the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jun 2024
Causal relationships between vegetation productivity, water availability, and atmospheric dryness at the catchment scale
Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of two months in the intra-annual variability of catchment water supply-productivity and atmospheric water demand-productivity relationships, with hysteresis patterns varying with the catchment’s hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with Horton index and in phase with evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause-effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of two months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities of ecosystem responses to climate change.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(10251 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(10251 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1748', Anonymous Referee #1, 10 Jul 2024
The authors investigated the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity across 341 catchments in the contiguous US. They employed various statistical methods, including circularity statistics, correlation analysis, and causality tests, to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. I found this work interesting, as it enhances our understanding and predictive capabilities regarding ecosystem responses to climate change. I have few minor suggestions:
1. What are the potential reasons for a strong positive causal link between the current and the preceding month’s GPP?
2. This is an interesting finding: "Vegetation response lagged behind changes in Wetness, and changes in VPD followed the vegetation response, resulting in a hysteresis phenomenon." Please comment if such hysteresis phenomena are likely to change in space and time.
3. Cations for figures 3-5 seem to be swapped. Please correct.
4. In Section 5, please develop your discussion in the context of prior similar studies and articulate your major contributions.
5. In Section 5 or 6, please include one paragraph on the limitations of your study. It is often quite complex to study the non-linear relationship between selected variables.
5. Can we use Soil moisture products (e.g., remote sensing products) instead of W minus (deltaS)?
6. Please justify why you made 6 groups to represent 341 catchments.
7. How did you analyze various data sets when they have different spatial and temporal resolutions? For example, the GPP dataset features a spatial resolution of 30 meters and a temporal resolution of 16 days, while other data sets are of varying resolution. How it was handled in the analysis?
Citation: https://doi.org/10.5194/egusphere-2024-1748-RC1 - AC1: 'Reply on RC1', Hongyi Li, 23 Aug 2024
-
RC2: 'Comment on egusphere-2024-1748', Anonymous Referee #2, 20 Aug 2024
This paper investigated the relationships among catchment water availability, vapor pressure deficit, and gross primary productivity using causality analysis, circularity statistics, Principal Component Analysis, etc. The topic is novel and meaningful, the findings are interesting. Here are some concerns and suggestions:
- Lines 95 to 99: How did you divide the catchments into six vegetation groups? Find the primary vegetation type based of the percentage of each vegetation type in the catchment? What are the criteria?
- Lines 218 to 224, what are the reasons causing the different lag time (e.g., 0, 1 month, 2 month) from the perspective of catchment chrematistics?
- You used PCA and found that the first two principal components accounted for most of the variability of the size of the hysteresis loops across catchments. Did you also research on the importance of those selected variables used for PCA to see the dominant factors?
- The last two paragraphs in the Discussion section seem more like results and conclusions. I suggest adding a discussion about whether any previous studies support or contradict your findings.
- Lines 119 – 120: The sentence has grammar error.
- For Figure 4, the title states that “The letters on the color bar represent months, with J for January, F for February, and so on.”, please double check it.
Citation: https://doi.org/10.5194/egusphere-2024-1748-RC2 - AC2: 'Reply on RC2', Hongyi Li, 23 Aug 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1748', Anonymous Referee #1, 10 Jul 2024
The authors investigated the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity across 341 catchments in the contiguous US. They employed various statistical methods, including circularity statistics, correlation analysis, and causality tests, to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. I found this work interesting, as it enhances our understanding and predictive capabilities regarding ecosystem responses to climate change. I have few minor suggestions:
1. What are the potential reasons for a strong positive causal link between the current and the preceding month’s GPP?
2. This is an interesting finding: "Vegetation response lagged behind changes in Wetness, and changes in VPD followed the vegetation response, resulting in a hysteresis phenomenon." Please comment if such hysteresis phenomena are likely to change in space and time.
3. Cations for figures 3-5 seem to be swapped. Please correct.
4. In Section 5, please develop your discussion in the context of prior similar studies and articulate your major contributions.
5. In Section 5 or 6, please include one paragraph on the limitations of your study. It is often quite complex to study the non-linear relationship between selected variables.
5. Can we use Soil moisture products (e.g., remote sensing products) instead of W minus (deltaS)?
6. Please justify why you made 6 groups to represent 341 catchments.
7. How did you analyze various data sets when they have different spatial and temporal resolutions? For example, the GPP dataset features a spatial resolution of 30 meters and a temporal resolution of 16 days, while other data sets are of varying resolution. How it was handled in the analysis?
Citation: https://doi.org/10.5194/egusphere-2024-1748-RC1 - AC1: 'Reply on RC1', Hongyi Li, 23 Aug 2024
-
RC2: 'Comment on egusphere-2024-1748', Anonymous Referee #2, 20 Aug 2024
This paper investigated the relationships among catchment water availability, vapor pressure deficit, and gross primary productivity using causality analysis, circularity statistics, Principal Component Analysis, etc. The topic is novel and meaningful, the findings are interesting. Here are some concerns and suggestions:
- Lines 95 to 99: How did you divide the catchments into six vegetation groups? Find the primary vegetation type based of the percentage of each vegetation type in the catchment? What are the criteria?
- Lines 218 to 224, what are the reasons causing the different lag time (e.g., 0, 1 month, 2 month) from the perspective of catchment chrematistics?
- You used PCA and found that the first two principal components accounted for most of the variability of the size of the hysteresis loops across catchments. Did you also research on the importance of those selected variables used for PCA to see the dominant factors?
- The last two paragraphs in the Discussion section seem more like results and conclusions. I suggest adding a discussion about whether any previous studies support or contradict your findings.
- Lines 119 – 120: The sentence has grammar error.
- For Figure 4, the title states that “The letters on the color bar represent months, with J for January, F for February, and so on.”, please double check it.
Citation: https://doi.org/10.5194/egusphere-2024-1748-RC2 - AC2: 'Reply on RC2', Hongyi Li, 23 Aug 2024
Peer review completion
Journal article(s) based on this preprint
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 355 | 181 | 26 | 562 | 27 | 20 |
- HTML: 355
- PDF: 181
- XML: 26
- Total: 562
- BibTeX: 27
- EndNote: 20
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Guta Wakbulcho Abeshu
Mingjie Shi
Ruby Leung
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(10251 KB) - Metadata XML