Identifying the Pockets Most Affected by Temperature Rise and Evaluating the Repercussions on Urban Communities and Their Agricultural Lands

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper utilized MOD11 LST products to analyze the spatiotemporal distributions of LST and TCI. However, the manuscript structure is chaotic and needs to be further adjusted, and the results could also be further optimized and validated. 

1. The TCI values are calculated using the MOD11C3 LST product. The general trend of LST analysis was utilized MOD11A2. Why did you choose two LST products?

2. Where does the population distribution, land cover, and water bodies map utilized in this paper come from? It was suggested that the data be added with more clarity.

3. What is the innovation of this work?

4. What are the limitations and implications of this study? Please add it at the end of the discussion.

5. The drought results, e.g., 23 drought-affected areas, from TCI should be validated.

Comments on the Quality of English Language

The English language of the manuscript can be further improved, and it is recommended to pay attention to the logic of the manuscript and the professionalism of the English language.

Author Response

1. The TCI values are calculated using the MOD11C3 LST product. The general trend of LST analysis was utilized MOD11A2. Why did you choose two LST products?

The choice to use both the MOD11A2 and MOD11C3 products, despite their different spatial resolutions, was guided by the need to address both the detailed thermal trends at the surface level and the broader climatic context over time.

MOD11A2, with its higher spatial resolution (1 km), was essential for calculating the annual surface temperature slope using Mann-Kendall and Sen's slope over a period of 22 years. This allows us to capture precise local variations in surface temperature and detect areas experiencing significant thermal increases. Such detailed analysis is critical for understanding short-term extreme weather conditions, such as heatwaves, which are key indicators of thermal stress on a localized level.

On the other hand, MOD11C3, with a coarser spatial resolution but regular monthly coverage, was selected for calculating the Temperature Condition Index (TCI). This product provides a broader perspective on long-term climate trends and helps identify areas experiencing recurrent drought conditions. By focusing on the broader spatial patterns, this product reveals the larger-scale extreme climate variability that affects human activities such as agriculture, water resources, and energy supply.

The combination of these two products allows us to capture both short-term weather-driven fluctuations and long-term climate dynamics, providing a more comprehensive understanding of drought from a thermal perspective. The higher-resolution product (MOD11A2) gives insight into the increasing intensity of heat over time, while the lower-resolution product (MOD11C3) offers a macro-level view of extreme climate pressures that persist over longer periods.

2. Where does the population distribution, land cover, and water bodies map utilized in this paper come from? It was suggested that the data be added with more clarity.

National Environmental Observatory at the Ministry of Local Administration and Environment. We added the reference in the Section 2.2 of the resubmitted manuscript.

3. What is the innovation of this work?

In light of the absence of ground-based climate monitoring stations in conditions of long-term civil wars, the level of confidence in the results must be enhanced by developing a methodology that relies on the use of remote sensing products to identify the spatial pockets most affected by rising temperatures and verifying using the impact and context analysis method (interpreting the population migration emanating from these pockets since 2000, which was monitored in the period before the war for more than 10 years towards areas with relative stability in rainfall rates) and deducing the most important direct repercussions on human and economic activity in these pockets with the highest temperatures.

Lines 125-146 of the resubmitted revised manuscript presented more of scientific contributions of this research.

4. What are the limitations and implications of this study? Please add it at the end of the discussion.

The prolonged war has created extremely challenging working conditions. The destruction of meteorological monitoring stations and the inability to conduct field surveys on climate impacts throughout the conflict—due to the fragmented control of territories by different groups—have severely limited direct data collection. Moreover, the restricted or unsafe movement of researchers during the war has made field surveys virtually impossible.

Given these constraints, remote sensing methods, when cross-referenced with findings from various studies that have examined the impacts of climate change on migration patterns, population movements, and agricultural production, can significantly enhance the reliability of analytical results. Integrating these data sources offers a comprehensive and scientifically robust approach to understanding climate-related changes despite the absence of conventional field-based observations.

This was added as new subsection (3.6) in the revised manuscript.

5. The drought results, e.g., 23 drought-affected areas, from TCI should be validated.

Currently we don’t have any ground-truth measurements or any official information about annual agricultural production at the scale national or regional, in addition to the limitations of doing any fields survey and so comparison with other studies is the only option we have.

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a valuable contribution to the understanding of drought impacts in Syria. The manuscript addresses a critical gap by focusing not only on the general trend of drought but also on pinpointing the exact locations where interventions are most needed. It is very interested to readers. There are some comments which may help authors to improve the quality of the manuscript. 
1. The manuscript could provide a more detailed explanation of data preprocessing steps, especially regarding the handling of missing data and cloud cover in LST retrievals in the method seciton. 

2. It is recommended that the authors include a map of the study area in Section 2.1, which includes a map of the surrounding geography (e.g., a map of Syria plus a map of the neighbouring countries), so that readers can get a clear picture of the geographic features of the study area.
3. If possible, include validation of the LST-derived drought pockets with ground-based observations, such as meteorological station data or documented drought events.
4. It is suggested that the authors add a discussion section, where the content of the results section in section 3 can be put into the discussion section. and also, discuss the limitations of using LST and TCI as sole indicators of drought without considering precipitation and soil moisture data.
5. Improve the clarity of figures and tables by enhancing resolution and ensuring that all labels and legends are legible.
7. line 200, '3.1. Examination the General Trend of LST:' better should be 'Examination the General Trend of LST...' ; line 161, '. Sen’ slope' should be ' Sen’ s slope', similar problem should be checked all through manuscript.

I am looking forward to seeing your revised manuscript.

Author Response

1. The manuscript could provide a more detailed explanation of data preprocessing steps, especially regarding the handling of missing data and cloud cover in LST retrievals in the method section. 

In the section 2 of the revised manuscript, we presented more explanation about the data and the preprocessing steps.

Concerning the handling of missing data and cloud cover in LST retrievals, we did not use the QA file included inside the used MODIS products for keeping the maximum of data and the continuity of time series in the analysis. In addition, several references used in this research had taken the same choice. We thought about this point when deepening our future research in each discovered affected pocket.  

2. It is recommended that the authors include a map of the study area in Section 2.1, which includes a map of the surrounding geography (e.g., a map of Syria plus a map of the neighboring countries), so that readers can get a clear picture of the geographic features of the study area.

Done in the revised manuscript.

3. If possible, include validation of the LST-derived drought pockets with ground-based observations, such as meteorological station data or documented drought events.

In absence of ground-truth meteorologic measurements in Syria where the majority of weather stations is old and not automated, it was difficult to achieve this task especially when working with maximum registered temperature used in used MODIS products.

4. It is suggested that the authors add a discussion section, where the content of the results section in section 3 can be put into the discussion section. and also, discuss the limitations of using LST and TCI as sole indicators of drought without considering precipitation and soil moisture data.

We covered this point in the introduction, especially in the lines 92-114 of the revised manuscript.  

5. Improve the clarity of figures and tables by enhancing resolution and ensuring that all labels and legends are legible.

Done in the revised manuscript.

6. line 200, '3.1. Examination the General Trend of LST:' better should be 'Examination the General Trend of LST...' ; line 161, '. Sen’ slope' should be ' Sen’ s slope', similar problem should be checked all through manuscript.

Done in the revised manuscript.

 

Reviewer 3 Report

Comments and Suggestions for Authors

The article is well written and has a good structure. Nevertheless, some issues should be addressed. In particular:

1. In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation; for example [1], [1–3] or [1,3]. For embedded citations in the text with pagination, use both parentheses and brackets to indicate the reference number and page numbers; for example [5] (p. 10). or [6] (pp. 101–105).

2. Use the right format for tables for this journal.

3. Use the right format for presenting the references.

4. The agricultural stability zones are described but need additional clarity regarding their boundaries.

5. The explanation of precipitation levels (e.g., "not less than 200 mm in most years") is vague. Specify the criteria used to define these thresholds.

6. Figure 1 is referenced but not well-integrated into the text. Provide a brief discussion of each step in the flowchart to guide readers through the methodology.

7. The use of Mann-Kendall and Sen's slope is appropriate, but the confidence intervals and significance levels should be elaborated on. For instance, explain why a 95% confidence level was selected and if other levels (e.g., 99%) were tested.

8. The article does not discuss the validation of the TCI results.

9. The MOD11A2 and MOD11C3 datasets have different resolutions (1 km vs. 5.6 km). Address how these differences were handled to ensure consistency in the analysis.

Comments on the Quality of English Language

A revision of the  English language is required.

Author Response

1. In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation; for example [1], [1–3] or [1,3]. For embedded citations in the text with pagination, use both parentheses and brackets to indicate the reference number and page numbers; for example [5] (p. 10). or [6] (pp. 101–105).

Done in the revised manuscript.

2. Use the right format for tables for this journal.

We modified it in the revised manuscript.

3. Use the right format for presenting the references.

Done in the revised manuscript.

4. The agricultural stability zones are described but need additional clarity regarding their boundaries.

More explanation about the agricultural stability zones was added in the section 2.2. of the revised manuscript.

5. The explanation of precipitation levels (e.g., "not less than 200 mm in most years") is vague. Specify the criteria used to define these thresholds.

As this note is related to the previous, we can present the following explanations:

Syria’s agricultural stability zones are categorized based on average total rainfall into five distinct zones. The thresholds for these zones, as defined by the Land Degradation Neutrality Target Setting Program (LDN TSP) in 2020, are generally as follows:

1. Irrigated Zones: > 600 mm of rainfall
2. Semi-Arid Zones: 400 - 600 mm of rainfall
3. Sub-Humid Zones: 300 - 400 mm of rainfall
4. Arid Zones: 200 - 300 mm of rainfall
5. Hyper-Arid Zones: < 200 mm of rainfall

These thresholds help in understanding agricultural productivity, land use planning, and environmental management efforts in the region.

These categories are essential for:

- Understanding Agricultural Productivity: Different zones have varied capacities for supporting crops.

- Land Use Planning: Helps in making informed decisions regarding agricultural practices.

- Environmental Management: Aids in developing strategies for sustainability and conservation efforts in each zone.

The categories of agricultural stability zones in Syria, defined by the Land Degradation Neutrality Target Setting Program (LDN TSP), are based on hydrological and climatic criteria, specifically average total annual rainfall. The thresholds help identify the potential for agricultural productivity and guide land use planning and management strategies.

The criteria are typically informed by:

1. Climatic Data: Historical and current rainfall data collected from meteorological stations throughout Syria.
2. Soil Types: Understanding of how soil characteristics interact with rainfall to influence agricultural potential.
3. Agricultural Studies: Previous research and assessments looking at crop yields in relation to rainfall patterns.
4. Environmental Impact Assessments: Evaluations that consider the impact of land use on both productivity and ecological health.

These thresholds and categories serve as a framework for managing resources effectively in arid and semi-arid environments, addressing environmental issues like land degradation.

For specific reference, documents or reports from the LDN TSP or related studies on Syria’s agricultural practices would provide detailed methodologies and justifications for these categorizations.

6. Figure 1 is referenced but not well-integrated into the text. Provide a brief discussion of each step in the flowchart to guide readers through the methodology.

It became figure 2 in the revised manuscript where it was well discussed and detailed graphically as well as in the text.

7. The use of Mann-Kendall and Sen's slope is appropriate, but the confidence intervals and significance levels should be elaborated on. For instance, explain why a 95% confidence level was selected and if other levels (e.g., 99%) were tested.

The study scale and the nature of the data used are general and the absence of field observations required adopting 95 % Confidence Level.

95 % Confidence Level:

- Usage: It is the most commonly used level in research, indicating that there is a 95% chance that the results obtained reflect the truth in the population.

- When to Use: Typically used in studies that require a balance between result accuracy and available resources, providing an acceptable level of assurance.

99% Confidence Level:

- Usage: Indicates a 99% chance that the results are correct, meaning the results are more precise and reliable.

- When to Use: Employed in studies that require a high level of certainty, such as medical research or experiments that significantly impact critical decisions where errors are unacceptable.

Conclusion

- If the data requires a higher level of precision, a 99% confidence level can be used.

- For more general studies, 95% is often sufficient and considered an acceptable standard.

8. The article does not discuss the validation of the TCI results.

Currently we don’t have any ground-truth measurements or any official information about annual agricultural production at the scale national or regional, in addition to the limitations of doing any fields survey and so comparison with other studies is the only option we have.

 

9. The MOD11A2 and MOD11C3 datasets have different resolutions (1 km vs. 5.6 km). Address how these differences were handled to ensure consistency in the analysis.

The choice to use both the MOD11A2 and MOD11C3 products, despite their different spatial resolutions, was guided by the need to address both the detailed thermal trends at the surface level and the broader climatic context over time.

MOD11A2, with its higher spatial resolution (1 km), was essential for calculating the annual surface temperature slope using Mann-Kendall and Sen's slope over a period of 22 years. This allows us to capture precise local variations in surface temperature and detect areas experiencing significant thermal increases. Such detailed analysis is critical for understanding short-term extreme weather conditions, such as heatwaves, which are key indicators of thermal stress on a localized level.

On the other hand, MOD11C3, with a coarser spatial resolution but regular monthly coverage, was selected for calculating the Temperature Condition Index (TCI). This product provides a broader perspective on long-term climate trends and helps identify areas experiencing recurrent drought conditions. By focusing on the broader spatial patterns, this product reveals the larger-scale extreme climate variability that affects human activities such as agriculture, water resources, and energy supply.

The combination of these two products allows us to capture both short-term weather-driven fluctuations and long-term climate dynamics, providing a more comprehensive understanding of drought from a thermal perspective. The higher-resolution product (MOD11A2) gives insight into the increasing intensity of heat over time, while the lower-resolution product (MOD11C3) offers a macro-level view of extreme climate pressures that persist over longer periods.

Reviewer 4 Report

Comments and Suggestions for Authors

I don’t recommend this manuscript for publication in its present form. A major revision is required. My concerns were listed as follows,

• The abstract must be well organized to highlight the new methods and new findings derived from a quantitative analysis. This section only alliteratively addresses its contribution but lacks of persuasive evidence.
• Scientific importance of this manuscript must be particularly reinforced.  
• Research gaps of previous studies must be clearly addressed. What are the disadvantages of site-based weather records? To what extent can the remotely sensed data can overcome the shortcomings of weather records?  
• In lines 190-194, LSTs derived from MOD11A2 and MOD11C3 datasets have different resolution. Why use these two datasets? Details of processing these datasets must be clearly provided.
• In Figure 1, only MOD11A2 LST was shown. Does this mean MOD11C3 data was abandoned?
• What are the new findings that can cover the knowledge gaps between previous studies? What are the innovatory design of this study?
• Please address the linkage between draught, war conflicts, and pockets affected using the combinations of remotely sensed LST and nighttime light and socioeconomic data. If it is possible, a multiple regression using these indicators is expected.

Author Response

1. The abstract must be well organized to highlight the new methods and new findings derived from a quantitative analysis. This section only alliteratively addresses its contribution but lacks of persuasive evidence.

We improved the abstract in the revised manuscript and it would be more coherent with the its adjusted structure.

2. Scientific importance of this manuscript must be particularly reinforced.  

We added to the introduction a new paragraph (lines 125-146 of the revised manuscript) containing the key scientific contributions of this research.

3. Research gaps of previous studies must be clearly addressed. What are the disadvantages of site-based weather records? To what extent can the remotely sensed data can overcome the shortcomings of weather records?  

The absence of ground monitoring mechanisms, the age and quality of weather stations before the war, and their inappropriate geographical distribution, in addition to the destruction of a large number of them due to the long civil war in the country are the main disadvantages of weather stations in Syria.

The introduction in the revised version tried to improve the discussion of research gaps of previous studies, and also give more explanations about the state of meteorological information in Syria and the advantages of used remote sensing data in such research in underdevelopment countries suffering a long-term civil war.  

4. In lines 190-194, LSTs derived from MOD11A2 and MOD11C3 datasets have different resolution. Why use these two datasets? Details of processing these datasets must be clearly provided.

We improved the section of methodology and the graphic farmwork in the revised version where each step of data processing was detailed.

The new text clarified the justification of using the two mentioned products which could be explained as following:

The choice to use both the MOD11A2 and MOD11C3 products, despite their different spatial resolutions, was guided by the need to address both the detailed thermal trends at the surface level and the broader climatic context over time.

MOD11A2, with its higher spatial resolution (1 km), was essential for calculating the annual surface temperature slope using Mann-Kendall and Sen's slope over a period of 22 years. This allows us to capture precise local variations in surface temperature and detect areas experiencing significant thermal increases. Such detailed analysis is critical for understanding short-term extreme weather conditions, such as heatwaves, which are key indicators of thermal stress on a localized level.

On the other hand, MOD11C3, with a coarser spatial resolution but regular monthly coverage, was selected for calculating the Temperature Condition Index (TCI). This product provides a broader perspective on long-term climate trends and helps identify areas experiencing recurrent drought conditions. By focusing on the broader spatial patterns, this product reveals the larger-scale extreme climate variability that affects human activities such as agriculture, water resources, and energy supply.

The combination of these two products allows us to capture both short-term weather-driven fluctuations and long-term climate dynamics, providing a more comprehensive understanding of drought from a thermal perspective. The higher-resolution product (MOD11A2) gives insight into the increasing intensity of heat over time, while the lower-resolution product (MOD11C3) offers a macro-level view of extreme climate pressures that persist over longer periods.

5. In Figure 1, only MOD11A2 LST was shown. Does this mean MOD11C3 data was abandoned?

It became figure 2 in the revised manuscript where it was well discussed and detailed graphically as well as in the text. We gave more details and explanations about MOD11A2 and MOD11C3 and the utility of their use for tracking and understanding the temperature rise in Syria.

6. What are the new findings that can cover the knowledge gaps between previous studies? What are the innovatory design of this study?

In addition to the discussion of previous studies apported in the introduction, lines 125-146 of the resubmitted revised manuscript presented more of scientific contributions of this research.

7. Please address the linkage between draught, war conflicts, and pockets affected using the combinations of remotely sensed LST and nighttime light and socioeconomic data. If it is possible, a multiple regression using these indicators is expected.

In this research we tried to present a deeper spatial understanding of state of temperature rise in Syria. The employed methodology, with its advantages and limitations, could quantify some impacts of this phenomenon. Our review of previous studies about climate change in Syria and its influence on the stability socio-economic and politic, allowed us to relate subjectively these mentioned elements.

The experimentation of any kind of multiple regression necessitates the availability official census or some accurate information about the demographic behavior, agricultural products, water resource variation, etc which is note the case of destroyed country such as Syria. With the new political change in Syria maybe we need more time to build a new spatial database allowing do such task or at minimum achieve a field survey to measure directly several dimensions of such research problematic. 

Reviewer 5 Report

Comments and Suggestions for Authors

Here are my comments about the manuscript titled ‘Identifying the Pockets Most Affected by Rising Temperatures and Assessing Their Impact on Population Centers and Their Agricultural Lands’.

 

• Both MOD11A2 and MOD11C3 were used in this study. MOD11A2 was used to examinate the temperature rise, while MOD11C3 was used to identify the pockets affected by temperature rise. It is difficult to understand the relationship between the two steps, because the above datasets have different spatial and temporal resolutions.
• Line 173-174, why 40% were chosen as the threshold, the basic should be added.
• To the best of my knowledge, drought is a long-term process, can LST alone characterize it?
• Due to the significant influence of solar radiation on LST, only daytime LST was used in this work, is the result consistent at nighttime?
• ASE was included in the manuscript, is there any new insights?
• The authors mentioned the population, how to obtain the spatial distribution of the dataset?
• Some errors should be checked carefully, e.g., Line 211, ‘special’?

Comments on the Quality of English Language

 The English could be improved to more clearly express the research.

Author Response

1. Both MOD11A2 and MOD11C3 were used in this study. MOD11A2 was used to examinate the temperature rise, while MOD11C3 was used to identify the pockets affected by temperature rise. It is difficult to understand the relationship between the two steps, because the above datasets have different spatial and temporal resolutions.

The choice to use both the MOD11A2 and MOD11C3 products, despite their different spatial resolutions, was guided by the need to address both the detailed thermal trends at the surface level and the broader climatic context over time.

MOD11A2, with its higher spatial resolution (1 km), was essential for calculating the annual surface temperature slope using Mann-Kendall and Sen's slope over a period of 22 years. This allows us to capture precise local variations in surface temperature and detect areas experiencing significant thermal increases. Such detailed analysis is critical for understanding short-term extreme weather conditions, such as heatwaves, which are key indicators of thermal stress on a localized level.

On the other hand, MOD11C3, with a coarser spatial resolution but regular monthly coverage, was selected for calculating the Temperature Condition Index (TCI). This product provides a broader perspective on long-term climate trends and helps identify areas experiencing recurrent drought conditions. By focusing on the broader spatial patterns, this product reveals the larger-scale extreme climate variability that affects human activities such as agriculture, water resources, and energy supply.

The combination of these two products allows us to capture both short-term weather-driven fluctuations and long-term climate dynamics, providing a more comprehensive understanding of drought from a thermal perspective. The higher-resolution product (MOD11A2) gives insight into the increasing intensity of heat over time, while the lower-resolution product (MOD11C3) offers a macro-level view of extreme climate pressures that persist over longer periods.

2. Line 173-174, why 40% were chosen as the threshold, the basic should be added.

The research of Kogan 1997 (mentioned in the text as [37]) discovered that when the temperature become close of its maximum historic, where the difference is lower than 35-40%, the agricultural production will be affected.

According to your comment we added this fact.

3. To the best of my knowledge, drought is a long-term process, can LST alone characterize it?

Surly it cannot characterize it alone, but in underdevelopment countries such Syria suffering from long-term civil war and lacks a reliable meteorological database, we have to find a reasonable alternative as like as the wide used MOD11C3 and MOD11A2 products. Anyway we covered this point in the introduction, especially in the lines 92-114 of the revised manuscript. 

4. Due to the significant influence of solar radiation on LST, only daytime LST was used in this work, is the result consistent at nighttime?

The used algorithm in MOD11C3 and MOD11A2 calculate the maximum monthly and 8-days, temperature successively. The platform TERRA crosses Syria only in the morning at local time.

5. ASE was included in the manuscript, is there any new insights?

We gave more insight about it in the revised manuscript.

6. The authors mentioned the population, how to obtain the spatial distribution of the dataset?

The new revised manuscript gives additional description of the used population layer, but we cannot confirm the method used by the provider to produce this layer. The Urban Community layer was initially based on the 2004 population census (Central Bureau of Statistics), with subsequent updates in 2011 and 2019, reflecting demographic changes over time.

7. Some errors should be checked carefully, e.g., Line 211, ‘special’?

We corrected the mentioned errors in the revised manuscript.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Thank you for addressing my comments. I believe that you have improved your manuscript.

Author Response

I would like to express our sincere gratitude for your insightful evaluations of our resubmitted manuscript

Reviewer 4 Report

Comments and Suggestions for Authors

I am glad to read this revised manuscript that adds many useful details to make it more readable and understandable. The authors' effort in presenting the spatiotemporal pattern of draught stress and related famine and humanitarian assistance is very important to help the rest of the world to understand what happened in Syria.  I recommend this manuscript for publication after a minor revision. I suggest the authors to 1) replace the current zonal lines with grey and dim colors with more highlighted ones;2)add the web linkes for data sources used in section 2.2.

Author Response

The authors are very thankful for your thoughtful reading and valuable suggestions, which we hope helped improve the manuscript's quality and clarity. 

1) replace the current zonal lines with grey and dim colors with more highlighted ones;

We have replaced the figure with more clear zonal lines.

2)add the web links for data sources used in section 2.2.

We have added the link for the dataset used in this study

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

All the comments have been addressed, and I have no further question.

Author Response

I would like to express our sincere gratitude for your insightful evaluations of our resubmitted manuscript