Dobslaw, H., Dill, R., Bagge, M., Klemann, V., Boergens, E., Thomas, M., Dahle, C., and Flechtner, F.: Gravitationally Consistent Mean Barystatic Sea Level Rise From Leakage-Corrected Monthly GRACE Data, J. Geophys. Res.-Sol. Ea., 125, e2020JB020923, https://doi.org/10.1029/2020jb020923, 2020.
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
Forootan, E., Schumacher, M., Mehrnegar, N., Bezděk, A., Talpe, M. J., Farzaneh, S., Zhang, C., Zhang, Y., and Shum, C. K.: An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data, Remote Sens., 12, 1639, https://doi.org/10.3390/rs12101639, 2020.
Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y. H.: The causes of sea-level rise since 1900, Nature, 584, 393–397, https://doi.org/10.1038/s41586-020-2591-3, 2020.
Galdyn, F., Sosnica, K., Zajdel, R., Meyer, U., and Jaggi, A.: Long-term ice mass changes in Greenland and Antarctica derived from satellite laser ranging, Remote Sens. Environ., 302, 113994, https://doi.org/10.1016/j.rse.2024.113994, 2024.
Gentner, L. Q., Gou, J., Tourian, M. J., Borger, L., Soja, B., and Sneeuw, N.: DeepRec: Global Terrestrial Water Storage Reconstruction Since 1941 Using Spatiotemporal-Aware Deep Learning Model, ESS Open Archive [preprint], https://doi.org/10.22541/essoar.175138855.54947789/v1, 2025.
Han, S.-C., Jekeli, C., and Shum, C. K.: Time-variable aliasing effects of ocean tides, atmosphere, and continental water mass on monthly mean GRACE gravity field, J. Geophys. Res., 109, B04403, https://doi.org/10.1029/2003JB002501, 2004.
Huang, Z., Jiao, J. J., Luo, X., Pan, Y., and Jin, T.: Drought and Flood Characterization and Connection to Climate Variability in the Pearl River Basin in Southern China Using Long-Term GRACE and Reanalysis Data, J. Climate, 34, 2053–2078, https://doi.org/10.1175/jcli-d-20-0332.1, 2021.
Humphrey, V. and Gudmundsson, L.: GRACE-REC: a reconstruction of climate-driven water storage changes over the last century, Earth Syst. Sci. Data, 11, 1153–1170, https://doi.org/10.5194/essd-11-1153-2019, 2019.
Jean, Y., Meyer, U., and Jäggi, A.: Combination of GRACE monthly gravity field solutions from different processing strategies, J. Geodesy, 92, 1313–1328, https://doi.org/10.1007/s00190-018-1123-5, 2018.
Ji, K., and Herring, T.: A method for detecting transient signals in GPS position time-series: smoothing and principal component analysis, Geophys. J. Int., 193, 171–186, https://doi.org/10.1093/gji/ggt003, 2013.
Ji, K., Shen, Y., Chen, Q., Li, B., and Wang, W.: An Adaptive Regularized Solution to Inverse Ill-Posed Models, IEEE T. Geosci. Remote Sens., 60, 1–15, https://doi.org/10.1109/tgrs.2022.3205572, 2022.
Jing, W., Zhang, P., Zhao, X., Yang, Y., Jiang, H., Xu, J., Yang, J., and Li, Y.: Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure, J. Hydrol.y, 590, 125239, https://doi.org/10.1016/j.jhydrol.2020.125239, 2020.
Klokočník, J., Wagner, C., Kostelecký, J., and Bezděk, A.: Ground track density considerations on the resolvability of gravity field harmonics in a repeat orbit, Adv. Space Res., 56, 1146–1160, https://doi.org/10.1016/j.asr.2015.06.020, 2015.
Koch, K. R.: Parameter estimation and hypothesis testing in linear models, 2nd Edn., Berlin, Springer, https://doi.org/10.1007/978-3-662-03976-2, 1999.
Kusche, J.: Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models, J. Geodesy, 81, 733–749, https://doi.org/10.1007/s00190-007-0143-3, 2007.
Kvas, A., Behzadpour, S., Ellmer, M., Klinger, B., Strasser, S., Zehentner, N., and Mayer-Gurr, T.: ITSG-Grace2018: Overview and evaluation of a new GRACE-only gravity field time series, J. Geophys. Res.-Sol. Ea., 124, 9332–9344, https://doi.org/10.1029/2019jb017415, 2019.
Li, F., Kusche, J., Chao, N., Wang, Z., and Locher, A.: Long-term (1979–present) total water storage anomalies over the global land derived by reconstructing GRACE data, Geophys. Res. Lett., 48, e2021GL093492, https://doi.org/10.1029/2021GL093492, 2021.
Löcher, A. and Kusche, J.: A hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging, J. Geodesy, 95, 6, https://doi.org/10.1007/s00190-020-01460-x, 2021.
Löcher, A., Kusche, J., and Nie, Y.: A 40-year record of the Earth's time-variable gravity field from SLR and DORIS, Adv. Space Res., 76, 1281–1291, https://doi.org/10.1016/j.asr.2025.05.089, 2025.
Long, D., Longuevergne, L., and Scanlon, B. R.: Global analysis of approaches for deriving total water storage changes from GRACE satellites, Water Resour. Res., 51, 2574–2594, https://doi.org/10.1002/2014WR016853, 2015.
Loomis, B. D., Rachlin, K. E., Wiese, D. N., Landerer, F. W., and Luthcke, S. B.: Replacing GRACE/GRACE-FO C-30 With Satellite Laser Ranging: Impacts on Antarctic Ice Sheet Mass Change, Geophys. Res. Lett., 47, e2019GL085488, https://doi.org/10.1029/2019gl085488, 2020.
Lück, C., Kusche, J., Rietbroek, R., and Locher, A.: IGG-Swarm: Temporal Gravity Models from Swarm, GFZ Data Services [data set], https://doi.org/10.5880/icgem.2021.002, 2021.
Mandal, N., Das, P., and Chanda, K.: Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data, Earth Syst. Sci. Data, 17, 2575–2604, https://doi.org/10.5194/essd-17-2575-2025, 2025.
Meyer, U., Jean, Y., Kvas, A., Dahle, C., Lemoine, J. M., and Jaggi, A.: Combination of GRACE monthly gravity fields on the normal equation level, J. Geodesy, 93, 1645–1658, https://doi.org/10.1007/s00190-019-01274-6, 2019.
NASA-SSH: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6 Version 5.2, NASA Physical Oceanography Distributed Active Archive Center [data set], https://doi.org/10.5067/GMSLM-TJ152, 2024.
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Peltier, W. R., Argus, D. F., and Drummond, R.: Comment on “An Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model” by Purcell et al., J. Geophys. Res.-Sol. Ea., 123, 2019–2028, https://doi.org/10.1002/2016jb013844, 2018.
Rodell, M. and Li, B.: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, Nature Water, 1, 241–248, https://doi.org/10.1038/s44221-023-00040-5, 2023.
Saemian, P.: Analyzing and characterizing spaceborne observation of water storage variation: Past, present, future, Doctoral dissertation, Universität Stuttgart, https://doi.org/10.18419/opus-13923, 2024.
Saemian, P., Tourian, M. J., AghaKouchak, A., Madani, K., and Sneeuw, N.: How much water did Iran lose over the last two decades?, J. Hydrol. Reg. Stud., 41, 101095, https://doi.org/10.1016/j.ejrh.2022.101095, 2022.
Saemian, P., Tourian, M. J., Elmi, O., Sneeuw, N., and AghaKouchak, A.: A probabilistic approach to characterizing drought using satellite gravimetry, Water Resour. Res., 60, e2023WR036873, https://doi.org/10.1029/2023WR036873, 2024.
Saemian, P., Tourian, M. J., Douch, K., Foster, J., Gou, J., Wiese, D., AghaKouchak, A., and Sneeuw, N.: A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX), Sci. Data, 13, 142, https://doi.org/10.1038/s41597-026-06604-w, 2026.
Sasgen, I., Steinhoefel, G., Kasprzyk, C., Matthes, H., Westermann, S., Boike, J., and Grosse, G.: Atmosphere circulation patterns synchronize pan-Arctic glacier melt and permafrost thaw, Commun. Earth Environ., 5, 375, https://doi.org/10.1038/s43247-024-01548-8, 2024.
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Muller Schmied, H., van Beek, L. P. H., Wiese, D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Doll, P., and Bierkens, M. F. P.: Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data, P. Natl. Acad. Sci. USA, 115, E1080–E1089, https://doi.org/10.1073/pnas.1704665115, 2018.
Scanlon, B. R., Zhang, Z., Rateb, A., Sun, A., Wiese, D., Save, H., Beaudoing, H., Lo, M. H., Muller-Schmied, H., Doll, P., van Beek, R., Swenson, S., Lawrence, D., Croteau, M., and Reedy, R. C.: Tracking seasonal fluctuations in land water storage using global models and GRACE satellites, Geophys. Res. Lett., 46, 5254–5264, https://doi.org/10.1029/2018GL081836, 2019.
Schrama, O., Wouters, B., and Lavallee, D.: Signal and noise in Gravity Recovery and Climate Experiment (GRACE) observed surface mass variations, J. Geophys. Res.-Sol. Ea., 112, B08407, https://doi.org/10.1029/2006jb004882, 2007.
Sharifi, E., Haas, J., Boergens, E., Dobslaw, H., and Güntner, A.: Technical note: GRACE-compatible filtering of water storage data sets via spatial autocorrelation analysis, Hydrol. Earth Syst. Sci., 29, 6985–6998, https://doi.org/10.5194/hess-29-6985-2025, 2025.
Shen, Y., Xu, P., and Li, B.: Bias-corrected regularized solution to inverse ill-posed models, J. Geodesy, 86, 597–608, https://doi.org/10.1007/s00190-012-0542-y, 2012.
Shen, Y., Wang, F., and Chen, Q.: Weighted multichannel singular spectrum analysis for post-processing GRACE monthly gravity field models by considering the formal errors, Geophys. J. Int., 226, 1997–2010, https://doi.org/10.1093/gji/ggab199, 2021.
Siemes, C., Ditmar, P., Riva, R. E. M., Slobbe, D. C., Liu, X. L., and Farahani, H. H.: Estimation of mass change trends in the Earth's system on the basis of GRACE satellite data, with application to Greenland, J. Geodesy, 87, 69–87, https://doi.org/10.1007/s00190-012-0580-5, 2013.
Sun, Z., Long, D., Yang, W., Li, X., and Pan, Y.: Reconstruction of grace data on changes in total water storage over the global land surface and 60 basins, Water Resour. Res., 56, e2019WR026250, https://doi.org/10.1029/2019WR026250, 2020.
Tapley, B. D., Bettadpur, S., Watkins, M., and Reigber, C.: The gravity recovery and climate experiment: Mission overview and early results, Geophys. Res. Lett., 31, L09601, https://doi.org/10.1029/2004gl019920, 2004.
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019.
Teunissen, P. J. G.: Adjustment theory: An introduction, Delft University Press, https://doi.org/10.59490/tb.95, 2000.
Tourian, M. J., Saemian, P., Ferreira, V. G., Sneeuw, N., Frappart, F., and Papa, F.: A copula-supported Bayesian framework for spatial downscaling of GRACE-derived terrestrial water storage flux, Remote Sens. Environ., 295, 113685, https://doi.org/10.1016/j.rse.2023.113685, 2023.
Uz, M., Akyilmaz, O., Shum, C. K., Atman, K. G., Olgun, S., and Gunes, O.: High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021, Sci. Data, 11, 71, https://doi.org/10.1038/s41597-023-02887-5, 2024.
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res.-Sol. Ea., 103, 30205–30229, https://doi.org/10.1029/98jb02844, 1998.
Wang, F., Chen, Y., Li, Z., Fang, G., Li, Y., Wang, X., Zhang, X., and Kayumba, P. M.: 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 Sens., 13, 889, https://doi.org/10.3390/rs13050889, 2021a.
Wang, F., Shen, Y., Chen, Q., and Wang, W.: Bridging the gap between GRACE and GRACE follow-on monthly gravity field solutions using improved multichannel singular spectrum analysis, J. Hydrol., 594, 125972, https://doi.org/10.1016/j.jhydrol.2021.125972, 2021b.
Wang, W., Shen, Y., Chen, Q., and Wang, F.: High-resolution mascon solutions reveal glacier-scale mass changes over the Greenland Ice Sheet from 2002 to 2022, Geophys. J. Int., 236, 494–515, https://doi.org/10.1093/gji/ggad439, 2024.
Wen, Z., Saemian, P., Xu, F., Sun, W., and Tourian, M. J.: Black Sea level variability and its driving factors in the last two decades, Geophys. J. Int., 243, ggaf316, https://doi.org/10.1093/gji/ggaf316, 2025.
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull., 1, 80–83, https://doi.org/10.2307/3001968, 1945.
Xu, P., Shen, Y., Fukuda, Y., and Liu, Y.: Variance component estimation in linear inverse ill-posed models, J. Geodesy, 80, 69–81, https://doi.org/10.1007/s00190-006-0032-1, 2006.
Yi, S. and Sneeuw, N.: A novel spatial filter to reduce north–south striping noise in GRACE spherical harmonic coefficients, J. Geodesy, 96, 23, https://doi.org/10.1007/s00190-022-01614-z, 2022.
Yi, S., Saemian, P., Sneeuw, N., and Tourian, M. J.: Estimating runoff from pan-Arctic drainage basins for 2002–2019 using an improved runoff-storage relationship, Remote Sens. Environ., 298, 113816, https://doi.org/10.1016/j.rse.2023.113816, 2023.
Yin, J., Slater, L. J., Khouakhi, A., Yu, L., Liu, P., Li, F., Pokhrel, Y., and Gentine, P.: GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present, Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, 2023.
Zhang, B., Liu, L., Yao, Y., van Dam, T., and Khan, S. A.: Improving the estimate of the secular variation of Greenland ice mass in the recent decades by incorporating a stochastic process, Earth Planet. Sc. Lett., 549, 116518, https://doi.org/10.1016/j.epsl.2020.116518, 2020.
Zhang, L., Shen, Y., Chen, Q., and Ji, K.: An improved parameter filtering approach for processing GRACE gravity field models using first-order Gauss–Markov process, J. Geodesy, 98, 56, https://doi.org/10.1007/s00190-024-01871-0, 2024.
Zhang, L., Shen, Y., Chen, Q., and Wang, F.: Influence factors and mechanisms of 2015–2016 extreme flood in Pearl River Basin based on the WSDI from GRACE, J. Hydrol. Reg. Stud., 47, 101376, https://doi.org/10.1016/j.ejrh.2023.101376, 2023.
Zhang, L., Shen, Y., Ji, K., and Chen, Q.: An Enhanced Parameter Filtering Approach for Postprocessing GRACE Monthly Gravity Field Models, IEEE Geosci. Remote Sens. Lett., 22, 1–5, https://doi.org/10.1109/LGRS.2025.3575197, 2025a.
Zhang, L., Shen, Y., Sneeuw, N., Ji, K., and Ju, X.: One-step estimation of non-seasonal terrestrial water storage variation in Southeastern China, Environ. Res. Lett., 20, 084071, https://doi.org/10.1088/1748-9326/adeff4, 2025b.
Zhang, L., Shen, Y., Sneeuw, N., Saemian, P., Ji, K., Chen, Q., and Wang, F.: A combination of Time-Variable Gravity Field Solutions from Multi-Satellite Datasets (1993–2024) via Constrained Collocation Model, Zenodo [data set], https://doi.org/10.5281/zenodo.18589507, 2026.
Zhong, L., Sośnica, K., Weigelt, M., Liu, B., and Zou, X.: Time-variable gravity field from the combination of HLSST and SLR, Remote Sens., 13, 3491, https://doi.org/10.3390/rs13173491, 2021.
Zhou, H., Wang, P., Tang, L., and Luo, Z.: A New GRACE Filtering Approach Based on Iterative Image Convolution, J. Geophys. Res.-Sol. Ea., 128, e2023JB026553, https://doi.org/10.1029/2023jb026553, 2023.
