the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Nov 2022
Marked observed interannual differences in the vegetation response to the trend towards a warmer and wetter climate in northwest China
Abstract. Located in the interior of Eurasia and to the north of the Qinghai–Tibet Plateau, the Northwest China experiences severe drought conditions as moist air from the ocean is unable to travel the long distance and penetrate the region’s mountain barriers. These special geo-climatic conditions result in Northwest China being highly sensitive to climate change. In this study, the characteristics of the response of the normalized difference vegetation index (NDVI) to the trend towards a warmer and wetter climate in Northern China from 1982 to 2019 were investigated. The results show that there were significant differences between these trends for the periods 1982–2000 and 2000–2019, with overall precipitation decreasing before 2000 but increasing afterwards. After 2000, the rate of temperature increase also slowed down, whereas the NDVI increased at an obviously faster rate. Compared with the period 1982–2000, during the period 2000–2019, the NDVI was more affected by precipitation than by the temperature. The results of a normalized linear regression also show that, for most vegetation types, the temperature played a more dominant role during the period 1982–2000, whereas precipitation had a more significant effect on the NDVI during the period 2000–2019. However, it was also found that, throughout the study period, the precipitation had a greater impact on forest NDVI and the temperature had a greater impact on the NDVI in areas of bare land. In addition, the results show that the strength of the relationship between the NDVI and climate in northwest China changed over time, with the relationship between NDVI and precipitation tending to become stronger and the relationship between NDVI and temperature tending to become weaker. The results will provide a new understanding of the relationship between vegetation and climate in northwest China and help to better cope with the risks brought by climate change.
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RC1: 'Comment on egusphere-2022-1110', Anonymous Referee #1, 16 Dec 2022
This study examines the response of vegetation NDVI to climate change in Northwest China over the recent 40 years. The results show that the relationship between NDVI and temperature weakens over time; however, the relationship between NDVI and precipitation strengthens. There are many such studies to date and some results require method support. Therefore, major revision is required. My suggestions are listed below:
(1) The description of Introduction is inadequate. For example, no sufficient evidence is provided to support why Northwest China is selected. In addition, I do not fully agree with the author’s statement that previous studies pay little attention to the long term changes of vegetation growth to climate change in Northwest China, as this region is usually included in a larger spatial extent, such as northern China, Central Eurasia or even the drylands of the Northern Hemisphere. Meanwhile, the diverse response of vegetation growth to climate variables across land surfaces has always been a hot topic and many interesting findings are found. Above all, the summary of previous studies is insufficient and arbitrary. As a result, the author is unable to give a clear scientific hypothesis.
(2) According to the results, the authors say the year of 2000 is an important turning point in time. However, there is no method description for defining the time turning point. It is unclear whether the turning point is robust and varies in space.
(3) The authors are suggested to add statistical analysis to compare the correlation coefficient of NDVI with temperature and precipitation, such as the results shown in Figure 5.
(4) It is unclear why a nine-year sliding window is used to show the time-varying sensitivity of NDVI to temperature and precipitation. How to consider the impact of solar radiation on vegetation growth? Is it a major driver?
(5) Actually it is difficult to integrate data of different spatial scales. For example, the NDVI data is at the pixel scale; however, the social statistical data is at the county or even provincial scales. It is questionable whether the human activity rather than climate can play a dominant role in shaping regional NDVI. Besides, how to distinguish the effects of afforestation on NDVI, as the analysis of land-use and-cover changes are missing in this study. The relationship between NDVI and social-economic divers such as GDP and population is very complex that should not be the focus of this study. I think the authors should focus on the topic why the response of vegetation NDVI changes over time. Ecosystem adaptation (e.g. changes in vegetation structure) or changes in environmental conditions, such as background soil moisture?
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC1 -
AC1: 'Reply on RC1', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results
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AC1: 'Reply on RC1', Shijun Zheng, 29 Mar 2023
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RC2: 'Comment on egusphere-2022-1110', Anonymous Referee #2, 16 Dec 2022
Zheng et al. investigate the variation of vegetation greenness driven by temperature and precipitation in Northwest China using GIMMS NDVI data and climate data obtained based on meteorological station observations. Their study suggests that NDVI changes were predominately driven by variation in temperature in 1982-2000 and driven by precipitation in 2000-2019. This study topic is not new both at the global and regional scale. Also, there is a lot of weakness in this study in their method, writing, and interpretation of the results. At this stage, I don't think this paper can be published on the ESD. My comments are listed as follows.
Introduction
- The first paragraph is too short to introduce the background and I find a weak linkage between the two sentences.
- Line 60 It's difficult to know what is the "relationship between vegetation and climate" represents here. The long-term trend or the interannual variability of climate? for vegetation greenness? Productivity? Growth? Or others? Also, 'As climate varies with climate', it's not clear "climate" for what?
- The previous efforts on the study topic, the knowledge gap as well as the aim of this study are very ambiguous in Line 60-69. The authors highlight the potential problems in time scales, different time periods and vegetation types, but it's confusing these problems for what? For example, I don't know what are the "different periods", what are the "different vegetation types", and what is the "study period".
- Given that this research topic is not new, they didn't clarify their improvements or their novelty in this study.
Methods
- I can't understand why they use the combined NDVI from GIMMS and MODIS datasets because as far as I know, the GIMMS NDVI has released data at least to the end of 2018.
- It’s not clear how to combine the two NDVI datasets. The authors only say that they used the pixel-wise linear regression but how to realize it in detail? They need to provide more details to show how they cope with the trend and variability of the newly combined NDVI time series. From Fig. 2, I can only see they compare the pixels of expanded NDVI with the GIMMS NDVI and MODIS NDVI for the overlapped years but this figure can't verify the time series of the expanded NDVI are credible for their long-term trend and variability.
- As the previous study (Frankenberg et al., 2020, science) points out, there are systematic biases in the AVHRR for the pre-2000 time series, the authors should take caution with the interpretation of potential change before and after 2000 when using GIMMS NDVI.
- The authors didn't state if the climate and vegetation data were detrended prior to calculating the correlations to avoid the issues like independence of the statistical analysis. If not, I think their results are not credible.
Results
- The authors completely fail to interpret their results because they didn't provide any data to support what they claimed in the result section. For example, the values of the temporal trend, the values of the correlation, etc.
- They only show the results from their figures but they never explain why those findings they got, what is mechanisms underlying their findings, and any literature to support their findings. Therefore, I don't know if their findings are robust and reliable. I think they need to discuss their results by comparing with previous studies.
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC2 -
AC2: 'Reply on RC2', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results.
-
RC3: 'Comment on egusphere-2022-1110', Anonymous Referee #3, 21 Dec 2022
This study examines the response of vegetation NDVI to climate change and human activity in Northwest China over the recent 40 years. The results showed a divergent response of vegetation to precipitation and temperature in different periods. Also, the results state that human activity played an important role. However, the study analysis is not solid and neat. Also, the topic is not clear. What message do authors what to convey (human activity vs. climate factors or both)? At this stage, I don't think this paper can be published on the ESD. My comments are listed as follows.
Major:
1 The discussion part emphasizes that the human factor is very important, but the draft spent most of the time discussing temperature and precipitation impacts on vegetation. It's better to highlight the key message author wants to convey.
2 The analysis method is not solid. For example, when mentioning significance, I have no idea how to measure it?
3 All results have no quantitive description but only a qualitative description.
4 There is no discussion behind the results.
Minor
- The landcover map is not given, it can be added to Figure 1. Also. There may be problems with the NDVI of the bare land
- How to synthesize MODIS (2000-2019) and GIMMS (1982-2015)? It is not stated clearly in the method section.
- Figure 5 and Figure 6 are hard to read, and simplifying them will be better.
- Fig11 f, y-axis mislabeled
- Fig12 How to derive GDP in no man's land (e.g., areas in northern Xinjiang)?
- In all figures, how to define no data areas? If you masked NDVI below a certain threshold, please note them in the figure and method.
- Line 80: MODIS NDVI from 2000-2019 and GIMMS NDVI from 1982-2015, but this sentence is not clear
- Line 85: Please provide the source link; this is not the source from which GIMMS is.
- Line 89: there should be a citation for the MVC method.
- What are the advantages and disadvantages of using MODIS and GIMMS in studying the response of vegetation to climate change in these areas? Please provide some references.
- Lines 114 and 124: please cite the original author
- Line 149-158: e.g., "more region" lacks a quantitative description
- Do not add transparency to the color of the figure4. It is better to set the alpha value to 1, and the leftmost triangle and rectangle of the color bar are separated.
- Line 194-196: rephrase and it is hard to understand
- Line 241-243: delete this sentence.
- Line 274: how to calculate significance?
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC3 -
AC3: 'Reply on RC3', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results.
Status: closed
-
RC1: 'Comment on egusphere-2022-1110', Anonymous Referee #1, 16 Dec 2022
This study examines the response of vegetation NDVI to climate change in Northwest China over the recent 40 years. The results show that the relationship between NDVI and temperature weakens over time; however, the relationship between NDVI and precipitation strengthens. There are many such studies to date and some results require method support. Therefore, major revision is required. My suggestions are listed below:
(1) The description of Introduction is inadequate. For example, no sufficient evidence is provided to support why Northwest China is selected. In addition, I do not fully agree with the author’s statement that previous studies pay little attention to the long term changes of vegetation growth to climate change in Northwest China, as this region is usually included in a larger spatial extent, such as northern China, Central Eurasia or even the drylands of the Northern Hemisphere. Meanwhile, the diverse response of vegetation growth to climate variables across land surfaces has always been a hot topic and many interesting findings are found. Above all, the summary of previous studies is insufficient and arbitrary. As a result, the author is unable to give a clear scientific hypothesis.
(2) According to the results, the authors say the year of 2000 is an important turning point in time. However, there is no method description for defining the time turning point. It is unclear whether the turning point is robust and varies in space.
(3) The authors are suggested to add statistical analysis to compare the correlation coefficient of NDVI with temperature and precipitation, such as the results shown in Figure 5.
(4) It is unclear why a nine-year sliding window is used to show the time-varying sensitivity of NDVI to temperature and precipitation. How to consider the impact of solar radiation on vegetation growth? Is it a major driver?
(5) Actually it is difficult to integrate data of different spatial scales. For example, the NDVI data is at the pixel scale; however, the social statistical data is at the county or even provincial scales. It is questionable whether the human activity rather than climate can play a dominant role in shaping regional NDVI. Besides, how to distinguish the effects of afforestation on NDVI, as the analysis of land-use and-cover changes are missing in this study. The relationship between NDVI and social-economic divers such as GDP and population is very complex that should not be the focus of this study. I think the authors should focus on the topic why the response of vegetation NDVI changes over time. Ecosystem adaptation (e.g. changes in vegetation structure) or changes in environmental conditions, such as background soil moisture?
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC1 -
AC1: 'Reply on RC1', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results
-
AC1: 'Reply on RC1', Shijun Zheng, 29 Mar 2023
-
RC2: 'Comment on egusphere-2022-1110', Anonymous Referee #2, 16 Dec 2022
Zheng et al. investigate the variation of vegetation greenness driven by temperature and precipitation in Northwest China using GIMMS NDVI data and climate data obtained based on meteorological station observations. Their study suggests that NDVI changes were predominately driven by variation in temperature in 1982-2000 and driven by precipitation in 2000-2019. This study topic is not new both at the global and regional scale. Also, there is a lot of weakness in this study in their method, writing, and interpretation of the results. At this stage, I don't think this paper can be published on the ESD. My comments are listed as follows.
Introduction
- The first paragraph is too short to introduce the background and I find a weak linkage between the two sentences.
- Line 60 It's difficult to know what is the "relationship between vegetation and climate" represents here. The long-term trend or the interannual variability of climate? for vegetation greenness? Productivity? Growth? Or others? Also, 'As climate varies with climate', it's not clear "climate" for what?
- The previous efforts on the study topic, the knowledge gap as well as the aim of this study are very ambiguous in Line 60-69. The authors highlight the potential problems in time scales, different time periods and vegetation types, but it's confusing these problems for what? For example, I don't know what are the "different periods", what are the "different vegetation types", and what is the "study period".
- Given that this research topic is not new, they didn't clarify their improvements or their novelty in this study.
Methods
- I can't understand why they use the combined NDVI from GIMMS and MODIS datasets because as far as I know, the GIMMS NDVI has released data at least to the end of 2018.
- It’s not clear how to combine the two NDVI datasets. The authors only say that they used the pixel-wise linear regression but how to realize it in detail? They need to provide more details to show how they cope with the trend and variability of the newly combined NDVI time series. From Fig. 2, I can only see they compare the pixels of expanded NDVI with the GIMMS NDVI and MODIS NDVI for the overlapped years but this figure can't verify the time series of the expanded NDVI are credible for their long-term trend and variability.
- As the previous study (Frankenberg et al., 2020, science) points out, there are systematic biases in the AVHRR for the pre-2000 time series, the authors should take caution with the interpretation of potential change before and after 2000 when using GIMMS NDVI.
- The authors didn't state if the climate and vegetation data were detrended prior to calculating the correlations to avoid the issues like independence of the statistical analysis. If not, I think their results are not credible.
Results
- The authors completely fail to interpret their results because they didn't provide any data to support what they claimed in the result section. For example, the values of the temporal trend, the values of the correlation, etc.
- They only show the results from their figures but they never explain why those findings they got, what is mechanisms underlying their findings, and any literature to support their findings. Therefore, I don't know if their findings are robust and reliable. I think they need to discuss their results by comparing with previous studies.
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC2 -
AC2: 'Reply on RC2', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results.
-
RC3: 'Comment on egusphere-2022-1110', Anonymous Referee #3, 21 Dec 2022
This study examines the response of vegetation NDVI to climate change and human activity in Northwest China over the recent 40 years. The results showed a divergent response of vegetation to precipitation and temperature in different periods. Also, the results state that human activity played an important role. However, the study analysis is not solid and neat. Also, the topic is not clear. What message do authors what to convey (human activity vs. climate factors or both)? At this stage, I don't think this paper can be published on the ESD. My comments are listed as follows.
Major:
1 The discussion part emphasizes that the human factor is very important, but the draft spent most of the time discussing temperature and precipitation impacts on vegetation. It's better to highlight the key message author wants to convey.
2 The analysis method is not solid. For example, when mentioning significance, I have no idea how to measure it?
3 All results have no quantitive description but only a qualitative description.
4 There is no discussion behind the results.
Minor
- The landcover map is not given, it can be added to Figure 1. Also. There may be problems with the NDVI of the bare land
- How to synthesize MODIS (2000-2019) and GIMMS (1982-2015)? It is not stated clearly in the method section.
- Figure 5 and Figure 6 are hard to read, and simplifying them will be better.
- Fig11 f, y-axis mislabeled
- Fig12 How to derive GDP in no man's land (e.g., areas in northern Xinjiang)?
- In all figures, how to define no data areas? If you masked NDVI below a certain threshold, please note them in the figure and method.
- Line 80: MODIS NDVI from 2000-2019 and GIMMS NDVI from 1982-2015, but this sentence is not clear
- Line 85: Please provide the source link; this is not the source from which GIMMS is.
- Line 89: there should be a citation for the MVC method.
- What are the advantages and disadvantages of using MODIS and GIMMS in studying the response of vegetation to climate change in these areas? Please provide some references.
- Lines 114 and 124: please cite the original author
- Line 149-158: e.g., "more region" lacks a quantitative description
- Do not add transparency to the color of the figure4. It is better to set the alpha value to 1, and the leftmost triangle and rectangle of the color bar are separated.
- Line 194-196: rephrase and it is hard to understand
- Line 241-243: delete this sentence.
- Line 274: how to calculate significance?
Citation: https://doi.org/10.5194/egusphere-2022-1110-RC3 -
AC3: 'Reply on RC3', Shijun Zheng, 29 Mar 2023
We really appreciate your helpful suggestions and comments. We have carefully revised the manuscript and addressed all comments. In terms of content, we mainly increased the experimentation of CRU and ERA5 meteorological data to enhance the reliability of the article results. We also conducted a mechanism analysis to investigate how drought regulates the relationship between vegetation and precipitation (temperature). As for the methodology, we detrended all variables before studying the vegetation-climate relationship to prevent statistical analysis independence. Instead of comparing the NDVI and climate relationship between pre- and post-2000, we used multiple sliding windows to emphasize the inter-annual variability of this relationship. In writing, we improved the language use throughout the entire article, made significant changes to the introduction to highlight the innovation of this study, and added descriptions of the interpretation of the results, as well as discussions of the results.
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