146 citations as recorded by crossref.

1. Improving Reservoir Outflow Estimation for Ungauged Basins Using Satellite Observations and a Hydrological Model Z. Han et al. https://doi.org/10.1029/2020WR027590
2. Map and Quantify the Ground Deformation Around Salt Lake in Hoh Xil, Qinghai-Tibet Plateau Using Time-Series InSAR From 2006 to 2018 Z. Zhang et al. https://doi.org/10.1109/JSTARS.2020.3031893
3. Lake Level Reconstructed From DEM-Based Virtual Station: Comparison of Multisource DEMs With Laser Altimetry and UAV-LiDAR Measurements P. Zhan et al. https://doi.org/10.1109/LGRS.2021.3086582
4. Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China L. Song et al. https://doi.org/10.1016/j.rse.2023.113657
5. What induces the interdecadal shift of the dipole patterns of summer precipitation trends over the Tibetan Plateau? Y. Liu et al. https://doi.org/10.1002/joc.7122
6. Assessment indices of littoral habitat condition for lakes in Maine and New England, United States J. Deeds et al. https://doi.org/10.1080/10402381.2023.2207490
7. Integrating ICESat-2 laser altimeter observations and hydrological modeling for enhanced prediction of climate-driven lake level change C. Liu et al. https://doi.org/10.1016/j.jhydrol.2023.130304
8. Hydrologic regime dynamics and driving factors of Anglaren Co on the Tibetan Plateau based on multi-source remote sensing data D. Huang et al. https://doi.org/10.1016/j.jhydrol.2025.133645
9. Extreme and compound events in lakes R. Woolway et al. https://doi.org/10.1038/s43017-025-00710-w
10. Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine Lakes on the Tibetan Plateau Y. Du et al. https://doi.org/10.1002/lno.12180
11. Accuracy Assessment of Estimated River Water Surface Elevations from Landsat 8 and 9 Imagery among Twenty Water Indices F. Pan https://doi.org/10.3390/rs16163054
12. Modeling Dynamics of Water Balance for Lakes in the Northwest Tibetan Plateau with Satellite-Based Observations J. Yan et al. https://doi.org/10.3390/rs17091618
13. New method improves extraction accuracy of lake water bodies in Central Asia Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2021.127180
14. Response of downstream lakes to Aru glacier collapses on the western Tibetan Plateau Y. Lei et al. https://doi.org/10.5194/tc-15-199-2021
15. Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques Y. Shen et al. https://doi.org/10.3390/rs14040815
16. A Practical Plateau Lake Extraction Algorithm Combining Novel Statistical Features and Kullback–Leibler Distance Using Synthetic Aperture Radar Imagery X. Zhou et al. https://doi.org/10.1109/JSTARS.2020.3016349
17. Insar Time-Series Deformation Forecasting Surrounding Salt Lake Using Deep Transformer Models j. wang et al. https://doi.org/10.2139/ssrn.4197041
18. Drainage basin reorganization and endorheic-exorheic transition triggered by climate change and human intervention S. Lu et al. https://doi.org/10.1016/j.gloplacha.2021.103494
19. Spatiotemporal water dynamic modelling of Ramsar-listed lakes on the Victorian Volcanic Plains using Landsat, ICESat-2 and airborne LiDAR data Z. Zhang et al. https://doi.org/10.1016/j.ecoinf.2022.101789
20. Driving factors and changes in components of terrestrial water storage in the endorheic Tibetan Plateau H. Deng et al. https://doi.org/10.1016/j.jhydrol.2022.128225
21. Understanding the impact of climatic variability on terrestrial water storage in the Qinghai-Tibet Plateau of China J. Xie et al. https://doi.org/10.1080/02626667.2022.2044482
22. Conceptual hydrological model-guided SVR approach for monthly lake level reconstruction in the Tibetan Plateau M. Hou et al. https://doi.org/10.1016/j.ejrh.2022.101271
23. Effects of environmental factors on the river water quality on the Tibetan Plateau: a case study of the Xoirong River, China M. Wu et al. https://doi.org/10.1007/s11356-023-30259-4
24. Glacier mass change and evolution of Petrov Lake in the Ak-Shyirak massif, central Tien Shan, from 1973 to 2023 using multisource satellite data Y. Wang et al. https://doi.org/10.1016/j.rse.2024.114437
25. Remote Sensing-Based Long-Term Assessment of Water Dynamics and Influencing Factors in Abaya and Chamo Lakes, East African Rift Valley, Ethiopia B. Mulu et al. https://doi.org/10.1177/11786221241299932
26. Spatio-temporal variations in terrestrial water storage and its controlling factors in the Eastern Qinghai-Tibet Plateau Y. Zhu et al. https://doi.org/10.2166/nh.2020.039
27. Changing Patterns of Lakes on The Southern Tibetan Plateau Based on Multi-Source Satellite Data F. Sun et al. https://doi.org/10.3390/rs12203450
28. Assessment of Algorithms for Estimating Chlorophyll-a Concentration in Inland Waters: A Round-Robin Scoring Method Based on the Optically Fuzzy Clustering S. Bi et al. https://doi.org/10.1109/TGRS.2021.3058556
29. A novel method for improving bathymetry-based estimation of lake water storage on the Tibetan Plateau B. Qiao et al. https://doi.org/10.1016/j.jhydrol.2026.135329
30. Spatio-Temporal Variations of Water Vapor Budget over the Tibetan Plateau in Summer and Its Relationship with the Indo-Pacific Warm Pool D. Meng et al. https://doi.org/10.3390/atmos11080828
31. Water circulation and water resources of Asia’s water tower: The past and future Q. Zhang et al. https://doi.org/10.1360/TB-2023-0381
32. Effects of climate change and land use/cover change on the volume of the Qinghai Lake in China H. Wang et al. https://doi.org/10.1007/s40333-022-0062-4
33. Lake area monitoring based on land surface temperature in the Tibetan Plateau from 2000 to 2018 W. Zhao et al. https://doi.org/10.1088/1748-9326/ab9b41
34. Synthesis of the ICESat/ICESat-2 and CryoSat-2 observations to reconstruct time series of lake level Y. Feng et al. https://doi.org/10.1080/17538947.2023.2166134
35. DSRC: An Improved Topographic Correction Method for Optical Remote-Sensing Observations Based on Surface Downwelling Shortwave Radiation W. Zhao et al. https://doi.org/10.1109/TGRS.2021.3083754
36. InSAR time-series deformation forecasting surrounding Salt Lake using deep transformer models J. Wang et al. https://doi.org/10.1016/j.scitotenv.2022.159744
37. Constraining the contribution of glacier mass balance to the Tibetan lake growth in the early 21st century L. Ke et al. https://doi.org/10.1016/j.rse.2021.112779
38. Lake volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019 J. Wang et al. https://doi.org/10.1038/s41597-022-01711-w
39. Remote sensing of lake chlorophyll-a in Qinghai-Tibet Plateau responding to climate factors: Implications for oligotrophic lakes F. Chen et al. https://doi.org/10.1016/j.ecolind.2024.111674
40. A dataset for lake level changes in the Tibetan Plateau from 2002 and 2010 to 2021 using multi-altimeter data J. Chen et al. https://doi.org/10.5194/essd-17-4235-2025
41. Analysis of Atmospheric Aerosol Changes in the Qinghai-Tibetan Plateau Region during 2009–2019 Using a New Fusion Algorithm Z. Zhao & H. Tonooka https://doi.org/10.3390/atmos15060712
42. Can groundwater storage in turn affect the cryospheric variables? A new perspective from nonlinear dynamic causality detection Y. Zhao et al. https://doi.org/10.1016/j.jhydrol.2023.129910
43. LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images W. Liu et al. https://doi.org/10.3390/rs13010056
44. What drives the rapid water-level recovery of the largest lake (Qinghai Lake) of China over the past half century? C. Fan et al. https://doi.org/10.1016/j.jhydrol.2020.125921
45. Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes N. Dong et al. https://doi.org/10.1029/2022WR033026
46. Linking ground ice and glacier melting to lake volume change in the Dogai Coring watershed on the Tibetan Plateau Z. Zhang et al. https://doi.org/10.1016/j.jhydrol.2023.130581
47. Contribution of ground ice melting to the expansion of Selin Co (lake) on the Tibetan Plateau L. Wang et al. https://doi.org/10.5194/tc-16-2745-2022
48. Increasing lake water storage on the Inner Tibetan Plateau under climate change B. Jia et al. https://doi.org/10.1016/j.scib.2023.02.018
49. Reservoir dominated spatio-temporal changes of the surface water area in the Yangtze River Basin during past three decades L. Chang et al. https://doi.org/10.1016/j.ejrh.2024.101948
50. Microbial Responses to Simulated Salinization and Desalinization in the Sediments of the Qinghai–Tibetan Lakes J. Huang et al. https://doi.org/10.3389/fmicb.2020.01772
51. Variations in lake water storage over Inner Mongolia during recent three decades based on multi-mission satellites Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2022.127719
52. Late Pleistocene lake overspill and drainage reversal in the source area of the Yellow River in the Tibetan Plateau X. Huang et al. https://doi.org/10.1016/j.epsl.2022.117554
53. Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations T. Chen et al. https://doi.org/10.3390/rs15030780
54. An improved modeling of precipitation phase and snow in the Lancang River Basin in Southwest China Z. Han et al. https://doi.org/10.1007/s11431-020-1788-4
55. Hydrological Controls on Dissolved Organic Matter Composition throughout the Aquatic Continuum of the Watershed of Selin Co, the Largest Lake on the Tibetan Plateau L. Zhou et al. https://doi.org/10.1021/acs.est.2c08257
56. Investigating different timescales of terrestrial water storage changes in the northeastern Tibetan Plateau P. Zhan et al. https://doi.org/10.1016/j.jhydrol.2022.127608
57. Climate change accelerates the evolution of reorganized river-lake systems on the Tibetan Plateau X. Kuang et al. https://doi.org/10.1038/s43247-025-02865-2
58. Mapping inundated bathymetry for estimating lake water storage changes from SRTM DEM: A global investigation K. Liu et al. https://doi.org/10.1016/j.rse.2023.113960
59. A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring C. Yuan et al. https://doi.org/10.3390/rs16060974
60. Estimating and Assessing Monthly Water Level Changes of Reservoirs and Lakes in Jiangsu Province Using Sentinel-3 Radar Altimetry Data J. Xu et al. https://doi.org/10.3390/rs16050808
61. Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion F. Xu et al. https://doi.org/10.1038/s41561-024-01446-w
62. Exploring the topographical pattern beneath the water surface: Global bathymetric volume-area-height curves (BVAH) of inland surface water bodies S. Zhu et al. https://doi.org/10.1016/j.geog.2024.06.005
63. Long and short‐term assessment of surface area changes in saline and freshwater lakes via remote sensing N. Yagmur et al. https://doi.org/10.1111/wej.12608
64. An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach X. Li & D. Long https://doi.org/10.1016/j.rse.2020.111966
65. On the capabilities of the SWOT satellite to monitor the lake level change over the Third Pole J. Xiong et al. https://doi.org/10.1088/1748-9326/acbfd1
66. What Controls Lake Contraction and Then Expansion in Tibetan Plateau’s Endorheic Basin Over the Past Half Century? W. Chen et al. https://doi.org/10.1029/2022GL101200
67. Enhancing hydrological model performance through multi-source open-data utilization in the highly managed, data-scarce basin J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.132860
68. A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation P. Han et al. https://doi.org/10.1016/j.jhydrol.2021.125979
69. Assessment of hydrological changes in inland water body using satellite altimetry and Landsat imagery: A case study on Tsengwen Reservoir C. Lee et al. https://doi.org/10.1016/j.ejrh.2022.101227
70. Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients X. Li et al. https://doi.org/10.5194/tc-17-349-2023
71. Recent water-level fluctuations, future trends and their eco-environmental impacts on Lake Qinghai P. Hou et al. https://doi.org/10.1016/j.jenvman.2023.117461
72. Deriving the Terrestrial Water Storage Anomaly From GRACE Spherical Harmonic Coefficients Using a Convolutional Neural Network Q. Zhang et al. https://doi.org/10.1029/2023EA003023
73. A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data B. Hu et al. https://doi.org/10.5194/hess-29-6023-2025
74. The Feasibility of Monitoring Great Plains Playa Inundation with the Sentinel 2A/B Satellites for Ecological and Hydrological Applications H. Tripp et al. https://doi.org/10.3390/w14152314
75. 100 years of lake evolution over the Qinghai–Tibet Plateau G. Zhang et al. https://doi.org/10.5194/essd-13-3951-2021
76. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. https://doi.org/10.5194/essd-14-3791-2022
77. Monitoring lake level changes in China using multi-altimeter data (2016–2019) J. Chen & J. Liao https://doi.org/10.1016/j.jhydrol.2020.125544
78. Water loss over the Tibetan Plateau endangers water supply security for Asian populations https://doi.org/10.1038/s41558-022-01451-0
79. Surface-Water-Level Changes During 2003–2019 in Australia Revealed by ICESat/ICESat-2 Altimetry and Landsat Imagery N. Xu et al. https://doi.org/10.1109/LGRS.2020.2996769
80. Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry S. Huang et al. https://doi.org/10.3390/rs18050763
81. The Impact of Climate Change on the State of Moraine Lakes in Northern Tian Shan: Case Study on Four Moraine Lakes N. Sydyk et al. https://doi.org/10.3390/w17172533
82. A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data S. Ma et al. https://doi.org/10.1080/20964471.2023.2295632
83. Refinement of ICESat-2 derived inland water surface levels with the TG20 local geoid model: In the case of Türkiye lakes Y. Kaya et al. https://doi.org/10.1016/j.pce.2025.103900
84. Influencing mechanism and hydrogeological implications of water level fluctuation of lakes in the northern Qaidam Basin, Qinghai-Tibet Plateau Y. Cheng et al. https://doi.org/10.1007/s00343-022-2185-z
85. HydroSat: geometric quantities of the global water cycle from geodetic satellites M. Tourian et al. https://doi.org/10.5194/essd-14-2463-2022
86. An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau Y. Wang et al. https://doi.org/10.5194/essd-12-1789-2020
87. Characterizing slope correction methods applied to satellite radar altimetry data: A case study around Dome Argus in East Antarctica G. Hai et al. https://doi.org/10.1016/j.asr.2021.01.016
88. Monitoring Spatial-Temporal Variations of Lake Level in Western China Using ICESat-1 and CryoSat-2 Satellite Altimetry J. Chen & Z. Duan https://doi.org/10.3390/rs14225709
89. Interannual variation in lake areas over 50 km² on the Tibetan Plateau from 1986 to 2020 based on remote sensing big data X. Wang et al. https://doi.org/10.1080/17538947.2023.2300308
90. Lake Water Storage and Long-Term Variation of Nganga Rinco on the Tibetan Plateau Revealed by ICESat-2 and Satellite Imagery Y. Li et al. https://doi.org/10.1109/JSTARS.2025.3570487
91. Revisiting the Terrestrial Water Storage Changes in the Northeastern Tibetan Plateau Using GRACE/GRACE-FO at Different Spatial Scales Considering the Impacts of Large Lakes and Reservoirs Z. Zhu et al. https://doi.org/10.3390/rs17193272
92. Enhancing Hydrological Model Calibration for Flood Prediction in Dam-Regulated Basins with Satellite-Derived Reservoir Dynamics C. Li et al. https://doi.org/10.3390/rs18020193
93. Rapid glacier mass loss in the Southeastern Tibetan Plateau since the year 2000 from satellite observations F. Zhao et al. https://doi.org/10.1016/j.rse.2021.112853
94. Model Estimates of China's Terrestrial Water Storage Variation Due To Reservoir Operation N. Dong et al. https://doi.org/10.1029/2021WR031787
95. Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations L. Huang et al. https://doi.org/10.1016/j.scitotenv.2024.173189
96. High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River Y. Wang et al. https://doi.org/10.1016/j.rse.2023.113575
97. Limited Contribution of Glacier Mass Loss to the Recent Increase in Tibetan Plateau Lake Volume F. Brun et al. https://doi.org/10.3389/feart.2020.582060
98. Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes A. Liibusk et al. https://doi.org/10.3390/rs12244062
99. Climate change threatens terrestrial water storage over the Tibetan Plateau X. Li et al. https://doi.org/10.1038/s41558-022-01443-0
100. Lake expansion drives chlorophyll-a decline on the Qinghai–Tibet Plateau X. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134617
101. A review of altimetry waveform retracking for inland water levels X. Deng et al. https://doi.org/10.1016/j.geog.2025.02.001
102. A Bigger Picture of how the Tibetan Lakes Have Changed Over the Past Decade Revealed by CryoSat‐2 Altimetry L. Jiang et al. https://doi.org/10.1029/2020JD033161
103. Global Estimation and Assessment of Monthly Lake/Reservoir Water Level Changes Using ICESat-2 ATL13 Products N. Xu et al. https://doi.org/10.3390/rs13142744
104. Monitoring Multi-Temporal Changes of Lakes on the Tibetan Plateau Using Multi-Source Remote Sensing Data from 1992 to 2019: A Case Study of Lake Zhari Namco J. Wu et al. https://doi.org/10.1007/s12583-022-1639-8
105. What Drive Regional Changes in the Number and Surface Area of Lakes Across the Yangtze River Basin During 2000–2019: Human or Climatic Factors? Y. Xu et al. https://doi.org/10.1029/2021WR030616
106. Refined estimation of lake water level and storage changes on the Tibetan Plateau from ICESat/ICESat-2 S. Luo et al. https://doi.org/10.1016/j.catena.2021.105177
107. Simulation of the Water Storage Capacity of Siling Co Lake on the Tibetan Plateau and Its Hydrological Response to Climate Change Y. Tang et al. https://doi.org/10.3390/w14193175
108. Precipitation Dominates Long-Term Water Storage Changes in Nam Co Lake (Tibetan Plateau) Accompanied by Intensified Cryosphere Melts Revealed by a Basin-Wide Hydrological Modelling X. Zhong et al. https://doi.org/10.3390/rs12121926
109. Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker J. Chen et al. https://doi.org/10.1109/JSTARS.2020.3035686
110. Retrieving water surface elevation over arctic thermokarst lakes using ICESat-2 and Sentinel-3 alti metry data S. Yekeen et al. https://doi.org/10.1016/j.rsase.2025.101751
111. Daily Continuous River Discharge Estimation for Ungauged Basins Using a Hydrologic Model Calibrated by Satellite Altimetry: Implications for the SWOT Mission Q. Huang et al. https://doi.org/10.1029/2020WR027309
112. Water Residence Time and Temperature Drive the Dynamics of Dissolved Organic Matter in Alpine Lakes in the Tibetan Plateau Y. Du et al. https://doi.org/10.1029/2020GB006908
113. Machine learning revealed monthly change characteristics of lakes on the Tibet Plateau over the past two decades J. Wu et al. https://doi.org/10.1016/j.ejrh.2025.102839
114. Fusing SAR image and CYGNSS data for monitoring river water level changes by machine learning Y. Jia et al. https://doi.org/10.1016/j.rse.2025.114927
115. A Low-Cost Approach for Lake Volume Estimation on the Tibetan Plateau: Coupling the Lake Hypsometric Curve and Bottom Elevation K. Liu et al. https://doi.org/10.3389/feart.2022.925944
116. Dynamic Monitoring and Ecological Risk Analysis of Lake Inundation Areas in Tibetan Plateau D. Wang et al. https://doi.org/10.3390/su142013332
117. Evolution patterns, driving mechanisms, and ecological indicative effects of 730 lakes water color in the Yangtze River Basin (1984–2023) Q. Chen et al. https://doi.org/10.1016/j.jhydrol.2025.132695
118. Divergent hydrologic regimes of mega-rivers originated from High Mountain Asia uncovered by satellite virtual station-densified water levels F. Zeng et al. https://doi.org/10.1016/j.jhydrol.2025.133214
119. Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin, Ethiopia W. Asfaw et al. https://doi.org/10.1016/j.jag.2020.102095
120. Progress in remote sensing study on lake hydrologic regime S. Chunqiao et al. https://doi.org/10.18307/2020.0514
121. Solid Water Melt Dominates the Increase of Total Groundwater Storage in the Tibetan Plateau Y. Zou et al. https://doi.org/10.1029/2022GL100092
122. GEDI: A New LiDAR Altimetry to Obtain the Water Levels of More Lakes on the Tibetan Plateau J. Wu et al. https://doi.org/10.1109/JSTARS.2023.3268558
123. Integrated hydrogeological and hydrogeochemical dataset of an alpine catchment in the northern Qinghai–Tibet Plateau Z. Pan et al. https://doi.org/10.5194/essd-14-2147-2022
124. Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data T. Wu et al. https://doi.org/10.1109/JSTARS.2021.3064580
125. The state and fate of lake ice thickness in the Northern Hemisphere X. Li et al. https://doi.org/10.1016/j.scib.2021.10.015
126. Response of lake dynamics to climate change in the Hala Lake Basin of Tibetan Plateau from 1986 to 2015 D. LI et al. https://doi.org/10.31497/zrzyxb.20210218
127. The Spatio-Temporal Reconstruction of Lake Water Levels Using Deep Learning Models: A Case Study on Altai Mountains L. Yue et al. https://doi.org/10.1109/JSTARS.2022.3182646
128. Unprecedented lake expansion in 2017–2018 on the Tibetan Plateau: Processes and environmental impacts Y. Lei et al. https://doi.org/10.1016/j.jhydrol.2023.129333
129. Assessing water storage changes of Lake Poyang from multi-mission satellite data and hydrological models Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2020.125229
130. Global dominance of seasonality in shaping lake-surface-extent dynamics L. Li et al. https://doi.org/10.1038/s41586-025-09046-3
131. Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data Z. Huang et al. https://doi.org/10.3390/rs16010128
132. Retracking Cryosat-2 Data in SARIn and LRM Modes for Plateau Lakes: A Case Study for Tibetan and Dianchi Lakes X. Deng et al. https://doi.org/10.3390/rs13061078
133. Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning X. Chen et al. https://doi.org/10.3390/w17010068
134. Rapid shoreline flooding enhances water turbidity by sediment resuspension: An example in a large Tibetan lake H. Mi et al. https://doi.org/10.1002/esp.5000
135. The slowdown of increasing groundwater storage in response to climate warming in the Tibetan Plateau L. Wang et al. https://doi.org/10.1038/s41612-024-00840-w
136. Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al. https://doi.org/10.1016/j.fmre.2025.11.012
137. Lake Evaporation and Its Effects on Basin Evapotranspiration and Lake Water Storage on the Inner Tibetan Plateau L. Wang et al. https://doi.org/10.1029/2022WR034030
138. Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al. https://doi.org/10.1007/s11431-023-2757-2
139. Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions M. Sun et al. https://doi.org/10.3389/feart.2021.640553
140. The Shrinkage of Lakes on the Semi-Arid Inner Mongolian Plateau Is Still Serious J. Bai et al. https://doi.org/10.3390/w17213056
141. Determination of Suitable Ecological Intervals for Arid Terminal Lakes via Multi-Source Remote Sensing: A “Morphometry–Security–Efficiency” Framework Applied to Ebinur Lake J. Liu et al. https://doi.org/10.3390/rs18050771
142. An intra-annual 30-m dataset of small lakes of the Qilian Mountains for the period 1987–2020 C. Li et al. https://doi.org/10.1038/s41597-023-02285-x
143. Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau J. Zhou et al. https://doi.org/10.1016/j.scib.2021.11.010
144. Seasonal trends and cycles of lake-level variations over the Tibetan Plateau using multi-sensor altimetry data F. Xu et al. https://doi.org/10.1016/j.jhydrol.2021.127251
145. Area dynamics of alpine lakes in the Yamzhog Yumco Basin: Optimized water indices reveal spatiotemporal patterns and key drivers M. Zhe & S. Sun https://doi.org/10.1016/j.ecolind.2025.114415
146. Hydrological Model Calibration for Dammed Basins Using Satellite Altimetry Information R. Zhong et al. https://doi.org/10.1029/2020WR027442