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Zhou, Fangrong, Hao Pan, Zhenyu Gao, Xuyong Huang, Guochao Qian, Yu Zhu, and Feng Xiao. "Fire Prediction Based on CatBoost Algorithm." Mathematical Problems in Engineering 2021 (July 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/1929137.
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In recent years, increasingly severe wildfires have posed a significant threat to the safe and stable operation of transmission lines. Wildfire risk assessment and early warning have become an important research topic in power grid risk assessment. This study proposes a fire prediction model on the basis of the CatBoost algorithm to effectively predict the fire point. Five wildfire risk factors, including vegetation factors, meteorological factors, human factors, terrain factors, and land surface temperature, were combined using the feature selection method on the basis of the gradient boosting decision tree model and principal component analysis to achieve dimensionality reduction of redundant data and create a fire prediction model. The MODIS fire point product is used as the model evaluation data. The verification result uses the AUC value as the evaluation factor. The accuracy of the model is 0.82, and the AUC value is 0.83. The obtained fire point evaluation results are in good agreement with the actual fire points. Results show that this model can effectively predict the risk of wildfires.
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Adaktylou, Nektaria, Dimitris Stratoulias, and Rick Landenberger. "Wildfire Risk Assessment Based on Geospatial Open Data: Application on Chios, Greece." ISPRS International Journal of Geo-Information 9, no. 9 (August 28, 2020): 516. http://dx.doi.org/10.3390/ijgi9090516.
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Wildfires burn tens of thousands of hectares of forest, chaparral and grassland in Mediterranean countries every year, giving rise to landscape, ecologic, economic, and public safety concerns. On the Greek island of Chios and in many other Mediterranean landscapes, areas affected by fire are difficult to access and control due to rugged terrain, requiring wildfire preparedness and response plans that support fire fighting. This study utilized open source data and a weighted linear combination to extract factors that determine wildfire risk. Landsat satellite imagery and publicly available geospatial data were used to create a Geographic Information System and a multi-criteria analysis to develop a methodology for spatially modeling fire risk on Chios, a Greek island with frequent fire occurrence. This study focused on the static, structural component of the risk assessment to produce a spatial distribution of fire risk as a thematic map. Fire weather conditions were accounted for using Fuel Moisture Content, which reflected dryness of dead fuels and water deficit of live biomass. To assess the results, historic fire data representing actual occurrence of fire incidents were compared with probable fire locations predicted by our GIS model. It was found that there was a good agreement between the ground reference data and the results of the created fire risk model. The findings will help fire authorities identify areas of high risk for wildfire and plan the allocation of resources accordingly. This is because the outputs of the designed fire risk model are not complex or challenging to use in Chios, Greece and other landscapes.
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Palaiologou, Palaiologos, Alan A. Ager, Cody R. Evers, Max Nielsen-Pincus, Michelle A. Day, and Haiganoush K. Preisler. "Fine-scale assessment of cross-boundary wildfire events in the western United States." Natural Hazards and Earth System Sciences 19, no. 8 (August 14, 2019): 1755–77. http://dx.doi.org/10.5194/nhess-19-1755-2019.
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Abstract. We report a fine-scale assessment of cross-boundary wildfire events for the western US. We used simulation modeling to quantify the extent of fire exchange among major federal, state, and private land tenures and mapped locations where fire ignitions can potentially affect populated places. We examined how parcel size affects wildfire transmission and partitioned the relative amounts of transmitted fire between human and natural ignitions. We estimated that 85 % of the total predicted wildfire activity, as measured by area burned, originates from four land tenures (Forest Service, Bureau of Land Management, private, and state lands) and 63 % of the total amount results from natural versus human ignitions. On average, one-third of the area burned by predicted wildfires was nonlocal, meaning that the source ignition was on a different land tenure. Land tenures with smaller parcels tended to receive more incoming fire on a proportional basis, while the largest fires were generated from ignitions in national parks, national forests, and public and tribal lands. Among the 11 western states, the amount and pattern of cross-boundary fire varied substantially in terms of which land tenures were mostly exposed, by whom, and to what extent. We also found spatial variability in terms of community exposure among states, and more than half of the predicted structure exposure was caused by ignitions on private lands or within the wildland–urban interface areas. This study addressed gaps in existing wildfire risk assessments that do not explicitly consider cross-boundary fire transmission and do not identify the source of fire. The results can be used by state, federal, and local fire planning organizations to help improve risk mitigation programs.
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Salis, Michele, Liliana Del Giudice, Peter R. Robichaud, Alan A. Ager, Annalisa Canu, Pierpaolo Duce, Grazia Pellizzaro, et al. "Coupling wildfire spread and erosion models to quantify post-fire erosion before and after fuel treatments." International Journal of Wildland Fire 28, no. 9 (2019): 687. http://dx.doi.org/10.1071/wf19034.
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Wildfires are known to change post-fire watershed conditions such that hillslopes can become prone to increased erosion and sediment delivery. In this work, we coupled wildfire spread and erosion prediction modelling to assess the benefits of fuel reduction treatments in preventing soil runoff. The study was conducted in a 68000-ha forest area located in Sardinia, Italy. We compared no-treatment conditions v. alternative strategic fuel treatments performed in 15% of the area. Fire behaviour before and after treatments was estimated by simulating 25000 wildfires for each condition using the minimum travel time fire-spread algorithm. The fire simulations replicated historic conditions associated with severe wildfires in the study 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 fuel treatment scenarios. The most efficient treatment alternative was that implemented near the road network. We also evaluated 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 optimise proactive risk management activities intended to reduce post-fire erosion.
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Kotroni, Vassiliki, Constantinos Cartalis, Silas Michaelides, Julia Stoyanova, Fillipos Tymvios, Antonis Bezes, Theodoros Christoudias, et al. "DISARM Early Warning System for Wildfires in the Eastern Mediterranean." Sustainability 12, no. 16 (August 18, 2020): 6670. http://dx.doi.org/10.3390/su12166670.
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This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.
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Tutsch, Matthew, Wolfgang Haider, Ben Beardmore, Kenneth Lertzman, Andrew B. Cooper, and Robert C. Walker. "Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern Gulf Islands, British Columbia, Canada." Canadian Journal of Forest Research 40, no. 11 (November 2010): 2104–14. http://dx.doi.org/10.1139/x10-159.
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Wildfire risk assessment research has made considerable progress towards estimating the probability of wildfires but comparatively little progress towards estimating the expected consequences of potential fires. One challenge with estimating wildfire consequences has been to identify a common metric that can be applied to consequences measured in different units. In this paper, we use the preferences of representatives of local fire management agencies as the common consequences metric and apply it to a case study in the southern Gulf Islands, British Columbia, Canada. The method uses an expert survey and a maximum-difference conjoint analysis to establish the relative importance of specific fire consequences. A fire with a major potential for loss of life was considered to be about three times worse than major damage to houses and 4.5 times worse than loss of a rare species. Risk ratings were very sensitive to changes in fire consequences ratings. As the complexity of values at risk and number of stakeholders increase, the most efficient allocation of wildfire prevention, protection, and suppression resources becomes increasingly challenging to determine. Thus, as the complexity of stakeholder representation and values at risk increases, we need to pay increasing attention to quantitative methods for measuring wildfire consequences.
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Mahmoud, Hussam, and Akshat Chulahwat. "Assessing wildland–urban interface fire risk." Royal Society Open Science 7, no. 8 (August 2020): 201183. http://dx.doi.org/10.1098/rsos.201183.
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Recent wildfire events, in the United States (USA) and around the world, have resulted in thousands of homes destroyed and many lives lost, leaving communities and policy makers, once again, with the question as to how to manage wildfire risk. This is particularly important given the prevalent trend of increased fire frequency and intensity. Current approaches to managing wildfires focus on fire suppression and managing fuel build-up in wildlands. However, reliance on these strategies alone has clearly proven inadequate. As such, focus should be shifted towards minimizing potential losses to communities. Achieving this goal, however, requires detailed understanding of the factors that contribute to community vulnerability and the interplay between probability of ignition, vulnerability and calculated risk. In this study, we evaluate wildfire risk for four different communities across the USA for the duration of May to September to communicate a different perspective of risk assessment. We show, for the first time, that community risk is closely related to wind speed and direction, pattern of surrounding wildland vegetation, and buildings layout. The importance of the findings lies in the need for exploring unique viable solutions to reduce risk for every community independently as opposed to embracing a generalized approach as is currently the case.
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I, Lavanya. "A Neural Network Model for Wildfire Scale Prediction using Meteorological Factors." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2646–50. http://dx.doi.org/10.22214/ijraset.2021.35258.
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Forest fires are natural hazards defined as movements of fire through unregulated and uncontrolled forested areas. They pose a permanent risk of loss of forest and forest land. The ability to reliably forecast the region that could be involved in a forest fire incident will help to optimize fire prevention efforts. It appears that Portugal may theoretically make better use of the wildfire risk assessment. More than any other region in Europe, it is a country overrun by wildfires. It has a large amount of forest. Forest fires have a long-term impact on the climate because they contribute to deforestation and global warming, which is one of the main causes of the phenomenon. This research employs Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models with meteorological parameters as inputs to anticipate forest fires as a means of safeguarding forest biodiversity. The results indicate that using meteorological data, it is possible to anticipate the severity of a forest fire at the beginning.
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Parisien, Marc-André, Denyse A. Dawe, Carol Miller, Christopher A. Stockdale, and O. Bradley Armitage. "Applications of simulation-based burn probability modelling: a review." International Journal of Wildland Fire 28, no. 12 (2019): 913. http://dx.doi.org/10.1071/wf19069.
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Wildland fire scientists and land managers working in fire-prone areas require spatial estimates of wildfire potential. To fulfill this need, a simulation-modelling approach was developed whereby multiple individual wildfires are modelled in an iterative fashion across a landscape to obtain location-based measures of fire likelihood and fire behaviour (e.g. fire intensity, biomass consumption). This method, termed burn probability (BP) modelling, takes advantage of fire spread algorithms created for operational uses and the proliferation of available data representing wildfire patterns, fuels and weather. This review describes this approach and provides an overview of its applications in wildland fire research, risk analysis and land management. We broadly classify the application of BP models as (1) direct examination, (2) neighbourhood processes, (3) fire hazard and risk and (4) integration with secondary models. Direct examination analyses are those that require no further processing of model outputs; they range from a simple visual examination of outputs to an assessment of alternate states (i.e. scenarios). Neighbourhood process analyses examine patterns of fire ignitions and subsequent spread across land designations. Fire hazard combines fire probability and a quantitative assessment of fire behaviour, whereas risk is the product of fire likelihood and potential impacts of wildfire. The integration with secondary models represents situations where BP model outputs are integrated into, or used in conjunction with, other models or modelling platforms.
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Guillaume, Bruno, Bernard Porterie, Antonio Batista, Phil Cottle, and Armand Albergel. "Improving the uncertainty assessment of economic losses from large destructive wildfires." International Journal of Wildland Fire 28, no. 6 (2019): 420. http://dx.doi.org/10.1071/wf18104.
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Currently, as fire risk is considered a high-frequency and low-severity risk, actuarial and underwriting pricing and risk management methods have stuck to methods based purely on historical loss data. In the global context of both increasing fire severity with climate change and increasing wildland–urban interface area, the use of environmental-based dynamical modelling tools offers a good alternative to better evaluate fire risk. A new method is presented here that combines the raster-based fire spread model SWIFFT and a stochastic approach for generating the spatial and temporal distribution of ignition points. Monte Carlo simulations are conducted, and the uncertainties of hazard and losses are evaluated. Adapted and applied to the landscape conditions of a Brazilian plantation, it is shown to be well suited for a precise reconstruction of the fire burnt area. Finally, the uncertainty assessment of losses for this study zone is presented. We conclude by discussing this new method, which has a high level of traceable uncertainty and how fire risk insurance can deal with it, as well as the progress of future research that will benefit from this method.
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Log, Torgrim, Vigdis Vandvik, Liv Guri Velle, and Maria-Monika Metallinou. "Reducing Wooden Structure and Wildland-Urban Interface Fire Disaster Risk through Dynamic Risk Assessment and Management." Applied System Innovation 3, no. 1 (March 18, 2020): 16. http://dx.doi.org/10.3390/asi3010016.
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In recent years, severe and deadly wildland-urban interface (WUI) fires have resulted in an increased focus on this particular risk to humans and property, especially in Canada, USA, Australia, and countries in the Mediterranean area. Also, in areas not previously accustomed to wildfires, such as boreal areas in Sweden, Norway, and in the Arctic, WUI fires have recently resulted in increasing concern. January 2014, the most severe wooden town fire in Norway since 1923 raged through Lærdalsøyri. Ten days later, a wildfire raged through the scattered populated community of Flatanger and destroyed even more structures. These fires came as a surprise to the fire brigades and the public. We describe and analyze a proposed way forward for exploring if and how this increasing fire incidence can be linked to concomitant changes in climate, land-use, and habitat management; and then aim at developing new dynamic adaptive fire risk assessment and management tools. We use coastal Norway as an example and focus on temporal changes in fire risk in wooden structure settlements and in the Norwegian Calluna vulgaris L. dominated WUI. In this interface, the fire risk is now increasing due to a combination of land-use changes, resulting in large areas of early successional vegetation with an accumulation of biomass, and the interactive effects of climatic changes resulting in increased drought risk. We propose a novel bow-tie framework to explore fire risk and preventive measures at various timescales (years, months, weeks, hours) as a conceptual model for exploring risk contributing factors and possibilities for risk management. Ignition is the top event of the bow-tie which has the potential development towards a fire disaster as a worst case outcome. The bow-tie framework includes factors such as changes in the built environment and natural habitat fuel moisture content due to the weather conditions, WUI fuel accumulation, possibly improved ecosystem management, contribution by civic prescribed burner groups, relevant fire risk modeling, and risk communication to the fire brigades and the public. We propose an interdisciplinary research agenda for developing this framework and improving the current risk understanding, risk communication, and risk management. This research agenda will represent important contributions in paving the road for fire disaster prevention in Norway, and may provide a model for other systems and regions.
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Adelaine, Sabrina A., Mizuki Sato, Yufang Jin, and Hilary Godwin. "An Assessment of Climate Change Impacts on Los Angeles (California USA) Hospitals, Wildfires Highest Priority." Prehospital and Disaster Medicine 32, no. 5 (June 13, 2017): 556–62. http://dx.doi.org/10.1017/s1049023x17006586.
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AbstractIntroductionAlthough many studies have delineated the variety and magnitude of impacts that climate change is likely to have on health, very little is known about how well hospitals are poised to respond to these impacts.Hypothesis/ProblemThe hypothesis is that most modern hospitals in urban areas in the United States need to augment their current disaster planning to include climate-related impacts.MethodsUsing Los Angeles County (California USA) as a case study, historical data for emergency department (ED) visits and projections for extreme-heat events were used to determine how much climate change is likely to increase ED visits by mid-century for each hospital. In addition, historical data about the location of wildfires in Los Angeles County and projections for increased frequency of both wildfires and flooding related to sea-level rise were used to identify which area hospitals will have an increased risk of climate-related wildfires or flooding at mid-century.ResultsOnly a small fraction of the total number of predicted ED visits at mid-century would likely to be due to climate change. By contrast, a significant portion of hospitals in Los Angeles County are in close proximity to very high fire hazard severity zones (VHFHSZs) and would be at greater risk to wildfire impacts as a result of climate change by mid-century. One hospital in Los Angeles County was anticipated to be at greater risk due to flooding by mid-century as a result of climate-related sea-level rise.ConclusionThis analysis suggests that several Los Angeles County hospitals should focus their climate-change-related planning on building resiliency to wildfires.AdelaineSA, SatoM, JinY, GodwinH. An assessment of climate change impacts on Los Angeles (California USA) hospitals, wildfires highest priority. Prehosp Disaster Med. 2017;32(5):556–562.
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Alcasena-Urdíroz, F. J., C. Vega-García, A. A. Ager, M. Salis, N. J. Nauslar, F. J. Mendizabal, and R. Castell. "Metodología de evaluación del riesgo de incendios forestales y priorización de tratamientos multifuncionales en paisajes mediterráneos." Cuadernos de Investigación Geográfica 45, no. 2 (September 4, 2019): 571. http://dx.doi.org/10.18172/cig.3716.
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In Mediterranean areas, the combined effects of the rural exodus, lack of forest management, and fire suppression policies have substantially contributed to increased forest fuel loadings and continuity over large areas. The result is a growing incidence of wildfires that exceed fire suppression capacity. Economic resources for landscape management are limited, and thus they must be prioritized towards the protection of valued assets where there is a high expectation of loss and the fuel treatments on strategic locations that restrict fires spreading into communities. We completed a case study in the Juslapeña Valley (Navarra, Spain) to demonstrate prioritization of fuel management activities. The study area has frequent and large forest fires that have caused significant damage to forest values and assets in rural communities. We first generated a wildfire risk map for valued assets, and then designed the optimal treatment mosaic within the community fireshed considering the wildfire exposure to forestlands and fire transmission to residential housing. We also identified overstocked stands where the timber or firewood production might supply the needs of local communities and partially cover the treatment cost. We found that the highest economic losses were obtained in residential houses located in the southern portion of the study area, mainly due to a higher burn probability. Fires ignited outside of the study area also exposed communities, and thus the extent considered in wildfire management plans needs to be adjusted to reflect the source and scale of risk to communities. The assessment framework presented in this study can be adapted to the multi-functional forest management in any fire-prone Mediterranean region elsewhere.
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Novitasari, Novitasari, Joko Sujono, Sri Harto, Azwar Maas, and Rachmad Jayadi. "Drought Index for Peatland Wildfire Management in Central Kalimantan, Indonesia During El Niño Phenomenon." Journal of Disaster Research 14, no. 7 (October 1, 2019): 939–48. http://dx.doi.org/10.20965/jdr.2019.p0939.
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Peatland wildfires, especially in tropical ecosystems, are often caused by drought, and lead to smoke and other related problems in all aspects of community life in Indonesia, especially in Central Kalimantan. Drought is worsened by the number of dry days in the dry season, known as the El Niño phenomenon, and the drainage system in a peatland. Additionally, drought decreases the water table and increases the probability of occurrence of wildfires in peatland areas. This study aims to modify the numerical formula of the drought factor (DFt) in the Keetch–Byram drought index (KBDI) based on tropical peatland wildfire conditions in Central Kalimantan during the El Niño phenomenon in 2015. Furthermore, it applies a revised peatland water table reference of 400 mm below the ground surface, based on previous research and the Government regulation on peatland ecosystem protection and management in Indonesia. These El Niño conditions caused a rain decline of approximately 35% in Block A, Ex-Mega Rice Project, Mantangai sub-District, Kapuas District, Central Kalimantan Province. The modified KBDI is compared with the Number of Fire Alerts (NFA) using NASA’s Active Fire Data in 2015. The analysis results demonstrate that the modified DFt under tropical peatland conditions leads to an increase in the drought index value, beginning on the driest days between July and November 2015. The value of the KBDI drought index increases from the high to the extreme index from September to November 2015, when as many as 61 extreme drought indices became indicators for peatland wildfire risk assessment. The extreme KBDI is directly proportional to the NFA recorded during 2015, and the highest number of fire alerts is observed for October 2015, with 1746 fire alerts within 31 days and extreme drought indices from 27 days. Hence, this modified formula is suitable for wildfire conditions on this peatland in Central Kalimantan. Overall, the modified DFt can be successfully applied to the El Niño phenomenon in 2015.
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Athanasopoulou, E., D. Rieger, C. Walter, H. Vogel, A. Karali, M. Hatzaki, E. Gerasopoulos, et al. "Fire risk, atmospheric chemistry and radiative forcing assessment of wildfires in eastern Mediterranean." Atmospheric Environment 95 (October 2014): 113–25. http://dx.doi.org/10.1016/j.atmosenv.2014.05.077.
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Stavi, Ilan. "Wildfires in Grasslands and Shrublands: A Review of Impacts on Vegetation, Soil, Hydrology, and Geomorphology." Water 11, no. 5 (May 20, 2019): 1042. http://dx.doi.org/10.3390/w11051042.
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Wildfires are prevalent in grasslands and shrublands. The objective of this study is to provide land managers with a general overview, by assessing the main impacts of wildfire, including those on plant communities (e.g., secondary succession and species invasion), soil characteristics (e.g., water repellency (hydrophobicity), aggregation and structure stability, and contents of organic carbon and nutrients), and surface processes (e.g., ash deposition, ground surface clogging, water runoff, soil erosion, hillslope debris flow, and dry ravel). Additionally, the study discusses the effects of livestock grazing on the functioning of post-fire grasslands and shrublands. Although mesic regions are mentioned, this review focuses on drylands. The comparatively low-to-moderate fuel loads that characterize grasslands and shrublands generate wildfires of relatively moderate intensity, resulting in moderate burn severity. Yet, it seems that because of decreased soil aggregate stability following burning, the hoof action of livestock that access burnt lands shortly after the fire increases the shearing and detachment of mineral material from the ground surface; this increases soil erodibility, with the possible risk of accelerated land degradation. The review ends with an assessment of general implications for environmental sustainability and health, and provides recommendations on wildfire control in rangelands, and on restoration of burnt lands.
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Janiec, Piotr, and Sébastien Gadal. "A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia." Remote Sensing 12, no. 24 (December 18, 2020): 4157. http://dx.doi.org/10.3390/rs12244157.
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The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling. The random forest method showed better results in the macroscale, however, the maximum entropy model was better in the microscale. The exclusion of variables that do not show a high correlation, does not always improve the modeling results. The random forest presence prediction model is a more accurate method and significantly reduces the risk territory. The reverse is the method of maximum entropy, which is not as accurate and classifies very large areas as endangered. Further study of this topic requires a clearer and conceptually developed approach to the application of remote sensing data. Therefore, this work makes sense to lay the foundations of the future, which is a completely automated fire risk assessment application in the Republic of Sakha. The results can be used in fire prophylactics and planning fire prevention. In the future, to determine the risk well, it is necessary to combine the obtained maps with the seasonal risk determined using indices (for example, the Nesterov index 1949) and the periodic dynamics of forest fires, which Isaev and Utkin studied in 1963. Such actions can help to build an application, with which it will be possible to determine the risk of wildfire and the spread of fire during extreme events.
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Yang, Xihua, Mingxi Zhang, Lorena Oliveira, Quinn R. Ollivier, Shane Faulkner, and Adam Roff. "Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment." Remote Sensing 12, no. 22 (November 20, 2020): 3805. http://dx.doi.org/10.3390/rs12223805.
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The Australian Black Summer wildfires between September 2019 and January 2020 burnt many parts of eastern Australia including major forests within the Sydney drinking water catchment (SDWC) area, almost 16.000 km2. There was great concern on post-fire erosion and water quality hazards to Sydney’s drinking water supply, especially after the heavy rainfall events in February 2020. We developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, remote sensing, Google Earth Engine (GEE), Geographical Information Systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE). The event-based rainfall erosivity was estimated from radar-derived rainfall accumulations for all storm events after the wildfires. Satellite data including Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to estimate the fractional vegetation covers and the RUSLE cover-management factor. The study reveals that the average post-fire erosion rate over SDWC in February 2020 was 4.9 Mg ha−1 month−1, about 30 times higher than the pre-fire erosion and 10 times higher than the average erosion rate at the same period because of the intense storm events and rainfall erosivity with a return period over 40 years. The high post-fire erosion risk areas (up to 23.8 Mg ha−1 month−1) were at sub-catchments near Warragamba Dam which forms Lake Burragorang and supplies drinking water to more than four million people in Sydney. These findings assist in the timely assessment of post-fire erosion and water quality risks and help develop cost-effective fire incident management and mitigation actions for such an area with both significant ecological and drinking water assets. The methodology developed from this study is potentially applicable elsewhere for similar studies as the input datasets (satellite and radar data) and computing platforms (GEE, GIS) are available and accessible worldwide.
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Ramón Vallejo, Victoriano, Susana Bautista, and José Antonio Alloza. "Restauración de montes quemados en condiciones mediterráneas." Recursos Rurais, no. 5 (September 11, 2018): 55–60. http://dx.doi.org/10.15304/rr.id5303.
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Wildfires may produce ecosystem damages that would require post-fire mitigation and/or restoration actions. The question is what are the criteria to identify those burned areas that show high degradation risk in order to plan and prioritise restoration projects. To address that question it is necessary to start with the analysis of fire impact, and from that analysis to derive predictive tools for assessing the fragility and regeneration capacity of burned ecosystems. The identification of post-fire degradation mechanisms provides the basis for developing the corresponding specific mitigation/restoration actions. The diagnostic of ecological impact of wildfires together with the established forest management objectives allow deriving mitigation/restoration strategies and the subsequent implementation projects.We present our experience on the evaluation of post-fire ecosystem vulnerability and on the assessment of restoration planning derived from recent and ongoing EC research projects. This includes the development of short-term restoration techniques suited for degraded soils and dry Mediterranean conditions, where fire-induced degradation is complicated with water shortage for regenerating vegetation.
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Bustillo Sánchez, Marcela, Marj Tonini, Anna Mapelli, and Paolo Fiorucci. "Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest." Geosciences 11, no. 5 (May 20, 2021): 224. http://dx.doi.org/10.3390/geosciences11050224.
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Wildfires are expected to increase in the near future, mainly because of climate changes and land use management. One of the most vulnerable areas in the world is the forest in central-South America, including Bolivia. Despite that this country is highly prone to wildfires, literature is rather limited here. To fill this gap, we implemented a dataset including the burned area that occurred in the department of Santa Cruz in the period of 2010–2019, and the digital spatial data describing the predisposing factors (i.e., topography, land cover, ecoregions). The main goal was to develop a model, based on Random Forest, in which probabilistic outputs allowed to elaborate wildfires susceptibility maps. The overall accuracy was finally estimated by using 5-fold cross-validation. In addition, the last three years of observations acted as the testing dataset, allowing to evaluate the predictive performance of the model. The quantitative assessment of the variables revealed that “flooded savanna” and “shrub or herbaceous cover, flooded, fresh/saline/brakish water” are respectively the ecoregions and land cover classes with the highest probability of predicting wildfires. This study contributes to the development and validation of an innovative mapping tool for fire risk assessment, implementable at a regional scale in different areas of the globe.
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Abbate, Longoni, Ivanov, and Papini. "Wildfire Impacts on Slope Stability Triggering in Mountain Areas." Geosciences 9, no. 10 (September 25, 2019): 417. http://dx.doi.org/10.3390/geosciences9100417.
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Landslides over steep slopes, floods along rivers plains and debris flows across valleys are hydrogeological phenomena typical for mountain regions. Such events are generally triggered by rainfall, which can have large variability in terms of both its intensity and volume. Furthermore, terrain predisposition and the presence of some disturbances, such as wildfires, can have an adverse effect on the potential risk. Modelling the complex interaction between these components is not a simple task and cannot always be carried out using instability thresholds that only take into account the characteristics of the rainfall events. In some particular cases, external factors can modify the existing delicate equilibrium on the basis of which stability thresholds are defined. In particular, events such as wildfires can cause the removal of vegetation coverage and the modification of the soil terrain properties. Therefore, wildfires can effectively reduce the infiltration capacity of the terrain and modify evapotranspiration. As a result, key factors for slope stability, such as the trend of the degree of saturation of the terrain, can be strongly modified. Thus, studying the role of wildfire effects on the terrain’s hydrological balance is fundamental to establish the critical conditions that can trigger potential slope failures (i.e., shallow landslides and possible subsequent debris flows). In this work, we investigate the consequences of wildfire on the stability of slopes through a hydrological model that takes into account the wildfire effects and compare the results to the current stability thresholds. Two case studies in the Ardenno (IT) and Ronco sopra Ascona (CH) municipalities were chosen for model testing. The aim of this paper is to propose a quantitative analysis of the two cases studies, taking into account the role of fire in the slope stability assessment. The results indicate how the post-fire circumstances strongly modify the ability of the terrain to absorb rainfall water. This effect results in a persistently drier terrain until a corner point is reached, after which the stability of the slope could be undermined by a rainfall event of negligible intensity.
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Luo, Lifeng, Ying Tang, Shiyuan Zhong, Xindi Bian, and Warren E. Heilman. "Will Future Climate Favor More Erratic Wildfires in the Western United States?" Journal of Applied Meteorology and Climatology 52, no. 11 (November 2013): 2410–17. http://dx.doi.org/10.1175/jamc-d-12-0317.1.
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AbstractWildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions of Haines index are calculated from simulations of current (1971–2000) and future (2041–70) climate using multiple regional climate models in the North American Regional Climate Change Assessment Program. Despite some differences among the projections, the simulations indicate that there may be not only more days but also more consecutive days with HI ≥ 5 during August in the future. This result suggests that future atmospheric environments will be more conducive to erratic wildfires in the mountainous regions of the western United States.
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Williams, C. Jason, Frederick B. Pierson, Peter R. Robichaud, and Jan Boll. "Hydrologic and erosion responses to wildfire along the rangeland–xeric forest continuum in the western US: a review and model of hydrologic vulnerability." International Journal of Wildland Fire 23, no. 2 (2014): 155. http://dx.doi.org/10.1071/wf12161.
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The recent increase in wildfire activity across the rangeland–xeric forest continuum in the western United States has landscape-scale consequences in terms of runoff and erosion. Concomitant cheatgrass (Bromus tectorum L.) invasions, plant community transitions and a warming climate in recent decades along grassland–shrubland–woodland–xeric forest transitions have promoted frequent and large wildfires, and continuance of the trend appears likely if warming climate conditions prevail. These changes potentially increase overall hydrologic vulnerability by spatially and temporally increasing soil exposure to runoff and erosion processes. Plot and hillslope-scale studies demonstrate burning may increase event runoff or erosion by factors of 2–40 over small-plot scales and more than 100-fold over large-plot to hillslope scales. Reports of flooding and debris flow events from rangelands and xeric forests following burning show the potential risk to natural resources, property, infrastructure and human life. We present a conceptual model for evaluating post-fire hydrologic vulnerability and risk. We suggest that post-fire risk assessment of potential hydrologic hazards should adopt a probability-based approach that considers varying site susceptibility in conjunction with a range of potential storms and that determines the hydrologic response magnitudes likely to affect values-at-risk. Our review suggests that improved risk assessment requires better understanding in several key areas including quantification of interactions between varying storm intensities and measures of site susceptibility, the varying effects of soil water repellency, and the spatial scaling of post-fire hydrologic response across rangeland–xeric forest plant communities.
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Tonini, Marj, Joana Parente, and Mário G. Pereira. "Global assessment of rural–urban interface in Portugal related to land cover changes." Natural Hazards and Earth System Sciences 18, no. 6 (June 13, 2018): 1647–64. http://dx.doi.org/10.5194/nhess-18-1647-2018.
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Abstract. The rural–urban interface (RUI), known as the area where structures and other human developments meet or intermingle with wildland and rural area, is at present a central focus of wildfire policy and its mapping is crucial for wildfire management. In the Mediterranean Basin, humans cause the vast majority of fires and fire risk is particularly high in the proximity of infrastructure and of rural/wildland areas. RUI's extension changes under the pressure of environmental and anthropogenic factors, such as urban growth, fragmentation of rural areas, deforestation and, more in general, land use/land cover change (LULCC). As with other Mediterranean countries, Portugal has experienced significant LULCC in the last decades in response to migration, rural abandonment, ageing of population and trends associated with the high socioeconomic development. In the present study, we analyzed the LULCC occurring in this country in the 1990–2012 period with the main objective of investigating how these changes affected RUI's evolution. Moreover, we performed a qualitative and quantitative characterization of burnt areas within the RUI in relation to the observed changes. Obtained results disclose important LULCC and reveal their spatial distribution, which is far from uniform within the territory. A significant increase in artificial surfaces was registered near the main metropolitan communities of the northwest, littoral-central and southern regions, whilst the abandonment of agricultural land near the inland urban areas led to an increase in uncultivated semi-natural and forest areas. Within agricultural areas, heterogeneous patches suffered the greatest changes and were the main contributors to the increase in urban areas; moreover, this land cover class, together with forests, was highly affected by wildfires in terms of burnt area. Finally, from this analysis and during the investigated period, it appears that RUI increased in Portugal by more than two-thirds, while the total burnt area decreased by one-third; nevertheless, burnt area within RUI doubled, which emphasizes the significance of RUI monitoring for land and fire managers.
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Mell, William E., Samuel L. Manzello, Alexander Maranghides, David Butry, and Ronald G. Rehm. "The wildland - urban interface fire problem - current approaches and research needs." International Journal of Wildland Fire 19, no. 2 (2010): 238. http://dx.doi.org/10.1071/wf07131.
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Wildfires that spread into wildland–urban interface (WUI) communities present significant challenges on several fronts. In the United States, the WUI accounts for a significant portion of wildland fire suppression and wildland fuel treatment costs. Methods to reduce structure losses are focussed on fuel treatments in either wildland fuels or residential fuels. There is a need for a well-characterised, systematic testing of these approaches across a range of community and structure types and fire conditions. Laboratory experiments, field measurements and fire behaviour models can be used to better determine the exposure conditions faced by communities and structures. The outcome of such an effort would be proven fuel treatment techniques for wildland and residential fuels, risk assessment strategies, economic cost analysis models, and test methods with representative exposure conditions for fire-resistant building designs and materials.
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Terêncio, Daniela Patrícia Salgado, Rui Manuel Vitor Cortes, Fernando António Leal Pacheco, João Paulo Moura, and Luís Filipe Sanches Fernandes. "A Method for Estimating the Risk of Dam Reservoir Silting in Fire-Prone Watersheds: A Study in Douro River, Portugal." Water 12, no. 11 (October 22, 2020): 2959. http://dx.doi.org/10.3390/w12112959.
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Forest fires are an increasing problem over recent decades. The fires, among other consequences, lead to an increase in the soil vulnerability to water erosion and a consequent increase in sedimentation rates. When barriers are present, such as dams or weirs, there is an amplified risk of sediment and ash deposition in their reservoirs, causing siltation. Thus, there is an interest in studying in more detail the risk of siltation of barriers and reservoirs in the Douro River watershed following wildfires. A detailed barrier inventory was lacking for the Douro River, hampering the identification of siltation-prone areas. In order to fill in this gap, an extensive inventory of barriers in the Douro river basin was carried out for the present study. The result was an abundant and reliable dataset on the Douro River barriers, which allowed a prognosis on the watershed siltation risk. The method for calculating the siltation risk relied on the relationship between the frequency of forest fires, the erosion risk and the frequency of reservoirs. The sub-basins with the greater siltation risk are the Tâmega, Corgo, Sousa and Paiva river basins. Most reservoirs with the highest siltation risk were from small dams. The modelling results were compared with stream connectivity and concentrations of stream water phosphorus (associated with the sediments that flow into the rivers due to the fires). With regard to connectivity, only two reservoirs were at high risk of sedimentation due to fires, so the categories of connectivity risk and fire-based sedimentation risk are probably not related. With regard to risk of high phosphorus loadings, in 8 basins the upper classes for fire-based erosion risk coincided with the upper class for phosphorus loadings suggesting that high phosphorus loading could be associated with fire-based erosion. This study works as a simple but reliable example on the assessment and mapping of siltation risk in stream networks intersected by abundant barriers. It allowed for identifying barriers that can accumulate a large quantity of fine sediments and ashes, interfering with water quality and soil erosion as well as with the storage capacity of the respective barriers.
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Di Napoli, Mariano, Palmira Marsiglia, Diego Di Martire, Massimo Ramondini, Silvia Liberata Ullo, and Domenico Calcaterra. "Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach." Remote Sensing 12, no. 15 (August 4, 2020): 2505. http://dx.doi.org/10.3390/rs12152505.
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Climate change has increased the likelihood of the occurrence of disasters like wildfires, floods, storms, and landslides worldwide in the last years. Weather conditions change continuously and rapidly, and wildfires are occurring repeatedly and diffusing with higher intensity. The burnt catchments are known, in many parts of the world, as one of the main sensitive areas to debris flows characterized by different trigger mechanisms (runoff-initiated and debris slide-initiated debris flow). The large number of studies produced in recent decades has shown how the response of a watershed to precipitation can be extremely variable, depending on several on-site conditions, as well as the characteristics of precipitation duration and intensity. Moreover, the availability of satellite data has significantly improved the ability to identify the areas affected by wildfires, and, even more importantly, to carry out post-fire assessment of burnt areas. Many difficulties have to be faced in attempting to assess landslide risk in burnt areas, which present a higher likelihood of occurrence; in densely populated neighbourhoods, human activities can be the cause of the origin of the fires. The latter is, in fact, one of the main operations used by man to remove vegetation along slopes in an attempt to claim new land for pastures or construction purposes. Regarding the study area, the Camaldoli and Agnano hill (Naples, Italy) fires seem to act as a predisposing factor, while the triggering factor is usually represented by precipitation. Eleven predisposing factors were chosen and estimated according to previous knowledge of the territory and a database consisting of 400 landslides was adopted. The present work aimed to expand the knowledge of the relationship existing between the triggering of landslides and burnt areas through the following phases: (1) Processing of the thematic maps of the burnt areas through band compositions of satellite images; and (2) landslide susceptibility assessment through the application of a new statistical approach (machine learning techniques). The analysis has the scope to support decision makers and local agencies in urban planning and safety monitoring of the environment.
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Maraveas, Chrysanthos, Dimitrios Loukatos, Thomas Bartzanas, and Konstantinos G. Arvanitis. "Applications of Artificial Intelligence in Fire Safety of Agricultural Structures." Applied Sciences 11, no. 16 (August 22, 2021): 7716. http://dx.doi.org/10.3390/app11167716.
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Artificial intelligence applications in fire safety of agricultural structures have practical economic and technological benefits on commercial agriculture. The FAO estimates that wildfires result in at least USD 1 billion in agriculture-related losses due to the destruction of livestock pasture, destruction of agricultural buildings, premature death of farm animals, and general disruption of agricultural activities. Even though artificial neural networks (ANNs), genetic algorithms (GAs), probabilistic neural networks (PNNs), and adaptive neurofuzzy inference systems (ANFISs), among others, have proven useful in fire prevention, their application is limited in real farm environments. Most farms rely on traditional/non-technology-based methods of fire prevention. The case for AI in agricultural fire prevention is grounded on the accuracy and reliability of computer simulations in smoke movement analysis, risk assessment, and postfire analysis. In addition, such technologies can be coupled with next-generation fire-retardant materials such as intumescent coatings with a polymer binder, blowing agent, carbon donor, and acid donor. Future prospects for AI in agriculture transcend basic fire safety to encompass Society 5.0, energy systems in smart cities, UAV monitoring, Agriculture 4.0, and decentralized energy. However, critical challenges must be overcome, including the health and safety aspects, cost, and reliability. In brief, AI offers unlimited potential in the prevention of fire hazards in farms, but the existing body of knowledge is inadequate.
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Palaiologou, Palaiologos, Alan A. Ager, Max Nielsen-Pincus, Cody R. Evers, and Kostas Kalabokidis. "Using transboundary wildfire exposure assessments to improve fire management programs: a case study in Greece." International Journal of Wildland Fire 27, no. 8 (2018): 501. http://dx.doi.org/10.1071/wf17119.
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Numerous catastrophic wildfires in Greece have demonstrated that relying on fire suppression as the primary risk-management strategy is inadequate and that existing wildfire-risk governance needs to be re-examined. In this research, we used simulation modelling to assess the spatial scale of wildfire exposure to communities and cultural monuments in Chalkidiki, Greece. The study area typifies many areas in Greece in terms of fire regimes, ownership patterns and fire-risk mitigation. Fire-transmission networks were built to quantify connectivity among land tenures and populated places. We found that agricultural and unmanaged wildlands are key land categories that transmit fire exposure to other land tenures. In addition, fires ignited within protected lands and community boundaries are major sources of structure exposure. Important cultural monuments in the study area had fairly low exposure but higher potential for fires with moderate to high intensity. The results show how the spatial diversity of vegetation and fuels, in combination with vegetation management practices on private and public tracts of land, contribute to transboundary risk. We use the results to motivate a discussion of integrating transboundary risk assessments to improve the current wildfire-risk rating system and begin the process of reforming risk governance in Greece.
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Areu-Rangel, Omar S., Rosanna Bonasia, Federico Di Traglia, Matteo Del Soldato, and Nicola Casagli. "Flood Susceptibility and Sediment Transport Analysis of Stromboli Island after the 3 July 2019 Paroxysmal Explosion." Sustainability 12, no. 8 (April 17, 2020): 3268. http://dx.doi.org/10.3390/su12083268.
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On 3 July 2019, Stromboli volcanic island experienced a paroxysmal explosion that triggered wildfires on vegetated areas in the south, southwestern, and eastern part of the island. This study analyzes the runoff and the transport of sediment originating from rainfall, to verify whether the vegetation loss due to wildfire changed the hydrogeological structure of the affected area and the flooding hazard. A preliminary hydrological study was conducted to analyze the superficial runoff due to rainfall. According to local planning, the hydrogeological study and flood risk assessment were carried out for the return periods corresponding to 50, 100, and 300 years. The flooding levels were calculated using the hydrodynamic module of the IBER software. The IBER sediment transport module was applied in a non-stationary regime for erosion and sedimentation analysis. The results showed that the fire caused an increase of the water discharge rates between 0.06 and 0.16 m2/s, for the 50 year return period, in the Ginostra inhabited area. The great differences of the flood levels between pre- and post-eruptive scenarios, for the highest return periods, were recognized. The analysis of sediment transport showed that rains could exert an erosion and re-sedimentation effect that would transport from 0.1 m to more than 1 m of re-mobilized material in the Ginostra area, which could cause inconvenience in the inhabited area of the island.
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Bedia, J., S. Herrera, and J. M. Gutiérrez. "Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain." Natural Hazards and Earth System Sciences 14, no. 1 (January 7, 2014): 53–66. http://dx.doi.org/10.5194/nhess-14-53-2014.
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Abstract. Most fire protection agencies throughout the world have developed forest fire risk forecast systems, usually building upon existing fire danger indices and meteorological forecast data. In this context, the daily predictability of wildfires is of utmost importance in order to allow the fire protection agencies to issue timely fire hazard alerts. In this study, we address the predictability of daily fire occurrence using the components of the Canadian Fire Weather Index (FWI) System and related variables calculated from the latest ECMWF (European Centre for Medium Range Weather Forecasts) reanalysis, ERA-Interim. We develop daily fire occurrence models in peninsular Spain for the period 1990–2008 and, considering different minimum burned area thresholds for fire definition, assess their ability to reproduce the inter-annual fire frequency variability. We based the analysis on a phytoclimatic classification aiming the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climate/fuel conditions. We then extend the analysis in order to assess the predictability of monthly burned areas. The sensitivity of the models to the level of spatial aggregation of the data is also evaluated. Additionally, we investigate the gain in model performance with the inclusion of socioeconomic and land use/land cover (LULC) covariates in model formulation. Fire occurrence models have attained good performance in most of the phytoclimatic zones considered, being able to faithfully reproduce the inter-annual variability of fire frequency. Total area burned has exhibited some dependence on the meteorological drivers, although model performance was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, highlighting the adequacy of the FWI system for fire occurrence prediction in the study area. The results were improved when using aggregated data across regions compared to when data were sampled at the grid-box level. The inclusion of socioeconomic and LULC covariates contributed marginally to the improvement of the models, and in most cases attained no relevant contribution to total explained variance – excepting northern Spain, where anthropogenic factors are known to be the major driver of fires. Models of monthly fire counts performed better in the case of fires larger than 0.1 ha, and for the rest of the thresholds (1, 10 and 100 ha) the daily occurrence models improved the predicted inter-annual variability, indicating the added value of daily models. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as a response variable. Our results leave the door open to the development a more complex modelling framework based on daily data from numerical climate model outputs based on the FWI system.
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Adamis, D., V. Papanikolaou, R. C. Mellon, and G. Prodromitis. "The impact of wildfires on mental health of residents in a rural area of Greece. A case control population based study." European Psychiatry 26, S2 (March 2011): 1188. http://dx.doi.org/10.1016/s0924-9338(11)72893-0.
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IntroductionPsychopathological disturbances are common in the aftermaths of a disaster. The consequences of these disorders can be long lasting. In August of 2007 an intense and destructive wildfire broke out in the Peloponnesus peninsula in Greece.ObjectivesTo investigate psychological and psychiatric morbidity in individuals who had experienced severe exposure to a wildfire disaster in a part of Greece and to indentify risk factors for the post disaster psychological problems.AimsTo investigate a broader spectrum of mid-term psychological and psychiatric morbidity in victims, to evaluate the proportion of psychopathology that could be accredited to the disaster, to estimate the association of losses with different psychological symptoms, to indentify risk factors for psychopathology.MethodsA Cross sectional case control study of adult population (18–65 years old). Data collected among others were demographic, Symptom Checklist 90-Revised for assessment of psychological difficulties, type and number of losses.ResultsThose damnified from the disaster scored significantly higher (p < 0.05) in the symptoms of somatisation, depression, anxiety, hostility, phobic anxiety, paranoia, and had significantly more symptoms (PST) and were more distressed by them (GSI) compared to controls. In addition risk factors for someone to be a psychiatric case were to be a victim from the fire, to have finished primary school, to be windowed and to have damages to his property.ConclusionsWildfires can cause considerable psychological symptoms in victims and there are reasons for public health policy makers to create services in order to help and improve the mental health of those affected.
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McEvoy, Andy, Becky K. Kerns, and John B. Kim. "Hazards of Risk: Identifying Plausible Community Wildfire Disasters in Low-Frequency Fire Regimes." Forests 12, no. 7 (July 16, 2021): 934. http://dx.doi.org/10.3390/f12070934.
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Optimized wildfire risk reduction strategies are generally not resilient in the event of unanticipated, or very rare events, presenting a hazard in risk assessments which otherwise rely on actuarial, mean-based statistics to characterize risk. This hazard of actuarial approaches to wildfire risk is perhaps particularly evident for infrequent fire regimes such as those in the temperate forests west of the Cascade Range crest in Oregon and Washington, USA (“Westside”), where fire return intervals often exceed 200 years but where fires can be extremely intense and devastating. In this study, we used wildfire simulations and building location data to evaluate community wildfire exposure and identify plausible disasters that are not based on typical mean-based statistical approaches. We compared the location and magnitude of simulated disasters to historical disasters (1984–2020) in order to characterize plausible surprises which could inform future wildfire risk reduction planning. Results indicate that nearly half of communities are vulnerable to a future disaster, that the magnitude of plausible disasters exceeds any recent historical events, and that ignitions on private land are most likely to result in very high community exposure. Our methods, in combination with more typical actuarial characterizations, provide a way to support investment in and communication with communities exposed to low-probability, high-consequence wildfires.
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Shankar, Uma, Jeffrey P. Prestemon, Donald McKenzie, Kevin Talgo, Aijun Xiu, Mohammad Omary, Bok Haeng Baek, Dongmei Yang, and William Vizuete. "Projecting wildfire emissions over the south-eastern United States to mid-century." International Journal of Wildland Fire 27, no. 5 (2018): 313. http://dx.doi.org/10.1071/wf17116.
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Wildfires can impair human health because of the toxicity of emitted pollutants, and threaten communities, structures and the integrity of ecosystems sensitive to disturbance. Climate and socioeconomic factors (e.g. population and income growth) are known regional drivers of wildfires. Reflecting changes in these factors in wildfire emissions estimates is thus a critical need in air quality and health risk assessments in the south-eastern United States. We developed such a methodology leveraging published statistical models of annual area burned (AAB) over the US Southeast for 2011–2060, based on county-level socioeconomic and climate projections, to estimate daily wildfire emissions in selected historical and future years. Projected AABs were 7 to 150% lower on average than the historical mean AABs for 1992–2010; projected wildfire fine-particulate emissions were 13 to 62% lower than those based on historical AABs, with a temporal variability driven by the climate system. The greatest differences were in areas of large wildfire impacts from socioeconomic factors, suggesting that historically based (static) wildfire inventories cannot properly represent future air quality responses to changes in these factors. The results also underscore the need to correct biases in the dynamical downscaling of wildfire climate drivers to project the health risks of wildfire emissions more reliably.
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Baijnath-Rodino, Janine A., Mukesh Kumar, Margarita Rivera, Khoa D. Tran, and Tirtha Banerjee. "How Vulnerable Are American States to Wildfires? A Livelihood Vulnerability Assessment." Fire 4, no. 3 (August 27, 2021): 54. http://dx.doi.org/10.3390/fire4030054.
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Quantifying livelihood vulnerability to wildland fires in the United States is challenging because of the need to systematically integrate multidimensional variables into its analysis. We aim to measure wildfire threats amongst humans and their physical and social environment by developing a framework to calculate the livelihood vulnerability index (LVI) for the top 14 American states most recently exposed to wildfires. The LVI is computed by assessing each state’s contributing factors (exposure, sensitivity, and adaptive capacity) to wildfire events. These contributing factors are determined through a set of indicator variables that are categorized into corresponding groups to produce an LVI framework. The framework is validated by performing a principal component analysis (PCA), ensuring that each selected indicator variable corresponds to the correct contributing factor. Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability. In contrast, California, Florida, and Texas experience the least livelihood vulnerability. While California has one of the highest exposures and sensitivity to wildfires, results indicate that it has a relatively high adaptive capacity, in comparison to the other states, suggesting it has measures in place to withstand these vulnerabilities. These results are critical to wildfire managers, government, policymakers, and research scientists for identifying and providing better resiliency and adaptation measures to support states that are most vulnerable to wildfires.
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Zou, Yufei, Susan M. O’Neill, Narasimhan K. Larkin, Ernesto C. Alvarado, Robert Solomon, Clifford Mass, Yang Liu, M. Talat Odman, and Huizhong Shen. "Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment." International Journal of Environmental Research and Public Health 16, no. 12 (June 17, 2019): 2137. http://dx.doi.org/10.3390/ijerph16122137.
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Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.
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Ziel, Robert H., Peter A. Bieniek, Uma S. Bhatt, Heidi Strader, T. Scott Rupp, and Alison York. "A Comparison of Fire Weather Indices with MODIS Fire Days for the Natural Regions of Alaska." Forests 11, no. 5 (May 3, 2020): 516. http://dx.doi.org/10.3390/f11050516.
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Research Highlights: Flammability of wildland fuels is a key factor influencing risk-based decisions related to preparedness, response, and safety in Alaska. However, without effective measures of current and expected flammability, the expected likelihood of active and problematic wildfires in the future is difficult to assess and prepare for. This study evaluates the effectiveness of diverse indices to capture high-risk fires. Indicators of drought and atmospheric drivers are assessed along with the operational Canadian Forest Fire Danger Rating System (CFFDRS). Background and Objectives: In this study, 13 different indicators of atmospheric conditions, fuel moisture, and flammability are compared to determine how effective each is at identifying thresholds and trends for significant wildfire activity. Materials and Methods: Flammability indices are compared with remote sensing characterizations that identify where and when fire activity has occurred. Results: Among these flammability indicators, conventional tools calibrated to wildfire thresholds (Duff Moisture Code (DMC) and Buildup Index (BUI)), as well as measures of atmospheric forcing (Vapor Pressure Deficit (VPD)), performed best at representing the conditions favoring initiation and size of significant wildfire events. Conventional assessments of seasonal severity and overall landscape flammability using DMC and BUI can be continued with confidence. Fire models that incorporate BUI in overall fire potential and fire behavior assessments are likely to produce effective results throughout boreal landscapes in Alaska. One novel result is the effectiveness of VPD throughout the state, making it a potential alternative to FFMC among the short-lag/1-day indices. Conclusions: This study demonstrates the societal value of research that joins new academic research results with operational needs. Developing the framework to do this more effectively will bring science to action with a shorter lag time, which is critical as we face growing challenges from a changing climate.
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Cochrane, M. A., C. J. Moran, M. C. Wimberly, A. D. Baer, M. A. Finney, K. L. Beckendorf, J. Eidenshink, and Z. Zhu. "Estimation of wildfire size and risk changes due to fuels treatments." International Journal of Wildland Fire 21, no. 4 (2012): 357. http://dx.doi.org/10.1071/wf11079.
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Human land use practices, altered climates, and shifting forest and fire management policies have increased the frequency of large wildfires several-fold. Mitigation of potential fire behaviour and fire severity have increasingly been attempted through pre-fire alteration of wildland fuels using mechanical treatments and prescribed fires. Despite annual treatment of more than a million hectares of land, quantitative assessments of the effectiveness of existing fuel treatments at reducing the size of actual wildfires or how they might alter the risk of burning across landscapes are currently lacking. Here, we present a method for estimating spatial probabilities of burning as a function of extant fuels treatments for any wildland fire-affected landscape. We examined the landscape effects of more than 72 000 ha of wildland fuel treatments involved in 14 large wildfires that burned 314 000 ha of forests in nine US states between 2002 and 2010. Fuels treatments altered the probability of fire occurrence both positively and negatively across landscapes, effectively redistributing fire risk by changing surface fire spread rates and reducing the likelihood of crowning behaviour. Trade offs are created between formation of large areas with low probabilities of increased burning and smaller, well-defined regions with reduced fire risk.
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Wilkinson, S. L., A. K. Furukawa, B. M. Wotton, and J. M. Waddington. "Mapping smouldering fire potential in boreal peatlands and assessing interactions with the wildland–human interface in Alberta, Canada." International Journal of Wildland Fire 30, no. 7 (2021): 552. http://dx.doi.org/10.1071/wf21001.
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Treed peatlands exhibit both crown and smouldering fire potential; however, neither are included in Canadian wildfire management models and, as such, they are not formally represented in management decision-making. The lack of smouldering fire risk assessment is a critical research gap as these fires can represent heavy resource draws and are predominant sources of smoke, air pollutants and atmospheric carbon. Here, for the first time, we combine existing knowledge of the controls on smouldering peat fire with expert opinion-based weightings through a multi-criteria decision analysis, to map the smouldering fire potential (i.e. hazard) of treed peatlands in the Boreal Plains, Alberta, Canada. We find that smouldering potential varies considerably between treed peatlands and that areas of sparser peatland coverage may contain high smouldering-potential peatlands. Further, we find that treed peatlands are a common feature in the wildland–human interface and that proportionally, the area of high smouldering potential is greater closer to roads compared with farther away. Our approach enables a quantitative measure of smouldering fire potential and evidences the need to incorporate peatland–wildfire interactions into wildfire management operations. We suggest that similar frameworks could be used in other peatland dominated regions as part of smouldering fire risk assessments.
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Trucchia, Andrea, Mirko D’Andrea, Francesco Baghino, Paolo Fiorucci, Luca Ferraris, Dario Negro, Andrea Gollini, and Massimiliano Severino. "PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator." Fire 3, no. 3 (July 6, 2020): 26. http://dx.doi.org/10.3390/fire3030026.
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PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks—fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.
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Ratknic, Tatjana, Mihailo Ratknic, Nikola Rakonjac, Ivana Zivanovic, and Zoran Poduska. "Development of a national index for the purpose of forest fire risk assessments on the example of southern Serbia." Thermal Science 23, no. 6 Part A (2019): 3307–16. http://dx.doi.org/10.2298/tsci190412276r.
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The paper presents the results on the study of the possible application of the Canadian Forest Fire Weather Index and the Modified Angstrom Index in forest fire risk assessments. The daily values of these indices for the period 2005-2015 were related to the forest fire database. It was found that there is a relatively weak to moderate correlation between forest fires and the values of the Canadian Forest Fire Weather Index. In order to improve the wildfire risk assessments (including forest fires), the index was modified. The modified index has a significantly greater correlation with the actual events of forest fires and consequently a much wider application in southern Serbia. The modified index can be of great importance in the future concepts of forest fire risk management.
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Chen, Weijie, You Zhou, Enze Zhou, Zhun Xiang, Wentao Zhou, and Junhan Lu. "Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data." Sensors 21, no. 2 (January 18, 2021): 634. http://dx.doi.org/10.3390/s21020634.
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Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.
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Nasanbat, Elbegjargal, and Ochirkhuyag Lkhamjav. "WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 469–73. http://dx.doi.org/10.5194/isprsarchives-xli-b1-469-2016.
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Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.
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Nasanbat, Elbegjargal, and Ochirkhuyag Lkhamjav. "WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 469–73. http://dx.doi.org/10.5194/isprs-archives-xli-b1-469-2016.
Full textAbstract:
Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.
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Yang, Xuhong, Xiaobin Jin, and Yinkang Zhou. "Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China." Forests 12, no. 10 (September 23, 2021): 1299. http://dx.doi.org/10.3390/f12101299.
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Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%.
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Reisen, Fabienne, Sandra M. Duran, Mike Flannigan, Catherine Elliott, and Karen Rideout. "Wildfire smoke and public health risk." International Journal of Wildland Fire 24, no. 8 (2015): 1029. http://dx.doi.org/10.1071/wf15034.
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Wildfire activity is predicted to increase with global climate change, resulting in longer fire seasons and larger areas burned. The emissions from fires are highly variable owing to differences in fuel, burning conditions and other external environmental factors. The smoke that is generated can impact human populations spread over vast geographical areas. Wildfire smoke is a complex mixture of pollutants that can undergo physical and chemical transformation processes during transport and can have major impacts on air quality and public health. This review looks at the main features of smoke that should be considered in the assessment of public health risk. It describes the current state of knowledge and discusses how smoke is produced, what factors affect emissions and smoke distribution, and what constituents of smoke are most likely to cause adverse health effects.
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Chu, T., and X. Guo. "An assessment of fire occurrence regime and performance of Canadian fire weather index in south central Siberian boreal region." Natural Hazards and Earth System Sciences Discussions 2, no. 7 (July 23, 2014): 4711–42. http://dx.doi.org/10.5194/nhessd-2-4711-2014.
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Abstract. Wildfire is the dominant natural disturbance in Eurasian boreal region, which acts as a major driver of the global carbon cycle. An effectiveness of wildfire management requires suitable tools for fire prevention and fire risk assessment. This study aims to investigate fire occurrence patterns in relation to fire weather conditions in the remote south central Siberia region. The Canadian Fire Weather Index derived from large-scale meteorological reanalysis data was evaluated with respects to fire regimes during 14 consecutive fire seasons in south central Siberian environment. All the fire weather codes and indices, including the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC), the Drought Code (DC), the Buildup Index (BUI), the Initial Spread Index (ISI), and the Fire Weather Index (FWI), were highly reflected inter-annual variation of fire activity in south central Siberia. Even though human-caused fires were major events in Russian boreal forest including south central Siberia, extreme fire years were strongly correlated with ambient weather conditions (e.g. Arctic Oscillation, air temperature, relative humidity and wind), showing by in-phase (or positive linear relationship) and significant wavelet coherence between fire activity and DMC, ISI, BUI, and FWI. Time series observation of 14 fire seasons showed that there was an average of about 3 months lags between the peaks of fire weather conditions and fire activity, which should take into account when using coarse scale fire weather indices in the assessment of fire danger in the study area. The results are expected to contribute to a better reconstruction and prediction of fire activity using large-scale reanalysis data in remote regions in which station data are very few.
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Narayanaraj, Ganapathy, and Michael C. Wimberly. "Influences of forest roads on the spatial pattern of wildfire boundaries." International Journal of Wildland Fire 20, no. 6 (2011): 792. http://dx.doi.org/10.1071/wf10032.
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Although many studies have examined fire ignition and fire spread, the cessation of fire is largely unexplored at a landscape scale. Therefore, we sought to examine the effects of forest roads on fire boundary locations. We analysed six fires within the Wenatchee National Forest in Washington State. Geographic Information System datasets of fire boundaries and environmental variables were obtained from government agencies. Case points were sampled on the fire boundaries, and five matching control points were sampled inside the fire boundary for each case. Matched case–control logistic regression was performed to identify the environmental differences between each case and its set of control points. We found that for four of the fires, fire boundaries were significantly closer to roads than to random control points. Various terrain and vegetation attributes were also correlated with fire boundary locations. In the fires where road effects were significant, they had stronger effects than any of the other environmental constraints on fire boundary locations. These results suggest that road effects on fire cessation should be incorporated more explicitly into landscape-level assessment of wildfire risk and analyses of fuel treatment effectiveness.
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Hyde, Kevin, Matthew B. Dickinson, Gil Bohrer, David Calkin, Louisa Evers, Julie Gilbertson-Day, Tessa Nicolet, Kevin Ryan, and Christina Tague. "Research and development supporting risk-based wildfire effects prediction for fuels and fire management: status and needs." International Journal of Wildland Fire 22, no. 1 (2013): 37. http://dx.doi.org/10.1071/wf11143.
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Wildland fire management has moved beyond a singular focus on suppression, calling for wildfire management for ecological benefit where no critical human assets are at risk. Processes causing direct effects and indirect, long-term ecosystem changes are complex and multidimensional. Robust risk-assessment tools are required that account for highly variable effects on multiple values-at-risk and balance competing objectives, to support decision making. Providing wildland fire managers with risk-analysis tools requires a broad scientific foundation in fire behaviour and effects prediction as well as high quality computer-based tools and associated databases. We outline a wildfire risk-assessment approach, highlight recent developments in fire effects science and associated research needs, and recommend developing a comprehensive plan for integrated advances in wildfire occurrence, behaviour and effects research leading to improved decision support tools for wildland fire managers. We find that the current state of development in fire behaviour and effects science imposes severe limits on the development of risk-assessment technology. In turn, the development of technology has been largely disconnected from the research enterprise, resulting in a confusing array of ad hoc tools that only partially meet decision-support needs for fuel and fire management. We make the case for defining a common risk-based analytic framework for fire-effects assessment across the range of fire-management activities and developing a research function to support the framework.
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Krsnik, Goran, Eduard Busquets Olivé, Míriam Piqué Nicolau, Asier Larrañaga, Adrián Cardil, Jordi García-Gonzalo, and José Ramón González Olabarría. "Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning." Remote Sensing 12, no. 24 (December 17, 2020): 4124. http://dx.doi.org/10.3390/rs12244124.
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Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia.