This paper reviews data assimilation techniques and applications that combine GRACE water storage data with hydrological modeling to improve estimation of water storages and fluxes. The paper is of interest to the HESS readership and generally informative and well written. Overall, I enjoyed reading the paper, but I think there is room for increasing impact and accessibility of the work. My comments are detailed below.

1. Contribution of the paper: the introduction asserts that "a thorough synthesis of existing studies is not yet available" (line 119). There have been previous review papers on GRACE data assimilation, so this statement needs clarification and the specific contribution of this review paper needs to be articulated better.

2. Structure of the paper: the paper could benefit from a more systematic structure. For example, the authors could start by introducing the three main components of a GRACE DA system (observation model and errors, hydrological model and errors, DA algorithm and setup) and then systematically present previous work, best practices, open problems, and future directions for those three components. I mention these three components, because that is what the conclusions section ("synthesis" section 7) uses. The other sections of the paper do not really follow this pattern (or at least less obviously so). A more consistent structure could help readability and coherence.

3. It's interesting that a paper on data assimilation manages to avoid any mathematical equations. On the one hand, this makes the material readable, but on the other hand it can also make things less precise/concrete. For example, section 3.5 introduces "innovations" and "increments" without defining these in an equation. The meaning of these terms is described on lines 458-460, but for readers not familiar with data assimilation the connection between them may remain a bit vague without an equation. Do the authors think this is a problem?

4. Observation errors:

4a. The use of grid scaling factors to restore some of the signal in GRACE data is mentioned on lines 180-181: is any advancement or alternative approach needed here since these scaling factors are derived from hydrological models and therefore contaminated by various errors (as also acknowledged on lines 224-225)?

4b. Line 240: why are the relationships nonlinear? It would help to specify which variables we are talking about here. For example, the relations between fine-scaled storages and fluxes (precipitation, ET...) and large-scale storage are actually linear, since both the water balance and spatial averaging are linear operations.

4c. Line 259, "DA methods take care of the horizontal and vertical downscaling by design": what if the modeling domain does not fully cover all GRACE 'grid' cells that overlap with the modeling domain? 

4d. Line 281-282, "the trend of the GRACE/-FO observations is kept to correct missing trends in the model". To what extent is the trend in GRACE observations also subject to error (e.g. different GRACE products showing different trends)? 

4e. Line 393, "uniform and uncorrelated errors": I suppose "uniform" means "spatially uniform"? Would be good to also specify which probability distribution is used for the random errors, e.g. Gaussian.

4f. Section 3.3: this section discusses how random errors in GRACE observations are modeled, separate from systematic errors (bias) discussed in section 2.5. However, how the two are modeled (noise and bias) is linked, i.e. a modeling choice in one affects the other, so I wonder why the two are discussed separately.

5. Forecast errors:

5a. More thorough and critical review could be useful here. For example, a table that summarizes how forecast errors are computed in different GRACE DA studies (e.g. which variables are perturbed, by how much, and how; whether there is accounting for spatial/temporal correlation in forcing...). This can lead to identifying gaps and give pointers for future studies. Section 5.3 does identify some open issues with forecast errors, but it's not clear how these should be tackled. E.g. how can the suggestions in section 6.1 help with the issue of bias due to nonlinearity mentioned in section 5.3?

5b. It seems generating the forecast ensemble is mostly done offline, e.g. the authors mention sensitivity analysis. Are there opportunities for calibrating forecast uncertainties as part of the DA system, i.e. in an automated fashion? 

6. DA algorithm and setup:

6a. The paper would benefit from more clearly discussing the relation between the intended goal of the DA application and how DA is implemented. The first sentence of the abstract suggests that the main goal here is reanalysis, i.e. create consistent historical datasets of water storage and fluxes that incorporate information from GRACE. However, it seems most of the paper discusses studies and implementations that are more related to operational DA (for use in e.g. early drought warning systems), as evidenced by the focus on filtering implementations (or smoothing implementations that only look at the last month) where DA is used to update initial storages for the next forecast. For reanalysis purposes it seems more appropriate to use smoothing implementations that make use of the entire historical record. Do the authors agree with this? If so, it would be helpful to include papers that use smoothing (not just last month) to assimilate GRACE data. 

6b. Likewise, when the aim is in generating consistent estimates of the various water storages and fluxes, it is interesting that the paper reports (line 669) that most DA studies violate the water balance (which clearly introduces inconsistency in the estimates). I guess this relates to which variables are being updated by the DA system. Most DA studies cited in the paper only update the storages, but there are various studies that use Kalman filtering and smoothing techniques to update other variables of the water balance as well with the aim of maintaining a closing water balance. 

6c. Line 357, "the EnKF and EnKS are optimal and unbiased only when assuming Gaussian errors": these algorithms are 'optimal' when you assume the errors are Gaussian or when that assumption is correct? 

7. Abstract: would benefit from a rewrite, as it currently reads more like an introduction and does not contain concrete information on the findings ("best practices and future directions" as promised by the title of the paper).

8. Conclusions: several open issues identified in the text don't find their way into the conclusions.

9. Edits: (line 30) at the other hand --> on the other hand, (line 381) weighing --> weighting, (line 247) GARCE --> GRACE.

General comment:  

In this manuscript, the authors provide a comprehensive review of current practices and future directions in assimilating terrestrial water storage (TWS) data from the GRACE and GRACE-FO missions into hydrological and land surface models. It draws on approximately 200 references to highlight advancements in frameworks like the Ensemble Kalman Filter (EnKF) and its variants, while addressing practical issues such as scale mismatches, error correlations, and applications in drought monitoring and climate trend analysis. The emphasis on geophysical corrections (e.g., glacial isostatic adjustment) and the integration of multi-sensor data (e.g., with SMAP or SWOT) provides a cohesive narrative that bridges theoretical DA with operational hydrology. The manuscript excels in its comprehensive scope and accessibility, making it a valuable resource for early-career researchers and practitioners seeking an entry point into GRACE/-FO DA. It includes detailed tables (e.g., Table 1: models that evaluated TWS; Table 2: DA frameworks) and illustrative figures (e.g., Figure 1: TWS components scheme; Figure 3: statistics from evaluations made by models).

The manuscript is timely, given the impending launch of missions like GRACE-C (~2030) or NGGM and advancements in Earth System Modeling (ESM) frameworks. In general, this work is worth publishing in the Hydrology and Earth System Sciences journal, as it fills a gap in synthesizing post-GRACE-FO literature and could serve as a foundational reference for advancing standardized practices in hydrological DA, however some improvements are necessary, including deeper critical analysis and enhanced didactic elements. Some suggestions for revisions are included below and these points do not detract from its value; rather, they could enhance its rigor and relevance.

Moderate comments:

While the manuscript is a solid synthesis, it requires moderate revisions to elevate its critical depth and utility. As a review paper, it should compile and critically evaluate the literature, identifying inconsistencies and unresolved debates. At present, the discussion is somewhat descriptive, with limited critique of methodological limitations or comparative assessments. To ensure the article meets the standards for publication, the authors must address the following critical points:

1. Enhance your critical evaluation and gap analysis. In order to avoid a mere listing of works, I suggest to add a dedicated sub-section in Section 5 to quantify gaps. One option would be to use meta-analysis of DA performance metrics from recent studies (2020-2025).
2. Improve validation and uncertainty discussions in Section 4, since it lacks depth in metrics; mandate inclusion of quantitative benchmarks (e.g., Triple Collocation Analysis) and error propagation models. I suggest, for instance, to address how non-Gaussianity affects DA reliability, proposing solutions to make recommendations more actionable.
3. Add didactic elements to enhance educational value, incorporate summary equations for core DA techniques in Section 3. I suggest adding schematic diagrams, such as a flowchart comparing sequential vs. smoothing DA methods, or a matrix illustrating error sources in GRACE processing.

Specific comments (Line-by-line comments):

Abstract:

• 1-16: The abstract is concise but could better emphasize the synthesis's novelty (e.g., "This review synthesizes over “n” studies to propose unified best practices..."). Add a sentence on key gaps identified, such as non-Gaussian challenges.

Introduction:

• Clarify the distinction between GRACE and GRACE-FO data continuity; reference Vishwakarma et al. (2021) for gap-filling techniques. I suggest to add a simple timeline figure of GRACE missions to make the historical context more didactic.

Sections 2 to 7:

• Sub-section 2.3: Expand on geophysical corrections with an equation for leakage error correction.
• Sub-section 2.4: Include a recent reference (Gerdener et al., 2024) on GRACE/-FO data uncertainties. Propose a schematic diagram illustrating the GRACE processing chain from raw measurements to TWS anomalies.
• Sub-section 2.5: Although the article is a review paper on the application of data assimilation techniques, mainly from the Kalman filter family, I recommend at least adding the basic EnKF equations for accessibility.
• Sub-section 3.2: I strongly suggest a table or matrix comparing DA methods' computational costs and assumptions.
• Sub-section 6.1: Strengthen ML discussion with a hybrid DA-ML schematic (e.g., EnKF with LSTM for error modeling).

References and General:

Ensure all citations are up-to-date, specially from 2020-2025. The manuscript could benefit from an appendix with a glossary of DA terms for non-experts.