This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.

  • Although the water volume changes derived from these methods are highly consistent, the two methods are still affected by different constraints.

  • The limitations and regional applicability of the two methods were analyzed in detail.

  • The DEM method is more suitable for estimating lake water volumes in the Tibetan Plateau Lake Region, and the DAHITI method is suitable for lakes with complete water level data.

Lakes play an important role in the global water cycle and act as key indicators of climate change. China has more than 2,000 lakes (>1 km2), cumulatively covering an area exceeding 80,000 km2 (Zhang & Song 2022). These lakes show significant spatial heterogeneity in terms of water volume change, hydrological regimes, and climate interactions. Considering the spatial characteristics of natural environments and resource utilization, the geographic distribution of Chinese lakes has been categorized into five distinct regions: the Tibetan Plateau (TP), Eastern China Plain, Mongolia-Xinjiang, Northeastern China Plain, and Yunnan-Guizhou Lake Region (Wang & Dou 1998). Among these, the TP and Mongolian Regions, which together constitute more than 70% of China, have the largest lakes with individual lakes exceeding 20 km2 in area (Liu et al. 2022). In particular, the TP has the largest number of lakes in China with more than 1,000 lakes in areas larger than 1 km2 (Zhang 2018). The Xinjiang and the Eastern China Plains occupy 9.1 and 21.73% of the national total lake area, respectively (Shang et al. 2021).

The spatial variability in lake dynamics, increasingly influenced by both climate change and anthropogenic activities, has become more pronounced in recent years (Zhang et al. 2013). In the Mongolia-Xinjiang Lake Region, lakes such as Boston Lake have shrunk due to an increase in irrigation for agriculture (Liu et al. 2006). In the Eastern China Plains, especially in the Yangtze River Basin (such as Tai Lake, Poyang Lake), lakes are subject to significant hydrological alterations driven by monsoonal shifts and human interventions (Guo et al. 2008; Dronova et al. 2011; Ji et al. 2019). However, the TP alpine lakes are generally expanding, a trend attributed to accelerated glacial melt and increased precipitation (Zhang et al. 2011).

The water balance of a lake refers to the net effect of inputs (such as surface runoff inflow and precipitation) against outputs (such as surface runoff outflow and lake evaporation) (Zhang et al. 2013). In remote areas with harsh conditions, these variables are often not directly measurable at hydrological stations. Remote sensing technology provides a solution, enabling the estimation of lake water balance through the analysis of satellite-derived water level (from radar or laser altimeters) combined with surface area data obtained from satellite imagery (Zhang et al. 2013; Duan et al. 2018). The advent of advanced remote sensing techniques has facilitated the development of multiple methods for estimating lake water volumes, typically incorporating data from individual satellites to estimate lake levels and areas, or integrating data from multiple remote sensing sources to improve temporal and spatial coverages (Zhang et al. 2011; Duan & Bastiaanssen 2013; Feng et al. 2022). For example, Zhang et al. (2013) derived lake levels from ICESat-1 altimetry data and lake surface area from Landsat images and further used them to estimate the water balances of the 10 largest lakes in China during 2003–2009. Xu et al. (2022) used multi-mission satellite data to systematically analyze water storage changes in Inner Mongolia's major lakes.

Existing studies usually use empirical formulas to calculate the water volume change in a single area based on lake area and water level (Crétaux et al. 2016; Cai et al. 2020). The method of integrating digital elevation model (DEM) and satellite optical images is also an important technical supplement for estimating water volume change, and this method has acceptable accuracy with an overall average error of 4.98% (Yang et al. 2017). Most existing studies have relied on a single estimation method, with comparative analyses between different methods remaining scarce. Satellite altimetry-based estimation methods have been widely used, particularly for large lakes with comprehensive data sets such as Selin Co and Qinghai Lake. Satellite altimeters are profiling tools rather than imaging devices, and they can only observe lakes along their ground tracks, meaning that many lakes are not or only partially measured. Thus, questions remain regarding the reliability of water volume change estimates for lakes with incomplete data records. The DEM-based method has been well applied in the TP region (Yang et al. 2017), its suitability for lakes in other regions has not been sufficiently investigated. Moreover, comparative analyses of the two different methods are even more scarce.

To address the above-mentioned gaps, this study aimed at conducting a comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes in China. Specifically, we leveraged satellite altimetry-derived lake water levels provided by the Database for Hydrological Time Series of Inland Waters (DAHITI) in combination with Landsat-derived lake areas to estimate the water volume changes for 34 selected lakes from 2005 to 2020. For the DEM-based method, we used the Shuttle Radar Topography Mission (SRTM) DEM and Landsat images to estimate water volume changes in the same lakes during the same period. The objectives of this study are to (1) evaluate the two methods and examine their strengths and limitations for estimating lake water volume changes and (2) assess the regional applicability of the two methods across a large geographical region. This study is expected to help us achieve a better understanding of the performance and applicability of these two methods and potentially to synthesize these two methods for an improved estimate of lake water volume changes.

This study focused on 34 selected lakes in China (Figure 1) with water level time series data provided by the DAHITI at the time of writing. These lakes were selected for a comparative analysis of water volume estimation methods. The 34 lakes are distributed in three lake regions as follows: TP, Xinjiang, and Eastern China Plain Lake Region. As shown in Figure 1, The TP Lake Region includes 27 alpine lakes such as Selin Co, Nam Co, Dagze Co, Gyaring Co, and so on. Xinjiang Lake Region includes two inland lakes such as Bosten Lake and Ulungur Lake. In the Eastern China Plain Lake Region, Hulun Lake is located in the Amur River Basin; Chao Lake, Poyang Lake, Junshan Lake, and Tai Lake are distributed in the Yangtze River Basin.
Figure 1

Distribution map of the studied 34 lakes in China. The gray lines indicate the lake region boundaries, and the blue shaded area indicates the extent of all lakes. The circled numbers indicate all lakes studied in China.

Figure 1

Distribution map of the studied 34 lakes in China. The gray lines indicate the lake region boundaries, and the blue shaded area indicates the extent of all lakes. The circled numbers indicate all lakes studied in China.

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Most of the 27 lakes on the TP are located in the endorheic region and are less affected by human activity; however, they are extremely sensitive to climatic and cryospheric changes. The TP is a regional Earth system showing very strong interactions among its lithosphere, hydrosphere, cryosphere, biosphere, atmosphere, and anthrosphere (Yang et al. 2011; Yao et al. 2015). The inland lakes in the arid region of Xinjiang are an important link in the water resource cycle and a comprehensive reflection of the effect of climate change and human activities on water resources (Wu et al. 2012). Lake volumes in the Eastern China Plain Lake Region show significant inter- and intra-annual variations caused by precipitation fluctuations during the summer monsoon. This phenomenon is more significant in the mid-lower reaches of the Yangtze River basin (Chen et al. 2011; Zheng et al. 2016).

We used Landsat images from 2005 to 2020 to extract the lake extents/surface areas over different periods. On this basis, we estimated lake water volume changes using two different methods: (1) satellite altimetry-based method that uses the empirical formula derived from DAHITI water level data and (2) DEM-based method that uses SRTM DEM data (Figure 2).
Figure 2

The flowchart for lake water volume estimation with two different methods (satellite altimetry-based and DEM-based) in this study.

Figure 2

The flowchart for lake water volume estimation with two different methods (satellite altimetry-based and DEM-based) in this study.

Close modal

Landsat images and processing

Launched by NASA, the Landsat satellites have contributed greatly to our understanding of the Earth's environment. Data used in this study were downloaded from the United States Geological Survey (http://glovis.usgs.gov.landsat). These data are widely used in lake extent extraction (Crétaux et al. 2016; Markham et al. 2018). We selected 113 Landsat TM/ETM/OLI images, each with less than 5% cloud cover and acquired around October, were selected. These image dates align with DAHITI water level data within a 2-month margin. The amount of precipitation and interannual precipitation fluctuation are very small in the TP and Xinjiang (Jin et al. 2021). Furthermore, alpine lakes in the TP are also influenced to some extent by the supply of glacial meltwater. Therefore, the water level changes of lakes in the TP and Xinjiang Lake Region are relatively stable and the estimation results are not significantly affected by flood peak. In addition, the limited number of lakes in the Eastern China Plain Lake Region has a minimal impact on the estimation of water volume. We also consulted relevant literature to ensure that the estimated time point we chose was not during the peak flood period of the eastern lake. The times at which Landsat images were used for lake extent extraction are listed in Table 1. The normalized difference water index (NDWI) method was used to extract lake boundaries and further to calculate lake surface areas (Gao 1996). The formula is described as follows:
formula
(1)
where indicates the water index; and indicate the reflectance in the green band and near-infrared band of the Landsat image.
Table 1

Temporal variation trend of water volume changes (km3) in 34 lakes estimated from the satellite altimetry-based method with the DAHITI data

Lake nameStart datePeriod 1Time 1Period 2Time 2Period 3End dateWater balance
Seling Co 09/04 +5.95 08/10 +3.41 11/15 +3.34 10/20 +12.70 
Ang Laren Co     10/16 +1.08 10/20 +1.08 
Zhari Namco 09/04 +1.47 10/10 −0.4 10/15 +3.52 10/20 +4.59 
TangraYumco 11/05 +1.18 10/10 +0.11 10/15 +1.48 10/20 +2.77 
Ngangze Co   11/09 +0.35 11/15 +0.74 11/20 +1.09 
Dagze Co 09/04 +0.96 10/09 +0.69 09/15 +1.11 10/20 +2.76 
Nam Co 11/05 +0.21 11/10 −0.17 11/15 +1.38 10/20 +1.42 
Dogaicoring 09/04 +0.77 09/10 +0.72 09/15 +1.02 10/20 +2.51 
Dorge Co     10/16 +0.04 09/20 +0.04 
Junshan     11/16 +0.20 09/20 +0.2 
Dogai Coring 09/04 +0.58 09/10 +0.42 10/15 +0.88 10/20 +1.88 
Kusai   09/10 +1.79 11/15 +0.08 10/20 +1.87 
Qinghai 09/05 +0.27 08/10 +3.78 09/15 +8.75 10/20 +12.8 
Jieze Caka     10/16 +0.12 10/20 +0.12 
Har 09/04 +0.42 07/10 +0.4 10/15 +1.3 10/20 +2.12 
La'nga Co   10/10 −0.18 09/15 −0.22 10/20 −0.40 
Taro Co   10/13 −0.33 09/15   −0.33 
Gyaring Co     10/15 +0.19 10/20 +0.19 
Gyado Tso     10/16 +0.07 10/20 +0.07 
Mishima     10/16 +0.18 10/20 +0.18 
Gongzhu     10/16 +0.04 10/20 +0.04 
Jianshuei     10/18 +0.08 10/20 +0.08 
Dulishi     10/16 +0.16 10/20 +0.16 
Luotuo     10/16 +0.13 10/20 +0.13 
Bairad Co     10/16 −0.07 10/20 −0.07 
Mugchu Tso     10/15 +0.01 10/20 +0.01 
Ulungar 08/05 +1.01 08/10 −0.53 10/15 +0.64 08/20 +1.12 
Ayakkum 10/05 +1.25 09/10 +1.49 08/15 +2.03 10/18 +4.77 
Bosten   08/10 −0.01 09/15 +1.74 10/20 +1.73 
Aqqikkol   10/11 +1.25 10/15 +0.77 10/18 +2.02 
Tai 11/05 +0.26 09/10 −1.19 10/15   −0.93 
Hulun 10/05 −2.98 09/10 +5.60 09/15 +0.09 11/20 +2.71 
Chao 11/05 +0.33 10/09 −0.12 12/16 +0.73 10/20 +0.94 
Poyang 09/05 −1.15.69 08/08 −9.15 09/15 +17.74 09/20 +7.44 
Lake nameStart datePeriod 1Time 1Period 2Time 2Period 3End dateWater balance
Seling Co 09/04 +5.95 08/10 +3.41 11/15 +3.34 10/20 +12.70 
Ang Laren Co     10/16 +1.08 10/20 +1.08 
Zhari Namco 09/04 +1.47 10/10 −0.4 10/15 +3.52 10/20 +4.59 
TangraYumco 11/05 +1.18 10/10 +0.11 10/15 +1.48 10/20 +2.77 
Ngangze Co   11/09 +0.35 11/15 +0.74 11/20 +1.09 
Dagze Co 09/04 +0.96 10/09 +0.69 09/15 +1.11 10/20 +2.76 
Nam Co 11/05 +0.21 11/10 −0.17 11/15 +1.38 10/20 +1.42 
Dogaicoring 09/04 +0.77 09/10 +0.72 09/15 +1.02 10/20 +2.51 
Dorge Co     10/16 +0.04 09/20 +0.04 
Junshan     11/16 +0.20 09/20 +0.2 
Dogai Coring 09/04 +0.58 09/10 +0.42 10/15 +0.88 10/20 +1.88 
Kusai   09/10 +1.79 11/15 +0.08 10/20 +1.87 
Qinghai 09/05 +0.27 08/10 +3.78 09/15 +8.75 10/20 +12.8 
Jieze Caka     10/16 +0.12 10/20 +0.12 
Har 09/04 +0.42 07/10 +0.4 10/15 +1.3 10/20 +2.12 
La'nga Co   10/10 −0.18 09/15 −0.22 10/20 −0.40 
Taro Co   10/13 −0.33 09/15   −0.33 
Gyaring Co     10/15 +0.19 10/20 +0.19 
Gyado Tso     10/16 +0.07 10/20 +0.07 
Mishima     10/16 +0.18 10/20 +0.18 
Gongzhu     10/16 +0.04 10/20 +0.04 
Jianshuei     10/18 +0.08 10/20 +0.08 
Dulishi     10/16 +0.16 10/20 +0.16 
Luotuo     10/16 +0.13 10/20 +0.13 
Bairad Co     10/16 −0.07 10/20 −0.07 
Mugchu Tso     10/15 +0.01 10/20 +0.01 
Ulungar 08/05 +1.01 08/10 −0.53 10/15 +0.64 08/20 +1.12 
Ayakkum 10/05 +1.25 09/10 +1.49 08/15 +2.03 10/18 +4.77 
Bosten   08/10 −0.01 09/15 +1.74 10/20 +1.73 
Aqqikkol   10/11 +1.25 10/15 +0.77 10/18 +2.02 
Tai 11/05 +0.26 09/10 −1.19 10/15   −0.93 
Hulun 10/05 −2.98 09/10 +5.60 09/15 +0.09 11/20 +2.71 
Chao 11/05 +0.33 10/09 −0.12 12/16 +0.73 10/20 +0.94 
Poyang 09/05 −1.15.69 08/08 −9.15 09/15 +17.74 09/20 +7.44 

The starting date of each lake is 2005 and the ending date is 2020. Each monitoring cycle is five years. If the water level data of the monitoring time point were missing in the DAHITI data, the time point was extended by one year or calculated one year later. Water level data of most lakes were missing from 2005 to 2010 but were relatively complete from 2015 to 2020.

Satellite altimetry-based method with DAHITI data

The DAHITI data incorporate altimetry data from various satellites, such as ENVISat, Jason-1, Jason-2, and TOPEX/Poseidon. DAHITI uses an extended outlier rejection and a Kalman filter approach. The DAHITI was developed by the Technical University of Munich and now provides more than 600 water elevation time series of rivers, reservoirs, and lakes, using multi-source and multi-sensor satellite altimetry data (https://dahiti.dgfi.tum.de/en/map/) (Schwatke et al. 2015). Lake levels from DAHITI have been well validated in many lakes (Busker et al. 2019; Liu et al. 2019a, 2019b). Schwatke et al. (2015) demonstrated the water level performance of DAHITI for numerous lakes and rivers in North and South America. Comprehensive validation was performed by comparison with in situ gauge data and results from external inland altimeter databases. The lake-level datasets computed using the DAHITI approach yielded errors between 4 and 36 cm. At the time of our study and writing, the DAHITI database includes water level time series (2002–2021) of 83 (the number will be continuously increased) water bodies in China (including large inland lakes and rivers in the Yangtze River basin, Zhujiang River basin, and other river basins). The duration of a monitoring period is five years (the corresponding monitoring years are 2005, 2010, 2015, and 2020). To use the satellite altimetry-based method we combined DAHITI water level data with lake area data derived from Landsat images to derive a time series of water volume changes for different monitoring periods from 2005 to 2020. The water volume changes were estimated using Equation (2), as demonstrated in detail by Taube (2000):
formula
(2)
where V is the volume change from elevation and area to elevation and area .

SRTM DEM and processing

The SRTM DEM is a near-global topographic database generated from satellite images (Jakob & Van 2001) at a resolution of 30 m (Rabus et al. 2003). The SRTM provides a global high-quality DEM which covers the Earth between latitudes 60°N and 56°S and is acquired with the same sensor in a single mission and produced with single-technique synthetic aperture radar (SAR) interferometry (Jakob & Van 2001). A great advantage is the homogeneous quality of the DEM (Rabus et al. 2003). Yang et al. (2017) assessed the accuracy of lake water volume change results calculated using the SRTM DEM and found the accuracy of this method acceptable. Therefore, these two methods in this study were not necessary to verify the accuracy.

First, the lake area ASRTM was extracted from the SRTM DEM data and the corresponding lake surface area Ai (i = 1, 2, 3 …) at every 1 m increment in elevation. Therefore, Ai (i = 1, 2, 3 …) at an elevation of i m above ASRTM was calculated. Second, using Equation (3) to calculate Vi which expresses the volume between the lake surface ASRTM and Ai the volume change when the lake surface rises from ASRTM to Ai we obtained a series of (Vi, Ai) data pairs and established a regression equation (Equation (4)) between them – Vi is estimated as a function of Ai to calculate the increase in lake volume relative to the lake surface area ASRTM when the lake reaches a certain surface area.

For example, at the time ta, the lake surface area increases to Aa. When Aa is substituted into Equation (4), the volume change of the lake relative to ASRTM can be obtained. Assuming that the slope below ASRTM is similar to that above ASRTM within a certain range, the volume change when the lake shrinks from ASRTM to Ab at moment tb can be estimated using Equation (4).

Therefore, when calculating the water volume change (Vb,a) of the lake between two times (tatb), it is only necessary to obtain the lake surface areas Aa and Ab of the lake at times ta and tb, respectively (Aa and Ab were obtained from Landsat images) and substitute them into Equation (5):
formula
(3)
formula
(4)
formula
(5)
For illustrative purposes, we used Aqqikkol Lake as an example to explain the calculation procedures. Figure 3(a) represents a cross-sectional view of the lake boundary extraction process. Figure 3(b) represents a plan view of lake boundary delineation.
Figure 3

Schematic diagram of the water level height difference method based on lake shore terrain simulation. The blue and red lines represent the extent of Aqqikkol Lake in 2015 and 2020, respectively.

Figure 3

Schematic diagram of the water level height difference method based on lake shore terrain simulation. The blue and red lines represent the extent of Aqqikkol Lake in 2015 and 2020, respectively.

Close modal
Equation (4) was used to calculate Vi and establish the regression equation of Aqqikkol Lake (Figure 4).
Figure 4

Regression of lake surface area change and lake water volume change for Aqqikkol Lake.

Figure 4

Regression of lake surface area change and lake water volume change for Aqqikkol Lake.

Close modal

Last, by inserting and (obtained from Landsat images) into Equation (5), the water volume change of Aqqikkol Lake from 2015 to 2020 was estimated. Then we repeated the same procedures mentioned earlier to estimate the water volume changes of all lakes with this DEM-based method.

Water volume change estimation results

We analyzed the lake water volume changes by using SRTM DEM, which has more complete estimation results relative to DAHITI missing the early water level information seriously (Table 2). The results show that the overall water volume of lakes in the study region is on the rise. From 2005 to 2020, except for the reduction of the water volume of three lakes such as La'nga Co, the total water volume of the remaining lakes has increased to varying degrees. In the three monitoring periods, the fastest increase period is from 2015 to 2020, with a total increase of 26.07 km3 in all lakes. The slowest increase period is from 2005 to 2010 with some lakes showing a significant decrease, and the total water volume of all lakes only increased by 11.47 km3. Therefore, the water volume growth rate of China's lakes in the past 15 years shows an accelerating trend.

Table 2

Temporal trend of water volume variations (km3) in 34 lakes estimated from the DEM-based method with the SRTM DEM data. The time point of each lake monitoring period is the same as in Table 1 

Lake nameStart datePeriod 1Time 2Period 2Time 3Period 3End dateWater balance
Seling Co 09/04 +3.22 08/10 +6.40 11/15 +1.56 10/20 +11.18 
Ang Laren Co 10/05 +0.68 10/10 −0.49 10/16 +1.39 10/20 +1.58 
Zhari Namco 09/04 +1.21 10/10 −0.35 10/15 +2.01 10/20 +2.87 
TangraYumco 11/05 +1.29 10/10 +0.11 10/15 +1.15 10/20 +2.55 
Ngangze Co 10/05 +0.3 11/09 +0.27 11/15 +0.80 10/20 +1.37 
Dagze Co 09/04 +1.07 10/09 +0.53 09/15 +1.21 10/20 +2.81 
Nam Co 11/05 +0.47 11/10 −0.18 11/15 +0.49 10/20 +0.78 
Dogaicoring 09/04 +0.73 09/10 +0.67 09/15 +0.91 10/20 +2.31 
Dorge Co 08/05 +0.20 09/10 −0.20 10/16 +0.04 09/20 +0.04 
Junshan 10/05 −0.01 08/08 +0.02 11/16 +0.08 09/20 +0.09 
Dogai Coring 09/04 +0.29 09/10 +0.43 10/15 +0.71 10/18 +1.43 
kusai 10/05 +0.85 09/10 +1.46 11/15 +0.04 10/20 +2.35 
Qinghai 09/05 +0.16 08/10 +4.99 09/15 +9.05 10/20 +14.20 
Jieze Caka 10/05 +0.14 10/10 +0.13 10/16 +0.13 10/20 +0.40 
Har 09/04 +0.85 07/10 +0.45 10/15 +1.47 10/20 +2.77 
La'nga Co 11/05 −0.1 10/10 −0.22 09/15 −0.32 10/20 −0.64 
Taro Co 10/05 +0.31 10/13 −0.23 09/15 +0.84 10/20 +0.92 
Gyaring Co 10/04 −0.35 10/09 −0.27 10/15 +0.29 10/20 −0.33 
Gyado Tso 10/05 +0.02 10/10 +0.11 10/16 +0.09 10/20 +0.22 
Mishima 11/05 +0.01 11/10 −0.03 10/16 +0.16 10/20 +0.14 
Gongzhu 10/05 +0.06 10/10 +0.03 10/16 +0.11 10/20 +0.20 
Jianshuei 09/05 +0.57 09/10 +1.18 09/18 +0.08 10/20 +1.83 
Dulishi 11/05 +0.17 11/10 +0.18 10/16 +0.17 10/20 +0.52 
Luotuo 10/05 +0.04 10/10 +0.06 10/16 +0.11 10/20 +0.21 
Bairad Co 10/05 −0.02 09/10 −0.06 10/16 −0.07 10/20 −0.15 
Mugchu Tso 10/05 −0.003 04/13 +0.02 10/15 +0.02 10/20 +0.04 
Ulungar 10/05 +0.78 10/10 +0.002 10/15 +0.64 10/20 +1.42 
Ayakkum 10/05 +1.04 09/10 +1.38 08/15 +1.94 10/18 +4.36 
Bosten         
Aqqikkol 10/05 +0.31 10/11 +1.42 10/15 +0.89 10/18 +2.62 
Tai         
Hulun 10/05 −2.82 09/10 +5.00 09/15 +0.08 11/20 +2.26 
Chao         
Poyang         
Lake nameStart datePeriod 1Time 2Period 2Time 3Period 3End dateWater balance
Seling Co 09/04 +3.22 08/10 +6.40 11/15 +1.56 10/20 +11.18 
Ang Laren Co 10/05 +0.68 10/10 −0.49 10/16 +1.39 10/20 +1.58 
Zhari Namco 09/04 +1.21 10/10 −0.35 10/15 +2.01 10/20 +2.87 
TangraYumco 11/05 +1.29 10/10 +0.11 10/15 +1.15 10/20 +2.55 
Ngangze Co 10/05 +0.3 11/09 +0.27 11/15 +0.80 10/20 +1.37 
Dagze Co 09/04 +1.07 10/09 +0.53 09/15 +1.21 10/20 +2.81 
Nam Co 11/05 +0.47 11/10 −0.18 11/15 +0.49 10/20 +0.78 
Dogaicoring 09/04 +0.73 09/10 +0.67 09/15 +0.91 10/20 +2.31 
Dorge Co 08/05 +0.20 09/10 −0.20 10/16 +0.04 09/20 +0.04 
Junshan 10/05 −0.01 08/08 +0.02 11/16 +0.08 09/20 +0.09 
Dogai Coring 09/04 +0.29 09/10 +0.43 10/15 +0.71 10/18 +1.43 
kusai 10/05 +0.85 09/10 +1.46 11/15 +0.04 10/20 +2.35 
Qinghai 09/05 +0.16 08/10 +4.99 09/15 +9.05 10/20 +14.20 
Jieze Caka 10/05 +0.14 10/10 +0.13 10/16 +0.13 10/20 +0.40 
Har 09/04 +0.85 07/10 +0.45 10/15 +1.47 10/20 +2.77 
La'nga Co 11/05 −0.1 10/10 −0.22 09/15 −0.32 10/20 −0.64 
Taro Co 10/05 +0.31 10/13 −0.23 09/15 +0.84 10/20 +0.92 
Gyaring Co 10/04 −0.35 10/09 −0.27 10/15 +0.29 10/20 −0.33 
Gyado Tso 10/05 +0.02 10/10 +0.11 10/16 +0.09 10/20 +0.22 
Mishima 11/05 +0.01 11/10 −0.03 10/16 +0.16 10/20 +0.14 
Gongzhu 10/05 +0.06 10/10 +0.03 10/16 +0.11 10/20 +0.20 
Jianshuei 09/05 +0.57 09/10 +1.18 09/18 +0.08 10/20 +1.83 
Dulishi 11/05 +0.17 11/10 +0.18 10/16 +0.17 10/20 +0.52 
Luotuo 10/05 +0.04 10/10 +0.06 10/16 +0.11 10/20 +0.21 
Bairad Co 10/05 −0.02 09/10 −0.06 10/16 −0.07 10/20 −0.15 
Mugchu Tso 10/05 −0.003 04/13 +0.02 10/15 +0.02 10/20 +0.04 
Ulungar 10/05 +0.78 10/10 +0.002 10/15 +0.64 10/20 +1.42 
Ayakkum 10/05 +1.04 09/10 +1.38 08/15 +1.94 10/18 +4.36 
Bosten         
Aqqikkol 10/05 +0.31 10/11 +1.42 10/15 +0.89 10/18 +2.62 
Tai         
Hulun 10/05 −2.82 09/10 +5.00 09/15 +0.08 11/20 +2.26 
Chao         
Poyang         

The water volume change data for Bosten, Poyang, Tai, and Chao Lakes is missing as DEM cannot be used to estimate this change.

Water volume changes derived from the SRTM DEM showed considerably spatial heterogeneity with 24 lakes in the TP Lake Region showing a marked increasing water volume trend. Among these, Selin Co and Qinghai Lake, with vast water storage capacities, showed the largest increase (both above 10 km3) with average annual changes of 0.70 and 0.95 km3/a, respectively. Lakes with lower water storage capacities, such as Dorge Co and Mugchu Tso, increased more slowly, with an average annual change of 0.003 km3/a. In contrast, the Bairad Co, La' nga Co, and Gyaring Co Lakes exhibited a certain degree of shrinkage. The water volume in the Xinjiang Lake Region showed a slowly increasing trend. The annual increase in water volume in Ulunger Lake was 0.09 km3/a, whilst the water volume changes in the East China Plain Lake Region showed fluctuating tendencies. The water volume change in Hulun Lake increased by more than 1 km3.

Figure 5 shows the water volume change of all lakes studied. In general, the lake water volume in China is on the rise. In the TP, two lakes showed a decrease in water volume, the other inland lakes have an increased trend of water volume change. In the Eastern China Plain Lake Region, four lakes showed an increasing trend in water volume change. In Xinjiang Lake Region, the water volumes of all lakes are increasing.
Figure 5

Spatial distribution of lake water volume change from 2015 to 2020 based on DEM. Lakes that cannot have their water volume changes estimated using DEM-based methods are represented using estimation results based on satellite altimetry-based methods. The red and blue color triangles indicate a decrease and increase in lake volume, respectively. The red line indicates the boundary of the lake region. Tai Lake is not shown in this figure as it lacks DAHITI data and DEM-based methods cannot be used to estimate its water volume change.

Figure 5

Spatial distribution of lake water volume change from 2015 to 2020 based on DEM. Lakes that cannot have their water volume changes estimated using DEM-based methods are represented using estimation results based on satellite altimetry-based methods. The red and blue color triangles indicate a decrease and increase in lake volume, respectively. The red line indicates the boundary of the lake region. Tai Lake is not shown in this figure as it lacks DAHITI data and DEM-based methods cannot be used to estimate its water volume change.

Close modal

Comparison of water volume change estimation with two different methods

We used two methods to estimate the water volume changes in 34 lakes and then compared their results. Overall, there is a strong correlation between the two results. For the satellite altimetry-based method with DAHITI data, not all lakes had a complete water level time series for each of the three cycles spanning 2005–2020. Nevertheless, the data from 2015 to 2020 were considerably more comprehensive, and this period was chosen for the comparative analysis of 29 lakes. Figure 6 shows high agreement between the two results with the coefficient of determination R2 of 0.93 and high confidence in the comparison, with most lakes falling within the 95% confidence interval, particularly those in the TP. The confidence levels for two lakes, Selin Co and Zhari Namco, were slightly lower.
Figure 6

Correlation analysis and confidence interval map of water volume change estimated by two different methods. The white circles represent the calculation results of every lake studied. The red strip indicates the confidence interval of 95%.

Figure 6

Correlation analysis and confidence interval map of water volume change estimated by two different methods. The white circles represent the calculation results of every lake studied. The red strip indicates the confidence interval of 95%.

Close modal
The deviations in the estimated results for individual lakes were quantified using a proportional algorithm. As shown in Figure 7, the deviations for most lakes are small, but the deviations of the Gongzhu and Kusai Lakes are relatively large. This may be attributed to the small surface areas of these lakes and the minor changes in water volume, which could amplify the discrepancies between the two methods.
Figure 7

Deviation of the estimated results of water volume changes in 29 lakes. The blue histogram and red line represent the estimated results using the DEM-based method and satellite altimetry-based method with the DAHITI data, respectively. The black short line represents the deviation of water volume changes obtained using the two methods.

Figure 7

Deviation of the estimated results of water volume changes in 29 lakes. The blue histogram and red line represent the estimated results using the DEM-based method and satellite altimetry-based method with the DAHITI data, respectively. The black short line represents the deviation of water volume changes obtained using the two methods.

Close modal

Limitations of the two methods

Limitations of the DEM-based method with the SRTM DEM data

The vertical accuracy of SRTM DEM data is reported to be 3–5 m (Bhang et al. 2006), while the horizontal accuracy is 3–6 m (Passini & Jacobsen 2007). Its mean absolute error (MAE) is measured at 3.60 m, ranking second among all global public DEMs (Liu et al. 2019a, 2019b). The high accuracy of SRTM DEM data has led to its widespread application in lake volume estimation studies (Yang et al. 2017; Liu et al. 2024). It is necessary to point out that we used the same SRTM DEM data to estimate the water volume changes of all lakes for different monitoring periods. Lake water volume changes are estimated based on variations in the lake level and area at different time periods. In short, the vertical errors present in SRTM DEM data can impact the estimation of lake water volume changes, although generally within acceptable margins. Yang et al. (2017) validated the accuracy of lake water volume change estimates using SRTM DEM by comparing them with depth measurement data. They found that the results were highly accurate. Liu et al. (2024) demonstrated the great utility of SRTM DEM in monitoring lake water storage changes through mapping inundated bathymetry. However, the lakeshore terrain plays a critical role in the applicability of the DEM-based method. The precision of contour line extraction is directly influenced by the slope of the lakeshore. For example, as shown in Figure 8(a) and 8(b), the lakeshore to the northeast and southwest of Chao Lake is relatively flat, whereas that in the eastern and northern mountainous areas is relatively steep. By comparing the extraction results of the contour line with the terrain slope binary map, it was found that the extraction of the contour line in areas with terrain slopes >1.5° – such as the eastern and northern shores of Chao Lake – was more accurate (within the range of the purple dashed lines in Figure 8(c). In contrast, in flat terrain areas with a slope <1.5°, the contour lines are confusing. Through extensive experiments, we set the slope condition (threshold) of the DEM method for estimating the lake water volume to >1.5°.
Figure 8

Threshold setting for extracting the contour lines of the Chao Lake shore. (a,b) represent the surrounding terrain and slope of Chao Lake, respectively. (c) represents the slope threshold setting for accurately extracting the lake shore using contour lines (gray and black areas represent areas with slopes < and >1.5°, respectively). The red lines represent the contour lines, and the area circled by the purple dashed line represents the relatively accurate area of the lake shoreline extracted by the contour lines.

Figure 8

Threshold setting for extracting the contour lines of the Chao Lake shore. (a,b) represent the surrounding terrain and slope of Chao Lake, respectively. (c) represents the slope threshold setting for accurately extracting the lake shore using contour lines (gray and black areas represent areas with slopes < and >1.5°, respectively). The red lines represent the contour lines, and the area circled by the purple dashed line represents the relatively accurate area of the lake shoreline extracted by the contour lines.

Close modal
In summary, the shorelines of most lakes with steep terrains can be accurately fitted using the DEM-based method (Figure 9(a), Gongzhu Lake), but those with relatively flat terrains are difficult to extract accurately. In addition, some large lakes have defects and only a portion of the shoreline can be accurately obtained. For example, the eastern and southern shores of Bosten Lake (Figure 9(b)) are close to mountain ranges, and the contour lines can effectively extract the lakeshore boundary. However, the contour lines deviate severely from the lakeshore boundary on the relatively flat plains to the west. In this situation, it is difficult to establish regression equations that accurately simulate changes in lakeshore topography, leading to an inability to accurately estimate the water volume changes in these lakes.
Figure 9

The contour line cannot describe the lake shore extent. (a) Gongzhu Lake and (b) Boston Lake. The blue area and red line represent the lake extent and contour line of the lake shore, respectively.

Figure 9

The contour line cannot describe the lake shore extent. (a) Gongzhu Lake and (b) Boston Lake. The blue area and red line represent the lake extent and contour line of the lake shore, respectively.

Close modal

Limitations of the satellite altimetry-based method with the DAHITI data

The satellite altimetry-based method with the DAHITI data integrates multi-source satellite data, and the monitoring period for each satellite is different. This resulted in partially missing water level data for some lakes which affected the lake water volume change estimation for some lakes in the TP Lake Region. Based on the data of the ‘water balance’ column in Tables 1 and 2 (the total water balance over the past 15 years), there is a marked difference in the total water balance of 21 of the lakes. These lakes limited water level data from DAHITI for the past 15 years, making it impossible to estimate the results of water volume changes during this period. Therefore, missing water level data is an extremely unfavorable factor for exploring the temporal variation characteristics of lake volumes using satellite altimetry data.

In addition, for water volume estimation in a single lake, the limited DAHITI water level data may also affect the value of the estimation results. Table 3 shows the deviation between the estimated results for Junshan and Dulishi Lakes. The deviation for Dulishi Lake is 0.01 km3, while that of Junshan Lake is 0.12 km3. Junshan Lake is located in the Eastern China Plain Lake Region, and its daily water level changes significantly owing to seasonal precipitation changes and human activities. However, the time difference between the water level and area is approximately two months, leading to a notable deviation in the estimation results. In contrast, the water level time series for Dulishi Lake is relatively complete, and the estimated results had a small deviation.

Table 3

Several examples of studied lakes with the large time offset between water level time and area extraction time

Lake nameTime of water levelTime of areaTime offset (day)Water volume offset (km3)
Junshan 21/11 05/10 47 0.12 
Dulishi 14/10 20/10 0.01 
Lake nameTime of water levelTime of areaTime offset (day)Water volume offset (km3)
Junshan 21/11 05/10 47 0.12 
Dulishi 14/10 20/10 0.01 

The date in the table represents the time point of the lake in a monitoring period.

Regional applicability of two methods

The DEM-based method has the advantage for estimating lake water volume changes without temporal constraints and can be applied to inland lakes worldwide because of the coverage of DEM data. However, the DEM-based method is highly dependent on lakeshore terrain. For some lakes with complete water level time series, such as the Hulun and Poyang Lakes, the satellite altimetry-based method is more suitable for monitoring the water volume change. However, the limited satellite altimetry-derived water levels from the DAHITI data constrain the applicability of this method.

As summarized in Table 4, the two methods demonstrate different regional applicability. The DEM-based method has a good applicability rate of 100% in the TP Lake Region which has a greater lakeshore slope, while in the Xinjiang and Eastern China Plain Lake Region with flatter lakeshore slopes, the water volume changes of some lakes cannot be estimated as there are applicability rates of only 50 and 40%, respectively. In contrast, the lack of long-term water level data across a period of 10 years for 10 lakes, such as Gongzhu Lake, greatly restricts the use of DAHITI data for estimating volumetric changes in these lakes.

Table 4

Regional applicability of the two methods for 34 lakes

Lake regionLake nameDAHITI applicabilityMissing period (year)DEM applicability
TP Lake Region Gyado Tso, Mishima, Gongzhu, Dulishi, Luotuo, Bairad Co, Ang Laren Co, Dorge Co, Jieze Caka Missing data 05–15 Available 
Taro Co Missing data 16–20 Available 
Seling Co, Zhari Namco, TangraYumco, Nam Co, Dogaicoring, Dogai Coring, Qinghai, Dagze Co, Har Available Complete Available 
Ngangze Co, Missing data 05–08 Available 
kusai, La'nga Co Missing data 05–09 Available 
Aqqikkol Missing data 05–10 Available 
Gyaring Co Missing data 05–14 Available 
Jianshuei Missing data 05–17 Available 
Ayakkum Missing data 18–20 Available 
Mugchu Tso Missing data 05–14 Available 
Eastern China Plain Lake Region Tai Missing data 16–20 Unavailable 
Chao, Poyang Available Complete Unavailable 
Junshan Missing data 05–15 Available 
Hulun Available Complete Available 
Xinjiang Lake Region Bosten Missing data 05–09 Unavailable 
Ulungar Available Complete Available 
Lake regionLake nameDAHITI applicabilityMissing period (year)DEM applicability
TP Lake Region Gyado Tso, Mishima, Gongzhu, Dulishi, Luotuo, Bairad Co, Ang Laren Co, Dorge Co, Jieze Caka Missing data 05–15 Available 
Taro Co Missing data 16–20 Available 
Seling Co, Zhari Namco, TangraYumco, Nam Co, Dogaicoring, Dogai Coring, Qinghai, Dagze Co, Har Available Complete Available 
Ngangze Co, Missing data 05–08 Available 
kusai, La'nga Co Missing data 05–09 Available 
Aqqikkol Missing data 05–10 Available 
Gyaring Co Missing data 05–14 Available 
Jianshuei Missing data 05–17 Available 
Ayakkum Missing data 18–20 Available 
Mugchu Tso Missing data 05–14 Available 
Eastern China Plain Lake Region Tai Missing data 16–20 Unavailable 
Chao, Poyang Available Complete Unavailable 
Junshan Missing data 05–15 Available 
Hulun Available Complete Available 
Xinjiang Lake Region Bosten Missing data 05–09 Unavailable 
Ulungar Available Complete Available 

Those with relatively complete DAHITI data and accurate extraction of lakeshore terrain are represented by ‘complete’ and ‘available’. Lakes with partially missing DAHITI data or lakes whose shores cannot be accurately extracted by DEM-based methods are represented by ‘missing data’ and ‘unavailable’. In addition, the period of missing DAHITI water level data for lakes is also indicated.

In summary, these two methods, along with their limitations and advantages, are technical complements. Therefore, when assessing lake water volume changes over a large region and over a long time series, more suitable methods can be selected based on the different lake region types. For lakes in regions that lack water level data for a certain period, the DEM-based method can be prioritized to estimate water volume changes during this period. For lakes with a relatively flat lakeshore terrain in the region and the availability of altimeter observations, satellite altimetry-based methods can be used to estimate water volume changes.

This study used and compared two different methods, the satellite altimetry-based method with the DAHITI data and the DEM-based method with the SRTM DEM data, to estimate the water volume change results of 34 lakes in China. This study discussed the limitations and regional applicability of the two methods. The estimation results obtained using the two methods were highly consistent, with an R2 of 0.93. However, both methods have advantages and limitations. The satellite altimetry-based method with the DAHITI data can only obtain lake water volume changes during the time period having altimetry data. In addition, for the eastern lakes, the huge daily change in lake water levels requires that the water level and lake range monitoring time points, i.e., the corresponding date, must be completely consistent. The DEM-based method with the SRTM DEM data can be used to estimate the water volume change of any closed lake in any region since 2000; but its accuracy is dependent on the lake shore topography. In our studied lakes in China, extracting accurate lake shorelines is challenging for lakes with slopes less than 1.5°. Because of the accuracy of lake shore extraction, the DEM-based method with the SRTM DEM is more suitable for estimating lake water volumes in the TP Lake Region (with an applicability rate of 100%); however, its applicability is lower in Xinjiang and Eastern China Plain Lake Region (with an applicability rate of 50 and 40%, respectively). Owing to the limited water level data for some lakes, the satellite altimetry-based method with the DAHITI data is more suitable for lakes with more complete water level data. A strategic selection of methods is recommended for analyzing lake water volume changes over extensive regions and timeframes. The DEM-based method is recommended for lakes with steeper shorelines and missing water level data. The satellite altimetry-based method with the DAHITI data is better suited for lakes with flatter shore terrain and comprehensive water level data. By combining these two methods, more complete and accurate estimates of lake water volume changes can be achieved.

This study provides valuable insights for researchers to effectively integrate these methods and obtain more complete and accurate estimates of lake water volume changes, especially when estimating changes over large spatial regions and long time series to understand the underlying patterns. Future researchers can further enhance the effectiveness of these methods by incorporating additional data. The new SWOT mission (launched at the end of 2022) promises to significantly advance lake monitoring capabilities. It is interesting and relevant to evaluate the actual performance of SWOT data for estimating lake water volume changes.

This work is supported by National Natural Science Foundation of China (41901129) and the University Natural Sciences Research Project of Anhui Educational committee (KJ2020JD06). Zheng Duan acknowledges the support from the Joint China–Sweden Mobility Grant funded by NSFC and STINT (CH2019-8250).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Busker
T.
,
Roo
A. D.
,
Gelati
E.
,
Schwatke
C.
,
Adamovic
M.
,
Bisselink
B.
,
Pekel
J. F.
&
Cottam
A.
2019
A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry
.
Hydrol. Earth Syst. Sci.
23
(
2
),
669
690
.
Cai
Y.
,
Ke
C. Q.
&
Shen
X. Y.
2020
Variations in water level, area and volume of Hongze Lake, China from 2003 to 2018
.
J. Great Lakes Res.
46
(
6
),
1511
1520
.
Crétaux
J. F.
,
Abarca-del-Río
R.
,
Berge-Nguyen
M.
,
Arsen
A.
,
Drolon
V.
,
Clos
G.
&
Maisongrande
P.
2016
Lake volume monitoring from space
.
Surv. Geophys.
37
,
269
305
.
Feng
Y. H.
,
Zhang
H.
,
Tao
S. L.
,
Ao
Z.
,
Song
C. Q.
,
Chave
J.
,
Toan
T. L.
,
Xue
B. L.
,
Zhu
J. L.
,
Pan
J. M.
,
Wang
S. P.
,
Tang
Z. Y.
&
Fang
J. Y.
2022
Decadal lake volume changes (2003–2020) and driving forces at a global scale
.
Remote Sens.
14
(
4
),
1032
.
Ji
H. P.
,
Wu
H. Y.
&
Wu
J.
2019
Variation of inflow and outflow of Lake Taihu in 1986–2017
.
J. Lake Sci.
31
(
6
),
1525
1533
.
Liu
C. S.
,
Du
L. J.
,
Chen
X.
&
Qiao
J. H.
2006
The change of effectively irrigated land area in China during the past 20 years
.
Resour. Sci.
28
(
2
),
8
12
.
Liu
K.
,
Song
C. Q.
,
Ke
L. H.
,
Jiang
L.
,
Pan
Y. Y.
&
Ma
R. H.
2019b
Global open-access DEM performances in Earth's most rugged region high mountain Asia: A multi-level assessment
.
Geomorphology
338
,
16
26
.
Liu
D.
,
Zhang
M.
,
Cao
Z. G.
,
Shen
M.
,
Qi
T. C.
,
Ma
J. G.
&
Duan
H. T.
2022
Monthly mean remote sensing water transparency dataset of large lakes in China during 2000–2020
.
Natl. Remote Sens. Bull.
26
(
1
),
221
230
.
Liu
K.
,
Song
C. Q.
,
Zhao
S.
,
Wang
J.
,
Chen
T.
,
Zhan
P. F.
,
Fan
C. Y.
&
Zhu
J.
2024
Mapping inundated bathymetry for estimating lake water storage changes from SRTM DEM: A global investigation
.
Remote Sens. Environ.
301
,
113960
.
Markham, B. L., Arvidson, T. J., Barsi, A., Choate, M., Kaita, M. E., Levy, R., Lubke, M. & Masek, J. G. 2018 Landsat program - Sciencedirect. In: Comprehensive Remote Sensing (Liang, S. L., ed.). Elsevier, Oxford, UK, 27–90.
Passini
R.
&
Jacobsen
K.
2007
Accuracy analysis of SRTM height models
. In:
Proceedings of 2007 American Society for Photogrammetry and Remote Sensing Annual Conference
,
Tampa, FL, USA
.
711
,
2529
.
Shang
B. X.
,
Xiao
C. L.
,
Zhao
D.
,
Zhu
Z. Z.
&
Zhang
G. Q.
2021
Distribution characteristics of lakes in China and suggestions for ecological protection and restoration of typical river basins
.
Geology. Sur. China
8
(
6
),
114
125
.
Taube
C. M.
,
2000
Three methods for computing the volume of a lake
. In:
Manual of Fisheries Survey Methods II: With Periodic Updates
(
Schneider
J. C.
ed.).
Michigan Department of Natural Resources, Ann Arbor, MI, USA.
Wang
S. M.
&
Dou
H. S.
1998
Records of Chinese Lakes
.
Science Press, Beijing, China
.
Wu
J. L.
,
Zeng
H.
,
Ma
L.
&
Bai
R. D.
2012
Recent changes of selected lake water resources in arid XinJiang, northwestern China
.
Quaternary Sci.
32
(
1
),
142
150
.
Yang
K.
,
Ye
B. S.
,
Zhou
D. G.
,
Wu
B. Y.
,
Foken
T.
,
Qin
J.
&
Zhou
Z. Y.
2011
Response of hydrological cycle to recent climate changes in the Tibetan Plateau
.
Climatic Change
109
(
3
),
517
534
.
Yang
R. M.
,
Zhu
L. P.
,
Wang
J.
,
Ju
J.
,
Ma
Q.
,
Turner
F.
&
Guo
Y.
2017
Spatiotemporal variations in volume of closed lakes on the Tibetan Plateau and their climatic responses from 1976 to 2013
.
Climatic Change
140
(
3–4
),
621
633
.
Yao
T. D.
,
Wu
F. Y.
,
Ding
L.
,
Sun
J. M.
,
Zhu
L. P.
,
Piao
S. L.
,
Deng
T.
,
Ni
X. J.
,
Zheng
H. B.
&
Yang
H. O.
2015
Multispherical interactions and their effects on the Tibetan Plateau's earth system: A review of the recent researches
.
Natl. Sci. Rev.
2
(
4
),
468
488
.
Zhang
G. Q.
,
Xie
H. J.
,
Kang
S. C.
,
Yi
D. H.
&
Ackley
S. F.
2011
Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009)
.
Remote Sens. Environ.
115
(
7
),
1733
1742
.
Zhang
G. Q.
,
Xie
H. J.
,
Yao
T. D.
&
Kang
S. C.
2013
Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
.
Chin. Sci. Bull.
58
,
3815
3829
.
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