The dynamics and drivers of fuel and fire in the Portuguese public forest

The impacts of climate change on groundwater quality: A review

Groundwater has been known as the second largest freshwater storage in the world, following surface water. Over the years, groundwater has already been under overwhelming pressure to satisfy human needs for artificial activities around the world. Meanwhile, the most noticeable footprint of human activities is the impact of climate change. Climate change has the potential to change the physical and chemical properties of groundwater, thereby affecting its ecological functions. This study summarizes existing research affiliated with the possible effects of a changing climate on the quality of groundwater, including changes in water availability, increased salinity and pollution from extreme weather events, and the potentiality of seawater intrusion into coastal aquifers. Previous works dealing with groundwater-induced responses to the climate system and climate impacts on groundwater quality through natural and anthropogenic processes have been reviewed. The climate-induced changes in groundwater quality including pH, dissolved oxygen level, salinity, and concentrations of organic and inorganic compounds were assessed. Some future research directions are proposed, including exploring the potential changes in the occurrences and fate of micropollutants in groundwater, examining the relationship between the increase of microcystin in groundwater and climate change, studying the changes in the stability of metals and metal complexation, and completing studies across different regional climate regions.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015-2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.

Climate- and fire-smart landscape scenarios call for redesigning protection regimes to achieve multiple management goals

Integrated management of biodiversity and ecosystem services (ES) in heterogeneous landscapes requires considering the potential trade-offs between conflicting objectives. The UNESCO's Biosphere Reserve zoning scheme is a suitable context to address these trade-offs by considering multiple management zones that aim to minimise conflicts between management objectives. Moreover, in Mediterranean ecosystems, management and planning also needs to consider drivers of landscape dynamics such as wildfires and traditional farming and forestry practices that have historically shaped landscapes and the biodiversity they host. In this study, we applied a conservation planning approach to prioritise the allocation of management zones under future landscape and climate scenarios. We tested different landscape management scenarios reflecting the outcomes of climate-smart and fire-smart policies. We projected the expected landscape dynamics and associated changes on the distribution of 207 vertebrate species, 4 ES and fire hazard under each scenario. We used Marxan with Zones to allocate three management zones, replicating the Biosphere Reserves zoning scheme ("Core area", "Buffer zone" and "Transition area") to address the various management objectives within the Biosphere Reserve. Our results show that to promote ES supply and biodiversity conservation, while also minimising fire hazard, the reserve will need to: i) Redefine its zoning, especially regarding Core Areas, which need a considerable expansion to help mitigate changes in biodiversity and accommodate ES supply under expected changes in climate and species distribution. ii) Revisit current management policies that will result in encroached landscapes prone to high intensity, uncontrollable wildfires with the potential to heavily damage ecosystems and compromise the supply of ES. Our results support that both climate- and fire-smart policies in the Meseta Ibérica can help develop multifunctional landscapes that help mitigate and adapt to climate change and ensure the best possible maintenance of biodiversity and ES supply under uncertain future climate conditions.

Evaluating priority locations and potential benefits for building a nation-wide fuel break network in Portugal

Despite growing interest in developing extensive fuel treatment programs to prevent catastrophic wildfires in the Mediterranean region, there is little information on the projected effectiveness of fuel treatments in terms of avoided exposure and risk. In Portugal, a fuel management plan aiming to prevent loss of lives, reduce large fires (>500 ha), and reduce annual burned area is under implementation, with particular emphasis on the nation-wide fuel break network (FBN). In this study, we evaluated the effectiveness of the planned FBN in terms of meeting fire management objectives, costs, and benefits. We first estimated the overall effectiveness of the FBN at intersecting modeled large fires (>500 ha) and at reducing exposure to protected areas and residential buildings using wildfire simulation modeling. Then, the fuel break burn-over percentage, i.e. the percentage of fires that are not contained at the FBN, was modeled as a function of pre-defined flame length thresholds for individual FBN segments. For the planned FBN, the results suggested a potential reduction of up to 13% in the annual burned area due to large fires (ca. 13,000 ha), of up to 8% in the annual number of residential buildings exposed (ca. 100 residential buildings), and up to 14% in the annual burned area in protected areas (ca. 2400 ha). The expected burn-over percentage was highly variable among the segments in response to estimated fire intensity, and an average decrease of 40% of the total benefits was estimated. The most important fuel breaks typically showed a higher percentage of fire burn-over, and hence reduction in effectiveness. We also showed that the current implementation of FBN follows a random sequence, suboptimal for all objectives. Our results suggest that additional landscape-scale fuel reduction strategies are required to meet short-term national wildfire management targets.

Biogeographic variability in wildfire severity and post-fire vegetation recovery across the European forests via remote sensing-derived spectral metrics

Wildfires have large-scale and profound effects on forest ecosystems, and they force burned forest areas toward a wide range of post-fire successional trajectories from simple reduction of ecosystem functions to transitions to other stable non-forest states. Fire disturbances represent a key driver of changes in forest structure and composition due to post-fire succession processes, thus contributing to modify ecosystem resilience to subsequent disturbances. Here, we aimed to provide useful insights into wildfire severity and post-fire recovery processes at the European continental scale, contributing to improved description and interpretation of large-scale wildfire spatial patterns and their effects on forest ecosystems in the context of climate change. We analyzed fire severity and short-term post-fire vegetation recovery patterns across the European forests between 2004 and 2015 using Corine Land Cover Forest classes and bioregions, based on MODIS-derived spectral metrics of the relativized burn ratio (RBR), normalized difference vegetation index (NDVI) and relative recovery indicator (RRI). The RBR-based fire severity showed geographic differences and interannual variability in the Boreal bioregion compared to that in other biogeographic regions. The NBR-based RRI showed a slower post-fire vegetation recovery rate with respect to the NDVI, highlighting the differential sensitivities of the analyzed remote sensing-spectral metrics. Moreover, the RRI showed a significant decreasing trend during the observation period, suggesting a growing lag in post-fire vegetation recovery across European forests.

Biophysical controls over fire regime properties in Central Portugal

The concept of fire regime can be used to describe, with different degrees of complexity, the spatial and temporal patterns of fires and their effects within a given area and over a given period. In this work, we explore the relations between fire regime and a set of potential biophysical controls at a local scale, for 972 civil parishes in central Portugal. The fire regime was characterized with reference to a 44-year period (1975-2018) using three properties: cumulative percentage of parish area burned, area-weighted total number of wildfires, and the Gini concentration index of burned area over time. Potential control variables included topography, seasonal temperature and rainfall, and land use/land cover type and patch fragmentation. Ordinal logistic regression was used to model the relations between the fire regime properties and the potential control factors. Results show that the fire regime properties have important spatial contrasts within the study area, and that land use/land cover distribution, spring rainfall and summer temperatures are the major controls over their variability. The percentage of each parish occupied by shrubland and spontaneous herbaceous vegetation is the single most important factor influencing cumulative percentage of parish area burned and the Gini concentration index of burned area, whereas spring rainfall is the foremost factor regarding area-weighted total number of wildfires. Along with the role of spring rainfall in promoting fuel availability later in the year, our results highlight the importance of the speed of regrowth of shrubland and spontaneous herbaceous vegetation after burning, pointing out the need of tailoring fuel management strategies to the properties of each parish.

Livestock Management for the Delivery of Ecosystem Services in Fire-Prone Shrublands of Atlantic Iberia

In the northwest of the Iberian Peninsula, characterized by its humid climate, large rural areas are being abandoned, mostly in less-favoured areas covered by heathlands, which present a low nutritive quality for livestock production. The high combustibility of these shrublands is driving a high wildfire incidence with negative environmental and economic effects. In this review, some aspects on wildfire occurrence and the potential of grazing livestock to reduce woody phytomass and fire risk in heathland-dominated areas whilst maintaining quality production and preserving biodiversity are summarized. Heathlands may be partially improved—converted to grassland—to better meet animals’ nutritional requirements while acting as ‘natural’ firebreaks. The specific grazing behaviour offers the opportunity to combine different domestic herbivores (mixed grazing) to achieve sustainable systems utilizing heterogeneous resources. Cattle, sheep, goats, and horses may have a role in the provision of different ecosystem services such as food production and biodiversity conservation. Genotype x environment interactions shape the ability of animals to cope with poor vegetation conditions, with smaller species and breeds performing better than larger animals. Goats and horses are indicated to arrest woody encroachment. Sustainable grazing systems are affordable in heathland–grassland mosaics by selecting appropriate livestock species and breeds for quality production, thus favouring rural economies and lowering fire risk.

Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

Evaluation of fire severity in fire prone-ecosystems of Spain under two different environmental conditions

Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recurrence.

A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment

The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a Repeatedly Random Undersampling (RRU) was proposed to address the randomness problem faced in Single Random Sampling. However, the RRU cannot guarantee that the generated classifier has the best classification performance during the repeatedly random sampling process. To address this weakness, in this study we proposed an optimized RRU, which follows the idea of RRU, and then changing its rule to find a best classifier. Then, the selected classifier, the actual most accurate classifier (MAC), was employed to compute the probability of hazard occurrence. Support Vector Machine (SVM) was selected as the analysis method, and Genetic Algorithm was employed to compute the parameters of SVM. Forest fire susceptibility was assessed in Huichang County in China due to its forest values and frequent fire events. The results indicated that compared with the RRU, the optimized RRU can find out an actual MAC which has the best classification performance among possible MACs; also, the fire susceptibility map generated by the actual MAC comforts to objective facts. The generated fire susceptibility map can provide useful decision supports for local government to reduce forest fire risks. Moreover, the proposed sampling method, the optimized RRU, presented an enhanced approach for selecting negative samples, which makes the results of forest fire susceptibility assessment more reliable and accurate.

Assessing the biophysical and social drivers of burned area distribution at the local scale

Understanding the characteristics of wildfire-affected communities and the importance of particular factors of different dimensions, is paramount to improve prevention and mitigation strategies, tailored to people's needs and abilities. In this study, we explored different combinations of biophysical and social factors to characterize wildfire-affected areas in Portugal. By means of machine-learning methods based on classification trees, we assessed the predictive ability of various models to discriminate different levels of wildfire incidence at the local scale. The model with the best performance included a reduced set of both biophysical and social variables and we found that, oveall, the exclusion of specific variables improved prediction rates of group classification. The most important variables were related to landcover; the civil parishes covered by more than 20% of shrublands were more fire-prone, whereas those parishes with at least 40% of agricultural land were less affected by wildfires. Regarding social variables, the most-affected parishes showed a lower proportion of foreign residents and lower purchasing power, conditions likely associated with the socioeconomic context of inland low-density rural areas, where rural abandonment, depopulation and ageing trends have been observed in the last decades. Further research is needed to investigate how other particular parameters representing the social context, and its evolution, can be integrated in wildfire occurrence modelling, and how these interact with the biophysical conditions over time.

Fire regime dynamics in mainland Spain. Part 2: A near-future prospective of fire activity

The current research belongs to a series of two manuscripts aiming at describing spatial-temporal dynamics of fire regime and its drivers in Spain. In this work, we present the first attempt to produce a spatial-temporal delimitation of homogeneous fire regime zones in Spain providing insights into the near future. The analyses were based on historical fire records; leveraging autoregressive models to project fire features into the near future. We evaluated the spatial extent of homogenous fire regime zones in three different periods: past (1974-1994), current (1995-2015) and future (2016-2036). To do so, we applied Principal Component Analysis and Ward's hierarchical clustering to identify zones of fire regime on the basis of the spatial and temporal arrangement of their main fire features: number of fires, burned area, burnt area from natural-caused fires, incidence of large fires (> 100 ha) and seasonality. Clusters of fire regime were trained in the current period, being later projected into the past and future periods using of k-Nearest Neighbor classification. ARIMA modeling forecasted a shrinkage in all fire features except natural-caused fires that remained stable. Overall, we detected a transition from significant fire incidence in the past towards a situation with moderate impact of fires in the near future. The Mediterranean coast experienced the largest decline in fire activity with few locations maintaining the historical levels of occurrence of large fires. On the other hand, the Northwestern end of Spain depicted a progression towards winter fire activity while still linked to large fires. This pattern persisted in the near future along the northern coast, whereas an intermix of minor fire progression and regression was expected thorough the hinterlands and the Mediterranean.

Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

Analysing eucalypt expansion in Portugal as a fire-regime modifier

Eucalypts, especially blue gum (Eucalyptus globulus), have been extensively planted in Portugal and nowadays dominate most of its forest landscapes. Large-scale forestation programs can intensify fire activity, and blue gum plantations are often viewed as highly flammable due to the nature and structure of the fuel complex. The role of eucalypt plantations in the fire regime of Mediterranean climate regions is increasingly debated following the recent catastrophic wildfires in Portugal and elsewhere. In this study we examined the effects of eucalypt forestation on burned area (BA), fire size, and fire severity in Portugal. This was based on fire and vegetation mapping and statistics, fire weather data, satellite imagery, and national forest inventory data. Eucalypt BA comprised an average of 12.5% of total BA (1980-2017) and did not increase over time and with eucalypt expansion. Eucalypt metrics did not explain interannual BA variability after accounting for the effects of other variables. Forest fires started within eucalypt stands were the least likely to become large, and large fire size was irresponsive to forest composition. Likewise, forest type was a generally minor influence in mega-fire severity and accounted for just 1.4-8.6% of surface fuel-hazard metrics variation. In general, large-scale conversion of maritime pine to eucalypt stands (1970-2015) implied lower fuel accumulation. Fire activity results are consistent with fuel hazard results and express trade-offs between short-rotation forestry and fire behaviour in blue gum stands, with high spotting potential versus modest crown fire likelihood. We found no support for the contention of a modified fire regime as a result of eucalypt forestation in Portugal, but the rising undermanaged and abandoned blue gum estate, especially after large-fire seasons, is a concern for the future. However, it remains to be determined whether post-fire eucalypt regrowth is a higher fire threat than native vegetation in the same context.

Regional fire scenarios in Spain: Linking landscape dynamics and fire regime for wildfire risk management

Fire scenarios are multiscale land-type planning units for a fire regime with different applications at national, regional, and local scales. The main aim of this research is to implement a methodology integrating landscape dynamics and wildfire risk from a context-specific approach, to identify current fire scenarios at a regional scale in the Spanish Central Mountain Range. These homogeneous areas are linked to different stages of a territorial dynamics model called wildfire generations and related to land use diversity and land management trends. A place-based methodology has been developed to characterize fire scenarios using Geographic Information Systems and statistical analysis, resulting in analytical and diagnostic mapping that can be used as a decision support tool for spatial planning and wildfire risk management. Its implementation has led to the delimitation of 91 discrete geographic units in the Central Mountain Range, classified according to wildfire generations and land use-land cover criteria. In conclusion, the fire scenarios concept is a potential approach to manage uncertainty by moving from the operational level of fire suppression to the strategic level of integrated fire management.

Wildfires and the role of their drivers are changing over time in a large rural area of west-central Spain

During the last decades, wildfires have been changing in many areas across the world, due to changes in climate, landscapes and socioeconomic drivers. However, how the role of these drivers changed over time has been little explored. Here, we assessed, in a spatially and temporally explicit way, the changing role of biophysical and human-related factors on wildfires in a rural area in west-central Spain from 1979 to 2008. Longitudinal Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) mixed models, with time as interacting factor (continuous and categorical), were used to model the number of fires of increasing size (≥1–10 ha, >10–100 ha, >100 ha) per 10 × 10 km cell per year, based on fire statistics. We found that the landscape was rather dynamic, and generally became more hazardous over time. Small fires increased and spread over the landscape with time, with medium and large fires being stable or decreasing. NB models were best for modelling small fires, while ZINB for medium and large; models including time as a categorical factor performed the best. Best models were associated to topography, land-use/land cover (LULC) types and the changes they underwent, as well as agrarian characteristics. Climate variables, forest interfaces, and other socioeconomic variables played a minor role. Wildfires were initially more frequent in rugged topography, conifer forests, shrublands and cells undergoing changes in LULC types of hazardous nature, for all fire sizes. As time went by, wildfires lost the links with the initial fire-prone areas, and as they spread, became more associated to lower elevation areas, with higher solar radiation, herbaceous crops, and large size farms. Thus, the role of the fire drivers changed over time; some decreased their explaining power, while others increased. These changes with time in the total number of fires, in their spatial pattern and in the controlling drivers limit the ability to predict future fires.

Regeneration of Native Forest Species in Mainland Portugal: Identifying Main Drivers

Persistence of native forests is a global concern. We aimed at unveiling the main factors affecting tree recruitment in Portuguese native forests, modelling sapling data collected during the 5th Portuguese Forest Inventory, for five main Quercus taxa. Zero-inflated count data models allowed us to examine simultaneously (i) the absence of tree recruitment and (ii) the density of tree recruitment. Using Akaike weights, we obtained importance values for 15 relevant explanatory variables. Results showed that seed availability and climatic variables were determinant to understand regional absence of regeneration for all taxa. Seed availability was also an important driver of sapling density, except for Quercus suber. Other variables impacted on regeneration density: grazing hindered Q. suber regeneration; regeneration of Q. rotundifolia and Q. suber was lower in flat areas; recurrent fire hampered the regeneration of Q. robur and Q. pyrenaica; Q. broteroi and Q. pyrenaica showed depressed regeneration in regions where forest plantations abound, while Q. robur and Q. suber seemed selectively protected. We conclude that caution is warranted when analysing pooled data for Quercus spp. regeneration, as different variables affected Quercus taxa differently. Finally, we suggest dedicated management actions to enhance the establishment of new native forests.

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Predicting wildfire spread is a challenging task fraught with uncertainties. 'Perfect' predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any fire model user to do so.

Probabilistic fire spread forecast as a management tool in an operational setting

Background

An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events.

Results

Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected.

Conclusion

This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires.

Electronic supplementary material

The online version of this article (doi:10.1186/s40064-016-2842-9) contains supplementary material, which is available to authorized users.

Wildfire patterns and landscape changes in Mediterranean oak woodlands

Fire is infrequent in the oak woodlands of southern Portugal (montado) but large and severe fires affected these agro-forestry systems in 2003-2005. We hypothesised transition from forest to shrubland as a fire-driven process and investigated the links between fire incidence and montado change to other land cover types, particularly those related with the presence of pioneer communities (generically designed in this context as "transitions to early-successional communities"). We present a landscape-scale framework for assessing the probability of transition from montado to pioneer communities, considering three sets of explanatory variables: montado patterns in 1990 and prior changes from montado to early-successional communities (occurred between 1960 and 1990), fire patterns, and spatial factors. These three sets of factors captured 78.2% of the observed variability in the transitions from montado to pioneer vegetation. The contributions of fire patterns and spatial factors were high, respectively 60.6% and 43.4%, the influence of montado patterns and former changes in montado being lower (34.4%). The highest amount of explained variation in the occurrence of transitions from montado to early-successional communities was related to the pure effect of fire patterns (19.9%). Low spatial connectedness in montado landscape can increase vulnerability to changes, namely to pioneer vegetation, but the observed changes were mostly explained by fire characteristics and spatial factors. Among all metrics used to characterize fire patterns and extent, effective mesh size provided the best modelling results. Transitions from montado to pioneer communities are more likely in the presence of high values of the effective mesh size of total burned area. This cross-boundary metric is an indicator of the influence of large fires in the distribution of the identified transitions and, therefore, we conclude that the occurrence of large fires in montado increases its probability of transition to shrubland.