# Smouldering Combustion Dynamics of a Soil from a Pinus halepensis Mill. Forest. A Case Study of the Rocallaura Fires in Northeastern Spain

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Material and Methods

#### 2.1. Study Area and Fire History

^{2}/ha to 30 m

^{2}/ha. Leaf biomass oscillates between 3 and 5 t/ha, and total biomass between 60 t/ha and 120 t/ha [27].

^{2}. On 19 July the second fire broke out affecting a total area of 790 ha of forest and cultivated fields and threaten the Wildland Urban Interface in a day with simultaneous fire events. The re-ignition of the same area was attributable to the smouldering combustion that persisted in the areas where the previous fire was suppressed.

#### 2.2. Experimental Design and Field Work

#### 2.3. Laboratory Analysis

^{3}), Fo is the organic fraction (kgo/kg), Dbo is the bulk density when Fo = 1, and Dbm is the bulk density when Fo = 0. The relation arises from assuming that (i) Dbo, the bulk density of “pure” organic matter, and Dbm, the bulk density of “pure” mineral matter, are constant and (ii) in a mixture, the volumes occupied by the organic mass and the mineral mass are additive. When these parameters are known, Db can be estimated from Fo, which is more easily measured. When Fo is greater than 0.1 kgo/kg, the organic mass per unit soil volume (FoDb), or organic density, is approximately constant at 0.1 Mgo/m

^{3}.

#### 2.4. Statistical Analysis

#### 2.5. Probability of Smoldering Fire Ignition in Organic Soils

^{−(B0}

^{+B1}

^{xH}

^{+B2}

^{xIM}

^{+B3}

^{xBD}

^{)})

_{0}, B

_{1}, B

_{2}and B

_{3}are coefficients that vary according to the probabilistic distribution of each sample group. H is the moisture content, in %, IM is the mineral content, in %, and BD is the bulk density kg/m

^{−3}. The calculation of the moisture content was done at a probability of ignition of 50% (P

_{50}) for each sample. Then, the result is expressed as the maximum moisture content given for ignition to occur. Isolating the percentage of maximum moisture content at a probability of 50% results in Equation (2):

_{0}+ B

_{2}xIM + B

_{3}xBD)/B

_{1}

_{0}, B

_{1}, B

_{2}and B

_{3}) are Frandsen [8] for the sample group ‘Pine duff (Seney)’ (Table 3). This sample group have similar mean values of inorganic matter (IM, 36.5 ± 16%), organic matter (OM, 63.5 ± 16%) and bulk density (BD, 190 ± 19 kg/m

^{−3}) at a 5 cm depth as the samples collected in Rocallaura (Table 2).

#### 2.6. Experimental Design to Examine the Effect of Soil Moisture and Bulk Density on the Smoldering Spread Rate

^{3}to 150 kg/m

^{3}) and, therefore, comparable to the bulk densities of the organic stratum (Oe + Oa) of this study (48 kg/m

^{3}and 190 kg/m

^{3}). The greatest difference with the samples from this study lay in the mineral content (mean 35%).

_{0}+ A

_{1}× MC + A

_{2}× BD + A

_{3}× MC × BD + ɛ

^{3}), A

_{0}, A

_{1}, A

_{2}and A

_{3}are the coefficients of the dependent variables and ɛ is the residual term. Given that we do not have the necessary tools to perform the same experiments and so obtain the model coefficients, we opted to use those that provide the best fit with the linear model proposed by Prat-Guitart et al. [9] (R

^{2}= 0.77) and as is detailed in Table 4.

## 3. Results

#### 3.1. Comparative Findings: Control Zone vs. Post-fire Zone in Mineral Soil

#### 3.2. Descriptive Analysis of the Duff Layer in the Control Zone

_{8}and D

_{24}, the means range between 3.67 and 3.33 cm. In the lowest part of the transect (D

_{32}), the mean depth is 5 ± 3.6 cm, with depths ranging from 2 to 9 cm. Bulk density values are the inverse of those of depth (Figure 5b). The mean density is 157 ± 134 kg/m

^{3}but values fluctuate greatly, especially in D

_{16}. The highest part of the transects presents a mean density of 64.44 ± 27.9 kg/m

^{3}, which is much lower than at other parts of the transect. The mean inorganic content is 35.52 ± 10.5%. It can be seen (Figure 5c) that the inorganic matter content rises (D

_{0}= 28.80%) along the transect to a distance of 24 m (D

_{24}= 46.93%). Thereafter, the mineral content decreases until the end of the transect (D

_{32}= 28.10%) (Figure 5c). The organic content is, logically, inversely proportional to that of the inorganic content, with a mean value of 64.48 ± 10.5% and a maximum of 80.84% and a minimum of 44.39% (Figure 5d).

#### 3.3. Duff Ignition Probability

_{50}) in all the duff samples (N = 15) and in six soil samples taken close to the surface (F 0–5 cm, N = 15) from the area exposed to low-intensity burning. All the surface soil samples (C 0–5 cm, N = 15) taken from the control zone, in contrast, do not present a P

_{50}of ignition.

_{50}and percentage inorganic matter (% IM) has a very high magnitude (F = 104.5, p < 0.001) and a coefficient of determination (R

^{2}= 0.846) (Table 6a). In the separate linear regression model between % MC at an ignition of P

_{50}and BD expressed in kg/m

^{3}, the magnitude (F = 70.9, p < 0.001) and the coefficient of determination are also high (R

^{2}= 0.789) (Table 6b).

^{3}in the duff and 373.90 ± 114 kg/m

^{3}in the F−0–5 cm (Figure 6b).

_{32}from T6. The next lowest value is 6.80%, corresponding to sample D

_{24}from T5. These two results mark the maximum limit of inorganic content and density for P

_{50}(76.36% and 74.26% of IM, 478.31 kg/m

^{3}and 439.87 kg/m

^{3}of BD, respectively).

_{50}when moisture content is as lower as 33.60%, IM is 19.16% and bulk density is 580 kg/m

^{3}(sample D

_{16}from T3), although the sample is not an extreme that can be considered representative of the whole layer. The next lowest moisture content 55.62% recorded in sample D

_{24}from T1 associated with an IM of 55.61% and a BD of 193.33 kg/m

^{3}. This value can be considered as being more representative of the duff layer. The highest bulk densities are recorded in samples D

_{32}from T2 and D

_{8}from T3 with 290 kg/m

^{3}each.

^{3}in order to reach a 50% probability of ignition (Table 7). In the case of duff, the regression line obtained was inversely proportional to the inorganic content and the density of the samples. Table 6 shows the results of linear regression model in order for a soil of the same physicochemical characteristics to have a 50% probability of combustion. In the model, the interaction between the two variables (IM × BD) was not included because the magnitude of the equation did not vary significantly. Figure 7a shows that if soil moisture increases then both the inorganic content and the density have to fall for there to be a 50% probability of ignition. In the case of duff, the higher its organic content, the lower the level of moisture needed for combustion at P

_{50}(Figure 7b).

#### 3.4. Percentage Fuel Consumption in the Duff Layer

_{24}from T5 with 63.4% and 60.67% consumption for 0% and 5% MC, respectively; and sample D

_{40}from T6 with 93.06% and 90.32% for 0% and 5% MC, respectively. At a moisture level between 10% and 25%, only in sample D

_{40}from T6 was more than 60% of the material consumed.

_{24}). From D

_{0}(0 m) to D

_{24}(24 m), the percentage of consumption decreases as the altitude gradient falls. Between D

_{24}and D

_{32}, percentage consumption rises again.

^{2}= 0.919) and a high magnitude (F = 34.09, p < 0.01). The regression line can be expressed as:

_{90}= 49.28 − 1.21 × MC (%)

_{90}is the percentage of samples of which more than 90% is consumed and MC (%) is the respective soil moisture content. This line seeks to provide an estimate of the maximum humidity limit so between 90% and 100% of each sample is consumed, which means that smouldering combustion can propagate easily. At rates of soil moisture between 5% and 15%, it is estimated that between 35% and 40% of the samples will be consumed more than 90% of their organic content. At 25% MC, the number of samples falls to 20%.

^{2}= 0.969) and a very high magnitude (F = 295.26, p < 0.001). The quadratic regression can be expressed as:

_{60}= 106.087 − 0.475 × MC − 0.08 × MC

^{2}

_{60}is the percentage of samples of which more than 60% is consumed and MC (%) is the respective moisture. This line seeks to provide an estimate of the maximum moisture limit so that more than 60% of each sample is consumed and, therefore, of the point at which smouldering combustion is sustained.

#### 3.5. Smoldering Combustion Spread Rate

## 4. Discussion

#### 4.1. Smouldering Fires in the Duff Layer

_{0}), which means it is more porous and can burn more easily [3]. The low moisture content (<15% MC) of the duff layer, resulting from the meteorological conditions that occurred during the fire season, is another factor explaining why part of the duff layer burned.

#### 4.2. Impacts of Smouldering for the Mediterranean Ecosystem

#### 4.3. Probability of Ignition and Fuel Consumption in the Duff Layer

^{2}during 3 min of high temperatures and 5.8 kW/m

^{2}during 30 min [45]. The composition of the duff (mineral content, moisture, bulk density and depth) also appear to determine the probability of its ignition and consumption [7]. The mineral content is one of the most important properties determining the smouldering combustion of fine organic layers [46], as it was in this case study.

_{50}) is defined as the threshold at which ignition occurs [7] (Figure 6 and Figure 7). The ignition results show that, due to extremely low levels of moisture content in the period of fire risk in the Mediterranean, the soil duff layer can easily combust. For ignition to occur in this study, approximately 75% mineral content (25% organic content) at 5% moisture content was estimated to be sufficient. The results also show that an increase in bulk density reduces ignition and propagation probability [8,9].

^{2+}cations in the mineral soil it can be considered that the mineral composition of the duff layer is playing a role on the estimated combustion parameters.

_{90}and Cons

_{60}respectively) and to achieve subsequent propagation [46], the mineral content values need to be lower than 55% and 40%, for moisture contents of 5% and 25%, respectively. Here, it is worth mentioning that Garlough and Keyes [23] did not include bulk density in their model because they did not observe a significant influence in the percentage variation of the consumption of their samples.

#### 4.4. Determination of Smoldering Combustion Spread Rate

^{3}. If the surface in the study area were homogeneous, smoldering combustion could begin at 92.16 ± 8.16 cm/day.

^{2+}and Mg

^{2+}cations could well increase heat transfer and, therefore, the spread rate [46]. Thus, we only estimated rates that might differ from those in reality, but, nevertheless, they should serve as a reference for further studies in Mediterranean areas.

#### 4.5. Future Fire-regime Changes and the Integration of the Risk of Smouldering Fires in Forest Planning and Management

## 5. Conclusions

^{3}. The study identifies the potential total consumption of the duff layer after a fire [that is > 90% of its organic content (Cons

_{90})], which can make up between 20% and 35% of a given surface, with typical moisture content levels for a period of high fire risk (30% and 5%, respectively). The consumption potential at which smouldering combustion can be propagated [>60% (Cons

_{60})] would extend over 93% to 100% of a given surface area. The average propagation velocity at which the combustion would consume falls within a range of 4.08 cm/h to 3.21 cm/h with a moisture content of 5% and 25%, respectively. However, the high heterogeneity in the composition of the fermentation and humus horizons (that is, their mineral content, depth and bulk density) and their moisture content influence the propagation variability of smouldering combustion.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Rein, G. Smouldering Combustion Phenomena in Science and Technology. Int. Rev. Chem. Eng.
**2009**, 1, 3–18. Available online: http://www.era.lib.ed.ac.uk/handle/1842/1152 (accessed on 25 November 2016). - Ohlemiller, T. Modeling of smoldering combustion propagation. Prog. Energy Combust. Sci.
**1985**, 11, 277–310. [Google Scholar] [CrossRef] - Rein, G. Smoldering Combustion. In SFPE Handbook of Fire Protection Engineering; Hurley, E.M.J., Gottuk, D.T., Harada, K., Kuligowski, E.D., Puchovsky, M., Wieczorek, C.J., Eds.; Springer: New York, NY, USA, 2016; pp. 581–603. [Google Scholar] [CrossRef] [Green Version]
- Rein, G. Smouldering Fires and Natural Fuels. In Fire Phenomena and the Earth System; Wiley: Hoboken, NJ, USA, 2013; pp. 15–33. [Google Scholar]
- Drysdale, D. An Introduction to Fire Dynamics; Wiley: Hoboken, NJ, USA, 2011. [Google Scholar]
- Frandsen, W.H. The influence of moisture and mineral soil on the combustion limits of smoldering forest duff. Can. J. For. Res.
**1987**, 17, 1540–1544. [Google Scholar] [CrossRef] - Rein, G.; Cleaver, N.; Ashton, C.; Pironi, P.; Torero, J. The severity of smouldering peat fires and damage to the forest soil. Catena
**2008**, 74, 304–309. [Google Scholar] [CrossRef] [Green Version] - Frandsen, W.H. Ignition probability of organic soils. Can. J. For. Res.
**1997**, 27, 1471–1477. [Google Scholar] [CrossRef] - Prat-Guitart, N.; Rein, G.; Hadden, R.M.; Belcher, C.M.; Yearsley, J. And Propagation probability and spread rates of self-sustained smouldering fires under controlled moisture content and bulk density conditions. Int. J. Wildland Fire
**2016**, 25, 456. [Google Scholar] [CrossRef] [Green Version] - Benscoter, B.; Thompson, D.K.; Waddington, J.M.; Flannigan, M.; Wotton, B.M.; De Groot, W.J.; Turetsky, M.R. Interactive effects of vegetation, soil moisture and bulk density on depth of burning of thick organic soils. Int. J. Wildland Fire
**2011**, 20, 418–429. [Google Scholar] [CrossRef] [Green Version] - Rodríguez-Carreras, R.; Úbeda, X.; Outeiro, L.; Áspero, F. Perceptions of social and environmental changes in a Mediterranean Forest during the last 100 yera: The Gavarres massif. J. Environ. Manag.
**2014**, 138, 75–86. [Google Scholar] [CrossRef] - SMC. Base de Datos Climáticos de Las Estaciones de Rocallaura y Tárrega. (1950–2015). Generalitat de Catalunya. Servei Meteorològic de Catalunya. Àrea de climatologia. Serveis Climàtics. Available online: http://www.meteo.cat/wpweb/climatologia/serveis-i-dades-climatiques/series-climatiques-historiques/ (accessed on 6 February 2017).
- Martinez, M.D.; Serra, C.; Burgueño, A.; Lana, X. Response to the comments on ‘time trends of daily maximum and minimum temperatures in Catalonia (NE Spain) for the period 1975–2004’. Int. J. Clim.
**2010**, 31, 153–157. [Google Scholar] [CrossRef] [Green Version] - Calbó, J.; Doblas-Reyes, F.; Gonçalves, M.; Guemas, V.; Barrera, J.; Cunillera, J.; Altava, V. Projeccions Climàtiques i Escenaris de Futur. En Tercer Informe Sobre El Canvi Climàtic a Catalunya (TICCC); Institut d’Estudis Catalans y Generalitat de Catalunya: Barcelona, Spain, 2016; pp. 113–133. [Google Scholar]
- Mouillot, F.; Rambal, S.; Joffre, R. Simulating climate change impacts on fire frequency and vegetation dynamics in a Mediterranean-type ecosystem. Glob. Chang. Biol.
**2002**, 8, 423–437. [Google Scholar] [CrossRef] - Viola, F.; Daly, E.; Vico, G.; Cannarozzo, M.; Porporato, A. Transient soil-moisture dynamics and climate change in Mediterranean ecosystems. Water Resour. Res.
**2008**, 44, 1–12. [Google Scholar] [CrossRef] - Pausas, J.G.; Fernández-Muñoz, S. Fire regime changes in the Western Mediterranean Basin: From fuel-limited to drought-driven fire regime. Clim. Chang.
**2011**, 110, 215–226. [Google Scholar] [CrossRef] [Green Version] - BAIC. Butlletí Anual d’Indicadors Climàtics Any 2015; Generalitat de Catalunya, Departament de Territori i Sostenibilitat, Servei Meteorològic de Catalunya, Àrea de Climatologia, Equip de Canvi Climàtic: Barcelona, Spain, 2016. [Google Scholar]
- Cunillera, J.; Barrera, A.; Baldasano, J.M.; Gonçalves, M.; Guerreiro, D. Generació d’escenaris climàtics amb alta resolució a Catalunya. Projecte ESCAT; BSC, Servei Meteorològic de Catalunya: Barcelona, Spain, 2012. [Google Scholar]
- Barrera-Escoda, A.; Gonçalves-Ageitos, M.; Guerreiro, D.; Cunillera, J.; Baldasano, J.M. Projections of temperature and precipitation extremes in the North Western Mediterranean Basin by dynamical downscaling of climate scenarios at high resolution (1971–2050). Clim. Chang.
**2013**, 122, 567–582. [Google Scholar] [CrossRef] - Gonçalves-Ageitos, M.; Barrera-Escoda, A.; Guerreiro, D.; Baldasano, J.M.; Cunillera, J. Seasonal to yearly assessment of temperature and precipitation trends in the North Western Mediterranean Basin by dynamical downscaling of climate scenarios at high resolution (1971–2050). Clim. Chang.
**2013**, 122, 243–256. [Google Scholar] [CrossRef] [Green Version] - Huang, X.; Rein, G. Computational study of critical moisture and depth of burn in peat fires. Int. J. Wildland Fire
**2015**, 24, 798. [Google Scholar] [CrossRef] [Green Version] - Garlough, E.C.; Keyes, C.R. Influences of moisture content, mineral content and bulk density on smouldering combustion of ponderosa pine duff mounds. Int. J. Wildland Fire
**2011**, 20, 589–596. [Google Scholar] [CrossRef] - Castellnou, M.; Prat-Guitart, N.; Arilla, E.; Larrañaga, A.; Nebot, E.; Castellarnau, X.; Vendrell, J.; Pallàs, J.; Herrera, J.; Monturiol, M.; et al. Empowering strategic decision-making for wildfire management: Avoiding the fear trap and creating a resilient landscape. Fire Ecol.
**2019**, 15, 1–17. [Google Scholar] [CrossRef] - Cambra, J.; Carreras, J.; Carrillo, E.; Curcó, A.; Farré, A.; Font, X.; Vilar, L. Cartografia dels hàbitats a Catalunya; Manual d’interpretació. Generalitat de Catalunya, Departament d’Agricultura Ramaderia, Pesca i Alimentació. Recuperado a Partir de, 2006. Available online: http://www.gencat.cat/docs/dmah/Home/Ambits dactuacio/Medi natural/Sistemes dinformacio/Habitats/Documents complementaris/Documents/mill_introduccio.pdf (accessed on 2 February 2017).
- ICGC. NDVI (Normalized Difference Vegetation Index [WMS]. 2 × 2. Generalitat de Catalunya. Institut Cartogràfic i Geològic de Catalunya. 2012. Available online: http://www.icc.cat/NDVI/NDVIServletWMS? (accessed on 12 November 2016).
- ICGC. Variables biofísiques de l’arbrat de Catalunya. [Mapa WMS]. 20 × 20 m. Institut Cartogràfic i Geològic de Catalunya. Generalitat de Catalunya. Institut Cartogràfic i Geològic de Catalunya. Recuperado a Partir de. 2016. Available online: https://www.instamaps.cat/geocatweb/visor.html?businessid=23c3cce6559920283d3f4954158c1381&title=Variables biofísiques de l%27arbrat de Catalunya#11/41.4139/0.9709 (accessed on 12 November 2016).
- ICGC. Ortofoto de Catalunya. Vuelo americano serie A (1945–1946) [WMS]. 1:10000. Generalitat de Catalunya. Institut Cartogràfic i Geològic de Catalunya. Available online: http://www.icgc.cat/en/Public-Administration-and-Enterprises/Services/Online-services-Geoservices/WMS-i-WMTS/WMS-of-raster-cartography/WMS-of-old-orthophotos (accessed on 4 February 2017).
- ICGC. Ortofoto de Catalunya [WMS]. 1:2500. Generalitat de Catalunya. Institut Cartogràfic i Geològic de Catalunya. Recuperado a Partir de. 2015. Available online: http://www.icgc.cat/en/Public-Administration-and-Enterprises/Services/Online-services-Geoservices/WMS-i-WMTS/WMS-of-raster-cartography/WMS-of-current-maps-and-orthophotos (accessed on 4 February 2017).
- Moreira, L.M. Recueil des Plans du Roussilon, de Catalogne: Des Chasteaux, Villages, Eglises, Chapelles & Maisons qui peuvent servir de Postes en temps de guerre; et de Quelques Endroits de France & dʼEspagne. Par le Sr. Pennier Ingenieur et Geographe du Roy. by the Institut Cartogràfic i Geològic de Catalunya. Imago Mundi
**2019**, 71, 212–213. [Google Scholar] [CrossRef] - Soil Survey Staff. Claves para la Taxonomía de Suelos. Mdp.Edu.Ar (Décimo seg). Departamento de Agricultura de los Estados Unidos. Servicio de Conservación de Recursos Naturales. Recuperado a Partir de. 2014. Available online: http://www.mdp.edu.ar/agrarias/grado/723_Genesis/archivos/Taxonomia_Suelos_2010.pdf (accessed on 12 November 2016).
- Úbeda, X.; Outeiro, L.; Sala, M. Vegetation regrowth after a differential intensity forest fire in a Mediterranean environment, northeast Spain. Land Degrad. Dev.
**2006**, 17, 429–440. [Google Scholar] [CrossRef] - Heiri, O.; Lotter, A.F.; Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: Reproducibility and comparability of results. J. Paleolimnol.
**2001**, 25, 101–110. [Google Scholar] [CrossRef] - Santisteban, J.I.; Mediavilla, R.; López-Pamo, E.; Dabrio, C.J.; Zapata, M.B.R.; Gil García, M.J.; Castaño, S.C.; Martínez-Alfaro, P.E. Loss on ignition: A qualitative or quantitative method for organic matter and carbonate mineral content in sediments? J. Paleolimnol.
**2004**, 32, 287–299. [Google Scholar] [CrossRef] [Green Version] - Perie, C.; Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci.
**2008**, 88, 315–325. [Google Scholar] [CrossRef] - Federer, C.A.; Turcotte, D.E.; Smith, C.T. The organic fraction–bulk density relationship and the expression of nutrient content in forest soils. Can. J. For. Res.
**1993**, 23, 1026–1032. [Google Scholar] [CrossRef] - Keeley, J. Ecology and evolution of pine life histories. Ann. For. Sci.
**2012**, 69, 445–453. [Google Scholar] [CrossRef] [Green Version] - Gillon, D.; Houssard, C.; Valette, J.; Rigolot, E. Nitrogen and phosphorus cycling following prescribed burning in natural and managed Aleppo pine forests. Can. J. For. Res.
**1999**, 29, 1237–1247. [Google Scholar] [CrossRef] - Certini, G.; Nocentini, C.; Knicker, H.; Arfaioli, P.; Rumpel, C. Wildfire effects on soil organic matter quantity and quality in two fire-prone Mediterranean pine forests. Geoderma
**2011**, 167, 148–155. [Google Scholar] [CrossRef] - Mastrolonardo, G.; Francioso, O.; Di Foggia, M.; Bonora, S.; Rumpel, C.; Certini, G. Application of thermal and spectroscopic techniques to assess fire-induced changes to soil organic matter in a Mediterranean forest. J. Geochem. Explor.
**2014**, 143, 174–182. [Google Scholar] [CrossRef] - Hartford, R.; Frandsen, W. When It’s Hot, It’s Hot. Or Maybe It’s Not! (Surface Flaming May Not Portend Extensive Soil Heating). Int. J. Wildland Fire
**1992**, 2, 139–144. [Google Scholar] [CrossRef] - Almendros, G.; González-Vila, F.J. Wildfires, soil carbon balance and resilient organic matter in Mediterranean ecosystems: A review. Span. J. Soil Sci.
**2012**, 2, 8–33. [Google Scholar] [CrossRef] - Pérez-Gorostiaga, P.; Vega, J.A.; Fonturbel, T.; Fernández, C.; Jimenez, E. Efectos de la Severidad del Fuego Forestal en el Suelo Sobre la Germinación y Supervivencia Inicial de Plántulas de Pinus Pinaster Ait. en Galicia; Junta de Castilla y León, E.S.E.C.F., Ed.; 5o Congreso Forestal Español Sociedad Española de Ciencias Forestales: Ávila, Spain, 2009; pp. 1–10. [Google Scholar]
- Madrigal, J.; Hernando, C.; Guijarro, M.; Vega, J.A.; Fonturbel, T.; Pérez-Gorostiaga, P. Smouldering fire-induced changes in a Mediterranean soil (SE Spain): Effects on germination, survival and morphological traits of 3-year-old Pinus pinaster Ait. Ecology
**2009**, 208, 279–292. [Google Scholar] [CrossRef] - Huang, X.; Rein, G.; Chen, H. Computational smoldering combustion: Predicting the roles of moisture and inert contents in peat wildfires. Proc. Combust. Inst.
**2015**, 35, 2673–2681. [Google Scholar] [CrossRef] [Green Version] - Reardon, J.; Hungerford, R.; Ryan, K. Factors affecting sustained smouldering in organic soils from pocosin and pond pine woodland wetlands. Int. J. Wildland Fire
**2007**, 16, 107–118. [Google Scholar] [CrossRef] - Miyanishi, K.; A Johnson, E. Process and patterns of duff consumption in the mixedwood boreal forest. Can. J. For. Res.
**2002**, 32, 1285–1295. [Google Scholar] [CrossRef] - Finér, L.; Jurgensen, M.; Palviainen, M.; Piirainen, S.; Page-Dumroese, D.S. Does clear-cut harvesting accelerate initial wood decomposition? A five-year study with standard wood material. For. Ecol. Manag.
**2016**, 372, 10–18. [Google Scholar] [CrossRef] [Green Version] - Kreye, J.K.; Varner, J.M.; Dugaw, C.; Cao, J.; Szecsei, J.; Engber, E.A. Pine cones facilitate ignition of forest floor duff. Can. J. For. Res.
**2013**, 43, 512–516. [Google Scholar] [CrossRef] [Green Version] - Huang, X.; Restuccia, F.; Gramola, M.; Rein, G. Experimental study of the formation and collapse of an overhang in the lateral spread of smouldering peat fires. Combust. Flame
**2016**, 168, 393–402. [Google Scholar] [CrossRef] [Green Version] - Prat-Guitart, N.; Rein, G.; Hadden, R.M.; Belcher, C.M.; Yearsley, J. Effects of spatial heterogeneity in moisture content on the horizontal spread of peat fires. Sci. Total Environ.
**2016**, 572, 1422–1430. [Google Scholar] [CrossRef] - Hille, M.; Ouden, J.D. Fuel load, humus consumption and humus moisture dynamics in Central European Scots pine stands. Int. J. Wildland Fire
**2005**, 14, 153. [Google Scholar] [CrossRef] - Moriondo, M.; Good, P.; Durão, R.; Bindi, M.; Giannakopoulos, C.; Corte-Real, J.A.M. Potential impact of climate change on fire risk in the Mediterranean area. Clim. Res.
**2006**, 31, 85–95. [Google Scholar] [CrossRef] - Batllori, E.; Parisien, M.-A.; Krawchuk, M.A.; Moritz, M.A. Climate change-induced shifts in fire for Mediterranean ecosystems. Glob. Ecol. Biogeogr.
**2013**, 22, 1118–1129. [Google Scholar] [CrossRef] - Lloret, F. Canvi Global i Règim d’Incendis a Catalunya. En «Què Hem Après Dels Grans Incendis Del 1994» XI Jornada CREAF SCB ICHN. CREAF; Societat Catalana de Biologia. Institució Catalana d’Història Natural: Barcelona, Spain, 2014. [Google Scholar]
- De Marco, A.; Gentile, A.E.; Arena, C.; De Santo, A.V. Organic matter, nutrient content and biological activity in burned and unburned soils of a Mediterranean maquis area of southern Italy. Int. J. Wildland Fire
**2005**, 14, 365–377. [Google Scholar] [CrossRef] - Pinol, J.; Castellnou, M.; Beven, K.J. Conditioning uncertainty in ecological models: Assessing the impact of fire management strategies. Ecol. Model.
**2007**, 207, 34–44. [Google Scholar] [CrossRef] - Miralles, M.; Kraus, D.; Molina, D.M.; Loureiro, C.; Delogu, G.; Ribet, N.; Vilalta, O. Improving Suppression Fire Capacity. Towards Integrated Fire Management—Outcomes of the European Project Fire Paradox; Research Report, 23; Silva, J.S., Rego, F., Fernandes, P., Rigolot, E., Eds.; European Forest Institute: Joensuu, Finland, 2010; pp. 189–201. [Google Scholar]
- Calbo-Aicart, C. Estrategias De Resistencia a Sequía En Pinus Halepensis: Hacia Una Caracterización Fenotípica Integradora De La Variabilidad Intraespecífica Adaptativa; Forestales, E.S.E.d.C., Ed.; 6o Congreso Forestal Español: Vitoria-Gasteiz, Spain, 2013; pp. 1–12. [Google Scholar]
- Vericat, P.; Piqué, M.; Trasobares, A. Factores Ambientales Que Afectan Al Crecimiento De Las Cuatro Principales Especies De Coníferas En Cataluña; Forestales, E.S.E.d.C., Ed.; 6o Congreso Forestal Español: Vitoria-Gasteiz, Spain, 2013; pp. 1–13. Available online: https://doi.org/6CFE01-025 (accessed on 10 February 2017).

**Figure 1.**Land use change in the area affected by the 2016 Rocallaura Fires. Sampling area located with a point. Left: orthophoto of the study area as it is in 2015. Right: historical orthophoto of 1945–1946 (American flight series 45–46). Adapted from: Institut Cartogràfic i Geològic de Catalunya (ICGC) [28,29].

**Figure 2.**Images of the study area close to re-ignition spots. Left: tree distribution and understory overview. Right: organic horizon and moss cover.

**Figure 3.**Map indicating the perimeters of the area affected by the 2016 fires. Source: Catalan Fire Service.

**Figure 4.**Left: diagram showing the transects designed following the Rocallaura fire and the distances of each sampling point (D0, D8, D16, D24, D32, D40) and their slope. In green (T1, T2, T3), the transects located in the unburned zone and in orange (T4, T5, T6) those in the burnt zone. Right: soil and duff sampling on the field (top) and samples burned with loss-on-ignition (LOI) method (bottom).

**Figure 5.**Box plots showing the variability in the physicochemical properties of the duff layer (Oe + Oa) with distance along the transects (D0, D8, D16, D24 y D32): (

**a**) depth (cm); (

**b**) bulk density (BD (kg/m

^{3})); (

**c**) inorganic matter content (IM (%)); (

**d**) organic matter content (OM (%)). Error bars represent the variability of data and indicate the error or uncertainty in each case.

**Figure 6.**(

**a**) Relationship between the moisture content (MC) at an ignition probability of 50% (P

_{50}) and inorganic matter content (IM) R

^{2}= 0.846, SE = 8.11, N = 20. 6. (

**b**) Relationship between moisture content at an ignition probability of 50% and bulk density (BD): R

^{2}= 0.789, SE = 6.39, N = 20. Values expressed by distance (D

_{0}= 0 m, D

_{8}= 8 m, D

_{16}= 16 m, D

_{24}= 24 m, D

_{32}= 32 m, D

_{40}= 40 m) along the transect (T1, T2 and T3 located in the control zone, T4, T5 and T6 in the burned zone). All symbols and colours represent distances (D

_{x}) along the transect.

**Figure 7.**(

**a**) Linear regression (Table 6) between the moisture content (MC) at a 50% probability of ignition (P

_{50}), the bulk density (BD) and inorganic matter (IM). R

^{2}= 0.763, et = 0.24, N = 20. (

**b**) Relationship between moisture content at P

_{50}, bulk density and organic matter (OM). The triangles represent density and the points represent IM or OM, N = 20.

**Figure 8.**Relationship between the % of the sample consumed with distance along the transect to different percentages of humidity, N = 48. (

**a**) MC = 0%; (

**b**) MC = 5%. Blue: duff or organic stratum (Oe + Oa); green: control area 0–5 cm; and grey: post-fire zone at 0–5 cm.

**Figure 9.**Relationship between the % of the sample consumed with distance along the transect to different moisture levels, N = 15. (

**a**) MC = 25%; (

**b**) MC = 50% and (

**c**) MC = 100%.

**Figure 10.**(

**a**) Linear regression between the % of organic layer samples consumed (Cons

_{90}) according to % MC. R

^{2}= 0.919, SE = 2.89, N = 5. (

**b**) Quadratic regression curve between the % of organic layer samples consumed (Cons

_{60}) according to the % MC. R

^{2}= 0.969, SE = 6.44, N = 20.

**Figure 11.**Spatial variability of the spread rate with a fixed 25% moisture content and according to the bulk density (BD) of each simple. N = 15. T1, T2 and T3 are the transects.

Horizon | Depth (cm) | pH | Calcium Carbonate (%) | Fine Soil (%) | Organic Matter (%) | Inorganic Matter (%) | Total Carbon (%) | C/N Ratio | Density (kg/m^{3}) |
---|---|---|---|---|---|---|---|---|---|

O * | 1–12 | - | 2.0–6.7 | - | 44.4–80.8 | 19.2–55.6 | 25.0–41.3 | - | 48–191 |

A | 5–40 | 7.6–8.5 | 11.3–36.3 | 53–98 | 7.1–20.7 | 79.3–92.9 | 8.0–13.1 | 23–45 | 550–1570 |

R (Calcareous) | >40 | - | - | - | - | - | - | - | - |

**Table 2.**Comparison of mean values of soil properties with past studies of modelling organic soil combustion.

Study Site | Pinus resinosa [8] | Pinus ponderosa [12] | Commercial Peat [9] | |
---|---|---|---|---|

Depth (cm) | 4.98 | 5 | 4–8 | 6 |

IM (%) | 35.5 | 36.5 | 25 | 3 |

BD (kg/m^{3}) | 160 | 190 | 50–275 | 50–150 |

**Table 3.**Coefficients used for the probabilistic model [8].

B_{0} | B_{1} | B_{2} | B_{3} | |
---|---|---|---|---|

Pine duff (Seney) | 45.1778 | −0.3227 | −0.3644 | −0.362 |

**Table 4.**Linear regression model coefficients described by Prat-Guitart et al. [9].

A_{0} | A_{1} | A_{2} | A_{3} | ɛ |
---|---|---|---|---|

0.514 | −0.545 | −0.325 | 0.151 | 0.173 |

**Table 5.**Descriptive statistics of the mineral soils analyzed. Bulk density (BD), organic matter content (OM) and inorganic matter content (IM). For C (control zone) and F (post-fire zone) at 0–5 cm or 5–10 cm. Mean at 95%; N = 66.

Soil Properties | Samples | Mean | SD | Median | Variance | Min | Max |
---|---|---|---|---|---|---|---|

BD (kg/m^{3}) | C (0–5) | 737.76 | 143.55 | 702.89 | 20.61 | 546.29 | 949.77 |

C (5–10) | 1043.30 | 257.33 | 972.70 | 66.22 | 649.28 | 1567.90 | |

F (0–5) | 593.45 | 138.01 | 597.32 | 19.05 | 295.34 | 797.79 | |

F (5–10) | 851.19 | 87.66 | 869.18 | 7.68 | 689.50 | 1044.47 | |

OM (%) | C (0–5) | 15.73 | 3.00 | 16.02 | 9.01 | 11.82 | 20.68 |

C (5–10) | 11.16 | 2.66 | 11.55 | 7.08 | 7.14 | 17.36 | |

F (0–5) | 19.83 | 6.28 | 18.88 | 39.44 | 14.10 | 38.50 | |

F (5–10) | 13.29 | 1.39 | 12.92 | 1.93 | 10.74 | 16.34 | |

IM (%) | C (0–5) | 84.28 | 3.00 | 83.98 | 9.01 | 79.32 | 88.18 |

C (5–10) | 88.84 | 2.66 | 88.45 | 7.08 | 82.64 | 92.86 | |

F (0–5) | 80.17 | 6.28 | 81.12 | 39.44 | 61.5 | 85.90 | |

F (5–10) | 86.72 | 1.39 | 87.08 | 1.93 | 83.66 | 89.26 |

**Table 6.**Results of the analysis of the two linear regressions. (

**a**) Moisture (%) at P

_{50}of ignition and mineral content (IM (%)): R

^{2}= 0.846, N = 20; (

**b**) humidity (%) at P

_{50}and bulk density (BD (kg/m

^{3}): R

^{2}= 0.789, N = 20.

B | SE | t | p-Value | ||
---|---|---|---|---|---|

(a) | (Constant) | 141.432 | 8.110 | 17.440 | 0.000 |

IM (%) | −1.743 | 0.170 | −10.222 | 0.000 | |

(b) | (Constant) | 109.271 | 6.387 | 17.108 | 0.000 |

BD (kg/m^{3}) | −0.196 | 0.023 | −8.42 | 0.000 |

**Table 7.**Results of the linear regression analysis between the maximum P50 moisture limit, with the bulk density (BD) and the inorganic content (IM): R

^{2}= 0.763, N = 20.

B | SE | t | p-Value | |
---|---|---|---|---|

(Constant) | 1.499 | 0.122 | 12.312 | 0.000 |

IM (%) | –0.010 | 0.003 | −3.264 | 0.002 |

BD (kg/m^{3}) | –0.001 | 0.000 | −2.899 | 0.006 |

**Table 8.**Linear regression models of the percentage of samples consumed: (a) Cons

_{90}, R

^{2}= 0.919, N = 5 and; (b) Cons

_{60}according to soil moisture content (%), R

^{2}= 0.969, N = 20.

B | SE | t | p-Value | ||

(a) | Constant | 49.279 | 2.887 | 17.069 | 0 |

Moisture Content (%) | −1.207 | 0.207 | −5.839 | 0.01 | |

B | SE | t | p-Value | ||

(b) | Constant | 106.087 | 3.925 | 27.027 | 0.000 |

Moisture Content (%) | −0.475 | 0.192 | −2.478 | 0.024 | |

Moisture Content (%)^{2} | −0.008 | 0.002 | −3.918 | 0.001 |

**Table 9.**Descriptive statistics of the estimated spread rate (Vp) according to the moisture content (MC) in the duff layer or organic stratum of the control area. N = 15.

Vp (cm/h) | Variance | Std. Dev | Min. | Max. | |
---|---|---|---|---|---|

5% MC | 4.08 | 0.13 | 0.36 | 2.99 | 4.41 |

10% MC | 3.84 | 0.11 | 0.34 | 2.84 | 4.14 |

25% MC | 3.21 | 0.07 | 0.26 | 2.42 | 3.44 |

50% HMC | 2.38 | 0.03 | 0.17 | 1.86 | 2.53 |

100% MC * | 1.30 | 0.00 | 0.07 | 1.10 | 1.36 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Xifré-Salvadó, M.À.; Prat-Guitart, N.; Francos, M.; Úbeda, X.; Castellnou, M.
Smouldering Combustion Dynamics of a Soil from a *Pinus halepensis* Mill. Forest. A Case Study of the Rocallaura Fires in Northeastern Spain. *Appl. Sci.* **2020**, *10*, 3449.
https://doi.org/10.3390/app10103449

**AMA Style**

Xifré-Salvadó MÀ, Prat-Guitart N, Francos M, Úbeda X, Castellnou M.
Smouldering Combustion Dynamics of a Soil from a *Pinus halepensis* Mill. Forest. A Case Study of the Rocallaura Fires in Northeastern Spain. *Applied Sciences*. 2020; 10(10):3449.
https://doi.org/10.3390/app10103449

**Chicago/Turabian Style**

Xifré-Salvadó, Miquel Àngel, Núria Prat-Guitart, Marcos Francos, Xavier Úbeda, and Marc Castellnou.
2020. "Smouldering Combustion Dynamics of a Soil from a *Pinus halepensis* Mill. Forest. A Case Study of the Rocallaura Fires in Northeastern Spain" *Applied Sciences* 10, no. 10: 3449.
https://doi.org/10.3390/app10103449