the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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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Status: open (until 22 Nov 2024)
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RC1: 'Comment on gmd-2024-170', Anonymous Referee #1, 22 Oct 2024
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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
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