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.

Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene.

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

Putting fire on the map of Brazilian savanna ecoregions.

The Brazilian savanna (Cerrado) is considered the most floristically diverse savanna in the world, home to more than seven thousand species. The region is a mosaic of savannas, grasslands and forests whose unique biophysical and landscape attributes are on the basis of a recent ecoregional map, paving the way to improved region-based strategies for land management actions. However, as a fire-prone ecosystem, Cerrado owes much of its distribution and ecological properties to the fire regime and contributes to an important parcel of South America burned area. Accordingly, any attempt to use ecoregion geography as a guide for management strategies should take fire into account, as an essential variable. The main aim of this study is to complement the ecoregional map of the Cerrado with information related to the fire component. Using remotely sensed information, we identify patterns and trends of fire frequency, intensity, seasonality, extent and scar size, and combine this information for each ecoregion, relying on a simple classification that summarizes the main fire characteristics over the last two decades. Results show a marked north-south fire activity gradient, with increased contributions from MATOPIBA, the latest agricultural frontier. Five ecoregions alone account for two thirds of yearly burned area. More intense fires are found in the Arc of Deforestation and eastern ecoregions, while ecoregions in MATOPIBA display decreasing fire intensity. An innovative analysis of fire scars stratified by size class shows that infrequent large fires are responsible for the majority of burned area. These large fires display positive trends over many ecoregions, whereas smaller fires, albeit more frequent, have been decreasing in number. The final fire classification scheme shows well defined spatially-aggregated groups, where trends are found to be the key factor to evaluate fire within their regional contexts. Results presented here provide new insights to improve fire management strategies under a changing climate.

Assessing the conservation of Miombo timber species through an integrated index of anthropogenic and climatic threats.

AIM: Angolan Miombo woodlands, rich in timber species of the Leguminosae family, go through one of the highest rates of deforestation in sub-Saharan Africa. This study presents, on the basis of updated information of the distribution of Leguminosae timber species native to Angola, an integrated index framing the main threats for trees, which aims to support new conservation measures. LOCATION: Sub-Saharan Africa, Republic of Angola. METHODS: The current distribution areas of six Leguminosae timber species (i.e., Afzelia quanzensis, Brachystegia spiciformis, Guibourtia coleosperma, Isoberlinia angolensis, Julbernardia paniculata, and Pterocarpus angolensis) were predicted through ensemble modeling techniques. The level of threat to each species was analyzed, comparing the species potential distribution with a threat index map and with the protected areas. The threat index of anthropogenic and climatic factors encompasses the effects of population density, agriculture, proximity to roads, loss of tree cover, overexploitation, trends in wildfires, and predicted changes in temperature and precipitation. RESULTS: Our results revealed that about 0.5% of Angola's area is classified as of "Very high" threat, 23.9% as "High" threat, and 66.5% as "Moderate" threat. Three of the studied species require special conservation efforts, namely B. spiciformis and I. angolensis, which have a large fraction of predicted distribution in areas of high threat, and G. coleosperma since it has a restricted distribution area and is one of the most valuable species in international markets. The priority areas for the conservation of Leguminosae timber species were found in Benguela and Huíla. MAIN CONCLUSIONS: This study provides updated data that should be applied to inform policymakers, contributing to national conservation planning and protection of native flora in Angola. Moreover, it presents a methodological approach for the predictions of species distribution and for the creation of a threat index map that can be applied in other poorly surveyed tropical regions.

Fire spread predictions: Sweeping uncertainty under the rug.

Predicting fire spread and behavior correctly is crucial to minimize the dramatic consequences of wildfires. However, our capability of accurately predicting fire spread is still very limited, undermining the utility of such simulations to support decision-making. Improving fire spread predictions for fire management purposes, by using higher quality input data or enhanced models, can be expensive, unfeasible or even impossible. Fire managers would benefit from fast and inexpensive ways of improving their decision-making. In the present work, we focus on i) understanding if fire spread predictions can be improved through model parameter calibration based on information collected from a set of large historical wildfires in Portugal; and ii) understanding to what extent decreasing parametric uncertainty can counterbalance the impact of input data uncertainty. Our results obtained with the Fire Area Simulator (FARSITE) modeling system show that fire spread predictions can be continuously improved by 'learning' from past wildfires. The uncertainty contained in the major input variables (wind speed and direction, ignition location and fuel models) can be 'swept under the rug' through the use of more appropriate parameter sets. The proposed framework has a large potential to improve future fire spread predictions, increasing their reliability and usefulness to support fire management and decision making processes, thus potentially reducing the negative impacts of wildfires.

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.

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.

Religious Affiliation Modulates Weekly Cycles of Cropland Burning in Sub-Saharan Africa.

Vegetation burning is a common land management practice in Africa, where fire is used for hunting, livestock husbandry, pest control, food gathering, cropland fertilization, and wildfire prevention. Given such strong anthropogenic control of fire, we tested the hypotheses that fire activity displays weekly cycles, and that the week day with the fewest fires depends on regionally predominant religious affiliation. We also analyzed the effect of land use (anthrome) on weekly fire cycle significance. Fire density (fire counts.km-2) observed per week day in each region was modeled using a negative binomial regression model, with fire counts as response variable, region area as offset and a structured random effect to account for spatial dependence. Anthrome (settled, cropland, natural, rangeland), religion (Christian, Muslim, mixed) week day, and their 2-way and 3-way interactions were used as independent variables. Models were also built separately for each anthrome, relating regional fire density with week day and religious affiliation. Analysis revealed a significant interaction between religion and week day, i.e. regions with different religious affiliation (Christian, Muslim) display distinct weekly cycles of burning. However, the religion vs. week day interaction only is significant for croplands, i.e. fire activity in African croplands is significantly lower on Sunday in Christian regions and on Friday in Muslim regions. Magnitude of fire activity does not differ significantly among week days in rangelands and in natural areas, where fire use is under less strict control than in croplands. These findings can contribute towards improved specification of ignition patterns in regional/global vegetation fire models, and may lead to more accurate meteorological and chemical weather forecasting.

Modelling fire frequency in a Cerrado savanna protected area.

Covering almost a quarter of Brazil, the Cerrado is the world's most biologically rich tropical savanna. Fire is an integral part of the Cerrado but current land use and agricultural practices have been changing fire regimes, with undesirable consequences for the preservation of biodiversity. In this study, fire frequency and fire return intervals were modelled over a 12-year time series (1997-2008) for the Jalapão State Park, a protected area in the north of the Cerrado, based on burned area maps derived from Landsat imagery. Burned areas were classified using object based image analysis. Fire data were modelled with the discrete lognormal model and the estimated parameters were used to calculate fire interval, fire survival and hazard of burning distributions, for seven major land cover types. Over the study period, an area equivalent to four times the size of Jalapão State Park burned and the mean annual area burned was 34%. Median fire intervals were generally short, ranging from three to six years. Shrub savannas had the shortest fire intervals, and dense woodlands the longest. Because fires in the Cerrado are strongly responsive to fuel age in the first three to four years following a fire, early dry season patch mosaic burning may be used to reduce the extent of area burned and the severity of fire effects.

Wildfire selectivity for land cover type: does size matter?

Previous research has shown that fires burn certain land cover types disproportionally to their abundance. We used quantile regression to study land cover proneness to fire as a function of fire size, under the hypothesis that they are inversely related, for all land cover types. Using five years of fire perimeters, we estimated conditional quantile functions for lower (avoidance) and upper (preference) quantiles of fire selectivity for five land cover types - annual crops, evergreen oak woodlands, eucalypt forests, pine forests and shrublands. The slope of significant regression quantiles describes the rate of change in fire selectivity (avoidance or preference) as a function of fire size. We used Monte-Carlo methods to randomly permutate fires in order to obtain a distribution of fire selectivity due to chance. This distribution was used to test the null hypotheses that 1) mean fire selectivity does not differ from that obtained by randomly relocating observed fire perimeters; 2) that land cover proneness to fire does not vary with fire size. Our results show that land cover proneness to fire is higher for shrublands and pine forests than for annual crops and evergreen oak woodlands. As fire size increases, selectivity decreases for all land cover types tested. Moreover, the rate of change in selectivity with fire size is higher for preference than for avoidance. Comparison between observed and randomized data led us to reject both null hypotheses tested ([Formula: see text] = 0.05) and to conclude it is very unlikely the observed values of fire selectivity and change in selectivity with fire size are due to chance.

Relationships between human population density and burned area at continental and global scales.

We explore the large spatial variation in the relationship between population density and burned area, using continental-scale Geographically Weighted Regression (GWR) based on 13 years of satellite-derived burned area maps from the global fire emissions database (GFED) and the human population density from the gridded population of the world (GPW 2005). Significant relationships are observed over 51.5% of the global land area, and the area affected varies from continent to continent: population density has a significant impact on fire over most of Asia and Africa but is important in explaining fire over < 22% of Europe and Australia. Increasing population density is associated with both increased and decreased in fire. The nature of the relationship depends on land-use: increasing population density is associated with increased burned are in rangelands but with decreased burned area in croplands. Overall, the relationship between population density and burned area is non-monotonic: burned area initially increases with population density and then decreases when population density exceeds a threshold. These thresholds vary regionally. Our study contributes to improved understanding of how human activities relate to burned area, and should contribute to a better estimate of atmospheric emissions from biomass burning.