| 30 Oct 2025
Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data
Abstract. The Gravity Recovery And Climate Experiment (GRACE) and its follow-on mission, GRACE-FO, have observed global mass changes and transports, expressed as terrestrial water storage anomalies (TWSA), for over two decades. However, for climate model evaluation, climate change attribution and other applications, multi-decadal TWSA time series are required. This need has triggered several studies on reconstructing TWSA via regression approaches or machine learning techniques, with the help of predictor variables such as rainfall, land or sea surface temperature. Here, we combine such an approach, for the first time, with large-scale time-variable gravity information from geodetic satellite laser ranging (SLR) and Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) tracking. The new reconstruction TWSTORE (Terrestrial Water STOrage REconstruction) is formulated in a GRACE-derived empirical orthogonal functions (EOFs) basis and complemented with the Löcher et al. (2025) approach, in which global gravity fields are solved from SLR ranges and DORIS observations in EOF space for the pre-GRACE time frame. Our approach is highly modular, allowing to use different data sets at several steps in the workflow.
We reconstruct GRACE-like TWSA for the global land, excluding Greenland and Antarctica, from 1984 onward. We find that the new combined reconstruction inherits information from the geodetic method, mainly at longer timescales. In contrast, at the seasonal scale, the climate-driven reconstruction and the geodetic product are already surprisingly consistent. In comparison to other reconstructions, we find thus major differences mainly at the multi-decadal timescale. All in all, our study confirms the presence of significant changes in storage trends, showing that GRACE-derived results should not be extrapolated to the past. The reconstructed fields and corresponding uncertainty information are available at https://doi.org/10.5281/zenodo.15827789 (Hacker, 2025). We also derive evaporation based on the water balance equation and the presented reconstruction for 11 river basins. The corresponding time series are available at https://doi.org/10.5281/zenodo.16643628 (Gutknecht, 2025).
Review of Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data by Hacker et al. submitted to Earth System Science Data.
In this study, Hacker et al. provide a new GRACE-like TWSA reconstruction back to 1984. The major contribution is to involve the SLR/DORIS-derived TWSA as additional input to keep the fidelity of reconstructed products. The benefits of this approach have been demonstrated through multiple internal/external validations.
Reconstructing GRACE-like TWSA products is generally important for the community, especially for analyzing long-term variations. This study makes an important contribution to the community by involving the alternative geodetic observations in the pre-GRACE era. I only have several rather moderate comments before the manuscript can be published. Most of them are related to possible improvements of the evaluation approaches.
General concerns:
The evaluations/validations can be further improved. As a data description paper for ESSD, rigorous validation is quite important. However, the evaluations in 4.1 and 4.2 are not fully independent as the SLR/GRACE data are used for inputs/targets, while 4.3 only presents the internal comparisons. The authors should try to perform more independent evaluations so that the audience will be more confident in the data. Here are two thoughts from my side:
1. An independent comparison can be performed by computing the TWS-derived global mean sea level (GMSL) variations, like Fig. 7 in Gentner et al. (2025). The data can be obtained from Frederikse et al. (2020). To this end, the trend and inter-annual signals contained in TWSTORE can be better demonstrated, and the benefits of including SLR/DORIS observations should be better argued.
2. Section 5.2 in the manuscript actually provides another good way for evaluating the data based on water balance. It might be a good idea to perform a global study based on some global dataset of precipitation, evapotranspiration, and runoff, and report some quantitative metrics. It is basically an extension of the current section 5.2 to the whole globe.
Besides, the comparison with other reconstructions mentioned in lines 71 – 73 is relevant. However, this comparison could be further improved to better demonstrate the contribution of this study. It is clearly mentioned in Humphrey and Gudmundsson (2019) that their reconstruction is only “climate-driven” and the long-term trends are removed. And the trends in Li et al. (2019) are based on linear extrapolation to the past, so they are also not that reliable. It would be good to involve some more recent studies that made additional efforts to reconstruct long-term variability, such as Yin et al. (2023), Palazzoli et al. (2025), Gentner et al. (2025). All those products are publicly available and easily accessible. Moreover, some more quantitative comparison results in addition to the current Section 4.3 are appreciated.
Specific comments:
[1] line 41: Please be clearer if you mean the missing input uncertainties, or the missing uncertainty information associated with those reconstructions, or maybe both.
[2] lines 43 – 44: “learned relations between climate and water storage can almost certainly not be transferred straightforwardly to the past” is a great motivation to involve the observations (SLR/DORIS) to further constraint the past reconstructions. Please highlight this point a bit more.
[3] Section 2 Methods. A schematic diagram showing the whole workflow would be beneficial for the audience to understand the full method, especially the relationship between EOF analysis and the three selected regression methods.
[4] lines 116 – 120: Dividing the whole world into different basins can indeed enhance the local performance. But will this strategy also introduce some discontinuities at the basin boundaries? Please comment on it. It may relate to the argument on lines 124 – 129. Is the missing cross-basin correlation a desired feature of the method or maybe a limitation?
[5] Fig. 2: Some of the known strong linear-trend signals are associated with high RMSDs (such as Alaska or the North of India), but some of them are not (such as the High Plain Aquifer and North China Plain). Could you please comment on the reasons behind these differences?
[6] Page 19 / Section 5.1: This section includes many short paragraphs. Please consider merging some of them.
References
Frederikse et al. (2020). The causes of sea-level rise since 1900. https://doi.org/10.1038/s41586-020-2591-3
Gentner et al. (2025). DeepRec: Global Terrestrial Water Storage Reconstruction Since 1941 Using Spatiotemporal-Aware Deep Learning Model. https://doi.org/10.22541/essoar.175138855.54947789/v1
Palazzoli et al. (2025). GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks. https://doi.org/10.1038/s41597-025-04403-3
Yin et al. (2023). GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present. https://doi.org/10.5194/essd-15-5597-2023