Wildfire Scenarios Research Articles

Tiny-Object Detection Based on Optimized YOLO-CSQ for Accurate Drone Detection in Wildfire Scenarios

Wildfires, which are distinguished by their destructive nature and challenging suppression, present a significant threat to ecological environments and socioeconomic systems. In order to address this issue, the development of efficient and accurate fire detection technologies for early warning and timely response is essential. This paper addresses the complexity of forest and mountain fire detection by proposing YOLO-CSQ, a drone-based fire detection method built upon an improved YOLOv8 algorithm. Firstly, we introduce the CBAM attention mechanism, which enhances the model’s multi-scale fire feature extraction capabilities by adaptively adjusting weights in both the channel and spatial dimensions of feature maps, thereby improving detection accuracy. Secondly, we propose an improved ShuffleNetV2 backbone network structure, which significantly reduces the model’s parameter count and computational complexity while maintaining feature extraction capabilities. This results in a more lightweight and efficient model. Thirdly, to address the challenges of varying fire scales and numerous weak emission targets in mountain fires, we propose a Quadrupled-ASFF detection head for weighted feature fusion. This enhances the model’s robustness in detecting targets of different scales. Finally, we introduce the WIoU loss function to replace the traditional CIoU object detection loss function, thereby enhancing the model’s localization accuracy. The experimental results demonstrate that the improved model achieves an mAP@50 of 96.87%, which is superior to the original YOLOV8, YOLOV9, and YOLOV10 by 10.9, 11.66, and 13.33 percentage points, respectively. Moreover, it exhibits significant advantages over other classic algorithms in key evaluation metrics such as precision, recall, and F1 score. These findings validate the effectiveness of the improved model in mountain fire detection scenarios, offering a novel solution for early warning and intelligent monitoring of mountain wildfires.

Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Rising wildfire incidents in South America, potentially exacerbated by climate change, require an exploration of sustainable approaches for fire risk reduction. This study investigates wildfire-prone meteorological conditions and assesses the susceptibility in Colombia’s megadiverse northern region. Utilizing this knowledge, we apply a machine learning model and the Monte Carlo approach to evaluate sustainability strategies for mitigating fire risk. The findings indicate that a substantial number of fires occur in the southern region, especially in the first two seasons of the year, and in the northeast in the last two seasons. Both are characterized by high temperatures, minimal precipitation, strong winds, and dry conditions. The developed model demonstrates significant predictive accuracy with the HIT, FAR, and POC of 87.9%, 28.3%, and 95.7%, respectively, providing insights into the probabilistic aspects of fire development. Various scenarios showed that a decrease in soil temperature reduces the risk mostly in lower altitudes and leaf skin reservoir content in the highest altitudes, as well as in the north region. Sustainability strategies, such as tree belts, agroforestry mosaics, and forest corridors emerge as crucial measures. The results underscore the importance of proactive measures in mitigating wildfire impact, offering actionable insights for crafting effective sustainability strategies amid escalating fire risks.

Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions

Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes.

Interaction of the flow and flame dynamics of a line wildfire in the atmospheric wake flow of a ridge

Understanding the flow and flame dynamics is important due to its relevance in wildfires, which become severe in recent years. The flow and flame dynamics of wildfire have been extensively studied on flat and inclined surface but are rarely studied under rough surface, such as the condition of a ridge or a hill, which is relevant for many of the wildfire scenarios in mountain areas. To investigate the interaction of wildfire with the wake flow over a ridge, this is the first study that uses a large-eddy simulation to model the flow and flame dynamics of a line fire located at the lee side of a ridge. The results show that the flame dynamics can be significantly influenced by the wake flow, and their interaction features cyclic four stages.

Dual-Dataset Deep Learning for Improved Forest Fire Detection: A Novel Hierarchical Domain-Adaptive Learning Approach

This study introduces a novel hierarchical domain-adaptive learning framework designed to enhance wildfire detection capabilities, addressing the limitations inherent in traditional convolutional neural networks across varied forest environments. The framework innovatively employs a dual-dataset approach, integrating both non-forest and forest-specific datasets to train a model adept at handling diverse wildfire scenarios. The methodology leverages a novel framework that combines shared layers for broad feature extraction with specialized layers for forest-specific details, demonstrating versatility across base models. Initially demonstrated with EfficientNetB0, this adaptable approach could be applicable with various advanced architectures, enhancing wildfire detection. The research’s comparative analysis, benchmarking against conventional methodologies, showcases the proposed approach’s enhanced performance. It particularly excels in accuracy, precision, F1-score, specificity, MCC, and AUC-ROC. This research significantly reduces false positives in wildfire detection through a novel blend of multi-task learning, dual-dataset training, and hierarchical domain adaptation. Our approach advances deep learning in data-limited, complex environments, offering a critical tool for ecological conservation and community protection against wildfires.

Oil sands restoration with warm‐adapted trees improves outcomes under moderate but not severe warming scenarios

AbstractSuccessful restoration of human‐disturbed landscapes and ecosystems will be increasingly compromised by the impacts of climate warming. Assisted migration and climate‐informed restoration, in which populations and species adapted to future climates are selected for restoration planting, have emerged as management tools to mitigate climate change effects. However, it is unclear whether climate‐informed restoration could offset the negative effects of climate change and enable successful restoration. We used a forest landscape model to evaluate the potential for reclamation activities to restore western Canadian boreal forest landscapes severely degraded by oil sands mining. We parametrized tree populations adapted to growing in warmer climates and then simulated the planting of local or southern tree populations under different climate change, mining, and wildfire disturbance scenarios. We found that planting trees better adapted to a warmer climate mitigated climate‐change and wildfire‐caused decreases in biomass across the landscape, but only under moderate climate change scenarios. The compensatory effect of planting populations adapted to warmer southern climates disappeared under a more severe climate change scenario. The advantage of planting southern populations also disappeared under wildfire scenarios, generally doubling the biomass loss compared with scenarios without wildfire. With wildfire and strong climate change effects, forest cover disappeared from much of the landscape, regardless of the planting scenario, causing it to change markedly from present‐day continuous boreal forest cover. We argue that such conditions would have large ecological and economic consequences. Scenario modeling with forest landscape models could be used as a tool to identify the long‐term success of restoration actions and to understand possible consequences of climate‐informed restoration.

The analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire

Evacuation is a crucial policy to mitigate wildfire impacts. Understanding traffic dynamics during a wildfire evacuation can help authorities to improve in improving emergency management plans, thus improving life safety. In this study, we developed a methodology to extract historical traffic data from vehicle detector stations and automate the analysis of traffic dynamics for actual wildfire evacuations. This has been implemented in an open-access tool called Traffic Dynamic Analyser (TDA) which generates speed-density and flow-density relationships from data using both commonly used macroscopic traffic models as well as machine learning techniques (e.g., support vector regression). The use of the methodology is demonstrated with a case study of the 2020 Glass Fire in California, USA. The results from TDA showed a slight reduction in speeds and flows on US Highway 101 during the evacuation scenario, compared with the routine scenario. Moreover, background traffic has been shown to play a key role in the 2020 Glass Fire compared with previous wildfire evacuation scenarios (e.g., the 2019 Kincade fire). The case study showed that the methodology implemented in the TDA can be used to understand traffic evacuation dynamics in wildfire scenarios and to validate evacuation models.

O explorador: desenvolvimento de um jogo digital inclusivo

Aplicativos estão em todos os lugares e ganham cada vez mais espaço na vida das pessoas. Junto com o crescimento da tecnologia, as relações sociodigitais também crescem, como esferas e pontes de integração e desenvolvimento humano. O presente projeto tem como objetivo propor um jogo acessível ao grupo de pessoas com deficiência visual, visando incluí-los socialmente e oferecer, como em qualquer outro jogo, diversão e lazer. Para isso, foram realizadas várias pesquisas sobre o tema da deficiência, inclusão e restrição da participação social, bem como pesquisas sobre o desenvolvimento de jogos, recursos de acessibilidade e meios de inclusão em jogos. Aqui apresentamos um jogo criado especificamente para pessoas cegas, demonstrando as funcionalidades do mesmo e suas potencialidades.

Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation

To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

Spot-fire distance increases disproportionately for wildfires compared to prescribed fires as grasslands transition to Juniperus woodlands.

Woody encroachment is one of the greatest threats to grasslands globally, depleting a suite of ecosystem services, including forage production and grassland biodiversity. Recent evidence also suggests that woody encroachment increases wildfire danger, particularly in the Great Plains of North America, where highly volatile Juniperus spp. convert grasslands to an alternative woodland state. Spot-fire distances are a critical component of wildfire danger, describing the distance over which embers from one fire can cause a new fire ignition, potentially far away from fire suppression personnel. We assess changes in spot-fire distances as grasslands experience Juniperus encroachment to an alternative woodland state and how spot-fire distances differ under typical prescribed fire conditions compared to conditions observed during wildfire. We use BehavePlus to calculate spot-fire distances for these scenarios within the Loess Canyons Experimental Landscape, Nebraska, U.S.A., a 73,000-ha ecoregion where private-lands fire management is used to reduce woody encroachment and prevent further expansion of Juniperus fuels. We found prescribed fire used to control woody encroachment had lower maximum spot-fire distances compared to wildfires and, correspondingly, a lower amount of land area at risk to spot-fire occurrence. Under more extreme wildfire scenarios, spot-fire distances were 2 times higher in grasslands, and over 3 times higher in encroached grasslands and Juniperus woodlands compared to fires burned under prescribed fire conditions. Maximum spot-fire distance was 450% greater in Juniperus woodlands compared to grasslands and exposed an additional 14,000 ha of receptive fuels, on average, to spot-fire occurrence within the Loess Canyons Experimental Landscape. This study demonstrates that woody encroachment drastically increases risks associated with wildfire, and that spot fire distances associated with woody encroachment are much lower in prescribed fires used to control woody encroachment compared to wildfires.

Parametric evaluation of heat transfer mechanisms in a WUI fire scenario

Background Wildland–urban interface (WUI) fires are becoming more frequent and catastrophic as they are associated with the effects of climate change, demographic pressure, human activities, abandonment of rural areas and activities promoting dangerous fuel continuity. For example, in the central regions of Portugal, Chile and California, severe direct and indirect impacts have been observed, with a catastrophic number of fatalities. Aims Mitigating and reducing the impacts of wildfires in the WUI requires understanding heat transfer mechanisms from forest fires and understanding how structures ignite is crucial to define and implement new mitigation strategies. Methods Adopting Computational Fluid Dynamics is essential to assess the WUI fire problem by simulating fire behaviour and quantifying its characteristics. In this paper, a building is exposed to several wildfire scenarios, assessing the influence of parameters such as materials, fuels, topography and meteorological conditions. Key results The investigated scenarios were developed considering validated Fire Dynamics Simulator (FDS) models of single trees on fire and the influence of governing parameters was quantified. Conclusions For the selected scenarios, the impacts on the building were assessed and compared, quantifying heat release rates, radiative heat flux and adiabatic surface temperature. Implications This research contributes to a Performance-Based Design (PBD) approach for buildings in the WUI.

Improved logistic models of crown fire probability in Canadian conifer forests

Background Crown fires are an ecologically necessary but hazardous process in conifer forests. Prediction of their behaviour in Canada has largely depended on the Canadian Forest Fire Behaviour Prediction System, in which fire weather indices drive primarily fixed fuel type models. The Crown Fire Initiation and Spread (CFIS) system presents a more flexible approach to predicting crown fire occurrence than fixed fuel type models. Aims Using a multi-decadal database of experimental fires carried out in conifer plots (1960–2019, n = 113), our aim was to develop updated models based on the CFIS system approach, fitting crown fire occurrence models to fire environment variables using logistic regression. Methods We tested alternative fuel moisture estimates and compared various model forms using repeated cross-validation. In two-storeyed stands, crown fire occurrence was defined as the involvement of lower canopy stratum fuels. Key results Final models based on wind speed, fuel strata gap, litter moisture and surface fuel consumption predicted crowning events correctly in up to 92% of cases in training data (89% in cross-validation). Conclusions and implications These new models offer improved accuracy and flexibility that will help users assess how competing environmental factors interact under different fuel treatments and wildfire scenarios.

How do large wildfires impact sediment redistribution over multiple decades?

AbstractWildfires have become an increasing threat for Mediterranean ecosystems, due to increasing climate change‐induced wildfire activity and changing land management practices. In addition to the initial risk, wildfires can alter the soil in various ways—depending on fire severity—and cause enhanced post‐fire erosion. Usually, post‐fire erosion studies focus on a short time window and lack the attention for sediment dynamics at larger spatial scales. Yet, these large spatial and temporal scales are fundamental for a better understanding of long‐term destructive effects of multiple recurring wildfires on post‐fire erosion processes and catchment sediment dynamics. In this study the landscape evolution model LAPSUS was used to simulate erosion and deposition in the 404 km2 Águeda catchment in north‐central Portugal over a 41‐year (1979–2020) timespan, including eight wildfires each burning >1000 ha. To include variation in fire severity and its impact on the soil, four burn severity classes, represented by the difference normalized burn ratio (dNBR), were parameterized. Although model calibration was difficult due to lack of spatial and temporal measured data, the results show that long‐term post‐fire net erosion rates were significantly higher in the wildfire scenarios (5.95 ton ha−1 yr−1) compared to those of a non‐wildfire scenario (0.58 ton ha−1 yr−1). Furthermore, erosion values increased with burn severity and multiple wildfires increased the overall catchment sediment build‐up. Simulated erosion patterns showed great spatial variability, with large deposition and erosion rates inside streams. This variability made it difficult to identify land uses that were most sensitive for post‐fire erosion, because some land uses were located in more erosion‐sensitive areas (e.g. streams, gullies) or were more affected by high burn severity levels than others. Despite these limitations, LAPSUS performed well on addressing spatial sediment processes and can contribute to pre‐fire management strategies, by identifying locations at risk of post‐fire erosion.

Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke.

Introduction:Wildfires are a threat to public health world-wide that are growing in intensity and prevalence. The biological mechanisms that elicit wildfire-associated toxicity remain largely unknown. The potential involvement of cross-tissue communication via extracellular vesicles (EVs) is a new mechanism that has yet to be evaluated.Methods:Female CD-1 mice were exposed to smoke condensate samples collected from the following biomass burn scenarios: flaming peat; smoldering peat; flaming red oak; and smoldering red oak, representing lab-based simulations of wildfire scenarios. Lung tissue, bronchoalveolar lavage fluid (BALF) samples, peripheral blood, and heart tissues were collected 4 and 24 h post-exposure. Exosome-enriched EVs were isolated from plasma, physically characterized, and profiled for microRNA (miRNA) expression. Pathway-level responses in the lung and heart were evaluated through RNA sequencing and pathway analyses.Results:Markers of cardiopulmonary tissue injury and inflammation from BALF samples were significantly altered in response to exposures, with the greatest changes occurring from flaming biomass conditions. Plasma EV miRNAs relevant to cardiovascular disease showed exposure-induced expression alterations, including miR-150, miR-183, miR-223-3p, miR-30b, and miR-378a. Lung and heart mRNAs were identified with differential expression enriched for hypoxia and cell stress-related pathways. Flaming red oak exposure induced the greatest transcriptional response in the heart, a large portion of which were predicted as regulated by plasma EV miRNAs, including miRNAs known to regulate hypoxia-induced cardiovascular injury. Many of these miRNAs had published evidence supporting their transfer across tissues. A follow-up analysis of miR-30b showed that it was increased in expression in the heart of exposed mice in the absence of changes to its precursor molecular, pri-miR-30b, suggesting potential transfer from external sources (e.g., plasma).Discussion:This study posits a potential mechanism through which wildfire exposures induce cardiopulmonary responses, highlighting the role of circulating plasma EVs in intercellular and systems-level communication between tissues.

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems.

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

Status and Practical Implications of Forest Fire Management in Nepal

Forest fire occurs globally on various scales every year, causing economic, social, ecological, and environmental damage. In a developing country like Nepal, a critical review of policy envisions and its translation concerning the prevailing wildfire scenarios is lacking which is crucial for sustainable forest fire management. We examine the existing disaster management policies relevant to forest fire, and analyse forest fire incidences and their impact on forest cover loss and eCO2 (equivalent carbon dioxide) emissions using the data from Global Forest Watch (GFW) between 2001 to 2021. We also assessed public finance scenarios for forest fire management in Nepal and correlated and compared these data with each other. Our results find that the ‘Forest Fire Management Strategy 2010’ is the only comprehensive policy document that guides forest fire management in Nepal. Despite having low political and financial support (dedicated forest fire management budget is <0.5 per cent out of all forestry sector), awareness raising programme such as celebration of forest fire management week, broadcasting awareness raising messages and information, use of forest firefighting tools and equipements are some of the ground level interventions to prevent and control forest fire. GFW data shows that 29443 visual infrared imaging radiometer suite (VIIRS) fire alerts record (available only for 2012 to 2021), burns 207956.5 hectares (ha) area, 3.28 ha of forest cover loss, and 1132 Mg eCO2 emissions every year on average. These figures are 0.07 per cent, 0.05 per cent, and 0.0176 per cent compared to total forest cover loss, total emission (eCO2) from the forest, and proportion of tree cover loss to burnt area, respectively. Results entail that the existing forest fire management policy requires a revisit considering the changed national and international context, forest fire should be considered as a high priority by all sectors and actors because of its widespread socio-economic, ecological, and environmental impacts. Ignorance of forest fire management could severely hinder the target for national and international commitments including performance-based carbon trading and maintaining forest cover. The findings would provide insights for policy decisions to manage forest fires sustainably.

Targeted grazing and mechanical thinning enhance forest stand resilience under a narrow range of wildfire scenarios

AbstractIncreasing wildfire activity has spurred ecological resilience‐based management that aims to reduce the vulnerability of forest stands to wildfire by reducing the probability of crown fire. Targeted grazing is increasingly being used to build forest resilience to wildfire, either on its own or in combination with treatments such as mechanical thinning; however, it is unclear how effective this method is at altering the probability of crown fire in forest stands. We use crown fire simulation models to quantify to what extent targeted grazing, mechanical thinning targeting the vertical fuel stratum, and a combination of both treatments alter eastern ponderosa pine savanna stand resilience to wildfire by modeling their relative impacts on the fuel stratum gap and subsequent crown fire occurrence under six different wildfire risk scenarios generated by altering wind and fuel moisture conditions. We then model changes in the probability of crown fire occurrence resulting from treatments across 75 field‐sampled sites in the Pine Ridge region of Nebraska relative to predicted crown fire occurrence when sites are left untreated. We find that mechanical (vertical) thinning has the potential to alter the probability of crown fire in ponderosa pine stands to a much greater extent than targeted grazing. Combining both approaches had a slightly higher probability of reducing crown fire risk across the greatest range of wildfire risk scenarios. Across 75 sample sites, targeted grazing was only predicted to prevent crown fire occurrence at two sites expected to experience crown fire under observed stand conditions across all six of our wildfire risk scenarios. In contrast, targeted grazing combined with mechanical thinning was predicted to prevent crown fire at approximately half of the sites expected to experience crown fire under observed conditions for mild and moderate wildfire risk scenarios. Thus, targeted grazing should be combined with mechanical thinning to best enhance forest resilience to wildfire. No combination of targeted grazing or mechanical thinning was able to alter the probability of crown fire under wildfire risk scenarios most conducive to wildfire, confirming that relying solely on vertical thinning and targeted grazing is unlikely to sufficiently enhance resilience of forest stands to future wildfire conditions.

Hydroclimatic Conditions, Wildfire, and Species Assemblages Influence Co-Occurrence of Bull Trout and Tailed Frogs in Northern Rocky Mountain Streams

Although bull trout (Salvelinus confluentus) and tailed frogs (Ascaphus montanus) have co-existed in forested Pacific Northwest streams for millennia, these iconic cold-water specialists are experiencing rapid environmental change caused by a warming climate and enhanced wildfire activity. Our goal was to inform future conservation by examining the habitat associations of each species and conditions that facilitate co-occupancy. We repurposed data from previous studies in the northern Rocky Mountains to assess the efficacy of bull trout electrofishing surveys for determining the occurrence of tailed frogs and the predictive capacity of habitat covariates derived from in-stream measurements and geospatial sources to model distributions of both species. Electrofishing reliably detected frog presence (89.2% rate). Both species were strongly associated with stream temperature and flow regime characteristics, and less responsive to riparian canopy cover, slope, and other salmonids. Tailed frogs were also sensitive to wildfire, with occupancy probability peaking around 80 years after a fire. Co-occupancy was most probable in locations with low-to-moderate frequencies of high winter flow events, few other salmonids, a low base-flow index, and intermediate years since fire. The distributions of these species appear to be sensitive to environmental conditions that are changing this century in forests of the northern Rocky Mountains. The amplification of climate-driven effects after wildfire may prove to be particularly problematic in the future. Habitat differences between these two species, considered to be headwater specialists, suggest that conservation measures designed for one may not fully protect the other. Additional studies involving future climate and wildfire scenarios are needed to assess broader conservation strategies and the potential to identify refuge streams where both species are likely to persist, or complementary streams where each could exist separately into the future.

A model comparison of fire return interval impacts on carbon and species dynamics in a southeastern U.S. pineland

AbstractEcosystem process models can be used to predict forest response to disturbances at a range of scales. Selection of the spatial class of model should depend on the scale of the research or management question, and model type should depend on the ecosystem attributes of interest. In some cases, multiple classes of models could be used to address a single research question, with evaluations at each scale having potential benefits and drawbacks. This study examines two classes of models relative to how fire return intervals impact carbon and species dynamics in a southeastern U.S. pineland. A model that can be run as a global class model (ED) and a landscape class model (LANDIS‐II) were parameterized with species inventory data from an experimental Pinus Palustris (longleaf pine) forest in southwest Georgia, and simulations were calibrated with literature values, then validated with eddy‐covariance data from the study site. A variety of fire scenarios that included prescribed fire with a 2‐yr return interval, fire exclusion, and three wildfire scenarios (20‐, 50‐, and 100‐yr return intervals) were used for model runs. Results were compared and evaluated with regard to ecosystem carbon and species dynamics. Both models illustrated that prescribed fire provided the greatest carbon sequestration potential and most stable aboveground biomass through time when compared to the wildfire scenarios. The fire exclusion scenario for LANDIS‐II was the only scenario where prescribed fire did not provide the greatest carbon sequestration potential. However, fire exclusion on the order of centuries was a condition of this outcome and the occurrence of such long fire‐free periods is considered unrealistic in this fire‐prone landscape. Differences between models were primarily the result of the underlying characteristics of each model class, namely the spatial resolution and number of species included. In the end, two vastly different scale models supported the conclusion that high frequency prescribed fire in southeastern U.S. pinelands stabilizes carbon and maintains species composition in an ecosystem that is a known ecological hotspot.

Reconstruction of the Spring Hill Wildfire and Exploration of Alternate Management Scenarios Using QUIC-Fire

New physics-based fire behavior models are poised to revolutionize wildland fire planning and training; however, model testing against field conditions remains limited. We tested the ability of QUIC-Fire, a fast-running and computationally inexpensive physics-based fire behavior model to numerically reconstruct a large wildfire that burned in a fire-excluded area within the New York–Philadelphia metropolitan area in 2019. We then used QUIC-Fire as a tool to explore how alternate hypothetical management scenarios, such as prescribed burning, could have affected fire behavior. The results of our reconstruction provide a strong demonstration of how QUIC-Fire can be used to simulate actual wildfire scenarios with the integration of local weather and fuel information, as well as to efficiently explore how fire management can influence fire behavior in specific burn units. Our results illustrate how both reductions of fuel load and specific modification of fuel structure associated with frequent prescribed fire are critical to reducing fire intensity and size. We discuss how simulations such as this can be important in planning and training tools for wildland firefighters, and for avenues of future research and fuel monitoring that can accelerate the incorporation of models like QUIC-Fire into fire management strategies.