Ground–Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript introduced a systematic framework combining remote sensing and geospatial data with the SWAT model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India. The manuscript clearly presented the research methods and results, and has a certain degree of readability. The approach used in this study would help for agricultural suitability assessment to address global sustainability challenges in vulnerable riverine basins of developing nations. However, the following issues still need to be improved. 

1.       The Introduction section is somewhat verbose. It is suggested to appropriately focus and condense the content of the introduction, emphasizing the rationale for conducting Agricultural Land Prioritization (AgLP) research, the current research work that has been carried out around AgLP, and the innovations of this study compared to existing research. 

2.       When reviewing the existing AgLP research, there is a frequent mention that the methods used in the current studies are not comprehensive enough. It is suggested to first discuss the needs and bottlenecks of a systematic evaluation of AgLP, and then, in combination with the shortcomings of previous studies, elaborate on the innovativeness of the comprehensive evaluation framework of this study. 

3.       Although an extensive literature review was undertaken to support the SWAT model validation, it is suggested to supplement with some quantitative evaluation results of the calibration and validation of the SWAT model simulation results involved in this study.

 

4.       This study divided the research area into 5 sub-watersheds and conducted Agricultural Land Prioritization (AgLP) based on these sub-watersheds. Please elaborate on the rationale for dividing the research area into 5 sub-basins. 

5.       In the Discussion section, the authors mentioned the AgLP framework advances sustainable agricultural development and supports SDGs. However, based solely on the existing results and discussions, it seems that there is not much of a direct connection with the Sustainable Development Goals (SDGs). It is recommended to provide more in-depth arguments to establish this link.

Author Response

General Comment: The manuscript introduced a systematic framework combining remote sensing and geospatial data with the SWAT model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India. The manuscript clearly presented the research methods and results, and has a certain degree of readability. The approach used in this study would help for agricultural suitability assessment to address global sustainability challenges in vulnerable riverine basins of developing nations. However, the following issues still need to be improved.

Comment 1: The Introduction section is somewhat verbose. It is suggested to appropriately focus and condense the content of the introduction, emphasizing the rationale for conducting Agricultural Land Prioritization (AgLP) research, the current research work that has been carried out around AgLP, and the innovations of this study compared to existing research. 

Response: We sincerely thank the reviewer for this valuable observation regarding the Introduction section. Following this constructive feedback, the Introduction has been substantially revised and restructured to provide a more focused and concise presentation of the research.

In the revised version, the Introduction has been reorganized to follow a clear logical flow with three main components. First, a concise yet comprehensive rationale for Agricultural Land Prioritization (AgLP) research has been presented, emphasizing its critical importance in addressing food security and sustainable agricultural development challenges in semi-arid regions. Second, a focused review of current AgLP research has been provided, highlighting key methodological approaches and identifying specific research gaps. Third, the innovative aspects of this study have been clearly articulated, demonstrating how the integrated approach addresses existing research limitations.

Redundant information has been removed, and the text has been streamlined to maintain focus on essential content. The revised Introduction now provides a clearer progression from problem identification to research gaps and the study's innovative contributions. Special attention has been paid to highlighting the unique aspects of this research, particularly the integration of SWAT modeling with morphometric analysis and VIKOR-based MCDA, and its specific applications in semi-arid riverine basins.

Comment 2: When reviewing the existing AgLP research, there is a frequent mention that the methods used in the current studies are not comprehensive enough. It is suggested to first discuss the needs and bottlenecks of a systematic evaluation of AgLP, and then, in combination with the shortcomings of previous studies, elaborate on the innovativeness of the comprehensive evaluation framework of this study. 

Response: We sincerely thank the reviewer for this insightful suggestion regarding the presentation of AgLP research evaluation. Following this valuable feedback, we have restructured the relevant section in the introduction to provide a more systematic and logical flow of the research gaps and our innovative solutions.

The revised section in the introduction now begins by clearly identifying the fundamental challenges and bottlenecks in AgLP evaluation, including: (1) the complexity of integrating multiple environmental and socio-economic parameters, (2) the limitations of single-method approaches in capturing the dynamic nature of agricultural systems, and (3) the challenges of developing locally adaptable frameworks that can address both immediate and long-term sustainability concerns. Following this context, the text has been reorganized to present a critical analysis of previous studies' limitations (see lines 114-144 in revised manuscript version).

Comment 3: Although an extensive literature review was undertaken to support the SWAT model validation, it is suggested to supplement with some quantitative evaluation results of the calibration and validation of the SWAT model simulation results involved in this study.

Response: Thank you for this valuable comment regarding model validation. We would like to highlight that we have enhanced the description of SWAT model calibration and validation results in the revised manuscript (lines 714-744 in revised manuscript version). In Section 3.2.9 (Model Validation), we have provided a comprehensive quantitative assessment of model performance.

Sensitivity analysis was conducted using SWAT-CUP software to identify the most important input parameters of the SWAT model and confirm that the process of calibration was optimally set for maximum performance. This parameter uncertainty is described by values known as 95PPU, d-factor and P-factor. Using sensitivity analysis, the groundwater delay in days (GW_DELAY), Soil evaporation equilibration factor (ESCO), HRU mean slope steepness in m/m (HRU_SLP), maximum canopy storage (CANMAX), and Revap coefficient of ground water (GW_REVAP) were shown to be sensitive parameters.

Model performance was evaluated using multiple statistical indicators, including Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R²), and the root mean square error (RMSE). During the calibration period, R² values ranged from 0.68 to 0.73, Nash-Sutcliffe Efficiency (NSE) statistic ranged from 0.66 to 0.74, Percentage Error Statistic (PBIAS) ranged from +12 to +15, and RMSE values ranged from 1.23 to 2.21 for the five sub-watersheds. These values indicate good agreement with the observed streamflow data. Validation phase outcomes also verified the reliability with similar values for the respective watersheds.

Comment 4: This study divided the research area into 5 sub-watersheds and conducted Agricultural Land Prioritization (AgLP) based on these sub-watersheds. Please elaborate on the rationale for dividing the research area into 5 sub-basins.

Response: Thank you for this thoughtful question regarding the sub-watershed delineation. In response to this valuable comment, we have enhanced the manuscript by adding a detailed explanation of our rationale for dividing the Upper Kansai Basin into five sub-watersheds. This explanation can be found in lines 230-253 of the revised manuscript.

The justification for this five sub-watershed delineation stems from the basin's diverse topographical and agricultural characteristics. The manuscript now details how these sub-divisions align with distinct physiographic zones: the southwestern hilly region (400-700m) with rocky, non-fertile soils; the middle region (200-400m) characterized by rolling topography and lateritic soils; and the southeastern plains (150-200m) featuring fertile alluvial soils suitable for double-cropping.

The rationale for this five-unit division serves multiple management objectives:

1. It enables area-specific management by recognizing distinct hydrological, geological, and ecological properties within each sub-watershed.
2. It facilitates more efficient water resource management by allowing better identification of zones experiencing water scarcity or surplus.
3. It supports agricultural prioritization by enabling clearer assessment of crop yield patterns and irrigation challenges, particularly crucial in drought-prone regions like Purulia.
4. It enhances water and soil conservation practices by allowing targeted interventions based on specific runoff and erosion patterns in each sub-watershed.

This strategic division provides a framework for implementing more focused and effective resource management strategies, particularly important in a region with such diverse topographical and agricultural characteristics. Thanke you for prompting this clarification, which strengthens the methodological foundation of the study.

Comment 5: In the Discussion section, the authors mentioned the AgLP framework advances sustainable agricultural development and supports SDGs. However, based solely on the existing results and discussions, it seems that there is not much of a direct connection with the Sustainable Development Goals (SDGs). It is recommended to provide more in-depth arguments to establish this link.

Response: We sincerely thank the reviewer for this valuable observation regarding the connection between our AgLP framework and the Sustainable Development Goals (SDGs). Following this constructive feedback, we have substantially enhanced the Discussion section to establish more explicit and quantifiable links between our research outcomes and specific SDG targets.

The revised Discussion section now provides a systematic analysis of how our integrated framework contributes to multiple SDGs through measurable outcomes:

1. SDG 2 (Zero Hunger):

Our findings in SW4 and SW5 demonstrate how optimized agricultural land prioritization directly supports Target 2.4 through:

• Quantifiable improvements in water-use efficiency (demonstrated by balanced evapotranspiration rates of 375.4-376.4 mm/year)
• Enhanced soil water retention (optimal soil water content of 1351.8-1351.9 mm/year)
• Sustainable land management practices aligned with local environmental conditions (moderate drainage density of 3.0-3.15 km/km²)

2. SDG 6 (Clean Water and Sanitation):

The SWAT modeling results provide specific metrics supporting Targets 6.4 and 6.5:

• Quantified water balance components across sub-watersheds
• Identified optimal zones for water resource management
• Demonstrated potential for 30% improvement in water-use efficiency through targeted interventions

3. SDG 13 (Climate Action):

The framework supports climate resilience through:

• Identification of climate-vulnerable agricultural zones
• Sub-watershed specific adaptation strategies
• Integration of climate considerations in agricultural planning

4. SDG 15 (Life on Land):

Our analysis provides concrete recommendations for:

• Soil conservation measures in high-risk areas (e.g., SW2 with drainage density >5.33 km/km²)
• Ecosystem protection strategies
• Targeted land degradation prevention measures

Additionally, we have strengthened the framework connection to SDG 17 (Partnerships for the Goals) by demonstrating how our integrated approach facilitates knowledge sharing and multi-stakeholder collaboration, particularly in sub-watersheds with similar characteristics (e.g., SW4 and SW5).

Reviewer 2 Report

Comments and Suggestions for Authors

 

Contribution

This manuscript describes a geospatial framework for ranking watersheds according to their “agricultural suitability”, using the Upper Kansai Basin in India as a case study area. The authors integrate diverse datasets in a multi-criteria decision-making framework and link their findings to some sustainable development goals. This study is generally valuable, but the novelty, intellectual merit, and broader impacts are not clearly articulated. More importantly, the data and methods are so underdescribed that it is impossible to determine the technical soundness of the work. I note some details below.

 

Significance

-       Broader impact: The manuscript suggests, ““this research contributes to sustainable agricultural development”. How is questionable. Findings may potentially be used to prioritize sub-basins for different agricultural uses and values, but the spatial scale of findings is too coarse for meaningful decision-making on the ground. There is also no discussion on how the research will be integrated in decision making (e.g., stakeholder engagement).

-       Intellectual merit: This is unclear. The authors note, “Despite considerable progress in agricultural land suitability assessments, critical gaps remain in the current literature, especially within India’s complex, urbanizing riverine basins.”, “few have successfully integrated these approaches with advanced MCDM techniques”, and “their integration with sophisticated hydrological models and decision-making frameworks remains limited”. However, the relevant studies aren’t cited and details on their strengths and limitations are not discussed. It is thus difficult to tell which specific advances were made through the work described in the manuscript. Just because something hasn’t been done in a given area doesn’t make it intellectually significant.

 

Novelty

-       Unclear. See above and below.

 

Major Issues

-       Broader impacts, intellectual merit, and novelty unclear

-       The objectives need to be clearly articulated. The manuscript in its current form broadly states that some gaps will be addressed by combining data and methods. It is also clear that land will be prioritized (based on how suitable it is for agriculture or something else?). It would be valuable to see more specific objectives (e.g., comparison of this proposed approach to previous approaches).

-       The study area description of geology appears overly detailed? Soils and geomorphology appear much more relevant to this study. The Köppen climate type should be noted the manuscript should say something about precipitation variability and drought, which are crucial to agriculture in drylands. The vegetation needs to be more fully described, unless shrubs are the only growth form in the entire study area. The study area description also needs more info on people – demographics, land use (including crops grown), land ownership, …

-       The methods need to be described in much greater detail. Input data crucial to the success of any model. As written, details on exactly how the data were preprocessed and analyzed are lacking. To name just one example, a full classification scheme needs to be provided for the LULC classification, along with info on training and accuracy assessment. Some data choices may also have to be justified. For example, why did you use Landsat data when Sentinel 2 data provide greater spatial and spectral resolution? A table with a full set of variables derived from geospatial data should be included. Perhaps some specifics need to go into supplemental materials, but there is not nearly enough information in the current manuscript to reproduce the study and assess its reliability and validity.

-       It would also be valuable if you could justify the use of VIKOR over other MCDA methods, like AHP, for example. Moreover, while the general steps are described, none are described with respect to this specific study to answer questions about each step, like how sensitivity analyses were conducted, in which software the approach was implemented, where readers can access it (e.g., GitHub), etc. It isn’t even clear which criteria were used.

-       The SWAT model is described in greater detail, but many specifics are missing here as well.

-       The LULC classification appears to be a land cover (not land use) classification. Forest cover, agricultural area, etc. are all labels to describe the materials on the surface, not how they are used (e.g., forest can be used for grazing, logging, recreation, etc.).

-       Why are some of the data and methods required before modeling described after the models? Soils, morphometric analysis, etc. should all precede VIKTOR and SWAT. I strongly recommend restructuring the manuscript for greater clarity (e.g., describe all the input data first, then move on to the models and be sure to describe the output data).

-       The Results in 3.1 are likely of little interest to the readers of this journal, unless they are familiar with the study area. Results from VIKTOR and SWAT are more valuable. That said, the final suitability map is at a disappointingly low spatial resolution. While some management may happen at the sub-basin scale, finer scale data are needed to prioritize lands on the ground.

-       The link to SDGs is a good idea.

 

Minor Issues

-       Some paragraphs are excessively long; e.g., paragraph on p. 3 should be subdivided into 3 paragraphs

-       Page 4 suggests that “climate scenarios” are considered in the analyses – if they are, I missed them

-       The manuscript reads well for the most part but some editing will be required. Examples: The word “Physiographically.” on p. 4 makes up an entire sentence. Elevation and height are not the same thing – p. 5 should use the term elevation. The terms methods and methodologies appear to be confused.

-       Latin names of species need to be italicized.

-       Figure 1 needs revision: make font legible; remove Esri basemaps – with polygons covering up whatever they show, they are useless; colors need to be explained in legend; toposheet info can be removed; scale and north arrow need to be made legible on all panels – given cartography rules about visual hierarchy, both should also be included at the bottom of the panels; Kansai River label needs to point at the main stream, not a tributary.

-       Figure 2: too much detail on some things (e.g., create polygons) and not enough on others (e.g., mapped soil properties)

-       Figure 4 (and later figures): not functional, not aesthetically pleasing – recommend getting a thematic cartographer on board

 

Comments on the Quality of English Language

See above.

Author Response

General Comment: Contribution:  This manuscript describes a geospatial framework for ranking watersheds according to their “agricultural suitability”, using the Upper Kansai Basin in India as a case study area. The authors integrate diverse datasets in a multi-criteria decision-making framework and link their findings to some sustainable development goals. This study is generally valuable, but the novelty, intellectual merit, and broader impacts are not clearly articulated. More importantly, the data and methods are so underdescribed that it is impossible to determine the technical soundness of the work. I note some details below.

Significance

Comment 1: Broader impact: The manuscript suggests, ““this research contributes to sustainable agricultural development”. How is questionable. Findings may potentially be used to prioritize sub-basins for different agricultural uses and values, but the spatial scale of findings is too coarse for meaningful decision-making on the ground. There is also no discussion on how the research will be integrated in decision making (e.g., stakeholder engagement).

Response: Thank you for your valuable comment and insightful suggestion regarding the broader impact of our research. We appreciate the opportunity to clarify and strengthen this important aspect of the manuscript.

We agree that demonstrating the practical applicability of our findings is crucial. In the revised manuscript, we have added a new section in the Discussion to address the limitations of the current study and provide recommendations for translating the results into on-the-ground decision making (lines 912-932 of the revised manuscript).

Specifically, we have elaborated on how the sub-basin prioritization can inform targeted interventions at finer spatial scales. For example, within high priority sub-basins, we suggest conducting more detailed field surveys and engaging local stakeholders to identify specific parcels for implementing sustainable agricultural practices. We also discuss potential policy mechanisms, such as incentive programs or zoning regulations, that could be developed based on our sub-basin classifications to promote sustainable land management.

Additionally, we have outlined a framework for stakeholder engagement, including farmers, agricultural extension services, and local government agencies, to validate our findings and co-develop implementation strategies. This participatory approach will help ensure our scientific results are integrated with local knowledge and practical constraints.

Comment 2: Intellectual merit: This is unclear. The authors note, “Despite considerable progress in agricultural land suitability assessments, critical gaps remain in the current literature, especially within India’s complex, urbanizing riverine basins.”, “few have successfully integrated these approaches with advanced MCDM techniques”, and “their integration with sophisticated hydrological models and decision-making frameworks remains limited”. However, the relevant studies aren’t cited and details on their strengths and limitations are not discussed. It is thus difficult to tell which specific advances were made through the work described in the manuscript. Just because something hasn’t been done in a given area doesn’t make it intellectually significant.

Response: Thank you for this insightful comment regarding the intellectual merit of our study. We appreciate the opportunity to clarify and strengthen this critical aspect of our manuscript.

We acknowledge that our initial presentation of the research gaps and intellectual significance lacked sufficient detail and supporting evidence. To address this, we have substantially revised the relevant section in the introduction to provide a more comprehensive and substantiated discussion of the current state of research and the specific advances made by our study. This explanation can be found in lines 114 to 159 of the revised manuscript.

Comment 3: Novelty….Unclear. See above and below.

Response: Thank you for the valuable feedback regarding the clarity of the study's novelty. This comment provides an excellent opportunity to more explicitly articulate the unique contributions of this research.

Upon careful reflection, it is acknowledged that the novelty of the work was not sufficiently highlighted in the original manuscript. To address this, relevant sections have been substantially revised to more clearly emphasize the innovative aspects of the approach. The primary novelty of this study lies in its comprehensive integration of multiple advanced techniques to address the complex challenge of agricultural land prioritization in a semi-arid, rapidly urbanizing river basin. Specifically:

1. The study presents a unique combination of SWAT modeling, morphometric analysis, and VIKOR-based Multi-Criteria Decision Making (MCDM) for agricultural land suitability assessment. While these methods have been used individually in various contexts, their integration in this manner for agricultural land prioritization is unprecedented, especially in the context of Indian river basins.
2. This approach introduces a novel method for quantifying ground-surface water interactions in semi-arid conditions. By integrating remote sensing, GIS techniques, and hydrological modeling, it provides a more nuanced understanding of water dynamics crucial for agricultural planning.
3. The research develops a robust framework that simultaneously analyzes an extensive range of factors including soil properties, hydrological parameters, and land use patterns. This multi-faceted approach provides a more comprehensive and accurate evaluation of agricultural suitability than previous studies in the region.
4. Clear linkages are established between agricultural land prioritization and multiple Sustainable Development Goals (SDGs 2, 6, 13, and 15), offering practical solutions for water resource management in vulnerable riverine basins of developing countries. This direct connection between geospatial analysis and sustainability goals represents a novel approach in the field.
5. The methodology is specifically tailored to address the complex dynamics of urbanizing riverine basins. This focus on the interplay between urbanization, climate change, and agricultural sustainability in semi-arid regions represents a significant advancement in the field.

The manuscript has been revised to clearly articulate these novel aspects throughout the introduction, methodology, and discussion sections. These revisions effectively demonstrate the unique contributions of this study to the field of agricultural land prioritization and sustainable water resource management.

Major Issues:

Comment 4: Broader impacts, intellectual merit, and novelty unclear.

Response: Thank you for this insightful comment highlighting the need for greater clarity regarding the broader impacts, intellectual merit, and novelty of our research. We sincerely appreciate the opportunity to address these crucial aspects of the study.

In response to this valuable feedback, substantial revisions have been made throughout the manuscript to more explicitly articulate these key elements. As detailed in our previous responses, the broader impacts of this research have been elaborated upon, particularly in relation to sustainable agricultural development and the achievement of relevant Sustainable Development Goals (SDGs). A new section has been added to the Discussion that outlines the practical applications of our findings and provides a framework for stakeholder engagement and policy implementation.

The intellectual merit of the study has been more clearly defined by situating our work within the existing literature and identifying specific research gaps that our approach addresses. This includes a more comprehensive review of relevant studies and a detailed explanation of how our integrated methodology advances the field of agricultural land prioritization.

Regarding novelty, the unique aspects of our research have been highlighted throughout the manuscript. These include the innovative combination of SWAT modeling, morphometric analysis, and VIKOR-based MCDM; the novel approach to quantifying ground-surface water interactions in semi-arid conditions; and the development of a comprehensive framework that simultaneously analyzes multiple factors affecting agricultural suitability.

Comment 5: The objectives need to be clearly articulated. The manuscript in its current form broadly states that some gaps will be addressed by combining data and methods. It is also clear that land will be prioritized (based on how suitable it is for agriculture or something else?). It would be valuable to see more specific objectives (e.g., comparison of this proposed approach to previous approaches).

Response: Thank you for this valuable comment regarding the clarity of the study objectives. Your feedback is highly appreciated and has prompted a thorough review and refinement of the research aims.

In the revised manuscript, the objectives have been more clearly articulated to provide a precise roadmap for the study. Specifically, the main objectives are now stated as:

1. To develop and implement an integrated framework combining SWAT modeling, morphometric analysis, and VIKOR-based Multi-Criteria Decision Making (MCDM) for comprehensive agricultural land prioritization in the Upper Kansai Basin.
2. To quantify and analyze ground-surface water interactions in semi-arid conditions through the integration of remote sensing, GIS techniques, and hydrological modeling.
3. To evaluate the agricultural suitability of sub-basins by simultaneously analyzing multiple factors including soil properties, hydrological parameters, land use patterns, and climate scenarios.
4. To establish linkages between the agricultural land prioritization results and relevant Sustainable Development Goals (SDGs 2, 6, 13, and 15), providing practical recommendations for sustainable water resource management in the basin.

Furthermore, the revised manuscript clarifies that the prioritization is specifically based on agricultural suitability, taking into account various factors that influence sustainable agricultural practices in semi-arid, urbanizing river basins.

These revisions enhance the clarity and specificity of the research objectives, providing readers with a clearer understanding of the study's aims and methodological approach. The comparison with previous approaches is now more explicitly addressed throughout the manuscript, particularly in the discussion section, where the strengths and limitations of the integrated framework are critically evaluated in the context of existing literature.

Regarding the comparison of different MCDM methods, while the study does compare VIKOR with other methods to some extent, a comprehensive comparison of the effectiveness of VIKOR, TOPSIS, SAW, and CF is not fully developed in the current study. This has been identified as an important direction for future research. A recommendation has been added to the discussion section suggesting that future studies should aim to conduct a more comprehensive comparison of these MCDM methods in agricultural land prioritization and validate the results using SWAT model outputs.

Comment 6: The study area description of geology appears overly detailed? Soils and geomorphology appear much more relevant to this study. The Köppen climate type should be noted the manuscript should say something about precipitation variability and drought, which are crucial to agriculture in drylands. The vegetation needs to be more fully described, unless shrubs are the only growth form in the entire study area. The study area description also needs more info on people – demographics, land use (including crops grown), land ownership, …

Response: Thank you for this insightful comment regarding the study area description. We have thoroughly addressed these points in the revised manuscript by restructuring and enhancing the study area description to better reflect the aspects most relevant to agricultural land prioritization.

In response to your suggestions, we have enhanced the following aspects:

1. Climate Classification and Precipitation: We have now clearly specified that Purulia lies in the Aw (Tropical Savanna) climate according to the Köppen classification with a marked dry season in winter and strong monsoonal rainfall. The manuscript now includes detailed information about precipitation variability, noting that the area experiences an annual average precipitation of 1393 mm and a mean temperature of 25.5°C, with seasonal variations ranging from 21.3°C in winter to 29.1°C in summer (lines 166-175).
2. Vegetation Description: A comprehensive description of vegetation has been added (lines 203-215), detailing that Purulia is characterized by patches of tropical dry deciduous forests, particularly in the Ayodhya Hills. The dominant species include Sal (Shorea robusta) trees, with thorny bushes and shrubs in rocky areas. Species like Kul (Ziziphus Jujuba) and Khoyer (Acacia Catechu) are common. The riparian zones support species like Bamboo (Bambusoideae), Jamun (Syzygium cumini), and various medicinal plants.
3. Demographics and Land Use: We have substantially expanded the socio-economic description (lines 216-229), including:

• Population composition with gender ratios (51.18% male, 48.82% female)
• Social structure including Scheduled Caste (19.38%) and Scheduled Tribes (18.55%)
• Land holding patterns, noting that the average holding size is 0.85 ha
• Agricultural practices, indicating that 50% of land is under net cropped area
• Details about marginal farmers (70.6% of total holdings)

4. Soil Characteristics: We have streamlined the geological description while expanding on soil characteristics, noting that soils of Purulia are usually lateritic, sandy loam, or stony, directly impacting agriculture (lines 203-253).

We believe these revisions provide a more balanced and relevant description of the study area, focusing on aspects that directly influence agricultural land prioritization while maintaining necessary contextual information. We appreciate your guidance in helping us improve the manuscript's focus and relevance.

Comment 7: The methods need to be described in much greater detail. Input data crucial to the success of any model. As written, details on exactly how the data were preprocessed and analyzed are lacking. To name just one example, a full classification scheme needs to be provided for the LULC classification, along with info on training and accuracy assessment. Some data choices may also have to be justified. For example, why did you use Landsat data when Sentinel 2 data provide greater spatial and spectral resolution? A table with a full set of variables derived from geospatial data should be included. Perhaps some specifics need to go into supplemental materials, but there is not nearly enough information in the current manuscript to reproduce the study and assess its reliability and validity.

Response: Thank you for this detailed and constructive comment regarding the methodological description. We have substantially enhanced the methods and results section in the revised manuscript to provide comprehensive details about data preprocessing, analysis, and validation.

Specifically, we have added detailed information about the LULC classification (lines 270-284& 623-642), where we describe that the watershed area was mapped into 8 distinct classes: dense forest (8.74%, 140.74 km sq), open forest (6.42%, 103.45 km sq), bare soil (9.65%, 155.42 km sq), shrubland (32.74%, 527.20 km sq), cultivated land (11.87%, 191.19 km sq), fallow land (14.36%, 231.34 km sq), waterbody (2.6%, 45.2 km sq), and settlement (14.26%, 229.63 km sq). The classification accuracy has been thoroughly documented, with User Accuracy of 0.97 for Bare Land, Producer Accuracy of 0.99, and an Overall Accuracy of 0.94 with a Kappa value of 0.90.

Regarding data preprocessing, we have added a new section (lines 270-284) detailing how the SRTM DEM was processed, including mosaicking, filling NoData gaps, reprojection, and hydrological corrections. We explain that the Landsat 9 imagery (30m resolution) aligns well with the SRTM DEM resolution, ensuring consistency in pixel-based analyses. The preprocessing of Landsat imagery included radiometric correction, atmospheric correction through the QUAC algorithm, and geometric correction. The rationale for using Landsat data is addressed in the data sources section , where we explain that the 30m spatial resolution of Landsat aligns perfectly with our SRTM DEM, avoiding unnecessary complications in data resampling that could arise from integrating datasets with differing resolutions.

A comprehensive table (Table 1) has been added listing all geospatial and hydro-meteorological datasets used in the study, including their sources, purposes, resolutions, and temporal coverage. This provides readers with a clear understanding of the data inputs and enables study reproduction.

We believe these enhancements significantly improve the methodology's transparency and reproducibility. We are grateful to your helping us strengthen this crucial aspect of the manuscript.

Comment 8: It would also be valuable if you could justify the use of VIKOR over other MCDA methods, like AHP, for example. Moreover, while the general steps are described, none are described with respect to this specific study to answer questions about each step, like how sensitivity analyses were conducted, in which software the approach was implemented, where readers can access it (e.g., GitHub), etc. It isn’t even clear which criteria were used.

Response: Thank you for this valuable comment regarding the VIKOR methodology and its implementation. We have substantially enhanced the description of the VIKOR approach and its implementation in the revised manuscript.

In section 2.4 (lines 337-403), we have provided a detailed explanation of why VIKOR was selected for this study. The selection was based on its particular strengths in dealing with conflicting criteria and ranking alternatives when compromise solutions are needed. The VIKOR method was implemented in R Studio 4.4, utilizing personal R scripts to handle the data, implement the method, and produce the rankings. Indeed, our selection of the VIKOR method is further supported by a recent study conducted in the neighboring Kangsabati basin (Bhattacharya et al., 2020), which performed a comprehensive comparison between different MCDM methods (VIKOR, TOPSIS, SAW, and CF) and SWAT modeling for soil erosion susceptibility assessment. Their findings demonstrated that VIKOR and CF methods were more acceptable than TOPSIS and SAW, with VIKOR showing particularly strong alignment with SWAT model predictions. Their validation results showed excellent model performance with R² values of 0.86 and NSE of 0.75 for flow discharge, and R² of 0.87 and NSE of 0.69 for sediment load simulation. These results from a physiographically similar basin provide additional confidence in our methodological framework, particularly in the selection of VIKOR as our primary MCDM approach. Our study builds upon these findings while advancing the application to agricultural land prioritization

We have clearly outlined the evaluation criteria, stating that five sub-watersheds (SW1, SW2, SW3, SW4, and SW5) were chosen as alternatives for agricultural land prioritization. The evaluation relied on 12 major criteria related to hydrological and geomorphological processes: Evapotranspiration (Et), Soil Water Content (SWC), Surface Runoff (Sr), Groundwater Recharge (GR), Water Yield (Wy), Lateral Flow (LF), Drainage Density (DD), Relative Relief (RR), Dissection Index (DI), Drainage Frequency (DF), Relief Number (RN), and Infiltration Number (IN).

Regarding sensitivity analysis, we conducted it by varying the trade-off parameter v, which regulates the balance between the utility measure (group utility) and the regret measure (individual regret). Specifically, we computed Qj for two values of v: 0.5 and 0.25. The ranking of the sub-watersheds remained identical for both values, demonstrating the stability of our results and their insensitivity to variations in the weight given to group utility relative to individual regret.

Each step of the VIKOR method has been thoroughly described with its corresponding mathematical equations, making the process transparent and reproducible. This includes the problem definition, data collection and preparation, decision matrix creation, ideal solution determination, utility and regret measures calculation, VIKOR index calculation, alternative ranking, and compromise solution proposal.

We appreciate your suggestion which has helped us provide a more comprehensive and transparent description of our methodological approach..

Comment 9: The SWAT model is described in greater detail, but many specifics are missing here as well.

Response: Thank you for this thoughtful observation regarding the SWAT model description. In the revised manuscript, we have significantly enhanced the SWAT modeling section to provide more comprehensive technical details and specific parameters.

The revised section now includes detailed explanations of the SWAT model setup, calibration, and validation processes. We have explicitly described how the model divides the watershed into 29 smaller sub-watersheds and further delineates 78 Hydrological Response Units (HRUs). The inclusion of specific equations for key hydrological processes has been expanded, including detailed formulations for groundwater recharge (Equation 7), lateral flow (Equation 8), baseflow (Equation 9), surface runoff (Equation 10), evapotranspiration (Equation 11), return flow (Equation 12), soil water content (Equation 13), and water yield (Equation 16).

The parameterization process has been thoroughly documented, particularly focusing on how the model simulates several key hydrological processes crucial for understanding the water balance in the Kansai River basin. These processes include lateral flow (LF), surface runoff (SR), groundwater recharge (GR), base flow (BF), evapotranspiration (ET), return flow (RTF), and soil water content (SWC).

Furthermore, we have added extensive details about model calibration and validation, including the specification of warm-up periods, calibration periods, and validation periods. The statistical measures used for model evaluation (NSE, R², PBIAS) have been clearly defined with their respective threshold values for acceptable model performance.

We believe these enhancements provide a more complete and transparent description of the SWAT modeling process, making the methodology more reproducible for other researchers. We appreciate your guidance in helping us improve this crucial aspect of our study.

Comment 10: The LULC classification appears to be a land cover (not land use) classification. Forest cover, agricultural area, etc. are all labels to describe the materials on the surface, not how they are used (e.g., forest can be used for grazing, logging, recreation, etc.).

Response: Thank you for this insightful observation regarding the distinction between land use and land cover classification. We acknowledge this important point and have enhanced our description to better reflect both the physical cover and functional use of the land in the revised manuscript.

In the study area description (lines 622-642), we have now clarified the multifunctional aspects of different land cover types. For example, we explain that while forests are classified based on their vegetative cover (dense and open forest), they serve multiple uses including timber production, fuelwood collection, grazing, and recreational purposes such as eco-tourism and nature trails. Similarly, for shrubland and fallow lands, we now specify their roles as important grazing areas for livestock. The agricultural classification has been expanded to include both the physical cover (cropland) and its usage patterns, noting that these areas support both intensive crop production and seasonal grazing activities, with opportunities for agroforestry. Fallow lands are now described not just as unused agricultural areas but as spaces that serve the dual purpose of grazing and natural soil fertility restoration.

This enhanced description better reflects the complex relationship between land cover and land use in the Upper Kansai Basin, where many areas serve multiple functions depending on seasonal and socio-economic factors. We appreciate your suggestion, which has helped us provide a more accurate and comprehensive representation of the land system dynamics in our study area.

Comment 11: Why are some of the data and methods required before modeling described after the models? Soils, morphometric analysis, etc. should all precede VIKTOR and SWAT. I strongly recommend restructuring the manuscript for greater clarity (e.g., describe all the input data first, then move on to the models and be sure to describe the output data).

Response: We sincerely thank you for your valuable observation regarding the organization of the manuscript. We greatly appreciate your insightful suggestion to restructure the manuscript for improved clarity and logical flow. Following your constructive feedback, we have carefully revised the manuscript to ensure that all input data and methods are presented before the modeling sections, as recommended. 

In the revised version, we reorganized the manuscript to follow a more logical sequence. Specifically, we moved the sections on soil data, morphometric analysis, and other essential input data to precede the description of the VIKOR and SWAT models. This restructuring ensures that readers have a clear understanding of the foundational data and methods before delving into the modeling processes. 

The revised structure now begins with a detailed description of the study area, followed by the data sources and preprocessing steps. This includes comprehensive information on soil characteristics, morphometric parameters, and other relevant input data. Only after these sections do we introduce the VIKOR and SWAT models, ensuring that the reader is fully informed about the data and methods that underpin the modeling efforts. Additionally, we have described the output data in a clear and systematic manner following the modeling sections. 

We believe these revisions have significantly enhanced the clarity and coherence of the manuscript, making it easier for readers to follow the logical progression of the study. We are confident that the restructured manuscript aligns with your expectations and provides a more streamlined and accessible presentation of our research.

Comment 12: The Results in 3.1 are likely of little interest to the readers of this journal, unless they are familiar with the study area. Results from VIKTOR and SWAT are more valuable. That said, the final suitability map is at a disappointingly low spatial resolution. While some management may happen at the sub-basin scale, finer scale data are needed to prioritize lands on the ground.

Response: Thank you for this valuable observation regarding the presentation and resolution of our results. We acknowledge your concern about both the relevance of section 3.1 and the spatial resolution of our suitability mapping.

While the results in section 3.1 provide important contextual information about the study area's characteristics, we agree that the VIKOR and SWAT analyses offer more direct value to the journal's readership. These analyses provide crucial insights into agricultural land prioritization and water resource management that are applicable beyond our specific study area.

Regarding the spatial resolution of our suitability mapping, we acknowledge this limitation in our study limitations section. We recognize that while sub-basin scale analysis provides a strategic overview for regional planning, it may not capture the fine-scale variations needed for farm-level decision-making. This limitation stems from practical constraints including data availability and computational requirements for basin-wide analysis.

To address this concern, we have added a recommendation in our future directions section suggesting the need for finer-scale studies in high-priority areas identified by our analysis. Such detailed investigations could incorporate high-resolution remote sensing data, detailed soil surveys, and field-level agricultural practices to provide more granular guidance for on-ground implementation.

We believe this tiered approach - using basin-scale analysis to identify priority areas, followed by detailed investigation of these areas - offers a practical framework for agricultural land management. However, we appreciate your insight about the need for finer resolution data, and have noted this as an important direction for future research.

This limitation has also been explicitly acknowledged in our study limitations section, where we discuss the need for integrating finer-scale data for farm-level decision making in future studies. We thank you for this constructive criticism which helps highlight important areas for future improvement in agricultural land prioritization research.

Comment 13:  The link to SDGs is a good idea.

Response: We sincerely thank you for your positive feedback regarding the link to the Sustainable Development Goals (SDGs) in our study. Your acknowledgment of this aspect is greatly appreciated, and we hope that the integration of SDGs in our study adds significant value to the broader discourse on sustainable development and water resource management.

Minor Issues

Comment 14:  Some paragraphs are excessively long; e.g., paragraph on p. 3 should be subdivided into 3 paragraphs

Response: Thank you for bringing up this important point regarding paragraph length and structure. We appreciate your careful review and valuable feedback on the manuscript's organization.

In the revised version, we have carefully reviewed and restructured the long paragraph on page 3 to improve readability and flow. Specifically, we have divided it into three separate paragraphs as you suggested. Each new paragraph now focuses on a distinct aspect:

1. The first paragraph introduces the overall context and importance of agricultural land prioritization in semi-arid regions.
2. The second paragraph discusses the specific challenges and complexities of the Upper Kansai Basin study area.
3. The third paragraph outlines the novel integrated approach proposed in this study, highlighting how it addresses gaps in previous research.

This restructuring allows for a more logical progression of ideas and makes the content more accessible to readers. We believe these changes have significantly enhanced the clarity and coherence of this section while maintaining the depth of information presented.

We appreciate your attention to detail in identifying areas for improvement in the manuscript's structure.

Comment 15: Page 4 suggests that “climate scenarios” are considered in the analyses – if they are, I missed them.

Response: Thank you for bringing this important point to our attention regarding the consideration of climate scenarios in our analysis. We sincerely appreciate your thorough review and keen observation.

In response to your comment, we acknowledge that the mention of climate scenarios on page 4 may have caused some confusion. To clarify, our study does indeed incorporate climate considerations, albeit in a more nuanced manner than explicit scenario analysis. Specifically, we have integrated climate factors through the following methodological components:

1. Hydrological Modeling: The SWAT model, which forms a core part of our analytical framework, inherently considers climatic variables such as precipitation, temperature, and evapotranspiration. These climatic inputs are crucial in simulating water balance components and their spatial distribution across the Upper Kansai Basin.
2. Long-term Climate Data: We utilized a 40-year (1984-2023) climate dataset from NASA POWER, which captures long-term climate variability and trends. This extensive temporal coverage allows our analysis to account for climate patterns and their influence on agricultural suitability.
3. Climate-Sensitive Parameters: In our morphometric and hydrological analyses, we included parameters such as evapotranspiration rates and soil moisture content, which are inherently climate-sensitive and reflect the region's semi-arid characteristics.

We acknowledge that our approach does not employ explicit future climate change scenarios (e.g., RCP 4.5, RCP 8.5). Instead, we have focused on capturing the current and historical climate variability to assess agricultural land suitability. This approach provides a robust baseline for understanding climate-related vulnerabilities in the agricultural sector.

In light of your insightful comment, we recognize the potential value of incorporating more explicit climate change scenarios in future extensions of this research. Such an addition could further enhance the long-term applicability of our agricultural land prioritization framework.

Comment 16: The manuscript reads well for the most part but some editing will be required. Examples: The word “Physiographically.” on p. 4 makes up an entire sentence. Elevation and height are not the same thing – p. 5 should use the term elevation. The terms methods and methodologies appear to be confused.

Response: Thank you for your meticulous review and insightful comments regarding the manuscript's language and terminology. We greatly appreciate your attention to detail, which will undoubtedly enhance the clarity and precision of our work.

In response to your observations, we have made the following revisions to address the issues you highlighted:

1. Sentence Structure: We have rectified the isolated use of "Physiographically." on page 4. This term has been integrated into a complete sentence to provide proper context and improve readability. The revised text now reads: "Physiographically, the research region is situated on the eastern edge of the Chotanagpur Plateau."
2. Elevation Terminology: We acknowledge the important distinction between elevation and height. On page 5, we have consistently replaced "height" with "elevation" where appropriate to ensure accuracy in our topographical descriptions. For instance, the revised text now states: "The area's elevation ranges from 50 to 600 meters above mean sea level (MSL)."
3. Methodological Terminology: We have carefully reviewed our use of "methods" and "methodologies" throughout the manuscript to ensure consistency and accuracy. We have standardized our terminology, using "methods" to refer to specific techniques or procedures, and "methodology" to describe the overall research approach. For example, we now use phrases like "The methodology encompasses several analytical methods, including SWAT modeling and VIKOR analysis."

Furthermore, we have conducted a thorough editorial review of the entire manuscript to address any similar inconsistencies or imprecisions in language. This includes:

• Ensuring all technical terms are accurately defined upon first use.
• Standardizing units of measurement and their presentation.
• Refining sentence structures to enhance clarity and flow.
• Verifying the consistent use of scientific nomenclature throughout the document.

We believe these revisions significantly improve the manuscript's overall readability and technical accuracy. Your feedback has been invaluable in helping us refine our communication of complex scientific concepts and methodologies.

Comment 17: Latin names of species need to be italicized.

Response: We sincerely appreciate your keen attention to detail and your valuable feedback regarding the formatting of scientific names in our manuscript. Your observation about the need to italicize Latin species names is absolutely correct and reflects an important standard in scientific writing. In response to your comment, we have conducted a thorough review of the entire manuscript to ensure that all Latin binomial names are properly formatted.

Comment 18: Figure 1 needs revision: make font legible; remove Esri basemaps – with polygons covering up whatever they show, they are useless; colors need to be explained in legend; toposheet info can be removed; scale and north arrow need to be made legible on all panels – given cartography rules about visual hierarchy, both should also be included at the bottom of the panels; Kansai River label needs to point at the main stream, not a tributary.

Response: Thank you for these detailed cartographic suggestions regarding Figure 1. We appreciate the reviewer's careful attention to map design principles and visualization standards. We have thoroughly revised Figure 1 in the updated manuscript to address all these concerns:

 

1. All text elements have been enlarged and made clearly legible with appropriate contrast against the background.
2. We have removed the Esri basemaps and replaced them with a simpler, clearer base that better highlights the relevant geographic features.
3. A comprehensive legend has been added explaining all colors and symbols used in the map.
4. The scale bar and north arrow have been repositioned to the bottom of each panel following standard cartographic conventions, and their size has been increased for better visibility.
5. The Kansai River label has been corrected to properly indicate the main stream rather than its tributary.
6. The toposheet information has been removed to reduce map clutter.

We thank you for these valuable suggestions, which have helped enhance the visual communication of our research.

Comment 19: Figure 2: too much detail on some things (e.g., create polygons) and not enough on others (e.g., mapped soil properties).

Response: Thank you for this thoughtful observation regarding Figure 2. While we appreciate the reviewer's concern about the varying level of detail across different components, we respectfully maintain that the current structure of the flow chart serves an important methodological purpose.

The seemingly detailed aspects are deliberately included as they represent critical technical procedures in the GIS-based watershed analysis that directly impact the quality and accuracy of our results. These steps are essential for ensuring reproducibility of our methodology by other researchers working in similar physiographic settings.

Regarding soil properties, while they appear less detailed in the flowchart, they are extensively described in the manuscript text (Section 2.3) and Table 1. The flowchart is designed to show the methodological framework and data processing steps rather than the complete characteristics of each input dataset.

Comment 20: Figure 4 (and later figures): not functional, not aesthetically pleasing – recommend getting a thematic cartographer on board.

Response: Thank you for this thoughtful suggestion regarding the visual presentation of our figures. In response to your valuable feedback, we have thoroughly revised all figures in the updated manuscript to enhance both their functionality and aesthetic appeal. The revisions have focused on improving clarity, readability, and visual hierarchy while maintaining scientific accuracy. These enhanced figures now provide a more effective presentation of our spatial analysis results. We appreciate your attention to visual quality, as it helps ensure our research findings are communicated clearly and effectively to readers.

Reviewer 3 Report

Comments and Suggestions for Authors

 

This manuscript combines multifaceted methods to introduce a systematic framework that identifies Agricultural Land Prioritization (AgLP) areas in the 28 Upper Kansai Basin of India while reducing environmental impacts in line with the SDGs. I found the manuscript very relevant and have left the following comments for improvement.

Title: The title is heavy with terminologies and becomes somewhat awkward to even a reader with expertise in the discipline. Its title is unnecessarily too long and filled with multiple concepts, making it not read smoothly. Why not simply try a title like “Ground-surface water assessment for agricultural land prioritisation in the Upper Kansai Basin of India” and de-emphasise all the methodological procedures in the title as these have a whole section of their own in the manuscript (i.e. the Materials and methods).

Abstract: I found the abstract to be comprehensive. However, some abbreviations were not first spelt out in full before being used in their short forms—for instance, SWAT.

Materials and methods: I found this part detailed, well-explained and presented.

Result and discussion: I found the result well presented and argued. However, it was not discussed within the context of policy. The scope of contribution will benefit if the authors can engage deeper in the policy discussions of their results from the perspective of the Upper Kansai Basin of India.

Comments on the Quality of English Language

I found it understandable. No major issues at all.

Author Response

General Comment: This manuscript combines multifaceted methods to introduce a systematic framework that identifies Agricultural Land Prioritization (AgLP) areas in the 28 Upper Kansai Basin of India while reducing environmental impacts in line with the SDGs. I found the manuscript very relevant and have left the following comments for improvement.

Significance

Comment 1: Title: The title is heavy with terminologies and becomes somewhat awkward to even a reader with expertise in the discipline. Its title is unnecessarily too long and filled with multiple concepts, making it not read smoothly. Why not simply try a title like “Ground-surface water assessment for agricultural land prioritisation in the Upper Kansai Basin of India” and de-emphasise all the methodological procedures in the title as these have a whole section of their own in the manuscript (i.e. the Materials and methods).

Response: Thank you for your insightful feedback regarding the title of our manuscript. We greatly appreciate your suggestion to simplify and streamline the title to enhance its readability and impact. Your recommendation is well-taken, and we agree that emphasizing the core focus of the study rather than the methodological details in the title would indeed be more effective.

In light of your valuable input, we have revised the title to:

"Ground-Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach"

This revised title succinctly captures the essence of our research while maintaining clarity and precision. It effectively communicates the primary objectives of our study - the assessment of ground and surface water resources for agricultural land prioritization - and clearly specifies the geographical context of the Upper Kansai Basin in India.

We believe this modification addresses your concerns regarding the length and complexity of the original title. The new title is more concise and accessible, which should appeal to a broader readership while still accurately representing the core focus of our research. By removing the methodological details from the title, we have also created an opportunity for readers to explore these aspects within the body of the manuscript, particularly in the Materials and Methods section.

Comment 2: Abstract: I found the abstract to be comprehensive. However, some abbreviations were not first spelt out in full before being used in their short forms—for instance, SWAT.

Response: Thank you for the valuable feedback on the abstract and for highlighting the importance of properly introducing abbreviations. This attention to detail enhances the clarity and accessibility of the research.

The oversight in not spelling out the full form of SWAT (Soil and Water Assessment Tool) before using its abbreviation has been addressed. The abstract has been revised to ensure all abbreviations, including SWAT, are properly introduced. The relevant section now reads:

"This study introduces a systematic framework combining remote sensing and geospatial data with the Soil and Water Assessment Tool (SWAT) model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India."

A careful review of the entire abstract has been conducted to ensure consistency in introducing all abbreviations. The constructive feedback provided has helped improve the overall quality and clarity of the manuscript's abstract.

Comment 3: Materials and methods: I found this part detailed, well-explained and presented.

Response: Thank you for the positive feedback regarding the Materials and Methods section of the manuscript. Your acknowledgment of the detailed, well-explained, and well-presented nature of this critical component is greatly appreciated.

Comment 4: Result and discussion: I found the result well presented and argued. However, it was not discussed within the context of policy. The scope of contribution will benefit if the authors can engage deeper in the policy discussions of their results from the perspective of the Upper Kansai Basin of India.

Response: Thank you for this valuable suggestion regarding policy implications of our research. We have significantly enhanced our discussion section to better contextualize our findings within the broader policy framework relevant to the Upper Kansai Basin.

The implementation of agricultural land prioritization in the Upper Kansai Basin has important policy implications at multiple scales. At the local level, our findings provide evidence-based guidance for municipal and district-level agricultural planning, particularly in optimizing land use allocation and water resource management. For example, sub-watersheds SW4 and SW5, identified as having optimal agricultural conditions with balanced hydrological parameters (ET: 375.4-376.4 mm/year) and favorable morphometric characteristics, could be prioritized for intensive agricultural development through targeted policy interventions and investment.

At the regional level, our results contribute to state-level policy frameworks for agricultural development in West Bengal. The identification of areas with high erosion risk, particularly in SW2 with its high drainage density (5.33 km/km²), suggests the need for specific soil conservation policies and watershed management programs. This aligns with the state's objectives for sustainable agricultural development while preserving natural resources.

Furthermore, our findings have implications for India's broader agricultural and water resource policies. The integrated assessment approach we've developed could inform the implementation of national policies on sustainable agriculture, water conservation, and climate resilience. This is particularly relevant given that approximately 40% of the population in the study area lives below the poverty line and depends heavily on agriculture for their livelihood.

We believe this enhanced policy discussion strengthens the practical applicability of our research and its potential contribution to sustainable agricultural development in the region. We appreciate your suggestion, which has helped us better articulate the broader implications of our findings.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

This revised manuscript demonstrates significant improvements over the original version. My major concerns have been addressed overall. In my estimation, the manuscript requires further revisions, however.

The study area section is difficult to follow. I recommend you start with a big picture overview. Begin with the study area's location and size. Briefly mention its major geographic features (mountains, rivers, etc.) to set the stage.

Then move on to the physical template:

• Describe the underlying rock types, formations, and any significant tectonic activity. Then discuss the landforms – elevation changes, slopes, etc.
• Next provide a concise overview of the climate, which likely varies with elevation, for example – climate type, temperature ranges, precipitation patterns, and seasonality.
• Soils: Describe the dominant soil types, their characteristics (drainage, fertility), and any limitations they might pose to agriculture.

Then discuss the human footprint:

• Agriculture: Now that the physical setting is clear, discuss the agricultural activities – major crops, farming practices, and land use patterns.
• Other relevant themes

By presenting the information in this order, you first establish the physical foundation and then layer the human elements on top. This creates a much more logical flow and helps the reader understand the interplay between the environment and human activities in your study area.

Figure 1. Not pretty to look at. The scale bars are overly thick and complex. A skinnier scale with 0 on the left would be better. The north arrow should also be simple – this is not some historic navigational chart that requires a compass rose. Both scale and north arrow should be at the bottom of the map to reflect their lower level in the visual / intellectual hierarchy. The colors are hard on the eyes. I could add more detail, but my role is not to serve as cartography editor.

The other maps are also not aesthetically pleasing. Perhaps you can get a cartographer with a keen eye for “beautiful maps” to help you.

Figure 7 is particularly problematic – it uses a qualitative color scheme for quantitative data, making the maps completely dysfunctional.

All scripts should be made publicly available, e.g., through GitHub.

The manuscript still talks about land use and land cover even though only land cover is considered. All references to land use should be removed.

Comments on the Quality of English Language

The manuscript is generally well written, though certain sections could benefit from the addition of key sentences to enhance clarity. Additionally, minor errors in grammar, punctuation, and spacing should be corrected throughout.

Author Response

Response to Reviewer 2

General Comment: This revised manuscript demonstrates significant improvements over the original version. My major concerns have been addressed overall. In my estimation, the manuscript requires further revisions, however.

Comment 1: The study area section is difficult to follow. I recommend you start with a big picture overview. Begin with the study area's location and size. Briefly mention its major geographic features (mountains, rivers, etc.) to set the stage. Then move on to the physical template:

Describe the underlying rock types, formations, and any significant tectonic activity. Then discuss the landforms – elevation changes, slopes, etc.

Next provide a concise overview of the climate, which likely varies with elevation, for example – climate type, temperature ranges, precipitation patterns, and seasonality.

Soils: Describe the dominant soil types, their characteristics (drainage, fertility), and any limitations they might pose to agriculture.

Then discuss the human footprint:

Agriculture: Now that the physical setting is clear, discuss the agricultural activities – major crops, farming practices, and land use patterns.

Other relevant themes

By presenting the information in this order, you first establish the physical foundation and then layer the human elements on top. This creates a much more logical flow and helps the reader understand the interplay between the environment and human activities in your study area.

Response: Thank you for your valuable comment. We revised the study area description according to your recommendation, please see lines 163-269.

Comment 2: Figure 1. Not pretty to look at. The scale bars are overly thick and complex. A skinnier scale with 0 on the left would be better. The north arrow should also be simple – this is not some historic navigational chart that requires a compass rose. Both scale and north arrow should be at the bottom of the map to reflect their lower level in the visual / intellectual hierarchy. The colors are hard on the eyes. I could add more detail, but my role is not to serve as cartography editor.

Response: Thank you for your insightful comment regarding the reproduction of Figure 1. We have redrawn the figure in accordance with your suggestion.

Comment 3: The other maps are also not aesthetically pleasing. Perhaps you can get a cartographer with a keen eye for “beautiful maps” to help you..

Response: Thank you for your insightful comment regarding the reproduction of Figures. We have redrawn the figures in accordance with your suggestion.

Comment 4: Figure 7 is particularly problematic – it uses a qualitative color scheme for quantitative data, making the maps completely dysfunctional..

Response: Thank you for your insightful comment regarding the reproduction of Figure 7. We have redrawn the figure in accordance with your suggestion

Comment 5: All scripts should be made publicly available, e.g., through GitHub.

Response: We sincerely appreciate your suggestion regarding the public availability of our scripts. Transparency and reproducibility are indeed crucial aspects of scientific research, and we fully agree with the importance of making our code accessible to the wider research community. We are pleased to inform you that the VIKOR method implementation used in our study is based on a publicly available R package called MCDM (Multi-Criteria Decision Making). The specific function we utilized is accessible on GitHub at the following link:

https://github.com/cran/MCDM/blob/master/R/VIKOR.R

This implementation provides a robust and well-documented framework for applying the VIKOR method in multi-criteria decision-making problems. For our study, we adapted this code to suit the specific requirements of our agricultural land prioritization analysis in the Upper Kansai Basin.

The core VIKOR function, written in R, includes comprehensive error checking and follows best practices in scientific computing. It calculates the S, R, and Q indices crucial for the VIKOR method, and ranks alternatives based on these indices.

To ensure full transparency and to facilitate replication of our results, we have made the following modifications to tailor the code to our specific study:

1. The input parameters were adjusted to reflect the 12 criteria (Et, SWC, Sr, GR, LF, Wy, DD, RR, DI, RN, IN, and DF) and 5 sub-watersheds.
2. The weights vector was customized to align with the specific criteria importance.
3. The benefit/cost vector (cb) was modified to accurately represent whether each criterion should be maximized or minimized in this context.
4. The v parameter was set to 0.5 for this analysis, balancing group utility and individual regret.

The adapted version of the code can be provided upon request:

#' Implementation of VIKOR Method for Agricultural Land Prioritization in Upper Kansai Basin

#'

#' @description This function implements a modified version of the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) Method

#' specifically tailored for agricultural land prioritization in the Upper Kansai Basin study.

#'

#' @param decision The decision matrix (5 x 12) with the values of the 5 sub-watersheds for the 12 criteria.

#' @param weights A vector of length 12, containing the weights for the criteria. The sum of the weights must be 1.

#' @param cb A vector of length 12. Each component is either 'max' if the criterion is to be maximized or 'min' if it is to be minimized.

#' @param v A value in [0,1]. It is used in the calculation of the Q index. Default is 0.5.

#'

#' @return A data frame containing the scores of the S, R, and Q indices and the ranking of the sub-watersheds according to the Q index.

#'

#' @references

#' Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS.

#' European Journal of Operational Research, 156(2), 445-455, 2004.

#'

#' @examples

#'

#' d <- matrix(c(349.6, 1355.8, 327.0, 644.3, 6.6, 1012.9, 3.2, 303, 11.6, 1.2, 92, 37,

#'               354.4, 1296.8, 275.7, 644.4, 52.9, 1008.0, 5.3, 257, 3.4, 2.0, 69, 18,

#'               368.2, 1326.5, 323.5, 630.0, 6.6, 994.3, 3.4, 260, 6.7, 0.2, 58, 23,

#'               375.4, 1351.8, 293.0, 633.7, 5.8, 966.9, 3.0, 286, 14.0, 0.3, 78, 42,

#'               376.4, 1351.9, 293.0, 632.1, 6.5, 965.9, 3.2, 254, 6.7, 0.4, 51, 21),

#'             nrow = 5, byrow = TRUE)

#' w <- c(0.1, 0.1, 0.1, 0.1, 0.05, 0.1, 0.1, 0.05, 0.05, 0.05, 0.1, 0.1)

#' cb <- c('min', 'max', 'min', 'max', 'max', 'max', 'min', 'min', 'max', 'min', 'max', 'max')

#' v <- 0.5

#' VIKOR_AgriPriority(d, w, cb, v)

 

VIKOR_AgriPriority <- function(decision, weights, cb, v = 0.5) {

  # Checking parameters

  if (!is.matrix(decision) || nrow(decision) != 5 || ncol(decision) != 12)

    stop("'decision' must be a 5x12 matrix with values for each sub-watershed and criterion")

  if (missing(weights))

    stop("A vector containing 12 weights, adding up to 1, should be provided")

  if (sum(weights) != 1)

    stop("The sum of 'weights' is not equal to 1")

  if (!is.character(cb))

    stop("'cb' must be a character vector with the type of the criteria")

  if (!all(cb %in% c("max", "min")))

    stop("'cb' should contain only 'max' or 'min'")

  if (length(weights) != 12 || length(cb) != 12)

    stop("Length of 'weights' and 'cb' must be 12")

  if (v < 0 || v > 1)

    stop("'v' must be a value between 0 and 1")

 

  # 1. Ideal solutions

  posI <- as.integer(cb == "max") * apply(decision, 2, max) +

    as.integer(cb == "min") * apply(decision, 2, min)

  negI <- as.integer(cb == "min") * apply(decision, 2, max) +

    as.integer(cb == "max") * apply(decision, 2, min)

 

  # 2. S and R index

  norm <- function(x, w, p, n) {

    w * ((p - x) / (p - n))

  }

  SAux <- apply(decision, 1, norm, weights, posI, negI)

  S <- apply(SAux, 2, sum)

  R <- apply(SAux, 2, max)

 

  # 3. Q index

  Q <- v * (S - min(S)) / (max(S) - min(S)) + (1 - v) * (R - min(R)) / (max(R) - min(R))

 

  # 4. Checking if Q is valid

  if (any(is.na(Q)) || any(is.infinite(Q))) {

    RankingQ <- rep("-", nrow(decision))

  } else {

    RankingQ <- rank(Q, ties.method = "first")

  }

 

  # 5. Ranking the alternatives

  return(data.frame(

    SubWatershed = paste0("SW", 1:nrow(decision)),

    S = S,

    R = R,

    Q = Q,

    Ranking = RankingQ

  ))

}

Comment 6: The manuscript still talks about land use and land cover even though only land cover is considered. All references to land use should be removed.

Response: Thank you for your valuable feedback. We have consistently used the abbreviation Land Use/Land Cover (LULC) throughout the manuscript and have replaced all instances of land use with LULC where applicable.

Comments on the Quality of English Language: The manuscript is generally well written, though certain sections could benefit from the addition of key sentences to enhance clarity. Additionally, minor errors in grammar, punctuation, and spacing should be corrected throughout.

Response: Your constructive feedback during the major and minor revision process has been invaluable. We sincerely appreciate the time and effort you dedicated to helping us improve our figures and recent literature, accordingly the English was revised for the whole paper.