Optimizing Spatial Discretization According to Input Data in the Soil and Water Assessment Tool: A Case Study in a Coastal Mediterranean Watershed
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsReview for water-3365761:
The manuscript is valuable, and the materials presented in the paper raise some important issues that may help researchers consider them in future investigations. However, after considering all recommendations in the following, the manuscript could be considered for publication in the journal as a technical paper:
(1) However, the manuscript, in its present form, contains several weaknesses. Appropriate revisions to the following points should be undertaken to justify the recommendation for publication. (2) For readers to quickly catch your contribution, it would be better to highlight major difficulties and challenges, and your original achievements to overcome them, in a clearer way in the abstract and introduction. (3) It is mentioned that spatial discretization in hydrological models has a strong impact on computation times and its influence on model performance is investigated using the SWAT model. What are other feasible alternatives? What are the advantages of adopting this model over others in this case? How will this affect the results? The authors should provide more details on this. (4) The introduction is poorly written and it does not properly refer to previously published studies. The authors need to carefully review the published literature, identify the gaps in the literature, and propose their approach to fill the gap. (5) It is mentioned that sixty-eight SWAT models were created using various soil and land use datasets and 17 discretization configurations, evaluated from 2001-2021 with the Kling- Gupta efficiency (KGE) metric. What are other feasible alternatives? What are the advantages of adopting these models and metrics over others in this case? How will this affect the results? The authors should provide more details on this. (6) The manuscript could be cited on recent literature about the simulation of streameflow with SWAT or other hydrological models, as follows:
- “Sensitivity analysis of streamflow parameters with SWAT calibrated by NCEP CFSR and future runoff assessment with developed Monte Carlo Model”
- “Investigating the performance of data mining, lumped, and distributed models in runoff projected under climate change”
(7) Much more explanations and interpretations should be added for the results, which are not enough. (8) It is suggested to compare the results of the present study with previous studies and analyze their results completely. (9) The discussion section in the present form is relatively weak and should be strengthened with more details and justifications. (10) In the conclusion section, the limitations of this study, suggested improvements of this work and future directions should be highlighted.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study quantifies how spatial discretization (number of sub-basins and of hydrological response units (HRUs)) affects the SWAT model’s performance at simulating daily streamflow, and whether this effect depends on the choice of soil and land use input datasets, by employing 68 SWAT models created using various soil and land use datasets and discretization configurations from 2001-2021 with KGE. This study suggested that minimizing HRUs may improve both the accuracy of streamflow simulations and the computational efficiency of the SWAT model. The findings in this study can provide useful information for SWAT model applications in similar watersheds subjected to extreme and rapid floods.
Although this is a good manuscript, there are still the following issues for improvement:
1. Model performance evaluation was employed daily observed and simulated streamflow or not? Please specify in Section 2.6.
2. There are a lot of Erreur ! Source du renvoi introuvable. ? Please troubleshoot similar errors and correct them in the full text.
3. Line 426-428 mentioned that Compared to the other input sets, no HRU threshold is clearly identified for the moderate-low cross-resolution input sets using DSMW and CLC (Figure 8c), probably because this threshold is below the maximum number of HRUs considered in the present study, are you sure? I consider that it might be because this threshold exceeds the maximum number of HRUs considered in the present study. Please verify it.
4. Line 498 Section 0 may be a clerical error, please verify and correct it.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsQ1. In the abstract, please numerically label major findings, e.g., (1), (2), etc.
Q2. The introduction requires several improvements and clarifications, as follows:
- L28-29: Please provide references for this claim.
- L44: Please provide references for this claim.
- The introduction outlines objectives such as assessing the effects of spatial discretization (sub-basins and HRUs) and input data resolution on SWAT performance, but it lacks specific hypotheses or scientific questions to guide the study. For instance, the authors state they will evaluate performance impacts but do not articulate clear, testable hypotheses like "Higher-resolution datasets will lead to X% improvement in KGE performance at the cost of Y% increased computational time".
- Please provide the full name of USGS.
- L26-27: Please be specific on each of these references cited, such as “Hydrological models support water-related risk decisions such as flood prediction (suggest 10.1016/j.scitotenv.2024.174289), drought monitoring (suggest 10.1016/j.jenvman.2024.121375) dam monitoring (suggest 10.1007/s00382-024-07319-7), planning for irrigation (suggest 10.1016/j.scitotenv.2024.175523), agricultural management (suggest 10.1109/jstars.2024.3380514) and production (suggest 10.3850/978-90-833476-1-5_iahr40wc-p1339-cd), and climate change investigation (10.3389/fenvs.2023.1304845).”
- L103-114: Please numerically label objectives, e.g., (1), (2), etc.
Q3. Please correct these errors in references “Erreur ! Source du renvoi introuvable.”
Q4. Figure 1, what do red dots represent?
Q5. Please review documentation for DSOLMap and label these soil types in Figure 2.
Q6. The authors mention that soil and land use data affect SWAT simulations but fail to explain how these datasets interact scientifically (e.g., through hydrological soil group classification, CN2 values, or soil-water processes). This weakens the argument for investigating their combined effects.
Q7. While the manuscript focuses on spatial discretization and input dataset resolutions, it lacks a detailed explanation of the scientific trade-offs between model performance and spatial complexity. For instance, the authors do not elaborate on the underlying hydrological processes (e.g., surface runoff, infiltration, baseflow) affected by finer or coarser discretization levels. Moreover, no scientific justification is provided for the thresholds chosen for the number of sub-basins (e.g., 4, 12, 18) or HRUs. The rationale for their selection remains arbitrary, which undermines the study’s methodological depth
Q8. The introduction fails to address the scale dependency of hydrological processes in SWAT. For example, finer discretization can capture small-scale variability in land use or soil properties, but it also introduces spatial heterogeneity that may not be linearly scaled with model performance. Important questions such as “At what scale does spatial resolution no longer improve accuracy?” or “How does sub-basin size influence parameter calibration and uncertainty propagation?” remain unaddressed.
Q9. The introduction does not acknowledge how SWAT calibration methods might interact with spatial discretization. SWAT uses a global calibration approach, where sensitive parameters like CN2 and groundwater coefficients are adjusted uniformly. This may introduce errors as HRUs increase, particularly because spatial variability in soil-land use properties is averaged out. There is no mention of conducting a parameter sensitivity analysis to evaluate how increasing the number of HRUs affects the calibration process. Also, why SUFI-2 but not DDS or others? SUFI-2 helps in reducing parameter’ range with lower numbers of iterations, but it also contains other disadvantages. The authors should review this for your discussion (10.1016/j.ejrh.2024.102134).
Q10. The introduction highlights the Mediterranean region’s vulnerability to floods but fails to connect climate variability (e.g., extreme rainfall, droughts) with spatial discretization in SWAT. For example, there are 2 questions: (a) How does finer discretization improve or hinder the simulation of extreme hydrological events? and (b) Does spatial heterogeneity of soil and land use become more critical during floods, when runoff processes dominate?
Q11. Please provide limitations and future works section.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe quality of this revision and also its structure has improved significantly. The authors did a good job answering comments. I recommend to accept the revised manuscript
Reviewer 3 Report
Comments and Suggestions for AuthorsN/A
