the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Soil smoldering in temperate forests: A neglected contributor to fire carbon emissions revealed by atmospheric mixing ratios
Abstract. Fire is considered as an essential climate variable, emitting greenhouse gas in the combustion process. Current global assessments of fire emissions traditionally rely on coarse remotely-sensed burned area data, along with biome-specific combustion completeness and emission factors, to provide near real-time information. However, large uncertainties persist regarding burned areas, biomass affected, and emission factors. Recent increases in resolution have improved previous estimates of burned areas and aboveground biomass, while increasing the information content used to derive emission factors, complemented by airborne sensors deployed in the Tropics. To date, temperate forests, characterized by a lower fire incidence and stricter aerial surveillance restrictions near wildfires, have received less attention. In this study, we leveraged the distinctive fire season of 2022, which impacted Western European temperate forests, to investigate fire emissions monitored by the atmospheric tower network. We examined the role of soil smoldering combustion responsible for higher carbon emissions, locally reported by firefighters but not accounted for in global fire emission budgets. We assessed the CO/CO2 ratio released by major fires in the Mediterranean, Atlantic pine, and Atlantic temperate forests of France. Our findings revealed low Modified Combustion Efficiency (MCE) for the two Atlantic temperate regions, supporting the assumption of heavy smoldering combustion. This type of combustion was associated with specific fire characteristics, such as long-lasting thermal fire signals, and affected ecosystems encompassing needle leaf species, peatlands, and superficial lignite deposits in the soils. Thanks to high-resolution data (approximately 10 meters) on burned areas, tree biomass, peatlands, and soil organic matter, we proposed a revised combustion emission framework consistent with the observed MCEs. Our estimates revealed that 6.15 MtCO2 (± 2.65) were emitted, with belowground stock accounting for 51.75 % (± 16.05). Additionally, we calculated a total emission of 1.14 MtCO (± 0.61), with 84.85 % (± 3.75) originating from belowground combustion. As a result, the carbon emissions from the 2022 fires in France amounted to 7.95 MteqCO2 (± 3.62). These values exceed by 2-fold the generic GFAS global estimates of 4.18 MteqCO2 (CO and CO2). Fires represent 1.97 % (± 0.89) of the country’s annual carbon footprint, corresponding to a reduction of 30 % of the forest carbon sink this year. Consequently, we conclude that current European fire emissions estimates should be revised to account for soil combustion in temperate forests. We also recommend the use of atmospheric mixing ratios as an effective monitoring system of prolonged soil fires that have the potential to reignite in the following weeks.
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RC1: 'Comment on egusphere-2023-2421', Anonymous Referee #1, 06 Feb 2024
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This paper combined satellite observations of fire behaviour, tower-based CO and CO2 mixing ratios and bottom-up approach to estimate forest fire emissions in France with a focus on improving emissions from smoldering. I found the methods used by the authors in general credible and the paper advances the quantification of fire emissions induced by forest fires. I have a few major comments mainly regarding clarifications of the methods being used and some minor technical comments (detailed below).
Major comments:
- I suggest adding a paragraph giving an overview of the methods, preferably with a flowchart figure, focusing on how different approaches are combined and connected.
- What’s the major purpose of Hysplit model? I don’t really see how it is connected with the selection of tower sites and determination of background measurement…. Is it only used to justify that most of the ICOS sites are free of influences of Mediterranean forest fires and hence their measurement could be considered as background ones? Fig. 3 is nice but also quite unique I guess. Is it a sufficient example to argue that, based on Hysplit simulations, most of the ICOS sites are free of influences of Mediterranean forest fires and hence their measurement could be considered as background ones?
- If I understand well, it seems that the characterization of fire behaviour using satellite data is independent of the bottom-up estimation of fire emissions, in particular, the fire behaviour information has not been used to determine the key parameters in Equation (2) (e.g., SFp) and parameters in lines 330-334. This is somewhat a little disappointing. Following this logic, it then seems that the key strength/advancement of the paper is that the authors compiled a nice Table 2, a range of more credible sources of fuel load, and the used satellite-derived fire information and power-based MCE to *indirectly* verify their bottom-up estimate of emissions? Is this correct? This point has to be made clearer when the authors address my first major comment.
- The authors examined three typical fires, or fires in three typical forests using satellite-based fire behaviour and power-based mixing ratio measurements. These are then used to support their bottom-up approach. Then then the challenge is how we can ensure that the upscaling to the national level using their bottom-up approach is also reliable, given that fires are highly temporal and spatially heterogeneous in terms of fire bebaviour, fraction of flaming versus smoldering, combustion completeness etc. (I believe the authors have tried to address well the spatial heterogeneity in fuel load)?
Minor comments:
Line 139: some introduction on VIIRS data is necessary because it seems an important limitation on what fires have been analyzed.
Line 145: “beyond the fire outbreak ”. What does ‘beyond’ mean here?
Line 146–147: I don’t see how the approach described here (visual examination of RGB spectrum) could be reconciled with BAMTS… So what is exactly the role of BAMTS in burned area detection? And how are these two further linked with random forest classifier and how the classifier is used and for which purpose?
Line 203: “corresponding to a single grid cell. ”. Which model does this grid cell refer to? What is the spatial resolution of Hysplit?
Table 1: Better to report R2 rather than R. The same for the texts.
Line 168: what is this 6-hour data?
Line 199: Is this 600 per hour particle numbers typically used in transportation modeling? How does this influence the results?
Line 201: “By tracking the arrival times of these particles within an influence region surrounding each atmospherictower, we successfully attributed a source to each anomaly”, I don’t understand the latter half. Could you please explain?
Line 298-299: I don’t understand what you mean by ‘baseline’ here.
Table 2: I cannot reconcile/connect Table 2 with lines 330–335. (1) you provide only constant SF values in Table 2. But if SF values do no change among the flaming phase, mixed phase and smoldering phase, then how is this used in Equation (2)? (2) lines 330-335 seems giving proportions of fuels being affected by fire, what is the difference between this and CC in Table 2? Seems that lines 330-335 should be better integrated with Table 2 so that you have only a single source to present the parameters used in emissions calculation. (3) how the information in lines 330-335 is used in Equation (2)? (4) how do you choose CC values between its min and max values in Table 2?
Line 352: TROPOMI data not explained in Methods.
Figure 4: what is the difference between 1-hour and 1-minute? Are they the temporal resolutions of the data ? what is the temporal resolution of measurement over the towers?
Citation: https://doi.org/10.5194/egusphere-2023-2421-RC1
Data sets
Fire emissions in France for 2022 fire season Lilian Vallet and Florent Mouillot https://oreme.org/observation/foret/incendies/
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