the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Dec 2025
Reconstruction of Climate-Driven Global Terrestrial Water Storage Variations (2002–2021)
Abstract. Terrestrial water storage anomalies (TWSA), jointly influenced by climatic variability and human activities, serve as a key indicator of global climate change. TWSA exhibits pronounced fluctuations across multiple temporal scales, a substantial portion of which can be attributed to climatic variability, such as the El Niño–Southern Oscillation (ENSO). Empirical reconstruction of climate-driven water storage based on relationships between GRACE satellite gravity observations and meteorological forcing data has become a common approach; however, existing models often neglect the regulating role of temperature in the transformation of precipitation into water storage. In this study, we propose a linear, four-parameter coupled recursive model that explicitly incorporates temperature effects on both the conversion and dissipation efficiency of water storage. Using GRACE/GRACE-FO satellite observations and meteorological forcing data, we reconstructed climate-driven TWSA over the global land grid (excluding Antarctica) at a daily temporal resolution and 0.5° spatial resolution for the period 2002 to 2021. For 116 major global river basins, we further derived basin-scale TWSA reconstructions and quantitatively evaluated the fraction of precipitation converted into TWSA. Finally, the reconstructed data were compared with existing reconstruction datasets. Compared with existing reconstruction products, the results indicate that: (1) the proposed method achieves substantially faster parameter convergence, improving computational efficiency by several tens of times during the TWSA reconstruction process; (2) the proposed model demonstrates superior performance in approximately 89 % of river basins and 62 % of global land grid cells. Specifically, the Nash–Sutcliffe efficiency (NSE) exceeds 0.7 in 84 out of 116 basins, and 62 % of global land grids exhibit NSE values greater than 0. This study enhances the understanding of the mechanisms governing terrestrial water storage variations at both global and regional scales, provides a quantitative assessment of climate-driven water storage changes, and offers a solid foundation for disentangling the respective impacts of climatic variability and human activities on water resources.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(1335 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-5991', Yulong Zhong, 05 Jan 2026
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AC1: 'Reply on RC1', Pu Xie, 20 Feb 2026
Response to Reviewer
We sincerely thank you for your careful review of our manuscript and for your valuable comments. Your suggestions have not only precisely identified areas that required clarification or improvement, but have also provided clear guidance for further strengthening the manuscript. We have carefully considered each comment and addressed them individually in the revised version. The primary changes in the manuscript are as follows:
- Following the reviewer's suggestion, the manuscript title has been revised to more clearly convey the main objective and contribution of the study. The new title is: "Reconstructing Climate-Driven Global Terrestrial Water Storage (2002–2021) Using a Four-Parameter Linear Recursive Model."
- To address concerns regarding the absence of CSR mascon products, we have incorporated CSR-based reconstruction results into the comparative analysis. The corresponding figures and analyses in the manuscript have been updated accordingly to provide a more complete assessment of reconstruction performance.
- To avoid ambiguity regarding temperature forcing datasets, we have added clarifications in the Methods section to explicitly distinguish GLDAS-2.2 temperature variables from ERA5-Land meteorological forcing data. In addition, new supplementary figures have been included to illustrate spatial and temporal differences between these datasets.
The point-by-point responses are provided in the attached document.
Sincerely,
Pu Xie and Shuang Yi
February 2026
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AC1: 'Reply on RC1', Pu Xie, 20 Feb 2026
-
RC2: 'Comment on egusphere-2025-5991', Anonymous Referee #2, 09 Jan 2026
The study presents a four-parameter, linear recursive model that reconstructs daily terrestrial water storage anomalies (TWSA) at 0.5° resolution for 2002–2021 using precipitation and temperature. The authors claim higher computational efficiency and better basin-scale performance than that of previous statistical reconstructions. While the work is interesting and potentially useful for data-scarce regions, my major concerns focus on (i) the lack of daily-scale validation, (ii) the arbitrary 116-basin subdivision and its global mapping strategy, and (iii) the insufficient demonstration of added value against physically based assimilation products such as GLDAS CLSM. These issues must be addressed before the paper can be considered for publication. I recommend Major Revision.
Major comments
- The model is trained on de-seasonalised and de-trended monthly TWSA from GRACE/GRACE-FO, and daily fields are produced but only monthly datasets are assessed. Although GRACE provides only monthly observations, the daily reconstruction should also be evaluated by comparison with other independent datasets such as model simulations and aridity indexes.
- In the work, TWS is reconstructed on land grids, but why do you evaluate β at the basin scale? In my view, you should not use this method to reconstruct grid-scale TWS, because the increase in TWS may be greater than precipitation due to runoff processes, which can lead to β > 1. Actually, Zhong et al. (2025) also apply a similar method to reconstruct basin-scale TWS, which can ignores the impact of runoff.
- I think you should divide the datasets into training periods and validation periods, as in machine learning, to avoid the over-fitting risk.
- Parameters a, b, c, d are claimed to be "physically meaningful" but lack independent corroboration such as ET/P partitioning or field infiltration measurements. Therefore, you should add scatter plots of a vs. observed ET/P and d vs. estimated groundwater turnover time, and discuss sign mismatches.
- As you mentioned, GLDAS CLSM (0.25°, assimilates GRACE) offers a quasi-independent reference. GLDAS provides reasonable TWS estimates. You should compare the results between the reconstruction and GLDAS, and illustrate the advantages of your method.
Minor comments
- Line 233: add the missing equation number.
- Line 333: "2.81 % of grids with NSE > 0.8" disagrees with the histogram.
- In Figures 6 and 16 (b–j), the letters (a, b, c, etc.) in panel (a) are preferable to ID numbers.
Citation: https://doi.org/10.5194/egusphere-2025-5991-RC2 -
AC2: 'Reply on RC2', Pu Xie, 20 Feb 2026
Response to Reviewers
We sincerely thank your constructive and insightful comments, which have significantly helped improve the clarity, scientific rigor, and presentation of our manuscript. We carefully considered all comments and have revised the manuscript accordingly. The primary revisions made in the manuscript are summarized as follows:- The basin subdivision strategy has been clarified and improved, including clearer documentation of data sources and revised ordering of basins in Table S1 to enhance transparency.
- Additional basin-scale comparisons with GLDAS CLSM have been included to demonstrate the added value of the proposed reconstruction method. The results have been incorporated into Section 4.2.2 of the revised manuscript, and the section has been extensively revised accordingly.
- New validations of daily-scale reconstruction results have been added through comparisons with independent datasets, now included as a new section in the manuscript. The corresponding figure has been included in the revised manuscript.
- Following the reviewer’s suggestion, we conducted an independent validation experiment by separating training and validation periods. The results have been added as a new section in the manuscript, with supporting figures included in the Supplementary Materials as Figs. S8 and S9.
- Following the reviewer’s suggestion regarding the physical interpretability of model parameters, we have substantially revised and expanded the derivation and interpretation of model parameters in the manuscript. The physical meanings of parameters a,b,c,and d are now more clearly clarified. The full mathematical derivation and supporting results have been moved to the revised manuscript, with supporting figures included in the Supplementary Materials as Figs. S10–S12.
The point-by-point responses are provided in the attached document.
Sincerely,
Pu Xie and Shuang Yi
February 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-5991', Yulong Zhong, 05 Jan 2026
This study presents a novel linear recursive model incorporating temperature effects on both conversion and dissipation to reconstruct climate-driven terrestrial water storage anomalies (TWSA). Using GRACE/GRACE-FO and meteorological data, the authors generate daily, high-resolution global reconstructions from 2002 to 2021. The proposed method demonstrates significantly faster parameter convergence and superior performance compared to existing datasets in a majority of river basins and land grids. By refining the analysis of precipitation's contribution to TWS, the approach offers improved insights for separating climatic and anthropogenic impacts on water resources. The paper demonstrates substantial work, supported by extensive experimental testing. The title could be improved to more accurately capture the paper's core contribution.
- The authors could provide the rationale for not using the CSR Mascon data product in this study.
- The GLDAS-2.2 data is also forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System. Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields. The GLDAS-2.2 data are archived and distributed in NetCDF format. Thus, the authors should explain the differences between the two temperature datasets.
Minor comments:
- L50, Usage of ‘Eq’ is not defined before.
- L135-136, the citation format ‘(Lan and Wenke, 2022)’ is wrong. The same for ‘(Muñoz-Sabater et al., 2021; Muñoz Sabater, 2019)’.
- L179 and L185, abbreviations with ‘where’ at the beginning of a sentence are unnecessary.
- L680, ‘In some other basins’ to ‘In other basins’.
Citation: https://doi.org/10.5194/egusphere-2025-5991-RC1 -
AC1: 'Reply on RC1', Pu Xie, 20 Feb 2026
Response to Reviewer
We sincerely thank you for your careful review of our manuscript and for your valuable comments. Your suggestions have not only precisely identified areas that required clarification or improvement, but have also provided clear guidance for further strengthening the manuscript. We have carefully considered each comment and addressed them individually in the revised version. The primary changes in the manuscript are as follows:
- Following the reviewer's suggestion, the manuscript title has been revised to more clearly convey the main objective and contribution of the study. The new title is: "Reconstructing Climate-Driven Global Terrestrial Water Storage (2002–2021) Using a Four-Parameter Linear Recursive Model."
- To address concerns regarding the absence of CSR mascon products, we have incorporated CSR-based reconstruction results into the comparative analysis. The corresponding figures and analyses in the manuscript have been updated accordingly to provide a more complete assessment of reconstruction performance.
- To avoid ambiguity regarding temperature forcing datasets, we have added clarifications in the Methods section to explicitly distinguish GLDAS-2.2 temperature variables from ERA5-Land meteorological forcing data. In addition, new supplementary figures have been included to illustrate spatial and temporal differences between these datasets.
The point-by-point responses are provided in the attached document.
Sincerely,
Pu Xie and Shuang Yi
February 2026
-
RC2: 'Comment on egusphere-2025-5991', Anonymous Referee #2, 09 Jan 2026
The study presents a four-parameter, linear recursive model that reconstructs daily terrestrial water storage anomalies (TWSA) at 0.5° resolution for 2002–2021 using precipitation and temperature. The authors claim higher computational efficiency and better basin-scale performance than that of previous statistical reconstructions. While the work is interesting and potentially useful for data-scarce regions, my major concerns focus on (i) the lack of daily-scale validation, (ii) the arbitrary 116-basin subdivision and its global mapping strategy, and (iii) the insufficient demonstration of added value against physically based assimilation products such as GLDAS CLSM. These issues must be addressed before the paper can be considered for publication. I recommend Major Revision.
Major comments
- The model is trained on de-seasonalised and de-trended monthly TWSA from GRACE/GRACE-FO, and daily fields are produced but only monthly datasets are assessed. Although GRACE provides only monthly observations, the daily reconstruction should also be evaluated by comparison with other independent datasets such as model simulations and aridity indexes.
- In the work, TWS is reconstructed on land grids, but why do you evaluate β at the basin scale? In my view, you should not use this method to reconstruct grid-scale TWS, because the increase in TWS may be greater than precipitation due to runoff processes, which can lead to β > 1. Actually, Zhong et al. (2025) also apply a similar method to reconstruct basin-scale TWS, which can ignores the impact of runoff.
- I think you should divide the datasets into training periods and validation periods, as in machine learning, to avoid the over-fitting risk.
- Parameters a, b, c, d are claimed to be "physically meaningful" but lack independent corroboration such as ET/P partitioning or field infiltration measurements. Therefore, you should add scatter plots of a vs. observed ET/P and d vs. estimated groundwater turnover time, and discuss sign mismatches.
- As you mentioned, GLDAS CLSM (0.25°, assimilates GRACE) offers a quasi-independent reference. GLDAS provides reasonable TWS estimates. You should compare the results between the reconstruction and GLDAS, and illustrate the advantages of your method.
Minor comments
- Line 233: add the missing equation number.
- Line 333: "2.81 % of grids with NSE > 0.8" disagrees with the histogram.
- In Figures 6 and 16 (b–j), the letters (a, b, c, etc.) in panel (a) are preferable to ID numbers.
Citation: https://doi.org/10.5194/egusphere-2025-5991-RC2 -
AC2: 'Reply on RC2', Pu Xie, 20 Feb 2026
Response to Reviewers
We sincerely thank your constructive and insightful comments, which have significantly helped improve the clarity, scientific rigor, and presentation of our manuscript. We carefully considered all comments and have revised the manuscript accordingly. The primary revisions made in the manuscript are summarized as follows:- The basin subdivision strategy has been clarified and improved, including clearer documentation of data sources and revised ordering of basins in Table S1 to enhance transparency.
- Additional basin-scale comparisons with GLDAS CLSM have been included to demonstrate the added value of the proposed reconstruction method. The results have been incorporated into Section 4.2.2 of the revised manuscript, and the section has been extensively revised accordingly.
- New validations of daily-scale reconstruction results have been added through comparisons with independent datasets, now included as a new section in the manuscript. The corresponding figure has been included in the revised manuscript.
- Following the reviewer’s suggestion, we conducted an independent validation experiment by separating training and validation periods. The results have been added as a new section in the manuscript, with supporting figures included in the Supplementary Materials as Figs. S8 and S9.
- Following the reviewer’s suggestion regarding the physical interpretability of model parameters, we have substantially revised and expanded the derivation and interpretation of model parameters in the manuscript. The physical meanings of parameters a,b,c,and d are now more clearly clarified. The full mathematical derivation and supporting results have been moved to the revised manuscript, with supporting figures included in the Supplementary Materials as Figs. S10–S12.
The point-by-point responses are provided in the attached document.
Sincerely,
Pu Xie and Shuang Yi
February 2026
Peer review completion
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Pu Xie
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(5562 KB) - Metadata XML
-
Supplement
(1335 KB) - BibTeX
- EndNote
- Final revised paper
This study presents a novel linear recursive model incorporating temperature effects on both conversion and dissipation to reconstruct climate-driven terrestrial water storage anomalies (TWSA). Using GRACE/GRACE-FO and meteorological data, the authors generate daily, high-resolution global reconstructions from 2002 to 2021. The proposed method demonstrates significantly faster parameter convergence and superior performance compared to existing datasets in a majority of river basins and land grids. By refining the analysis of precipitation's contribution to TWS, the approach offers improved insights for separating climatic and anthropogenic impacts on water resources. The paper demonstrates substantial work, supported by extensive experimental testing. The title could be improved to more accurately capture the paper's core contribution.
Minor comments: