the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 May 2024
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
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 -
AC1: 'Reply on RC1', Albert Alvarez, 27 Jul 2024
Answer to Reviewer #1: Anonymous Referee
Manuscript presents and interesting exercise of evaluation of consumed biomass and emissions estimated during a wildfire and the differences between Pinus and Quercus stands.
Thank you very much for your reply. We appreciate the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.
We agree with your comment about the important uncertainties of the method in each one of their steps. We have written a short section in the discussion with the different uncertainties from the method and what we tried to do to reduce these uncertainties “Section 4.4. Uncertainties in emissions estimates and limitations” on (Lines X).
We used Seiler and Crutzen (1980) method and the equation;
EM = A x B x C x D,
where EM are the total emissions (Mg/ha), A is the area burned (ha), B is the available biomass before the fire (Mg/ha), C is the combustion factor (%) and D is the emission factor (g/kg).
If the burned area is precisely defined, as we think it was in our case, the greatest uncertainties are in the estimation of biomass prior to the fire and the combustion factor (Ottmar et al., 2008; Bacciu et al., 2015; Fernandes et al., 2022), but also on the use of emission factors available. We discuss below each of these components.
The quantification of different fuel types before the fire is one of the main sources of uncertainty, indeed, variations in fuel characteristics may contribute to 83 percent uncertainties in estimates of wildfires emissions (Ottmar et al., 2008; Fernandes et al., 2022). Moreover, in the south-European forests, the high spatial variability of fuel loadings, the fuel structure which describes how fuel loads are vertically distributed in a stand and how fire moves through a site are critical factors when describing forest fuels and determine final pollutant emissions (Carvalho et al., 2007; French et al., 2011). We have tried to reduce this uncertainly with the identification of species at different layers and by using specific allometric equations from a combination of data from National Forest Inventory (NFI) for canopies and shrubs but especially from Ecological Forestry Inventory of Catalunya for litter assessment (IEFC) (Gracia, 2001; Vayreda et al., 2016). Although biomass allometries are also a source of uncertainty, the range values in which they move fit with real data from the inventories and are at least differentiated between plots instead of using a general value for all plots. There are more precise allometric equations for shrublands in Spain that we used but they are also difficult to apply after the fire when sometimes you cannot identify variables such as height or diameter of the shrub. The joint of these data allowed reduction in the uncertainty of the total fuel load available especially including the litter component that is not usually assessed.
In fact, previous studies considered that forest floor emissions were the most uncertain component when modelling carbon emissions from forest fires, since its consumption can range from near 0 to 100% (Vilen and Fernandes 2011). In addition, considering the difficulty in estimating combustion of subsurface carbon and that 65% of the total fire-wide carbon emissions may come from the combustion of litter, duff, and mineral soil carbon (Campbell et al., 2007), we consider that most of the uncertainty in our estimate of total emissions may arise from uncertainty in combustion of these fuels. Therefore, our results should be taken with caution because of the complexity of litter distribution and the variables that could influence its amount and variation. We highly recommend to develop better models for assessing litter for different forest species because of its high percentage in the two forest types studied. This will be the future challenge since for the moment remote sensing is not useful for assessing these fuel loads. If the content of litter is not assessed, there will be a constant underprediction of fuel load and emissions especially when there are extreme wildfires.
Combustion factors for each layer are another important source of uncertainty with values over 30% (Ottmar et al., 2008; Fernandes et al., 2022) and values that could be higher depending on the position of the fire (head, flank, back) (Surawski et al., 2016). There are few examples from field works especially when you apply Seiler and Crutzen (1980) method with remote sensing tools (De Santis et al., 2010; Jimenez et al., 2013). However, we reduced that uncertainty with direct observation of the percentage consumed after the fire at the three layers. This is especially important when you are comparing emissions between different fire severities. It is also important to distinguish what crown fraction is consumed in crown fires in wind fires since it is usually overestimated because it is often considered that up to 0.6-2.5cm all branches are consumed when, in this fire type, we saw that in some cases not all the fine material had been consumed. Although the estimation method was visual, it helps to understand what fraction of the canopies can be consumed in this type of fires. However, it would be necessary to replicate the measures in convective and topographic fires where perhaps more canopies can be consumed. Overall, these variations in crown consumption could affect the total biomass but the largest amount of biomass consumed is clearly that of shrubs and litter, the biomass most poorly estimated in studies on emissions and therefore the greatest source of uncertainty.
Regarding emission factors, they are also one of the main uncertainty sources in emissions estimations. It is important to work on this topic with more field measurements, in particular for southern European conditions variability (Fernandes et al. 2022). EFs variation (mainly due to type of pollutant, type and arrangement of fuel, and combustion factor) that could contribute to about 16% of the total error associated with emissions (Ottmar et al., 2008; Bacciu et al., 2015; Fernandes et al., 2022) is mainly available for United States of America (USA) forests (Urbanski, 2013), but it is not a suitable proxy for wildfires in Europe, due to the different vegetation cover and the differences in combustion characteristics (e.g. flaming and smouldering phases).
There are also other limitations with the methods used in the current study. They include not considering fuel load from herbs, not taking into account the influence of topography or fuel moisture on the general emission factors and the use of litter emission factors from Q. pubescens (the only available) instead of those from Q. suber. We also did not differentiate between flaming or smoldering phase of combustion and we did not consider fuel moisture.
References:
Bacciu, V., Spano, D., and Salis, M.: Emissions from Forest Fires: Methods of Estimation and National Results, in: The Greenhouse Gas Balance of Italy: An Insight on Managed and Natural Terrestrial Ecosystems, edited by: Valentini, R. and Miglietta, F., Springer, Berlin, Heidelberg, 87–102, Germany, https://doi.org/10.1007/978-3-642-32424-6_6, 2015.
Campbell, J., Donato, D., Azuma, D., and Law, B.: Pyrogenic carbon emission from a large wildfire in Oregon, United States, J. Geophys. Res. Biogeosciences, 112, https://doi.org/10.1029/2007JG000451, 2007.
Carvalho, A., Monteiro, A., Flannigan, M., Solman, S., Miranda, A., and Borrego, C.: Forest fire emissions under climate change: impacts on air quality, in: Seventh Symposium on Fire and Forest Meteorology, The Turrets, USA, 23 October 2007, https://ams.confex.com/ams/7firenortheast/techprogram/paper_126854.htm (last access: 23 April 2024), 2007.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Fernandes, A. P., Lopes, D., Sorte, S., Monteiro, A., Gama, C., Reis, J., Menezes, I., Osswald, T., Borrego, C., Almeida, M., Ribeiro, L. M., Viegas, D. X., and Miranda, A. I.: Smoke emissions from the extreme wildfire events in central Portugal in October 2017, Int. J. Wildl. Fire, 31, 989-1001, https://doi.org/10.1071/WF21097, 2022.
French, N. H. F., de Groot, W. J., Jenkins, L. K., Rogers, B. M., Alvarado, E., Amiro, B., de Jong, B., Goetz, S., Hoy, E., Hyer, E., Keane, R., Law, B. E., McKenzie, D., McNulty, S. G., Ottmar, R., Perez-Salicrup, D. R., Randerson, J., Robertson, K. M., and Turetsky, M.: Model comparisons for estimating carbon emissions from North American wildland fire, J. Geophys. Res., 116, https://doi.org/10.1029/2010JG001469, 2011.
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013.
Ottmar, R. D., Miranda, A. I., and Sandberg, D. V: Chapter 3 Characterizing Sources of Emissions from Wildland Fires, in: Developments in Environmental Science Wildland Fires and Air Pollution, vol. 8, edited by: Bytnerowicz, A., Arbaugh, M. J., Riebau, A. R., and Andersen, C., Elsevier, 61–78, 2008.
Surawski, N. C., Sullivan, A. L., Roxburgh, S. H., and Polglase, P. J.: Estimates of greenhouse gas and black carbon emissions from a major Australian wildfire with high spatiotemporal resolution, J. Geophys. Res. Atmos., 121, 9892–9907, https://doi.org/10.1002/2016JD025087, 2016.
Urbanski, S. P.: Combustion efficiency and emission factors for wildfire-season fires in mixed conifer forests of the northern Rocky Mountains, US, Atmos.Chem.Phys., 13, 7241–7262, 2013.
Vilen, T. and Fernandes, P. M.: Forest fires in Mediterranean countries: CO2 emissions and mitigation possibilities through prescribed burning, Environ. Manage., 48, 558–567, 2011.
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.
OK, we will reconsider both the title and the formulation of the main objective to make them coherent between them. However, as the other reviewers may also propose changes in this direction, we prefer to wait for having all comments and then reformulate them.
Line 102. Regeneration of Quercus ilex under Q. ilex stands?
No, this was incorrectly written it was Quercus suber under Q. suber stands. We have changed that on line 102.
Line 102. What about mixed forest? Are there mixed oak-pine forests in burned area?
Yes, there were areas with mixed forests. In the north-west forests of Q. suber changed to Q. ilex, but this was burned with south winds in the days following the start of the fire and there were not enough cases to include in the study. There were also mixed areas of P. halepensis and Q. ilex, but this type of forest was not dominant in the landscape and we only carried out plots in areas with pure forest species, either pines or oaks.
Line 112. This is not considering a large spot distance during extreme wildfire events (e.g. see Tedim et al. 2019)
You are right, spot distances of 200-400 meters with a maximum of 1km are not considered large spot distances but intermediate-range spotting during this extreme wildfire event EWE (Tedim et al., 2018). This is especially true when you compare with other countries and forest types (Martin and Hillen, 2016; Cruz et al., 2012) and accepted definitions to describe extreme fire event (Tedim et al., 2018).
We have upgraded the section 2.2 “Fire description and weather conditions during the fire” to justify better the reason because this fire was considered an EWE following Tedim et al., (2018) among other articles.
References:
Cruz, M. G., Sullivan, A. L., Gould, J. S., Sims, N. C., Bannister, A. J., Hollis, J. J., and Hurley, R. J.: Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia, For. Ecol. Manage., 284, 269–285, 2012.
Bombers. Report of the Jonquera wildfire. Bombers de la Generalitat de Catalunya, Departament de interior de la Generalitat de Catalunya. https://agricultura.gencat.cat/web/.content/06-medi-natural/boscos/gestio-forestal/obres/restauracio-forestal/restauracio-hidrologica/fitxers-binaris/jonquera_informe_incendi.pdf (last access: 23 July 2024), 2012.
DARP: Report on the forest fire of July 22, 2012 in La Jonquera (Alt Empordà), Generalitat de Catalunya, Departament d'Agricultura, Ramaderia, Pesca, Alimentació i Medi Natural, Girona, Spain, https://agricultura.gencat.cat/web/.content/06-medi-natural/boscos/gestio-forestal/obres/restauracio-forestal/restauracio-hidrologica/fitxers-binaris/jonquera_informe_incendi.pdf (last access: 23 July 2024), 2012.
Martin, J. and Hillen, T.: The spotting distribution of wild fires, Appl. Sci., 6, https://doi.org/10.3390/app6060177, 2016.
Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, R. M., Delogu, M. G., Fernandes, M. P., Ferreira, C., McCaffrey, S., McGee, K. T., Parente, J., Paton, D., Pereira, G. M., Ribeiro, M. L., Viegas, X. D., and Xanthopoulos, G.: Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts, Fire 2018, 1(1), 9; https//doi.org/10.3390/fire1010009, 1, 2018.
Line 120. “monspeliensis” Lowercase
OK, changed (Line 120).
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.
After the fire, during August 2012 we did not know if we would have the opportunity to do the study and some of us were working with firefighters collecting data from fires. Until September 2012 we could not start to plan the field work and we did not have much time because we knew that the timber extraction work was going to start immediately as it was happened in other fires where we did field work (Alvarez et al., 2012).
A recording flight of the fire had to be carried out by the fire department, so due to our collaboration with them, we were able to participate in the flight, obtaining videos and hundreds of photos of the entire fire. Therefore, it was not a specific flight for the work but a complementary activity. These were used to make an initial estimate of the burned area by each severity and to locate areas where to find severities that were not charred. In fact, we thought about the possibility of apply any remote sensing index but we did not know if it could identify these areas with lower severity better than with the flight material obtained. Finally, we were lucky to participate in the flight because we could also understand better the global spread of the fire that helped us to obtain other data related to fire behavior.
In section 2.3 “Field plot data and fire severity estimation” we have included a sentence to clarify the reason because we used a flight instead of the use of remote sensing tools to determine distribution of fire severity. We will highlight the lack of time, the opportunity of obtaining a multifunctional data from flights and the doubt about the precision to distinguish green and scorch areas in this massive scorch wildfire.
References:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, For. Sci., 59, 139–150, https://doi.org/10.5849/forsci.10-140, 2013.
Line 150-155. This hierarchical and deterministic classification following Alvarez et al. 2012 must be justified for studied area. e.g. using cluster analysis
We used this classification in the studied area because it was created and tested in a fire only 30 km away from this fire in Pinus halepensis stands, one of the two main species of forest types of this study with also similar fuel types. Moreover, the classification was made not only to apply to one species but in general, according to common but critical variables that determine fire behavior independently of forest types: the density of trees, which determines horizontal continuity, and the percentage of trees based on height, which determines vertical continuity.
At the beginning of the study, we concluded that we captured the variable forest structures in Q. suber plots, just as the classification method was designed to be applied to other types of forests and allow comparisons. The result was really good because it was possible to identify the differences in the fuel types between the two different types of forests and the effects on fire severity and consumption. We have added a small comment justifying this point in section "2.3 Field plot data and fire severity estimation".
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).
We agree with you about the difficulty of the visual estimation of fine crown and shrub percentage after the fire and we also understand the concern about the high degree of uncertainty that this measure can cause. However, according to De Santis et al. (2010), biomass consumption was traditionally estimated using a two-step methodology which includes:
- the estimation of pre-fire biomass by applying allometric regression equations using destructive sampling or biomass values per species and
- the post-fire biomass estimated by field-based weighting or by visual examination.
When we started the fieldwork, we visited the few areas unburned within the fire perimeter to understand what possible fuels we could find and to identify species. Moreover, we visited areas that immediately bordered the perimeter of the fire when we had plots near the perimeter. When we started to measure the plots, we invested long time to measure all the different possible diameters from shrubs and fine branches from trees with a caliper.
All plots were done by the same two people in order to avoid observer bias that could cause a significant influence in the kind of measures that we took, the percentages of fuel types after the fire. The value of each percentage was an average value from the two people to avoid errors of perception. We also take dozens of photos from all angles from each plot to capture trees, shrubs and litter. It was useful because at the beginning every night we contrasted percentages given to each plot and adjusted them comparing with previous plots when it was necessary. After this first training and as we made more plots, we had a more balanced vision (which was far from perfect) of the percentages we gave, so that the quantitative differences we appreciated were relatively small. The fieldwork was intensive from Octobre 2012 to March 2013, only stopped on very windy or rainy days. At the end of the work, we obtained near 12,000 photos from plots and their surrounding area that also helped us to calibrate dubious plots.
On the other hand, we transparently recognize that the potential shrub or litter cover measurements before the fire based on the number of shrubs, and comparing it with what we saw in unburned areas inside could be not as accurate as it could be using other methods. However, these quantitative percentages reflected the difference between plots that we saw qualitatively and the results obtained from the fuel loads from shrubs were within the ranges that we obtained from the IFN3 plots and bibliography.
We have updated the section “2.4.1 Area burned and pre-fire available biomass”, including a synthesis of the training method to obtain the percentage of fuel consumed. Moreover, in the discussion or/and in the new section “4.4 Uncertainties in emissions estimates and limitations” we have included the implications over the uncertainties and potential overestimation of shrub and crown fuel loads before and after the fire.
References:
Ottmar, R. D., Vihnanek, R. E., and Wright, C. S.: Stereo photo series for quantifying natural fuels Volume X : sagebrush with grass and ponderosa pine-juniper types in central Montana, USDA For. Serv. Pacific Northwest Res. Station. Gen. Tech. Rep., X, 2007.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Surawski, N. C., Sullivan, A. L., Roxburgh, S. H., and Polglase, P. J.: Estimates of greenhouse gas and black carbon emissions from a major Australian wildfire with high spatiotemporal resolution, J. Geophys. Res. Atmos., 121, 9892–9907, https://doi.org/10.1002/2016JD025087, 2016.
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.
For the first point, NFI data from shrubs are a measure of 10 m radius plot in the center of each NFI plot (https://www.mapa.gob.es/es/desarrollo-rural/publicaciones/publicaciones-de-desarrollo-rural/librobiomasadigital_tcm30-538563.pdf). Regarding the use of IFN3 plots in shrub load assessment, we developed models that related tree cover (at the time of IFN3) with shrub fuel (also at IFN3), estimated with the model MEDFATE (De Càceres et al., 2019). Then these allometries are applied to the field data. Field data for shrubs were the species identification and the shrub fraction cover, which was used in the equations created with the data from the IFN3 results applying MEDFATE. We have included in the section “4.4 Uncertainties in emissions estimates and limitations” the potential overestimation of biomass with the measure of shrub cover. However, after the fieldwork and comparing plots this percentage was logical with the qualitative estimation among all the different plots.
IFN3 plots were done from 2000 to 2001 and the fire was in 2012. However, we did not used the original values of those plots ten years later. We used IFN3 plots combined with measures of MEDFATE model to obtain allometric equations used later with the field data to assess the shrub fuel load. Therefore, there was not a problem with the growing of shrubs in the IFN plots.
References:
De Càceres, M., Casals, P., Gabriel, E., and Castro, X.: Scaling-up individual-level allometric equations to predict stand-level fuel loading in Mediterranean shrublands, Ann. For. Sci., 76, 87, https://doi.org/10.1007/s13595-019-0873-4, 2019
Pasalodos-Tato, M., Ruiz-Peinado, R., Ri¢, M. de., and Montero, G.: Shrub biomass accumulation and growth rate models to quantify carbon stocks and fluxes for the Mediterranean region, Eur. J. For. Res., 134, 537–553, 2015.
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
The question about how we estimated the % of consumed biomass has been explained in detail in the previous question “Line 170-174.”.
Regarding the fact that needles fall before measuring the amount remaining on the tree after the fire, it is an uncertainty that is inevitable. It is true that as more time passes after the fire, the number fallen needles in areas of moderate/low severities increases. We took this phenomenon into account by observing the number of needles on the burned ground, which were therefore not present previously. We considered whether there was a slope to determine if the needles in the area could correspond to the sampled trees and the condition of the trunk and branches of the trees before decide the percentage of needles unburned.
Moreover, the fall of the needles, and in general the alteration of the conditions of the plots, depends greatly on the days of rain and wind that occur after the fire. In 2012, after the fire, the number of rainy days in the area burned was very few. Despite some windy days, the interruptions to fieldwork were generally very few (perhaps less than five). We have also included these considerations warning of the effect on the measurements of this phenomenon, and that it was taken into account in section “2.3 Field plot data and fire severity 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
Yes, it is also true that Q. pubescens and Q. suber ecosystems may be quite different. However we only used the emission factors for the litter component from Pallozi et al. (2018) because it was the only available study and because at least Pinus halepensis was correctly represented and the other species was of the same genus. We also have included this consideration in the section “4.4 Uncertainties in emissions estimates and limitations”.
References:
Pallozzi, E., Lusini, I., Cherubini, L., Hajiaghayeva, R. A., Ciccioli, P., and Calfapietra, C.: Differences between a deciduous and a conifer tree species in gaseous and particulate emissions from biomass burning, Environ. Pollut., 234, 457–467, https://doi.org/10.1016/j.envpol.2017.11.080, 2018.
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
We will try to test the presence of spatial autocorrelation in the new version of the manuscript.
In my opinion results and discussion could be different if method is refocused. Authors must justify well their decision to assess robust results
We agree with this point and we have included a new paragraph that we hope could reflect the uncertainty and limitations of the work done and its potential implications. After your comments we expect to explain better the weakest points of the work, recognizing the limitations but highlighting the potential knowledge it can also provide.
We attach these same responses in PDF format.
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AC1: 'Reply on RC1', Albert Alvarez, 27 Jul 2024
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RC2: 'Comment on egusphere-2024-1355', Paulo Fernandes, 17 Jul 2024
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 -
AC2: 'Reply on RC2', Albert Alvarez, 19 Aug 2024
Answer to Reviewer :2
Reviewer #2: Paulo Fernandes
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.
Thank you very much for your reply, we sincerely appreciate your words and the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.We understand some of the problems that have arisen regarding certain terminology used in the text and we have clarified it.
Regarding the use of biomass consumption, we agree that the use of fuel consumption would be more appropriate. We have already changed this term throughout the text. It is interesting to note that the term biomass consumption has been used alone or combined with fuel consumption in some articles mixing both terms (e.g., De Santis et al.,2010; Jiménez et al., 2017; Domingo et al., 2017; Molina et al.,2019, Nolan et al., 2022; Balde et al., 2023).
We agree that “available biomass” was not correctly used in the text. We wanted to refer to the total possible fuel consumed, which includes all litter and shrub fuel loading, but only a portion of crown fuel loading. Since not all crown biomass is consumable, we wanted to distinguish it from the total biomass. We have already changed this term throughout the text.
Regarding the concepts of forest structure and fuel type, it is true that in some sections, such in the abstract, objectives or section 2.3 “Field plot data and fire severity estimation” these terms has been expressed in an unclear manner, making understanding difficult.
However, we believe that we understand the meaning of forest structure and fuel type, so we have clarified it throughout the entire text. Forest structure has been defined in the introduction to avoid potential confusions with fuel type. Moreover, we have removed “forest structure” in lines where it was not necessary or when “fuel type” was more appropriate to avoid misunderstandings. The whole text is about fuel types not about forest structures but we cannot break the link between forest structures and fuel types from the original references to maintain the coherence with the terminology used in those articles. For this reason, “Table S1. Main characteristics of the four fuel types in Pinus and Quercus forests” will remain the same.
The term forest structure is usually used to describe some characteristic of the spatial distribution of biomass in forested vegetation, both vertically and horizontally. Different researchers have used numerous metrics to quantify forest structure (Rowell et al., 2006; Kane et al., 2010; Bottalico et al., 2017; Hoff et al., 2019).
We have defined forest structure (only overstory forest structure characteristics were used because litter, herbs, and shrubs were burned) based on canopy closure, number of tree layers, % of the different types of trees (small, medium, large) and tree density (Alvarez et al., 2012a). We think this definition is not far from Fernades (2009), who defines forest structure as a “combination of generic stand density (closed or open) and height (low or tall). The definition of forest structure is not directly linked to potential fire types, fire behavior or fire hazard groups that determine or define differences between fuel types. It is only a description of forest vegetation with common characteristics based the tree layer.
When we associated forest structure (in our case only related to trees) with the potential fire types (using real data from field work) is when we had fuel types defined in the article as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987; CIFFC 2023).
It is important highlight that our fuel types are based on the tree component (canopy closure, tree density and percentage of tree type density depending on tree height), so within each fuel type there is a variety of possible combinations of shrubs that could not be considered because the fuel types were created from post-fire field data. This lack of other characteristics like shrub o litter component could help to misunderstand our fuel types with forest structures. We will try to highlight this point in Section 2.3.
It is also true that the fuel types have been applied to another species than Pinus halepensis (the main species for which they were created) without previous fuel types, which is the case of Quercus suber. This is an assumption that we will be described in section 2.3 as well as in the uncertainty section. We believe that the use of the same fuel types for the two species is a valid first approximation to compare severity, consumption and emissions between species when there are no more previous data from one species. We have included in the Supplementary Material a table where we show the photographs of different fuel types in the two species and their equivalence.
We will also answer the specific questions below regarding these terms.
References:
Balde, B., Vega-Garcia, C., Gelabert, P. J., Ameztegui, A., and Rodrigues, M.: The relationship between fire severity and burning efficiency for estimating wildfire emissions in Mediterranean forests, J. For. Res., 34, 1195–1206, https://doi.org/10.1007/s11676-023-01599-1, 2023.
Bottalico, F., Chirici, G., Giannini, R., Mele, S., Mura, M., Puxeddu, M., McRoberts, R. E., Valbuena, R., and Travaglini, D.: Modeling Mediterranean forest structure using airborne laser scanning data, Int. J. Appl. Earth Obs. Geoinf., 57, https://doi.org/10.1016/j.jag.2016.12.013, 2017.
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Domingo, D., Lamelas-Gracia, M. T., Montealegre-Gracia, A. L., and de la Riva-Fern ndez, J.: Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest, Eur. J. Remote Sens., 50, 384–396, https://doi.org/10.1080/22797254.2017.1336067, 2017.
Fernandes, P. M.: Combining forest structure data and fuel modelling to classify fire hazard in Portugal, Ann. For. Sci., 66, 415p1-415p9, https://doi.org/10.1051/forest/2009013, 2009.
Hoff, V., Rowell, E., Teske, C., Queen, L., and Wallace, T.: Assessing the relationship between forest structure and fire severity on the north rim of the grand canyon, Fire, 2, https://doi.org/10.3390/fire2010010, 2019.
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013.
Kane, van R., McGaughey, R. J., Bakker, J. D., Gersonde, R. F., Lutz, J. A., and Franklin, J. F.: Comparisons between field- and LiDAR-based measures of stand structural complexity, Can. J. For. Res., 40, https://doi.org/10.1139/X10-024, 2010.
Merrill, D. F. and Alexander, M. E.: Glossary of forest fire management terms, Can. Comm. For. Fire Manag. Natl. Res. Counc. Canada Ottawa, ON, Canada, 1987.
Molina, J. R., Herrera, M. A., and Silva, F.: Wildfire-induced reduction in the carbon storage of Mediterranean ecosystems: An application to brush and forest fires impacts assessment, Environ. Impact Assess. Rev., 76, 88–97, https://doi.org/10.1016/j.eiar.2019.02.008, 2019.
Nolan, R. H., Price, O. F., Samson, S. A., Jenkins, M. E., Rahmani, S., and Boer, M. M.: Framework for assessing live fine fuel loads and biomass consumption during fire, For. Ecol. Manage., 504, https://doi.org/10.1016/j.foreco.2021.119830, 2022.
Rowell, E., Selelstad, C., Vierling, L., Queen, L., and Shepperd, W.: Using laser altimetry-based segmentation to refine automated tree identification in managed forests of the Black Hills, South Dakota, in: Photogrammetric Engineering and Remote Sensing, https://doi.org/10.14358/PERS.72.12.1379, 2006.
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.
OK, thank you by the comment, we have changed the text accordingly in the abstract and throughout the text.
We did not want to say that all extreme fires are wind-driven fires, indeed, there are wind-driven fires in shrublands without crown fires, but it has been the type of fire (wind-driven, topographic, convective) that in last two decades has burned more in Catalonia exceeding firefighting capabilities, creating entrapment situations or burning as a large fires with extreme fire behavior and in urban interface (WUI) (e.g. La Jonquera 2012, Ventallò 2006, Cistella 2006, etc). While convective fire needs some time to develop its potential, a wind-driven fire can easily escape from the initial response of firefighters, so, starts a large fire.
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)?The sentence was not clear, fuel types were not equaled to forest types (oak and pine forests).
We agree with you that a simpler definition of fuel type could be “A fuel type by definition is a fuel-complex that has distinctive fire behaviour”. We originally defined fuel type as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987; CIFFC 2023).
We have changed “among layers of fuel types” by “fuel types” for clarity, we hope it will be better expressed in the abstract.
References:
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
Merrill, D. F. and Alexander, M. E.: Glossary of forest fire management terms, Can. Comm. For. Fire Manag. Natl. Res. Counc. Canada Ottawa, ON, Canada, 1987.
L16. “available biomass consumed” is redundant. By definition, available = consumed, not consumable (potentially consumed).We have change “available biomass consumed” by “fuel loading” in the abstract and throughout the text.
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.It is true that the available biomass depends on fuel moisture, which is variable and determines what kind of “land cover types” (crops, grasslands, shrublands, forests) are available to spread fire during a fire season. When referring to available biomass, we wanted to indicate that not all the biomass was counted in terms of consumption, but only a part according to its size.
We have changed pre-fire available biomass by pre-fire fuel loading in the abstract and throughout the rest of the text., but we were not sure if “pre-fire” was also redundant now. If it was also redundant, we would remove it.
L20. Again, this suggests the authors are misunderstanding what a fuel type is.As we commented in the first answer, we believe that we understand what a fuel type is. However, it is possible that the in this sentence of the line 20 “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” it seems that we are describing a forest structure instead a fuel type.
This is caused by two factors. The first is that our fuel types were described using only overstory components (canopy closure, number of tree layers, % of the different types of trees (small, medium, large) and tree density (Alvarez et al., 2012a)) because the original objective when were created them in 2011 was to study the crown fire potential, but also the other fire types (surface fires, passive and active crown fires) associated to those forest structures.
The second point is that the process of description of the fuel type was post-fire. For this reason, the surface and ground components could not be included in the description of the fuel types. In the case of surface fuels, we measured the influence of different surface fuels in the difference fire behavior but it was not significant (Alvarez et al., 2013). Hence, we did not have more detailed description of the fuel type including surface fuels variations or litter.
For example, when we defined fuel type 2, we observed that sometimes it had more or less surface fuel density, however, that difference did not affect the fire type, therefore, we could not distinguish two separate fuel types (Alvarez et al., 2013).
We understand that these fuel types may seem simplifications since they only describe the tree layer, but it is also important to note that the tree component, especially the density of trees and the percentage of trees with different heights, is what most influenced the behavior of the fire and the presence or absence of crown fires. This in turn influences the behavior of the fire, the fire severity and therefore, the consumption and pollutant emissions.
We think that this approximation may be valid as a first approximation and as a starting point for possible continuations in which the fuel types may later give rise to more complete descriptions of the entire fuel complex and not only of the overstory forest structure but with surface fuels with shrubs and litter, but also associated with a description of potential fire types that we could obtain from real fires or fire behavior simulations that finally could define if from one fuel type there are different fuel types with different characteristics, not only from the fuel complex composition but from their different fire behavior of fire type.
We will also include a brief explanation of the origin of the fuel types (post-fire events), the objective for which they were defined and their basis (overstory components that describe species, form, size, arrangement and continuity) in Section 2.3 and therefore the limitations they may have.
References:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, https://doi.org/10.5849/forsci.10-140, 2013.
L21. Correct “with has”.Thank you, we have corrected this on line XX.
L21-22. “non-charred fire severity” is awkward, rephrase.We have change “non-charred fire severity” by “lowest fire severity” to refer to plots with more percentage of green and scorch instead of charred. Moreover, we have rewritten the sentence on lines XX.
L23-24. Rephrase, a comparison between forest types and fire severity class does not make sense.OK, we have rewritten the sentence on lines XX.
L24-25. Rephrase, quite hard to follow and understand.OK, we have rephrased the sentence.
L25. Rephrase: the large fire had fires? Massive is not needed, here and elsewhere.Yes, you are right the sentence is not clear. We have rewritten the sentence for clarity:
“We analyzed a large wind-driven wildfire” instead of the previous sentence “The wildfire analyzed was a large fire with massive crown and high-intensity surface fires….” on lines XX)
L50. Advise not to cite Fernandes et al. (2022), the results are not plausible.Ok, thank you we have removed the reference here on line 50 and we will consider if we include the cite throughout the text.
L52. Rephrase, a fire does not experience severity.OK, we have changed it on lines XX.
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.We understand your skepticism regarding this statement. In fact, this was a perception that we got after the field work and seeing all the plots. Unfortunately, we could not replicate the work as is in more wind driven fires or convective or topographic fires to compare this perception with real data. When we wrote the introduction, we thought it would be interesting to highlight this point since there is few information for or against this statement.
Regarding the references, in Jimenez et al., (2013a), there was a mistake, Jimenez et al., (2013a) was not used in the last version of the manuscript and the correct reference was Jimenez et al., (2013b) also included in the manuscript. This has been changed.
In Jimenez et al., (2013b), we found that “In the present study, higher wind velocities were found in the fire with lower crown consumption (Sobradelo)”.
But it is also true that later it is written “However, most of the components of Canadian Forest Fire Weather Index values were higher in the Pentes and Matama fires than in the Sobradelo fire (Table 1), which probably reflects weather conditions that were more favourable for higher fire severity. The latter is another possible factor supporting the relatively higher canopy fuel load consumption in the Matama and Pentes fires than in the Sobradelo fire”.
Regarding Stocks (1987a), we found the idea that not all kind of crown fuel size is consumed during a crown fire “In contrast, crown fires in living conifer forests consume only foliage and fine branchlets.”.
In fact, in Stocks (1987b), now included in the article to clarify the sentence the relation between crown consumption and wind speed is described but also dependent with FFMC “Crown fuel consumption, namely needles and small dead branch wood in the tree crown, is related to the Initial Spread Index (ISI), a numerical rating of the expected rate of fire spread (Eq. 6a). The ISI combines the effects of wind and the Fine Fuel Moisture Code (FFMC), a numerical rating of the moisture content of cured fine fuels. As would therefore be expected, crown fuel consumption is also strongly related to wind speed and FFMC when expressed individually (Eq. 6b).”
The sentence we wrote was not well expressed; therefore, we have nuanced the sentence in the text and corrected the meaning. What we wanted to highlight is that:
"In some cases, crown consumption can be lower despite having higher speeds (Jimenez et al., 2013b), and that there are works in which wind speed is also related to crown consumption but link windspeed to increased crown consumption (Stocks 1987). However, not only the wind speed but the combination with fine fuel moisture is what ultimately determines the crown consumption among other factors.”
References upgraded
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013b.
Stocks, B.J.: Fire behavior in immature jack pine. Can. J. For. Res. 17, 80–86, 1987b.
References removed from the text:
Jiménez, E., Vega, J. A., Fernández-Alonso, J. M., Vega-Nieva, D., Álvarez-González, J. G., and Ruiz-González, A. D.: Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances, Can. J. For. Res., 43, 149–158, https://doi.org/10.1139/cjfr-2012-0374, 2013a.
Stocks, B. J.: Fire Potential in the Spruce Budworm-damaged Forests of Ontario, For. Chron., 63,https://doi.org/10.5558/tfc63008-1, 1987.
L67. Fire severity is already expressing change, and not just through fuel consumption but through vegetation alteration in general, i.e. including scorch.We completed the sentence with your clarification “through vegetation alteration in general” on line XXX.
L73. None of these references is a primary reference for the concept of fuel type.You are right that these references were not the primary, we used these references to avoid using old refences only but temporally graduated cites that used fuel types defined in the similar way. However, we have change references for the primary reference also used when we defined the term fuel type in 2012 (Alvarez et al., 2012). We also included a reference to a current glossary in which the definition is the same,
…fuel types, defined as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987, CIFFC 2023).
References:
Alvarez, A., Gracia, M., and Retana, J.: Fuel types and crown fire potential in Pinus halepensis forests, Eur. J. For. Res., 131, 463–474, https://doi.org/10.1007/s10342-011-0520-6, 2012.
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
Merrill DF, Alexander ME (1987) Glossary of forest fire management terms. National Research Council of Canada, Canadian Committee on Forest Fire Management, Ottawa.
L74. Not sure what “combustion factors according to fire severity” means. Which functions?You are right, the sentence is not clear. We have rewritten again on lines XX.
L75-76. What does fuel layers have to do with landscape heterogeneity? The former is local.Again, the sentence was not clear, we have changed “fuel layers” by “fuel types” on lines XX.
L78. Isn’t forest inventory data field data?Yes, you are right, forest inventory data is also field data. We have rewritten the sentence talking about postfire field sampling on lines XX.
L80. Forest structure does not “make” a fuel type, only variability within a fuel type.You are right, the text is confused, we have removed forest structure from the sentence and left only fuel types. We agree that forest structures can make variations within a fuel type but only if there is a different fire behavior. We also have rewritten the main objective for clarity.
L82. The cause-effect relationship is inverted: fuel consumption determines fire severity, not the other way around.OK, fuel consumption determines fire severity. We have changed the sentence on lines XX.
L83. Analyse what? Quantity, variability?We have rewritten the main objective of the study (lines XX) and complete the specific objectives including quantify and compare instead of analyze (lines XX).
L98-100. Rephrase. It states that structure determines structure?We have changed the paragraph for clarity on lines XX.
L102. scorpius, not Scorpius.OK, changed, thank you.
L111. No need to qualify a crown fire as “massive”. Delete or be more specific.OK, we have removed “massive” from “massive crown fires” on the whole text and on line xxx.
L111. Spotting, not spots.Ok, changed.
L115. Relative humidity, not moisture, right?Ok, changed.
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.We agree, but it was not been possible to obtain that concrete value from DC. The first author of the study was working with firefighters in that time and he was sure that DC was not extreme but we do not have the exact value.
L120. Again, decapitalize the species.Thank you again.
L129. Not 3 types, 3 classes. A reference for what fire severity is, is needed, preferably the original one (Ryan & Noste).Ok, it has been changed “classes” instead of “types”. However, we are not sure if the cite you proposed was “Ryan, K. and Noste, N.: Evaluating prescribed fires, Symp. Work. Wilderness Fire, 1985.”?
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.
This percentage refers to the % of trees classified as green, scorch or charred at plot level, none at tree level. One tree was green, scorch or charred depending the proportion burned in each severity as it was described more accurately on lines 164-168. The categories of trees depending on fire severity was described on lines 164-168. “Thus, we categorized the tree into three types; firstly, green trees, which could be partially scorch but had at least 20% green crown; secondly scorch trees, which were mostly affected by radiant and convective heat and had less than 20% green crown, although normally they were fully scorch with abundant fine fuels (needles and small branches with <6 mm) on the tree or on the ground but not consumed; and thirdly charred trees, which were skeletons mainly consumed without fine materials on the tree or on the ground (Alvarez et al., 2013)”. If you prefer this definition may be moved to the beginning of the section “2.3 Field plot data and fire severity estimation”.
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.
Sorry, we are not entirely sure we understand what part you are referring to. Are you reefing to Table S1. “Main characteristics of the four fuel types in Pinus and Quercus forests?”
We did not consider that the four fuel types are structural variants but a group of forest structures with common synthetized characteristics that could burn with similar fire behavior or fire type. The main issue, and this is the possible reason because the misunderstanding, is that our fuel types are based only in overstory forest structure as we have explained previously.
We will explain this point in the description of Table S1 to clarify the reason because there are only stand characteristics described. In addition, we think that including the potential fire behavior and dominant fire type associated to general structural characteristics, we are showing the four groups are more than “fuel structural types” but fuel types following the definition “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions”. In our case, in the table, there is a different characteristic fire behavior under defined burning conditions, in this case a wind-driven fire, different from convective or topographic fire with its own characteristics. This also can be added to de table S1 description.
However, you are right in your logical concern about applying the same definition of fuel type for two different cover species. Now, this point has been explained in the text, by specifying that these fuel types were created in a very nearby fire, under similar conditions but for Pinus halepensis. At the beginning of the field work, we wanted to test whether the same thresholds that defined the forest structures (quantitative characteristic of the stand that can help to differentiate plots in the field work) and later grouped into the 4 fuel types according to fire types burned (using the previous study in Pinus halepensis wildfire) could be applied for both species and allow us to identify and differentiate forest structures in Q. suber forests. This explanation has been introduced in the text in section 2.3 “Field plot data and fire severity estimation” and also clarified in the table of the supplementary material “table S1”. It has also been described as a source of uncertainty in section 4.4 Uncertainties in emissions estimates and limitations.
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.Yes, it might not be the same not the same “left alive” than “left green”. We have changed “left alive” by the sentence “For each tree, fire severity was assessed using the proportion of residual crown severity (green, scorch, black).
L164, 165, 166. Scorched, not scorch.OK, it has been changed.
L168. Rectify: species is not “measured”.Ok, thank you. We have changed “measured” by “identified”
L170. Why are char heights within parentheses? Were they an additional variable measured? If that is the case they should be outside parentheses.It was an additional variable and we have removed the parentheses after the definition of crown base height with this result.
“… total height and crown base height (measured at the lowest part of the crown with vertical continuity of branches) and higher and lower char height measured on tree steam.”
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?- We measured the percentages of consumption % visually but following all precautions to reduce biases and uncertainties associated to visually assessments.
It was really difficult the visual estimation of fine crown and shrub percentage after the fire and we also understand the concern about the high degree of uncertainty that this measure can cause. However, according to De Santis et al. (2010), biomass consumption was traditionally estimated using a two-step methodology which includes:
- the estimation of pre-fire biomass by applying allometric regression equations using destructive sampling or biomass values per species and
- the post-fire biomass estimated by field-based weighting or by visual examination.
But it is also clear that, as suggested by different authors (e.g. Gallagher et al.2020), the fact that post-fire percent cover is often estimated visually can introduce a bias into the calculations that is difficult to quantify.
When we started the fieldwork, we visited the few areas unburned within the fire perimeter to understand what possible fuels we could find and to identify species. Moreover, we visited areas that immediately bordered the perimeter of the fire when we had plots near the perimeter. When we started to measure the plots, we invested long time to measure all the different possible diameters from shrubs and fine branches from trees with a caliper.
All plots were done by the same two people in order to avoid observer bias that could cause a significant influence in the kind of measures that we took, the percentages of fuel types after the fire. The value of each percentage was an average value from the two people to avoid errors of perception. We also take dozens of photos from all angles from each plot to capture trees, shrubs and litter. It was useful because at the beginning every night we contrasted percentages given to each plot and adjusted them comparing with previous plots when it was necessary. After this first training and as we made more plots, we had a more balanced vision (which was far from perfect) of the percentages we gave, so that the quantitative differences we appreciated were relatively small. On the other hand, we transparently recognize that the potential shrub or litter cover measurements before the fire based on the number of shrubs, and comparing it with what we saw in unburned areas inside could be not as accurate as it could be using other methods. However, these quantitative percentages reflected the difference between plots that we saw qualitatively and the results obtained from the fuel loads from shrubs were within the ranges that we obtained from the IFN3 plots and bibliography.
We have updated the section “2.3 Field plot data and fire severity estimation”, including a synthesis of the training method to obtain the percentage of fuel consumed on lines (xxx). Moreover, in the discussion or/and in the new “4.4 Uncertainties in emissions estimates and limitations” we have included the implications over the uncertainties and potential overestimation of shrub and crown fuel loads before and after the fire on lines XXX.
References:
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Gallagher, M. R., Skowronski, N. S., Lathrop, R. G., McWilliams, T., and Green, E. J.: An Improved Approach for Selecting and Validating Burn Severity Indices in Forested Landscapes, https://doi.org/10.1080/07038992.2020.1735931, 2020.
L173. The time-lag concept is only for dead fuels, so replace it with diameter ranges (<6mm, 6-25 mm, etc.).Thank you for the observation. It has been changed in the section “2.3 Field plot data and fire severity estimation”
L179. Eliminate “available”, because available fuel is indicated by the combustion factor.It has been changed.
L182. Crews, not corps.OK, done.
L183. What plots are these? Are they different from the severity plots? Clarify.We clarified what kind of plots are. Yes, these are the plots done during the field work, where we measured fire severity and the rest of quantitative variables (tree density, tree height, percentages of consume etc).
“During the field work, we made plots of two sizes depending on the heterogeneity of the area and the density, with a range between 316 to 716 m2. To obtain the fine biomass before the fire in these field work plots, we distinguished three components: trees, shrubs and litter” at the begging of the section “2.4.1 Area burned and pre-fire available biomass”.
L185. Delete available and don’t present what follows as bullet points.Ok, we have changed it.
L214. This (the field component) overlaps with section 2.3. Should be moved/merged. Still, no description of how this was done (classes?).You are right partially, we repeated that the method of assessment of combustion factor was obtained visually, but here we explain what we did with the percentages of consumption assessed from field work for trees, shrubs and litter, and each of their fraction (leaves and fine branches lower than 6mm for crowns, two categories for shrubs and one for litter). We will move part of the long paragraph (lines 255-260) to the upgraded section “2.3 Field plot data and fire severity estimation”. Here, we will only explain how we obtain the average values at plot level for trees, shrubs and litter.
No, we assessed percentages of consumption after a long process explained in detail when we answered the question “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?”. With the percentage of consumption considering the different categories depending of their size we obtained an average value per plot and fuel size related to crown, shrub, and only one for litter. total height and crown base height
L219. Quite hard to do, unless consumption is nil or is total. How did you manage to have a reference for preburn litter?As you have said, litter consumption was total that it was de case of charred plots and many cases of scorched and until some green plots when trees were larger and surface fire was intense. When there were green plots, it was relatively easy if the green plot burned with a low surface fire intensity. For the few intermediate cases we followed a long process. But this process was applied to all plots to maintain the consistency. We explain it below.
In the section 2.3 we have explained how the consumption percentage was estimated: there are different phases. First, after observing green areas to understand the quantity of litter that we could expect to find when it was possible, a consumption percentage was taken visually from the observation that two people made (it was an average or consensual if there were significant differences, something that not usually happened) throughout the work to avoid observer bias errors. During the field work, we also considered important variables that could explain the percentage of remaining litter or their lack. Thus, we considered if we were in a plot with high slope, we observed if the absence of litter could be explained by the slope and the area surrounding the plot. After this first measure, and after analyzing and comparing many plots during the field work, we could adjust better the percentage of a new plot comparing with previous plots. After the field work, and using dozens of photos from the plots focussed only in the soil, we could compare visually if the percentage assessed could describe what we were seeing and if the percentage was logical in relation to the other values. We have explained briefly the methodology that we applied to obtain an assessment of the litter consumption in section 2.3.
L225. So, after all this how did you calculate EM? Averages of B, C and D for the whole fire? Another method? Explain.No, we applied the formula (EM = A x B x C x D) for each plot, that is, the initial biomass in the burned area of each plot in each layer, the percentage of consumption of each layer (2 fractions by crowns, 2 by shrubs, 1 by litter. We applied that percentage of consumption on the initial biomass in each stratum, and we applied the emission factor on the biomass consumed to obtain the total emission per plot.
Later, we had a gas or particle emission value per plot, each plot corresponded to one species (Pinus vs Quercus), fuel type (1-4) and fire severity (green, scorched, charred). Finally, we calculated average values and carried out statistical tests from the grouped plots depending of these variables (species, fuel type and fire severity).
L228. Unclear what was the spatial scale of analyses here. Plots?Yes, the comparison always is from plots. We will apply the results of this work to continue to assess the emissions at fire scale with the rest of material made (fire severity maps vs remote sensing tools, etc.). We have clarified the first sentence of the section 2.5 to notices this point.
L233. What determines fire severity differences? Fire severity class?The first thing that differentiate or classify severity at tree level was the percentage of the crown remained after the fire, classifying trees in green, scorched and charred. Then, we calculated the percentage of trees from the three-tree fire severity categories defining each plot as a green, scorched and charred. We hope it is now explained more clearly in the section “2.3 Field plot data and fire severity estimation”
L235. Define coarse fuel in methods.We have defined coarse fuels in the section “2.3 Field plot data and fire severity estimation”. coarse fuels for shrubs were fuel size higher than leaves and fine branches (<6mm). However, we did not consider trunks because we did not see them.
L238, 240. Scorched. This is a systematic error across the paper. Replace also in the figures.Thank you again. We have modified it throughout the manuscript.
L245. Two “available” to delete.Done, thank you.
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.Ok we will carry out the figure 3 with the untransformed values.
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.Yes, we have written fuel loading instead of available biomass in figures 3,4 and throughout the text and we have clarified the caption of figure 4.
L266. Rephrase, otherwise it looks like a methods sentence.We have rephrased the sentence in the new version.
L278. A more meaningful way to say this is “Crown fire was predominant …”.We have changed the sentence accordingly.
Table 1: add standard deviations or, perhaps better, coefficients of variation.Ok, the new version will include the standard deviations of the values.
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.Yes, you are right, it is not totally true as you pointed, there are many factors that influence the potential biomasses such as site quality, including the soil type, the slope position, percentage of stoniness. We have removed this sentence to clarify and reduce the discussion.
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/2009013Thank you, we will include them in the discussion.
New references:
Fernandes, P. M.: Combining forest structure data and fuel modelling to classify fire hazard in Portugal, Ann. For. Sci., 66, 415p1-415p9, 2009.
Fernandes, P. M., Guiomar, N., and Rossa, C. G.: Analysing eucalypt expansion in Portugal as a fire-regime modifier, Sci. Total Environ., 666, https://doi.org/10.1016/j.scitotenv.2019.02.237, 2019.
L345. Note that other studies might be using different definitions, as very often only fine fuels and coarse dead fuels are considered.Yes, you are right, it is very difficult to compare the fuel load values estimated in other studies because each one measures relatively different things, but they can cause large changes in the totals. Only the inclusion or not of litter in the total biomass can completely alter the biomass totals as we have seen.
We have included this point as a reflection in the sentence before comparing results of pre-fire fuel loads.
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.Thank you for this new reference, we will include it and will search for more examples to broaden the perspective of the results.
New reference:
Fernandes, P. M., Fernandes, M. M., and Loureiro, C.: Post-fire live residuals of maritime pine plantations in Portugal: Structure, burn severity, and fire recurrence, For. Ecol. Manage., 347, 170–179, 2015.
L367-368. Again the repetition of “fires” as if a single fire was composed of several fires.We have changed the sentence, thank you.
L368. You forgot the most important driver of fuel availability (combustion factor): fuel moisture content.You are right, we have included fuel moisture content as an important driver that determine fuel availability.
L374-375. This is circular reasoning: fuel consumption is fire severity, the latter is based on the former and not the other way around.Ok, thank you, we have reviewed that this relationship is correctly expressed throughout the text.
L378. Although common, this is a misunderstanding: fuel consumption is in general independent from fire spread rate.Ok, we have considered this point in the sentence.
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.Thank you for the reference, we will compare our CO2 with the results of the article in the discussion in section “4.3 Atmospheric pollutant emissions”.
New reference:
Fernandes, P. M. and Loureiro, C.: Fine fuels consumption and CO2 emissions from surface fire experiments in maritime pine stands in northern Portugal, For. Ecol. Manage., 291, 344–356, 2013.
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.Ok this paragraph will be modified to introduce nuances and reduce the tone.
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.Yes, we have also written a new section named “4.4 Uncertainties in emissions estimates and limitations” and we will include the point that you mention with the limitation when comparing our results with studies that actually measured emissions in the field.
L411. This last sentence needs referencing.Ok, we will search some references for this sentence.
Citation: https://doi.org/10.5194/egusphere-2024-1355-AC2
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AC2: 'Reply on RC2', Albert Alvarez, 19 Aug 2024
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RC3: 'Comment on egusphere-2024-1355', Anonymous Referee #3, 17 Jul 2024
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 -
AC3: 'Reply on RC3', Albert Alvarez, 19 Aug 2024
Reviewer #3: Anonymous Referee
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:
Thank you very much for your reply. We appreciate the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.
What we have done with all parametric tests is to represent the residuals of the models with the predicted values. Examining the graphical representation of the residuals against the expected values allows us to assess a series of assumptions made about the quality of the model fit: (i) Normality: the residuals are assumed to be normally distributed around each predicted value; (ii) Linearity: it is also assumed that there is a linear relationship between the residuals and the predicted values; (iii) Homoscedasticity: it is also assumed that the variance of the residuals is similar for different values of the dependent variable.
If the editor considers it appropriate to show all residual graphics in the supplementary material, we will include them.
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.We agree with your two suggestions. First, we have upgraded the discussion including more references to compare our results with other works or methodologies used to measure wildfire emissions. Second, there is a new section “4.4 Uncertainties in emissions estimates and limitations”, which includes the various limitations and uncertainties at the different levels of emissions estimation (field work, different components of the calculation method, i.e. calculation of the pre-fire fuel load, estimation of the combustion factor, emission factors, etc.). We have included in this section all comments from all of reviewers to enrich and clarify how can be considered the results of this study.
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.This terminology started to be used more frequently after the Fire Paradox project in Europe (Silva et al, 2010). The European Project ‘‘Fire Paradox’’ analyzed the spread of fire in historical wildfires and showed that there were similar spread schemes dominated by common factors (e.g. wind direction and speed). Depending on the spread scheme and the dominant spread factor, three fire types were defined: convection or plume dominated fires, wind-driven fires and topographic fires (Castellnou et al., 2013; Costa et al., 2011). Firstly, convection or plume-dominated fires are characterized by the accumulation of high quantity of available fuels and atmospheric instability. This fire type has such a high intensity and extreme behavior that produces its own fire environment and generates massive spotting. Secondly, wind-driven fires follow the speed and direction of strong winds when the meteorological window that produces the fire conditions is maintained, with the same intensity and velocity during day and night. In both of them, small changes in the landscape have little influence in the direction and behavior of these fire types, especially under extreme meteorological conditions. In contrast, topographic fires are dominated by local winds caused by slope and differences in solar heating of the earth surface (i.e. sea breeze, land breeze, valley and slope winds). The direction of this fire type changes with topography (e.g. hydrographic basins, main valley), and it has high intensity during the day and low intensity at night (Castellnou et al., 2013; Costa et al., 2011). In the latter fire type, wildfire is more sensitive to small changes, thus little variations of topographical wind, slope or aspect have higher influence on fire behavior (Lecina-Diaz et al., 2014).
The combination of two or three fire types in the same wildfire might be common in North America, Canada and Australia, since fire usually burns during many days or months and involves large areas of the landscape. Nevertheless, the majority of wildfires in Europe burn for 48 hours or less, thus fire has fewer opportunities to flip from one fire type to another.
References:
Castellnou, M., Pagés, J., Miralles, M., Piqué, M.: Tipificación de los incendios forestales de Catalunña. Elaboración del mapa de incendios de diseño como herramienta para la gestión forestal. Proceedings of the 5th Congreso Forestal Espanñool Ávila, Spain. Available: https://interior.gencat.cat/web/.content/home/030_arees_dactuacio/bombers/foc_forestal/jornades_recerca_cooperacio_internacional/articles_de_recerca_en_foc_forestal/articles_incendis_forestals/2009_Castellnou-et-al_tipificacion-IF-en-CAT_Mapa-incendios-de-diseno_CongrAvila.pdf (last access: 29 July 2024), 2009.
Costa, P., Castellnou, M., Larrañaga, A., Miralles, M., and Kraus, D.: Prevention of Large Wildfires using the Fire Types Concept, Departament de Interior.Generalitat de Catalunya., Cerdanyola del Vall‚s, Barcelona, Spain., https://interior.gencat.cat/ca/el_departament/publicacions/proteccio_civil/la_prevencio_dels_grans_incendis_forestals_adaptada_a_l_incendi_tipus/index.html (last access: 29 July 2024), 2011.
Lecina-Diaz, J., Alvarez, A., and Retana, J.: Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of mediterranean pine forests, PLoS One, https://doi.org/10.1371/journal.pone.0085127, 2014.
Silva, JS., Rego, F., Fernandes, P., Rigolot, E., editors Towards Integrated Fire Management - Outcomes of the European Project Fire Paradox. European Forest Institute Research Report 23. https://efi.int/publications-bank/towards-integrated-fire-management-outcomes-european-project-fire-paradox (last access: 29 July 2024), 2010.
Line (L) 15. … ‘one of the largest wildfires of the last decade’. In what context is this e.g. fires in Spain, fires in the Mediterranean region?You are right, we did not specify the location well enough in the abstract. The Jonquera fire was in north-eastern Spain, we have included this information in the abstract.
Please remove emotive phrases from the manuscript e.g. L25 ‘massive wildfire’, L56 ‘huge inaccuracies’We have revised the text to remove all emotive sentences and rephrase unnecessary nuances.
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.Thank you, you are right, we have included this point in the sentence.
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.Yes, thank you for the observation, we have written another brief paragraph to clarify the method is in comprehensive way.
Page 2 bottom paragraph. I’m wondering whether the paper below is worth citing to provide a technical definition for what you are referring to as ‘fire severity’?- 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
Yes, thank you, we have included a technical definition with your reference (Keeley, 2009) to clarify the meaning of fire severity, together with a suggestion from other reviewer that asks for including a reference for what fire severity is.
Page 3. L2 and L415. I would remove the phrase ‘unprecedented combination of …’. The type of investigation you are conducting is standard practice rather than unprecedented.Yes, we have removed “unprecedented combination”, but we have highlighted the novelty of the field work data in Spain at least, and the use of litter component in the total fuel load component.
L115. Moisture content. Is this fine fuel moisture content or something else?Yes, that was a mistake. It has been corrected to “Relative humidity”.
Around L130. When you refer to charred trees up to what height level are trees generally charred?We have clarified this description in the section “2.3 Field plot data and fire severity estimation”, which was split into two different paragraphs with a brief description of the fire severity classification from tree level to plot level following Alvarez et al. (2013).
Reference:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, For. Sci., 59, 139–150, https://doi.org/10.5849/forsci.10-140, 2013.
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.Thank you for the observation, the figure has been taken and adopted from Alvarez et al. (2012), so, probably we will redraw a new one to convey the same meaning.
Equation 1. Use multiplication signs rather than the letter x.Thank you, this has been changed.
L233. Log transform for normality. What test did you use for this and what was the result e.g. test statistic and p-value?As we have explained in the first response to the reviewer, we have examined the graphical representation of the residuals against the expected values allows us to assess a series of assumptions made about the quality of the model fit: normality, linearity and homoscedasticity. When we transformed the variable into logarithm, the graph of the residuals improved as you can see in the three factor ANOVA of available biomass among fuel types considering the three different layers (crown, shrub and litter) in the two species.
Untransformed available biomass (also in the pdf)
Log-transformed available biomass (also in the pdf)
L241. What was the required significance level for significant differences?The required significance level was 0.05, which corresponds to a 95% confidence level
Figure 3. Is the log base 10 or base e?It is base e.
Table 2. Is there any reason why nitrous oxide was excluded from your analysis since it is a major greenhouse gas?We understand the concern about the lack of nitrous oxide emission values. We only used those gases and pollutants with values from each stratum (crown, shrub, litter) but we did not find emission factors for litter from Pinus halepensis and Quercus suber. We have added one sentence highlining the importance of having more emission factors available for species especially for nitrous oxide and similar components because of their higher impact on greenhouse phenomenon in the new section “4.4 Uncertainties in emissions estimates and limitations”
We attach these same responses in PDF format.
Status: closed
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RC1: 'Comment on egusphere-2024-1355', Anonymous Referee #1, 26 Jun 2024
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 -
AC1: 'Reply on RC1', Albert Alvarez, 27 Jul 2024
Answer to Reviewer #1: Anonymous Referee
Manuscript presents and interesting exercise of evaluation of consumed biomass and emissions estimated during a wildfire and the differences between Pinus and Quercus stands.
Thank you very much for your reply. We appreciate the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.
We agree with your comment about the important uncertainties of the method in each one of their steps. We have written a short section in the discussion with the different uncertainties from the method and what we tried to do to reduce these uncertainties “Section 4.4. Uncertainties in emissions estimates and limitations” on (Lines X).
We used Seiler and Crutzen (1980) method and the equation;
EM = A x B x C x D,
where EM are the total emissions (Mg/ha), A is the area burned (ha), B is the available biomass before the fire (Mg/ha), C is the combustion factor (%) and D is the emission factor (g/kg).
If the burned area is precisely defined, as we think it was in our case, the greatest uncertainties are in the estimation of biomass prior to the fire and the combustion factor (Ottmar et al., 2008; Bacciu et al., 2015; Fernandes et al., 2022), but also on the use of emission factors available. We discuss below each of these components.
The quantification of different fuel types before the fire is one of the main sources of uncertainty, indeed, variations in fuel characteristics may contribute to 83 percent uncertainties in estimates of wildfires emissions (Ottmar et al., 2008; Fernandes et al., 2022). Moreover, in the south-European forests, the high spatial variability of fuel loadings, the fuel structure which describes how fuel loads are vertically distributed in a stand and how fire moves through a site are critical factors when describing forest fuels and determine final pollutant emissions (Carvalho et al., 2007; French et al., 2011). We have tried to reduce this uncertainly with the identification of species at different layers and by using specific allometric equations from a combination of data from National Forest Inventory (NFI) for canopies and shrubs but especially from Ecological Forestry Inventory of Catalunya for litter assessment (IEFC) (Gracia, 2001; Vayreda et al., 2016). Although biomass allometries are also a source of uncertainty, the range values in which they move fit with real data from the inventories and are at least differentiated between plots instead of using a general value for all plots. There are more precise allometric equations for shrublands in Spain that we used but they are also difficult to apply after the fire when sometimes you cannot identify variables such as height or diameter of the shrub. The joint of these data allowed reduction in the uncertainty of the total fuel load available especially including the litter component that is not usually assessed.
In fact, previous studies considered that forest floor emissions were the most uncertain component when modelling carbon emissions from forest fires, since its consumption can range from near 0 to 100% (Vilen and Fernandes 2011). In addition, considering the difficulty in estimating combustion of subsurface carbon and that 65% of the total fire-wide carbon emissions may come from the combustion of litter, duff, and mineral soil carbon (Campbell et al., 2007), we consider that most of the uncertainty in our estimate of total emissions may arise from uncertainty in combustion of these fuels. Therefore, our results should be taken with caution because of the complexity of litter distribution and the variables that could influence its amount and variation. We highly recommend to develop better models for assessing litter for different forest species because of its high percentage in the two forest types studied. This will be the future challenge since for the moment remote sensing is not useful for assessing these fuel loads. If the content of litter is not assessed, there will be a constant underprediction of fuel load and emissions especially when there are extreme wildfires.
Combustion factors for each layer are another important source of uncertainty with values over 30% (Ottmar et al., 2008; Fernandes et al., 2022) and values that could be higher depending on the position of the fire (head, flank, back) (Surawski et al., 2016). There are few examples from field works especially when you apply Seiler and Crutzen (1980) method with remote sensing tools (De Santis et al., 2010; Jimenez et al., 2013). However, we reduced that uncertainty with direct observation of the percentage consumed after the fire at the three layers. This is especially important when you are comparing emissions between different fire severities. It is also important to distinguish what crown fraction is consumed in crown fires in wind fires since it is usually overestimated because it is often considered that up to 0.6-2.5cm all branches are consumed when, in this fire type, we saw that in some cases not all the fine material had been consumed. Although the estimation method was visual, it helps to understand what fraction of the canopies can be consumed in this type of fires. However, it would be necessary to replicate the measures in convective and topographic fires where perhaps more canopies can be consumed. Overall, these variations in crown consumption could affect the total biomass but the largest amount of biomass consumed is clearly that of shrubs and litter, the biomass most poorly estimated in studies on emissions and therefore the greatest source of uncertainty.
Regarding emission factors, they are also one of the main uncertainty sources in emissions estimations. It is important to work on this topic with more field measurements, in particular for southern European conditions variability (Fernandes et al. 2022). EFs variation (mainly due to type of pollutant, type and arrangement of fuel, and combustion factor) that could contribute to about 16% of the total error associated with emissions (Ottmar et al., 2008; Bacciu et al., 2015; Fernandes et al., 2022) is mainly available for United States of America (USA) forests (Urbanski, 2013), but it is not a suitable proxy for wildfires in Europe, due to the different vegetation cover and the differences in combustion characteristics (e.g. flaming and smouldering phases).
There are also other limitations with the methods used in the current study. They include not considering fuel load from herbs, not taking into account the influence of topography or fuel moisture on the general emission factors and the use of litter emission factors from Q. pubescens (the only available) instead of those from Q. suber. We also did not differentiate between flaming or smoldering phase of combustion and we did not consider fuel moisture.
References:
Bacciu, V., Spano, D., and Salis, M.: Emissions from Forest Fires: Methods of Estimation and National Results, in: The Greenhouse Gas Balance of Italy: An Insight on Managed and Natural Terrestrial Ecosystems, edited by: Valentini, R. and Miglietta, F., Springer, Berlin, Heidelberg, 87–102, Germany, https://doi.org/10.1007/978-3-642-32424-6_6, 2015.
Campbell, J., Donato, D., Azuma, D., and Law, B.: Pyrogenic carbon emission from a large wildfire in Oregon, United States, J. Geophys. Res. Biogeosciences, 112, https://doi.org/10.1029/2007JG000451, 2007.
Carvalho, A., Monteiro, A., Flannigan, M., Solman, S., Miranda, A., and Borrego, C.: Forest fire emissions under climate change: impacts on air quality, in: Seventh Symposium on Fire and Forest Meteorology, The Turrets, USA, 23 October 2007, https://ams.confex.com/ams/7firenortheast/techprogram/paper_126854.htm (last access: 23 April 2024), 2007.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Fernandes, A. P., Lopes, D., Sorte, S., Monteiro, A., Gama, C., Reis, J., Menezes, I., Osswald, T., Borrego, C., Almeida, M., Ribeiro, L. M., Viegas, D. X., and Miranda, A. I.: Smoke emissions from the extreme wildfire events in central Portugal in October 2017, Int. J. Wildl. Fire, 31, 989-1001, https://doi.org/10.1071/WF21097, 2022.
French, N. H. F., de Groot, W. J., Jenkins, L. K., Rogers, B. M., Alvarado, E., Amiro, B., de Jong, B., Goetz, S., Hoy, E., Hyer, E., Keane, R., Law, B. E., McKenzie, D., McNulty, S. G., Ottmar, R., Perez-Salicrup, D. R., Randerson, J., Robertson, K. M., and Turetsky, M.: Model comparisons for estimating carbon emissions from North American wildland fire, J. Geophys. Res., 116, https://doi.org/10.1029/2010JG001469, 2011.
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013.
Ottmar, R. D., Miranda, A. I., and Sandberg, D. V: Chapter 3 Characterizing Sources of Emissions from Wildland Fires, in: Developments in Environmental Science Wildland Fires and Air Pollution, vol. 8, edited by: Bytnerowicz, A., Arbaugh, M. J., Riebau, A. R., and Andersen, C., Elsevier, 61–78, 2008.
Surawski, N. C., Sullivan, A. L., Roxburgh, S. H., and Polglase, P. J.: Estimates of greenhouse gas and black carbon emissions from a major Australian wildfire with high spatiotemporal resolution, J. Geophys. Res. Atmos., 121, 9892–9907, https://doi.org/10.1002/2016JD025087, 2016.
Urbanski, S. P.: Combustion efficiency and emission factors for wildfire-season fires in mixed conifer forests of the northern Rocky Mountains, US, Atmos.Chem.Phys., 13, 7241–7262, 2013.
Vilen, T. and Fernandes, P. M.: Forest fires in Mediterranean countries: CO2 emissions and mitigation possibilities through prescribed burning, Environ. Manage., 48, 558–567, 2011.
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.
OK, we will reconsider both the title and the formulation of the main objective to make them coherent between them. However, as the other reviewers may also propose changes in this direction, we prefer to wait for having all comments and then reformulate them.
Line 102. Regeneration of Quercus ilex under Q. ilex stands?
No, this was incorrectly written it was Quercus suber under Q. suber stands. We have changed that on line 102.
Line 102. What about mixed forest? Are there mixed oak-pine forests in burned area?
Yes, there were areas with mixed forests. In the north-west forests of Q. suber changed to Q. ilex, but this was burned with south winds in the days following the start of the fire and there were not enough cases to include in the study. There were also mixed areas of P. halepensis and Q. ilex, but this type of forest was not dominant in the landscape and we only carried out plots in areas with pure forest species, either pines or oaks.
Line 112. This is not considering a large spot distance during extreme wildfire events (e.g. see Tedim et al. 2019)
You are right, spot distances of 200-400 meters with a maximum of 1km are not considered large spot distances but intermediate-range spotting during this extreme wildfire event EWE (Tedim et al., 2018). This is especially true when you compare with other countries and forest types (Martin and Hillen, 2016; Cruz et al., 2012) and accepted definitions to describe extreme fire event (Tedim et al., 2018).
We have upgraded the section 2.2 “Fire description and weather conditions during the fire” to justify better the reason because this fire was considered an EWE following Tedim et al., (2018) among other articles.
References:
Cruz, M. G., Sullivan, A. L., Gould, J. S., Sims, N. C., Bannister, A. J., Hollis, J. J., and Hurley, R. J.: Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia, For. Ecol. Manage., 284, 269–285, 2012.
Bombers. Report of the Jonquera wildfire. Bombers de la Generalitat de Catalunya, Departament de interior de la Generalitat de Catalunya. https://agricultura.gencat.cat/web/.content/06-medi-natural/boscos/gestio-forestal/obres/restauracio-forestal/restauracio-hidrologica/fitxers-binaris/jonquera_informe_incendi.pdf (last access: 23 July 2024), 2012.
DARP: Report on the forest fire of July 22, 2012 in La Jonquera (Alt Empordà), Generalitat de Catalunya, Departament d'Agricultura, Ramaderia, Pesca, Alimentació i Medi Natural, Girona, Spain, https://agricultura.gencat.cat/web/.content/06-medi-natural/boscos/gestio-forestal/obres/restauracio-forestal/restauracio-hidrologica/fitxers-binaris/jonquera_informe_incendi.pdf (last access: 23 July 2024), 2012.
Martin, J. and Hillen, T.: The spotting distribution of wild fires, Appl. Sci., 6, https://doi.org/10.3390/app6060177, 2016.
Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, R. M., Delogu, M. G., Fernandes, M. P., Ferreira, C., McCaffrey, S., McGee, K. T., Parente, J., Paton, D., Pereira, G. M., Ribeiro, M. L., Viegas, X. D., and Xanthopoulos, G.: Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts, Fire 2018, 1(1), 9; https//doi.org/10.3390/fire1010009, 1, 2018.
Line 120. “monspeliensis” Lowercase
OK, changed (Line 120).
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.
After the fire, during August 2012 we did not know if we would have the opportunity to do the study and some of us were working with firefighters collecting data from fires. Until September 2012 we could not start to plan the field work and we did not have much time because we knew that the timber extraction work was going to start immediately as it was happened in other fires where we did field work (Alvarez et al., 2012).
A recording flight of the fire had to be carried out by the fire department, so due to our collaboration with them, we were able to participate in the flight, obtaining videos and hundreds of photos of the entire fire. Therefore, it was not a specific flight for the work but a complementary activity. These were used to make an initial estimate of the burned area by each severity and to locate areas where to find severities that were not charred. In fact, we thought about the possibility of apply any remote sensing index but we did not know if it could identify these areas with lower severity better than with the flight material obtained. Finally, we were lucky to participate in the flight because we could also understand better the global spread of the fire that helped us to obtain other data related to fire behavior.
In section 2.3 “Field plot data and fire severity estimation” we have included a sentence to clarify the reason because we used a flight instead of the use of remote sensing tools to determine distribution of fire severity. We will highlight the lack of time, the opportunity of obtaining a multifunctional data from flights and the doubt about the precision to distinguish green and scorch areas in this massive scorch wildfire.
References:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, For. Sci., 59, 139–150, https://doi.org/10.5849/forsci.10-140, 2013.
Line 150-155. This hierarchical and deterministic classification following Alvarez et al. 2012 must be justified for studied area. e.g. using cluster analysis
We used this classification in the studied area because it was created and tested in a fire only 30 km away from this fire in Pinus halepensis stands, one of the two main species of forest types of this study with also similar fuel types. Moreover, the classification was made not only to apply to one species but in general, according to common but critical variables that determine fire behavior independently of forest types: the density of trees, which determines horizontal continuity, and the percentage of trees based on height, which determines vertical continuity.
At the beginning of the study, we concluded that we captured the variable forest structures in Q. suber plots, just as the classification method was designed to be applied to other types of forests and allow comparisons. The result was really good because it was possible to identify the differences in the fuel types between the two different types of forests and the effects on fire severity and consumption. We have added a small comment justifying this point in section "2.3 Field plot data and fire severity estimation".
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).
We agree with you about the difficulty of the visual estimation of fine crown and shrub percentage after the fire and we also understand the concern about the high degree of uncertainty that this measure can cause. However, according to De Santis et al. (2010), biomass consumption was traditionally estimated using a two-step methodology which includes:
- the estimation of pre-fire biomass by applying allometric regression equations using destructive sampling or biomass values per species and
- the post-fire biomass estimated by field-based weighting or by visual examination.
When we started the fieldwork, we visited the few areas unburned within the fire perimeter to understand what possible fuels we could find and to identify species. Moreover, we visited areas that immediately bordered the perimeter of the fire when we had plots near the perimeter. When we started to measure the plots, we invested long time to measure all the different possible diameters from shrubs and fine branches from trees with a caliper.
All plots were done by the same two people in order to avoid observer bias that could cause a significant influence in the kind of measures that we took, the percentages of fuel types after the fire. The value of each percentage was an average value from the two people to avoid errors of perception. We also take dozens of photos from all angles from each plot to capture trees, shrubs and litter. It was useful because at the beginning every night we contrasted percentages given to each plot and adjusted them comparing with previous plots when it was necessary. After this first training and as we made more plots, we had a more balanced vision (which was far from perfect) of the percentages we gave, so that the quantitative differences we appreciated were relatively small. The fieldwork was intensive from Octobre 2012 to March 2013, only stopped on very windy or rainy days. At the end of the work, we obtained near 12,000 photos from plots and their surrounding area that also helped us to calibrate dubious plots.
On the other hand, we transparently recognize that the potential shrub or litter cover measurements before the fire based on the number of shrubs, and comparing it with what we saw in unburned areas inside could be not as accurate as it could be using other methods. However, these quantitative percentages reflected the difference between plots that we saw qualitatively and the results obtained from the fuel loads from shrubs were within the ranges that we obtained from the IFN3 plots and bibliography.
We have updated the section “2.4.1 Area burned and pre-fire available biomass”, including a synthesis of the training method to obtain the percentage of fuel consumed. Moreover, in the discussion or/and in the new section “4.4 Uncertainties in emissions estimates and limitations” we have included the implications over the uncertainties and potential overestimation of shrub and crown fuel loads before and after the fire.
References:
Ottmar, R. D., Vihnanek, R. E., and Wright, C. S.: Stereo photo series for quantifying natural fuels Volume X : sagebrush with grass and ponderosa pine-juniper types in central Montana, USDA For. Serv. Pacific Northwest Res. Station. Gen. Tech. Rep., X, 2007.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Surawski, N. C., Sullivan, A. L., Roxburgh, S. H., and Polglase, P. J.: Estimates of greenhouse gas and black carbon emissions from a major Australian wildfire with high spatiotemporal resolution, J. Geophys. Res. Atmos., 121, 9892–9907, https://doi.org/10.1002/2016JD025087, 2016.
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.
For the first point, NFI data from shrubs are a measure of 10 m radius plot in the center of each NFI plot (https://www.mapa.gob.es/es/desarrollo-rural/publicaciones/publicaciones-de-desarrollo-rural/librobiomasadigital_tcm30-538563.pdf). Regarding the use of IFN3 plots in shrub load assessment, we developed models that related tree cover (at the time of IFN3) with shrub fuel (also at IFN3), estimated with the model MEDFATE (De Càceres et al., 2019). Then these allometries are applied to the field data. Field data for shrubs were the species identification and the shrub fraction cover, which was used in the equations created with the data from the IFN3 results applying MEDFATE. We have included in the section “4.4 Uncertainties in emissions estimates and limitations” the potential overestimation of biomass with the measure of shrub cover. However, after the fieldwork and comparing plots this percentage was logical with the qualitative estimation among all the different plots.
IFN3 plots were done from 2000 to 2001 and the fire was in 2012. However, we did not used the original values of those plots ten years later. We used IFN3 plots combined with measures of MEDFATE model to obtain allometric equations used later with the field data to assess the shrub fuel load. Therefore, there was not a problem with the growing of shrubs in the IFN plots.
References:
De Càceres, M., Casals, P., Gabriel, E., and Castro, X.: Scaling-up individual-level allometric equations to predict stand-level fuel loading in Mediterranean shrublands, Ann. For. Sci., 76, 87, https://doi.org/10.1007/s13595-019-0873-4, 2019
Pasalodos-Tato, M., Ruiz-Peinado, R., Ri¢, M. de., and Montero, G.: Shrub biomass accumulation and growth rate models to quantify carbon stocks and fluxes for the Mediterranean region, Eur. J. For. Res., 134, 537–553, 2015.
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
The question about how we estimated the % of consumed biomass has been explained in detail in the previous question “Line 170-174.”.
Regarding the fact that needles fall before measuring the amount remaining on the tree after the fire, it is an uncertainty that is inevitable. It is true that as more time passes after the fire, the number fallen needles in areas of moderate/low severities increases. We took this phenomenon into account by observing the number of needles on the burned ground, which were therefore not present previously. We considered whether there was a slope to determine if the needles in the area could correspond to the sampled trees and the condition of the trunk and branches of the trees before decide the percentage of needles unburned.
Moreover, the fall of the needles, and in general the alteration of the conditions of the plots, depends greatly on the days of rain and wind that occur after the fire. In 2012, after the fire, the number of rainy days in the area burned was very few. Despite some windy days, the interruptions to fieldwork were generally very few (perhaps less than five). We have also included these considerations warning of the effect on the measurements of this phenomenon, and that it was taken into account in section “2.3 Field plot data and fire severity 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
Yes, it is also true that Q. pubescens and Q. suber ecosystems may be quite different. However we only used the emission factors for the litter component from Pallozi et al. (2018) because it was the only available study and because at least Pinus halepensis was correctly represented and the other species was of the same genus. We also have included this consideration in the section “4.4 Uncertainties in emissions estimates and limitations”.
References:
Pallozzi, E., Lusini, I., Cherubini, L., Hajiaghayeva, R. A., Ciccioli, P., and Calfapietra, C.: Differences between a deciduous and a conifer tree species in gaseous and particulate emissions from biomass burning, Environ. Pollut., 234, 457–467, https://doi.org/10.1016/j.envpol.2017.11.080, 2018.
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
We will try to test the presence of spatial autocorrelation in the new version of the manuscript.
In my opinion results and discussion could be different if method is refocused. Authors must justify well their decision to assess robust results
We agree with this point and we have included a new paragraph that we hope could reflect the uncertainty and limitations of the work done and its potential implications. After your comments we expect to explain better the weakest points of the work, recognizing the limitations but highlighting the potential knowledge it can also provide.
We attach these same responses in PDF format.
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AC1: 'Reply on RC1', Albert Alvarez, 27 Jul 2024
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RC2: 'Comment on egusphere-2024-1355', Paulo Fernandes, 17 Jul 2024
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 -
AC2: 'Reply on RC2', Albert Alvarez, 19 Aug 2024
Answer to Reviewer :2
Reviewer #2: Paulo Fernandes
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.
Thank you very much for your reply, we sincerely appreciate your words and the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.We understand some of the problems that have arisen regarding certain terminology used in the text and we have clarified it.
Regarding the use of biomass consumption, we agree that the use of fuel consumption would be more appropriate. We have already changed this term throughout the text. It is interesting to note that the term biomass consumption has been used alone or combined with fuel consumption in some articles mixing both terms (e.g., De Santis et al.,2010; Jiménez et al., 2017; Domingo et al., 2017; Molina et al.,2019, Nolan et al., 2022; Balde et al., 2023).
We agree that “available biomass” was not correctly used in the text. We wanted to refer to the total possible fuel consumed, which includes all litter and shrub fuel loading, but only a portion of crown fuel loading. Since not all crown biomass is consumable, we wanted to distinguish it from the total biomass. We have already changed this term throughout the text.
Regarding the concepts of forest structure and fuel type, it is true that in some sections, such in the abstract, objectives or section 2.3 “Field plot data and fire severity estimation” these terms has been expressed in an unclear manner, making understanding difficult.
However, we believe that we understand the meaning of forest structure and fuel type, so we have clarified it throughout the entire text. Forest structure has been defined in the introduction to avoid potential confusions with fuel type. Moreover, we have removed “forest structure” in lines where it was not necessary or when “fuel type” was more appropriate to avoid misunderstandings. The whole text is about fuel types not about forest structures but we cannot break the link between forest structures and fuel types from the original references to maintain the coherence with the terminology used in those articles. For this reason, “Table S1. Main characteristics of the four fuel types in Pinus and Quercus forests” will remain the same.
The term forest structure is usually used to describe some characteristic of the spatial distribution of biomass in forested vegetation, both vertically and horizontally. Different researchers have used numerous metrics to quantify forest structure (Rowell et al., 2006; Kane et al., 2010; Bottalico et al., 2017; Hoff et al., 2019).
We have defined forest structure (only overstory forest structure characteristics were used because litter, herbs, and shrubs were burned) based on canopy closure, number of tree layers, % of the different types of trees (small, medium, large) and tree density (Alvarez et al., 2012a). We think this definition is not far from Fernades (2009), who defines forest structure as a “combination of generic stand density (closed or open) and height (low or tall). The definition of forest structure is not directly linked to potential fire types, fire behavior or fire hazard groups that determine or define differences between fuel types. It is only a description of forest vegetation with common characteristics based the tree layer.
When we associated forest structure (in our case only related to trees) with the potential fire types (using real data from field work) is when we had fuel types defined in the article as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987; CIFFC 2023).
It is important highlight that our fuel types are based on the tree component (canopy closure, tree density and percentage of tree type density depending on tree height), so within each fuel type there is a variety of possible combinations of shrubs that could not be considered because the fuel types were created from post-fire field data. This lack of other characteristics like shrub o litter component could help to misunderstand our fuel types with forest structures. We will try to highlight this point in Section 2.3.
It is also true that the fuel types have been applied to another species than Pinus halepensis (the main species for which they were created) without previous fuel types, which is the case of Quercus suber. This is an assumption that we will be described in section 2.3 as well as in the uncertainty section. We believe that the use of the same fuel types for the two species is a valid first approximation to compare severity, consumption and emissions between species when there are no more previous data from one species. We have included in the Supplementary Material a table where we show the photographs of different fuel types in the two species and their equivalence.
We will also answer the specific questions below regarding these terms.
References:
Balde, B., Vega-Garcia, C., Gelabert, P. J., Ameztegui, A., and Rodrigues, M.: The relationship between fire severity and burning efficiency for estimating wildfire emissions in Mediterranean forests, J. For. Res., 34, 1195–1206, https://doi.org/10.1007/s11676-023-01599-1, 2023.
Bottalico, F., Chirici, G., Giannini, R., Mele, S., Mura, M., Puxeddu, M., McRoberts, R. E., Valbuena, R., and Travaglini, D.: Modeling Mediterranean forest structure using airborne laser scanning data, Int. J. Appl. Earth Obs. Geoinf., 57, https://doi.org/10.1016/j.jag.2016.12.013, 2017.
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Domingo, D., Lamelas-Gracia, M. T., Montealegre-Gracia, A. L., and de la Riva-Fern ndez, J.: Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest, Eur. J. Remote Sens., 50, 384–396, https://doi.org/10.1080/22797254.2017.1336067, 2017.
Fernandes, P. M.: Combining forest structure data and fuel modelling to classify fire hazard in Portugal, Ann. For. Sci., 66, 415p1-415p9, https://doi.org/10.1051/forest/2009013, 2009.
Hoff, V., Rowell, E., Teske, C., Queen, L., and Wallace, T.: Assessing the relationship between forest structure and fire severity on the north rim of the grand canyon, Fire, 2, https://doi.org/10.3390/fire2010010, 2019.
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013.
Kane, van R., McGaughey, R. J., Bakker, J. D., Gersonde, R. F., Lutz, J. A., and Franklin, J. F.: Comparisons between field- and LiDAR-based measures of stand structural complexity, Can. J. For. Res., 40, https://doi.org/10.1139/X10-024, 2010.
Merrill, D. F. and Alexander, M. E.: Glossary of forest fire management terms, Can. Comm. For. Fire Manag. Natl. Res. Counc. Canada Ottawa, ON, Canada, 1987.
Molina, J. R., Herrera, M. A., and Silva, F.: Wildfire-induced reduction in the carbon storage of Mediterranean ecosystems: An application to brush and forest fires impacts assessment, Environ. Impact Assess. Rev., 76, 88–97, https://doi.org/10.1016/j.eiar.2019.02.008, 2019.
Nolan, R. H., Price, O. F., Samson, S. A., Jenkins, M. E., Rahmani, S., and Boer, M. M.: Framework for assessing live fine fuel loads and biomass consumption during fire, For. Ecol. Manage., 504, https://doi.org/10.1016/j.foreco.2021.119830, 2022.
Rowell, E., Selelstad, C., Vierling, L., Queen, L., and Shepperd, W.: Using laser altimetry-based segmentation to refine automated tree identification in managed forests of the Black Hills, South Dakota, in: Photogrammetric Engineering and Remote Sensing, https://doi.org/10.14358/PERS.72.12.1379, 2006.
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.
OK, thank you by the comment, we have changed the text accordingly in the abstract and throughout the text.
We did not want to say that all extreme fires are wind-driven fires, indeed, there are wind-driven fires in shrublands without crown fires, but it has been the type of fire (wind-driven, topographic, convective) that in last two decades has burned more in Catalonia exceeding firefighting capabilities, creating entrapment situations or burning as a large fires with extreme fire behavior and in urban interface (WUI) (e.g. La Jonquera 2012, Ventallò 2006, Cistella 2006, etc). While convective fire needs some time to develop its potential, a wind-driven fire can easily escape from the initial response of firefighters, so, starts a large fire.
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)?The sentence was not clear, fuel types were not equaled to forest types (oak and pine forests).
We agree with you that a simpler definition of fuel type could be “A fuel type by definition is a fuel-complex that has distinctive fire behaviour”. We originally defined fuel type as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987; CIFFC 2023).
We have changed “among layers of fuel types” by “fuel types” for clarity, we hope it will be better expressed in the abstract.
References:
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
Merrill, D. F. and Alexander, M. E.: Glossary of forest fire management terms, Can. Comm. For. Fire Manag. Natl. Res. Counc. Canada Ottawa, ON, Canada, 1987.
L16. “available biomass consumed” is redundant. By definition, available = consumed, not consumable (potentially consumed).We have change “available biomass consumed” by “fuel loading” in the abstract and throughout the text.
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.It is true that the available biomass depends on fuel moisture, which is variable and determines what kind of “land cover types” (crops, grasslands, shrublands, forests) are available to spread fire during a fire season. When referring to available biomass, we wanted to indicate that not all the biomass was counted in terms of consumption, but only a part according to its size.
We have changed pre-fire available biomass by pre-fire fuel loading in the abstract and throughout the rest of the text., but we were not sure if “pre-fire” was also redundant now. If it was also redundant, we would remove it.
L20. Again, this suggests the authors are misunderstanding what a fuel type is.As we commented in the first answer, we believe that we understand what a fuel type is. However, it is possible that the in this sentence of the line 20 “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” it seems that we are describing a forest structure instead a fuel type.
This is caused by two factors. The first is that our fuel types were described using only overstory components (canopy closure, number of tree layers, % of the different types of trees (small, medium, large) and tree density (Alvarez et al., 2012a)) because the original objective when were created them in 2011 was to study the crown fire potential, but also the other fire types (surface fires, passive and active crown fires) associated to those forest structures.
The second point is that the process of description of the fuel type was post-fire. For this reason, the surface and ground components could not be included in the description of the fuel types. In the case of surface fuels, we measured the influence of different surface fuels in the difference fire behavior but it was not significant (Alvarez et al., 2013). Hence, we did not have more detailed description of the fuel type including surface fuels variations or litter.
For example, when we defined fuel type 2, we observed that sometimes it had more or less surface fuel density, however, that difference did not affect the fire type, therefore, we could not distinguish two separate fuel types (Alvarez et al., 2013).
We understand that these fuel types may seem simplifications since they only describe the tree layer, but it is also important to note that the tree component, especially the density of trees and the percentage of trees with different heights, is what most influenced the behavior of the fire and the presence or absence of crown fires. This in turn influences the behavior of the fire, the fire severity and therefore, the consumption and pollutant emissions.
We think that this approximation may be valid as a first approximation and as a starting point for possible continuations in which the fuel types may later give rise to more complete descriptions of the entire fuel complex and not only of the overstory forest structure but with surface fuels with shrubs and litter, but also associated with a description of potential fire types that we could obtain from real fires or fire behavior simulations that finally could define if from one fuel type there are different fuel types with different characteristics, not only from the fuel complex composition but from their different fire behavior of fire type.
We will also include a brief explanation of the origin of the fuel types (post-fire events), the objective for which they were defined and their basis (overstory components that describe species, form, size, arrangement and continuity) in Section 2.3 and therefore the limitations they may have.
References:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, https://doi.org/10.5849/forsci.10-140, 2013.
L21. Correct “with has”.Thank you, we have corrected this on line XX.
L21-22. “non-charred fire severity” is awkward, rephrase.We have change “non-charred fire severity” by “lowest fire severity” to refer to plots with more percentage of green and scorch instead of charred. Moreover, we have rewritten the sentence on lines XX.
L23-24. Rephrase, a comparison between forest types and fire severity class does not make sense.OK, we have rewritten the sentence on lines XX.
L24-25. Rephrase, quite hard to follow and understand.OK, we have rephrased the sentence.
L25. Rephrase: the large fire had fires? Massive is not needed, here and elsewhere.Yes, you are right the sentence is not clear. We have rewritten the sentence for clarity:
“We analyzed a large wind-driven wildfire” instead of the previous sentence “The wildfire analyzed was a large fire with massive crown and high-intensity surface fires….” on lines XX)
L50. Advise not to cite Fernandes et al. (2022), the results are not plausible.Ok, thank you we have removed the reference here on line 50 and we will consider if we include the cite throughout the text.
L52. Rephrase, a fire does not experience severity.OK, we have changed it on lines XX.
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.We understand your skepticism regarding this statement. In fact, this was a perception that we got after the field work and seeing all the plots. Unfortunately, we could not replicate the work as is in more wind driven fires or convective or topographic fires to compare this perception with real data. When we wrote the introduction, we thought it would be interesting to highlight this point since there is few information for or against this statement.
Regarding the references, in Jimenez et al., (2013a), there was a mistake, Jimenez et al., (2013a) was not used in the last version of the manuscript and the correct reference was Jimenez et al., (2013b) also included in the manuscript. This has been changed.
In Jimenez et al., (2013b), we found that “In the present study, higher wind velocities were found in the fire with lower crown consumption (Sobradelo)”.
But it is also true that later it is written “However, most of the components of Canadian Forest Fire Weather Index values were higher in the Pentes and Matama fires than in the Sobradelo fire (Table 1), which probably reflects weather conditions that were more favourable for higher fire severity. The latter is another possible factor supporting the relatively higher canopy fuel load consumption in the Matama and Pentes fires than in the Sobradelo fire”.
Regarding Stocks (1987a), we found the idea that not all kind of crown fuel size is consumed during a crown fire “In contrast, crown fires in living conifer forests consume only foliage and fine branchlets.”.
In fact, in Stocks (1987b), now included in the article to clarify the sentence the relation between crown consumption and wind speed is described but also dependent with FFMC “Crown fuel consumption, namely needles and small dead branch wood in the tree crown, is related to the Initial Spread Index (ISI), a numerical rating of the expected rate of fire spread (Eq. 6a). The ISI combines the effects of wind and the Fine Fuel Moisture Code (FFMC), a numerical rating of the moisture content of cured fine fuels. As would therefore be expected, crown fuel consumption is also strongly related to wind speed and FFMC when expressed individually (Eq. 6b).”
The sentence we wrote was not well expressed; therefore, we have nuanced the sentence in the text and corrected the meaning. What we wanted to highlight is that:
"In some cases, crown consumption can be lower despite having higher speeds (Jimenez et al., 2013b), and that there are works in which wind speed is also related to crown consumption but link windspeed to increased crown consumption (Stocks 1987). However, not only the wind speed but the combination with fine fuel moisture is what ultimately determines the crown consumption among other factors.”
References upgraded
Jiménez, E., Vega, J. A., Ruiz-González, A. D., Guijarro, M., Varez-González, J. G., Madrigal, J., Cuiñas, P., Hernando, C., and Fernández-Alonso, J. M.: Carbon emissions and vertical pattern of canopy fuel consumption in three Pinus pinaster Ait. active crown fires in Galicia (NW Spain), Ecol. Eng., 54, 202–209, https://doi.org/10.1016/j.ecoleng.2013.01.039, 2013b.
Stocks, B.J.: Fire behavior in immature jack pine. Can. J. For. Res. 17, 80–86, 1987b.
References removed from the text:
Jiménez, E., Vega, J. A., Fernández-Alonso, J. M., Vega-Nieva, D., Álvarez-González, J. G., and Ruiz-González, A. D.: Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances, Can. J. For. Res., 43, 149–158, https://doi.org/10.1139/cjfr-2012-0374, 2013a.
Stocks, B. J.: Fire Potential in the Spruce Budworm-damaged Forests of Ontario, For. Chron., 63,https://doi.org/10.5558/tfc63008-1, 1987.
L67. Fire severity is already expressing change, and not just through fuel consumption but through vegetation alteration in general, i.e. including scorch.We completed the sentence with your clarification “through vegetation alteration in general” on line XXX.
L73. None of these references is a primary reference for the concept of fuel type.You are right that these references were not the primary, we used these references to avoid using old refences only but temporally graduated cites that used fuel types defined in the similar way. However, we have change references for the primary reference also used when we defined the term fuel type in 2012 (Alvarez et al., 2012). We also included a reference to a current glossary in which the definition is the same,
…fuel types, defined as “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions” (Merrill and Alexander 1987, CIFFC 2023).
References:
Alvarez, A., Gracia, M., and Retana, J.: Fuel types and crown fire potential in Pinus halepensis forests, Eur. J. For. Res., 131, 463–474, https://doi.org/10.1007/s10342-011-0520-6, 2012.
CIFFC, C. I. F. F. C.: Canadian Wildland Fire Management Glossary, Can. Interag. For. Fire Cent. Winnipeg, MB, Canada, 2023.
Merrill DF, Alexander ME (1987) Glossary of forest fire management terms. National Research Council of Canada, Canadian Committee on Forest Fire Management, Ottawa.
L74. Not sure what “combustion factors according to fire severity” means. Which functions?You are right, the sentence is not clear. We have rewritten again on lines XX.
L75-76. What does fuel layers have to do with landscape heterogeneity? The former is local.Again, the sentence was not clear, we have changed “fuel layers” by “fuel types” on lines XX.
L78. Isn’t forest inventory data field data?Yes, you are right, forest inventory data is also field data. We have rewritten the sentence talking about postfire field sampling on lines XX.
L80. Forest structure does not “make” a fuel type, only variability within a fuel type.You are right, the text is confused, we have removed forest structure from the sentence and left only fuel types. We agree that forest structures can make variations within a fuel type but only if there is a different fire behavior. We also have rewritten the main objective for clarity.
L82. The cause-effect relationship is inverted: fuel consumption determines fire severity, not the other way around.OK, fuel consumption determines fire severity. We have changed the sentence on lines XX.
L83. Analyse what? Quantity, variability?We have rewritten the main objective of the study (lines XX) and complete the specific objectives including quantify and compare instead of analyze (lines XX).
L98-100. Rephrase. It states that structure determines structure?We have changed the paragraph for clarity on lines XX.
L102. scorpius, not Scorpius.OK, changed, thank you.
L111. No need to qualify a crown fire as “massive”. Delete or be more specific.OK, we have removed “massive” from “massive crown fires” on the whole text and on line xxx.
L111. Spotting, not spots.Ok, changed.
L115. Relative humidity, not moisture, right?Ok, changed.
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.We agree, but it was not been possible to obtain that concrete value from DC. The first author of the study was working with firefighters in that time and he was sure that DC was not extreme but we do not have the exact value.
L120. Again, decapitalize the species.Thank you again.
L129. Not 3 types, 3 classes. A reference for what fire severity is, is needed, preferably the original one (Ryan & Noste).Ok, it has been changed “classes” instead of “types”. However, we are not sure if the cite you proposed was “Ryan, K. and Noste, N.: Evaluating prescribed fires, Symp. Work. Wilderness Fire, 1985.”?
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.
This percentage refers to the % of trees classified as green, scorch or charred at plot level, none at tree level. One tree was green, scorch or charred depending the proportion burned in each severity as it was described more accurately on lines 164-168. The categories of trees depending on fire severity was described on lines 164-168. “Thus, we categorized the tree into three types; firstly, green trees, which could be partially scorch but had at least 20% green crown; secondly scorch trees, which were mostly affected by radiant and convective heat and had less than 20% green crown, although normally they were fully scorch with abundant fine fuels (needles and small branches with <6 mm) on the tree or on the ground but not consumed; and thirdly charred trees, which were skeletons mainly consumed without fine materials on the tree or on the ground (Alvarez et al., 2013)”. If you prefer this definition may be moved to the beginning of the section “2.3 Field plot data and fire severity estimation”.
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.
Sorry, we are not entirely sure we understand what part you are referring to. Are you reefing to Table S1. “Main characteristics of the four fuel types in Pinus and Quercus forests?”
We did not consider that the four fuel types are structural variants but a group of forest structures with common synthetized characteristics that could burn with similar fire behavior or fire type. The main issue, and this is the possible reason because the misunderstanding, is that our fuel types are based only in overstory forest structure as we have explained previously.
We will explain this point in the description of Table S1 to clarify the reason because there are only stand characteristics described. In addition, we think that including the potential fire behavior and dominant fire type associated to general structural characteristics, we are showing the four groups are more than “fuel structural types” but fuel types following the definition “identifiable associations of fuel elements with distinctive species, form, size arrangement, and continuity that exhibit characteristic fire behavior under defined burning conditions”. In our case, in the table, there is a different characteristic fire behavior under defined burning conditions, in this case a wind-driven fire, different from convective or topographic fire with its own characteristics. This also can be added to de table S1 description.
However, you are right in your logical concern about applying the same definition of fuel type for two different cover species. Now, this point has been explained in the text, by specifying that these fuel types were created in a very nearby fire, under similar conditions but for Pinus halepensis. At the beginning of the field work, we wanted to test whether the same thresholds that defined the forest structures (quantitative characteristic of the stand that can help to differentiate plots in the field work) and later grouped into the 4 fuel types according to fire types burned (using the previous study in Pinus halepensis wildfire) could be applied for both species and allow us to identify and differentiate forest structures in Q. suber forests. This explanation has been introduced in the text in section 2.3 “Field plot data and fire severity estimation” and also clarified in the table of the supplementary material “table S1”. It has also been described as a source of uncertainty in section 4.4 Uncertainties in emissions estimates and limitations.
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.Yes, it might not be the same not the same “left alive” than “left green”. We have changed “left alive” by the sentence “For each tree, fire severity was assessed using the proportion of residual crown severity (green, scorch, black).
L164, 165, 166. Scorched, not scorch.OK, it has been changed.
L168. Rectify: species is not “measured”.Ok, thank you. We have changed “measured” by “identified”
L170. Why are char heights within parentheses? Were they an additional variable measured? If that is the case they should be outside parentheses.It was an additional variable and we have removed the parentheses after the definition of crown base height with this result.
“… total height and crown base height (measured at the lowest part of the crown with vertical continuity of branches) and higher and lower char height measured on tree steam.”
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?- We measured the percentages of consumption % visually but following all precautions to reduce biases and uncertainties associated to visually assessments.
It was really difficult the visual estimation of fine crown and shrub percentage after the fire and we also understand the concern about the high degree of uncertainty that this measure can cause. However, according to De Santis et al. (2010), biomass consumption was traditionally estimated using a two-step methodology which includes:
- the estimation of pre-fire biomass by applying allometric regression equations using destructive sampling or biomass values per species and
- the post-fire biomass estimated by field-based weighting or by visual examination.
But it is also clear that, as suggested by different authors (e.g. Gallagher et al.2020), the fact that post-fire percent cover is often estimated visually can introduce a bias into the calculations that is difficult to quantify.
When we started the fieldwork, we visited the few areas unburned within the fire perimeter to understand what possible fuels we could find and to identify species. Moreover, we visited areas that immediately bordered the perimeter of the fire when we had plots near the perimeter. When we started to measure the plots, we invested long time to measure all the different possible diameters from shrubs and fine branches from trees with a caliper.
All plots were done by the same two people in order to avoid observer bias that could cause a significant influence in the kind of measures that we took, the percentages of fuel types after the fire. The value of each percentage was an average value from the two people to avoid errors of perception. We also take dozens of photos from all angles from each plot to capture trees, shrubs and litter. It was useful because at the beginning every night we contrasted percentages given to each plot and adjusted them comparing with previous plots when it was necessary. After this first training and as we made more plots, we had a more balanced vision (which was far from perfect) of the percentages we gave, so that the quantitative differences we appreciated were relatively small. On the other hand, we transparently recognize that the potential shrub or litter cover measurements before the fire based on the number of shrubs, and comparing it with what we saw in unburned areas inside could be not as accurate as it could be using other methods. However, these quantitative percentages reflected the difference between plots that we saw qualitatively and the results obtained from the fuel loads from shrubs were within the ranges that we obtained from the IFN3 plots and bibliography.
We have updated the section “2.3 Field plot data and fire severity estimation”, including a synthesis of the training method to obtain the percentage of fuel consumed on lines (xxx). Moreover, in the discussion or/and in the new “4.4 Uncertainties in emissions estimates and limitations” we have included the implications over the uncertainties and potential overestimation of shrub and crown fuel loads before and after the fire on lines XXX.
References:
De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.
Gallagher, M. R., Skowronski, N. S., Lathrop, R. G., McWilliams, T., and Green, E. J.: An Improved Approach for Selecting and Validating Burn Severity Indices in Forested Landscapes, https://doi.org/10.1080/07038992.2020.1735931, 2020.
L173. The time-lag concept is only for dead fuels, so replace it with diameter ranges (<6mm, 6-25 mm, etc.).Thank you for the observation. It has been changed in the section “2.3 Field plot data and fire severity estimation”
L179. Eliminate “available”, because available fuel is indicated by the combustion factor.It has been changed.
L182. Crews, not corps.OK, done.
L183. What plots are these? Are they different from the severity plots? Clarify.We clarified what kind of plots are. Yes, these are the plots done during the field work, where we measured fire severity and the rest of quantitative variables (tree density, tree height, percentages of consume etc).
“During the field work, we made plots of two sizes depending on the heterogeneity of the area and the density, with a range between 316 to 716 m2. To obtain the fine biomass before the fire in these field work plots, we distinguished three components: trees, shrubs and litter” at the begging of the section “2.4.1 Area burned and pre-fire available biomass”.
L185. Delete available and don’t present what follows as bullet points.Ok, we have changed it.
L214. This (the field component) overlaps with section 2.3. Should be moved/merged. Still, no description of how this was done (classes?).You are right partially, we repeated that the method of assessment of combustion factor was obtained visually, but here we explain what we did with the percentages of consumption assessed from field work for trees, shrubs and litter, and each of their fraction (leaves and fine branches lower than 6mm for crowns, two categories for shrubs and one for litter). We will move part of the long paragraph (lines 255-260) to the upgraded section “2.3 Field plot data and fire severity estimation”. Here, we will only explain how we obtain the average values at plot level for trees, shrubs and litter.
No, we assessed percentages of consumption after a long process explained in detail when we answered the question “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?”. With the percentage of consumption considering the different categories depending of their size we obtained an average value per plot and fuel size related to crown, shrub, and only one for litter. total height and crown base height
L219. Quite hard to do, unless consumption is nil or is total. How did you manage to have a reference for preburn litter?As you have said, litter consumption was total that it was de case of charred plots and many cases of scorched and until some green plots when trees were larger and surface fire was intense. When there were green plots, it was relatively easy if the green plot burned with a low surface fire intensity. For the few intermediate cases we followed a long process. But this process was applied to all plots to maintain the consistency. We explain it below.
In the section 2.3 we have explained how the consumption percentage was estimated: there are different phases. First, after observing green areas to understand the quantity of litter that we could expect to find when it was possible, a consumption percentage was taken visually from the observation that two people made (it was an average or consensual if there were significant differences, something that not usually happened) throughout the work to avoid observer bias errors. During the field work, we also considered important variables that could explain the percentage of remaining litter or their lack. Thus, we considered if we were in a plot with high slope, we observed if the absence of litter could be explained by the slope and the area surrounding the plot. After this first measure, and after analyzing and comparing many plots during the field work, we could adjust better the percentage of a new plot comparing with previous plots. After the field work, and using dozens of photos from the plots focussed only in the soil, we could compare visually if the percentage assessed could describe what we were seeing and if the percentage was logical in relation to the other values. We have explained briefly the methodology that we applied to obtain an assessment of the litter consumption in section 2.3.
L225. So, after all this how did you calculate EM? Averages of B, C and D for the whole fire? Another method? Explain.No, we applied the formula (EM = A x B x C x D) for each plot, that is, the initial biomass in the burned area of each plot in each layer, the percentage of consumption of each layer (2 fractions by crowns, 2 by shrubs, 1 by litter. We applied that percentage of consumption on the initial biomass in each stratum, and we applied the emission factor on the biomass consumed to obtain the total emission per plot.
Later, we had a gas or particle emission value per plot, each plot corresponded to one species (Pinus vs Quercus), fuel type (1-4) and fire severity (green, scorched, charred). Finally, we calculated average values and carried out statistical tests from the grouped plots depending of these variables (species, fuel type and fire severity).
L228. Unclear what was the spatial scale of analyses here. Plots?Yes, the comparison always is from plots. We will apply the results of this work to continue to assess the emissions at fire scale with the rest of material made (fire severity maps vs remote sensing tools, etc.). We have clarified the first sentence of the section 2.5 to notices this point.
L233. What determines fire severity differences? Fire severity class?The first thing that differentiate or classify severity at tree level was the percentage of the crown remained after the fire, classifying trees in green, scorched and charred. Then, we calculated the percentage of trees from the three-tree fire severity categories defining each plot as a green, scorched and charred. We hope it is now explained more clearly in the section “2.3 Field plot data and fire severity estimation”
L235. Define coarse fuel in methods.We have defined coarse fuels in the section “2.3 Field plot data and fire severity estimation”. coarse fuels for shrubs were fuel size higher than leaves and fine branches (<6mm). However, we did not consider trunks because we did not see them.
L238, 240. Scorched. This is a systematic error across the paper. Replace also in the figures.Thank you again. We have modified it throughout the manuscript.
L245. Two “available” to delete.Done, thank you.
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.Ok we will carry out the figure 3 with the untransformed values.
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.Yes, we have written fuel loading instead of available biomass in figures 3,4 and throughout the text and we have clarified the caption of figure 4.
L266. Rephrase, otherwise it looks like a methods sentence.We have rephrased the sentence in the new version.
L278. A more meaningful way to say this is “Crown fire was predominant …”.We have changed the sentence accordingly.
Table 1: add standard deviations or, perhaps better, coefficients of variation.Ok, the new version will include the standard deviations of the values.
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.Yes, you are right, it is not totally true as you pointed, there are many factors that influence the potential biomasses such as site quality, including the soil type, the slope position, percentage of stoniness. We have removed this sentence to clarify and reduce the discussion.
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/2009013Thank you, we will include them in the discussion.
New references:
Fernandes, P. M.: Combining forest structure data and fuel modelling to classify fire hazard in Portugal, Ann. For. Sci., 66, 415p1-415p9, 2009.
Fernandes, P. M., Guiomar, N., and Rossa, C. G.: Analysing eucalypt expansion in Portugal as a fire-regime modifier, Sci. Total Environ., 666, https://doi.org/10.1016/j.scitotenv.2019.02.237, 2019.
L345. Note that other studies might be using different definitions, as very often only fine fuels and coarse dead fuels are considered.Yes, you are right, it is very difficult to compare the fuel load values estimated in other studies because each one measures relatively different things, but they can cause large changes in the totals. Only the inclusion or not of litter in the total biomass can completely alter the biomass totals as we have seen.
We have included this point as a reflection in the sentence before comparing results of pre-fire fuel loads.
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.Thank you for this new reference, we will include it and will search for more examples to broaden the perspective of the results.
New reference:
Fernandes, P. M., Fernandes, M. M., and Loureiro, C.: Post-fire live residuals of maritime pine plantations in Portugal: Structure, burn severity, and fire recurrence, For. Ecol. Manage., 347, 170–179, 2015.
L367-368. Again the repetition of “fires” as if a single fire was composed of several fires.We have changed the sentence, thank you.
L368. You forgot the most important driver of fuel availability (combustion factor): fuel moisture content.You are right, we have included fuel moisture content as an important driver that determine fuel availability.
L374-375. This is circular reasoning: fuel consumption is fire severity, the latter is based on the former and not the other way around.Ok, thank you, we have reviewed that this relationship is correctly expressed throughout the text.
L378. Although common, this is a misunderstanding: fuel consumption is in general independent from fire spread rate.Ok, we have considered this point in the sentence.
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.Thank you for the reference, we will compare our CO2 with the results of the article in the discussion in section “4.3 Atmospheric pollutant emissions”.
New reference:
Fernandes, P. M. and Loureiro, C.: Fine fuels consumption and CO2 emissions from surface fire experiments in maritime pine stands in northern Portugal, For. Ecol. Manage., 291, 344–356, 2013.
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.Ok this paragraph will be modified to introduce nuances and reduce the tone.
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.Yes, we have also written a new section named “4.4 Uncertainties in emissions estimates and limitations” and we will include the point that you mention with the limitation when comparing our results with studies that actually measured emissions in the field.
L411. This last sentence needs referencing.Ok, we will search some references for this sentence.
Citation: https://doi.org/10.5194/egusphere-2024-1355-AC2
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AC2: 'Reply on RC2', Albert Alvarez, 19 Aug 2024
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RC3: 'Comment on egusphere-2024-1355', Anonymous Referee #3, 17 Jul 2024
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 -
AC3: 'Reply on RC3', Albert Alvarez, 19 Aug 2024
Reviewer #3: Anonymous Referee
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:
Thank you very much for your reply. We appreciate the time the reviewer spent on reviewing the manuscript. We have considered all of the reviewer comments and suggestions, and either incorporated them into the text or responded to them below.
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.
What we have done with all parametric tests is to represent the residuals of the models with the predicted values. Examining the graphical representation of the residuals against the expected values allows us to assess a series of assumptions made about the quality of the model fit: (i) Normality: the residuals are assumed to be normally distributed around each predicted value; (ii) Linearity: it is also assumed that there is a linear relationship between the residuals and the predicted values; (iii) Homoscedasticity: it is also assumed that the variance of the residuals is similar for different values of the dependent variable.
If the editor considers it appropriate to show all residual graphics in the supplementary material, we will include them.
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.We agree with your two suggestions. First, we have upgraded the discussion including more references to compare our results with other works or methodologies used to measure wildfire emissions. Second, there is a new section “4.4 Uncertainties in emissions estimates and limitations”, which includes the various limitations and uncertainties at the different levels of emissions estimation (field work, different components of the calculation method, i.e. calculation of the pre-fire fuel load, estimation of the combustion factor, emission factors, etc.). We have included in this section all comments from all of reviewers to enrich and clarify how can be considered the results of this study.
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.This terminology started to be used more frequently after the Fire Paradox project in Europe (Silva et al, 2010). The European Project ‘‘Fire Paradox’’ analyzed the spread of fire in historical wildfires and showed that there were similar spread schemes dominated by common factors (e.g. wind direction and speed). Depending on the spread scheme and the dominant spread factor, three fire types were defined: convection or plume dominated fires, wind-driven fires and topographic fires (Castellnou et al., 2013; Costa et al., 2011). Firstly, convection or plume-dominated fires are characterized by the accumulation of high quantity of available fuels and atmospheric instability. This fire type has such a high intensity and extreme behavior that produces its own fire environment and generates massive spotting. Secondly, wind-driven fires follow the speed and direction of strong winds when the meteorological window that produces the fire conditions is maintained, with the same intensity and velocity during day and night. In both of them, small changes in the landscape have little influence in the direction and behavior of these fire types, especially under extreme meteorological conditions. In contrast, topographic fires are dominated by local winds caused by slope and differences in solar heating of the earth surface (i.e. sea breeze, land breeze, valley and slope winds). The direction of this fire type changes with topography (e.g. hydrographic basins, main valley), and it has high intensity during the day and low intensity at night (Castellnou et al., 2013; Costa et al., 2011). In the latter fire type, wildfire is more sensitive to small changes, thus little variations of topographical wind, slope or aspect have higher influence on fire behavior (Lecina-Diaz et al., 2014).
The combination of two or three fire types in the same wildfire might be common in North America, Canada and Australia, since fire usually burns during many days or months and involves large areas of the landscape. Nevertheless, the majority of wildfires in Europe burn for 48 hours or less, thus fire has fewer opportunities to flip from one fire type to another.
References:
Castellnou, M., Pagés, J., Miralles, M., Piqué, M.: Tipificación de los incendios forestales de Catalunña. Elaboración del mapa de incendios de diseño como herramienta para la gestión forestal. Proceedings of the 5th Congreso Forestal Espanñool Ávila, Spain. Available: https://interior.gencat.cat/web/.content/home/030_arees_dactuacio/bombers/foc_forestal/jornades_recerca_cooperacio_internacional/articles_de_recerca_en_foc_forestal/articles_incendis_forestals/2009_Castellnou-et-al_tipificacion-IF-en-CAT_Mapa-incendios-de-diseno_CongrAvila.pdf (last access: 29 July 2024), 2009.
Costa, P., Castellnou, M., Larrañaga, A., Miralles, M., and Kraus, D.: Prevention of Large Wildfires using the Fire Types Concept, Departament de Interior.Generalitat de Catalunya., Cerdanyola del Vall‚s, Barcelona, Spain., https://interior.gencat.cat/ca/el_departament/publicacions/proteccio_civil/la_prevencio_dels_grans_incendis_forestals_adaptada_a_l_incendi_tipus/index.html (last access: 29 July 2024), 2011.
Lecina-Diaz, J., Alvarez, A., and Retana, J.: Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of mediterranean pine forests, PLoS One, https://doi.org/10.1371/journal.pone.0085127, 2014.
Silva, JS., Rego, F., Fernandes, P., Rigolot, E., editors Towards Integrated Fire Management - Outcomes of the European Project Fire Paradox. European Forest Institute Research Report 23. https://efi.int/publications-bank/towards-integrated-fire-management-outcomes-european-project-fire-paradox (last access: 29 July 2024), 2010.
Line (L) 15. … ‘one of the largest wildfires of the last decade’. In what context is this e.g. fires in Spain, fires in the Mediterranean region?You are right, we did not specify the location well enough in the abstract. The Jonquera fire was in north-eastern Spain, we have included this information in the abstract.
Please remove emotive phrases from the manuscript e.g. L25 ‘massive wildfire’, L56 ‘huge inaccuracies’We have revised the text to remove all emotive sentences and rephrase unnecessary nuances.
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.Thank you, you are right, we have included this point in the sentence.
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.Yes, thank you for the observation, we have written another brief paragraph to clarify the method is in comprehensive way.
Page 2 bottom paragraph. I’m wondering whether the paper below is worth citing to provide a technical definition for what you are referring to as ‘fire severity’?- 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
Yes, thank you, we have included a technical definition with your reference (Keeley, 2009) to clarify the meaning of fire severity, together with a suggestion from other reviewer that asks for including a reference for what fire severity is.
Page 3. L2 and L415. I would remove the phrase ‘unprecedented combination of …’. The type of investigation you are conducting is standard practice rather than unprecedented.Yes, we have removed “unprecedented combination”, but we have highlighted the novelty of the field work data in Spain at least, and the use of litter component in the total fuel load component.
L115. Moisture content. Is this fine fuel moisture content or something else?Yes, that was a mistake. It has been corrected to “Relative humidity”.
Around L130. When you refer to charred trees up to what height level are trees generally charred?We have clarified this description in the section “2.3 Field plot data and fire severity estimation”, which was split into two different paragraphs with a brief description of the fire severity classification from tree level to plot level following Alvarez et al. (2013).
Reference:
Alvarez, A., Gracia, M., Castellnou, M., and Retana, J.: Variables That Influence Changes in Fire Severity and Their Relationship with Changes Between Surface and Crown Fires in a Wind-Driven Wildfire, For. Sci., 59, 139–150, https://doi.org/10.5849/forsci.10-140, 2013.
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.Thank you for the observation, the figure has been taken and adopted from Alvarez et al. (2012), so, probably we will redraw a new one to convey the same meaning.
Equation 1. Use multiplication signs rather than the letter x.Thank you, this has been changed.
L233. Log transform for normality. What test did you use for this and what was the result e.g. test statistic and p-value?As we have explained in the first response to the reviewer, we have examined the graphical representation of the residuals against the expected values allows us to assess a series of assumptions made about the quality of the model fit: normality, linearity and homoscedasticity. When we transformed the variable into logarithm, the graph of the residuals improved as you can see in the three factor ANOVA of available biomass among fuel types considering the three different layers (crown, shrub and litter) in the two species.
Untransformed available biomass (also in the pdf)
Log-transformed available biomass (also in the pdf)
L241. What was the required significance level for significant differences?The required significance level was 0.05, which corresponds to a 95% confidence level
Figure 3. Is the log base 10 or base e?It is base e.
Table 2. Is there any reason why nitrous oxide was excluded from your analysis since it is a major greenhouse gas?We understand the concern about the lack of nitrous oxide emission values. We only used those gases and pollutants with values from each stratum (crown, shrub, litter) but we did not find emission factors for litter from Pinus halepensis and Quercus suber. We have added one sentence highlining the importance of having more emission factors available for species especially for nitrous oxide and similar components because of their higher impact on greenhouse phenomenon in the new section “4.4 Uncertainties in emissions estimates and limitations”
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