the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jul 2022
Long-time Series Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019)
Abstract. Soil conservation capacity (SC) is defined as the ability of the ecosystem to control soil erosion and protect soil function. A long-term and high-resolution estimation of soil conservation is urgent for ecological assessment and land management on a large scale. Here, a 300-m resolution SC dataset in China is established from 1992 to 2019 based on the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE modelling was conducted based on five key parameters, including the rainfall erosivity (interpolation of daily rainfall), land cover management (provincial data), conservation practices (weighted by terrain and crop types), topography (30 m), and soil properties (250 m). The dataset agrees with previous measurements (R2 > 0.5 in all the basins) and other regional simulations. The results show that China's SC decreased before 2003 and then increased up to now. The SC change exhibits the ecological effects of soil and water conservation policies in China, such as the Conversion of Farmland to Forests and Grass (Grain-for-Green), which unfolded many movements after 2000. Compared with current studies, the dataset has long-term, large-scale, and relatively high-resolution characteristics. This dataset will serve as a base to open out the mechanism of SC variations in China and could help assess the ecological effects of land management policies. This dataset is available at https://doi.org/10.11888/Terre.tpdc.272668 (Li et al., 2022).
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CC1: 'Comment on essd-2022-222', Song Leng, 05 Aug 2022
This dataset with nearly three-decade records provides an invaluable scientific basis, which can enhance our understandings of soil conservation and contribute to the guiding opinions for land and resources management.
Besides, I am wondering whether an updated datasets with higher spatial resolution can be processed and released in future?
Thank you.
Citation: https://doi.org/10.5194/essd-2022-222-CC1 -
CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
Thank you for your comments. The resolution of this data is determined after a trade-off between data precision and the feasibility of computation. In the future, it is possible to update data precision based on the latest released data, which may be 100 m resolution.
Citation: https://doi.org/10.5194/essd-2022-222-CC2
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CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
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CC3: 'Comment on essd-2022-222', Yuxia Liu, 07 Aug 2022
This is great work. It not only benefits soil conservation management in China, but also offers a crucial reference for the rest of the world. Just have a query about data acquisition. Can I download the dataset by specific year? Thanks.
Citation: https://doi.org/10.5194/essd-2022-222-CC3 -
CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
Thank you for your comments and questions. We plan to upload annual data in future updates. You can contact the corresponding author to obtain the data if you need the data now.
Citation: https://doi.org/10.5194/essd-2022-222-CC4
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CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
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CC5: 'Comment on essd-2022-222', Rong Gan, 08 Aug 2022
Nicely done! This dataset is of importance to a broad community interested in soil conservation. Hope this data can be further released to the latest year.
Citation: https://doi.org/10.5194/essd-2022-222-CC5 -
CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
Thanks for your comments and suggestions. Yes, the latest data is important, but some raw data has been difficult to acquire recently, possibly due to the impact of the Covid-19 pandemic. Once the data is acquired, we will update the data in the future.
Citation: https://doi.org/10.5194/essd-2022-222-CC6
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CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
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CC7: 'Comment on essd-2022-222', Sicong Gao, 08 Aug 2022
This dataset would benefit remote sensing modelling and validation. Will you keep updating this dataset? Extend to 2021?
Line 345: estimations in the Pear River Basin are higher than other simulations
(Fig.9b). The reason may be that the methods we used in this region are different from other studies.Please further explain the reason of the difference between your studies and others. Your explanation is not clear, it is true, but not clear. Readers may still be confused about this inconsistency. It would be great if you could explain which input factors contribute to the high estimations.
Citation: https://doi.org/10.5194/essd-2022-222-CC7 -
AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
Thank you for your comment and interest.
The difference between our study and others in the Pearl river is that we used a revised R-factor calculation method here. Other studies usually use one method in the whole study area, while we used a model which is more suitable for Karst regions.
We plan to update the data in the future if more people call for our work.
Citation: https://doi.org/10.5194/essd-2022-222-AC1
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AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
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RC1: 'Comment on essd-2022-222', Anonymous Referee #1, 18 Nov 2022
The topic is interesting. However, the article has some big flaws.
1.The dataset given in the manuscript is incomplete and not of great value. The manuscript mainly includes three datasets: rainfall erosivity factor (R) and vegetation cover factor (C) and soil conservation capacity (SC). The first two are based on station observations and remotely sensed vegetation. Unfortunately, only the mean values are given, but no annual data, which makes it less valuable. The third is computational results that are difficult to validate against other experiments or observations.
2. The main advantage of the data claimed in this manuscript is the long time series, however, the data presented are only averages, which renders it no advantage over other high resolution data, for example, Rao et al., 2013 in the reference list.
3. In the RUSLE model, the deriving of the P-factor on a large scale has always been a difficult problem for erosion evaluation. The method used in this study lacks novelty, and the data of P-factor is not given in the manuscript. As the authors said: “soil conservation capacity is defined as water erosion prevented by vegetations and practice measures” (line 90), however, this study only considered the practices applied on terraces using an assignment method. Hence, the changes in soil conservation capacity presented by the manuscript are mostly caused by changes in vegetation conditions.
4. Although the authors claim that they have improved the calculation method of R-factor, they only use two methods on a national scale with such a complex geographical background.
5. Since the soil conservation data presented in this study are only the result of changes in climatic and vegetation conditions, the various possible uses of the data claimed by the authors are necessarily limited.
Citation: https://doi.org/10.5194/essd-2022-222-RC1 -
RC2: 'Comment on essd-2022-222', Anonymous Referee #2, 04 Dec 2022
This study integrated spatial data of climate, soil, vegetation, and topography to establish a dataset of soil conservation potential related to soil erosion over China. This work is of interest in the community, and valuable for environmental protection. However, there are issues of uncertainty of the data, although they are validated over limited components, as there are complex combinations of vegetation, soil and climate over China.
(1)Regarding the framework of soil conservation capacity, the potential erosion (SEp) is defined by natural conditions for bare soil without vegetation, e.g. rainfall, slope, soil texture, which is calculated by Revised Universal Soil Loss Equation (RUSLE) model. After two more factors are included, the actual erosion (SEa) is obtained with modification of vegetation cover, and water and soil conservation measure, i.e. C and P. The issues of this method are,
(1)Effects of vegetation and climate (rainfall) are separated in SEp and SEa, but vegetation is actual determined by climate. SEp is higher while SEa is lower in wetter environment. This results in SC, the difference between SEp and SEa, is higher in the south than in the north. The capacity should be a term reflecting management, but this SC distribution in Fig 5(a) cannot replicate the highest potential of water and soil conservation in the Loess Plateau, the Northeast, etc.
(2)Both C and P elements are similar, including influence of management, and hard to quantify, especially over national scale. There are obvious simplifications of C factor using provincial coverage.
(3)RUSLE is a model for local scale, containing slope and length. It is difficult to calculate coarse grids over national scale.
It is advisable to modify the concept of SC and methods of calculation before the dataset can be accepted for published.Citation: https://doi.org/10.5194/essd-2022-222-RC2
Status: closed
-
CC1: 'Comment on essd-2022-222', Song Leng, 05 Aug 2022
This dataset with nearly three-decade records provides an invaluable scientific basis, which can enhance our understandings of soil conservation and contribute to the guiding opinions for land and resources management.
Besides, I am wondering whether an updated datasets with higher spatial resolution can be processed and released in future?
Thank you.
Citation: https://doi.org/10.5194/essd-2022-222-CC1 -
CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
Thank you for your comments. The resolution of this data is determined after a trade-off between data precision and the feasibility of computation. In the future, it is possible to update data precision based on the latest released data, which may be 100 m resolution.
Citation: https://doi.org/10.5194/essd-2022-222-CC2
-
CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
-
CC3: 'Comment on essd-2022-222', Yuxia Liu, 07 Aug 2022
This is great work. It not only benefits soil conservation management in China, but also offers a crucial reference for the rest of the world. Just have a query about data acquisition. Can I download the dataset by specific year? Thanks.
Citation: https://doi.org/10.5194/essd-2022-222-CC3 -
CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
Thank you for your comments and questions. We plan to upload annual data in future updates. You can contact the corresponding author to obtain the data if you need the data now.
Citation: https://doi.org/10.5194/essd-2022-222-CC4
-
CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
-
CC5: 'Comment on essd-2022-222', Rong Gan, 08 Aug 2022
Nicely done! This dataset is of importance to a broad community interested in soil conservation. Hope this data can be further released to the latest year.
Citation: https://doi.org/10.5194/essd-2022-222-CC5 -
CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
Thanks for your comments and suggestions. Yes, the latest data is important, but some raw data has been difficult to acquire recently, possibly due to the impact of the Covid-19 pandemic. Once the data is acquired, we will update the data in the future.
Citation: https://doi.org/10.5194/essd-2022-222-CC6
-
CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
-
CC7: 'Comment on essd-2022-222', Sicong Gao, 08 Aug 2022
This dataset would benefit remote sensing modelling and validation. Will you keep updating this dataset? Extend to 2021?
Line 345: estimations in the Pear River Basin are higher than other simulations
(Fig.9b). The reason may be that the methods we used in this region are different from other studies.Please further explain the reason of the difference between your studies and others. Your explanation is not clear, it is true, but not clear. Readers may still be confused about this inconsistency. It would be great if you could explain which input factors contribute to the high estimations.
Citation: https://doi.org/10.5194/essd-2022-222-CC7 -
AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
Thank you for your comment and interest.
The difference between our study and others in the Pearl river is that we used a revised R-factor calculation method here. Other studies usually use one method in the whole study area, while we used a model which is more suitable for Karst regions.
We plan to update the data in the future if more people call for our work.
Citation: https://doi.org/10.5194/essd-2022-222-AC1
-
AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
-
RC1: 'Comment on essd-2022-222', Anonymous Referee #1, 18 Nov 2022
The topic is interesting. However, the article has some big flaws.
1.The dataset given in the manuscript is incomplete and not of great value. The manuscript mainly includes three datasets: rainfall erosivity factor (R) and vegetation cover factor (C) and soil conservation capacity (SC). The first two are based on station observations and remotely sensed vegetation. Unfortunately, only the mean values are given, but no annual data, which makes it less valuable. The third is computational results that are difficult to validate against other experiments or observations.
2. The main advantage of the data claimed in this manuscript is the long time series, however, the data presented are only averages, which renders it no advantage over other high resolution data, for example, Rao et al., 2013 in the reference list.
3. In the RUSLE model, the deriving of the P-factor on a large scale has always been a difficult problem for erosion evaluation. The method used in this study lacks novelty, and the data of P-factor is not given in the manuscript. As the authors said: “soil conservation capacity is defined as water erosion prevented by vegetations and practice measures” (line 90), however, this study only considered the practices applied on terraces using an assignment method. Hence, the changes in soil conservation capacity presented by the manuscript are mostly caused by changes in vegetation conditions.
4. Although the authors claim that they have improved the calculation method of R-factor, they only use two methods on a national scale with such a complex geographical background.
5. Since the soil conservation data presented in this study are only the result of changes in climatic and vegetation conditions, the various possible uses of the data claimed by the authors are necessarily limited.
Citation: https://doi.org/10.5194/essd-2022-222-RC1 -
RC2: 'Comment on essd-2022-222', Anonymous Referee #2, 04 Dec 2022
This study integrated spatial data of climate, soil, vegetation, and topography to establish a dataset of soil conservation potential related to soil erosion over China. This work is of interest in the community, and valuable for environmental protection. However, there are issues of uncertainty of the data, although they are validated over limited components, as there are complex combinations of vegetation, soil and climate over China.
(1)Regarding the framework of soil conservation capacity, the potential erosion (SEp) is defined by natural conditions for bare soil without vegetation, e.g. rainfall, slope, soil texture, which is calculated by Revised Universal Soil Loss Equation (RUSLE) model. After two more factors are included, the actual erosion (SEa) is obtained with modification of vegetation cover, and water and soil conservation measure, i.e. C and P. The issues of this method are,
(1)Effects of vegetation and climate (rainfall) are separated in SEp and SEa, but vegetation is actual determined by climate. SEp is higher while SEa is lower in wetter environment. This results in SC, the difference between SEp and SEa, is higher in the south than in the north. The capacity should be a term reflecting management, but this SC distribution in Fig 5(a) cannot replicate the highest potential of water and soil conservation in the Loess Plateau, the Northeast, etc.
(2)Both C and P elements are similar, including influence of management, and hard to quantify, especially over national scale. There are obvious simplifications of C factor using provincial coverage.
(3)RUSLE is a model for local scale, containing slope and length. It is difficult to calculate coarse grids over national scale.
It is advisable to modify the concept of SC and methods of calculation before the dataset can be accepted for published.Citation: https://doi.org/10.5194/essd-2022-222-RC2
Data sets
Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019) Jialei Li, Hongbin He, Liding Chen, Ranhao Sun https://doi.org/10.11888/Terre.tpdc.272668
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