the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Sep 2024
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
Abstract. During the past decades, wildfires have undergone rapid changes while both the extent of fire activities and the resulting greenhouse gas (GHG) emissions from wildfires in China remain inadequately quantified. To explore national wildfire-induced emissions, we employed satellite-based data on burned vegetation to generate the China Wildfire Emission Dataset (ChinaWED). This dataset is constructed at monthly and kilometer scale under a consistent and quantifiable calculation framework, providing an average annual estimates of wildfire-induced GHG emissions of 78.13 ± 22.46 Tg CO2, 279.47 ± 82.01 Gg CH4, and 6.26 ± 1.67 Gg N2O for the past decade. We observed significant decreases in both wildfire occurrences and emissions within forests and grasslands. This trend, however, is counteracted by increasing agricultural fires, which constitute the primary type accounting for at least half of the national total fire emissions. The seasonal cycle of wildfire GHG emissions show an evident apex occurring during the transition from mid-spring to early-summer. At the regional scale, Northeast, Southwest and East China emerge as hotspots for wildfire-induced emissions. Our study offers new insights into understanding China's wildfire dynamics and provides a detailed regional model for the wildfire greenhouse gas emissions over China.
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RC1: 'Comment on gmd-2024-170', Anonymous Referee #1, 22 Oct 2024
Review of " China Wildfire Emission (ChinaWED v1) for the period 2012-2022" by Lin et al.
The study introduces the China Wildfire Emission (ChinaWED) model, integrating high-resolution satellite data, updated emission factors, and fuel load maps. It reveals significant seasonal patterns in GHG emissions, with peak emissions months usually from spring to early summer, largely driven by agricultural activities. The findings indicate that the implementation of fire prevention policies has resulted in a substantial reduction in both burned area and GHG emissions over the past decade. The ChinaWED model improves the identification of burned areas and incorporates more detailed parameters for emission factors and fuel load content. Compared to previous global wildfire emission models, it offers reliable estimates of wildfire emissions in China. Therefore, I recommend that this manuscript be considered for publication in Geoscientific Model Development, although some concerns need to be addressed.
- The authors always emphasize the capabilities of their developed China Wildfire Emission Dataset (ChinaWED) throughout the manuscript. For instance, in the abstract and introduction, they mention statements such as, “This dataset is constructed at monthly and kilometer scale,” and “The newly developed product is easily to update with”. However, for a journal like GMD, it would be more suitable to frame the manuscript from the perspective of model development and advancements. I strongly recommend that the authors revise the text accordingly. For example, Lines 48-49 could be modified to: "establish a national-scale wildfire emission model to reflect..."
- In the Lines 209-214, the authors state that agricultural wildfire emissions at the national scale have a decreasing trend. However, the description of an increasing trend in agricultural wildfire emissions in Lines 25-26 contradicts the statement that “All vegetation wildfires decreased at different magnitudes” and that “Agricultural fires had been gradually limited and demonstrated a decline in burned area.”
- Method section:
(1) I do not fully understand the method for extracting burned area. Why is it not possible to directly use active fire data to identify burned area, instead of using them merely as an auxiliary for burned area detection? I suspect there are limitations to this approach, but the authors do not point them out. Additionally, can active fire points be identified as burned areas only if they are located near the burned area? What is the size of the circular buffer used?
(2) In Lines 168-174, why is the combustion completeness of grassland and cropland related to the percentage of forest cover?
(3) I personally believe that the identification of small-sized wildfires is a highlight of the ChinaWED model. Figures S3 and S4 could be moved into the main text.
- Lines 194-195: the average annual wildfire-induced GHG emissions in China amounted to 78.13 Teragrams (Tg) CO2
- Line 208: the trend of -0.31 Mha yr-2 differs from the number presented in Figure 1.
- Lines 212-214: the three types of GHGs in the cropland?
- Line 295: why are there existing references? Aren’t these emissions derived from the ChinaWED dataset?
- Lines 344-346: why does the use of active fire data lead to an overestimation of wildfire emissions?
- Figure 2: the caption “Vertical lines illustrate the peak emissions on different land cover types” is somewhat unclear.The vertical line refers to the months of the year when wildfire emissions peak?
- Figure 4: what does the gray bar in the left panel represent, and what do the numbers represent?
- The order of the figures in the supplementary material is different from that mentioned in the main text.
Citation: https://doi.org/10.5194/gmd-2024-170-RC1 -
CC1: 'Comment on gmd-2024-170', Chao Yue, 15 Dec 2024
Lin et al. developed a new dataset for wildfire emissions in China and compared their data with other datasets. I welcome this effort. The approaches used make sense for me. One general question is that I don’t see how aboveground biomass data was used a proxy for fuel load. For forests, China’s fire is mostly surface fire so what we need is surface fuel load or surface litter. For cropland, I don’t know how much Spawn et al. data is reliable in China. I hope that the authors can provide some thoughts in this respect when revising the paper.
Minor comments:
Line 106-108: justifications are needed on why these two products are selected out of other potential products.Line 117: I don’t see how Fig. S1 shows the system… Do you mean Fig. S3? But Fig. S3 is not very clear either.
Line 125: Fig. S3 and S4 need more explanations. The current ones are not easy to follow.
Line 131: “burned areas” => you could name them as false active fire detections.
Line 132-133: the resampling to 1km is critical as the authors argued that cropland residual burning is very small in China. How this resampling is done and how will it affect the burned area product ?
Section 2.3 (line 156-157): I don’t see the need why AGB and land cover products have both 300m resolution while you decide to finally work on 1km resolution, followed by resample burned area to 1km?
Table S1: the setting of CC seems a little arbitrary. Justifications are needed.
Fig. 5: it does not quite make sense to use MCD64 for BA comparison because it is already used in the authors’ dataset? The other two are based on active fire and hence not a good source either. Why not using GFED5? ESA CCI burned area datasets?
Fig. 1: I am surprised to such a huge interannual variation in BA (about five times!) while it is still dominated by agricultural fires which are human dominated. Did the authors think about the reason?
Citation: https://doi.org/10.5194/gmd-2024-170-CC1 -
RC2: 'Comment on gmd-2024-170', Chao Yue, 15 Dec 2024
Lin et al. developed a new dataset for wildfire emissions in China and compared their data with other datasets. I welcome this effort. The approaches used make sense for me. One general question is that I don’t see how aboveground biomass data was used a proxy for fuel load. For forests, China’s fire is mostly surface fire so what we need is surface fuel load or surface litter. For cropland, I don’t know how much Spawn et al. data is reliable in China. I hope that the authors can provide some thoughts in this respect when revising the paper.
Minor comments:
Line 106-108: justifications are needed on why these two products are selected out of other potential products.Line 117: I don’t see how Fig. S1 shows the system… Do you mean Fig. S3? But Fig. S3 is not very clear either.
Line 125: Fig. S3 and S4 need more explanations. The current ones are not easy to follow.
Line 131: “burned areas” => you could name them as false active fire detections.
Line 132-133: the resampling to 1km is critical as the authors argued that cropland residual burning is very small in China. How this resampling is done and how will it affect the burned area product ?
Section 2.3 (line 156-157): I don’t see the need why AGB and land cover products have both 300m resolution while you decide to finally work on 1km resolution, followed by resample burned area to 1km?
Table S1: the setting of CC seems a little arbitrary. Justifications are needed.
Fig. 5: it does not quite make sense to use MCD64 for BA comparison because it is already used in the authors’ dataset? The other two are based on active fire and hence not a good source either. Why not using GFED5? ESA CCI burned area datasets?
Fig. 1: I am surprised to such a huge interannual variation in BA (about five times!) while it is still dominated by agricultural fires which are human dominated. Did the authors think about the reason?
Citation: https://doi.org/10.5194/gmd-2024-170-RC2
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