the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Calibrating on Downscaled Satellite Soil Moisture Data Can Improve Watershed Model Performance in Predicting Soil Moisture Variability
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.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5813', Anonymous Referee #1, 06 Jan 2026
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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
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- 1
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.