the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Nov 2021
A data-driven topsoil δ13C dataset and the drivers of spatial variability across the Tibetan Plateau
Abstract. Soil carbon isotopes (δ13C) provide reliable insights at a long-term scale for studying soil carbon turnover. The Tibetan Plateau (TP), called “the third pole of the earth” is one of the most sensitive areas to global climate change and exhibits an early warning signal of global warming. Although many studies detected the variability of soil δ13C at site scales, a knowledge gap still exists in the spatial pattern of topsoil δ13C across the TP. To fill the substantial knowledge gap, we first compiled a database of topsoil δ13C with 396 observations from published literatures. Then we applied a Random Forest (RF) algorithm – a machine learning approach, to predict the spatial pattern of topsoil δ13C and β (indicating the decomposition rate of soil organic carbon (SOC), calculated by δ13C divided by logarithmically converted SOC). Finally, two datasets – topsoil δ13C and β with a fine spatial resolution of 1 km across the TP were developed. Results showed that topsoil δ13C varied significantly among different ecosystem types (p < 0.001). Topsoil δ13C was −26.3 ± 1.60 ‰ (mean ± standard deviation) for forests, 24.3 ± 2.00 ‰ for shrublands, −23.9 ± 1.84 ‰ for grasslands, −18.9 ± 2.37 ‰ for deserts, respectively. RF could well predict the spatial variability of topsoil δ13C with a model efficiency of 0.62 and root mean square error of 1.12 ‰, enabling to derive data-driven δ13C and β products. Data-driven topsoil δ13C varied from −28.26 ‰ to −16.95 ‰, with the highest topsoil δ13C in the north and northwest TP and the lowest δ13C in Southeast or South TP, indicating strong spatial variabilities in topsoil δ13C. Similarly, there were strong spatial variabilities in data-driven β, with the lowest β values at the east and middle TP, indicating a higher SOC turnover in the east and middle TP compared that of other regions in the TP. This study was the first attempt to develop a fine resolution product of topsoil δ13C and β across the TP, which could provide an independent data-driven benchmark for biogeochemical cycling models to study SOC turnover and terrestrial carbon-climate feedbacks over the TP under climate change. The data-driven δ13C and β datasets are public available at https://doi.org/10.6084/m9.figshare.16641292.v2 (Tang, 2021).
- Preprint
(773 KB) - Metadata XML
-
Supplement
(747 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on essd-2021-411', Anonymous Referee #1, 06 Feb 2022
The manuscript "A data-driven topsoil δ13C dataset and the drivers of spatial variability across the Tibetan Plateau" is well written, and the language is good. Although the quality of the presentation is excellent, I do not recommend its publication for the following reasons:
- The manuscript's scientific relevance (environmental science, earth science) is not obvious for me. The spatial distribution of soil organic carbon d13C in the upper 3 cm layer of soils is not significant, since it does not represent soil organic matter in any way.
- The data on which the communication is based are dubious, in my opinion.
Critical comments:
- Data source (2.1). The paper is based on two sources: the first dataset by Lu et al. 2004; and the second dataset by Qi 2017. I have not found any dataset under the second source. I also found uncertainties in the first source. Authors reported sampling from 0 - 5 cm, while Lu et al. reported about 0-3 cm. I also have severe doubts about the applicability of the dataset since:
- It is impossible to completely remove organic debris from the 0-3 cm soil layer for many soil types. Consequently, the data quality is dubious.
- I could not find any information on the carbonate content of the soils in the area in the manuscript. Lu et al. reported on the treatment of their samples with 1N HCl. This treatment removes (an unknown) part of the SOM from both carbonate and carbonate-free soils. Consequently, the data quality is dubious.
- Sampling is limited to the valleys. However, no information is available on the geomorphological position of the sampling points. The geomorphological position (as an environmental variable) is critical! This can be a more important factor than the vegetation.
Data analysis (2.3). The authors applied one-way ANOVA. This is a good technique, but this is a kind of parametric test. This test can only be used if the dataset has a normal distribution. The authors have not mentioned testing this.
Citation: https://doi.org/10.5194/essd-2021-411-RC1 -
CC1: 'Comment on essd-2021-411', Kerong Zhang, 08 Feb 2022
This manuscript reported the topsoil δ13C dataset across the Tibetan Plateau. The dataset is important, however, the authors did not provide the information of soil organic carbon content and the δ13C of vegetation, which is essential for exploring the drivers of spatial variability of soil δ13C.
Citation: https://doi.org/10.5194/essd-2021-411-CC1 -
CC2: 'Comment on essd-2021-411', Brenton Ladd, 20 May 2022
I agree with the authors that the Tibetan plateau can be considered a third pole on planet earth and also that the documented rapid rise in air temperatures in the region make it an important area for monitoring and study and by extension that this synthesis of data for the region is an important contribution.
Line 100: to increase the utility of the data set the GIS derived variables described starting at line 100 should be added to the xcel file. Would also be useful to add actual measurements of soil properties in addition to the soil grid (GIS) derived measurements of soils, for the sites for which you have these data.
Re the beta values: from my reading you use %C measurements from a GIS source (soil Grids). In my experience GIS derived estimates of SOC can be quite variable / uncertain, therefore related to the point above it would be good if you could include in your database actual measurements of %C to compare against the soil grid derived estimates, even if it isn’t possible for all sites. Further upon rereading the Garten et al paper I see that their models for turnover times for soil C were made using measurements of isotope ratios and their changes across the soil depth profile (Rayleigh equations). Since you do not present this information, I am not convinced that you can use the approach of Garten et al to calculate beta values. this either needs to be rectified or deleted in the revised ms.
Discussion: didn’t review the discussion, the above points need addressing first.
Citation: https://doi.org/10.5194/essd-2021-411-CC2 -
RC2: 'Comment on essd-2021-411', Anonymous Referee #2, 25 May 2022
I agree with the authors that the Tibetan plateau can be considered a third pole on planet earth and also that the documented rapid rise in air temperatures in the region make it an important area for monitoring and study and by extension that this synthesis of data for the region is an important contribution.
Line 100: to increase the utility of the data set the GIS derived variables described starting at line 100 should be added to the xcel file. Would also be useful to add actual measurements of soil properties in addition to the soil grid (GIS) derived measurements of soils, for the sites for which you have these data.
Re the beta values: from my reading you use %C measurements from a GIS source (soil Grids). In my experience GIS derived estimates of SOC can be quite variable / uncertain, therefore related to the point above it would be good if you could include in your database actual measurements of %C to compare against the soil grid derived estimates, even if it isn’t possible for all sites. Further upon rereading the Garten et al paper I see that their models for turnover times for soil C were made using measurements of isotope ratios and their changes across the soil depth profile (Rayleigh equations). Since you do not present this information, I am not convinced that you can use the approach of Garten et al to calculate beta values. this either needs to be rectified or deleted in the revised ms.
Discussion: didn’t review the discussion, the above points need addressing first.
Citation: https://doi.org/10.5194/essd-2021-411-RC2
Status: closed
-
RC1: 'Comment on essd-2021-411', Anonymous Referee #1, 06 Feb 2022
The manuscript "A data-driven topsoil δ13C dataset and the drivers of spatial variability across the Tibetan Plateau" is well written, and the language is good. Although the quality of the presentation is excellent, I do not recommend its publication for the following reasons:
- The manuscript's scientific relevance (environmental science, earth science) is not obvious for me. The spatial distribution of soil organic carbon d13C in the upper 3 cm layer of soils is not significant, since it does not represent soil organic matter in any way.
- The data on which the communication is based are dubious, in my opinion.
Critical comments:
- Data source (2.1). The paper is based on two sources: the first dataset by Lu et al. 2004; and the second dataset by Qi 2017. I have not found any dataset under the second source. I also found uncertainties in the first source. Authors reported sampling from 0 - 5 cm, while Lu et al. reported about 0-3 cm. I also have severe doubts about the applicability of the dataset since:
- It is impossible to completely remove organic debris from the 0-3 cm soil layer for many soil types. Consequently, the data quality is dubious.
- I could not find any information on the carbonate content of the soils in the area in the manuscript. Lu et al. reported on the treatment of their samples with 1N HCl. This treatment removes (an unknown) part of the SOM from both carbonate and carbonate-free soils. Consequently, the data quality is dubious.
- Sampling is limited to the valleys. However, no information is available on the geomorphological position of the sampling points. The geomorphological position (as an environmental variable) is critical! This can be a more important factor than the vegetation.
Data analysis (2.3). The authors applied one-way ANOVA. This is a good technique, but this is a kind of parametric test. This test can only be used if the dataset has a normal distribution. The authors have not mentioned testing this.
Citation: https://doi.org/10.5194/essd-2021-411-RC1 -
CC1: 'Comment on essd-2021-411', Kerong Zhang, 08 Feb 2022
This manuscript reported the topsoil δ13C dataset across the Tibetan Plateau. The dataset is important, however, the authors did not provide the information of soil organic carbon content and the δ13C of vegetation, which is essential for exploring the drivers of spatial variability of soil δ13C.
Citation: https://doi.org/10.5194/essd-2021-411-CC1 -
CC2: 'Comment on essd-2021-411', Brenton Ladd, 20 May 2022
I agree with the authors that the Tibetan plateau can be considered a third pole on planet earth and also that the documented rapid rise in air temperatures in the region make it an important area for monitoring and study and by extension that this synthesis of data for the region is an important contribution.
Line 100: to increase the utility of the data set the GIS derived variables described starting at line 100 should be added to the xcel file. Would also be useful to add actual measurements of soil properties in addition to the soil grid (GIS) derived measurements of soils, for the sites for which you have these data.
Re the beta values: from my reading you use %C measurements from a GIS source (soil Grids). In my experience GIS derived estimates of SOC can be quite variable / uncertain, therefore related to the point above it would be good if you could include in your database actual measurements of %C to compare against the soil grid derived estimates, even if it isn’t possible for all sites. Further upon rereading the Garten et al paper I see that their models for turnover times for soil C were made using measurements of isotope ratios and their changes across the soil depth profile (Rayleigh equations). Since you do not present this information, I am not convinced that you can use the approach of Garten et al to calculate beta values. this either needs to be rectified or deleted in the revised ms.
Discussion: didn’t review the discussion, the above points need addressing first.
Citation: https://doi.org/10.5194/essd-2021-411-CC2 -
RC2: 'Comment on essd-2021-411', Anonymous Referee #2, 25 May 2022
I agree with the authors that the Tibetan plateau can be considered a third pole on planet earth and also that the documented rapid rise in air temperatures in the region make it an important area for monitoring and study and by extension that this synthesis of data for the region is an important contribution.
Line 100: to increase the utility of the data set the GIS derived variables described starting at line 100 should be added to the xcel file. Would also be useful to add actual measurements of soil properties in addition to the soil grid (GIS) derived measurements of soils, for the sites for which you have these data.
Re the beta values: from my reading you use %C measurements from a GIS source (soil Grids). In my experience GIS derived estimates of SOC can be quite variable / uncertain, therefore related to the point above it would be good if you could include in your database actual measurements of %C to compare against the soil grid derived estimates, even if it isn’t possible for all sites. Further upon rereading the Garten et al paper I see that their models for turnover times for soil C were made using measurements of isotope ratios and their changes across the soil depth profile (Rayleigh equations). Since you do not present this information, I am not convinced that you can use the approach of Garten et al to calculate beta values. this either needs to be rectified or deleted in the revised ms.
Discussion: didn’t review the discussion, the above points need addressing first.
Citation: https://doi.org/10.5194/essd-2021-411-RC2
Data sets
A data-driven estimate of topsoil (0-5 cm) isotope carbon across the Tibetan Plateau Xiaolu Tang https://doi.org/10.6084/m9.figshare.16641292.v2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 911 | 299 | 62 | 1,272 | 122 | 54 | 60 |
- HTML: 911
- PDF: 299
- XML: 62
- Total: 1,272
- Supplement: 122
- BibTeX: 54
- EndNote: 60
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1