Prediction of terrestrial water storage changes by using GRACE data over Nile river basin
DOI: https://doi.org/10.3846/gac.2026.22447Abstract
This research involved training two deep learning prediction models: Long Short-Term Memory (LSTM) and Dipper Throated Optimizations Fitness Grey Wolf-LSTM (DTOFGW-LSTM), utilizing data obtained from remote sensing to reconstruct and predict the Terrestrial Water Storage Changes (TWSC) over Nile River Basin (NRB). We evaluated factors including Terrestrial Water Storage Changes (TWSC) and Groundwater Storage Changes (GWSC), identified through the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow-On (GRACE-FO), alongside precipitation data collected by the Global Precipitation Climate Change Program (GPCP) to analyze the patterns of change within the research area. We utilized the LSTM and DTOFGW-LSTM algorithms to rebuild the TWSC and GWSC from 2018 to 2024. We utilized the precise model to forecast the GRACE gap from 2017 to 2018 and the TWSC from 2024 to 2030. The findings demonstrated the superiority of the suggested model (DTOFGW-LSTM) with a root mean square error (RMSE) of 0.51, a coefficient of determination (R²) of 0.99, and a mean absolute percentage error (MAPE) of 0.21.
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GRACE, GRACE-FO, DTOFGW-LSTM, LSTM, TWSC, GWSCHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Ahmed, M., Sultan, M., Elbayoumi, T., & Tissot, P. (2019). Forecasting GRACE data over the African watersheds using artificial neural networks. Remote Sensing, 11(15), Article 1769. https://doi.org/10.3390/rs11151769
Awange, J. L., Forootan, E., Kuhn, M., Kusche, J., & Heck, B. (2014). Water storage changes and climate variability within the Nile Basin between 2002 and 2011. Advances in Water Resources, 73, 1–15. https://doi.org/10.1016/j.advwatres.2014.06.010
Belda, M., Halenka, T., Holtanová, E., Kalvová, J., & Hlávka, Z. (2015). Evaluation of CMIP5 present climate simulations using the Köppen-Trewartha climate classification. Climate Research, 64, 201–212. https://doi.org/10.3354/cr01316
Duan, A., Zhong, Y., Xu, G., Yang, K., Tian, B., Wu, Y., Bai, H., & Hu, E. (2024). Quantifying the 2022 extreme drought in the Yangtze River Basin using GRACE-FO. Journal of Hydrology, 630, Article 130680. https://doi.org/10.1016/j.jhydrol.2024.130680
Gyawali, B., Ahmed, M., Murgulet, D., & Wiese, D. N. (2022). Filling temporal gaps within and between GRACE and GRACE-FO terrestrial water storage records: An innovative approach. Remote Sensing, 14(7), Article 1565. https://doi.org/10.3390/rs14071565
Hasan, E., & Tarhule, A. (2020). GRACE: Gravity recovery and climate experiment long-term trend investigation over the Nile River Basin: Spatial variability drivers. Journal of Hydrology, 586, Article 124870. https://doi.org/10.1016/j.jhydrol.2020.124870
Hasan, E., Tarhule, A., & Kirstetter, P. (2021). Using GRACE/GRACE-FO, data-driven and modeling to access the twentieth and twenty-first century water storage changes in the Nile River Basin. Preprints. https://doi.org/10.20944/preprints202101.0357.v1
Llovel, W., Becker, M., Cazenave, A., Crétaux, J.-F., & Ramillien, G. (2010). Global land water storage change from GRACE over 2002–2009; Inference on sea level. Comptes Rendus Géoscience, 342(3), 179–188. https://doi.org/10.1016/j.crte.2009.12.004
Long, D., Shen, Y., Sun, A., Hong, Y., Longuevergne, L., Yang, Y., Li, B., & Chen, L. (2014). Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sensing of Environment, 155, 145–160. https://doi.org/10.1016/j.rse.2014.08.006
Loomis, B. D., Felikson, D., Sabaka, T. J., & Medley, B. (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. Journal of Geophysical Research: Solid Earth, 126(12), Article e2021JB023024. https://doi.org/10.1029/2021JB023024
Lu, S., Li, W., Yao, G., Zhong, Y., Bao, L., Wang, Z., Bi, J., Zhu, C., & Guo, Q. (2024). The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data. Scientific Reports, 14, Article 16855. https://doi.org/10.1038/s41598-024-67611-8
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., & Wood, E. F. (2012). Multisource estimation of long-term terrestrial water budget for major global river basins. Journal of Climate, 25, 3191–3206. https://doi.org/10.1175/JCLI-D-11-00300.1
Pan, J., Jing, B., Jiao, X., & Wang, S. (2020). Analysis and application of grey wolf optimizer-long short-term memory. IEEE Access, 8, 121460–121468. https://doi.org/10.1109/access.2020.3006499
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I., (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878–3895. https://doi.org/10.1002/2016WR019933
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Schmied, H. M., Van Beek, L. P. H., Wiese, D. N., Wada, Y., Reedy, R. C., Longuevergne, L., Döll, P., & Bierkens, M. F. P. (2018). Global models underestimate large decadal decline and rising water storage trends relative to GRACE satellite data. Proceedings of National Academy of Sciences of The United States of America, 115(6), E1080–E1089. https://doi.org/10.1073/pnas.1704665115
Shahin, M. M. A. (1985). Hydrology of the Nile Basin (Vol. 21). Elsevier.
Strassberg, G., Scanlon, B. R., & Chambers, D. (2009). Evaluation of groundwater storage monitoring with the GRACE satellite: Case study of the High Plains aquifer, central United States. Water Resources Research, 45(5), Article W05410. https://doi.org/10.1029/2008WR006892
Tapley, B. D., Bettadpur, S., Watkins, M., & Reigber, C. (2004). The gravity recovery and climate experiment: Mission overview and early results. Geophysical Reserch Letters, 31(9), Article L09607. https://doi.org/10.1029/2004GL019920
Wang, F., Chen, Y., Li, Z., Fang, G., Li, Y., Wang, X., Zhang, X., & Kayumba, P. M. (2021). Developing a long short-term memory (LSTM)-based model for reconstructing terrestrial water storage variations from 1982 to 2016 in the Tarim river basin, Northwest China. Remote Sensing, 13(5), Article 889. https://doi.org/10.3390/rs13050889
Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065
Zhang, L., & Sun, W. (2022). Progress and prospects of GRACE Mascon product and its application. Reviews of Geophysics Planetary Physics, 53, 35–52 (in Chinese).
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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This work is licensed under a Creative Commons Attribution 4.0 International License.