Non-Negligible Urbanization Effects on Trend Estimates of Total and Extreme Precipitation in Northwest China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments to the Authors:

I suggest that you include a few key quantitative results in the abstract, such as estimated trends or percentage differences between urban and rural stations for the most relevant indices.

 

The Introduction provides a solid overview of previous research and clearly sets the context of the study. I suggest expanding on this with a brief summary of the significance of this research, to immediately highlight the value of the work for the reader. The final two sentences, which refer to the structure of the manuscript, seem unnecessary in the introduction and could be omitted or moved to a more appropriate section.

 

I recommend adding a scale bar and a north arrow to Figure 1 to improve orientation. It would also be helpful to include the locations of at least a few major cities within the study area, to enhance clarity and readability for the reader.

 

It might be useful to include information about the altitude of the precipitation stations in the methodology section, such as the average altitude and the number of stations in different elevation zones. This would help readers better understand the potential influence of altitude on the results.

 

Are the authors aware of the details related to precipitation observational stations, in the sense of whether some precipitation stations in the multi-decade period followed by the analysis experienced a transformation from rural to urban? If so, it would be useful to clarify how such stations are classified and treated in the analysis (whether and how the possible change in the degree of urbanization over time was taken into account)?

 

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

 

Thank you so much for your comments and suggestions. We have made a major revision of the manuscript, addressing all the concerns raised by you. Below are our responses to the comments, with the original comments in black and the responses in blue. The content in blue font with underline refers to the modifications or additions made in the article.

 

Best regards,

 

Panfeng Zhang

 

 

Comments 1: I suggest that you include a few key quantitative results in the abstract, such as estimated trends or percentage differences between urban and rural stations for the most relevant indices.

 

Response 1: Thanks a lot for the comments. In the revised manuscript, a few key quantitative results have been added in the abstract. Including the urbanization contribution of some indices which are significantly affected by urbanization in the sampled urban areas of NWC and Urumqi. The specific changes in the revised manuscript (line 27-28; line 30-32) are as follows:

 

" The R10mm, R95pTOT, R99pTOT, and PRCPTOT indices in the sampled urban areas of NWC exhibited statistically significant negative urbanization effects, reaching -0.08 days decade^-1, -3.8% decade^-1, -2.4% decade^-1, and -3.5% decade^-1, respectively. However, the R95pTOT, SDII, CDD, and CWD indices at the urban station of the largest city, Urumqi City, have been significantly positively affected by urbanization, which is inconsistent with the sampled urban areas of NWC, the urbanization effect reaching 6.9% decade^-1, 0.05 mm·d^-1 decade^-1, 0.23 days decade^-1, and 0.11 days decade^-1, respectively."

 

The urbanization contribution was calculated only when the urbanization effect is statistically significant at the level 0.05.

 

Comments 2: The Introduction provides a solid overview of previous research and clearly sets the context of the study. I suggest expanding on this with a brief summary of the significance of this research, to immediately highlight the value of the work for the reader. The final two sentences, which refer to the structure of the manuscript, seem unnecessary in the introduction and could be omitted or moved to a more appropriate section.

 

Response 2: Thanks for the comments. In the revised manuscript, a summary of the significance of this research has been added. The specific changes in the article (line 93 to 98) are as follows:

 

"By isolating the impact of urbanization from the changing trends of rainfall/extreme rainfall in NWC over the past few decades, we can obtain the changing trends of precipitation/extreme precipitation in the region under natural background conditions. This will enhance our understanding of the scientific facts regarding natural climate change in NWC."

 

Meanwhile, the final two sentences have been deleted.

 

Comments 3: I recommend adding a scale bar and a north arrow to Figure 1 to improve orientation. It would also be helpful to include the locations of at least a few major cities within the study area, to enhance clarity and readability for the reader.

 

Response 3: Thanks a lot. In the revised manuscript, a north arrow and a scale bar have been added to Figure 1. In addition, the name labels of five big cities, Urumqi, Xi'an, Lanzhou, Yinchuan and Xining, have been added, as shown in the following figure.

 

Considering the boundary issue, national boundaries, provincial boundaries and the boundaries of the study area are also added to Figure 1. Due to the above changes, we have also supplemented the description of the diagram. The specific changes in the article (line 125-137) are as follows:

"The thick black solid line with gray shadow is the national boundary, the thin solid line is the provincial boundary. In Figure 1 , the abbreviations XA, WLMQ, XN, LZ, and YC refer to Xi’an, Urumqi, Xining, Lanzhou, and Yinchuan, respectively. Due to the excessive number of LULC types, the legends in Figure 1 are overcrowded and overlap with one another. Therefore, the land cover types were reclassified into 7 major categories. During the reclassified process, the original urban areas, permanent snow, water bodies, and bare areas were preserved, and the remaining land were reclassified into three categories: grassland, cropland, and tree cover. Such as, the land cover of deciduous broadleaved tree, deciduous needleleaved tree, and mixed leaf type (broadleaved and needleleaved) are reclassified as tree cover."

 

Comments 4: It might be useful to include information about the altitude of the precipitation stations in the methodology section, such as the average altitude and the number of stations in different elevation zones. This would help readers better understand the potential influence of altitude on the results.

 

Response 4: Thanks for the comments. In the revised manuscript, the average altitude and altitude range of all stations used in the study are described in the method section. The specific description in the article (line 187 to 189) is as follows:

 

"The average altitude of the stations used in this study is 1400.2 m, distributed across different altitude tiers as follows: 43 stations below 500 m, 89 stations at 500-1000 m, 114 stations at 1000-1500 m, and 118 stations above 1500 m "

 

In the calculation process, it can be found that the altitudes of these stations in NWC vary greatly.

 

Comments 5: Are the authors aware of the details related to precipitation observational stations, in the sense of whether some precipitation stations in the multi-decade period followed by the analysis experienced a transformation from rural to urban? If so, it would be useful to clarify how such stations are classified and treated in the analysis (whether and how the possible change in the degree of urbanization over time was taken into account)?

 

Response 5: Thanks for the comments. We realize that precipitation stations may have undergone changes over the past 60 years. Using 2020 LULC data to divide urban and rural stations, on the one hand, the results of being classified as rural stations can indicate that they were also rural stations from 1961 to 2020, that is, they were not affected by urbanization. On the other hand, being classified as urban stations can indicate that they were affected by urbanization before 2020, but the starting time of urbanization was different. However, we do not need to specifically figure out in which exact year this station began to be affected by urbanization, i.e. the degree of urbanization impact over time; it is sufficient to know that the station is currently affected by urbanization to determine the impact of urbanization.

 

In response to your consideration, I can give an example to illustrate. Based on the results of the 2020 LULC partitioning, let's assume that one of these stations (number: 9999) is an urban station, and that station was transformed from a rural station to an urban station in 2000 (a year between 1961 and 2020). During the calculation of the difference time series (all stations - rural stations), the precipitation changes of the station before 2000 are roughly consistent with the background station changes, so the data of the station before 2020 did not change much compared to the data of the rural station when making the difference sequence. However, the difference sequence after 1990 will obtain urbanization effect from this station, so it can reflect the long-term effect of urbanization, which is consistent with our main research objective.

 

We hope this clarification addresses your concern. Thank you again for this valuable comment.

 

At the same time, the explanation for dividing urban and rural stations using only 2020 LULC has been added in this article (line 177-182)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The topic of extreme precipitation changes in Northwest China is of high significance, and the authors’ attempt to quantify urbanization effects adds potential value. However, in its current form, the manuscript faces important methodological and representativeness issues that substantially affect the robustness and credibility of the results. I recommend a major revision before this work can be considered for publication. My detailed comments are as follows:
1 The classification of urban and rural stations relies on land use data. If only the 2020 ESA CCI dataset was used, then the urban–rural distinction is only valid for that year, which is problematic for analyzing trends from 1961 to 2022. Please clarify whether multiple years of land cover data were applied. If not, the station classification lacks temporal representativeness.
2 The Figure 1 is visually overloaded, with no legend, national, or provincial boundaries. Referring readers to an external URL for the legend is not an appropriate solution. The figure should be simplified, ideally by reclassifying into a few major land cover types, while ensuring that political boundaries are included for clarity.
3 LINE 123-134. The manuscript only provides abbreviations of the 11 ETCCDI indices. While it is unnecessary to reproduce the full definitions, at least short descriptive phrases should be provided. Without these, non-specialist readers will find the section inaccessible.
4 In line 152, the author says that station elevation differs and affects precipitation, leading to high spatial variability in precipitation records. Is the difference in precipitation determined solely by elevation?
5 The choice of 2° × 2° resolution is very coarse, even for global-scale studies. For a regional analysis in Northwest China, this resolution likely undermines representativeness. The authors should justify this choice more convincingly, and consider whether higher-resolution reanalysis data (with validation against ground stations) might provide a more robust basis.
6 Ultimately, only 26 grid cells containing 194 stations (44 rural and 150 urban) were included. This is a relatively small and uneven sample for a region as vast as Northwest China, and the results may be more representative of a few major cities rather than the whole region. The authors should clearly acknowledge this limitation and avoid overgeneralizing the findings as representative of “entire NWC.”

Author Response

Dear Reviewer,

 

Thank you so much for your comments and suggestions. We have made a major revision of the manuscript, addressing all the concerns raised by you. Below are our responses to the comments, with the original comments in black and the responses in blue. The content in blue font with underline refers to the modifications or additions made in the article.

 

Best regards,

 

Panfeng Zhang

 

 

Comments 1: The classification of urban and rural stations relies on land use data. If only the 2020 ESA CCI dataset was used, then the urban–rural distinction is only valid for that year, which is problematic for analyzing trends from 1961 to 2022. Please clarify whether multiple years of land cover data were applied. If not, the station classification lacks temporal representativeness.

 

Response 1: Thanks for the comments. We only used the 2020 Land Use/Land Cover (LULC) dataset to classify urban and rural stations. The reason for this approach is as follows: if a station is identified as a rural station based on the 2020 LULC data, it can be assumed that the station has remained a rural station since its establishment and has not been affected by urbanization. In contrast, if a station is classified as an urban station using the 2020 LULC data, it can be inferred that the station began to be affected by urbanization (to a greater or lesser extent) at some year in or before 2020, and there is no need to determine the exact year when this urbanization impact started. This approach is quite common in studies investigating the impact of urbanization on climate change trends (such as those of temperature or precipitation), as exemplified by Zhang et al. (2021) and Tysa et al. (2019).

 

The above classification relies on the assumption that no historical relocations of the stations have occurred. However, some stations in China have been relocated. Therefore, we used a homogenized adjusted Chinese precipitation dataset to minimize the impact caused by such relocations as much as possible. Additionally, since the study period of our research is 1961-2022, the 2022 LULC dataset would have been the ideal choice. Nevertheless, when this study commenced, the most recent dataset released by the European Space Agency (ESA) was the 2020 version. For this reason, we used the 2020 LULC data to calculate the percentage of urban LULC for each station, which was then used to classify the stations into urban and rural categories. There may still have been some changes in LULC in northwestern China during 2021 and 2022, with an increase in urban LULC. As a result, a small number of stations currently classified as rural stations based on the 2020 LULC data might have been slightly affected by urbanization by 2022. However, we estimate that such changes are minimal, and the overall impact of urbanization can be neglected.

 

In response to your comments, we have added additional textual descriptions in the section of the method that explains the classification method for urban and rural stations to clarify this issue. For details, please refer to lines 177 - 182 in the main text. We hope this clarification addresses your concern. Thank you again for this valuable comment.

 

Comments 2: Figure 1 is visually overloaded, with no legend, national, or provincial boundaries. Referring readers to an external URL for the legend is not an appropriate solution. The figure should be simplified, ideally by reclassifying into a few major land cover types, while ensuring that political boundaries are included for clarity.

 

Response 2: Thanks a lot. In the revised manuscript, national boundaries, provincial boundaries and the boundaries of the study area are also added to Figure 1. The LULC types have been reclassified into several major types based on the relationships between legends. Such as, the LULC of deciduous broadleaved tree, deciduous needleleaved tree, and mixed leaf type (broadleaved and needleleaved) are reclassified as tree cover."

 

Considering the orientation and relative positions of different regions, a north arrow and a scale bar have been added to Figure 1. We also added five big cities in the figure: Urumqi, Xi'an, Lanzhou, Yinchuan and Xining, as shown in the following Figure.

 

Due to the above changes, we have also supplemented the description of the diagram. The specific changes in the article (line 125-137) are as follows:

"The thick black solid line with gray shadow is the national boundary, the thin solid line is the provincial boundary. In Figure 1, the abbreviations XA, WLMQ, XN, LZ, and YC refer to Xi’an, Urumqi, Xining, Lanzhou, and Yinchuan, respectively. Due to the excessive number of LULC types, the legends in Figure 1 are overcrowded and overlap with one another. Therefore, the LULC types were reclassified into 7 major categories. During the reclassified process, the original urban areas, permanent snow, water bodies, and bare areas were preserved, and the remaining LULC were reclassified into three categories: grassland, cropland, and tree cover. Such as, the LULC of deciduous broadleaved tree, deciduous needleleaved tree, and mixed leaf type (broadleaved and needleleaved) are reclassified as tree cover. "

 

Comments 3: LINE 123-134. The manuscript only provides abbreviations of the 11 ETCCDI indices. While it is unnecessary to reproduce the full definitions, at least short descriptive phrases should be provided. Without these, non-specialist readers will find the section inaccessible.

 

Response 3: Thanks a lot for the comments. In the revised manuscript, short descriptive phrases about the 11 ETDCCI indices have been added. The specific changes in the article (line 150-156) are as follows:

 

"(1) relative threshold indices: R95pTOT (Annual total PRCP when RR > 95p); R99pTOT (Annual total PRCP when RR > 99p), (2) absolute threshold indices: R10mm (Annual count of days when PRCP≥ 10mm); R20mm (Annual count of days when PRCP ≥ 20mm); CDD (maximum number of consecutive days with RR < 1mm); CWD (maximum number of consecutive days with RR ≥ 1mm), (3) extreme value indices: Rx1day (Monthly maximum 1-day precipitation); Rx5day (Monthly maximum 5-day precipitation:), and (4) other indices PRCPTOT (Annual total precipitation in wet days), SDII (Simple precipitation intensity index). "

 

Comments 4: In line 152, the author says that station elevation differs and affects precipitation, leading to high spatial variability in precipitation records. Is the difference in precipitation determined solely by elevation?

 

Response 4: Thank you to the reviewer for raising this important question. The high spatial variability of precipitation cannot be simply attributed to the factor of altitude differences. In the revised manuscript, the text of this section has been rewritten as “Station elevation differs and affects precipitation, apart from the altitude factor, other factors (such as distance from the sea, prevailing wind patterns, and local topography) can also lead to an unusually large spatial variability in precipitation.”, as shown in line 190-193.

 

 

Comments 5: The choice of 2° × 2° resolution is very coarse, even for global-scale studies. For a regional analysis in Northwest China, this resolution likely undermines representativeness. The authors should justify this choice more convincingly, and consider whether higher-resolution reanalysis data (with validation against ground stations) might provide a more robust basis.

 

Response 5: Thanks a lot for the comments. We fully agree that, ideally, using higher spatial resolution data for region analysis can indeed better capture details.

 

However, for the specific research area of NWC, the distribution of meteorological observation stations is relatively sparse and uneven, with most stations concentrated in oases and river valleys, and very few stations in vast deserts and high mountain areas. This uneven distribution brings a crucial trade-off to our analysis: the trade-off between spatial resolution and the effective sample size within each grid.

 

To find the most suitable resolution, we once systematically tested five grid sizes:

 

1. Resolution of 0.5°×0.5°: The maximum number of generated grids is achieved, but this results in most grids containing too few stations, which cannot meet the grid selection criteria (including at least one rural station and one urban station) and even leads to a large number of empty grids. Specifically, at this resolution, only 26 stations can be used for subsequent analysis, which cannot meet the needs of subsequent statistical analysis due to the significant difference in the number of stations between urban and rural areas. This will seriously weaken the statistical robustness of the results.
2. Resolution of 1°×1°: The number of generated grids is relatively large, and compared with Scheme A, the number of stations has increased, including 123 stations for subsequent analysis. However, due to the small number of stations and the uneven proportion of urban and rural stations, this will seriously weaken the statistical robustness of the results and cannot meet the requirements for subsequent statistical analysis.
3. Resolution of 1.5°×1.5°: The situation has improved, and the number of stations has increased to 189. However, the number of background stations is relatively small, and the ratio of background stations to all stations is 1:7. A small number of background stations may not fully represent the diversity of natural climate variability in the entire region. If these few background stations happen to be in a specific microclimate environment (such as in humid mountainous areas), then the precipitation benchmark of rural stations may be biased and cannot truly reflect the background situation of the region without the influence of urbanization. And due to the small sample size, the variance of its statistical results will be larger, making it more sensitive to outliers.
4. Resolution of 2°×2°: At this resolution, we successfully included 194 stations in the analysis, and the key is that at this resolution, the proportion (1:3) of urban and rural stations is uniform, providing us with the necessary statistical basis for conducting comparative analysis of urban and rural areas within the grid.
5. Resolution of 2.5°×2.5°: The resolution is too rough, and the number of stations has increased dramatically, including 314 stations, which cannot accurately reflect the differences between regions

 

In summary, under the current research framework based on station observation data, selecting a resolution of 2°×2° is based on the objective reality of station density in the study area, with the aim of prioritizing the robustness of statistical analysis. We appreciate the reviewer's suggestions, which have helped us clarify the methodological considerations behind this approach. The specific changes in the article (line 218-221) are as follows:

 

"When considering the gridding resolution in NWC, a series of experiments were carried out. Finally, the grid 2°×2° scheme was adopted as a compromise -- ensuring sufficient spatial coverage of the study area during gridding while avoiding excessively coarse resolution."

 

Comments 6: Ultimately, only 26 grid cells containing 194 stations (44 rural and 150 urban) were included. This is a relatively small and uneven sample for a region as vast as Northwest China, and the results may be more representative of a few major cities rather than the whole region. The authors should clearly acknowledge this limitation and avoid overgeneralizing the findings as representative of “in the sampled urban areas of NWC.”

 

Response 6: Thanks a lot. We clearly acknowledge this limitation and avoid overgeneralizing the findings as representative of “in the sampled urban areas of NWC”. We have changed all 'in the sampled urban areas of NWC' in the article to 'in the sampled urban areas of NWC'.

 

At the same time, we also highlighted the issue of insufficient spatial coverage in the article. The specific changes in the article (line 231-233) are as follows:

 

“At last, 26 representative grid cells containing 194 observational stations were ultimately selected for subsequent analysis, comprising 44 rural stations and 150 urban stations, which is a relatively small and uneven sample for a region as vast as NWC, and the results may be more representative of the sampled urban areas of NWC rather than the entire NWC.”

 

 

 

References

 

Tysa, S. K., G. Ren, Y. Qin, P. Zhang, Y. Ren, W. Jia, and K. Wen, 2019: Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations. J. Geophys. Res. Atmos., 124, 10646–10661, https://doi.org/10.1029/2019JD030948.

Zhang, P., and Coauthors, 2021: Urbanization Effects on Estimates of Global Trends in Mean and Extreme Air Temperature. Journal of Climate, 34, 1923–1945, https://doi.org/10.1175/JCLI-D-20-0389.1.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

I appreciate the paper and the argument, but I have some questions about the method used to identify the effect of urbanization on the precipitation trend:

1. I do not fully understand why you did not directly use the urban rainfall gauges to assess the rainfall trend, rather than relying on the difference between the total gauges and the rural ones. Could you provide a comparison?

2. Have you checked whether there are maps of the temporal evolution of urbanized areas, whether these areas have expanded, and whether there is a relationship between this expansion and your results?

3. It is not entirely clear to me whether you have investigated the effect of the size of urban areas on your results. Could you clarify this aspect in more detail?

Author Response

Dear Reviewer,

 

Thank you so much for your comments and suggestions. We have made a major revision of the manuscript, addressing all of the concerns raised by you. Below are our responses to the comments, with the original comments in black and the responses in blue. The content in blue font with underline refers to the modifications or additions made in the article.

 

Best regards,

 

Panfeng Zhang

 

 

Comments 1: I do not fully understand why you did not directly use the urban rainfall gauges to assess the rainfall trend, rather than relying on the difference between the total gauges and the rural ones. Could you provide a comparison?

 

Response 1: Thank you for this important question.

 

Our objective is to study the impact of urbanization on the changing trends of precipitation (or extreme precipitation). According to the hypothesis, the expansion of urban areas driven by the urbanization process causes some meteorological stations originally located in rural or suburban areas to gradually move closer to urban areas or become surrounded by cities. This leads to a decrease in wind speed around these meteorological stations, which further results in an increase in the precipitation (rainfall and snowfall) captured by the rain gauges at the stations. We believe that this increase in precipitation captured by rain gauges, caused by the decline in wind speed (attributed to urbanization), is not a real climate change under natural background conditions but rather a bias. Therefore, we use LULC data to identify truly rural stations unaffected by human activities, from which we obtain the real time series of regional precipitation (or extreme precipitation) changes under natural background. Subsequently, we calculate the difference between rural stations time series and all stations time series to derive a difference series. The trend of this difference series represents the impact caused by urbanization. Naturally, cities develop their own microclimates, such as urban rain islands or urban dry islands; thus, the impact of urbanization on precipitation is not entirely due to bias. For this reason, the title of our paper is not "A Study on Precipitation Bias Caused by Urbanization in Northwest China".

 

The research approach you mentioned, which only uses precipitation data from rain gauges at meteorological stations within urban areas, belongs to a different research direction: urban climate change research, i.e., the study of climate changes within the cities themselves. This approach only requires the use of urban stations. In contrast, our research falls under the category of urbanization impact studies, so we need to adopt the method of all stations minus rural stations. There have been many similar studies of urbanization effect conducted by previous researchers, such as those by Peterson (2003), Jones et al. (1990), Ren and Zhou et al. (2014), and Zhang et al. (2021).

 

Comments 2: Have you checked whether there are maps of the temporal evolution of urbanized areas, whether these areas have expanded, and whether there is a relationship between this expansion and your results?

 

Response 2: In our study, we did not utilize long-term LULC to check temporal evolution of urbanized areas in NWC. We only used the 2020 LULC dataset to classify urban and rural stations. The reason for this approach is as follows: if a station is identified as a rural station based on the 2020 LULC data, it can be assumed that the station has remained a rural station since its establishment and has not been affected by urbanization. In contrast, if a station is classified as an urban station using the 2020 LULC data, it can be inferred that the station began to be affected by urbanization (to a greater or lesser extent) at some year in or before 2020, and there is no need to determine the exact year when this urbanization impact started. This approach is quite common in studies investigating the impact of urbanization on climate change trends (such as those of temperature or precipitation), as exemplified by Zhang et al. (2021) and Tysa et al. (2019).

The above classification relies on the assumption that no historical relocations of the stations have occurred. However, some stations in China have been relocated. Therefore, we used a homogenized adjusted Chinese precipitation dataset to minimize the impact caused by such relocations as much as possible. Additionally, since the study period of our research is 1961-2022, the 2022 LULC dataset would have been the ideal choice. Nevertheless, when this study commenced, the most recent dataset released by the European Space Agency (ESA) was the 2020 version. For this reason, we used the 2020 LULC data to calculate the percentage of urban LULC for each station, which was then used to classify the stations into urban and rural categories. There may still have been some changes in LULC in northwestern China during 2021 and 2022, with an increase in urban LULC. As a result, a small number of stations currently classified as rural stations based on the 2020 LULC data might have been slightly affected by urbanization by 2022. However, we estimate that such changes are minimal, and the overall impact of urbanization can be neglected.

In response to your comments, we have added additional textual descriptions in the section of the method that explains the classification method for urban and rural stations to clarify this issue. For details, please refer to lines 177 - 182 in the main text. We hope this clarification addresses your concern. Thank you again for this valuable comment.

 

 

We hope this clarification addresses your concern. Thank you again for this valuable comment.

 

Comments 3: It is not entirely clear to me whether you have investigated the effect of the size of urban areas on your results. Could you clarify this aspect in more detail?

 

Response 3:

Thank you for raising this important point regarding the potential influence of urban area size on our results.

 

In our study, we did not directly calculate the impact of urban areas size on the results; instead, we calculated the percentage of urban LULC within a buffer radius of 1–12 km around each meteorological station and used this to classify the stations into urban and rural categories. This is because even for a large city, if the meteorological station affiliated with it has been in the rural area far from the city built-up extent all along, it may not be possible to detect the impact of urbanization from its data. Conversely, for a small or medium-sized city, if the meteorological station was initially built on the city's edge, it is highly likely that this station has experienced significant urbanization impacts over the past few decades amid the urbanization process.

 

Our buffer-based method directly quantifies the local LULC intensity around each station, which is a more precise and physically meaningful indicator of the potential for urbanization effects.

 

While the total urban area might have some broader, mesoscale climatic effects, our focus was on isolating the local-scale impact captured by the station's immediate surroundings.

 

We hope this clarification addresses your concern. Thank you again for this valuable comment.

 

 

 

References

Jones, P., P. Groisman, M. Coughlan, N. Plummer, W. Wang, and T. Karl, 1990: Assessment of Urbanization Effects in Time-Series of Surface Air-Temperature Over Land. Nature, 347, 169–172, https://doi.org/10.1038/347169a0.

Peterson, T. C., 2003: Assessment of urban versus rural in situ surface temperatures in the contiguous United States: No difference found. J. Clim., 16, 2941–2959, https://doi.org/10.1175/1520-0442(2003)016%253C2941:AOUVRI%253E2.0.CO;2.

Ren, G., and Y. Zhou, 2014: Urbanization Effect on Trends of Extreme Temperature Indices of National Stations over Mainland China, 1961-2008. J. Clim., 27, 2340–2360, https://doi.org/10.1175/JCLI-D-13-00393.1.

Tysa, S. K., G. Ren, Y. Qin, P. Zhang, Y. Ren, W. Jia, and K. Wen, 2019: Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations. J. Geophys. Res. Atmos., 124, 10646–10661, https://doi.org/10.1029/2019JD030948.

Zhang, P., and Coauthors, 2021: Urbanization Effects on Estimates of Global Trends in Mean and Extreme Air Temperature. Journal of Climate, 34, 1923–1945, https://doi.org/10.1175/JCLI-D-20-0389.1.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

My concern has been addressed.

Reviewer 3 Report

Comments and Suggestions for Authors

I would like to express my gratitude to the authors for the clarifications provided, which I greatly appreciated for their precision and clarity. Nevertheless, I would be interested in understanding how the results obtained for the year 2022 can be compared with those that could be derived from the analysis of data pertaining to other years.