PCA-SOM of GRACE-FO total water storage for global climate decisions
Abstract
The gravity recovery and climate experiment (GRACE) and GRACE-Follow on (FO) data provide valuable information about dynamic total water storage (TWS). The complexity of the computational process and the influence of various parameters on TWS changes are complicated in their interpretation. Principal component analysis (PCA) has been used to identify key components to amplify signals and reduce noise in observations. For this purpose, in this research, the Self-organizing map algorithm (SOM) has been used to cluster TWS in 4 categories. The results show that the western regions of Greenland and part of Antarctica are in the critical cluster and have a TWS rate of about –0.2 m/year, which indicates the melting of ice in these regions. The advantage of PCA-SOM is the easy interpretation of TWS, which reduces the impact of seasonal parameters, observation noise and measurement error, and facilitates global policy decisions in the face of climate change.
Keyword : GRACE-FO, SOM, TWS, GRACE, climate
How to Cite
Memarian Sorkhabi, O., & Kurdpour, I. (2022). PCA-SOM of GRACE-FO total water storage for global climate decisions. Geodesy and Cartography, 48(4), 243–247. https://doi.org/10.3846/gac.2022.15171
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Acevedo-Acosta, J. D., Martínez-López, A., Morales-Acoltzi, T., Albáñez-Lucero, M., & Verdugo-Díaz, G. (2021). Self-organization maps (SOM) in the definition of a “transfer function” for a diatoms-based climate proxy. Climate Dynamics, 56, 423–437. https://doi.org/10.1007/s00382-020-05482-1
Bryant, R. J., Oxley, J., Young, G. J., Lane, J. A., Metcalfe, C., Davis, M., Turner, E. L., Martin, R. M., Goepel, J. R., Varma, M., Griffiths, D. F., Grigor, K., Mayer, N., Warren, A. Y., Bhattarai, S., Dormer, J., Mason, M., Staffurth, J., Walsh, E., Rosario, D. J., Catto, J. W. F., Neal, D. E., Donovan, J. L., Hamdy, F. C., & ProtecT Study Group. (2020). The ProtecT trial: Analysis of the patient cohort, baseline risk stratification and dis-ease progression. BJU International, 125(4), 506–514. https://doi.org/10.1111/bju.14987
Gholami, V., Khaleghi, M. R., & Salimi, E. T. (2020). Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. Journal of Mountain Science, 17(7), 1724–1734. https://doi.org/10.1007/s11629-019-5483-y
Godah, W. (2019). IGiK–TVGMF: A MATLAB package for computing and analysing temporal variations of gravity/mass functionals from GRACE satellite based global geopotential models. Computers & Geosciences, 123, 47–58. https://doi.org/10.1016/j.cageo.2018.11.008
Jensen, L., Eicker, A., Stacke, T., & Dobslaw, H. (2020). Predictive skill assessment for land water storage in CMIP5 decadal hindcasts by a global reconstruction of GRACE satellite data. Journal of Climate, 33(21), 9497–9509. https://doi.org/10.1175/JCLI-D-20-0042.1
Jiang, D., Huang, S., & Han, D. (2014). Monitoring and modeling terrestrial ecosystems’ response to climate change. Advances in Meteorology, 2014, 429349. https://doi.org/10.1155/2014/429349
Khaki, M. (2020). Efficient assimilation of GRACE TWS into hydrological models. In Satellite remote sensing in hydrological data assimilation (pp. 51–74). Springer, Cham. https://doi.org/10.1007/978-3-030-37375-7_6
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H.-Y., Wiese, D. N., & Yuan, D.-N. (2020). Extending the global mass change data record: GRACE Follow‐On instrument and science data performance. Geophysical Research Letters, 47(12), e2020GL088306. https://doi.org/10.1029/2020GL088306
Li, J., Wang, S., & Zhou, F. (2016). Time series analysis of long-term terrestrial water storage over Canada from GRACE Satellites using principal component analysis. Canadian Journal of Remote Sensing, 42(3), 161–170. https://doi.org/10.1080/07038992.2016.1166042
Liu, X., Feng, X., Ciais, P., Fu, B., Hu, B., & Sun, Z. (2020). GRACE satellite-based drought index indicating increased impact of drought over major basins in China during 2002–2017. Agricultural and Forest Meteorology, 291, 108057. https://doi.org/10.1016/j.agrformet.2020.108057
Medvedev, I. P., Kulikov, E. A., Fine, I. V., & Kulikov, A. E. (2019). Numerical modeling of sea level oscillations in the Caspian Sea. Russian Meteor-ology and Hydrology, 44(8), 529–539. https://doi.org/10.3103/S1068373919080041
Memarian Sorkhabi, O., Asgari, J., & Amiri Simkooei, A. (2021). Analysis of Greenland mass changes based on GRACE four-dimensional wavelet decomposition. Remote Sensing Letters, 12(5), 499–509. https://doi.org/10.1080/2150704X.2021.1903608
Sasgen, I., Wouters, B., Gardner, A. S., King, M. D., Tedesco, M., Landerer, F. W., Dahle, C., Save, H., & Fettweis, X. (2020). Return to rapid ice loss in Greenland and record loss in 2019 detected by the GRACE-FO satellites. Communications Earth & Environment, 1, 8. https://doi.org/10.1038/s43247-020-0010-1
Sorkhabi, O. M., & Milani, M. (2022). Deep Learning of Ionosphere Single-Layer Model and Tomography. Geomagnetism and Aeronomy, 62(4), 474-481. https://doi.org/10.1134/S0016793222040120
Sorkhabi, O. M., Kurdpour, I., & Sarteshnizi, R. E. (2022a). Land subsidence and groundwater storage investigation with multi sensor and extended Kalman filter. Groundwater for Sustainable Development, 19, 100859. https://doi.org/10.1016/j.gsd.2022.100859
Sorkhabi, O. M., Milani, M., & Seyed Alizadeh, S. M. (2022b). Investigating the efficiency of deep learning methods in estimating GPS geodetic veloc-ity. Earth and Space Science, 9(10), e2021EA002202. https://doi.org/10.1029/2021EA002202
Sun, A. Y., Scanlon, B. R., Zhang, Z., Walling, D., Bhanja, S. N., Mukherjee, A., & Zhong, Z. (2019). Combining physically based modeling and deep learning for fusing GRACE satellite data: Can we learn from mismatch? Water Resources Research, 55(2), 1179–1195. https://doi.org/10.1029/2018WR023333
Tan, Y. C., Ke, K. Y., & Fang, H. T. (2019). Application of integrated SOM-and MOGA-SVM-based algorithms to forecast groundwater level in Choushui River Alluvial fan, Taiwan. In AGUFM 2019, H53Q-2052.
Xu, L., Chen, N., Zhang, X., & Chen, Z. (2019). Spatiotemporal changes in China’s terrestrial water storage from GRACE satellites and its possible drivers. Journal of Geophysical Research: Atmospheres, 124(22), 11976–11993. https://doi.org/10.1029/2019JD031147
Bryant, R. J., Oxley, J., Young, G. J., Lane, J. A., Metcalfe, C., Davis, M., Turner, E. L., Martin, R. M., Goepel, J. R., Varma, M., Griffiths, D. F., Grigor, K., Mayer, N., Warren, A. Y., Bhattarai, S., Dormer, J., Mason, M., Staffurth, J., Walsh, E., Rosario, D. J., Catto, J. W. F., Neal, D. E., Donovan, J. L., Hamdy, F. C., & ProtecT Study Group. (2020). The ProtecT trial: Analysis of the patient cohort, baseline risk stratification and dis-ease progression. BJU International, 125(4), 506–514. https://doi.org/10.1111/bju.14987
Gholami, V., Khaleghi, M. R., & Salimi, E. T. (2020). Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. Journal of Mountain Science, 17(7), 1724–1734. https://doi.org/10.1007/s11629-019-5483-y
Godah, W. (2019). IGiK–TVGMF: A MATLAB package for computing and analysing temporal variations of gravity/mass functionals from GRACE satellite based global geopotential models. Computers & Geosciences, 123, 47–58. https://doi.org/10.1016/j.cageo.2018.11.008
Jensen, L., Eicker, A., Stacke, T., & Dobslaw, H. (2020). Predictive skill assessment for land water storage in CMIP5 decadal hindcasts by a global reconstruction of GRACE satellite data. Journal of Climate, 33(21), 9497–9509. https://doi.org/10.1175/JCLI-D-20-0042.1
Jiang, D., Huang, S., & Han, D. (2014). Monitoring and modeling terrestrial ecosystems’ response to climate change. Advances in Meteorology, 2014, 429349. https://doi.org/10.1155/2014/429349
Khaki, M. (2020). Efficient assimilation of GRACE TWS into hydrological models. In Satellite remote sensing in hydrological data assimilation (pp. 51–74). Springer, Cham. https://doi.org/10.1007/978-3-030-37375-7_6
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H.-Y., Wiese, D. N., & Yuan, D.-N. (2020). Extending the global mass change data record: GRACE Follow‐On instrument and science data performance. Geophysical Research Letters, 47(12), e2020GL088306. https://doi.org/10.1029/2020GL088306
Li, J., Wang, S., & Zhou, F. (2016). Time series analysis of long-term terrestrial water storage over Canada from GRACE Satellites using principal component analysis. Canadian Journal of Remote Sensing, 42(3), 161–170. https://doi.org/10.1080/07038992.2016.1166042
Liu, X., Feng, X., Ciais, P., Fu, B., Hu, B., & Sun, Z. (2020). GRACE satellite-based drought index indicating increased impact of drought over major basins in China during 2002–2017. Agricultural and Forest Meteorology, 291, 108057. https://doi.org/10.1016/j.agrformet.2020.108057
Medvedev, I. P., Kulikov, E. A., Fine, I. V., & Kulikov, A. E. (2019). Numerical modeling of sea level oscillations in the Caspian Sea. Russian Meteor-ology and Hydrology, 44(8), 529–539. https://doi.org/10.3103/S1068373919080041
Memarian Sorkhabi, O., Asgari, J., & Amiri Simkooei, A. (2021). Analysis of Greenland mass changes based on GRACE four-dimensional wavelet decomposition. Remote Sensing Letters, 12(5), 499–509. https://doi.org/10.1080/2150704X.2021.1903608
Sasgen, I., Wouters, B., Gardner, A. S., King, M. D., Tedesco, M., Landerer, F. W., Dahle, C., Save, H., & Fettweis, X. (2020). Return to rapid ice loss in Greenland and record loss in 2019 detected by the GRACE-FO satellites. Communications Earth & Environment, 1, 8. https://doi.org/10.1038/s43247-020-0010-1
Sorkhabi, O. M., & Milani, M. (2022). Deep Learning of Ionosphere Single-Layer Model and Tomography. Geomagnetism and Aeronomy, 62(4), 474-481. https://doi.org/10.1134/S0016793222040120
Sorkhabi, O. M., Kurdpour, I., & Sarteshnizi, R. E. (2022a). Land subsidence and groundwater storage investigation with multi sensor and extended Kalman filter. Groundwater for Sustainable Development, 19, 100859. https://doi.org/10.1016/j.gsd.2022.100859
Sorkhabi, O. M., Milani, M., & Seyed Alizadeh, S. M. (2022b). Investigating the efficiency of deep learning methods in estimating GPS geodetic veloc-ity. Earth and Space Science, 9(10), e2021EA002202. https://doi.org/10.1029/2021EA002202
Sun, A. Y., Scanlon, B. R., Zhang, Z., Walling, D., Bhanja, S. N., Mukherjee, A., & Zhong, Z. (2019). Combining physically based modeling and deep learning for fusing GRACE satellite data: Can we learn from mismatch? Water Resources Research, 55(2), 1179–1195. https://doi.org/10.1029/2018WR023333
Tan, Y. C., Ke, K. Y., & Fang, H. T. (2019). Application of integrated SOM-and MOGA-SVM-based algorithms to forecast groundwater level in Choushui River Alluvial fan, Taiwan. In AGUFM 2019, H53Q-2052.
Xu, L., Chen, N., Zhang, X., & Chen, Z. (2019). Spatiotemporal changes in China’s terrestrial water storage from GRACE satellites and its possible drivers. Journal of Geophysical Research: Atmospheres, 124(22), 11976–11993. https://doi.org/10.1029/2019JD031147