Calibrating on Downscaled Satellite Soil Moisture Data Can Improve Watershed Model Performance in Predicting Soil Moisture Variability

Asfaw, Binyam Workeye; Maksud, Siam; Fuka, Daniel R.; Collick, Amy S.; White, Robin R.; Easton, Zachary M.

doi:10.5194/egusphere-2025-5813

Preprints

https://doi.org/10.5194/egusphere-2025-5813

Preprints

04 Dec 2025

| 04 Dec 2025

Calibrating on Downscaled Satellite Soil Moisture Data Can Improve Watershed Model Performance in Predicting Soil Moisture Variability

Binyam Workeye Asfaw, Siam Maksud, Daniel R. Fuka, Amy S. Collick, Robin R. White, and Zachary M. Easton

Abstract. Watershed streamflow is often the focus of hydrological model calibration and evaluation, despite other potential objectives, including water quality management, flood protection, and agricultural management. When hydrological models are calibrated on streamflow, intermediate processes such as those affecting soil moisture are not necessarily well represented. This research evaluated the performance of downscaled and bias corrected soil moisture calibrated models against streamflow calibrated models both under single and multi-objective scenarios on field scale soil moisture estimation performance. Downscaled satellite soil moisture data and streamflow data are used to calibrate a Soil and Water Assessment Tool – Variable Source Area model initialized using topographic index classes to create hydrologic response units. In-situ soil moisture measurements at 25 locations across a 4-ha mixed-grass pasture located in southwestern Virginia were used to estimate field scale average soil moisture variability for model evaluation. Leveraging downscaled satellite soil moisture data substantially improved estimation of temporal soil moisture variability without affecting the model performance in estimating streamflow. The multi-objective calibration using streamflow and satellite soil moisture improved overall model performance both in estimating streamflow and soil moisture. A three-class topographic index hydrologic response unit definition allowed for adequate representation of saturation excess runoff process. Downscaling enabled calibration in a small 14 km² watershed using coarse satellite soil moisture data.

Received: 24 Nov 2025 – Discussion started: 04 Dec 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

24 Sep 2026

Calibration using downscaled and bias-corrected satellite soil-moisture data can improve watershed model representation of soil-moisture variability

Binyam Workeye Asfaw, Siam Maksud, Daniel R. Fuka, Amy S. Collick, Robin R. White, and Zachary M. Easton

Hydrol. Earth Syst. Sci., 30, 5999–6018, https://doi.org/10.5194/hess-30-5999-2026,https://doi.org/10.5194/hess-30-5999-2026, 2026

Short summary

Binyam Workeye Asfaw, Siam Maksud, Daniel R. Fuka, Amy S. Collick, Robin R. White, and Zachary M. Easton

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5813', Anonymous Referee #1, 06 Jan 2026

This manuscript examines the use of streamflow and downscaled and bias-corrected satellite soil moisture data for calibrating a SWAT-VSA model in a small, saturation-excess dominated watershed. The topic is relevant and timely; however, some issues need to be addressed before the manuscript can be considered for publication.

1. The Introduction does not sufficiently clarify the specific novelty of this study relative to existing literature. If the main contribution lies in calibrating hydrological models using downscaled and bias-corrected satellite soil moisture, the authors should more clearly review and position their work within existing studies on satellite soil moisture downscaling, bias correction, and reported performance improvements. Similarly, if the key contribution is the application of SWAT-VSA in saturation-excess dominated watersheds, the importance and added value of this choice should be more explicitly articulated. At present, the objectives and innovation of the study are not clearly distinguished from prior work.

2. The study is conducted in a single small watershed (~14 km²) dominated by saturation-excess runoff and relies on the SWAT-VSA model. While this choice is physically appropriate for the study site, it substantially limits the generality of the conclusions. The manuscript should more explicitly discuss the limits of applicability of the proposed approach, and the conclusions should be clearly framed as primarily applicable to small, saturation-excess runoff dominated catchments.

3. The study relies on a pretrained mlhrsm downscaling model and an empirical, precipitation-threshold-based bias correction scheme. However, the regional applicability of the pretrained downscaling model is not evaluated, and no comparison is shown between original, downscaled, and bias-corrected soil moisture. The authors should explicitly show these differences and discuss how the downscaling and bias correction steps influence the soil moisture signal used for calibration and, consequently, the model results.

4. A key concern relates to the representativeness of the soil moisture observations. The 25 soil moisture sensors are located within a 4.2 ha pasture, whereas the modeled watershed covers approximately 14 km². It is unclear whether such a limited area is sufficient for evaluating model performance at the watershed scale.

5. An additional concern is that the evaluation of soil moisture performance throughout the manuscript is based primarily on soil moisture data that have undergone both downscaling and bias correction by the authors, without independent validation. While this preprocessing may be necessary, it raises the question of how the reliability of the calibrated model can be independently assessed.
The manuscript would benefit from a clearer explanation of how the authors ensure that the improved soil moisture performance reflects genuine model skill rather than agreement with a processed target dataset. In particular, the role of independent in-situ soil moisture observations in validating the calibration results should be more explicitly discussed, and the potential circularity introduced by calibrating and evaluating against processed soil moisture data should be acknowledged.

6. In addition, the authors should clarify why downscaling to 500 m resolution is necessary if the soil moisture data are ultimately averaged to the watershed scale for calibration. It should be discussed whether this averaging undermines the advantages of using a spatially explicit, VSA-based distributed model.

7. The Discussion section could be streamlined by reducing repetitive literature comparisons and would benefit from a more critical assessment of which aspects of soil moisture and streamflow dynamics remain poorly captured. The authors should discuss whether these limitations reflect structural constraints of the SWAT model (e.g., simplified representation of unsaturated flow processes), and whether the observed performance gains justify the additional complexity introduced by downscaling, bias correction, and multi-objective calibration. A clearer distinction between statistical improvement and process-level improvement would strengthen the interpretation of the results.

Minor comments
1. Figures in the Appendix (e.g., Fig. A1 and Fig. A2) are essential for understanding the workflow and study area and should be moved to the main text.

2. The description in Section 2.2 and Fig. A2 may give the misleading impression that the study area is the full 57 km² Stroubles Creek watershed rather than the upper ~14.5 km² watershed.

3. The number of calibration iterations is reported in Section 2.5.4 (Parameter Uncertainty) but should be clearly stated in the Calibration Strategy section.

4. The sensitivity analysis period (2015–2019) differs from the calibration and evaluation periods without sufficient justification. In addition, results in Fig. A3 are shown only for 2018, which requires explanation.

5. Figure 1 and Figure 4 do not clearly convey differences among the three calibration strategies and could be improved for clarity.

Citation: https://doi.org/10.5194/egusphere-2025-5813-RC1
- AC2: 'Reply on RC1', Zachary Easton, 12 Mar 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5813/egusphere-2025-5813-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5813-AC2
RC2:
'Comment on egusphere-2025-5813', Mikolaj Piniewski, 12 Feb 2026
General assessment
The manuscript tackles a relevant and timely problem in hydrological modelling and contains a solid technical foundation. Nevertheless, major revisions are required before it can be considered for publication. In particular, clearer justification of methodological choices, improved structuring of the Results and Discussion, and a more cautious interpretation of findings—especially regarding hydrological realism and applicability to ungauged basins—are needed to support the conclusions drawn.
Major issues
Scale consistency and representativeness

A central issue throughout the manuscript concerns consistency across spatial scales and data representativeness. The study combines watershed-scale calibration, field-scale in-situ soil moisture observations, downscaled satellite products, and HRU-based model outputs, but the implications of these scale mismatches are not sufficiently addressed. In particular, the use of a very coarse soil dataset (FAO–UNESCO digital soil map) alongside high-resolution DEM and land-use data raises questions about how soil spatial variability is represented and how soil parameters should be interpreted. These issues condition the interpretation of sensitivity analysis, parameter uncertainty, and claims of improved hydrological representation and should be discussed more explicitly.
Sensitivity analysis and parameter interpretation

The sensitivity analysis underpinning parameter selection lacks sufficient clarity. It is unclear whether sensitivity was assessed with respect to streamflow, soil moisture, or both, despite the use of multiple calibration objectives. Because sensitivity rankings can differ substantially depending on the target variable, presenting a single ranking without clarification limits interpretability and affects later discussion of parameter uncertainty. In addition, some parameter ranges (e.g., AWC) appear unrealistically wide given their dependence on soil texture, raising concerns about physical plausibility and parameter compensation during calibration.
Interpretation of results: statistical improvement vs. hydrological realism

While the Results clearly demonstrate improved statistical agreement of soil moisture estimates under soil moisture and multi-objective calibration, the manuscript does not sufficiently distinguish between improved fit to processed calibration targets and improved hydrological process representation. This distinction is critical given the reliance on downscaled and bias-corrected satellite soil moisture data. The Results section also mixes quantitative findings with interpretation, contributing to redundancy with the Discussion and obscuring the main messages. Either additional diagnostics demonstrating improved hydrological realism or a more cautious framing of the reported improvements is needed.
Generality of conclusions and ungauged basin claims

Several conclusions—particularly those related to applicability in ungauged basins—extend beyond what is directly demonstrated. Although satellite soil moisture data are used in calibration, the approach relies on bias-corrected products, and the bias correction itself is informed by local in-situ measurements. As a result, no truly ungauged scenario is evaluated. These limitations should be clearly articulated and reflected in the framing of the Conclusions, with broader implications presented as potential rather than demonstrated.

Section-specific and technical comments
Introduction
The opening sentence is overly method-focused. Consider starting with the hydrological relevance of soil moisture as a key state variable.

The literature review is extensive but largely descriptive; the specific conceptual gap addressed by this study should be articulated more clearly and earlier.

Methods
Move the Study Area subsection to the beginning of the Methods to better contextualize data choices, model structure, and calibration strategy.

Section 2.1.2: Clarify the soil moisture measurement method and sampling depths.

Section 2.5.1: Specify which observed variable(s) were used in the sensitivity analysis and describe the sensitivity method and ranking metric more clearly.

Line 225: The FAO–UNESCO digital soil map (1:5,000,000) is extremely coarse relative to watershed size and the study’s focus on soil processes. Clarify how many soil classes are represented, how soil parameters were derived, and why finer-resolution datasets were not used.

Results
Results are organized by calibration approach, making comparison difficult. Reorganizing or explicitly synthesizing results around key outcome variables (e.g., streamflow, soil moisture, water balance, parameter behavior) would improve clarity.

Interpretive statements appear throughout the Results, contributing to redundancy with the Discussion.

Discussion
Much of the Discussion reiterates Results or compares performance metrics with previous studies, but offers limited deeper synthesis.

Key limitations (scale mismatch, reliance on processed soil moisture products, single watershed and model structure) should be explicitly framed as constraints on interpretation.

Conclusions
The Conclusions would benefit from a more cautious and conditioned framing. Improved statistical performance should not be equated directly with improved hydrological realism.

Claims regarding applicability to ungauged basins should be tempered, given that bias correction relies on in-situ data and no ungauged scenario is tested.

Figures and tables
Table 1: Clarify the sensitivity target variable or present separate rankings for streamflow and soil moisture; justify the AWC parameter ranges in terms of physical plausibility.

Table 2 should be moved to the Results section or Appendix.

Figure A1: (1) The workflow diagram does not fully or accurately reflect the methodology described in the text. (1) It is unclear why the “Pre-processing” box is connected to “SWAT Initialization,” as the downscaled SMAP data are used for calibration rather than model setup. (2) The link between in-situ soil moisture and satellite soil moisture data—via the bias correction step—is missing. (3) The sensitivity and uncertainty analysis steps are not represented in the workflow, despite their importance in parameter selection and interpretation. (4) Objective functions are shown only for the model evaluation stage, even though they are central to model calibration; these should either be shown consistently for both stages or removed. After addressing these issues and improving the overall visualization, this figure would be more appropriate for inclusion in the main text rather than the Appendix.

Figure A2: The map layout is too low-resolution and visually unattractive. Both the legend and the two scale bars overlap with the maps. It is not clear where the study area is located on the right-hand side of the map. Adding a land-use map would be a useful improvement.

Figure 3: Simplify by removing the legend and axis labels, retaining only values as pie labels.

Figure 5: Specify the soil depth represented in the caption.

Line-specific comments
Lines 144–146: A reference is missing for the downscaling method; clarify why this approach was selected.

Line 154: Specify the soil depth.

Line 161: Clarify what is meant by “field scale” (likely the 4.2 ha pasture).

Line 180: Clarify why river stage data are of particular interest and how they are used. I think only discharge data were used in this study.

Lines 257–259: Specify the values of parameters d and k in Eq. (1).

Line 380: Remove the reference to “Figure 1”.

Code availability
The provided GitHub link does not work and should be corrected.
Citation: https://doi.org/10.5194/egusphere-2025-5813-RC2
- AC1: 'Reply on RC2', Zachary Easton, 12 Mar 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5813/egusphere-2025-5813-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5813-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (24 Mar 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (04 May 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 May 2026) by Roberto Greco

RR by Mikolaj Piniewski (13 Jun 2026)

RR by Anonymous Referee #1 (22 Jun 2026)

Suggestions for revision or reasons for rejection

I appreciate the substantial efforts the authors have made to revise and improve the manuscript in response to previous comments. The revised version provides a more detailed explanation of the study motivation, the role of downscaled and bias-corrected satellite soil moisture, and the applicability of SWAT-VSA in a small, saturation-excess-dominated watershed. However, I still have several concerns, and I therefore suggest that the following issues be addressed before the manuscript is considered for publication.

1. The manuscript aims to evaluate whether downscaled and bias-corrected satellite soil moisture data can improve hydrological model performance. However, the current experimental design does not clearly isolate the effects of the original satellite product, downscaling, and bias correction. The calibration strategies compare streamflow-only calibration, soil-moisture-only calibration using the downscaled and bias-corrected soil moisture product, and multi-objective calibration. This design demonstrates that using the final DSM product can improve soil moisture estimation relative to streamflow-only calibration, but it does not show whether the improvement is due to the use of satellite soil moisture in general, the downscaling step, or the bias-correction procedure. To support the stated contribution more rigorously, the authors should consider adding calibration experiments using: (i) the original or Enhanced SMAP product, (ii) the downscaled but uncorrected soil moisture product, and (iii) the downscaled and bias-corrected DSM product. Such a comparison would allow the authors to explicitly quantify the added value of downscaling and bias correction for both soil moisture and streamflow performance.

2. The role of the in-situ soil moisture measurements in model evaluation and model selection needs clarification. The manuscript states that in-situ soil moisture observations were used exclusively for independent model evaluation and were not involved in preprocessing or calibration. However, the calibration strategy section indicates that final model selection among Pareto-optimal solutions additionally considered skill in reproducing field-scale in-situ soil moisture observations. If the in-situ data were used to select the final model, then they are not fully independent validation data. This potential circularity should be explicitly addressed. The authors should clarify whether in-situ soil moisture was used only after final model selection, or whether it influenced selection of the final multi-objective solution. If the latter is true, the manuscript should avoid describing the in-situ evaluation as fully independent.

3. Figure 3 is used to support the statement that the bias-corrected DSM product shows improved temporal agreement with in-situ measurements. However, Figure 3 only shows the downscaled and bias-corrected/downscaled soil moisture time series and does not include the in-situ observations. Therefore, the figure can demonstrate that the bias correction increases peak responses after precipitation events, but it cannot directly demonstrate improved agreement with in-situ soil moisture. The authors should either add in-situ soil moisture observations to Figure 3, preferably for the same period, or provide quantitative statistics comparing the original downscaled product, the bias-corrected product, and in-situ observations. Without such evidence, the claim of improved agreement with in-situ observations is not fully supported by the figure.

4. The description of the bias-correction threshold appears inconsistent. In section 2.4.2, the threshold precipitation depth is described as the equivalent value required to bring the top 120 mm of soil from field capacity to saturation. In section 2.4.3, the threshold is described as representing the depth required to fill the top 0–50 mm soil layer from field capacity to saturation. This inconsistency is important because the satellite-calibrated soil moisture layer is treated as 0–50 mm, whereas the in-situ evaluation uses a 0–120 mm composite. The authors should clarify which soil depth was used to derive the threshold and ensure that the bias-correction rationale is consistent with the soil moisture layer used for calibration.

5. The parameter sensitivity analysis is based on streamflow NSE, even though the study focuses on both streamflow and soil moisture calibration. This may bias the sensitivity ranking toward parameters that control streamflow rather than those that control soil moisture dynamics. Since soil moisture is a central calibration target in this manuscript, the authors should consider reporting sensitivity results using soil moisture metrics as well, or at least discuss how relying only on streamflow-based sensitivity may influence parameter selection and interpretation.

6. The manuscript states that multi-objective calibration improves overall model performance for both streamflow and soil moisture. However, the streamflow results suggest that multi-objective calibration mainly preserves streamflow NSE and RMSE rather than clearly improving them relative to streamflow-only calibration. In addition, the multi-objective model shows a larger negative streamflow bias during the evaluation period. The authors should moderate this claim and more clearly distinguish between maintaining streamflow performance and improving streamflow performance. A more accurate interpretation may be that multi-objective calibration substantially improves soil moisture performance while maintaining streamflow skill at a level comparable to streamflow-only calibration.

7. The workflow shown in Figure 2 is difficult to follow and should be redesigned. The authors should simplify the workflow by separating it into clearer blocks.

8. The water-balance and parameter-behavior analysis is useful, but its purpose should be stated more explicitly. This section appears intended to evaluate whether improvements in statistical model performance correspond to physically realistic hydrologic behavior, or whether they result from compensatory parameter changes. The authors should make this purpose clearer at the beginning of the section.

9. There appears to be a unit error in Line 474. The manuscript reports an RMSE value for streamflow during the evaluation period using units of m³ m⁻³, which are soil moisture units. This should be corrected.

Hide

ED: Publish subject to revisions (further review by editor and referees) (23 Jun 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (04 Aug 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (17 Aug 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (17 Aug 2026) Author's response Manuscript

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5813', Anonymous Referee #1, 06 Jan 2026

This manuscript examines the use of streamflow and downscaled and bias-corrected satellite soil moisture data for calibrating a SWAT-VSA model in a small, saturation-excess dominated watershed. The topic is relevant and timely; however, some issues need to be addressed before the manuscript can be considered for publication.

1. The Introduction does not sufficiently clarify the specific novelty of this study relative to existing literature. If the main contribution lies in calibrating hydrological models using downscaled and bias-corrected satellite soil moisture, the authors should more clearly review and position their work within existing studies on satellite soil moisture downscaling, bias correction, and reported performance improvements. Similarly, if the key contribution is the application of SWAT-VSA in saturation-excess dominated watersheds, the importance and added value of this choice should be more explicitly articulated. At present, the objectives and innovation of the study are not clearly distinguished from prior work.

2. The study is conducted in a single small watershed (~14 km²) dominated by saturation-excess runoff and relies on the SWAT-VSA model. While this choice is physically appropriate for the study site, it substantially limits the generality of the conclusions. The manuscript should more explicitly discuss the limits of applicability of the proposed approach, and the conclusions should be clearly framed as primarily applicable to small, saturation-excess runoff dominated catchments.

3. The study relies on a pretrained mlhrsm downscaling model and an empirical, precipitation-threshold-based bias correction scheme. However, the regional applicability of the pretrained downscaling model is not evaluated, and no comparison is shown between original, downscaled, and bias-corrected soil moisture. The authors should explicitly show these differences and discuss how the downscaling and bias correction steps influence the soil moisture signal used for calibration and, consequently, the model results.

4. A key concern relates to the representativeness of the soil moisture observations. The 25 soil moisture sensors are located within a 4.2 ha pasture, whereas the modeled watershed covers approximately 14 km². It is unclear whether such a limited area is sufficient for evaluating model performance at the watershed scale.

5. An additional concern is that the evaluation of soil moisture performance throughout the manuscript is based primarily on soil moisture data that have undergone both downscaling and bias correction by the authors, without independent validation. While this preprocessing may be necessary, it raises the question of how the reliability of the calibrated model can be independently assessed.
The manuscript would benefit from a clearer explanation of how the authors ensure that the improved soil moisture performance reflects genuine model skill rather than agreement with a processed target dataset. In particular, the role of independent in-situ soil moisture observations in validating the calibration results should be more explicitly discussed, and the potential circularity introduced by calibrating and evaluating against processed soil moisture data should be acknowledged.

6. In addition, the authors should clarify why downscaling to 500 m resolution is necessary if the soil moisture data are ultimately averaged to the watershed scale for calibration. It should be discussed whether this averaging undermines the advantages of using a spatially explicit, VSA-based distributed model.

7. The Discussion section could be streamlined by reducing repetitive literature comparisons and would benefit from a more critical assessment of which aspects of soil moisture and streamflow dynamics remain poorly captured. The authors should discuss whether these limitations reflect structural constraints of the SWAT model (e.g., simplified representation of unsaturated flow processes), and whether the observed performance gains justify the additional complexity introduced by downscaling, bias correction, and multi-objective calibration. A clearer distinction between statistical improvement and process-level improvement would strengthen the interpretation of the results.

Minor comments
1. Figures in the Appendix (e.g., Fig. A1 and Fig. A2) are essential for understanding the workflow and study area and should be moved to the main text.

2. The description in Section 2.2 and Fig. A2 may give the misleading impression that the study area is the full 57 km² Stroubles Creek watershed rather than the upper ~14.5 km² watershed.

3. The number of calibration iterations is reported in Section 2.5.4 (Parameter Uncertainty) but should be clearly stated in the Calibration Strategy section.

4. The sensitivity analysis period (2015–2019) differs from the calibration and evaluation periods without sufficient justification. In addition, results in Fig. A3 are shown only for 2018, which requires explanation.

5. Figure 1 and Figure 4 do not clearly convey differences among the three calibration strategies and could be improved for clarity.

Citation: https://doi.org/10.5194/egusphere-2025-5813-RC1
- AC2: 'Reply on RC1', Zachary Easton, 12 Mar 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5813/egusphere-2025-5813-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5813-AC2
RC2:
'Comment on egusphere-2025-5813', Mikolaj Piniewski, 12 Feb 2026
General assessment
The manuscript tackles a relevant and timely problem in hydrological modelling and contains a solid technical foundation. Nevertheless, major revisions are required before it can be considered for publication. In particular, clearer justification of methodological choices, improved structuring of the Results and Discussion, and a more cautious interpretation of findings—especially regarding hydrological realism and applicability to ungauged basins—are needed to support the conclusions drawn.
Major issues
Scale consistency and representativeness

A central issue throughout the manuscript concerns consistency across spatial scales and data representativeness. The study combines watershed-scale calibration, field-scale in-situ soil moisture observations, downscaled satellite products, and HRU-based model outputs, but the implications of these scale mismatches are not sufficiently addressed. In particular, the use of a very coarse soil dataset (FAO–UNESCO digital soil map) alongside high-resolution DEM and land-use data raises questions about how soil spatial variability is represented and how soil parameters should be interpreted. These issues condition the interpretation of sensitivity analysis, parameter uncertainty, and claims of improved hydrological representation and should be discussed more explicitly.
Sensitivity analysis and parameter interpretation

The sensitivity analysis underpinning parameter selection lacks sufficient clarity. It is unclear whether sensitivity was assessed with respect to streamflow, soil moisture, or both, despite the use of multiple calibration objectives. Because sensitivity rankings can differ substantially depending on the target variable, presenting a single ranking without clarification limits interpretability and affects later discussion of parameter uncertainty. In addition, some parameter ranges (e.g., AWC) appear unrealistically wide given their dependence on soil texture, raising concerns about physical plausibility and parameter compensation during calibration.
Interpretation of results: statistical improvement vs. hydrological realism

While the Results clearly demonstrate improved statistical agreement of soil moisture estimates under soil moisture and multi-objective calibration, the manuscript does not sufficiently distinguish between improved fit to processed calibration targets and improved hydrological process representation. This distinction is critical given the reliance on downscaled and bias-corrected satellite soil moisture data. The Results section also mixes quantitative findings with interpretation, contributing to redundancy with the Discussion and obscuring the main messages. Either additional diagnostics demonstrating improved hydrological realism or a more cautious framing of the reported improvements is needed.
Generality of conclusions and ungauged basin claims

Several conclusions—particularly those related to applicability in ungauged basins—extend beyond what is directly demonstrated. Although satellite soil moisture data are used in calibration, the approach relies on bias-corrected products, and the bias correction itself is informed by local in-situ measurements. As a result, no truly ungauged scenario is evaluated. These limitations should be clearly articulated and reflected in the framing of the Conclusions, with broader implications presented as potential rather than demonstrated.

Section-specific and technical comments
Introduction
The opening sentence is overly method-focused. Consider starting with the hydrological relevance of soil moisture as a key state variable.

The literature review is extensive but largely descriptive; the specific conceptual gap addressed by this study should be articulated more clearly and earlier.

Methods
Move the Study Area subsection to the beginning of the Methods to better contextualize data choices, model structure, and calibration strategy.

Section 2.1.2: Clarify the soil moisture measurement method and sampling depths.

Section 2.5.1: Specify which observed variable(s) were used in the sensitivity analysis and describe the sensitivity method and ranking metric more clearly.

Line 225: The FAO–UNESCO digital soil map (1:5,000,000) is extremely coarse relative to watershed size and the study’s focus on soil processes. Clarify how many soil classes are represented, how soil parameters were derived, and why finer-resolution datasets were not used.

Results
Results are organized by calibration approach, making comparison difficult. Reorganizing or explicitly synthesizing results around key outcome variables (e.g., streamflow, soil moisture, water balance, parameter behavior) would improve clarity.

Interpretive statements appear throughout the Results, contributing to redundancy with the Discussion.

Discussion
Much of the Discussion reiterates Results or compares performance metrics with previous studies, but offers limited deeper synthesis.

Key limitations (scale mismatch, reliance on processed soil moisture products, single watershed and model structure) should be explicitly framed as constraints on interpretation.

Conclusions
The Conclusions would benefit from a more cautious and conditioned framing. Improved statistical performance should not be equated directly with improved hydrological realism.

Claims regarding applicability to ungauged basins should be tempered, given that bias correction relies on in-situ data and no ungauged scenario is tested.

Figures and tables
Table 1: Clarify the sensitivity target variable or present separate rankings for streamflow and soil moisture; justify the AWC parameter ranges in terms of physical plausibility.

Table 2 should be moved to the Results section or Appendix.

Figure A1: (1) The workflow diagram does not fully or accurately reflect the methodology described in the text. (1) It is unclear why the “Pre-processing” box is connected to “SWAT Initialization,” as the downscaled SMAP data are used for calibration rather than model setup. (2) The link between in-situ soil moisture and satellite soil moisture data—via the bias correction step—is missing. (3) The sensitivity and uncertainty analysis steps are not represented in the workflow, despite their importance in parameter selection and interpretation. (4) Objective functions are shown only for the model evaluation stage, even though they are central to model calibration; these should either be shown consistently for both stages or removed. After addressing these issues and improving the overall visualization, this figure would be more appropriate for inclusion in the main text rather than the Appendix.

Figure A2: The map layout is too low-resolution and visually unattractive. Both the legend and the two scale bars overlap with the maps. It is not clear where the study area is located on the right-hand side of the map. Adding a land-use map would be a useful improvement.

Figure 3: Simplify by removing the legend and axis labels, retaining only values as pie labels.

Figure 5: Specify the soil depth represented in the caption.

Line-specific comments
Lines 144–146: A reference is missing for the downscaling method; clarify why this approach was selected.

Line 154: Specify the soil depth.

Line 161: Clarify what is meant by “field scale” (likely the 4.2 ha pasture).

Line 180: Clarify why river stage data are of particular interest and how they are used. I think only discharge data were used in this study.

Lines 257–259: Specify the values of parameters d and k in Eq. (1).

Line 380: Remove the reference to “Figure 1”.

Code availability
The provided GitHub link does not work and should be corrected.
Citation: https://doi.org/10.5194/egusphere-2025-5813-RC2
- AC1: 'Reply on RC2', Zachary Easton, 12 Mar 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5813/egusphere-2025-5813-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5813-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (24 Mar 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (04 May 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 May 2026) by Roberto Greco

RR by Mikolaj Piniewski (13 Jun 2026)

RR by Anonymous Referee #1 (22 Jun 2026)

Suggestions for revision or reasons for rejection

Hide

ED: Publish subject to revisions (further review by editor and referees) (23 Jun 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (04 Aug 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (17 Aug 2026) by Roberto Greco

AR by Zachary Easton on behalf of the Authors (17 Aug 2026) Author's response Manuscript

Journal article(s) based on this preprint

24 Sep 2026

Calibration using downscaled and bias-corrected satellite soil-moisture data can improve watershed model representation of soil-moisture variability

Binyam Workeye Asfaw, Siam Maksud, Daniel R. Fuka, Amy S. Collick, Robin R. White, and Zachary M. Easton

Hydrol. Earth Syst. Sci., 30, 5999–6018, https://doi.org/10.5194/hess-30-5999-2026,https://doi.org/10.5194/hess-30-5999-2026, 2026

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Binyam Workeye Asfaw, Siam Maksud, Daniel R. Fuka, Amy S. Collick, Robin R. White, and Zachary M. Easton

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Month	HTML	PDF	XML	Total
Dec 2025	511	221	74	806
Jan 2026	445	75	30	550
Feb 2026	209	69	31	309
Mar 2026	222	60	30	312
Apr 2026	40	8	0	48
May 2026	46	9	3	58
Jun 2026	7	2	2	11
Jul 2026	5	4	2	11
Aug 2026	10	5	1	16
Sep 2026	45	18	9	72
Oct 2026	37	15	3	55

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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.

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We tested whether calibrating watershed models with downscaled satellite soil moisture improves predictions of soil moisture and streamflow. Using machine learning to refine satellite data, we found that incorporating soil moisture in calibration enhanced both soil moisture and streamflow estimates, reducing uncertainty. This approach can support water management in small or ungauged basins worldwide.


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