Assessing the Effectiveness of Silvicultural Treatments on Fire Behavior in the Hyrcanian Temperate Forests of Northern Iran.

We implemented a fire modeling approach to evaluate the effectiveness of silvicultural treatments in reducing potential losses to the Hyrcanian temperate forests of northern Iran, in the Siahkal National Forest (57,110 ha). We compared the effectiveness of selection cutting, low thinning, crown thinning, and clear-cutting treatments implemented during the last ten years (n = 241, 9500-ha) on simulated stand-scale and landscape-scale fire behavior. First, we built a set of fuel models for the different treatment prescriptions. We then modeled 10,000 fires at the 30-m resolution, assuming low, moderate, high, very high, and extreme weather scenarios and human-caused ignition patterns. Finally, we implemented a One-way ANOVA test to analyze stand-level and landscape-scale modeling output differences between treated and untreated conditions. The results showed a significant reduction of stand-level fire hazard, where the average conditional flame length and crown fire probability was reduced by about 12 and 22%, respectively. The conifer plantation patches presented the most significant reduction in the crown fire probability (>35%). On the other hand, we found a minor increase in the overall burn probability and fire size at the landscape scale. Stochastic fire modeling captured the complex interactions among terrain, vegetation, ignition locations, and weather conditions in the study area. Our findings highlight fuel treatment efficacy for moderating potential fire risk and restoring fuel profiles in fire-sensitive temperate forests of northern Iran, where the growing persistent droughts and fuel buildup can lead to extreme fires in the near future.

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.

Contrasting the efficiency of landscape versus community protection fuel treatment strategies to reduce wildfire exposure and risk.

We examined the financial efficiency and effectiveness of landscape versus community protection fuel treatments to reduce structure exposure and loss to wildfire on a large fire-prone area of central Idaho (USA). The study area contained 63,707 structures distributed in 20 rural communities and resorts, encompassing 13,804 km2. We used simulation modeling to estimate expected structure loss based on burn probability and characteristics of the home ignition zone. We then designed three fuel management strategies that targeted treatments to: 1) the surrounding areas predicted to be the source of exposure to communities from large fires, 2) the home ignition zone, and 3) a combination of the landscape and home ignition zone. We evaluated each treatment scenario in terms of exposure and expected structure loss compared to a no-treatment scenario. The potential revenue from wood products was estimated for each scenario to assess the cost-efficiency. We found that the combined landscape and home ignition zone treatment scenario which treated 5.7% of the study area resulted in the highest overall reduction in predicted exposure (47.5%, 100 structures yr-1) and predicted loss (69.1%, 57 structures yr-1). Home ignition zone treatments provided the best predicted economic and per area treated performance where exposure and loss were reduced by one structure by treating 89 and 111 ha per year, respectively, with an annual cost of $33,645 and $73,672. Revenue from thinning was the highest for landscape fuel treatments and covered 16% of the required investment. This work highlighted economic and risk tradeoffs associated with alternative fuel treatment strategies to protect developed areas from large wildland fires.

Predicting Paradise: Modeling future wildfire disasters in the western US.

The 2018 Camp fire destroyed the town of Paradise, California and resulted in 82 fatalities, the worst wildfire disaster in the US to date. Future disasters of similar or greater magnitude are inevitable given predicted climate change but remain highly uncertain in terms of location and timing. As with other natural disasters, simulation models are one of the primary tools to map risk and design prevention strategies. However, risk assessments have focused on estimation of mean values rather than predicting extreme events that are increasingly becoming a reality in many parts of the globe. Using the western US as a study area, we synthesized newer wildfire simulation and building location data (54 million fires, 25 million building locations) and compared the outputs to several sources of observed exposure data. The simulations used synchronized weather among spatial simulation subunits, thereby providing estimates of extreme fire seasons, fire events within them, and exceedance probabilities at multiple scales. We found that annual area burned was accurately replicated by simulations but building exposure was substantially overestimated, although the relatively small historical sample size might have influenced the comparison. We identified extreme fire seasons in the simulation record (10,000 fire years) that exceeded historical fire seasons by 278% in terms of area burned, and 1255% in terms of buildings exposed, under contemporary climate. Simulated building exposure peaked in specific regions along gradients of building density and burnable fuels. The study is the first to explore large scale extreme wildfire exposure in terms of both annual variability and magnitude, providing a broad foundation of methods to advance wildfire disaster prediction.

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region.

Despite escalating expenditures in firefighting, extreme fire events continue to pose a major threat to ecosystem services and human communities in Mediterranean areas. Developing a safe and effective fire response is paramount to efficiently restrict fire spread, reduce negative effects to natural values, prevent residential housing losses, and avoid causalties. Though current fire policies in most countries demand full suppression, few studies have attempted to identify the strategic locations where firefighting efforts would likely contain catastrophic fire events. The success in containing those fires that escape initial attack is determined by diverse structural factors such as ground accessibility, airborne support, barriers to surface fire spread, and vegetation impedance. In this study, we predicted the success in fire containment across Catalonia (northeastern Spain) using a model generated with random forest from detailed geospatial data and a set of 73 fire perimeters for the period 2008-2016. The model attained a high predictive performance (AUC = 0.88), and the results were provided at fine resolution (25 m) for the entire study area (32,108 km2 ). The highest success rates were found in agricultural plains along the nonburnable barriers such as major road corridors and largest rivers. Low levels of containment likelihood were predicted for dense forest lands and steep-relief mountainous areas. The results can assist in suppression resource pre-positioning and extended attack decision making, but also in strategic fuels management oriented at creating defensive locations and fragmenting the landscape in operational firefighting areas. Our modeling workflow and methods may serve as a baseline to generate locally adapted models in fire-prone areas elsewhere.

Modeling initial attack success of wildfire suppression in Catalonia, Spain.

In southern European regions, the few fires that escape initial attack (IA) account for most of the burned area. Nonetheless, limited effort has been conducted to develop spatiotemporal models aiming at improving pre-positioning and deployment of fire-fighting brigades on the first dispatch. To this end, we calibrated a model to assess the probability of containment of fire by IA in Catalonia (northeastern Spain). The model was trained using machine learning algorithms from georeferenced historical fire ignition locations, fire response and weather conditions. Our results indicated that early detection, ground accessibility, and aerial support governed the broad spatial pattern of fire containment probability, with strong gradients that ranged from lowest chances of containment in northwestern mountains to highest in the coastal belt. In turn, weather conditions and fire simultaneity were crucial to explain the differences during wildfire season. We found that fires igniting above the 85th percentile of temperature and wind speed, during simultaneous fire episodes (n > 10), and 12.5 km away from the nearest fire station will probably escape IA, and grow into large events. These hazardous fire danger conditions were met 13 days per year on average during the period 1998-2015, with 5 fire simultaneous episodes escaping IA that burned 1546 ha in total. Results were provided as a set of high-resolution raster grids (100 m), which replicated the most typical weather and fire occurrence scenarios that first responders are likely to face during the wildfire season. This study reveals existing limitations in the dominant fire exclusion policy of Mediterranean areas and advocates for a comprehensive long-term wildfire management solution. Our model may help inform science-based decision-making on IA and general fire response planning in the study area.

Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain.

Southern European countries rely largely on fire suppression and ignition prevention to manage a growing wildfire problem. We explored a more wholistic, long-term approach based on priority maps for the implementation of diverse management options aimed at creating fire resilient landscapes, restoring cultural fire regimes, facilitating safe and efficient fire response, and creating fire-adapted communities. To illustrate this new comprehensive strategy for fire-prone Mediterranean areas, we developed and implemented the framework in Catalonia (northeastern Spain). We first used advanced simulation modeling methods to assess various wildfire exposure metrics across spatially changing fire-regime conditions, and these outputs were then combined with land use maps and historical fire occurrence data to prioritize different fuel and fire management options at the municipality level. Priority sites for fuel management programs concentrated in the central and northeastern high-hazard forestlands. The suitable areas for reintroducing fires in natural ecosystems located in scattered municipalities with ample lightning ignitions and minimal human presence. Priority areas for ignition prevention programs were mapped to populated coastal municipalities and main transportation corridors. Landscapes where fire suppression is the principal long-term strategy concentrated in agricultural plains with a high density of ignitions. Localized programs to build defensible space and improve self-protection on communities could be emphasized in the coastal wildland-urban interface and inner intermix areas from Barcelona and Gerona. We discuss how the results of this study can facilitate collaborative landscape planning and identify the constraints that prevent a longer term and more effective solution to better coexist with fire in southern European regions.

Wildfire spread, hazard and exposure metric raster grids for central Catalonia.

We provide 40 m resolution wildfire spread, hazard and exposure metric raster grids for the 0.13 million ha fire-prone Bages County in central Catalonia (northeastern Spain) corresponding to node influence grid (NIG), crown fraction burned (CFB) and fire transmission to residential houses (TR). Fire spread and behavior data (NIG, CFB and fire perimeters) were generated with fire simulation modeling considering wildfire season extreme fire weather conditions (97th percentile). Moreover, CFB was also generated for prescribed fire (Rx) mild weather conditions. The TR smoothed grid was obtained with a geospatial analysis considering large fire perimeters and individual residential structures located within the study area. We made these raster grids available to assist in the optimization of wildfire risk management plans within the study area and to help mitigate potential losses from catastrophic events.

The wildland-urban interface raster dataset of Catalonia.

We provide the wildland urban interface (WUI) map of the autonomous community of Catalonia (Northeastern Spain). The map encompasses an area of some 3.21 million ha and is presented as a 150-m resolution raster dataset. Individual housing location, structure density and vegetation cover data were used to spatially assess in detail the interface, intermix and dispersed rural WUI communities with a geographical information system. Most WUI areas concentrate in the coastal belt where suburban sprawl has occurred nearby or within unmanaged forests. This geospatial information data provides an approximation of residential housing potential for loss given a wildfire, and represents a valuable contribution to assist landscape and urban planning in the region.

Optimizing prescribed fire allocation for managing fire risk in central Catalonia.

We used spatial optimization to allocate and prioritize prescribed fire treatments in the fire-prone Bages County, central Catalonia (northeastern Spain). The goal of this study was to identify suitable strategic locations on forest lands for fuel treatments in order to: 1) disrupt major fire movements, 2) reduce ember emissions, and 3) reduce the likelihood of large fires burning into residential communities. We first modeled fire spread, hazard and exposure metrics under historical extreme fire weather conditions, including node influence grid for surface fire pathways, crown fraction burned and fire transmission to residential structures. Then, we performed an optimization analysis on individual planning areas to identify production possibility frontiers for addressing fire exposure and explore alternative prescribed fire treatment configurations. The results revealed strong trade-offs among different fire exposure metrics, showed treatment mosaics that optimize the allocation of prescribed fire, and identified specific opportunities to achieve multiple objectives. Our methods can contribute to improving the efficiency of prescribed fire treatment investments and wildfire management programs aimed at creating fire resilient ecosystems, facilitating safe and efficient fire suppression, and safeguarding rural communities from catastrophic wildfires. The analysis framework can be used to optimally allocate prescribed fire in other fire-prone areas within the Mediterranean region and elsewhere.

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas.

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981-2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981-2010, 2011-2040, and 2041-2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041-2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

Assessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area.

We used a fire simulation modeling approach to assess landscape scale wildfire exposure for highly valued resources and assets (HVR) on a fire-prone area of 680 km(2) located in central Sardinia, Italy. The study area was affected by several wildfires in the last half century: some large and intense fire events threatened wildland urban interfaces as well as other socioeconomic and cultural values. Historical wildfire and weather data were used to inform wildfire simulations, which were based on the minimum travel time algorithm as implemented in FlamMap. We simulated 90,000 fires that replicated recent large fire events in the area spreading under severe weather conditions to generate detailed maps of wildfire likelihood and intensity. Then, we linked fire modeling outputs to a geospatial risk assessment framework focusing on buffer areas around HVR. The results highlighted a large variation in burn probability and fire intensity in the vicinity of HVRs, and allowed us to identify the areas most exposed to wildfires and thus to a higher potential damage. Fire intensity in the HVR buffers was mainly related to fuel types, while wind direction, topographic features, and historically based ignition pattern were the key factors affecting fire likelihood. The methodology presented in this work can have numerous applications, in the study area and elsewhere, particularly to address and inform fire risk management, landscape planning and people safety on the vicinity of HVRs.

Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy.

In this paper, we applied landscape scale wildfire simulation modeling to explore the spatiotemporal patterns of wildfire likelihood and intensity in the island of Sardinia (Italy). We also performed wildfire exposure analysis for selected highly valued resources on the island to identify areas characterized by high risk. We observed substantial variation in burn probability, fire size, and flame length among time periods within the fire season, which starts in early June and ends in late September. Peak burn probability and flame length were observed in late July. We found that patterns of wildfire likelihood and intensity were mainly related to spatiotemporal variation in ignition locations, fuel moisture, and wind vectors. Our modeling approach allowed consideration of historical patterns of winds, ignition locations, and live and dead fuel moisture on fire exposure factors. The methodology proposed can be useful for analyzing potential wildfire risk and effects at landscape scale, evaluating historical changes and future trends in wildfire exposure, as well as for addressing and informing fuel management and risk mitigation issues.