the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Nov 2023
Mapping the Future Afforestation Distribution of China Constrained by National Afforestation Plan and Climate Change
Shuaifeng Song
Xuezhen Zhang
Abstract. Afforestation has been considered a critical nature-based solution to mitigate global warming. China has announced an ambitious afforestation plan covering an area of 73.78×104 km2 from 2020 to 2050. However, it is unclear where it will be suitable for afforestation under future climate change. Here, we carried out a finer resolution (25 by 25 km) of climate change dynamic downscaling for China using the WRF model nested with bias-corrected MPI–ESM1–2–HR model; then, using the Holdridge life zone model forced by the WRF model output, we mapped the climatological suitability for forest in China. The results showed that the potential forestation domain (PFD) at present (1995–2014) approximated 500.75×104 km2, and it would increase to 518.25×104 km2, by about 3.49 %, to the period of 2041–2060 under the SSP2–4.5 scenario. Considering the expansion of the future PFD caused by climate change, the afforestation area for each province was allocated into grid cells following the climatological suitability for the forest. The newly afforestation grid cells would occur around and to the east of the Hu Line. Due to afforestation, the land cover would be modified. The conversion of grasslands to deciduous broadleaf forests in northern China covered most area, accounting for 41 % of the newly afforestation area. The grid cell-resolved afforestation dataset was consistent with the provincial afforestation plan and the future climatological forest suitability. It would be valuable for investigating the impacts of future afforestation on various aspects, including the carbon budget, ecosystem services, water resources, and surface climate.
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Shuaifeng Song et al.
Status: open (until 04 Jan 2024)
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RC1: 'Comment on bg-2023-177', Anonymous Referee #1, 02 Dec 2023
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General comments
The article “Mapping the Future Afforestation Distribution of China Constrained by National Afforestation Plan and Climate Change” explored the distribution of future potential afforestation areas based on future high-resolution climate data from the WRF model and HLZ model. It is highlighted that the afforestation scenario is constrained by both the climatological suitability for tree and national afforestation plan. The climatology suitability for tree is decided by future climate conditions and determines the potentially available afforestation domain. The national afforestation plan determines the total afforestation area. The potential value is to provide the design framework for locations of future afforestation. Overall, the article is suitable for the scope of Biogeosciences, I recommend that the authors address the concerns below in a minor revision prior to publication.
Specific comments
Method: I'm confused about the spatial resolution of the article and please provide an explanation. Firstly, the authors emphasize the “high-resolution simulations” in this article. However, the spatial resolution is only 25 km. The other high-resolution climate dataset product (i.e., WorldClim data, https://www.worldclim.org/data/index.html.) is available at the ~1km spatial resolution. I'm confused if that description is appropriate, and please illustrate the advantages of WRF simulation in this study. L115: Why the spatial resolution of ERA5 reanalysis data is 1.0°×1.0°. In ECMWF, the highest resolution of the ERA5 product is 0.25°×0.25°, which is close to WRF simulation (25 km). In the HIS_ERA experiment, is downscaling 1.0° ERA5 data to 25 km necessary? L89: The spatial resolution of MCD12Q1 is 500m, which is different from the WRF simulation (25km). How do you match it well? Please give some detailed information.
L218: “Areas with high precipitation are allowed priority afforestation.” In this study, precipitation is treated as a key meteorological factor that restricts forest distribution. Indeed, precipitation is critical for forest growth. However, a single climate variable is slightly simple rather than representing climatology suitability for tree. Multivariate comprehensive indicators affecting forest growth are more appropriate. In this study, the essence of the HLZ model is the distance to the three bioclimatic variables. I recommend considering the distance as a comprehensive indicator to quantify the climatology suitability for tree.
L204: In the section on the approach of the newly afforestation allocation, I'm confused about the definition of forest. Please clarify it. For the national afforestation plan (NFMP), the total afforestation area is 73.78×104 km2. How to define the total afforestation area? I wonder whether the definition from the State Forestry Administration of China agrees with this study.
L113: The authors use the SSP2–4.5 scenario (the middle-of-the-road development) to represent the climate future projections. However, this study only used one model projections rather than multiple model ensemble mean. Following the methodology of CMIP6 climate projection, scenario-based climate projection may have large uncertainties. It is suggested the revision to address this issue. It is also worthy to discuss effects of single model projection uncertainties on the research result of this study.
L353: “Our findings indicated that future afforestation in China would mostly occur around and to the east of the Hu Line, consistent with Zhang et al. (2022).” The authors try to compare other similar studies on future potential afforestation distribution. More result differences should be discussed. I suggest to highlight the innovation and implications of the article by comparing with existing studies.
L180-186: Why the Holdridge life zone (HLZ) model is suitable for simulating the potential vegetation types in China. The author simply describes the extensive application of the HLZ model. I suggest validating the accuracy of the HLZ model. It is necessary to compare potential vegetation types with true vegetation types. Please add it to the Supplement Material.
L125: The authors have done substantial work on numerical experiments. For example, the authors correct the lateral boundary conditions rather than the raw GCM before dynamic downscaling. It is a very good solution to reduce the underlying bias. I suggest adding the comparison of raw GCM, bias-corrected GCM, and observation.
L351: This article emphasizes “The dataset would be valuable for studying the effects of future afforestation on carbon budget, ecosystem service, water resources, surface climate”. Would the data set be available to the public, especially in Figure 7?
L234: “The WRF simulation generally overestimates TP in most regions with a national-average bias of 92.883 mm”. According to Figure 3d-3f, the obvious overestimate is over the southeast Tibetan Plateau. It is suggested to explain the potential reasons of these bias in the revision.
Table 1: Why this parameterization scheme of the WRF model is appropriate in this study. Please give a specific reason or reference.
L300: What is the meaning of “The corresponding annual total precipitation is over 353.6 mm among
the selected grids”? How to obtain the value of 353.6 mm. Please clarify it.
L311: “It is generally common sense that afforestation is highly constrained by precipitation.” Please add specific explanations or references.
L275: To what does “total area” refer to? Is it the whole nation? Please clarify.
Figure 5b: The flow diagrams are not clear, and please give specific values.
Eq. (2): “ , , and ”. Please correct it.
Figure 3 and Figure 4: For Figure 3 and Figure 4 captions, suggest not to use the abbreviations “HLZ”, “AT”, “TP”, and “PE”.
L208: “national afforestation plan” is redundant. Please use the “NFMP”.
L98: “The total national afforestation area is about 73.78×104 km2 from 2020 to 2050”. Please give specific forest cover.
Figure 2: No citation for Figure 2 in the text.
Figure 6: Please do not use the abbreviations in the figure captions.
L338: “woody savannas” replaces “Woody savannas”.
Citation: https://doi.org/10.5194/bg-2023-177-RC1
Shuaifeng Song et al.
Shuaifeng Song et al.
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