Anterior|Próximo

Análise da capacidade de observação do armazenamento de água do lago com base em dados simulados do satélite SWOT: Um estudo de lagos típicos do Planalto Tibetano

  • role： First author第一作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China

  • Email：liangwx23@mail2.sysu.edu.cn
  • Introduction：梁炜轩，研究方向为SWOT卫星与陆地水文。E-mail： liangwx23@mail2.sysu.edu.cn
LIANG Weixuan,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China

  • Email：fengwei@sysu.edu.cn
  • Introduction：冯伟，研究方向为卫星大地测量学与全球变化。E-mail：fengwei@sysu.edu.cn
FENG Wei*,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China

SUN Mingzhi,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China

ZHONG Min

Resumo

Os lagos, como um componente importante da água superficial, desempenham um papel fundamental no ciclo global da água, sendo também "sentinelas" das mudanças climáticas globais. O satélite Surface Water and Ocean Topography (SWOT), lançado em dezembro de 2022, realizará pela primeira vez uma observação bidimensional de larga escala dos lagos globais. Para melhor aplicação futura dos dados do satélite SWOT, este artigo realiza uma simulação abrangente e avaliação do potencial de aplicação do satélite SWOT. Este estudo utiliza a ferramenta de simulação hidrológica do Centro Nacional de Estudos Espaciais (CNES) para realizar um estudo de simulação dos dados de observação do satélite SWOT em quatro lagos localizados na região do Tibete-Qinghai: Lac La'angco, Lac Mapangyongco, Lac Anglarenco e Lac Renqingxiubco, avaliando a capacidade do satélite SWOT de observar a altura média da superfície da água, a área do lago e o armazenamento de água do lago. O estudo de simulação mostrou que para os quatro lagos típicos da região do Tibete-Qinghai, a precisão da altura média da superfície da água observada pelo SWOT é melhor que 0,02 m, o coeficiente de correlação entre a série simulada da altura da superfície da água e a série real ultrapassa 0,9, refletindo bem a variação sazonal da altura da superfície da água; o erro na observação da área do lago pelo SWOT está na maior parte dentro de 10%. O satélite SWOT tem grande potencial para a inversão do volume de água do lago. O erro resultante da estimativa da altura da superfície da água e da área gerada pelo SWOT tem pouco impacto na estimativa do volume estático de água do lago, enquanto o erro na estimativa da topografia do fundo do lago tem grande impacto, portanto, obter dados a priori de maior precisão da profundidade do lago é fundamental para obter um armazenamento de água do lago mais preciso no futuro.

Palavras-chave

Planalto Tibetano; água superficial e topografia oceânica (SWOT); ferramenta de simulação hidrológica CNES; área do lago; armazenamento de água do lago; topografia do fundo do lago

References

  1. 1.
    Altenau E H, Pavelsky T M, Durand M T, Yang X, de Moraes Frasson R P and Bendezu L. 2021. The surface water and ocean topography (SWOT) mission river database (SWORD): a global river network for satellite data products. Water Resources Research, 57(7): e2021WR030054
  2. 2.
    Biancamaria S, Lettenmaier D P and Pavelsky T M. 2016. The SWOT mission and its capabilities for land hydrology//Cazenave A, Champollion N, Benveniste J and Chen J, eds. Remote Sensing and Water Resources. Cham: Springer: 117-147
  3. 3.
    Birkett C, Reynolds C, Beckley B and Doorn B. 2011. From research to operations: the USDA global reservoir and lake monitor//Vignudelli S, Kostianoy A G, Cipollini P and Benveniste J, eds. Coastal Altimetry. Berlin: Springer: 19-50
  4. 4.
    Bonnema M and Hossain F. 2019. Assessing the potential of the surface water and ocean topography mission for reservoir monitoring in the Mekong River Basin. Water Resources Research, 55(1): 444-461
  5. 5.
    Bonnema M G, Sikder S, Hossain F, Durand M, Gleason C J and Bjerklie D M. 2016. Benchmarking wide swath altimetry-based river discharge estimation algorithms for the Ganges River system. Water Resources Research, 52(4): 2439-2461
  6. 6.
    Crétaux J F, Abarca-del-Río R, Bergé-Nguyen M, Arsen A, Drolon V, Clos G and Maisongrande P. 2016. Lake volume monitoring from space. Surveys in Geophysics, 37(2): 269-305
  7. 7.
    Crétaux J F, Arsen A, Calmant S, Kouraev A, Vuglinski V, Bergé-Nguyen M, Gennero M C, Nino F, Abarca Del Rio R, Cazenave A and Maisongrande P. 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
  8. 8.
    Downing J A, Prairie Y T, Cole J J, Duarte C M, Tranvik L J, Striegl R G, McDowell W H, Kortelainen P, Caraco N F, Melack J M and Middelburg J J. 2006. The global abundance and size distribution of lakes, ponds, and impoundments. Limnology and Oceanography, 51(5): 2388-2397
  9. 9.
    Durand M, Chen C, de Moraes Frasson R P, Pavelsky T M, Williams B, Yang X and Fore A. 2020. How will radar layover impact SWOT measurements of water surface elevation and slope, and estimates of river discharge? Remote Sensing of Environment, 247: 111883
  10. 10.
    Elmer N J, Hain C, Hossain F, Desroches D and Pottier C. 2020. Generating proxy SWOT water surface elevations using WRF‐hydro and the CNES SWOT hydrology simulator. Water Resources Research, 56(8): e2020WR027464
  11. 11.
    Elmer N J, McCreight J and Hain C. 2021. Hydrologic Model parameter estimation in ungauged basins using simulated SWOT discharge observations. Water Resources Research, 57(10): e2021WR029655
  12. 12.
    Fu L L, Alsdorf D, Morrow R, Rodriguez E and Mognard N. 2012. SWOT: the Surface Water and Ocean Topography Mission. Wide- Swath Altimetric Elevation on Earth. Jet Propulsion Laboratory.
  13. 13.
    Humphrey V, Rodell M and Eicker A. 2023. Using satellite-based terrestrial water storage data: a review. Surveys in Geophysics, 44(5): 1489-1517
  14. 14.
    Khazaei B, Read L K, Casali M, Sampson K M and Yates D N. 2022. GLOBathy, the global lakes bathymetry dataset. Scientific Data, 9(1): 36
  15. 15.
    Li Y, Gao H L, Zhao G and Tseng K H. 2020. A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry. Remote Sensing of Environment, 244: 111831
  16. 16.
    Messager M L, Lehner B, Grill G, Nedeva I and Schmitt O. 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7: 13603
  17. 17.
    Nair A S, Verma K, Karmakar S, Ghosh S and Indu J. 2022. Exploring the potential of SWOT mission for reservoir monitoring in Mahanadi Basin. Advances in Space Research, 69(3): 1481-1493
  18. 18.
    Nguyen U N T, Pham L T H and Dang T D. 2019. An automatic water detection approach using Landsat 8 OLI and google earth engine cloud computing to map lakes and reservoirs in New Zealand. Environmental Monitoring and Assessment, 191(4): 235
  19. 19.
    Ottlé C, Bernus A, Verbeke T, Pétrus K, Yin Z, Biancamaria S, Jost A, Desroches D, Pottier C, Perrin C, De Lavenne A, Flipo N and Rivière A. 2020. Characterization of SWOT water level errors on seine reservoirs and La Bassée gravel pits: impacts on water surface energy budget modeling. Remote Sensing, 12(18): 2911
  20. 20.
    Schwatke C, Dettmering D, Bosch W and Seitz F. 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
  21. 21.
    Soman M K and Indu J. 2022. Sentinel-1 based inland water dynamics mapping system (SIMS). Environmental Modelling and Software, 149: 105305
  22. 22.
    Sun M Z, Liu X, Wang H H, Yuan J J, Li C M and Guo J Y. 2022. Monitoring lake level change in La-ang Co from 1992 to 2020 using multi-altimeter data. National Remote Sensing Bulletin, 26(1): 126-137
  23. 23.
    Wang J. 2018a. Bathymetric data of Laangcuo Lake (2017). National Tibetan Plateau/Third Pole Environment Data Center.
  24. 24.
    Wang J. 2018b. Bathymetric data of Mapam Yumco Lake (2017). National Tibetan Plateau/Third Pole Environment Data.
  25. 25.
    Wang J. 2018c. Bathymetric data of Ngangla Ringco Lake (2017). National Tibetan Plateau/Third Pole Environment Data Center.
  26. 26.
    Wang J. 2018d. Bathymetric data of Renqingxiubucuo Lake (2017). National Tibetan Plateau/Third Pole Environment Data Center.
  27. 27.
    Woolway R I, Kraemer B M, Lenters J D, Merchant C J, O’Reilly C M and Sharma S. 2020. Global lake responses to climate change. Nature Reviews Earth and Environment, 1(8): 388-403
  28. 28.
    Xiong J H, Guo S L, Jiang L G, Yin J B and Wang J. 2023. Exploring the potential of SWOT for water level monitoring in the Qinghai Lake. National Remote Sensing Bulletin, 27(8): 1888-1898
  29. 29.
    Xu F L and Zhang G Q. 2021. Lake-level over the Tibetan Plateau using multi-sensor satellite altimetry data (2010-2020). National Tibetan Plateau/Third Pole Environment Data Center.
  30. 30.
    Yoon Y, Durand M, Merry C J, Clark E A, Andreadis K M and Alsdorf D E. 2012. Estimating river bathymetry from data assimilation of synthetic SWOT measurements. Journal of Hydrology, 464-465: 363-375
  31. 31.
    Zhang G Q. 2019a. Dataset of river basins map over the TP (2016). National Tibetan Plateau/Third Pole Environment Data Center.
  32. 32.
    Zhang G Q. 2019b. China lake dataset (1960s-2020). National Tibetan Plateau/Third Pole Environment Data Center.
  33. 33.
    Zhang G Q, Luo W, Chen W F and Zheng G X. 2019. A robust but variable lake expansion on the Tibetan Plateau. Science Bulletin, 64(18): 1306-1309
  34. 34.
    Zhang G Q, Xie H J, Kang S C, Yi D H and Ackley S F. 2011. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003-2009). Remote Sensing of Environment, 115(7): 1733-1742
  35. 35.
    Zhang Y, Zhang G Q and Zhu T T. 2020. Seasonal cycles of lakes on the Tibetan Plateau detected by Sentinel-1 SAR data. Science of the Total Environment, 703: 135563

Leia o texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website