Comment: The comparison is mainly implemented for different climate zones. However, as we known, GWS may dominate TWS in many regions, thus the comparison between the models and GRACE is better to be divided into the models with and without groundwater simulations.

Reply: Thank you for your valuable feedback. We appreciate your input and have taken your suggestion into consideration. As you rightly pointed out, groundwater storage (GWS) can indeed dominate total water storage (TWS) in many regions, like basins which can significantly impact the comparison between model simulations and GRACE data.

In response to your suggestion, we have divided our comparison over five river basins with major underlying aquifers (Congo, Amazon, Orinoco, Ganges-Brahmaputra, and California) into models with and without groundwater simulations. Please see Figure 5 and explanations in the Supplement. The comparison indicates that including a groundwater compartment in GHM/LSM models can apparently improve the presentation of water storage dynamics.

Comment: More investigations on the water cycle (e.g., P, ET and Q) and storage (e.g., GWS, SMS) compartments will be helpful for a better understanding on the reasons why GRACE and models differ from each other in the aspects of phase and amplitude.

Response: We sincerely appreciate your valuable suggestion regarding additional investigations into the water cycle components and storage compartments. Your comments are certainly insightful.

While we understand the potential benefits of delving deeper into parameters such as precipitation (P), evapotranspiration (ET), and streamflow (Q), as well as storage elements like groundwater storage (GWS) and soil moisture storage (SMS), we would like to express our perspective on this matter.

Considering the scope and objectives of the current study, we aimed to focus on specific aspects, i.e. seasonal total water storage dynamics, to maintain clarity in the analysis. Expanding the investigation to include a detailed examination of all these water cycle variables might introduce a level of complexity that could potentially divert attention from the primary research questions. However, we acknowledge that future studies dedicated specifically to the water cycle components and storage compartments could certainly enhance our understanding of the discrepancies between GRACE and the models.

While we aligned with the significance of your suggestion, we believe it would be best suited for a separate investigation, perhaps as an extension of this work or as an independent study. Your guidance is immensely valuable, and we will definitely consider your input for future research endeavors.

Comment: Figure 1-4: what the map colors mean?

Response: We provided the description of base map in captions of Figures 1-4. Base map represents KGClim Climate Zones classification.

Comment: Table 1: Statistical information on the phase and amplitude derived from models and GRACE needs to be provided as they are the key information. It can be included in Table 1 or summarized in another table.

Response: The statistical analysis on amplitude derived from models and GRACE is provided in Table 2 and phase analysis is provided in Figure 5.

Comment: Line 83: Full names are need for the abbreviation for R1 and R2 at its first time.

Response: Full names of R1 and R2 are provided; please see the Supplement (Water Resource Reanalysis tier-1 (R1) and tier-2 (R2)).

We have included our response to comments from Referee #2 in the supplement. 

Bibi et al. evaluated the reliability of 13 global models using the GRACE TWS for 29 river basins. They conclude that the modeled TWS does not compare well with the GRACE TWS. Authors analyzed amplitude and phase-difference and performed the comparisons based on 5 climate zones (Polar, boreal, temperate, arid, and tropical), 2 set of hydrological model types (LSM and GHM), and 2 sets of R1 and R2 model types. The authors find that R2 models have better correlations with GRACE than R1 models. Though this study provides new insights into the future improvement of large-scale hydrological models, there are some major concerns in this study. By addressing these concerns, the manuscript will better align with the standards of the HESS and provide a more compelling and novel contribution to the field. Please find my detailed review below-

Comment: Line 22- It would be easier for readers to understand if the meaning of the term ‘R2 models’ is provided here.

R1 and R2 models are Water Resource Reanalysis tier-1 and tier-2 products which provide a large set of LSMs and GHMs.

R1: 0.5° forced with ERA-Interim data (WFDEI) meteorological reanalysis dataset

R2: 0.25° forced with Multi-Source Weighted Ensemble Precipitation (MSWEP) dataset

Response: Thank you for your valuable comment regarding the terminology used in our manuscript. We appreciate your suggestion to clarify the meaning of 'R1 and R2’ models' for the benefit of our readers. In response to your comment, we have added a brief explanation of the terms in the manuscript to improve clarity. We hope that this addition will enhance the understanding of our work for all readers.

Comment: Lines 91-102: The authors have used only JPL-M and CSR-M solutions, why not GSFC Mascons as well? The authors did not provide the reason behind using linear interpolation of GRACE TWS data.

Response: Thank you for your valuable feedback, we appreciate your input and would like to address your comments:

In our study, we used the GRACE JPL-M and CSR-M solutions for several reasons. These two solutions are widely recognized and have been extensively validated in the literature. JPL-M and CSR-M are among the most commonly used GRACE solutions for global terrestrial water storage (TWS) estimates due to their accuracy. However, we acknowledge that incorporating the GSFC Mascons solution in future research could provide additional insights, and we will consider this for future work.

We used linear interpolation for GRACE TWS data primarily for data consistency and to align the GRACE TWS data with the temporal resolution of other datasets used in our study. Linear interpolation is a commonly used technique to estimate TWS values for time periods between GRACE satellite overpasses. While other interpolation methods, such as spline interpolation, can be used, we chose linear interpolation for simplicity and because it is a standard approach in the GRACE research community. We agree that providing a brief explanation of this choice in the manuscript would be beneficial for readers, and we will make sure to include such clarification in the revised manuscript.

Thank you for your thoughtful comments, and we will make the necessary revisions to address these concerns in our manuscript.

Comment: Lines 109-110: Please correct the sentence.

Response: Thank you for pointing out the issue with the sentence in lines 109-110 of our manuscript. We apologize for any confusion. We corrected the sentence to ensure it is clear and accurate in the revised manuscript. Your feedback is greatly appreciated.

Comment: Why only amplitude and phase of seasonal cycle of TWS was checked in this study? Why not the trend in the TWS data?

Response: Thank you for your valuable comment regarding the analysis of GRACE TWS data in our study. We appreciate your feedback, and we'd like to respond to your question:

In our study, we focused on analyzing the amplitude and phase of the seasonal cycle of TWS for several reasons. The primary objective of our research was to assess the seasonal variability of terrestrial water storage (TWS) in a specific region. Seasonal changes in TWS are of significant importance for various applications, such as hydrological modeling, agriculture, and water resource management. Therefore, our study aimed to understand how well the GRACE and models captured these seasonal variations. The amplitude and phase of the seasonal cycle provide crucial information about the timing and magnitude of TWS changes, which are particularly relevant for addressing certain research questions.

Our research objectives were specifically tailored to examine the seasonal patterns of TWS in the study area during a particular time frame. While assessing trends in TWS is indeed important for different research questions, it may require a separate analysis and may involve addressing different objectives. We decided to focus on the seasonal cycle for the sake of clarity and to maintain a concise scope within the context of our study.

However, we acknowledge that the analysis of trends in TWS data is a valuable avenue of research, and it can provide insights into long-term hydrological changes. We hope this explanation clarifies our choice to focus on the amplitude and phase of the seasonal cycle of TWS in this particular study. If you have any further questions or suggestions, please feel free to let us know. Your feedback is greatly appreciated.

Lines 138-139: Why only GRACE CSR_M seasonal cycle was used to validate the model results? As indicated above why GRACE JPL-M data was not used? Or the mean of the two datasets?

Response: We appreciate your question regarding the choice of using only the GRACE CSR_M seasonal cycle for validating model results. We would like to clarify our rationale for this decision. The primary reason for selecting the GRACE CSR_M dataset for validation is its well-established and widely recognized record of terrestrial water storage changes. However, we acknowledge the importance of discussing the choice and providing a more comprehensive explanation in our manuscript.

CSR_M has been widely used as a benchmark dataset in various hydrological and Earth science studies, making it a reliable reference for model validation. Researchers often use CSR_M to assess the performance of hydrological models due to its well-documented accuracy and reliability. While it is valuable to compare our model results with multiple GRACE datasets, focusing on CSR_M alone in the initial validation simplifies the validation process and allows us to clearly understand the model's behavior against a well-established dataset. Presenting results from a single validation dataset initially provides clarity in our manuscript, enabling readers to focus on the model's performance against CSR_M specifically. This approach simplifies the presentation and interpretation of validation results.

In our revised manuscript, we will include a more comprehensive discussion of the choice to use CSR_M for validation, emphasizing its strengths and limitations.

Thank you for your valuable feedback, which will help improve the clarity and comprehensiveness of our research.

Comment: Line 375-: The causes of discrepancies in seasonal amplitudes and phase between models and GRACE TWSC provided in section 4.1 are without any reference. There is no analysis shown to backup the claim. For example, how do the authors know that Model Parameterization is causing the difference in GRACE and model TWS data without doing any analysis and citing any literature? If it is well known then what is the contribution of this study?

Response: Thank you for your valuable feedback. We appreciate your suggestion to delve deeper into the causes of amplitude and phase differences in Section 4.1. In response to this comment, we expanded the discussion in Section 4.1 and provided a more comprehensive analysis of the factors contributing to the observed amplitude and phase differences with reference. For instance, we specifically discussed the contrasting performance difference in simulating groundwater storage changes between GHM/LSM models with and without a groundwater compartment.

Groundwater storage (GWS) can indeed dominate total water storage (TWS) in many regions, like basins with major underlying aquifers which can significantly impact the comparison between model simulations and GRACE data. In response to your comment, we have also divided our comparison over five river basins with major underlying aquifers (Congo, Amazon, Orinoco, Ganges-Brahmaputra, and California) into models with and without groundwater simulations. The comparison indicates that including a groundwater compartment in GHM/LSM models can apparently improve the presentation of water storage dynamics. These have been included in the revised manuscript.

Comment: Scanlon et al., (2018) already compared the model TWS trends against the GRACE TWS datasets. What are the novel contributions here? Please state them clearly.

 Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., Van Beek, L. P., ... & Bierkens, M. F. (2018). Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences, 115(6), E1080-E1089.

Response: Thank you for your thoughtful review comment. We appreciate your engagement with our work and the opportunity to clarify the novel contributions of our study in “Benchmarking multimodel terrestrial water storage (TWS) seasonal cycles against GRACE observations over major global river basins”, especially in light of the previous work by Scanlon et al. (2018).

While it is true that Scanlon et al. (2018) compared model TWS trends against GRACE TWS datasets, our study focuses on a distinct aspect of TWS analysis, namely, the seasonal cycle. We acknowledge the prior work of Scanlon et al., (2018), which primarily delved into decadal trends in TWS and highlighted discrepancies between models and GRACE observations over extended time periods. In contrast, our study shifts the focus to the seasonal variations in TWS, with the following novel contributions:

We specifically investigate the seasonal dynamics of TWS across major global river basins. Instead of examining long-term trends, our study provides a detailed examination of how TWS varies throughout the year. The phase difference between GRACE and the modeled TWS seasonal cycle was not generally covered in previous studies.

We employ 13 models, each with its own set of assumptions and parameters, to assess how well they capture the observed seasonal TWS variations. This approach enables us to assess the performance of different models in representing seasonal patterns, which can have important implications for water resource management, flood forecasting, and ecosystem health.

While Scanlon et al. (2018) did use GRACE data as a reference, our study explicitly benchmarks the seasonal TWS cycles produced by various hydrological models against GRACE observations. By doing so, we assess how well these models capture the seasonal dynamics observed from space, which can reveal model strengths and weaknesses in representing short-term hydrological processes.

In summary, our study takes a different angle in the assessment of TWS by focusing on seasonal variations and conducting a comprehensive benchmarking exercise using multiple models against GRACE observations. This approach offers valuable insights into the performance of hydrological models in simulating short-term TWS dynamics, providing critical information for applications such as water resource management, drought monitoring, and flood prediction.