the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Understanding the Effect of Revegetated Shrubs on Energy, Water and Carbon Fluxes in a Desert Steppe Ecosystem Using STEMMUS-SCOPE Model
Enting Tang
Yijian Zeng
Yunfei Wang
Zengjing Song
Danyang Yu
Hongyue Wu
Chenglong Qiao
Christiaan van der Tol
Lingtong Du
Zhongbo Su
Abstract. Revegetation is one of the most effective ways to combat desertification and soil erosion in semiarid and arid regions. However, the perturbation of revegetation on ecohydrological processes remains to be studied, especially its effect on the complex interaction between the hydrological processes and vegetation growth under water stress. This study evaluated the effects of revegetation on the energy, water and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, Northwest China, by simulating two vegetated scenarios (shrubs-grassland ecosystem and grassland ecosystem) using STEMMUS-SCOPE model. The model was validated by field observations from May to September of 2016–2019. The simulated energy, water and carbon fluxes in 2016 and 2019 were used to evaluate the difference between two vegetated scenarios. Higher leaf area index and root water uptake of C3 shrubs (Caragana Intermedia) resulted in the increased carbon fixation (+ 82 %) and transpiration (+ 99 %) in the shrubs-grassland ecosystem compared to C3 grassland ecosystem. In both scenarios, turbulent energy was dominated by latent heat flux, which was stronger in the shrubs-grassland ecosystem (+ 13 %). With the remarkable increase in transpiration, revegetation induced the soil water losses, especially the soil water content within the 0–200 cm soil depth (- 19 %), and exaggerated the excess of water consumption over the received precipitation. These results emphasize the importance of accounting for energy and water budget in water-limited ecosystems during ecological restoration, to prevent soil water depletion. As an example, the consequence of increased transpiration should be further examined.
- Preprint
(3682 KB) - Metadata XML
-
Supplement
(2139 KB) - BibTeX
- EndNote
Enting Tang et al.
Status: final response (author comments only)
-
RC1: 'Comment on bg-2023-70', Anonymous Referee #1, 17 Jun 2023
This study applied the STEMMUS-SCOPE model to a typical revegetation plot which consists of shrubs and grass, to simulate the impact of revegetated shrubs on surface fluxes (latent heat flux, sensible heat flux, and GPP) and soil moisture. While the manuscript describes a lot about the comparison between the two scenarios, I more focus on the model and the model configuration, and how this study can contribute to model development or deepen our understanding the effect of revegetated shrubs. In general, I think this part of work is weak.
- Line 29, in the introduction section, the scientific question is not clear. Generally, the authors thought root water uptake is a critical process in the modeling, and the dynamic root length density for estimating root water uptake is necessary. However, no contents about the root water uptake were presented in this study. What is the impact of dynamic root length density? What is the performance of root water uptake simulation? This is the major limitation of this study.
- Line 92. The quality control of flux data was missing. Moreover, how did you calculate GPP?
- Line 125. How did you determine the contributions for shrubland and grassland?
- Line 136, can 500 m-MODIS LAI represent the 30 m fenced area?
- Line 139-140, how did you determine the values of 2.33 and 1/4?
- Line 151, what is the difference between red and yellow dots?
- Line 176, why were root-related parameters not identified as influential parameters? This is the main focus of your study.
- Line 216, how did the authors optimize the parameters (best-fit trail in Line 196)? How to avoid the equifinality for the parameters of shrub and grass?
- Line 218, does it mean Vcmax of shrub and grass is the same (120)? Why?
- Line 237, the sensors were installed under the grassland, but the simulated soil water content is the average of shrub, grass, and bare soil. So, direct compassion of them may have a large bias.
- Line 373, why did not the author attempt to modify the model to simulate evaporation from the bare soil?
- Line 403. Why cannot the model capture the wet deep soil layer? Is it related to root water uptake? More analysis and simulation should be performed.
Citation: https://doi.org/10.5194/bg-2023-70-RC1 - AC2: 'Reply on RC1', Enting Tang, 12 Aug 2023
-
RC2: 'Comment on bg-2023-70', Anonymous Referee #2, 17 Jul 2023
The authors used the STEMMUS-SCOPE Model to analyze how the revegetation of shrubs in a desert steppe modifies the water, energy and GPP dynamics during wet and dry years. Since the paper is not a model development study, I will focus on its presentation aspects. Nonetheless, I think the paper is quite straightforward, but can be improved by taking care of my suggestions.
The title: Since the only carbon flux analyzed is GPP, I recommend the title be changed to “Understanding the Effect of Revegetated Shrubs on fluxes of Energy, Water and Gross Primary productivity in a Desert Steppe Ecosystem Using STEMMUS-SCOPE Model”. Additionally, if the EC tower measured CO2, how about also showing the net ecosystem exchange as one indicating effect of different vegetation? Or it is not possible to differentiate the vegetation effect from NEE?
Equation (1), what does C_shrub mean?
From the method description, it appears LAI is used as model input. Does this mean the model actually does not simulate the carbon cycle, aka it is a prescribed phenology simulation?
Figure 2, what is the uncertainty of the observed LAI? Can you also show it in the Figure?
2.4.1 Sensitivity analysis, it was mentioned that SA was only done for parameters of the shrubland simulation, why is it not done for grassland as well?
3.2 Model performance, in no place I found model spin up was mentioned. Do the model simulations include model spinup? How is it done?
Figure 4c, there are a few points aligning as a vertical line. Does it indicate there are some problems with the data or the model?
Figure 5c, I don’t quite understand the diurnal pattern. If you are average over a certain time period, please show the mean and as well as the variability (i.e., standard deviation).
Figure 6, could you also show the variability?
Section 3.3.3 on GPP, I don’t think the interpretation of midday depression is accurate. It may involve factors more than radiation. Perhaps you can show the diurnal patter of leaf temperature, and temperature dependence of carboxylation as well.
Discussion 4.2.1, paragraph 2, why don’t you use the Bowen ratio as an indicator, which is likely more informative than the ratio of LE/Rn here.
Section 4.2.3, on water fluxes. I am wondering if the model can do a good job in overall water mass balance. I am thinking changing from grass to shrub will lead to difference in column integrated water mass, could you show time series of total water storage in the model? Also, the drainage flux? How far the rainfall infiltration could go? I am then also wondering how sensitive is the model simulation to the represented soil depth, given the gravitational drainage condition is used at the lower boundary. Could you elaborate?
Citation: https://doi.org/10.5194/bg-2023-70-RC2 - AC1: 'Reply on RC2', Enting Tang, 11 Aug 2023
Enting Tang et al.
Model code and software
Understanding the Effect of Revegetated Shrubs on Energy, Water and Carbon Fluxes in a Desert Steppe Ecosystem Using STEMMUS-SCOPE Model Enting Tang, Yijian Zeng, Yunfei Wang, Zengjing Song, Danyang Yu, Hongyue Wu, Chenglong Qiao, Christiaan van der Tol, Lingtong Du, and Zhongbo Su https://doi.org/10.5281/zenodo.7986566
Enting Tang et al.
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
369 | 99 | 19 | 487 | 35 | 4 | 5 |
- HTML: 369
- PDF: 99
- XML: 19
- Total: 487
- Supplement: 35
- BibTeX: 4
- EndNote: 5
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1