable part and the outer variable part. As the variable water
area was of more concern in this study, ROIs for extracting changes in lake
area only cover the lake shoreline and its neighboring areas as shown in
Fig. 6. The inner part of the water body was calculated only once and
considered invariant, making the calculation more efficient on GEE.
Meanwhile, more images are available as the area of ROI becomes smaller,
because the possibility of clouds covering the ROI is reduced compared with
an ROI covering the entire lake. Landsat 7 ETM+ images after 2003 were not
included in this part of the calculation as gaps negatively affected the ROI for
lake area extraction. Similar to Sect. 3.2, we selected images with less
than 5 % cloud cover on an ROI to generate time series of changes in lake
area, obtaining 20–30 data points on average for regression. R2 values
for each lake are listed in Table 3, indicating that most lake basins agree well with the parabolic hypsometric
curve.
Field experiments in two typical lakes: (a) an overview map of the
experiment spot; (b) pressure-type water level sensor; (c) unmanned aerial
vehicle (UAV); (d) installation of the water level sensor; and (e) UAV image
of a portion of the bank of Nam Co.
Validation of data qualityField experiment
Most Tibetan lakes are located in remote and inaccessible regions, resulting
in the scarcity of ground-based in situ measurements that are
vital for data quality assessment. We made some in situ measurements in two
lakes to validate the data quality of Landsat-derived water levels. The data
quality of satellite altimetry on lakes or rivers has been widely
investigated, and thus it is beyond the scope of our study. Many studies used
in situ water levels to calculate statistical metrics, e.g., root mean
squired error (RMSE). However, results provided by different studies vary,
which could be associated with the cross-section width of the study water
body in the ground track panel (Nielsen et al., 2017). This means that
these results may not be comparable due to their unique applications. In
addition, it is not rigorous to use in situ data of only one lake to
represent the overall performance in the uncertainty assessment for
altimetry water levels. Instead, we used the standard deviation of valid
footprints acquired in the same cycle as an estimate of uncertainty in
satellite altimetry water levels. In contrast, the applicable condition of
Landsat-derived water levels is not so variable as that of satellite
altimetry data. Derivation of Landsat-derived water levels requires
a relatively flat bank as well as some altimetric information, which were
available in all lakes. Since these selected bank slopes were similarly
small (∼1/30), it was possible to use a few typical lakes to
represent all lakes. Therefore, we carried out a field experiment (Fig. 7) in Yamzhog
Yumco and Nam Co to validate the Landsat-derived water levels.
There were two main goals in our experiments: (1) collecting daily in situ
water level data in a TP lake to validate the corresponding Landsat-derived
water levels statistically and (2) testing the performance of extracting lake
shoreline positions from high-resolution optical images (GF-2) to provide a
theoretical uncertainty analysis of Landsat-derived water levels. On Yamzhog
Yumco, we installed a pressure-type water level sensor (type H5110-DY,
manufactured by Shenzhen Hongdian Technologies Co., Ltd.), which measured
water pressure and temperature at the installation depth that were converted
into water depths with a relative accuracy of ∼ 0.1 %. The
device was carried onto the lake and put ∼ 20 m below the
water surface and 0.5 m above the lake bottom, suggesting an absolute error
of ∼ 2 cm. As for GF-2 images, the spatial resolution of the
panchromatic band is 0.8 m, which is able to provide a very accurate
reference of lake boundaries for assessing water classification results for
Landsat images. We used three GF-2 images acquired at different seasons (two
in July and September 2015 and one in February 2016) and different places on the TP to
better represent the local conditions when extracting Landsat-derived water
levels or areas. Image coregistration was performed to make sure that there
was no obvious spatial shift between the GF-2 images and corresponding
Landsat images. The accuracy of the image coregistration was
∼ 2 m.
Uncertainty analysis of Landsat-derived water levels
Based on the in situ water level measurements made by the pressure-type
water level sensor, we evaluated the accuracy of Landsat-derived water
levels statistically. We first calculated anomalies of in situ water levels
and Landsat-derived water levels, and then water levels from the two sources
acquired on the same day were used for analysis. There were 16
Landsat-derived water level records available for the comparison against the
in situ measurements, indicating an RMSE of the water level anomaly of 0.11 m. The linear fit shows a slope close to 1 and an R2 of 0.89, suggesting
the consistency between the in situ water level measurements and the
Landsat-derived water levels (Fig. 8b). It should be noted that the
Landsat-derived water levels used for validation here were translated from
lake shoreline positions using parameters derived from fitting with
CryoSat-2 data, i.e., there is no in situ information involved in generating
the Landsat-derived water levels shown in Fig. 8.
(a) In situ water level anomaly versus Landsat-derived water level
anomaly in Yamzhog Yumco; (b) linear regression between the
Landsat-derived water levels and in situ water levels during the same
period.
Furthermore, we performed a theoretical uncertainty analysis of the
Landsat-derived water levels by looking at the original optical data and the
generation process with the help of high-resolution GF-2 images. First, we
took GF-2 images (after coregistration with the Landsat image for the same
period and after the coregistration errors were ∼ 2 m) as the
ground truth to determine the accurate position and shape of the lake
shoreline. Second, we performed water classification from the Landsat 8 OLI
image for the same period jointly using the water index method and Otsu
algorithm to derive the binarized image. Landsat image pixels where the lake
shorelines from the GF-2 images cross were delineated and marked as
shoreline pixels as shown in Fig. 9a. Then the water area in each
shoreline pixel was calculated.
(a–c) GF-2 images (upper layer) and corresponding Landsat 8 OLI images (bottom layer) acquired on 7 September 2015, 29 January 2016, and 5 July 2015; (d) Landsat 8 OLI shoreline pixels (the background is the GF-2 image) – blue pixels were classified as water, and yellow pixels were classified as
land; (e) the relationship between the water area ratio in a pixel and the frequency of the pixel being classified as water. Blue bars are sampled at a 0.04 bin space from the 4128 pixels. The red line shows the fitting curve based on the maximum likelihood method.
Given that these shoreline pixels were classified as either water or land, a
relationship between the water area ratio of the shoreline pixel and the
probability of the pixel being classified as water can be derived. This
relationship generally describes the function of the water classification
method by telling how likely a pixel is to be determined as water, given the
water area ratio of the pixel. Based on the observations of a total of 4128
Landsat shoreline pixels, a power function was chosen to represent the water
classifier as Eq. (4) shows:
f(x)=xn,
where x represents the water area ratio in the shoreline pixel, f(x) represents the probability of the shoreline pixel being classified as the water pixel, and n is the parameter that determines the shape of the curve. Parameter n was determined using the maximum likelihood method
(Fig. 9e).
As expected, the probability of the pixel being classified as water increases with
the water area ratio in the pixel (Fig. 9e). The enclosed area of the
fitting curve (y=x1.43) is smaller than that of y=x on [0, 1],
suggesting that there may be a lower probability of the occurrence of water
pixels that is associated with a systematic bias of the lake shoreline
detection. Note that the systematic bias can be removed when linearly
fitting the lake shoreline positions and altimetry water levels as long as
the bias is stable. Therefore, uncertainty in Landsat-derived water levels
developed in this study arises mainly from the variation in this systematic
bias.
F(X): probability density function of the bias (X) between the
classified water ratio (X1) and real water ratio (X0) in a shoreline
pixel.
To describe the variation in the systematic bias, a new random variable X was introduced to represent the bias between the classified water area and the
real water area in a shoreline pixel. Given the shape and position of the
lake shoreline, the real water area in each shoreline pixel is a complex
function of the relative position between the pixel and the shoreline. To
simplify the derivation, we assumed that the water area ratio in a shoreline
pixel is uniformly distributed on [0,1], meaning that the probability of any
value between 0 and 1 is equal. If we use X0 to represent the true water
area ratio in the shoreline pixel and X1 to represent
the classified results based on the water area ratio, the random variable
X can be expressed as
X=X1-X0,
where X1 can take on 1 or 0 (i.e., the classified results only tell us
whether a pixel is a water pixel or not), so X can only take on either
1-X0 or -X0. Because the range of X0 is [0,1], it
is obvious that the range of X is [-1,1]. A derivation of F(X), i.e., the probability density function (PDF) of X, can be found in the
Supplement (Part 2).
Overall, F(X) describes how the bias between the classified water ratio and
real water ratio in shoreline pixels is distributed as shown in Fig. 10. If there are N shoreline pixels in an ROI, we can take them as
N independent observations of X and calculate the mean value X‾. This value X‾ can represent an average shift of the detected lake shoreline from the real lake shoreline in the unit of 1-pixel width
(30 m). As we mentioned above, the systematic bias can be removed in the
regression between the lake shoreline positions and altimetry water levels.
As such, it is the variation of the bias that determines the accuracy of the
Landsat-derived water levels. We can calculate the standard variation of
X‾ to represent the uncertainty in lake shoreline position. Note that
there is a simple relationship between σx‾ and
σx:
σx‾=σxN.
One only needs to calculate σx:
7X‾=∫-11F(X)⋅XdX≈-0.09,8σx=∫-11F(X)⋅(X-X‾)2dX≈0.39.
Combined with Eqs. (4) and (7), Eq. (8) was resolved numerically,
resulting in ∼ 0.39-pixel width. Substituting σx
in Eq. (6) with Eq. (8) gives
σx‾=0.39N.
If the slope of the shoreline is known, e.g., tan θ, the uncertainty of
the Landsat-derived water level can be expressed as
σho=σx‾⋅d⋅tanθ=0.39×30×tanθN,
where σho is the uncertainty of Landsat-derived water levels
and d is the spatial resolution of the satellite image (30 m). In this study, a typical width of ROI for deriving Landsat-derived water levels is
∼ 10-pixel width, meaning that N is ∼ 10. In
addition, lake shores used for generating Landsat-derived water levels here
generally have a relatively mild slope of ∼1/30 or even
smaller, which can be roughly estimated from the maximum shoreline change
and altimetry water level change within a year. Here if we use 1/30 as the
slope tan θ, the uncertainty of the Landsat-derived water levels can
ultimately be estimated to be ∼ 0.12 m, which is very close to
the RMSE of 0.11 m based on the comparison between the optical water levels
and in situ water level measurements mentioned earlier.
However, for most cases we do not know the exact lake bank slope
tan θ, which is the reason why we performed the regression analysis
between the lake shoreline positions and altimetry-derived water levels.
Information on the real lake bank slope is implicitly expressed in the
linear fitting slope β (if the fitting line is y=βx+α). Uncertainty in altimetric information could evolve into the
fitting parameters and impact the accuracy of the generated Landsat-derived
water levels. Given that the observed lake shoreline position is X1
(e.g., X1=5.6, meaning that the observed lake shoreline position is
5.6 Landsat pixels away from the initial position corresponding to the lowest
water level, which is different from Eq. (5), X1 here can be a rational number
because it is determined by averaging all shoreline pixels in the ROI,
whereas in Eq. 5 we focused on only 1 shoreline pixel), combining Eq. (5), the Landsat-derived water level (y) can be expressed as
y=βX1-X1‾+Y‾=βX0-X0‾+βX-X‾+Y‾,
where X1-X1‾ denotes the
observed lake shoreline change (in the unit of a Landsat pixel), X1‾ denotes the mean of observed lake shoreline positions used for
linear regression, Y‾ denotes the mean of altimetry water levels used
for linear regression, X0-X0‾
denotes the real lake shoreline change, (X-X‾)
denotes the variation of the Landsat-derived shoreline position caused by
the water extraction method, and β is the linear fitting slope. It is
obvious that the expected value X-X‾ is 0. As
we discussed earlier, a systematic bias does not affect the accuracy of the
Landsat-derived water level but the variation of the systematic bias does.
Based on Eq. (11), the overall uncertainty of the Landsat-derived water level
σy can be given as
σy=σβ2∂y∂β2+σx‾2∂y∂X-X‾2+σY‾2∂y∂Y‾2=σβ2(X1-X1‾)2+σx‾2β2+σY‾2,
where β and σβ can be derived from the linear
regression analysis, σx‾ is given in Eq. (9), and σY‾ is the uncertainty of the mean altimetry water level which can
be estimated from the altimetry data. For a typical lake like Yamzhog Yumco,
β=0.35 m, σβ=0.02 m, Max(|X1-X1|‾)= 11, σx‾=0.13, and σY‾=0.015 m, which gives a maximum σy
of 0.22 m. Note that X0-X0‾ is
assumed to be the ground truth so there is no error associated with this
term. This relationship shows that the uncertainty in the Landsat-derived
water level increases with the distance from the center point
(X1‾, Y‾) represented by
(X1-X1‾)2.
Interpretation of this phenomenon is that extrapolation of Landsat-derived
water levels (far from the center point) may cause some errors and should be
used with caution. More detailed discussion on the extrapolation can be
found in the Supplement.
Overall, the uncertainty quantification of the Landsat-derived water levels
developed in this study indicates clearly that the accuracy of
Landsat-derived water levels depends on the width of an ROI, e.g., the
number of pixels/observations, slope of the lake shore, the effectiveness of
the water classification method, and the uncertainty in the altimetry water
level used for regression. One of the advantages of the Landsat-derived
water level is that an ROI does not necessarily cover a large area of lake
shores, which maximizes the potential of optical remote sensing images to
increase the spatial coverage and temporal resolution of lake water level
estimates that may not be realized by using satellite altimetry alone.
Optical remote sensing images provide important complementary information on
altimetry water levels and can subsequently facilitate lake water storage
estimation.
Cross validation of the TP lake level and storage
changes derived from our study with those provided by the LEGOS Hydroweb
database (Crétaux et al., 2011a): (a) trends in lake water levels
from 2003 to 2015 and (b) trends in lake water storage from 2003 to 2015.
Spatial distribution of trends in lake storage on the TP during
2000–2017. The black line shows the boundary of the endorheic basin of
the TP including 39 lakes in this study. The other 13 lakes are located
outside the endorheic basin.
Cross validation with similar products
We compared our product with a widely used lake water level/storage data set
provided by the LEGOS Hydroweb, indicating that the two products are, on the
whole, consistent with each other (shown in Fig. 11), but our product may
perform better in terms of the temporal continuity as well as the temporal
resolution (shown in Sect. 6.2). Both advantages are important in
improving our understanding of responses of lakes to climate change. There
are 21 lakes that are the same in both our study and LEGOS Hydroweb. Annual trends in
water level and lake storage during 2003–2015 are compared in Fig. 11,
indicating the overall consistency of the two products in terms of R2 of
the linear fit.
Data set description
The data sets cover 52 large lakes (50 lakes with a surface area larger
than 150 km2 and 2 lakes that are 100–150 km2) on the TP. The data
sets consist of two parts: (1) a table containing hypsometric curves and
corresponding regression statistics (R2 and the number of data pairs)
for each lake, with parameters of the hypsometric curves listed in separate
columns for the convenience of batch processing; and (2) time series for each
lake archived as 52 entities with geographic information (i.e., latitude,
longitude, and size of the lake) that can be checked in an online map
provided by PANGAEA, avoiding the confusion of lake names. The time series
of each lake include lake water levels and lake storage changes.
For data points in the water level time series, satellite or sensor type is
shown (i.e., from Jason-1/2/3, Envisat, ICESat, CryoSat, or optical images).
Uncertainty was calculated using the standard deviation of valid footprints
in the cycle (only for altimetry data). The lake water storage time series
were transformed from water level time series using the hypsometric
relationship so that they have equal data size. The lake water storage time
series represent changes in lake storage with respect to a reference water
level, which is listed in the corresponding hypsometric curve table as a
parameter. The overall uncertainty of Landsat-derived water levels within
the regression range (the range of altimetry water levels) is 0.1–0.2 m
based on the experiment and analysis in this paper. The extrapolation of
Landsat-derived water levels may occur during the time gap between
altimetric missions and before 2002. The average uncertainty of altimetry
water levels is 0.11 m.
ApplicationsSpatiotemporal analysis of changes in lake water storage in the Tibetan Plateau
Based on the lake water storage changes we derived, spatial patterns of lake
storage trends during 2000–2017 were shown in Fig. 12. In the endorheic basin of the TP, similar to some reported results (Yao et al., 2018b; Zhang et al., 2017a), most lakes have been
expanding rapidly; e.g., Selin Co (31.80∘ N, 89.00∘ E) gained 19.7±2.0 km3 of water during the study period, and Lake Kusai (35.70∘ N, 92.90∘ E)
experienced an abrupt expansion due to flood and gained 2.2±0.2 km3 of water in 2011, as reported in related work (Yao et al.,
2012). In contrast, some lakes in the southern part of the TP experienced
shrinkage, e.g., Yamzhog Yumco (28.93∘ N, 90.70∘ E) gained a total of 0.8±0.4 km3 water during 2000–2004 but has shrunk during the remaining 13 years (2005–2017) at a rate of -0.19±0.03 km3 yr-1. In contrast to Yamzhog Yumco, Lake Qinghai (36.90∘ N, 100.00∘ E) lost 2.2±0.7 km3 water during 2000–2004 but gained 7.7±0.6 km3 of water during 2005–2017. Similar patterns can be detected in adjacent lakes of Lake Qinghai, e.g., Lake Donggei Cuona (35.28∘ N, 98.55∘ E)
and Lake Ngoring (34.90∘ N, 97.70∘ E).
Discrepancy of lake storage trends in Goren Co between
Yao et al. (2018a) and our study.
(a) Total storage changes in the 52 lakes on the TP,
which can be generally divided into two stages: (1) a rapidly increasing
stage (2000–2011) with a higher increasing rate of 6.71 km3 yr-1 and (2) a mildly increasing stage (2012–2017) with an increasing rate of 1.98 km3 yr-1. (b) Histogram of intra-annual changes in lake water levels of
the 52 lakes on the TP.
However, spatial proximity cannot fully explain the intricate trend
distribution in the Selin Co basin, where large lakes such as Selin Co were
expanding, whereas smaller adjacent lakes showed an opposite decreasing
trend, e.g., Urru Co (31.70∘ N, 88.00∘ E), Lake Co Ngoin (31.60∘ N, 88.77∘ E),
and Goren Co (31.10∘ N, 88.37∘ E). In fact, we found that the decreasing
trends in some small lakes like Goren Co were not detected in Yao et al. (2018b), which is likely due to the lower temporal resolution as shown in
Fig. 13. The three shrinking lakes are located in
the upstream region and feed Selin Co through two small rivers. One of the
rivers links lakes Goren Co, Urru Co, and Selin Co, whereas the other links
lakes Co Ngoin and Selin Co.
A possible explanation of the disparity of changes in lake water storage in
the Selin Co basin could be the principle of minimum potential energy. If we
simplify the basin with the tank model and take the upstream small lake as a
tank with a leaking hole, the storage of the small lake is mainly controlled
by the height of the leaking hole. Given that surface water of the small
lake increased, most of the increased water would flow into the large lake
(a lower tank), and the outflow discharge of the small lake at higher
elevations would increase accordingly. The height of the leaking hole would
decline (erosion) so as to increase the overflow capacity, which eventually
results in the decrease in small lake storage. Another possible situation is
that the height of the leaking hole remains the same and the water surface
height of the small lake increases, but this situation is not consistent
with the minimum potential energy principle, as more water potential energy
is stored in the small lake. This phenomenon shows that river-lake
interactions may cause complex patterns of the regional surface water
distribution. Therefore, decreases in small lake water storage and increases
in water storage of Selin Co in the basin detected by our study seem
reasonable. Increases in small lake water storage in this basin reported in
some published studies may be associated with the sparse sampling of lake
water levels.
Similarities and differences between water level time series from
the LEGOS Hydroweb database (Crétaux et al., 2011a) and our study.
(a) Taro Co (31.14∘ N, 84.12∘ E). (b) Zhari Namco (30.93∘ N, 85.61∘ E). (c) Ngoring Lake (34.90∘ N, 97.70∘ E). Shading areas highlight the differences
between the two data sets. LSH represents lake surface height.
We averaged the total lake water storage change in each season to generate
time series shown in Fig. 14a. The overall
storage change in the 52 lakes is 100.1±5.7 km3. The total lake
water storage was increasing rapidly during the first 12 years but became
relatively stable since 2012. Intra-annual variation in the TP lakes can
also be investigated using the densified lake water level time series
generated by our study. We removed the linear trend (sometimes there were
multiple linear trends for a lake in different periods, which were removed
in a stepwise fashion) and calculated the mean monthly water level anomaly
for each lake over the study period. Then the intra-annual water level
change was represented by the difference between the maximum and minimum
values of the monthly water level anomaly. The histogram of the intra-annual
water level change in Fig. 14b shows
that most of the TP lakes have water level variations ranging from 0.3 to 0.75 m in a year on average. Similar work was performed by Lei et al. (2017), but only a small number of lakes were investigated in their study.
Quality assessment of similar data products
Some obvious discrepancies between the two data sources can be noticed,
e.g., water levels of Taro Co. Both Hydroweb data and our estimation used
ICESat and CryoSat-2 data. The difference lies in the fact that our
CryoSat-2 product was more updated with a longer time span but Hydroweb used
an additional altimetry satellite SARAL. Because the systematic biases of
both products were removed, it is possible that we chose different baselines
that resulted in the overall shift as shown in Fig. 15a. For
instance, we may use different sets of ellipsoid and geoid models. In
addition to the overall shift, some time-dependent discrepancy can be found
in Fig. 15, e.g., periods highlighted by shading areas.
The black curve shows the Landsat-derived water level we derived, which is a
critical reference for connecting two different altimetry data time series
without an overlap period. The Landsat-derived water level shows that the
last two samples of ICESat data should not be lower than the first few
samples of the CryoSat-2/SARAL data (see the dashed boxes). However, it is
apparent that Hydroweb data display a reverse relationship, showing that the
last two ICESat measurements are smaller than the first few CryoSat/SARAL
measurements. It is likely due to an unremoved systematic bias between
ICESat and CryoSat/SARAL time series from Hydroweb data in Taro Co.
Even though the Landsat-derived water levels were generated by linearly
fitting the lake shoreline positions with altimetry data, the relative
magnitude of water levels during different periods should not be largely
affected by the fitting parameters, e.g., if Landsat-derived water levels
show that Ha>=Hb, where Ha (Hb) means water
levels acquired in period A (B), the Ha>=Hb
relationship would not change with the fitting parameters used to generate
the Landsat-derived water levels. This is the main reason for us to use
Landsat-derived water levels as reference. Therefore, Hydroweb data may
overestimate the increasing trends in the water levels of Taro Co as their
ICESat data are ∼ 0.3 m lower than the SARAL/CryoSat data. A
similar issue can be observed in Zhari Namco and Ngoring lakes shown in
Fig. 15b–c, and the explanation is similar to that of Taro Co. This
problem may also exist in some similar studies when multisource altimetry
data without overlap periods were used.
Lake water level (left y axis) estimates from our approach for six
TP lakes. Black lines represent optical data and red dots represent
altimetry data. LSH represents lake surface height.
(a) Lake storage changes in Lake Zhuonai, Lake Kusai, and Lake
Salt corresponding to the outburst event in September 2011 and (b) storage changes
in relevant lakes during the outburst event (a magnified plot of the shading
area in a).
Changes in the water level of Lake Kusai after receiving the
outburst flood from Lake Zhuonai. Stage 1 was used to determine the range of
parameter C in Eq. (13). Stage 2 was used to compared the simulated lake
outflow from Kusai Lake based on Eq. (13) with the water gain estimate from
remote sensing of Lake Salt downstream during the same period; and (b) changes in water storage of Lake Salt derived from remote sensing using our developed method. There was 0.19 km3 of water gained in stage 2, which was comparable to the outflow estimate of Lake Kusai (0.22 km3) based
on Eq. (13).
As shown in Fig. 16, optical data can be less noisy than altimetry data in
certain lakes and significantly improve the continuity of lake level and
storage change monitoring. In addition, a more apparent seasonality in lake
level change can be seen from the generated lake level time series. These
advantages would largely benefit a better understanding of responses of TP
lakes to climate change and facilitate hydrologic modeling of lake basins,
regional water balance analysis, and even hydrodynamic analysis of lake
water bodies.
Lake overflow flood monitoring
As mentioned earlier in Sect. 5.1, Lake Kusai experienced an abrupt
expansion in 2011, resulting from the dike break of an upstream lake (Hwang
et al., 2019; Liu et al., 2016; Xiaojun et al., 2012), named Lake Zhuonai
(35.54∘ N, 91.93∘ E). The outburst of Lake Zhuonai occurred on 14 September
(Liu et al., 2016), with 2.47±0.06 km3 of water leaking
into the Kusai River (as shown in Fig. 17b), the main inflow of Lake
Kusai. The water level of Lake Kusai increased by up to 7.9±0.5 m
within 20 days (from 11 September to 1 October in 2011) based on Jason-2 data and then started to drop as water overflowed from the southeast corner into Lake
Haidingnuoer (35.55∘ N, 93.16∘ E) and Lake Salt (35.52∘ N, 93.40∘ E). Lake Salt,
the lowest part of the basin close to the basin boundary, has gained
3.0±0.1 km3 of water since 2011 and has become a critical threat to
the surrounding residents and railway ∼ 10 km southeast to the
boundary. Note that there are few satellite altimetry data available for
Lake Salt except several CryoSat-2 observations, where Landsat-derived water
levels can provide a near-real-time monitoring of changes in lake water
level and storage that are crucial to flood early warning and risk
management.
Aided by the high-temporal-resolution lake water level series, it was
possible to estimate the height of the outlet of Lake Kusai, an important
parameter for overflow estimation. The overflow of Lake Kusai can help
predict the water level rise in Lake Salt and even serve as an indicator of
flood forecast, as Jason-3 data with a 10-day revisit cycle are now
available on Lake Kusai. Several pairs of Landsat 8 OLI images and lake
water levels for the same period were compared to provide a range of
possible outlet heights, which are likely to be 4483.9 to 4484.1 m, as
shown in Fig. 18a. Then we measured the mean width of the outlet from
high-resolution optical images provided by Planet Explorer (Planet, 2017),
which is relatively stable in Dec 2011 at 31.5±2.3 m in recent years.
Given lake water levels and the outlet height and width, an estimation of overflow
can be made using the broad crest weir formula:
Q=C⋅b⋅H1.52g,
where C is a parameter mainly reflecting geometric characteristics of the weir that mainly varies from 0.3 to 0.4, b is the width of the weir, H is the water head with respect to the top of the weir, and g is the acceleration of gravity.
We determined C (∼ 0.3) by using stage 1 shown in Fig. 19 as
a calibration period. Details can be found in the supplementary file. Then
we applied this result to stage 2 shown in Fig. 19 to estimate the total
overflow from Lake Kusai and compared the overflow with total water gain in
stage 2 in Lake Salt. Since Lake Salt mainly relied on the replenishment of
Lake Kusai during that period, with little precipitation input and
negligible glacier meltwater in winter, the outflow of Lake Kusai can be
comparable with the water gain in Lake Salt derived from remote sensing,
though there was a small amount of evaporation loss. This relationship can
provide a straightforward validation of our developed method. However, it
was not available in stage 1, because the outflow of Lake Kusai first
replenished Lake Haidingnuoer until the latter began overflowing. Results
based on Eq. (13) indicate that the total outflow from Lake Kusai in stage 2
ranged from 0.21 to 0.22 km3, whereas the water gain in Lake Salt from
remote sensing was 0.19±0.01 km3. This indicates that our high-temporal-resolution lake water level time series are valuable in monitoring
and predicting lake outflow flooding that is crucial for the safety of
downstream residents and infrastructure.
Data availability
The derived TP lake water levels, hypsometric curves, and water storage
changes are archived and available at 10.1594/PANGAEA.898411 (Li et al., 2019).
Conclusion
In this study, we develop high-temporal-resolution (i.e., weekly to monthly
timescales) water levels and storage change data sets for 52 large lakes on
the TP during 2000–2017 by combining multiple altimetric missions and
optical remote sensing images. Generated from lake shoreline positions and
regression analysis with altimetry data, the Landsat-derived water level
serves as a unique reference covering the entire study period, enabling a
more consistent merging of multisource altimetry time series. Multisource
altimetry water levels are first extracted separately from spaceborne
altimetry products and then combined into a longer and denser altimetry
water level time series with systematic biases well removed using
Landsat-derived water levels as reference. The combined altimetry and
Landsat-derived water levels increase the overall sampling frequency to
submonthly regardless of the lake size.
By comparison with a widely used LEGOS Hydroweb data set, we show that
without Landsat-derived water levels as a reference there may be a
remaining bias in the combined altimetry water levels in certain lakes. Our
study has considerably improved the temporal resolution of the monitoring of
lake water level and storage changes in the TP. For most lakes examined in
the published studies, to our best knowledge, the estimates from our study
provide the observations of the highest temporal resolution that can better
reveal the interannual and intra-annual variability and trends in lake water
level and storage, even in some relatively small lakes whose annual trends
may, however, be incorrectly estimated by sparse sampling of lake water
levels. The developed data sets can also facilitate the monitoring of some
rapidly expanding lakes with overflow risks and provide important
information on flood prediction and early warning.
We evaluate the uncertainty in the Landsat-derived water levels by field
experiments and rigorous uncertainty analysis. Both methods are consistent
that the magnitude of the uncertainty is ∼ 0.1 m, which
suggests that Landsat-derived water levels are often more efficient and less
noisy than altimetry data when the altimeter footprints on the lake surface
are insufficient, especially for small lakes. Based on our estimates, 52
large TP lakes accounting for ∼ 60 % of the total TP lake
area have gained 100.1±5.7 km3 of water during the past 18 years.
Lakes in the endorheic basin on the TP have mostly expanded. The complex
spatial pattern of lake storage changes in the Selin Co basin was quantified
and a possible explanation was proposed in this study. Note that the quality
of the Landsat-derived water levels before 2002 may not be as good as those
after 2002, because no altimetry data before 2002 are used in this study.
Extrapolation of the relationship between lake shoreline positions and water
levels may not be stable if the water level during 2000–2001 was much lower
or higher than those from 2002 to 2017. Discussions on how the extrapolation
may affect the data quality can be found in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-11-1603-2019-supplement.
Author contributions
LD and LX designed the research. LX, LD, HQ, and ZF developed the approaches
and data sets. LX, HQ, HP, and LD carried out the field experiment. LX, LD,
and YW contributed to the analysis of results and writing of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Mingda Du's assistance in the field experiments, discussion on the use of satellite
altimetry data for lake monitoring with Gang Qiao from Tongji
University, and efforts in improving and archiving the data sets made by
Daniela Ransby from PANGAEA data publisher are acknowledged here.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 91547210 and 51722903) and the National Key Research and
Development Program of China (grant no. 2017YFC0405801).
Review statement
This paper was edited by Birgit Heim and reviewed by two anonymous referees.
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