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Volume 35 Issue 5
Oct 2024
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Juan Wu, Chang-Qing Ke, Yu Cai, Zheng Duan. 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. Journal of Earth Science, 2024, 35(5): 1679-1691. doi: 10.1007/s12583-022-1639-8
Citation: Juan Wu, Chang-Qing Ke, Yu Cai, Zheng Duan. 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. Journal of Earth Science, 2024, 35(5): 1679-1691. doi: 10.1007/s12583-022-1639-8

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

doi: 10.1007/s12583-022-1639-8
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  • Corresponding author: Chang-Qing Ke, kecq@nju.edu.cn
  • Received Date: 09 Dec 2021
  • Accepted Date: 22 Feb 2022
  • Issue Publish Date: 30 Oct 2024
  • Lake level, area and volume are sensitive indicators of climate change. At present, many studies have focused on the interannual water balance of lakes, but lake level and area can change remarkably with seasons, especially for lakes with seasonal ice cover. Zhari Namco, a seasonal frozen lake, was selected as an example to investigate its seasonal water balance. Multi-source altimetry and Landsat data were used to obtain the seasonal lake level and area from 1992 to 2019, and seasonal lake volume variations were also estimated. The results indicated the average lake level, area and volume in autumn were the largest. The lake level, area, and volume experienced three turning points approximately in 2000, 2010, and 2016, and showed an overall increasing trend from 1992 to 2019, with slopes of 0.15 m/year, 2.17 km2/year, and 0.14 km3/year, respectively. The lake area expanded significantly in autumn, which was related to the abundant precipitation. Delay time of land surface runoff, increased temperature, and evaporation may be the reason for the low lake level and volume in summer. The precipitation was the dominant factor of water balance, which explained 62.09%, 62.43%, and 62.10% of the variations in lake level, area, and volume, respectively.

     

  • Electronic Supplementary Materials: Supplementary materials (Tables S1–S3; Figures S1–S6) are available in the online version of this article at https://doi.org/10.1007/s12583-022-1639-8.
    Conflict of Interest
    The authors declare that they have no conflict of interest.
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  • Abshire, J. B., Sun, X. L., Riris, H., et al., 2005. Geoscience Laser Altimeter System (GLAS) on the ICESat Mission: On-Orbit Measurement Performance. Geophysical Research Letters, 32(21): L21S02. https://doi.org/10.1029/2005gl024028
    Bhang, K. J., Schwartz, F. W., Braun, A., 2007. Verification of the Vertical Error in C-Band SRTM DEM Using ICESat and Landsat-7, Otter Tail County, MN. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 36–44. https://doi.org/10.1109/tgrs.2006.885401
    Birkett, C., 2000. Synergistic Remote Sensing of Lake Chad Variability of Basin Inundation. Remote Sensing of Environment, 72(2): 218–236. https://doi.org/10.1016/s0034-4257(99)00105-4
    Brun, F., Berthier, E., Wagnon, P., et al., 2017. A Spatially Resolved Estimate of High Mountain Asia Glacier Mass Balances from 2000 to 2016. Nature Geoscience, 10: 668–673. https://doi.org/10.1038/ngeo2999
    Cai, Y., Ke, C. Q., Li, X. G., et al., 2019. Variations of Lake Ice Phenology on the Tibetan Plateau from 2001 to 2017 Based on MODIS Data. Journal of Geophysical Research: Atmospheres, 124(2): 825–843. https://doi.org/10.1029/2018jd028993
    Cai, Y., Ke, C. Q., Shen, X. Y., 2020. Variations in Water Level, Area and Volume of Hongze Lake, China from 2003 to 2018. Journal of Great Lakes Research, 46(6): 1511–1520. https://doi.org/10.1016/j.jglr.2020.08.024
    Capó, M., Pérez, A., Lozano, J. A., 2017. An Efficient Approximation to the K-Means Clustering for Massive Data. Knowledge-Based Systems, 117: 56–69. https://doi.org/10.1016/j.knosys.2016.06.031
    Crétaux, J. F., Arsen, A., Calmant, S., et al., 2011. SOLS: A Lake Database to Monitor in the near Real Time Water Level and Storage Variations from Remote Sensing Data. Advances in Space Research, 47(9): 1497–1507. https://doi.org/10.1016/j.asr.2011.01.004
    Donlon, C., Berruti, B., Buongiorno, A., et al., 2012. The Global Monitoring for Environment and Security (GMES) Sentinel-3 Mission. Remote Sensing of Environment, 120: 37–57. https://doi.org/10.1016/j.rse.2011.07.024
    Du, Y., Zhang, Y. H., Ling, F., et al., 2016. Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sensing, 8(4): 354. https://doi.org/10.3390/rs8040354
    Duan, Z., Bastiaanssen, W. G. M., 2013. Estimating Water Volume Variations in Lakes and Reservoirs from Four Operational Satellite Altimetry Databases and Satellite Imagery Data. Remote Sensing of Environment, 134: 403–416. https://doi.org/10.1016/j.rse.2013.03.010
    ESA (European Space Agency, Mullar Space Science Laboratory), 2012. CryoSat Product Handbook DLFE-3605, 101
    Fang, C. Y., Lai, Z. Q., Jing-yuanl, Y., et al., 2011. Study on the Nonuniform Spatial Distribution of Water Level in Poyang Lake Based on ASAR Images and DEM. Procedia Environmental Sciences, 10: 2540–2546. https://doi.org/10.1016/j.proenv.2011.09.395
    Hall, D. K., Riggs, G. A., Salomonson, V. V., 2001. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms. https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod10.pdf
    He, Y. Z., Yue, D. P., Da, X., et al., 2016. A Preliminary Study on Impact of Climatic Change and Human Activity on Water Resources: Taking Shaanxi Province as an Example. Acta Agriculturae Jiangxi, 28(12): 87–93 (in Chinese with English Abstract)
    Jain, S. K., Singh, R. D., Jain, M. K., et al., 2005. Delineation of Flood-Prone Areas Using Remote Sensing Techniques. Water Resources Management, 19(4): 333–347. https://doi.org/10.1007/s11269-005-3281-5
    Jiang, L. G., Nielsen, K., Andersen, O. B., et al., 2017. Monitoring Recent Lake Level Variations on the Tibetan Plateau Using CryoSat-2 SARIn Mode Data. Journal of Hydrology, 544: 109–124. https://doi.org/10.1016/j.jhydrol.2016.11.024
    Jiang, L. G., Nielsen, K., Andersen, O. B., et al., 2020. A Bigger Picture of how the Tibetan Lakes Have Changed over the Past Decade Revealed by CryoSat-2 Altimetry. Journal of Geophysical Research: Atmospheres, 125(23): e2020jd033161. https://doi.org/10.1029/2020jd033161
    Kleinherenbrink, M., Naeije, M., Slobbe, C., et al., 2020. The Performance of CryoSat-2 Fully-Focussed SAR for Inland Water-Level Estimation. Remote Sensing of Environment, 237: 111589. https://doi.org/10.1016/j.rse.2019.111589
    Li, B. Q., Zhang, J. Y., Yu, Z. B., et al., 2017. Climate Change Driven Water Budget Dynamics of a Tibetan Inland Lake. Global and Planetary Change, 150: 70–80. https://doi.org/10.1016/j.gloplacha.2017.02.003
    Li, X. D., Long, D., Huang, Q., et al., 2019. High-Temporal-Resolution Water Level and Storage Change Data Sets for Lakes on the Tibetan Plateau during 2000-2017 Using Multiple Altimetric Missions and Landsat-Derived Lake Shoreline Positions. Earth System Science Data, 11(4): 1603–1627. https://doi.org/10.5194/essd-11-1603-2019
    Long, Y. N., Yan, S. X., Jiang, C. B., et al., 2019. Inversion of Lake Bathymetry through Integrating Multi-Temporal Landsat and ICESat Imagery. Sensors, 19(13): 2896. https://doi.org/10.3390/s19132896
    Loveland, T. R., Irons, J. R., 2016. Landsat 8: The Plans, the Reality, and the Legacy. Remote Sensing of Environment, 185: 1–6. https://doi.org/10.1016/j.rse.2016.07.033
    Mann, H. B., 1945. Nonparametric Tests Against Trend. Econometrica, 13(3): 245. https://doi.org/10.2307/1907187
    Markus, T., Neumann, T., Martino, A., et al., 2017. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2): Science Requirements, Concept, and Implementation. Remote Sensing of Environment, 190: 260–273. https://doi.org/10.1016/j.rse.2016.12.029
    McFeeters, S. K., 1996. The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17(7): 1425–1432. https://doi.org/10.1080/01431169608948714
    McFeeters, S., 2013. Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach. Remote Sensing, 5(7): 3544–3561. https://doi.org/10.3390/rs5073544
    Medina, C. E., Gomez-Enri, J., Alonso, J. J., et al., 2008. Water Level Fluctuations Derived from ENVISAT Radar Altimeter (RA-2) and In-Situ Measurements in a Subtropical Waterbody: Lake Izabal (Guatemala). Remote Sensing of Environment, 112(9): 3604–3617. https://doi.org/10.1016/j.rse.2008.05.001
    Medina, C., Gomez-Enri, J., Alonso, J. J., et al., 2010. Water Volume Variations in Lake Izabal (Guatemala) from in situ Measurements and ENVISAT Radar Altimeter (RA-2) and Advanced Synthetic Aperture Radar (ASAR) Data Products. Journal of Hydrology, 382(1/2/3/4): 34–48. https://doi.org/10.1016/j.jhydrol.2009.12.016
    NSIDC, 2012. The Transformation between T/P Ellipsoid and WGS84. http://sidads.colorado.edu/pub/DATASETS/icesat/tools/idl/ellipsoid/README_ellipsoid.txt
    Olthof, I., 2017. Mapping Seasonal Inundation Frequency (1985–2016) along the St-John River, New Brunswick, Canada Using the Landsat Archive. Remote Sensing, 9(2): 143. https://doi.org/10.3390/rs9020143
    Pan, F. F., Nichols, J., 2013. Remote Sensing of River Stage Using the Cross-Sectional Inundation Area-River Stage Relationship (IARSR) Constructed from Digital Elevation Model Data. Hydrological Processes, 27(25): 3596–3606. https://doi.org/10.1002/hyp.9469
    Pekel, J. F., Cottam, A., Gorelick, N., et al., 2016. High-Resolution Mapping of Global Surface Water and Its Long-Term Changes. Nature, 540: 418–422. https://doi.org/10.1038/nature20584
    Pradhan, B., Tehrany, M. S., Jebur, M. N., 2016. A New Semiautomated Detection Mapping of Flood Extent from TerraSAR-X Satellite Image Using Rule-Based Classification and Taguchi Optimization Techniques. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 4331–4342. https://doi.org/10.1109/tgrs.2016.2539957
    Qiao, B. J., Zhu, L. P., Yang, R. M., 2019. Temporal-Spatial Differences in Lake Water Storage Changes and Their Links to Climate Change Throughout the Tibetan Plateau. Remote Sensing of Environment, 222: 232–243. https://doi.org/10.1016/j.rse.2018.12.037
    Rahman, M. A., Lou, Y. S., Sultana, N., 2017. Analysis and Prediction of Rainfall Trends over Bangladesh Using Mann-Kendall, Spearman's Rho Tests and ARIMA Model. Meteorology and Atmospheric Physics, 129(4): 409–424. https://doi.org/10.1007/s00703-016-0479-4
    Schutz, B. E., Zwally, H. J., Shuman, C. A., et al., 2005. Overview of the ICESat Mission. Geophysical Research Letters, 32(21): L21S01. https://doi.org/10.1029/2005gl024009
    Schwatke, C., Dettmering, D., Bosch, W., et al., 2015. DAHITI–An Innovative Approach for Estimating Water Level Time Series over Inland Waters Using Multi-Mission Satellite Altimetry. Hydrology and Earth System Sciences, 19(10): 4345–4364. https://doi.org/10.5194/hess-19-4345-2015
    Sentinel-3-Team, 2017. Sentinel-3 User Handbook. European Space Agency (ESA). https://sentinel.esa.int/documents/247904/4871083/Sentinel-3+SRAL+Land+User+Handbook+V1.1.pdf
    Shu, S., Liu, H. X., Beck, R. A., et al., 2020. Analysis of Sentinel-3 SAR Altimetry Waveform Retracking Algorithms for Deriving Temporally Consistent Water Levels over Ice-Covered Lakes. Remote Sensing of Environment, 239: 111643. https://doi.org/10.1016/j.rse.2020.111643
    Smith, L. C., 1997. Satellite Remote Sensing of River Inundation Area, Stage, and Discharge: A Review. Hydrological Processes, 11(10): 1427–1439. https://doi.org/10.1002/(sici)1099-1085(199708)11:101427:aid-hyp473>3.3.co;2-j doi: 10.1002/(sici)1099-1085(199708)11:101427:aid-hyp473>3.3.co;2-j
    Smith, L. C., Pavelsky, T. M., 2009. Remote Sensing of Volumetric Storage Changes in Lakes. Earth Surface Processes and Landforms, 34(10): 1353–1358. https://doi.org/10.1002/esp.1822
    Song, C. Q., Huang, B., Ke, L. H., et al., 2014. Seasonal and Abrupt Changes in the Water Level of Closed Lakes on the Tibetan Plateau and Implications for Climate Impacts. Journal of Hydrology, 514: 131–144. https://doi.org/10.1016/j.jhydrol.2014.04.018
    Song, C. Q., Huang, B., Richards, K., et al., 2014. Accelerated Lake Expansion on the Tibetan Plateau in the 2000s: Induced by Glacial Melting or other Processes? Water Resources Research, 50(4): 3170–3186. https://doi.org/10.1002/2013wr014724
    Song, C. Q., Ye, Q. H., Cheng, X., 2015. Shifts in Water-Level Variation of Namco in the Central Tibetan Plateau from ICESat and CryoSat-2 Altimetry and Station Observations. Science Bulletin, 60(14): 1287–1297. https://doi.org/10.1007/s11434-015-0826-8
    Song, K. S., Liu, G., Wang, Q., et al., 2020. Quantification of Lake Clarity in China Using Landsat OLI Imagery Data. Remote Sensing of Environment, 243: 111800. https://doi.org/10.1016/j.rse.2020.111800
    Sridevi, T., Sharma, R., Mehra, P., et al., 2016. Estimating Discharge from the Godavari River Using ENVISAT, Jason-2, and SARAL/AltiKa Radar Altimeters. Remote Sensing Letters, 7(4): 348–357. https://doi.org/10.1080/2150704x.2015.1130876
    Tao, S. L., Fang, J. Y., Zhao, X., et al., 2015. Rapid Loss of Lakes on the Mongolian Plateau. Proceedings of the National Academy of Sciences of the United States of America, 112(7): 2281–2286. https://doi.org/10.1073/pnas.1411748112
    Tong, X. H., Pan, H. Y., Xie, H., et al., 2016. Estimating Water Volume Variations in Lake Victoria over the Past 22 Years Using Multi-Mission Altimetry and Remotely Sensed Images. Remote Sensing of Environment, 187: 400–413. https://doi.org/10.1016/j.rse.2016.10.012
    Wang, X. B., Xie, S. P., Zhang, X. L., et al., 2018. A Robust Multi-Band Water Index (MBWI) for Automated Extraction of Surface Water from Landsat 8 OLI Imagery. International Journal of Applied Earth Observation and Geoinformation, 68: 73–91. https://doi.org/10.1016/j.jag.2018.01.018
    Wu, G. P., Liu, Y. B., 2015. Capturing Variations in Inundation with Satellite Remote Sensing in a Morphologically Complex, Large Lake. Journal of Hydrology, 523: 14–23. https://doi.org/10.1016/j.jhydrol.2015.01.048
    Xu, F. L., Zhang, G. Q., Yi, S., et al., 2022. Seasonal Trends and Cycles of Lake-Level Variations over the Tibetan Plateau Using Multi-Sensor Altimetry Data. Journal of Hydrology, 604: 127251. https://doi.org/10.1016/j.jhydrol.2021.127251
    Yang, K., Wu, H., Qin, J., et al., 2014. Recent Climate Changes over the Tibetan Plateau and Their Impacts on Energy and Water Cycle: A Review. Global and Planetary Change, 112: 79–91. https://doi.org/10.1016/j.gloplacha.2013.12.001
    Yang, X. W., Wang, N. L., Chen, A. A., et al., 2020. Changes in Area and Water Volume of the Aral Sea in the Arid Central Asia over the Period of 1960–2018 and Their Causes. CATENA, 191: 104566. https://doi.org/10.1016/j.catena.2020.104566
    Yao, T. D., Thompson, L., Yang, W., et al., 2012. Different Glacier Status with Atmospheric Circulations in Tibetan Plateau and Surroundings. Nature Climate Change, 2(9): 663–667. https://doi.org/10.1038/nclimate1580
    Yuan, C., Gong, P., Liu, C. X., et al., 2019. Water-Volume Variations of Lake Hulun Estimated from Serial Jason Altimeters and Landsat TM/ETM+ Images from 2002 to 2017. International Journal of Remote Sensing, 40(2): 670–692. https://doi.org/10.1080/01431161.2018.1516316
    Zhang, G. Q., Chen, W. F., Xie, H. J., 2019. Tibetan Plateau's Lake Level and Volume Changes from NASA's ICESat/ICESat-2 and Landsat Missions. Geophysical Research Letters, 46(22): 13107–13118. https://doi.org/10.1029/2019gl085032
    Zhang, G. Q., Xie, H. J., Kang, S. C., et al., 2011. Monitoring Lake Level Changes on the Tibetan Plateau Using ICESat Altimetry Data (2003–2009). Remote Sensing of Environment, 115(7): 1733–1742. https://doi.org/10.1016/j.rse.2011.03.005
    Zhang, G. Q., Yao, T. D., Chen, W. F., et al., 2019. Regional Differences of Lake Evolution across China during 1960s–2015 and Its Natural and Anthropogenic Causes. Remote Sensing of Environment, 221: 386–404. https://doi.org/10.1016/j.rse.2018.11.038
    Zhang, G. Q., Yao, T. D., Piao, S. L., et al., 2017a. Extensive and Drastically Different Alpine Lake Changes on Asia's High Plateaus during the Past Four Decades. Geophysical Research Letters, 44(1): 252–260. https://doi.org/10.1002/2016gl072033
    Zhang, G. Q., Yao, T. D., Shum, C. K., et al., 2017b. Lake Volume and Groundwater Storage Variations in Tibetan Plateau's Endorheic Basin. Geophysical Research Letters, 44(11): 5550–5560. https://doi.org/10.1002/2017gl073773
    Zhang, Y., Zhang, G. Q., Zhu, T. T., 2020a. Seasonal Cycles of Lakes on the Tibetan Plateau Detected by Sentinel-1 SAR Data. Science of the Total Environment, 703: 135563. https://doi.org/10.1016/j.scitotenv.2019.135563
    Zhang, G. Q., Yao, T. D., Xie, H. J., et al., 2020b. Response of Tibetan Plateau Lakes to Climate Change: Trends, Patterns, and Mechanisms. Earth-Science Reviews, 208: 103269. https://doi.org/10.1016/j.earscirev.2020.103269
    Zhang, X., Wu, Y. H., Zhang, X., 2015. Zhari Namco Water Level Change Detection Using Multi-Satellite Altimetric Data during 1992–2012. Journal of Natural Resources, 30(7): 1153–1162 (in Chinese with English Abstract)
    Zhao, W., Xiong, D. H., Wen, F. P., et al., 2020. Lake Area Monitoring Based on Land Surface Temperature in the Tibetan Plateau from 2000 to 2018. Environmental Research Letters, 15(8): 084033. https://doi.org/10.1088/1748-9326/ab9b41
    Zheng, J. J., Ke, C. Q., Shao, Z. D., et al., 2016. Monitoring Changes in the Water Volume of Hulun Lake by Integrating Satellite Altimetry Data and Landsat Images between 1992 and 2010. Journal of Applied Remote Sensing, 10(1): 016029. https://doi.org/10.1117/1.jrs.10.016029
    Zwenzner, H., Voigt, S., 2009. Improved Estimation of Flood Parameters by Combining Space Based SAR Data with very High Resolution Digital Elevation Data. Hydrology and Earth System Sciences, 13(5): 567–576. https://doi.org/10.5194/hess-13-567-2009
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