Combined ICESat and CryoSat-2 Altimetry for Accessing Water Level Dynamics of Tibetan Lakes over 2003–2014

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.

The Dynamic Changes of Lake Issyk-Kul from 1958 to 2020 Based on Multi-Source Satellite Data

Lake Issyk-Kul is the largest alpine lake in arid Central Asia. In recent years, the lake has become a subject of special concern due to the dramatic fluctuations in its water level. In this study, the long-term continuous changes in the water level of Lake Issyk-Kul were derived from hydro-meteorological stations, CryoSat-2, and ICESat-2 satellites. Changes in area were analyzed by the Joint Research Centre (JRC) Global Surface Water (GSW) dataset based on the Google Earth Engine and the variations in water volume were estimated by an empirical formula. The results indicate that the water level of Lake Issyk-Kul fluctuated between 1606.06 m and 1608.32 m during 1958–2020, showing a slight decrease of 0.02 m/year on average. The water level first experienced a significant decreasing trend of 0.05 m/year from 1958 to 1998, and then began to rise rapidly by 0.10 m/year during 1998–2006, followed by a fluctuating decline after 2006. The area of Lake Issyk-Kul exhibited a downward trend before 1998, then a rapid expansion during 1998–2006, and short-term fluctuations in decline thereafter. Meanwhile, changes in water volume of Lake Issyk-Kul followed a similar pattern of variations in water level and area. According to comprehensive analyses, the continuous downward trend of the water level before 1998 was primarily affected by substantial anthropogenic water consumption in the basin. However, since the 21st century, the increases in precipitation and glacier meltwater and the reduced water consumption have collectively facilitated the short-term recovery of Lake Issyk-Kul in water level, area, and water volume.

A Novel Method for Mapping Lake Bottom Topography Using the GSW Dataset and Measured Water Level

Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake’s dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect.

Decoding the dramatic hundred-year water level variations of a typical great lake in semi-arid region of northeastern Asia

Lakes in arid and semi-arid regions are experiencing dramatic variations in water level and volume, which has caused severe ecological and social problems. Long-term study of the lake dynamics in arid/semi-arid regions could provide particular insights into the mechanisms driving lake variations, while hydro-meteorological data were usually limited in these regions, especially before the instrumental period. In the present study, we focused on a typical great lake - Hulun Lake in semi-arid region in northern China, simulated the hydrological processes from 1904 to 2016 using SWAT model, CRUNCEP7 reanalysis data, and sparse records of lake level during 1900s-1950s, and investigated the mechanisms driving the dramatic variations of the lake at the hundred-year time scale. Results illustrated that the simplified Penman equation by Valiantzas (2006) could reproduce the evaporation dynamics of Hulun Lake, with monthly R² being 0.93-0.95. The long-term simulation since 1904 reproduced runoff dynamics, which were consistent with the dramatic variations of lake level over hundred years. The largest water level increase (~5.0 m in 1950s) and decrease (~4.5 m in 2000s) during 1904-2016 were jointly affected by river runoff, lake evaporation, and precipitation into the lake. Both the positive/negative phase and the multi-decadal trend of PDO clearly influenced the hydrological cycle of Hunlun Lake, especially for the period of 1904-1950 with low lake levels. Overall, the present study provided a methodology for investigating the hundred-year hydrological processes for lakes in semi-arid regions in northeastern Asia.

Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive.

Recent Abnormal Hydrologic Behavior of Tibetan Lakes Observed by Multi-Mission Altimeters

Inland lakes in the Tibetan Plateau (TP) with closed catchments and minimal human disturbance are an important indicator of climate change. However, the examination of changes in the spatiotemporal patterns of Tibetan lakes, especially water level variations, is limited due to inadequate access to measurements. This obstacle has been improved by the development of satellite altimetry observations. The more recent studies revealed that the trend of central TP to grow decreased or reversed between 2010 and 2016. However, thus far, this trend has not been investigated to determine whether this pattern would last for the following years. This study aims to combine the traditional (launched before 2010, e.g., TOPEX/POSEIDON, ERS-1, ERS-2, Jason-1/-2, and Envisat) and recently advanced (launched after 2010, e.g., SARAL and Sentinel-3) altimetry observations to understand the Tibetan lake changes further in recent years. Therefore, we acquired information on the continuous lake level changes in Tibetan lakes using the lake level sequence integration method based on multisource altimetry satellites. The results revealed that water level changes in 22 examined lakes showed abrupt rises in 2016–2018, but the onsets and magnitudes of the rises varied among the lakes. During the study period, the water levels of the lakes (except Nam Co) revealed a drastic rising tendency with a mean rate of 0.74 m/a, which was remarkably higher than the average rate of water level rise over the period 2010–2015 (approximately 0.28 m/a). Specifically, the water level of the nine lakes in the Northern TP (NTP) displayed a significant rising trend, with an average rate of 0.82 m/a. In the Central TP (CTP), the lake level changes were generally divided into two categories. The water levels for the lakes in the Western CTP rose rapidly, while, in the Eastern CTP, the lake water levels rose slowly, with an average rising rate less than 0.40 m/a. The water levels for the lakes in the Northeastern TP (NETP) and Northwestern TP (NWTP) kept a stable rising tendency. According to the results of the climate analysis, the spatial differences of the lake level rise rates were primarily caused by the spatial and temporal changes of precipitation over the TP.

Lake water volume fluctuations in response to climate change in Xinjiang, China from 2002 to 2018

Climate change has a global impact on the water cycle and its spatial patterns, and these impacts are more pronounced in eco-fragile regions. Arid regions are significantly affected by human activities like farming, and climate change, which influences lake water volumes, especially in different latitudes. This study integrates radar altimetry data from 2002 to 2018 with optical remote sensing images to analyze changes in the lake areas, levels, and volumes at different altitudes in Xinjiang, China. We analyzed changes in lake volumes in March, June, and October and studied their causes. The results showed large changes in the surface areas, levels, and volumes of lakes at different altitudes. During 2002–2010, the lakes in low- and medium-altitude areas were shrinking but lakes in high altitude areas were expanding. Monthly analysis revealed more diversified results: the lake water levels and volumes tended to decrease in March (−0.10 m/year, 37.55×10⁸ m³) and increase in June (0.03 m/year, 3.48×10⁸ m³) and October (0.04 m/year, 26.90×10⁸ m³). The time series lake water volume data was reconstructed for 2011 to 2018 based on the empirical model and the total lake water volume showed a slightly increasing trend during this period (71.35×10⁸ m³). We hypothesized that changes in lake water at high altitudes were influenced by temperature-induced glacial snow melt and lake water in low- to medium-altitude areas was most influenced by human activities like agricultural irrigation practices.

Monitoring Long-Term Lake Level Variations in Middle and Lower Yangtze Basin over 2002–2017 through Integration of Multiple Satellite Altimetry Datasets

Satellite altimetry has been effectively used for monitoring lake level changes in recent years. This work focused on the integration of multiple satellite altimetry datasets from ICESat-1, Envisat and Cryosat-2 for the long-term (2002–2017) observation of lake level changes in the middle and lower Yangtze River Basin (MLYB). Inter-altimeter biases were estimated by using the gauged daily water level data. It showed that the average biases of ICESat-1 and Cryosat-2 with respect to Envisat were 6.7 cm and 3.1 cm, respectively. The satellite-derived water levels were evaluated against the gauged data. It indicated significantly high correlations between the two datasets, and the combination of three altimetry data produced precise water level time series with high temporal and spatial resolutions. A liner regression model was used to estimate the rates of lake level changes over the study period after the inter-altimeter bias adjustment was performed. The results indicated that ~79% of observed lakes (41/52) showed increasing trends in water levels with rates up to 0.203 m/y during 2002–2017. The temporal analysis of lake level variations suggested that ~60% of measured lakes (32/53) showed decreasing trends during 2002–2009 while ~66% of measured lakes (79/119) exhibited increasing trends during 2010–2017. Most of measured reservoirs displayed rapidly rising trends during the study period. The driving force analysis indicated that the temporal heterogeneity of precipitation can be mainly used to explain the observed pattern of lake level changes. The operation of reservoirs and human water consumption were also responsible for the lake level variations. This work demonstrated the potential of integrating multiple satellite altimeters for the long-term monitoring of lake levels, which can help to evaluate the impact of climate change and anthropogenic activities on regional water resources.

Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission

Spaceborne global navigation satellite system reflectometry (GNSS-R) data collected by the UK TechDemoSat-1 (TDS-1) satellite is applied to retrieve global lake levels for the first time. Lake levels of 351 global lakes (area greater than 500 km2 and elevation lower than 3000 m each) are estimated using TDS-1 Level 1b data over 2015–2017. Strong correlations (overall R2 greater than 0.95) are observed among lake levels derived from TDS-1 and other altimetry satellites such as CryoSat-2, Jason, and Envisat (the latter two are collected by Hydroweb), although with large root-mean-square error (RMSE) (tens of meters) mainly due to the fact that TDS-1 is not dedicated for altimetry measuring purpose. Examples of the Caspian Sea and the Poyang Lake show consistent spatial and temporal variations between TDS-1 and other data sources. The results in this paper provide supportive information for further application of GNSS-R constellations to measure altimetry of inland water bodies.

Analysis of Retrackers’ Performances and Water Level Retrieval over the Ebro River Basin Using Sentinel-3

Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can, therefore, play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This study aimed to retrieve water levels from Level-1B Sentinel-3 data with focus on the minimization of the land contamination over small- to middle-sized water bodies (130 m to 4.5 km), where continuous clean waveforms rarely exist. Three specialized algorithms or retrackers, together with a new waveform portion selection method, were evaluated to minimize land contamination in the waveforms and to select the nadir return associated with the water body being overflown. The waveform portion selection method, with consideration of the Digital Elevation Model (DEM), was used to fit the multipeak waveforms that arise when overflying the continental water bodies, exploiting a subwaveform-based approach to pick up the one corresponding to the nadir. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Center of Gravity (OCOG) retracker and two-step SAR physical-based retracker, were compared. No significant difference was found in the results of the three retrackers. However, waveform portion selection using DEM information great improved the results. Time series of water levels were retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (approximately 1.8 km wide) and Ribarroja Reservoir (approximately 400 m wide), with unbiased root-mean-square errors (RMSEs) down to 0.28 m and 0.16 m, respectively, depending on the retracker.

Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin

The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer’s Drought Severity Index (PDSI), and El Niño Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88–1.46 m, and 1.41–1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence.

Temporal Variations in the Quantity of Groundwater Flow in Nam Co Lake

This paper aims to calculate and analyze the spatial and temporal variations in the groundwater flow quantity in Nam Co Lake based on the water balance principle. The results show that a large amount of groundwater was gradually lost and that, groundwater loss decreased from 1.9 billion m3 to 1.5 billion m3 from the period of 1980–1984 to 1995–2009. The comparative analysis in the current study indicates that the decrease in the groundwater index has a strong linear relationship with the temperature of the ground surface on the Tibetan Plateau, with a correlation coefficient as high as 0.92. Moreover, environmental variations such as large-scale engineering construction projects and increases in water storage may have played dominant roles in the sudden changes in the water quantities of plateau lakes (e.g., Nam Co Lake) during the periods of 1990–1995 and 2000–2009. The increased water levels resulted in reduced groundwater losses, which may lead to the substantial expansion or gradual shrinkage of the Qinghai–Tibet Plateau lakes over short periods of time. The results of this study provide an important reference for studying the mechanisms of lake water level changes on the Qinghai–Tibet Plateau.

Theoretical surface type classifier based on a waveform model of a satellite laser altimeter and its performance in the north of Greenland

Current land-cover classification methods using ICESat/GLAS's (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) datasets are based on empirical thresholds or machine learning by training multiple GLAS parameters, e.g., the reflectivity and elevation of the target and width, amplitude, kurtosis, and skewness of the return waveform. A theoretical classifier is derived based on a waveform model of an actual laser altimeter illuminating the sea surface. With given system parameters and the sea surface wind corresponding to the location of a laser footprint (the wind can be calculated by using the National Centers for Environmental Prediction dataset), a precise theoretical waveform can be generated as a reference. Compared with the measured waveform, a weighted total difference, which is very sensitive to small-scale sea ice within the laser footprint, can be calculated to classify the GLAS measured data as open water. In the north of Greenland, after discarding the saturated GLAS data, the new theoretical classifier performed better [overall accuracy (OA)=95.62%, Kappa coefficient=0.8959] compared to the classical support vector machine (SVM) classifier (OA=90.44%, Kappa=0.7901), but the SVM classifier showed a better result for the user's accuracy of sea ice. Benefiting from the synergies of the theoretical and SVM classifiers, the integrated theoretical and SVM classifier achieved excellent accuracy (OA=98.21%, Kappa=0.9588). In the future, the new ICESat-2 photon counting laser altimeter will also construct a "waveform" (elevation distribution) by selecting the photon cloud, and thus, this new analytical method will be potentially useful for detecting open water in the Arctic.

A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001-2015

Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km² each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as ‘the Roof of the World’ and ‘Asia’s water towers’, exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001–2015) nighttime and daytime LSWT for 374 lakes (≥10 km² each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc.