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.

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.

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.