Unravelling the Effect of Climate Change on Fire Danger and Fire Behaviour in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)

doi:10.21203/rs.3.rs-1150761/v1

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Research Article

Unravelling the Effect of Climate Change on Fire Danger and Fire Behaviour in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)

https://doi.org/10.21203/rs.3.rs-1150761/v1

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The impacts of wildfires are increasing in the Mediterranean Basin due to more extreme fire seasons that often overwhelm the response capacity of fire suppression forces. In this study, we quantified the effect of climate change on fire danger (components of the Canadian FWI System) and wildfire behaviour characteristics (rate of spread and fireline intensity) for the four major Mediterranean forest ecosystems in the transboundary biosphere reserve of the Meseta Ibérica under RCP4.5 and RCP8.5 scenarios. The effect of climate change on wildfire behaviour was also complemented by considering changes in net primary production (NPP), and hence available fuel. Our results show that the meteorological fire season will start earlier and end later, leading to a significant increase in the number of days with weather conditions that promote large wildfires. For fire behaviour, the most relevant changes will occur in pine forests, where a wildfire with median fireline intensity will offer serious resistance to control from spring to autumn. The severity of fire behaviour in shrublands also increases substantially when considering climate change, with high-intensity wildfires potentially occurring in any time of the year. Both deciduous and evergreen broadleaves forests are predicted to typically generate wildfires that remain within suppression capability. By adjusting NPP to future climate conditions, the fireline intensity may not increase during summer in deciduous and evergreen broadleaves forests, and can even be significantly reduced in shrublands. This study suggests that improved fire planning and management of wildfire-prone landscapes will counteract the effect of climate change on fire behaviour and impacts.

Forest Fire

Fuel Hazard

Fire Suppression

Climate Change

Landscape Planning

The occurrence of wildfires is greatly dependent on weather and climate conditions. Particularly, high temperatures, moderate annual precipitation and extended periods of drought are globally associated with the greatest wildfire burned areas (Aldersley et al. 2011). The latest Intergovernmental Panel on Climate Change (IPCC) report suggests that weather conditions driving wildfires have become more frequent over the last decades in many regions of the world, including southern Europe (IPCC, 2021). As a result, wildfires at the high end of the fire weather and fire behaviour range, which pose extraordinary challenges to firefighting regardless of the available resources (Fernandes et al. 2016), are increasing in size and/or frequency (Williams et al. 2019; Moreira et al. 2020; Abram et al. 2021). Recently, wildfires have had drastic consequences for human lives and livelihoods and ecosystems across the planet, from Europe (San-Miguel-Ayanz et al. 2020) and Russia (Ponomarev et al. 2015), to Australia (Nolan et al. 2020) and North (Brown, 2020; Mass and Ovens, 2019) and South America (de la Barrera et al. 2018; Lizundia-Loiola et al. 2020), which is also reflected in a major rise in wildfire-related fatalities throughout Southern Europe from the late 1970s onwards (Molina-Terrén et al. 2019). Even in the absence of fatalities or injuries, other medium and long-term effects on human health and society are often reported following large wildfires (Bowman et al. 2017).

Climate and weather conditions that promote large wildfires are common in Mediterranean Europe. The Mediterranean region is affected by recurring wildfires and, on average, about half-million hectares burn every year (San-Miguel-Ayanz et al. 2019). Such extent of burned area results from the combination of fire-prone weather conditions and ongoing rural abandonment and depopulation, which decrease land management practices that reduce fuel biomass (such as farming, grazing and forest management) and diminish landscape heterogeneity. This in turn leads to an expansion of forest and shrubland and consequently higher fuel accumulation and connectivity, i.e potentially higher fire intensity (Pausas and Fernández-Muñoz, 2012; Fernandes et al. 2014; Viedma et al. 2015; Moreira et al. 2020). Together, both factors, plus the expansion of wildland-urban interfaces, originated a complex and challenging scenario for wildfire management (Lahaye et al. 2018; Moreira et al. 2020).

The Mediterranean Basin is a hot spot of climate change (de Sherbinin, 2014). Regional climate change projections highlight warmer and drier climates throughout the region, which will significantly increase future fire danger, especially in late spring and early autumn (Calheiros et al. 2021). This is expected to lead to changes in fire regimes, with higher fire danger, and a longer and more severe fire season (Pereira et al. 2002; Turco et al. 2014; Ruffault et al. 2020; Calheiros et al. 2021), even if the Paris agreement efforts to limit the temperature increase to 1.5°C are successful (Turco et al. 2018). Such harsh conditions may decrease the effectiveness and opportunities for wildfire suppression (Mitsopoulos et al. 2016), pushing local authorities far beyond their response capacities (Castellnou et al. 2019). Nonetheless, climate-induced changes in fire behaviour might be limited by fuel availability (Dupuy et al. 2020).

Most fire regimes components, as well as the impacts of individual events, are a manifestation of fire behaviour characteristics, e.g. rate of spread and fireline intensity, as driven by the fire environment, i.e. the combination of fuel, weather and terrain influences. If climate change is expected to modify fire danger, then it will influence fire behaviour. However, despite the great focus of research on fire danger in southern Europe under climate change (Dupuy et al. 2020), including its effect on the burned area (a direct outcome of fire behaviour characteristics), very few studies analysed climate change effects on wildfire behaviour, which is acknowledged to be more important consequence of climate change than simply total burned area (Kolden, 2020; Moreira et al. 2020). Moreover, these applications focus more on the spatiality of fire behaviour change than on temporal change. This means that a single set of meteorological conditions is used to represent a future fire season, often for a period of 30 years (e.g. Kalabokidis et al. 2015; Lozano et al. 2017). To our knowledge, only a study (Matthews et al. 2012) assessed the temporal change in wildfire behaviour under climate change scenarios. We aim to fill this gap by characterizing the monthly effect of climate change in both fire danger and wildfire behaviour metrics for four broad Mediterranean ecosystems types and under two widely used climate change scenarios, RCP4.5 and RCP8.5. We specifically studied the region of the Meseta Ibérica, a Transboundary Biosphere Reserve located between Portugal and Spain and where wildfire incidence is significant.

Study area

The target region comprises a total area of 4444.25 km², located in a borderland between Portugal and Spain, in the Transboundary Biosphere Reserve of Meseta Ibérica (Fig. 1). Topography varies widely, from mountains in the north (maximum elevation of ca. 2500 m) to flatter, undulated terrain in the south (minimum elevation of 0 m). Climate is characterized by the prevalence of Csb (dry and warm summer) climate, with a few areas with a Csa (dry and hot summer) climate under the Koppen-Geiger classification (Beck et al. 2018). At the end of the century, considering the RCP8.5 scenario, the Csa climate type will expand to the entire study area (Beck et al. 2018). Annual precipitation varies considerably over the territory, from around 1200 mm year^{− 1} to 700 mm year^{− 1} following a north-south decreasing gradient. Temperature varies along the same gradient, from an annual average of approximately 12°C in the northern mountainous areas to 17°C in the southernmost areas.

The region went through has suffered significant socioeconomic changes over the last several decades, leading to depopulation and abandonment of marginal agricultural land. This, together with mountain afforestation programs in the mid-20th century, led to an increase in the number of wildfires and the burned area from 1990 to the mid-2000′s, with a small decline in the last decade (ICNF, 2015). A joint effect of changes in land use and land cover, topography and vegetation type and structure were found to affect fire behaviour in the area (Azevedo et al. 2011; ; Silva et al. 2011; Magalhães et al. 2017). Depending on latitude, elevation and human agency, the region is dominated by four distinct woody vegetation types that are broadly representative of those occurring across the Mediterranean Basin, respectively pine forests, evergreen sclerophyllous oak woodlands and forests, deciduous forests, and a range of shrubland communities.

Monthly burned area distribution in the study area is concentrated mainly from July to September (Fig. 7). Another period of relevant wildfire activity can be detected in February and April, mainly due to agricultural stubble burning and for pastures renewal. Typically, the fires occurring in pasture and agricultural lands are smaller than the ones occurring in shrublands and forest areas, restricted mainly to the summer period.

Climate scenarios and data

The future climate was characterized under two distinct climate change scenarios, RCP4.5 and RCP8.5. The former describes a radiative forcing of ≈ 4.5 W m⁻² (≈ 650 ppm CO₂ eq.), implying an increase in global mean surface air temperature of 1.8°C (1.1–2.6°C) by the end of the century relative to the historical period of 1986–2005 (Collins et al. 2013). RCP8.5 describes a high-end emission scenario (van Vuuren et al. 2011) radiative forcing of ≈ 8.5 Wm⁻² (≈ 1370 ppm CO₂ eq.), with a projected increase in global mean surface air temperature of 3.7°C (2.6–4.8°C) in 2100 relative to the same historical period (Collins et al. 2013). The inclusion of more than one climate scenario is useful to assist in the decision-making process (Pedersen et al. 2020).

The following atmospheric variables were selected to compute the fire danger indices used herein: daily total precipitation, daily minimum, mean and maximum temperature, daily minimum, mean and maximum relative humidity and daily mean wind speed. Both observational and simulated data generated by climate models were retrieved within the target region. The ERA5-Land reanalysis dataset (Muñoz-Sabater et al. 2021) was used to characterize the historic period (1989–2005). The climate model simulations were considered for the historic (1989–2005) and future (2031–2050) periods, under RCP4.5 and RCP8.5. A 4-member ensemble of Global Climate Model (GCM) – Regional Climate Model (RCM) chains was provided by the EURO-CORDEX project (https://www.euro-cordex.net/). The present study used the variables Near-Surface Temperature, Near-Surface specific Humidity, Precipitation, Near-Surface Wind Speed, Eastward Near-Surface Wind, Northward Near-Surface Wind, Daily Maximum Near-Surface temperature and Daily Minimum Near-Surface temperature from the driving models: MPI-M-MPI-ESM-LR, IPSL-IPSL-CM5A-MR, ICHEC-EC-EARTH and CNRM-CERFACS-CNRM-CM5. Each driving model represents an institute and an RCM model (Table 1). For these projections, the time-frequency is daily, the ensemble is r1i1p1 and the experiments were carried out for the historical, RCP4.5 and RCP8.5 anthropogenic forcing.

Table 1

List of regional climate models (RCM) and respective institute and driving model, used in this study (EURO-CORDEX Data, 2020; Fraga et al. 2020)
Institute	Driving Model	RCM model
CLMcom	MPI-M-MPI-ESM-LR	CCLM4-8-17
IPSL-INERIS	IPSL-IPSL-CM5A-MR	WRF331F
KNMI	ICHEC-EC-EARTH	RACMO22E
SMHI	CNRM-CERFACS-CNRM-CM5	RCA4

Daily temperatures and precipitation were bias-corrected at the daily timescale using the quantile-mapping approach and the observation-based E-OBS gridded dataset version 22.0e (Cornes et al. 2018) as a baseline in the historic period (1989–2005). For each model, the same bias correction was applied to both the historic and future periods. The raw climate model data were defined over a 0.11° latitude × 0.11° longitude (~10 km) and were re-gridded, by bilinear interpolation, to the same grid as ERA5-Land and E-OBS, at a native spatial resolution of 9 km (0.10° latitude × 0.10° longitude), thus warranting the overlap of grid boxes from all datasets and variables.

Future changes in the climate variables were calculated by computing the anomalies for each location between future monthly values and the median observed value in the reference period. Fire danger and fire behaviour were calculated for the entire EURO-CORDEX grid within the study area, per climate model and scenario. For each day in the temporal series, we calculated the percentile 95 and the median of the study area to represent the fraction of the study area with the highest fire danger and potentially exhibiting a more dangerous wildfire behaviour and the median hazard in the region, respectively. We then calculated the monthly value of fire danger and fire behaviour for each climate model and scenario. We used the median monthly value to ensemble the different metrics.

Fire danger and wildfire behaviour characteristics

The Canadian Forest Fire Weather (FWI) Index System (van Wagner, 1987) was evaluated as the preferred method for fire danger rating in the Mediterranean region, particularly for the summer season (Viegas et al. 1999) and it has become the standard to assess fire danger at regional to global scales, including in climate change applications (Bedia et al. 2015; Abatzoglou et al. 2019). We evaluated the influence of climate change in the Fire Weather Index (FWI) and its two major sub-indices. These indices are presented and characterized below (van Wagner, 1987):

The Initial Spread Index (ISI), that represents the atmospheric effect in fire-spread rate.
The Buildup Index (BUI), that reflects the drought effect on the amount of fuel available for combustion.
The Fire Weather Index (FWI), a combined outcome of the ISI and the BUI that rates potential fire intensity and is often used by fire management agencies to assess fire danger conditions in the near future.

The FWI codes are calculated from noon readings of air temperature and relative humidity at a 2-m height, open wind speed at 10 m, and 24-hour rainfall. However, because of the study emphasis on potential fire behaviour rather than on fire danger conditions per se, calculation of the indices with an atmospheric component (FFMC, ISI, FWI) was based on daily maximum temperature and minimum relative humidity to capture the peak burning conditions associated to minimum fine dead fuel moisture content. The alternative would be to use daily means of input variables, which is not recommended as it induces a negative bias on FWI estimation (Herrera et al. 2013).

The FWI varies greatly between regions (or ecozones), with FWI values associated with the occurrence of large wildfires and distinct fire activity levels differing between locations (Amiro et al. 2004). Recently, Fernandes (2019) defined FWI thresholds associated with large wildfires in each administrative region (Distrito) in Portugal. For the study area, mainly located in the Bragança distrito (nearly 60% of the study area), wildfires that burn ≥500 ha are associated with an FWI of 79.9, whereas wildfires that burn ≥1000 ha are associated with an FWI of 90.1. Here, we used these FWI values to calculate the number of days above each of these thresholds for the historical and future climate (both under RCP4.5 and RCP8.5).

In addition to the fire danger indices, we calculated fire behaviour characteristics, namely rate of spread and fireline intensity in common Mediterranean vegetation covers. This need comes from the fact that FWI does not consider important factors that affect fire ignition and spread (and consequently fire danger), such as vegetation cover. ROS and FLI are defined as follows:

Rate of Spread (ROS) is the linear rate of advance of the fire front. ROS determines the amount of resources needed to contain a wildfire and is important for shaping fireline intensity and consequently, suppression difficulty and firefighter safety (Sullivan and Gould, 2018)
Fireline Intensity (FLI) is the amount of heat released per unit time and unit length of the fire front (kW m^-1). FLI is a major driver of aboveground fire effects and is often used as a proxy for fire suppression difficulty (Alexander and Cruz, 2018).

Fire behaviour modelling

We considered the four generic vegetation types that prevail in the region, respectively long-needle pine forest (Pinus pinaster, P. pinea), broadleaved deciduous forest (Quercus pyrenaica, Castanea sativa), broadleaved evergreen forest (Quercus suber, Q. ilex), and shrubland dominated by Erica, Cytisus, Pterospartium and Cistus species. The forest types were assigned fuel models from the Portuguese catalogue (Cruz and Fernandes, 2008; Fernandes, 2009) for the purpose of fire behaviour simulation, respectively M-PIN, M-CAD and M-ESC. These fuel models assume surface fuel complexes composed of litter and shrubs and their development included parameterization based on field observations of fire behaviour characteristics.

The forward (maximum) spread rate of a surface fire was simulated with the model of Rothermel (1972) for flat terrain, which depicts landscape fire spread better (Sullivan et al. 2014), and for baseline live fuel moisture contents for each forest type (Fernandes, 2009); because the effect of live fuel moisture content in the model is a mathematical artefact (Catchpole and Catchpole, 1991) the estimates were subsequently adjusted as per Rossa and Fernandes (2018). Live fuel moisture content was estimated from the Drought Code (DC) of the Canadian FWI System and an ensemble of representative species in the region (Viegas et al. 2001). The simulations also require “midflame” wind speed, i.e. ~2-m wind speed, and the moisture content of fine dead fuels, estimated respectively from 10-m open wind speed and the Fine Fuel Moisture Code (FFMC) of the Canadian FWI System (van Wagner, 1987), and then adjusted for vegetation type (Fernandes, 2009; Pinto and Fernandes, 2014; Andrews, 2018). Calculation of fire-spread rate for shrubland (SHRUBL) followed the same procedures but was based on the robust empirical model of Anderson et al. (2015). Surface FLI was calculated as the product of ROS, heat of combustion (assumed constant at 18,000 kJ kg⁻¹) and fuel consumption (Byram, 1959; Alexander, 1982); the latter was estimated as an asymptotic function of the Duff Moisture Code (DMC) of the Canadian FWI (Forestry Canada Fire Danger Group, 1992; Fernandes and Loureiro, 2013). Assessment of surface fire transition to a crown fire and the type of crown fire (active or passive) followed Van Wagner (1977), but assuming that deciduous broadleaved forest does not support active crowning (Alexander and Cruz, 2006; de Groot et al. 2013). Crown fire rate of spread adopted the model of Cruz et al. (2005) for pine forest and assumed Anderson et al. (2015) in broadleaved evergreen forest. Surface and crown fire spread were weighted as a function of crown fraction burned (van Wagner, 1993) to calculate the overall fire-spread rate and FLI. Typical (median) values per vegetation type for the fuel and stand descriptors required by the calculations were computed from the national forest inventory plots located within the study region (Fernandes et al. 2019).

The influence of climate change in fire behaviour is presented by only considering the weather influence in ROS and FLI, and also by considering the potential effect that climate has over the biomass (i.e. the amount of fuel available to burn). To represent potential changes in fuel consumption during a wildfire due to climate change, we adjusted the Net Primary Production (NPP). NPP was calculated for each grid point, according to the global equations presented in Del Grosso et al. (2008) based on empirical non-linear relationships with temperature and precipitation. NPP was calculated differently for tree-dominated vegetation, i.e. forest, and for shrubland, respectively from precipitation and temperature and precipitation only.

Climate change and fire danger

Climate analysis

The analysis of future climate, namely in maximum temperature, precipitation, wind speed and minimum relative humidity, reveals significant changes in the short/medium term (p-value ≤ 0.05, except for wind speed; Fig. 2). The maximum temperature is expected to increase every month, with the highest increase being projected for summer and adjacent months (median increase of ca. + 5°C). Precipitation is projected to decrease throughout the year, with the highest decrease occurring during winter and autumn (median decrease of approximately 100 mm). Regarding the projected changes in wind speed, no particular trend can be identified, with null median monthly changes. The minimum relative humidity is projected to decrease every month, with the highest decreases occurring during autumn. The minimum relative humidity is also projected to decrease in the median between 15 and 10% during summer and adjacent months.

Initial Spread Index (ISI)

Figure 3 (top left) shows historical monthly and projected ISI. Historically, the highest values of ISI are restricted to late spring and the summer months (June, July, August and September). When considering future climate, both scenarios point to an increase in the median in all months, with the highest absolute increase in ISI occurring between May and October. Moreover, by mid-century, the months of April, May and October show an ISI value close to the historical summer values.

Buildup Index (BUI)

Historical and future BUI are represented in Fig. 3 (top right). Historically, the monthly BUI values for the period peak during summer, with the highest values occurring in August. However, under climate change scenarios, BUI is projected to increase every month, with the highest absolute increases occurring from June to October. In the mid-century period and under both climate scenarios, May, June and October reveal medians similar (when not greater) to those in the current summer months.

Fire Weather Index (FWI)

Figure 3 (bottom left) shows the monthly FWI for the historical period and climate change scenarios. For the historical period, the pattern is the same as previously identified, with the highest values limited to the summer months (July, August and September). Under both climate scenarios, the most relevant increases in FWI are projected to occur from May to October, mainly in the spring and autumn (despite a general increase throughout the year). This increase in fire danger results from the increase in both its atmospheric (ISI) and drought (BUI) components.

Finally, we quantified the number of days per year with the FWI associated with wildfires larger than 500 ha (FWI ≥ 79.9). Historically, fire danger associated with ≥500-ha wildfires is restricted to the summer (July, August and September – Fig. 3, bottom right). Under the climate scenarios RCP4.5 and RCP8.5, we project a 10-fold increase in the frequency of extreme FWI values, particularly due to an increase in the frequency of extreme FWI in the summer and adjacent months. This implies that, while in the historical period an FWI associated with ≥500-ha wildfires would occur once every 5 years (on average), in the future (2031-2050, for both RCP4.5 and RCP8.5) this same event may occur almost twice a year.

Climate change and fire behaviour

Rate of fire spread

Figure 4 depicts the monthly rate of fire spread for the different fuel models considered, under historical and future climate. As expected, ROS varies greatly amongst vegetation types: shrubland (SHRUBL) and pine forest (M-PIN) exhibit the highest values, while broadleaves, either deciduous (M-CAD) or evergreen (M-ESC) show less extreme values. ROS also shows pronounced intra-annual variability, with the highest ROS values occurring in summer months (July, August and September) for all fuel types. An increase in ROS is projected for all fuel model types under both climate change scenarios. M-PIN shows the highest dispersion of values, mainly in and around summer months, reflecting high variability. SHRUBL also shows an increase in ROS, more pronounced during spring and summer. M-CAD and M-ESC show a small increase in ROS in all months. Nevertheless, M-ESC displays a great number of outliers during summer that suggests some potential to occasionally exhibit extreme ROS values. The Wilcoxon Signed-Rank Test showed a significant ROS increase for all months and both climate scenarios when compared to the historical period (p ≤ 0.05). Monthly ROS is not statistically different between future climate scenarios (p > 0.05).

Fireline intensity

Figure 5 shows the monthly FLI for the historical period (1989-2005) and future climate conditions in the most fire-prone locations of the study area, without the adjustment in NPP (and consequently in the amount of available fuel). Historically, FLI is highly variable between vegetation types. FLI also shows a great intra-annual variance, with the highest values occurring always during summer, regardless of fuel type. Under historical fire danger, fire intensity is distinctively higher in shrubland (SHRUBL). In this fuel type, a wildfire can create problems to suppression during almost the entire year. M-PIN also shows the potential to generate wildfire that can pose a challenge to firefighting operations, mainly during summer (July, August and September). M-CAD and M-ESC are the most benign fuel types, as they typically do not generate wildfires intense enough to compromise effective wildfire suppression, even in summer.

The effect of climate change (under both scenarios) in FLI will lead to significant changes in all months when compared to historical values (p≤0.05). The most relevant changes are found for M-PIN during summer. Moreover, wildfires challenging suppression operations in M-PIN are no longer typically restricted to summer and become more frequent in spring (May and June) and autumn (October). These latter months are also more variable when compared to the historical FLI. Extreme fire behaviour is expected in SHRUBL throughout the year, and wildfires of ≥4000 kW m⁻¹ will potentially occur in any month. M-ESC shows an important increase in FLI, namely during the summer months, when the 4000 kW m⁻¹ threshold is frequently crossed. On the other hand, M-CAD shows the highest resistance to climate change, with the 75th percentile always below that same threshold. This means that the typical wildfire in the M-CAD fuel type will not imply ineffective fire suppression in any of the climate change scenarios considered.

Figure 6 is similar to Fig. 5, but now considering changes in NPP and thus in the amount of available fuel. When this adjustment is undertaken, all fuel types exhibit a smaller increase in FLI than when no adjustment in NPP is carried out. M-PIN shows the highest consistency from all models, with a significant increase in FLI in almost all months (p ≤ 0.05, except for May). For M-PIN, the increase in FLI is particularly relevant during May, June and October, with the projected FLI frequently above the 4000 kW/m threshold. Furthermore, a relevant increase in FLI is observed also in May, June, September and October, which points to more frequent extreme fires. Regarding M-CAD and M-ESC, both fuel types hint at a significant increase in FLI during most of the year, particularly in early spring and autumn. Nevertheless, FLI in both fuel types is not expected to increase during June and summer; p>0.05). Lastly, shrubland shows the greatest change in the future pattern of FLI when adjusting the NPP (compared to not considering changes in NPP). No significant changes in FLI from November to April (p>0.05) occur when adjusting NPP. Nonetheless, FLI decreases significantly in the May to October period when compared with historical FLI (p≤0.05). This is possibly due to the large decrease in precipitation projected for the study region, which is the only meteorological factor on which shrubland NPP is dependent.

A similar analysis considering the median of the study area instead of the most fire-prone locations (i.e., the 95th percentile) is provided herein as Supplementary Material (Fig. 8, 9, 10).

This study improves the current understanding of the potential impacts of climate change on future wildfire potential in typical southern Europe vegetation types, namely through fire behaviour modelling. Vast research had been conducted on the influence of climate change on fire danger prior to this study (e.g. Turco et al. 2014; Ruffault et al. 2020; Calheiros et al. 2021). Nevertheless, the lack of studies assessing the influence of climate change on wildfire behaviour is manifest. In the present study, we specifically address this gap by evaluating the potential influence of climate change on the monthly variability of wildfire behaviour characteristics.

Our analysis of the climate variables (maximum temperature, precipitation, wind speed and minimum relative humidity) revealed significant changes in their monthly values (apart from wind speed). The change to warmer and drier years, particularly during and around summer, is supported by previous studies (Andrade et al. 2014; Soares et al. 2015). As a consequence of the change in these variables, an FWI increase is projected, owing to an increase of both its atmospheric component and its drought component. FWI is indeed projected to increase every month, mainly during late spring, summer and early autumn, with a 10-fold increase in the frequency of FWI associated with wildfires larger than 500 ha in the region.

Following the projected strengthening in fire danger, a significant increase in wildfire behaviour is expected. Here we show that the ROS and FLI increase significantly for all the fuel models considered. Despite such increment, fuel type shapes how wildfire spread will unfold. According to the difficulty of suppression presented by Alexander and Cruz (2018), a median wildfire occurring between June and September during the historical period in pine forest may challenge fire suppression, while shrubland fires would present the same difficulties throughout the year. Both of these fuel types are known to generate high-intensity wildfires (Lecina-Diaz et al. 2014; Anderson et al. 2015). Shrub fuels are especially flammable and can sustain extreme rates of spread and fire intensities, even at moderate fire danger levels (Fernandes, 2001; De Luis et al. 2004). On the other hand, a median wildfire occurring in the broadleaved forest during the historical period would neither lead to fast nor high-intensity wildfire, which is consistent with burn severity studies (Fernandes et al. 2010; Saulino et al. 2020). This is because of intrinsic litter properties, either high compactness or fast decomposition (Varner et al. 2015), combined with shading levels that constrain flammability directly through micrometeorology and indirectly by selecting for mesophytic species in the understory (e.g. Nowacki and Abrams, 2008).

When considering climate change scenarios without the NPP adjustment resulting from changes in temperature and precipitation, and hence the amount of fuel, ROS and FLI significantly increase every month, for all the selected fuel types. The most relevant changes are projected to occur for pine forest, where a median wildfire in the period 2031-2050 can offer serious resistance to control from spring (May and June) to autumn (October). The magnitude of fire behaviour in shrublands also increases substantially, with high-intensity wildfires potentially occurring in any month. Similar results (increase in the intensity of the fires that exceed the capacity of suppression) were also obtained for the Canadian boreal forest (Wotton et al. 2017). Regarding deciduous and evergreen broadleaves, both are typically (median) predicted to generate wildfires with intensity low enough to be within suppression capability (except for the M-ESC type in August). This indicates that an expanded presence of broadleaved forest in the landscape, especially deciduous, would contribute to mitigating the consequences of climate change on wildfire activity and severity. However, climate change in the study region will also diminish the presence of the biophysical envelope under which those species thrive (Costa et al. 2017).

By adjusting the NPP and, consequently, the amount of fuel available to burn, our results highlight a different pattern than that previously discussed. The FLI in M-ESC and M-CAD may not increase during the summer months, and can even be significantly reduced in SHRUBL due to the underlying drier and warmer conditions throughout the year. On the other hand, M-PIN shows a significant increase in FLI in almost all months, even with less fuel amount to burn. Overall, either when considering changes in NPP and fuel available to burn or not, we show that fuel types will exhibit a more severe behaviour during spring and early autumn. This may lead not only to an expansion of the fire season (Calheiros et al. 2021) but also to the occurrence of extreme wildfire events (e.g. where ground-based suppression is not effective) in spring and autumn, namely in M-PIN and SHRUBL fuel types. Moreover, such reductions in biomass production may also suggest future problems with forest resilience and regeneration after a wildfire (Halofsky et al. 2020; Pontes-Lopes et al. 2021), promoting a transition to shrublands (Acácio et al. 2009) and, hence, increase the fire intensity and ROS.

Our findings show that the amount of biomass (i.e. fuel available to burn) should be considered when projecting future changes in fire behaviour. We acknowledge that various limitations can further contribute to the (already relevant) uncertainty of climate projections and scenarios (Hausfather and Peters, 2020; Peters et al. 2020; Pedersen et al. 2021), such as assuming a linear relationship between climate and NPP and biomass production (see e.g. Duveneck and Thompson, 2017). Notwithstanding, our results reveal that for the study area and period analysed none of the wildfire behaviour characteristics significantly differ between RCP4.5 and RCP8.5. This indicates that both climate scenarios are spatially and temporally consistent for this period (despite great changes at the end of the century (Calheiros et al. 2021). This can be largely explained by the similar changes in the climatic variables driven by pathways of anthropogenic radiative forcing that diverge more significantly only after 2050.

Our projected increases in fire behaviour magnitude indicate increased burned area, as the latter increases nonlinearly with fire-spread rate, and both the ROS and FLI constrain the ability to contain a wildfire. However, the projections are consistent but not directly comparable with the various studies that extrapolate (and probably exaggerate) future burned areas in Europe based on historic fire-weather relationships (Dupuy et al. 2020). Future wildfire activity will be dictated by fire danger in its atmospheric and drought (fuel availability) components, but also by changes in vegetation (including NPP) and fuels and in ignition rates (Bradstock, 2010), plus fire management policies (Moreira et al. 2020).

We have shown that climate change will increase fire danger, increasing the frequency of fire weather conditions associated with large wildfires in the target region. Both RCP4.5 and RCP8.5 point to an increase in fire-spread rate and fireline intensity in the fuel types considered, which are broadly representative of the Mediterranean basin. Nonetheless, while pine forests and shrublands may experience an increase in fire intensity to levels that further exceed wildfire suppression capacity, mainly from spring to autumn, broadleaved forests will not typically exceed such thresholds.

The consistency in results regardless of the climate scenario considered reinforces the need to address the effects of climate change in wildfires when planning future management (Moreira et al. 2020). Although we acknowledge that the “challenges of wildfire management through the 21st century include not just dealing with an increased number of fires, but also an increased incidence of unmanageable crown fire” (Wotton et al. 2017), our results clearly show that climate change effects in certain fuel types, such as broadleaved forests, may not imply further ineffectiveness of firefighting operations in the medium-term. Likewise, landscape-level fuel treatments that reduce fuel load will be crucial to mitigating the future fire regime. Hence, through better planning and management of wildfire-prone landscapes, decision-makers would be, consequently, protecting communities, even when considering climate change.

Funding

This research was funded by Portuguese national funds through FCT - Foundation for Science and Technology, I.P., under the FirESmart project (PCIF/MOG/0083/2017). The authors would like to acknowledge the support of FCT by providing funding to the Forest Research Centre (UIDB/00239/2020) and the Centre for the Research and Technology of Agro-environmental and Biological Sciences (UIDB/04033/2020). B.A. Aparício was also supported by the individual research grant from the FCT (UI/BD/150755/2020).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose

Data Availability

The datasets generated during this study are available from the corresponding author on reasonable request

Acknowledgment

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Unravelling the Effect of Climate Change on Fire Danger and Fire Behaviour in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)

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