the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Fuel types and fire severity effects on atmospheric pollutant emissions in an extreme wind-driven wildfire
Abstract. In the Mediterranean area, wind-driven wildfires with crown fires are rising, causing an increment in atmospheric pollutant emissions. Quantifying gas emissions in these wildfires requires a better understanding of the components that contribute to the total emission estimate. Here, we aimed to analyze the differences in pre-fire available biomass distribution among layers of fuel types in Pinus halepensis and Quercus suber (hereafter, pine and oak) forests burned in one of the largest wildfires (“La Jonquera”, 10,264 ha) of the past decades. This was done in order to try to unravel the differences in fire severity linked to the percentage of available biomass consumed in each layer and pollutant emissions (CO2, CO, CH4, PM2.5). We used field data (>100 post-fire plots) in which measures from crown, shrub and litter layers, fire severity and consumption assessments were combined with data from National Forest Inventories to quantify final atmospheric pollutant emissions.
Total pre-fire available biomass among pine and oak forests showed different vertical distribution. Pine forests had a higher percentage of crown fine and shrub biomass for all fuel types while oak had more litter biomass. The fuel types with large trees and low tree density, together with fuel types with has lower tree density and vertical continuity had the highest non-charred fire severity in pine and oak. The presence of Erica arborea caused higher fire severity in oak stands. Fuel types of pine were more resistant to the effects of surface fires because they had taller trees than oak. Percent biomass consumption was higher in pine and oak stands in low fire severities because the taller trees could withstand surface fire at high intensities without increasing fire severity. The wildfire analyzed was a large fire with massive crown and high-intensity surface fires, but only a small amount of the finest crown biomass and coarse surface fuels were consumed. Fire severity was the main factor determining different amount of emissions without significant influence of fuel types, and only emissions of CO2 and CH4 were higher in pine than in oak in low fire severities. Although remote sensing technologies are extremely useful for biomass and wildfire severity assessments, field data is essential to quantify biomass consumption, atmospheric pollutant emissions from different fuel types and fuel layers.
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RC1: 'Comment on egusphere-2024-1355', Anonymous Referee #1, 26 Jun 2024
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Manuscript presents and interesting exercise of evaluation of consumed biomass and emissions estimated during a wildfire and the differences between Pinus and Quercus stands. The exploitation of public databases and field sampling is a valuable approximation to this topic. Nevertheless, this method present important limitations that must be highlighted by authors. A high level of uncertainty is expected with proposed methodology; therefore authors and readers must be aware of this approximation to results and derived conclusions.
Next, I detail some comments and suggestions to improve the manuscript and some question to authors that must be clarified:
Line 80. The main objective is not completely in agreement with title. Reconsider rewrite de title please. Propose a hypothesis please.
Line 102. Regeneration of Quercus ilex under Q. ilex stands?
Line 102. What about mixed forest? Are there mixed oak-pine forests in burned area?
Line 112. This is not considering a large spot distance during extreme wildfire events (e.g. see Tedim et al. 2019)
Line 120. “monspeliensis” Lowercase
Line 125. Why do you not use remote sensing data to plan the inventory? Helicopter flight do not seem a very economic method and you probably obtain similar categories than remote sensing from Copernicus database (dNBR). Justify better please
Line 150-155. This hierarchical and deterministic classification following Alvarez et al. 2012 must be justified for studied area. e.g. using cluster analysis
Line 170-174. How? Visually estimation? In my opinion it is very difficult this estimation at crown level and the uncertainty of measure is very high. Consumed visually observed could be a good estimation if pre-fire data are available (very difficult or impossible during wildfires). In my opinion a good estimation of percentage of shrub and dead fuel data in opportunistic sampling need a comparison between burnt and unburnt plots (control unburnt plots is needed to ratify data obtained in 3FNI see below).
Line 197. To my understanding NFI data from shrubs are a estimation of 5 m radius plot in the centre of NFI plot, is it correct? Authors are expanding these data to plot level in field data. Be cautious please. At least authors must be honest highlighting the limitation of these data. In addition, what is the time lag between NFI3 and wildfire? Authors must be highlight or justify how including the growing of shrubs on results. On the contrary they must assume the underestimation of biomass during the combustion process. It is important because this value affects to estimated emissions. I recommend consulting models proposed by Montero et al. for potential estimation of shrub growing. This work includes correlation models for all Shrub communities in Spain and could be useful to compare results with models proposed by authors.
https://www.mapa.gob.es/es/desarrollo-rural/publicaciones/publicaciones-de-desarrollo-rural/librobiomasadigital_tcm30-538563.pdf
Line 215. Two months after fire (date of sampling) most of scorched needles in moderate and low severity fire have fallen. Explain better how you estimate % of consumed biomass please. In my opinion a visually observed evaluation must be carried out 1-2 week after fire in order to classify completely burned, scorched and not consumed crown fuels. Explain this point better please, or assume the limitation from this estimation
Line 225. I aware the difficulties to obtain emission factors for all species studied but in my opinion Q. pubescens is a very different species and ecosystem than Q. suber
Line 226. ANOVA assumes independent and randomized events for each plot. This is not true in studied plots (Figure 1). I suggest including a spatial correlation analysis
In my opinion results and discussion could be different if method is refocused. Authors must justify well their decision to assess robust results
Citation: https://doi.org/10.5194/egusphere-2024-1355-RC1 -
RC2: 'Comment on egusphere-2024-1355', Paulo Fernandes, 17 Jul 2024
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The study combines post-fire field data, national forest inventory and assumptions/models and assess fire severity and estimate fuel consumption and gaseous emissions. It features a clever method to estimate pre-fire fuel loadings and establishes relationships between consumption/emissions and forest structure. The findings are valuable and contribute to a topic of study that has received very low attention in Europe, namely the assessment of canopy fuel consumption, so it definitely warrants publication. Although the description of methods to evaluate fuel consumption is not totally complete/clear, it suggests some degree of subjectivity and so the implications in terms of uncertainty should be addressed in the Discussion.
Many small issues are present across the manuscript. Some relate to completeness/clarity of methods descriptions, others with fundamental concepts and terminology, e.g. “biomass consumption” would be better replaced by “fuel consumption” across the ms., and “available” when referring to fuel/biomass should disappear(explained below). Also, the concept of fuel type and its confusion with fuel structure.
Specific comments
L11. Replace “wind-driven wildfires with crown fires” by “wind-driven crown fires”. But not all extreme fires are wind-driven, so I advise to get rid of wind-driven as well.
L13-14. Can’t understand “among layers of fuel types”. A fuel type by definition is a fuel-complex that has distinctive fire behaviour. Or is it just poor grammar and fuel types are being equated to forest types (oak and pine)?
L16. “available biomass consumed” is redundant. By definition, available = consumed, not consumable (potentially consumed).
L19. It is impossible to know pre-fire available biomass, because it depends on fuel moisture and fire type. Replace by fuel loading, which in fact it’s what you are referring to.
L20. Again, this suggests the authors are misunderstanding what a fuel type is.
L21. Correct “with has”.
L21-22. “non-charred fire severity” is awkward, rephrase.
L23-24. Rephrase, a comparison between forest types and fire severity class does not make sense.
L24-25. Rephrase, quite hard to follow and understand.
L25. Rephrase: the large fire had fires? Massive is not needed, here and elsewhere.
L50. Advise not to cite Fernandes et al. (2022), the results are not plausible.
L52. Rephrase, a fire does not experience severity.
L61. I don’t think that stronger wind (faster fire) is associated with lower crown consumption (except perhaps under marginally moist conditions), because flame residence time is basically constant for any given combination of fuel particle size and arrangement. I doubt that any of the references cited stated such.
L67. Fire severity is already expressing change, and not just through fuel consumption but through vegetation alteration in general, i.e. including scorch.
L73. None of these references is a primary reference for the concept of fuel type.
L74. Not sure what “combustion factors according to fire severity” means. Which functions?
L75-76. What does fuel layers have to do with landscape heterogeneity? The former is local.
L78. Isn’t forest inventory data field data?
L80. Forest structure does not “make” a fuel type, only variability within a fuel type.
L82. The cause-effect relationship is inverted: fuel consumption determines fire severity, not the other way around.
L83. Analyse what? Quantity, variability?
L98-100. Rephrase. It states that structure determines structure?
L102. scorpius, not Scorpius.
L111. No need to qualify a crown fire as “massive”. Delete or be more specific.
L111. Spotting, not spots.
L115. Relative humidity, not moisture, right?
L117-118. It would be much better to indicate drought indices that actually refer to fuel drought, like the DC or the BUI of the Canadian FWI.
L120. Again, decapitalize the species.
L129. Not 3 types, 3 classes. A reference for what fire severity is, is needed, preferably the original one (Ryan & Noste).
L131. What this % refer to? Is it literally as written, i.e. green trees are totally green? Or are the % in relation to % canopy volume or % tree height? Trees are often completely scorched or burned, but rarely totally green after a wildfire.
L148. I strongly recommend to not designate these structural variants as fuel types, namely because it is being applied to two forest types that may be seen as distinct fuel types by themselves, i.e they will burn differently, at least under part of the fire weather spectrum. Simply, “fuel structure types” is adequate.
L163. Left alive or left green? Often, the fraction alive is higher than the fraction green. If field work was carried out a few months after the fire, what was recorded is the green fraction.
L164, 165, 166. Scorched, not scorch.
L168. Rectify: species is not “measured”.
L170. Why are char heights within parentheses? Were they an additional variable measured? If that is the case they should be outside parentheses.
L170. How was this assessed? Classes? Which classes? A different way? Same question for shrubs. Need to address the limitations (uncertainty) of the estimates in the Discussion. To what extent they impact on the emissions estimates?
L173. The time-lag concept is only for dead fuels, so replace it with diameter ranges (<6mm, 6-25 mm, etc.).
L179. Eliminate “available”, because available fuel is indicated by the combustion factor.
L182. Crews, not corps.
L183. What plots are these? Are they different from the severity plots? Clarify.
L185. Delete available and don’t present what follows as bullet points.
L214. This (the field component) overlaps with section 2.3. Should be moved/merged. Still, no description of how this was done (classes?).
L219. Quite hard to do, unless consumption is nil or is total. How did you manage to have a reference for preburn litter?
L225. So, after all this how did you calculate EM? Averages of B, C and D for the whole fire? Another method? Explain.
L228. Unclear what was the spatial scale of analyses here. Plots?
L233. What determines fire severity differences? Fire severity class?
L235. Define coarse fuel in methods.
L238, 240. Scorched. This is a systematic error across the paper. Replace also in the figures.
L245. Two “available” to delete.
Figure 3. You did the stats in log-transformed values but it would be much better to show the actual untransformed values in this figure.
Figure 4. Replace “available biomass” by fuel loading. Explain in the caption that this includes only fine fuels for the trees and all size classes for the shrubs.
L266. Rephrase, otherwise it looks like a methods sentence.
L278. A more meaningful way to say this is “Crown fire was predominant …”.
Table 1: add standard deviations or, perhaps better, coefficients of variation.
L338-339. This is not totally true, as it depends also on plant morphology. E.g. shrubland types in the same region can have very distinct potential biomasses depending on community composition.
L342. For a more recent analysis see https://doi.org/10.1016/j.scitotenv.2019.02.237 and for the general effect of forest structure on fuel load see http://dx.doi.org/10.1051/forest/2009013
L345. Note that other studies might be using different definitions, as very often only fine fuels and coarse dead fuels are considered.
L356. Please put this finding (FT2) in a more international context, as this is the pattern found in many pine forests elsewhere, namely in Portugal (https://www.sciencedirect.com/science/article/pii/S0378112715001528 ) and in north America (dozens of studies to choose from …). I also think the authors can do better in terms of discussions regarding the other FTs findings.
L367-368. Again the repetition of “fires” as if a single fire was composed of several fires.
L368. You forgot the most important driver of fuel availability (combustion factor): fuel moisture content.
L374-375. This is circular reasoning: fuel consumption is fire severity, the latter is based on the former and not the other way around.
L378. Although common, this is a misunderstanding: fuel consumption is in general independent from fire spread rate.
L392. And comparable to CO2 emissions in Portugal surface fire experiments in pine (https://doi.org/10.1016/j.foreco.2012.11.037) which considered litter.
L397, L426. I don’t think this is true and did a short literature search that confirmed it. It depends on the type of study and available fuel data. So please rephrase to introduce nuance and tone down.
L401. This study assumed emission factors from the literature that made emission estimates a function of vegetation type and fuel load. So, I advise mentioning this limitation when comparing with studies that actually measured emissions in the field.
L411. This last sentence needs referencing.
Citation: https://doi.org/10.5194/egusphere-2024-1355-RC2 -
RC3: 'Comment on egusphere-2024-1355', Anonymous Referee #3, 17 Jul 2024
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Thanks to their authors for their submission. The fire science discipline is always advantaged by studies on pre-burn biomass determination followed by assessing the impact of fire behaviour on fuel consumption and emissions. In a changing climate, such investigations are worthwhile so well done on getting the work to this point. Some corrections are required before a favourable decision can be reached on this article. Two higher-level questions involve:
- Statistical analysis. Multiple linear regression and two/three-way Analysis of Variance is used in this manuscript. I would recommend that the authors check (and report upon) whether the assumptions underpinning these techniques are satisfied or not. The conclusions of this manuscript hinge on statistical analysis of results so a robust effort is required here.
- The discussion section needs more work. In my opinion two extra sections are required. 1) An additional sub-section would compare your results with other inventories in your country either at the regional or national level. This will make it easier for the reader to see how your estimates quantitatively compare with previous work. 2) A section should be added on uncertainties in your emissions estimates since you rely on information source with error e.g. allometric equations for biomass determination, plot level sampling errors and emission factors with uncertainties.
Some other suggested corrections are:
The phrase ‘wind-driven wildfire’ is used in the manuscript. Is there such a thing as ‘non-wind-driven wildfire’. I thought wind would always be a necessary component for wildland fire.
Line (L) 15. … ‘one of the largest wildfire of the last decade’. In what context is this e.g. fires in Spain, fires in the Mediterranean region?
Please remove emotive phrases from the manuscript e.g. L25 ‘massive wildfire’, L56 ‘huge inaccuracies’.
L44. You mention health impacts from wildfire particulate matter. It is worth pointing out that gas phase pollutants from wildfire also have health effects as well.
L53. Referring to the ‘Seiler and Crutzen (1980) method’ strikes me as jargon. Technically, it is a fuel consumption method that Seiler and Crutzen (1980) developed.
Page 2 bottom paragraph. I’m wondering whether the paper below is worth citing to provide a technical definition for what your are referring to as ‘fire severity’?
- E. Keeley. Fire intensity, fire severity and burn severity: A brief review and suggested usage
International Journal of Wildland Fire
https://doi.org/10.1071/WF07049
Page 3. L2 and L415. I would remove the phrase ‘unprecedented combination of …’. The type of investigation your are conducting is standard practice rather than unprecedented.
L115. Moisture content. Is this fine fuel moisture content or something else?
Around L130. When you refer to charred trees up to what height level are trees generally charred?
Figure 2. Is this figure adapted or adopted from Alvarez et al. (2012)? If it is adopted you will need copyright permissions to use this figure.
Equation 1. Use multiplication signs rather than the letter x.
L233. Log transform for normality. What test did you use for this and what was the result e.g. test statistic and p-value?
L241. What was the required significance level for significant differences?
Figure 3. Is the log base 10 or base e?
Table 2. Is there any reason why nitrous oxide was excluded from your analysis since it is a major greenhouse gas?
Citation: https://doi.org/10.5194/egusphere-2024-1355-RC3
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