Fire Size Distribution Research Articles

Assessment of the effectiveness of coarse resolution fire products in monitoring long-term changes in fire regime within protected areas in South Africa

Although high-resolution satellite and in situ airborne observations are becoming a common data source for fire monitoring, reconstruction of the historical fire record is based on less suitable data. In many regions, managers and policy-makers rely solely on coarse resolution global fire datasets. This study assessed the suitability of readily available products to accurately depict several fire metrics by comparing them to the manually derived fire scars within three natural reserves in South Africa (2003–2020). Contrary to previous findings, we showed that MCD64A1 and FireCCI51 products detected the majority of the burned area (70–97 %) within protected areas and showed high temporal consistency. Additionally, active and burned area products accurately detected the length and the peak of the fire season. Nonetheless, we found neither medium nor coarse resolution data suitable for evaluating long-term changes in fire size distribution. Coarse resolution burned area products demonstrated a very limited capacity to detect fires smaller than 100 ha, while a lack of sufficient non-cloudy Landsat images led to substantial gaps in fire reconstruction, bringing an unknown level of uncertainties in fire metrics estimates. Similarly, the only available medium-resolution burned area product (GABAM) was unsuitable for fire monitoring since unmapped areas were classified as unburned, making no distinction between regions with an absence of data and an absence of fires. Meanwhile, the performance of FireCCILT11 was ranked the lowest among all the available fire products. Given the lack of resources in many protected areas in South Africa and the pressing need to evaluate current and historical fire management practices and policies, we conclude that, in the absence of a more accurate higher-resolution alternative, MCD64A1 was the most suitable product for capturing year-to-year fluctuations in fire activity and a combination of MODIS and VIIRS active fire products for monitoring changes in fire seasonality.

System-level feedbacks of active fire regimes in large landscapes

BackgroundClimate is a main driver of fire regimes, but recurrent fires provide stabilizing feedbacks at several spatial scales that can limit fire spread and severity—potentially contributing to a form of self-regulation. Evaluating the strength of these feedbacks in wildland systems is difficult given the spatial and temporal scales of observation required. Here, we used the REBURN model to directly examine the relative strengths of top-down and bottom-up drivers of fire over a 3000-year simulation period, within a 275,000-ha conifer-dominated landscape in north central Washington State, USA.ResultsWe found strong support for top-down and bottom-up spatial and temporal controls on fire patterns. Fire weather was a main driver of large fire occurrence, but area burned was moderated by ignition frequencies and by areas of limited fuels and fuel contagion (i.e., fire fences). Landscapes comprised of >40% area in fire fences rarely experienced large fire years. When large fires did occur during the simulation period, a recovery time of 100–300 years or more was generally required to recover pre-fire vegetation patterns.ConclusionsSimulations showed that interactions between fire weather, fuel contagion, topography, and ignitions manifest variability in fire size and severity patch size distributions. Burned and recovering vegetation mosaics provided functional stabilizing feedbacks, a kind of metastability, which limited future fire size and severity, even under extreme weather conditions. REBURN can be applied to new geographic and physiographic landscapes to simulate these interactions and to represent natural and culturally influenced fire regimes in historical, current, or future climatic settings.

MTTfireCAL Package for R—An Innovative, Comprehensive, and Fast Procedure to Calibrate the MTT Fire Spread Modelling System

Fire spread behavior models are used to estimate fire behavior metrics, fire hazard, exposure, and risk across the landscape. One of the most widely used fire spread models is the minimum travel time (MTT), which requires a very time-consuming, interactive, trial-and-error calibration process to reproduce observed fire regimens. This study presents the MTTfireCAL package for R, a tool that enables fast calibration of the MTT fire spread models by testing and combining multiple settings and then ranking them based on the model’s capacity to reproduce historical fire patterns, such as fire size distribution and fire frequency. Here, we explain the main methodological steps and validate the package by comparing it against the typical calibration procedures in two study areas. In addition, we estimate the minimum number of fire runs required to ensure a reliable calibration. Overall, the use of MTTfireCAL R package and the optimization of the number of ignitions used allowed for a faster calibration of the MTT modeling system than the typical trial-and-error calibration. The MTT modeling system calibrated using MTTfireCAL was also able to better reproduce the historical fire patterns. This tool has the potential to support the academic and operational community working with MTT.

Global Warming Reshapes European Pyroregions

AbstractWildland fire is expected to increase in response to global warming, yet little is known about future changes to fire regimes in Europe. Here, we developed a pyrogeography based on statistical fire models to better understand how global warming reshapes fire regimes across the continent. We identified five large‐scale pyroregions with different levels of area burned, fire frequency, intensity, length of fire period, size distribution, and seasonality. All other things being equal, global warming was found to alter the distribution of these pyroregions, with an expansion of the most fire prone pyroregions ranging respectively from 50% to 130% under 2° and 4°C global warming scenarios. Our estimates indicate a strong amplification of fire across parts of southern Europe and a subsequent shift toward new fire regimes, implying substantial socio‐ecological impacts in the absence of mitigation or adaptation measures.

Megaincendios 2020 en Córdoba: Incidencia del fuego en áreas de valor ecológico y socioeconómico

In 2020, the Sierras of Córdoba suffered the most extreme fire season in recent collective memory, causing severe ecological and socioeconomic damage. In this study, we mapped the fires of 2020 using Sentinel 2 imagery, we compared the season with historical statistics from 1987-2019 in terms of number of fires, burned area, fire size distribution and fire frequency. Additionally, we analyzed fire incidence in areas of ecological value (protected natural areas [PNA], priority areas for biodiversity conservation [CPA] and native forests protected by Federal Law [#26331/07]) and in areas of socioeconomic value (productive and residential lands, considering their tax value). In 2020, nearly 291866 ha burned in the Sierras of Córdoba —becoming the fire season with the second largest burned area in 34 years—, with a record of 5 megafires (≥10000 ha). Many burned areas had fire return intervals from 3 to 9 years on average, constituting as hotspot regions of fire in our study area. Flames affected more than 5% of PNA, 12% of CPA and 12% of forests protected by federal law. Fires affected mainly the rural areas, and to a lesser extent, urban lands with tax values of AR$8677 and 24592 million, respectively. Extreme drought and fuel availability were determinants for the occurrence of megafires, while the pandemic lockdown suggests that fires were not set by negligent urban inhabitants. The damages caused by the 2020 megafires and the possibility of similar scenarios in the future require policies towards an integral management of the fire issue in Córdoba province.

An aridity threshold model of fire sizes and annual area burned in extensively forested ecoregions of the western USA

Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability. Identifying the climatic conditions that trigger extensive fire years at regional scales can enable development of area burned models that are both spatially and temporally robust, which is crucial for understanding the impacts of past and future climate change. We identified region-specific thresholds in fire-season aridity that distinguish years with limited, moderate, and extensive area burned for 11 extensively forested ecoregions in the western United States. We developed a new area burned model using these relationships and demonstrate its application in the Southern Rocky Mountains using climate projections from five global climate models (GCMs) that bracket the range of projected changes in aridity. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. We project dramatic increases in area burned after 2020 under most GCMs and after 2060 with all GCMs as the frequency of extensive fire years increases. Our adaptable model can readily incorporate new observations (e.g., extreme fire years) to directly address the non-stationarity of fire-climate relationships as climatic conditions diverge from past observations. Our aridity threshold fire model provides a simple yet spatially robust approach to project regional changes in area burned with broad applicability to ecosystem and vegetation simulation models.

Predicting future patterns, processes, and their interactions: Benchmark calibration and validation procedures for forest landscape models

Process-based Forest Landscape Models (FLMs) rely on first principles to simulate ecological patterns and processes, making them uniquely powerful for forecasting ecological dynamics under unprecedented climatic and disturbance regimes. Persistent challenges with any ecological forecasting model are calibration (“tuning” the model) and validation (“proofing” the model). As no actual future data exist from which to conduct a formal model validation, model credibility is established through numerous tests against empirical datasets and comparisons with other types of models. The purpose of this study was to establish more consistent and generalizable standards for calibrating and validating LANDIS-II, a widely used, open-source FLM. We reviewed methods gleaned from a wide variety of previous FLM studies and advance some new techniques for evaluating the credibility of the model outputs. We used publicly available data with full coverage for the United States (US) so that our methods will be generalizable to other landscapes in the US, and we developed an ecologically meaningful set of validation metrics for evaluating the credibility of new applications.We found that LANDIS-II could be calibrated to reliably simulate empirical vegetation-disturbance-climate dynamics in diverse, mountainous terrain and fire-prone landscapes of the eastern Cascade Mountains. We performed an inter-model validation between LANDIS-II and the Forest Vegetation Simulator (FVS), demonstrating consistent projections of biomass dynamics for all tree-dominated ecoregions in the study domain. Similarly, simulated fires reliably approximated the empirical fire event size and severity patch size distributions based on observed fire activity from 1984 to 2019. By establishing rigorous, transparent, and repeatable standards for calibrating and validating FLM dynamics, we sought to remove some of the barriers to adapting LANDIS-II to new landscapes and climates, facilitate further validation of existing models, and aid independent assessment of the credibility of forest landscape models.

A comprehensive characterization of MODIS daily burned area mapping accuracy across fire sizes in tropical savannas

Daily global burned area mapping of Earth from remotely sensed data is of considerable interest and indicates a clear user need, despite well-known accuracy limitations. Many studies report global and regional indicators of spatial and temporal accuracy to evaluate the quality of burned area products, but tend to rely on relatively sparse validation data. In this paper, an extensive validation data set covering 223 million ha over the Brazilian Cerrado for the 2015 fire season is used to thoroughly evaluate how the accuracy of burned area products relying on distinct spectral channels (SWIR+NIR versus NIR) and spatial resolution (250 m, 500 m, and both) depends on fire size.Toward this end, we consider publicly available burned area products MCD64A1 (v006) at 500 m and FireCCI51 at 250 m and use a recently proposed graph-based patch algorithm, which is flexible enough to combine 250–500 m bands.The trade-off between spatial resolution and spectral channels for the problem at hand reveals that better spatial resolution (NIR at 250 m) is preferable to a broader range of spectral channels (NIR + SWIR at 500 m) for small burns (<250 ha), but that this relation is not preserved for large burns (>1000 ha). Also, this study shows that combining both spectral indices into a single classifier leads to the most accurate burned area map. The analysis by land cover type indicates that overall accuracy is highest for grasslands and savannas, with grasslands benefiting the most from the use of SWIR bands. Results also indicate that burned areas can be classified with accuracy measured by the Dice Coefficient close to 80% for fire sizes larger than 500 ha, which corresponds to 60% of the total burned area, but accuracy lower than 36% for fire sizes smaller than 250 ha. In particular, our analysis suggests that the validation of burned area products always should be linked to the description of fire size distribution since it is a fundamental driver of accuracy.Results show that MODIS daily burned area mapping at 250–500 m spatial resolution can exhibit high temporal accuracy, with almost unbiased estimates and standard deviation close to 1.5 days, indicating an accurate characterization of the spread of large fires.

Stochastic Thermal Demand and Resulting Capacity Loss of Concrete Tunnel Liners Subjected to Vehicle Fires

Fire resistant design of both structural and non-structural components in road tunnels is predicated on the determination of fire demand intensity. Current practice typically uses a conservative, deterministic fire curve that does not necessarily provide a representative evaluation of the spatial and temporal distribution of thermal demands in tunnels that are caused by large vehicle fires. This paper proposes a tunnel-specific probabilistic framework for evaluating vehicle fire frequency and intensity based on tunnel geometry and traffic information. The framework leverages a fast-running computational tool that has been previously developed by the authors for calculating fire-induced heat flux exposure on tunnel liners because of enclosed vehicle fires. The likelihood of a vehicular fire and the associated fire size distribution are used to generate probabilistic distributions of total fire exposure for the reinforced concrete tunnel liner. Critical heat flux values according to these probabilistic distributions are then used to assess reductions in concrete material strength and resulting losses in the structural performance of the system. A case study of the Fort Pitt Tunnel in Pittsburgh, PA, is included for demonstration. The proposed framework enables decision making regarding design and renovation of tunnels for fire resistance as well as post-fire inspection by quantifying the risk of capacity reduction in the concrete liner because of a realistic range of fire hazard intensities.

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima.

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30‐yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero‐inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump‐shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

Evidence for scale‐dependent topographic controls on wildfire spread

AbstractWildfire ecosystems are thought to be self‐regulated through pattern–process interactions between ignition frequency and location, and patterns of burned and recovering vegetation. Yet, recent increases in the frequency of large wildfires call into question the application of self‐organization theory to landscape resilience. Topography represents a stable bottom‐up template upon which fire interacts as both a physical and an ecological process. However, it is unclear how topographic control changes geographically and across spatial scales. We analyzed fire perimeter and topography data from 16 Bailey ecoregions across the State of California to identify spatial correspondence between ecoregional fire event and topographic patch size distributions. We found both sets of distributions followed a power‐law form and were statistically similar across several orders of magnitude, for most ecoregions. As a direct test of topographic controls on fire event perimeters, we used a paired t‐test across ~11,000 fires to identify differences in topographic attributes at fire boundaries versus fire interiors. Statistical significance was determined using 500 iterations of a neutral landscape model. Level of topographic control varied significantly by ecoregion and across topographic features. For example, north–south aspect breaks, valley bottoms, and roads showed a consistently high degree of spatial control on wildfire perimeters. Topographic controls were most pronounced in mountainous ecoregions and were least influential in arid regions. Ridgetops provided a low‐level control across all ecoregions. Spatial control was strongest for small (100–102 ha) to medium (103–104 ha) fire sizes, suggesting that controls were scale‐dependent rather than scale‐invariant. Roads were the dominant control across all ecoregions; however, removing roads from the analyses had no significant effect on the overall role of topography on wildfire extinguishment in this analysis. This result suggested that certain topographic settings show strong spatial control on fire growth, despite the presence of roads. Our results support the observation that both bottom‐up and top‐down factors constrain fire sizes and that there are likely scaling regions within fire size distributions wherein the dominance of these spatial controls varies. Human influences on fire spread may either diminish or enhance the role of some bottom‐up and top‐down factors, adding further complexity.

Management thresholds stemming from altered fire dynamics in present-day arid and semi-arid environments

Changes in fire frequency, size, and severity are driving ecological transformations in many systems. In arid and semi-arid regions that are adapted to fire, long-term fire exclusion by managers leads to declines in fire frequency, altered fire size distribution, and increased proportion of high severity fires. In arid and semi-arid systems where fire was historically rare, factors such as invasion by highly combustible non-native plants elevate fire frequency and size, elevating mortality of native species. Altered temperature and precipitation regimes may exacerbate these changes by increasing biomass and flammability. Current transformation in fire dynamics carry social as well as ecological consequences. Human cultures, livelihoods, values, and management behaviors are attuned to fire dynamics. Changes can make it costly or impossible to maintain traditional landscape use and economic activities. We review the ecological and social science literature to examine drivers of altered fire dynamics in arid and semi-arid systems worldwide and the conditions representing fire dynamics thresholds—points at which altered conditions may make it difficult or impossible to achieve management objectives, even via traditional adaptive management focusing on alternative management activities to achieve objectives. Such thresholds could force a wholesale shift in management objectives and practices and a new approach to adaptive management that redefines objectives when no viable adaptive action can be undertaken.

Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products—canopy height (CH) and canopy base height (CBH)—on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow.

Land cover, more than monthly fire weather, drives fire-size distribution in Southern Québec forests: Implications for fire risk management.

Fire activity in North American forests is expected to increase substantially with climate change. This would represent a growing risk to human settlements and industrial infrastructure proximal to forests, and to the forest products industry. We modelled fire size distributions in southern Québec as functions of fire weather and land cover, thus explicitly integrating some of the biotic interactions and feedbacks in a forest-wildfire system. We found that, contrary to expectations, land-cover and not fire weather was the primary driver of fire size in our study region. Fires were highly selective on fuel-type under a wide range of fire weather conditions: specifically, deciduous forest, lakes and to a lesser extent recently burned areas decreased the expected fire size in their vicinity compared to conifer forest. This has large implications for fire risk management in that fuels management could reduce fire risk over the long term. Our results imply, for example, that if 30% of a conifer-dominated landscape were converted to hardwoods, the probability of a given fire, occurring in that landscape under mean fire weather conditions, exceeding 100,000 ha would be reduced by a factor of 21. A similarly marked but slightly smaller effect size would be expected under extreme fire weather conditions. We attribute the decrease in expected fire size that occurs in recently burned areas to fuel availability limitations on fires spread. Because regenerating burned conifer stands often pass through a deciduous stage, this would also act as a negative biotic feedback whereby the occurrence of fires limits the size of nearby future for some period of time. Our parameter estimates imply that changes in vegetation flammability or fuel availability after fires would tend to counteract shifts in the fire size distribution favoring larger fires that are expected under climate warming. Ecological forecasts from models neglecting these feedbacks may markedly overestimate the consequences of climate warming on fire activity, and could be misleading. Assessments of vulnerability to climate change, and subsequent adaptation strategies, are directly dependent on integrated ecological forecasts. Thus, we stress the need to explicitly incorporate land-cover’s direct effects and feedbacks in simulation models of coupled climate–fire–fuels systems.

Mapping fire regimes in China using MODIS active fire and burned area data

Relatively little is known about vegetation fire regimes in China. In this study, we investigated fire regime characteristics and their potential drivers, utilizing information extracted from the MODerate resolution Imaging Spectrometer (MODIS) satellite. Twelve fire regime variables were selected and computed on a regular grid over all of China, using MODIS burned area and active fire data during the period 2001 to 2016, to identify fire incidence and its inter-annual variability, seasonality, intensity, fire size distribution and vegetation types affected by fire. The variables were normalized and clustered to define six fire regimes with distinctive fire attributes. Results show that 78.6% of the land in China was affected by fire during the study period. The barren or sparsely vegetated lands of western China are nearly fire-free. Active fires were observed in Central China, but area burned was not detectable from MODIS. Forest fires in northeastern China are relatively large, infrequent, with a short fire season that peaks in non-winter seasons and higher inter-annual variability, implying a high likelihood of accidental causes. In contrast, forest fires in southern China are relatively small, frequent, with a long fire season that peaks in non-summer seasons, and lower inter-annual variability, suggesting regular use as a land management tool. Low inter-annual variability and low fire intensity were associated with cropland fires, whereas grassland fires generally exhibit the opposite traits. We have also discussed the potential drivers of each fire regime characteristics.

Highlighting Biome-Specific Sensitivity of Fire Size Distributions to Time-Gap Parameter Using a New Algorithm for Fire Event Individuation

Detailed spatial-temporal characterization of individual fire dynamics using remote sensing data is important to understand fire-environment relationships, to support landscape-scale fire risk management, and to obtain improved statistics on fire size distributions over broad areas. Previously, individuation of events to quantify fire size distributions has been performed with the flood-fill algorithm. A key parameter of such algorithms is the time-gap used to cluster spatially adjacent fire-affected pixels and declare them as belonging to the same event. Choice of a time-gap to define a fire event entails several assumptions affecting the degree of clustering/fragmentation of the individual events. We evaluate the impact of different time-gaps on the number, size and spatial distribution of active fire clusters, using a new algorithm. The information produced by this algorithm includes number, size, and ignition date of active fire clusters. The algorithm was tested at a global scale using active fire observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Active fire cluster size distributions were characterized with the Gini coefficient, and the impact of changing time-gap values was analyzed on a 0.5° cell grid. As expected, the number of active fire clusters decreased and their mean size increased with the time-gap value. The largest sensitivity of fire size distributions to time-gap was observed in African tropical savannas and, to a lesser extent, in South America, Southeast Asia, and eastern Siberia. Sensitivity of fire individuation, and thus Gini coefficient values, to time-gap demonstrate the difficulty of individuating fire events in tropical savannas, where coalescence of flame fronts with distinct ignition locations and dates is very common, and fire size distributions strongly depend on algorithm parameterization. Thus, caution should be exercised when attempting to individualize fire events, characterizing their size distributions, and addressing their management implications, particularly in the African savannas.

Fuel fragmentation and fire size distributions in managed and unmanaged boreal forests in the province of Saskatchewan, Canada

Forest fires are an important disturbance factor of boreal forests, annually burning about 0.5% of the forested area in Canada. Wildfire regimes are influenced by climate and a number of studies project an increase in wildfire activity with climate change. Another factor influencing wildfires is human intervention (fire suppression), and one factor that has rarely been assessed is fuel fragmentation. Studies evaluating the effect of forest fire suppression concluded that in areas with strong suppression effort the burned area as well as the fire size decreased.Here we evaluate wildfire distributions over the last three decades for two areas that differ mainly in their level of forest management and fire suppression: the Boreal Shield (unmanaged) and the Boreal Plain regions (intensively managed) in the Canadian Province of Saskatchewan.We calculate a fuel fragmentation index and relate fire sizes and burned areas to fire weather. We use the concept of the characteristic fire size (CFS); hence we analyze how much burned area is contributed to the total burned area per fire size class.Both areas show a uni-modal distribution of the CFS, indicating that the majority of burned area was contributed by medium sized fires (Boreal Shield 6.39·104ha, the Boreal Plain 8.79·104ha). Burned area as well as fuel fragmentation is lower in the managed forest compared to the unmanaged area.The fuel fragmentation index constantly increased since the 1980s in both regions. Despite the large efforts of fire suppression in the Boreal Plains, the CFS is slightly larger in this managed region. Neither the burned area nor the fire size could be linked statistically to the weather conditions, at the time of the fire (using the Canadian Fire Weather Index).We argue that the high fragmentation over the last decades have decreased the burned area. The slightly higher characteristic fire size in the managed area might be explained by the considerably lower fragmentation, counteracting fire suppression efforts. Fuel fragmentation is likely to decrease over the next decades due re-growth. Though a strong link between fire weather and burned area at the fine scale of this study could not be detected we expect that a decrease in fragmentation in combination with an increase in fire prone weather conditions (as expected for the future) might increase the risk of large fires in both areas. We suggest that future fire risk analysis should include an assessment of the effect of fuel fragmentation.

Global fire size distribution: from power law to log-normal

Wildland fires are one of the main alleged examples of Self-Organised Criticality (SOC), with simple SOC models resulting in the expectation of a power-law fire size frequency distribution. Here, we test whether fire size distributions systematically follow a power law and analyse their spatial variation for eight distinct areas over the globe. For each of the areas, we examine the fire size frequency distribution using two types of plots, maximum likelihood estimation and chi-square tests. Log-normal emerges as a suitable option to fit the fire size distribution in this variety of environments. In only two of eight areas was the power law (which is a particular case of the log-normal) not rejected. Notably, the two parameters of log-normal are related to each other, displaying a general linear relation, which extends to the sites that can be described with a power law. These results do not necessarily refute the SOC hypothesis, but reveal the presence of other processes that are, at least, modulating the outcome of SOC in some areas.

Fire regime from 1973 to 2011 in north-western Patagonian grasslands

Fire is one of the most important disturbances in terrestrial ecosystems and has major ecological and socioeconomic impacts. Fire regime describes the variation of individual fire events in time and space. Few studies have characterised the fire regime in grasslands in spite of the importance of these ecosystems. The aim of this study was to describe the recent fire regime (from 1973 to 2011) of north-western Patagonian grasslands in terms of seasonality, frequency and burned area. Our study area covered 560 000 ha and we used a remote sensing approach combined with statistics obtained from operational databases. Fires occur during the summer in 2 of every 3 years with a frequency of 2.7 fires per year and a mean size of 823 ha. Fire size distribution is characterised by many small fires and few large ones which would respond to a distribution from the power law family. Eighty per cent of the total area affected by fire was burned in the span of a few years, which were also widespread fire years in forests and woodlands of north-western Patagonia. This work contributes to general knowledge about fire regimes in grasslands and we expect that our results will serve as a reference to further fire regime research.

Assessing the effect of a fuel break network to reduce burnt area and wildfire risk transmission

Wildfires pose complex challenges to policymakers and fire agencies. Fuel break networks and area-wide fuel treatments are risk-management options to reduce losses from large fires. Two fuel management scenarios covering 3% of the fire-prone Algarve region of Portugal and differing in the intensity of treatment in 120-m wide fuel breaks were examined and compared with the no-treatment option. We used the minimum travel time algorithm to simulate the growth of 150 000 fires under the weather conditions historically associated with large fires. Fuel break passive effects on burn probability, area burned, fire size distribution and fire transmission among 20 municipalities were analysed. Treatments decreased large-fire incidence and reduced overall burnt area up to 17% and burn probability between 4% and 31%, depending on fire size class and treatment option. Risk transmission among municipalities varied with community. Although fire distribution shifted and large events were less frequent, mean treatment leverage was very low (1 : 26), revealing a very high cost–benefit ratio and the need for engaging forest owners to act in complementary area-wide fuel treatments. The study assessed the effectiveness of a mitigating solution in a complex socioecological system, contributing to a better-informed wildland fire risk governance process among stakeholders.