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Estimations of River Discharge of the Congo and Orinoco Basins using Gravity-based Remote Sensing Technique

  • Younggyeong Lim (Department of Science Education, Seoul National University) ;
  • Jooyoung Eom (Department of Earth Science Education, Kyungpook National University) ;
  • Kookhyoun Youm (Department of Science Education, Seoul National University) ;
  • Taehwan Jeon (Department of Science Education, Seoul National University) ;
  • Ki-Weon Seo (Department of Earth Science Education, Seoul National University)
  • 투고 : 2024.09.25
  • 심사 : 2024.10.22
  • 발행 : 2024.10.31

초록

River discharge is a crucial indicator of climate change and requires accurate and continuous estimation for effective water resource management and environmental monitoring. This study used satellite gravimetry data to estimate river discharge in major basins with high discharge volumes, specifically the Congo and Orinoco basins. By enhancing the spatial resolution of gravity data through advanced post-processing techniques, including forward modeling and river routing schemes, we effectively detected changes in the water mass stored within river channels. Additionally, signals from surrounding regions were statistically removed using the Empirical Orthogonal Function (EOF) analysis to isolate river-specific discharge signals. These refined signals were then converted into river discharge data through seasonal calibration using the modeled discharge data. Our results demonstrate that this method yields accurate and reliable discharge estimates comparable to in-situ measurements from gauge stations, even without ground-based surveys such as an Acoustic Doppler Current Profiler (ADCP) field campaigns. This research highlights the significant potential of satellite-based gravity data as an alternative to traditional ground surveys, providing practical information on the hydrological status of regions associated with large-scale river systems.

키워드

과제정보

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2021R1F1A1061854 and 2022R1C1C200658613).

참고문헌

  1. Alsdorf, D., Beighley, E., Laraque, A., Lee, H., Tshimanga, R., O'Loughlin, F., Mahe, G., Dinga, B., Moukandi, G., and Spencer, R., 2016, Opportunities for hydrologic research in the Congo Basin. Reviews of Geophysics, 54, 378-409. https://doi.org/10.1002/2016RG000517
  2. Alsdorf, D., Han, S.C., Bates, P., and Melack, J., 2010, Seasonal water storage on the Amazon floodplain measured from satellites. Remote Sensing of Environment, 114, 2448-2456.
  3. Bjerklie, D., Durand, M., Lenoir, J., Dudley, R.W., Birkett, C., Jones, J.W., and Harlan, M., 2023, Satellite remote sensing of river discharge: a framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations. Journal of Applied Remote Sensing, 17, 14520
  4. Bjerklie, D.M., Birkett, C.M., Jones, J.W., Carabajal, C., Rover, J.A., Fulton, J.W., and Garambois, P.-A., 2018, Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska. Journal of Hydrology, 561, 1000-1018. https://doi.org/10.1016/j.jhydrol.2018.04.005
  5. Cheng, M., and Tapley, B.D., 2004, Variations in the Earth's oblateness during the past 28 years. Journal of Geophysical Research: Solid Earth, 109, https://doi.org/10.1029/2004JB003028
  6. Dziewonski, A.M., and Anderson, D.L., 1981, Preliminary reference Earth model. Physics of the earth and planetary interiors, 25, 297-356.
  7. Eicker, A., Schumacher, M., Kusche, J., Doll, P., and Schmied, H.M., 2014, Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results. Surveys in Geophysics, 35, 1285-1309. https://doi.org/10.1007/s10712-014-9309-8
  8. Eom, J., Seo, K.-W., and Ryu, D., 2017, Estimation of Amazon River discharge based on EOF analysis of GRACE gravity data. Remote Sensing of Environment, 191, 55-66. https://doi.org/10.1016/j.rse.2017.01.011
  9. Feng, D., Gleason, C.J., Yang, X., and Pavelsky, T.M., 2019, Comparing Discharge Estimates Made via the BAM Algorithm in High-Order Arctic Rivers Derived Solely From Optical CubeSat, Landsat, and Sentinel-2 Data. Water Resources Research, 55, 7753-7771. https://doi.org/10.1029/2019WR025599
  10. Gleason, C.J., and Durand, M.T., 2020, Remote Sensing of River Discharge: A Review and a Framing for the Discipline. Remote Sensing, 12, https://doi.org/10.3390/rs12071107
  11. Gualtieri, C., Yepez, S., Bermudez, M., and Laraque, A., 2022, Observations of hydrodynamics and sediment transport in the Orinoco River. Proceedings of the 39th IAHR World Congress, Place, 24 p.
  12. Harlan, M.E., Gleason, C.J., Altenau, E.H., Butman, D., Carter, T., Chu, V.W., Cooley, S.W., Dolan, W.D., Durand, M.T., Eidam, E., Fayne, J.V., Feng, D., Ishitsuka, Y., Kuhn, C., Kyzivat, E.D., Langhorst, T., Minear, J.T., Pavelsky, T.M., Peters, D.L., Pietroniro, A., Pitcher, L.H., and Smith, L.C., 2021, Discharge Estimation From Dense Arrays of Pressure Transducers. Water Resources Research, 57, https://doi.org/10.1029/2020WR028714
  13. Intergovernmental Panel on Climate, C., 2023, Climate Change 2021-The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, Total https://doi.org/DOI: 10.1017/9781009157896
  14. Jolliffe, I., 2005, Principal component analysis. Encyclopedia of statistics in behavioral science. 109-120p
  15. Jones, A.E., Hardison, A.K., Hodges, B.R., McClelland, J.W., and Moffett, K.B., 2019, An expanded rating curve model to estimate river discharge during tidal influences across the progressive-mixed-standing wave systems. Plos one, 14, https://doi.org/10.1371/journal.pone.0225758
  16. Kebede, M.G., Wang, L., Li, X., and Hu, Z., 2020, Remote sensing-based river discharge estimation for a small river flowing over the high mountain regions of the Tibetan Plateau. International Journal of Remote Sensing, 41, 3322-3345. https://doi.org/10.1080/01431161.2019.1701213
  17. Longuevergne, L., Scanlon, B.R., and Wilson, C.R., 2010, GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA. Water Resources Research, 46, https://doi.org/10.1029/2009WR008564
  18. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Pean, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M., 2021, IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, 2, 2391 p.
  19. Monahan, A.H., Fyfe, J.C., Ambaum, M.H., Stephenson, D.B., and North, G.R., 2009, Empirical orthogonal functions: The medium is the message. Journal of Climate, 22, 6501-6514.
  20. Peltier, R.W., Argus, D.F., and Drummond, R., 2018, Comment on "An assessment of the ICE-6G_C (VM5a) glacial isostatic adjustment model" by Purcell et al. Journal of Geophysical Research: Solid Earth, 123, 2019-2028.
  21. Rangelova, E., Sideris, M., and Kim, J., 2012, On the capabilities of the multi-channel singular spectrum method for extracting the main periodic and non-periodic variability from weekly GRACE data. Journal of geodynamics, 54, 64-78.
  22. Samba, G., Nganga, D., and Mpounza, M., 2008, Rainfall and temperature variations over Congo-Brazzaville between 1950 and 1998. Theoretical and Applied Climatology, 91, 85-97. https://doi.org/10.1007/s00704-007-0298-0
  23. Save, H., Bettadpur, S., and Tapley, B.D., 2016, High-resolution CSR GRACE RL05 mascons. Journal of Geophysical Research: Solid Earth, 121, 7547-7569. https://doi.org/10.1002/2016JB013007
  24. Scanlon, B.R., Zhang, Z., Reedy, R.C., Pool, D.R., Save, H., Long, D., Chen, J., Wolock, D.M., Conway, B.D., and Winester, D., 2015, Hydrologic implications of GRACE satellite data in the C olorado R iver B asin. Water Resources Research, 51, 9891-9903.
  25. Shiklomanov, A.I., Lammers, R.B., and Vorosmarty, C.J., 2002, Widespread decline in hydrological monitoring threatens Pan-Arctic Research. Eos, Transactions American Geophysical Union, 83, 13-17. https://doi.org/10.1029/2002EO000007
  26. Sichangi, A.W., Wang, L., and Hu, Z., 2018, Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River. Remote Sensing, 10, 1385.
  27. Sun, Y., Riva, R., and Ditmar, P., 2016, Optimizing estimates of annual variations and trends in geocenter motion and J2 from a combination of GRACE data and geophysical models. Journal of Geophysical Research: Solid Earth, 121, 8352-8370. https://doi.org/10.1002/2016JB013073
  28. Swenson, S., and Wahr, J., 2006, Post-processing removal of correlated errors in GRACE data. Geophysical Research Letters, 33, https://doi.org/10.1029/2005GL025285
  29. Syed, T.H., Famiglietti, J.S., Chen, J., Rodell, M., Seneviratne, S.I., Viterbo, P., and Wilson, C.R., 2005, Total basin discharge for the Amazon and Mississippi River basins from GRACE and a land-atmosphere water balance. Geophysical Research Letters, 32, https://doi.org/10.1029/2005GL024851
  30. Tapley, B.D., Bettadpur, S., Ries, J.C., Thompson, P.F., and Watkins, M.M., 2004, GRACE Measurements of Mass Variability in the Earth System. Science, 305, 503 p. https://doi.org/10.1126/science.1099192
  31. Tarpanelli, A., and Domeneghetti, A., 2021, Flow duration curves from surface reflectance in the near infrared band. Applied Sciences, 11, 3458.
  32. Tourian, M.J., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R., and Sneeuw, N., 2022, HydroSat: geometric quantities of the global water cycle from geodetic satellites. Earth System Science Data, 14, 2463-2486.
  33. Wang, H., Xiang, L., Jia, L., Jiang, L., Wang, Z., Hu, B., and Gao, P., 2012, Load Love numbers and Green's functions for elastic Earth models PREM, iasp91, ak135, and modified models with refined crustal structure from Crust 2.0. Computers & Geosciences, 49, 190-199.
  34. Watkins, M.M., Wiese, D.N., Yuan, D.-N., Boening, C., and Landerer, F.W., 2015, Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons. Journal of Geophysical Research: Solid Earth, 120, 2648-2671. https://doi.org/10.1002/2014JB011547
  35. Wieczorek, M.A., and Simons, F.J., 2005, Localized spectral analysis on the sphere. Geophysical Journal International, 162, 655-675.
  36. Wouters, B., and Schrama, E.J., 2007, Improved accuracy of GRACE gravity solutions through empirical orthogonal function filtering of spherical harmonics. Geophysical Research Letters, 34, https://doi.org/10.1029/2007GL032098
  37. Yamazaki, D., Lee, H., Alsdorf, D.E., Dutra, E., Kim, H., Kanae, S., and Oki, T., 2012, Analysis of the water level dynamics simulated by a global river model: A case study in the Amazon River. Water Resources Research, 48, https://doi.org/10.1029/2012WR011869
  38. Youm, K., Eom, J., Seo, K.-W., Chen, J., Wilson, C.R., and Oh, S., 2022, High-resolution surface mass loads in the Amazon Basin combining GRACE and river routing model. Geophysical Journal International, 232, 2105-2118. https://doi.org/10.1093/gji/ggac439