- Dr. Fermín Alcasena is a Lecturer in Landscape Restoration and Management at the Polytechnic University of Catalonia.... moreDr. Fermín Alcasena is a Lecturer in Landscape Restoration and Management at the Polytechnic University of Catalonia. He recently completed a postdoc at the University of Lleida, where he focused on understanding and managing wildfire risk, particularly through extreme fire modeling and optimizing fuel management in forests. After earning a degree in Forest Engineering, he embarked on his research journey in 2013 at the CMCC Foundation in Italy. He later pursued an MSc in Wildland Fire Science and Integrative Management, followed by a Ph.D. in Forest and Environmental Management. Subsequently, he spent three years at Oregon State University through the USDA Forest Service's J-1 International Visitor Program. However, before entering academia, he gained practical experience as an Operations Manager for a logging company and contributed to the management of public forests in rural communities in northern Spain.edit
Despite the need for preserving the carbon pools in fire-prone southern European landscapes, emission reductions from wildfire risk mitigation are still poorly understood. In this study, we estimated expected carbon emissions and carbon... more
Despite the need for preserving the carbon pools in fire-prone southern European landscapes, emission reductions from wildfire risk mitigation are still poorly understood. In this study, we estimated expected carbon emissions and carbon credits from fuel management projects ongoing in Catalonia (Spain). The planning areas encompass about 1000 km2 and represent diverse fire regimes and Mediterranean forest ecosystems. We first modeled the burn probability assuming extreme weather conditions and historical fire ignition patterns. Stand-level wildfire exposure was then coupled with fuel consumption estimates to assess expected carbon emissions. Finally, we estimated treatment cost-efficiency and carbon credits for each fuel management plan. Landscape-scale average emissions ranged between 0.003 and 0.070 T CO2 year−1 ha−1. Fuel treatments in high emission hotspots attained reductions beyond 0.06 T CO2 year−1 per treated ha. Thus, implementing carbon credits could potentially finance up to 14% of the treatment implementation costs in high emission areas. We discuss how stand conditions, fire regimes, and treatment costs determine the treatment cost-efficiency and long-term carbon-sink capacity. Our work may serve as a preliminary step for developing a carbon-credit market and subsidizing wildfire risk management programs in low-revenue Mediterranean forest systems prone to extreme wildfires.
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... more
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.
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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... more
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.
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In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on wildfire risk in fire-prone areas, a... more
In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on wildfire risk in fire-prone areas, a fire-spread modeling approach to evaluate the variations in wildfire potential induced by different spatial patterns and percentages of land abandonment was applied. The study was carried out in a 1200 km2 agropastoral area located in north-western Sardinia (Italy) mostly covered by herbaceous fuels. We compared nine land abandonment scenarios, which consisted of the control conditions (NA) and eight scenarios obtained by combining four intensity levels (10, 20, 30, 40%) and two spatial patterns of agropastoral land abandonment. The abandonment scenarios hypothesized a variation in dead fuel load and fuel depth within abandoned polygons with respect to the control conditions. For each abandonment scenario, wildfire hazard and likelihood at the landscape scal...
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Research Interests: Environmental Science, Algorithms, Environmental Management, Risk Management, Risk assessment, and 13 moreMedicine, Multidisciplinary, Probability, Italy, Humans, Computer Simulation, Weather, Fires, Geospatial Analysis, Theoretical Models, Environment Design, Risk Assessment, and Conservation of Natural Resources
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<p>High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction... more
<p>High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction modelling to evaluate the effects of fuel reduction treatments in preventing soil runoff in Mediterranean ecosystems. The study was carried out in a 68,000-ha forest area located in Northern Sardinia, Italy. We treated 15% of the study area, and compared no-treatment conditions vs alternative strategic fuel treatments. We estimated pre- and post-treatment fire behaviour by using the Minimum Travel Time (MTT) fire spread algorithm. For each fuel treatment scenario, we simulated 25,000 wildfires replicating the historic weather conditions associated with severe wildfires in the area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within the fuel treatment scenarios tested. The treatments realized nearby roads were the most efficient. We also evaluated the effects of other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimize proactive risk management activities aimed at reducing post-fire erosion in Mediterranean areas.</p>
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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... more
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.
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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... more
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 scal...
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We assessed climate change impacts on wildfires using fire modeling approach for three 30-years periods in the Mediterranean island of Sardinia (Italy). Climate projections based on the A1B IPCC emissions scenario have been carried out... more
We assessed climate change impacts on wildfires using fire modeling approach for three 30-years periods in the Mediterranean island of Sardinia (Italy). Climate projections based on the A1B IPCC emissions scenario have been carried out using the CMCC-CLM regional climate model (Rockel et al. 2008, spatial and temporal resolution of 14 km and 6 hours respectively) to derive the input data for fire spread modeling. The three studied periods were: baseline (1981-2010) and two future periods (2011-2040 and 2041-2070). To characterize the impacts of these projected changes in climate and fuel moisture conditions on fire behavior and exposure we applied a fire spread model based on the Minimum Travel Time algorithm (MTT, Finney 2002) called RANDIG. The fire simulations were run at 250 m of resolution, considering a set of 50000 fire ignitions randomly sampled from the historical database provided by JRC. To evaluate the fire exposure variations only due to climate changes, we supposed no ...
Rural-urban interface (RUI, synonym of WUI, wildland-urban interface) are key areas in land management and planning for wildfire risk mitigation. A tool for RUI mapping was developed to help decision makers cope with risk management.... more
Rural-urban interface (RUI, synonym of WUI, wildland-urban interface) are key areas in land management and planning for wildfire risk mitigation. A tool for RUI mapping was developed to help decision makers cope with risk management. Several methods for fire-risk assessment in RUI were developed, depending on the scale, the geographical context and data availability. Methods and tools were validated based on fire simulations and are now available. In addition, projections of future risk in RUI were obtained using land cover and climate change simulations. In the next four or five decades, mitigation of wildfire risk will depend on land managers capacity to control RUI development.
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... more
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 scal...