Forest Fire Danger Research Articles

Statistical Calibration of Long-Term Reanalysis Data for Australian Fire Weather Conditions

Abstract Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.

A novel fire danger rating model based on time fading precipitation model — A case study of Northeast China

With the increase of extreme climate and global warming, forest fires have become more frequent. Therefore, it is important to accurately predict whether fires will occur in forest in the future. Precipitation is an important factor that affects the probability of the occurrence of forest fires in the future. Previous models selected annual average precipitation, monthly average precipitation or drought days as the precipitation value, which the attenuation of precipitation is not considered. In this study, a time fading model is used to calculate the comprehensive precipitation index, which is an exponential weight decay model. The earlier the precipitation time, the smaller the weight. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, in this study, discrete fire points are converted into a continuous fire-point density. The structure of the prediction model is more reasonable, which is conducive to obtaining higher-precision prediction results. Besides, the SVM regression model was used to construct a forest fire danger rating model. In the same area, considering the comprehensive precipitation index compared with the average precipitation value, the accuracy of the three forest areas in northeastern China in the test set has been improved by about 5%. The accuracy rates of 90.13%, 93.04% and 87.5% can be achieved respectively.

Relationship between Forest Fuel Moisture Contents and Weather Factors During the Forest Fires Danger Season in Chuncheon, Gangwon

The amount of moisture contained in fallen leaves in the forest is closely related to the possibility of the ignition and spread of a forest fire. This moisture content is an important fundamental component of the forest fire danger rating system, as well as an essential parameter in forest fire management. This study examined the relationship between the moisture content of fuel in the forests and weather information obtained from HOBO data loggers installed in forests during the forest fire seasons of 2018 and 2020. The moisture content of the forest fuel was divided into two slopes (south and north), and the fallen leaves used were broad-leaved (Prunus serrulate and Quercus dentata) and coniferous (Pinus densiflora and Pinus koraiensis). The temperature (mean, minimum, and maximum) and relative humidity (mean and minimum) data obtained from the weather measuring devices installed in the forests were used to correlate weather factors with fuel moisture content. During the spring forest fire period, the temperature (mean, minimum, and maximum) and relative humidity (mean and minimum) of each slope and tree species were significant, while the average temperature and relative humidity were significant during the autumn. According to the correlations between the fuel moisture content and weather, factors with high significance were selected, and multiple regression analyses of the slope and species were found to be significant at 1 percent. The results of this study are expected to be used as primary data for calculating the fuel index of the forest fire forecasting system by accounting for the effects of weather factors on the moisture content of the fuel.

Analisis Tingkat Bahaya Karhutla (Kebakaran Hutan dan Lahan) di Kabupaten Ogan Ilir Provinsi Sumatera Selatan

Forest and land fires are disasters that occur every year in Ogan Ilir Regency. The largest forest and land fire disaster in this district was in 2015. Still, this disaster was repeated in the following year, from 2016 to 2020. This research is one of the efforts to mitigate forest and land fires to reduce the fire risk. This study aims to analyze the level of danger caused by forest fires and land. The method used in this study is a quantitative descriptive method using overlay analysis. This research is located in Ogan Ilir Regency because Ogan Ilir Regency is one of the regencies in the Sumatra Province of Sumatra, which often occurs in fires. The measurement of the hazard level of forest and land fires (forest and land fires) uses several parameters, including a map of land cover types in Ogan Ilir Regency, a rainfall map in Ogan Ilir Regency, and a map of soil types in Ogan Ilir Regency. The study's results show that the level of danger of forest and land fires in Ogan Ilir Regency is classified into three levels. The first level is high, with an area of 204,51 km² or 10,11% of the total area. Then the medium level has an area of 1182,06 Km² or 58,43% of the entire area. Then the last level is the low level which has an area of 363,45 Km² or about 31,46% of the total area of Ogan Ilir Regency. Research on the level of danger of a disaster becomes essential because it is auxiliary information to determine and analyze which locations are prone to a disaster.

PYROLOGICAL CHARACTERISTICS OF FOREST EDGES UNDER INTENSIVE RECREATIONAL LOADS

-The increase in fires in urban landscapes causes adverse and sometimes irreversible changes in forest ecosystems. The modern solution is to find the places of primary fires and the most dangerous areas. The purpose of this study was to find dependences between the forest typological features of forest stands and their fire danger. To fulfil this purpose, a systematic analysis of forest fire danger and phytoindication were performed. The composition of the flora of edge biotopes was figured out using the route geobotanical method. The article analyses forest fires that occurred in urban forests of Kyiv during 2014-2021. Primary ignition locations have been identified. It was proved that fires often occurred on the forest edges, as well as along highways and railways. Among the standard indicators of fire danger (number and area of forest fires), the method makes provision for establishing the fire frequencies in the same forest area. This indicator allows figuring out the most dangerous areas from the pyrogenic standpoint. It was found that the simple edge is characterised by a minimal taxonomic composition in the ground vegetation and its projective cover of up to 20%. The grass tier stabilises the environment and mitigates the effects of extreme natural phenomena. The vegetation cover of an elementary and multicomponent edge is one of the barriers that can stop surface fires. Taxonomic diversity, especially of deciduous species, determines the pyrological features of forest edges. Analysis of the flora of marginal biotopes showed that oligotrophs, xerophytes, and xeromesophytes predominate among the species of simple edges. The share of ruderal elements in the ground vegetation was 36%, which indicates a significant anthropogenic impact and synanthropisation of the flora of the edges. These processes adversely impact the conservation of typical species but have a fire-retaining effect. Analysis of forest fires dynamics under intensive recreational loads allows substantiating the vectors for fire safety improvement, choosing a strategy for ensuring fire safety, and increasing the economic and social efficiency of fire prevention measures. It is proved that the forest edge is one of the decisive factors in the fire behaviour arising from a nonwooded area. The formation of multicomponent edges will help minimise the risks of fires

Fire risk associated with landscape changes, climatic events and remote sensing in the Atlantic Forest using ARIMA model

The human influence on climate change has increased the occurrence of extreme events and made heat waves and droughts more frequent and severe, which leads to an increased number of fires. It is likely that in the past, the Atlantic Forest has responded to the occurrence of extreme events with fires that may have contributed to the reduction of forest cover in the state. Just as there is a hypothesis that in the future, with the greater recurrence of these events, the Atlantic Forest will be susceptible to greater risk of forest fires. This study aims to develop a model using the Autoregressive Integrated Moving Average Model (ARIMA) to evaluate the danger of forest fires for past and future climatic periods, in the State of Rio de Janeiro. Images from the Thematic Mapper sensor and the Enhanced Thematic Mapper sensor from 1985 to 2015 were used in order to classify them into anthropized and forest areas. A set of meteorological variables on a daily and monthly scale for the period 1985 to 2015 was used to calculate the F-index on a monthly scale. ARIMA was used to simulate the observed and future F-index data up to 2030. The results show higher values of Normalized Difference Fraction Index (NDFI) in areas to the south and southwest of the State, coinciding with the areas of greater predominance of Atlantic Forest. The most degraded regions are in the northeast and north, and the year 2000 showed the largest area of degraded forest. By analyzing the F-index for the past, it was possible to observe a gradual increase in fires, which were associated with extreme events, mainly La Niña. ARIMA modeling allowed us to identify a class change from high to very high in terms of fire danger in the past and future. In 2030, the minimum value of the F-index reached 2.98, which is considered very high in May and June. By analyzing the entire future period on a monthly scale, the highest fire danger values were found in August and September. It is important that actions be taken to minimize the effects of climate change, once that these changes causes an increase in the occurrence of extreme events, which favor a fire-prone climate, increasing the number of forest fires.

Assessment of the Uncertainty for the Spatial Distribution of Lightning Discharge Density Based on the Smoothed Bootstrap Procedure and WWLLN Data: a Case Study

Thunderstorm activity is the most common natural cause of forest fires. Such forest fires can affect the functioning of industrial and infrastructure facilities. An effective application of the deterministic-probabilistic approach is possible if there is a set of initial data, including cloud-to-ground lightning discharges. It is especially important to understand the boundaries of uncertainty in determining the spatial distribution of the density of lightning discharges in a controlled forest-covered area. The aim of this work is to develop a procedure for assessing the uncertainty of the spatial distribution of lightning discharge density using a smoothed bootstrap algorithm and World Wide Lightning Location Network (WWLLN) data. The novelty of the work is due to the use of WWLLN data in combination with bootstrap algorithm in order to assess uncertainty of spatial distribution of lightning activity. The main findings are: 1) algorithm of bootstrap procedure to assess uncertainty in lightning discharge density within spatial distribution; 2) program realization of this algorithm using QGIS and GRASS software; 3) case study results for the typical boreal zone, namely, Republic of Buryatia (Russian Federation). Conclusions based on the results of bootstrap assessment of lightning discharge density uncertainty are presented regarding spatial distribution of lightning discharges. These results are applicable for forest fire danger prediction in lightning activity conditions.

Mathematical Simulation of Forest Fuel Pyrolysis and Crown Forest Fire Impact for Forest Fire Danger and Risk Assessment

In order to predict and assess the danger from crown forest fires, it is necessary to study the thermal degradation of different forest fuels in a high-temperature environment. In this paper, the main characteristics of pyrolysis accompanied by moisture evaporation in a foliage sample of angiosperms (birch) were investigated within conditions typical for a crown forest fire. The heat and mass transfer in the forest fuel element is described by the system of non-stationary non-linear heat conduction equations with corresponding initial and boundary conditions. The considered problem is solved within the framework of the three-dimensional statement by the finite difference method. The locally one-dimensional method was used to solve three-dimensional equations for heat conduction. The simple iteration method was applied to solve nonlinear effects caused by the forest fuel pyrolysis and moisture evaporation. The fourth kind of boundary conditions are applicable at the interface between the sub-areas. Software implementation of the mathematical model is performed in the high-level programming language Delphi in the RAD Studio software. The characteristic changes in the sample temperature field and the phase composition under high-temperature exposure from a forest fire are presented. The induction period of the thermal decomposition of dry organic matter in the sample was determined. Recommendations are made about key features of accounting for the pyrolysis and evaporation processes when predicting forest fire danger. The research results can be used in the development and improvement of systems for predicting forest fire danger and environmental consequences of the forest fires.

Interaction of elevation and climate change on fire weather risk

Most wildfire studies are regional to global in scale; however, many of the values of interest and the weather are local phenomena that may give rise to large spatial variability in risk. We assessed the interaction of elevation and climate on fire weather for the Penticton Creek watershed in southwestern Canada for historic weather, and five climate change scenarios. Daily temperature and precipitation records spanning 100 years were generated using the LARS-WG5 weather generator and were used to calculate the fire weather indices of the Canadian Forest Fire Danger Rating System. Fire season length, restricted activity season, and fire season severity are all projected to increase by the 2050s and in some scenarios to increase further by the 2080s. Low and mid-elevations had substantially worsening risks, whereas at the highest elevations, risks were mitigated by the continuation of the snowpack. Increasing temperatures lengthened the fire season while decreasing (increasing) precipitation exacerbated (ameliorated) the intensity of the fire risk. These results indicate more variable climate change effects than in the literature. Over 24 million km2globally of forestland have a similar or higher degree of slope, highlighting the need for detailed topographic integration in fire studies to meet the needs of local planners.

Mathematical Modeling of Human Activity on Forested Areas from Point Objects of Railway Infrastructure in a Two-Dimensional Statement

Human activity causes a large quantity of wildfires in areas close to the location of different industrial or transport infrastructure. All the infrastructure facilities of JSC Russian Railways may be classified into linear, point and area sources of human activity. This work purpose is to suggest an approach for predicting, assessing, and monitoring the human activity on forested areas based on a deterministic mathematical model. Partial differential equations have been solved using the finite difference method. The program realization is executed in the RAD Studio software. Expressions like the heat conduction equation are used to describe the propagation of human activity around railway facilities. Different railway objects have produced different distribution of the virtual (possible) number of forest fires (VNF) caused by the different level of human activity. Different sizes of the railway facilities have also showed that a highest VNF is predicted for large one. The proposed mathematical model is applicable to predict wildfires due to human activity. This paper discusses the possibility of predicting forest fire dangers near point infrastructure facilities of JSC Russian Railways. The results obtained are part of a large project aimed to develop methods for predicting, assessing, and monitoring forest fire danger near infrastructure facilities of Russian Railways.

Study on forest fire danger prediction in plateau mountainous forest area

Due to the complex vegetation and topography conditioning the plateau mountainous area, forest fire is difficult to put out in this area once it occurred. The research on forest fire danger prediction is very significant for fire prevention and fire fighting in plateau mountain area. Based on fire dataset from 1997 to 2018 in Lijiang area, this paper applied the spatial overlay analysis, Kriging interpolation analysis and Logistic regression model to study the relationship between forest fire occurrence and elevation, vegetation types and meteorological factors. An optimal Logistic model was selected to predict the probability of forest fire occurrence and fire danger rating in Lijiang area. The results showed that elevation, slope, aspect, daily average temperature and daily average relative humidity were significantly correlated with forest fire occurrence. The prediction accuracy of the Logistic model is averagely 86.9%. According to the results of fire danger prediction, forest fires in Lijiang occurred mainly concentrated in the north of Yulong county, the south of Ninglang county and the central area of Huaping county. The results are of great significance for forest fire prevention in Lijiang area.

Estimating dead fine fuel moisture content of forest surface, based on wireless sensor network and back-propagation neural network

The dead fine fuel moisture content (FFMC) in forest affects the occurrence and spread of forest fires. Therefore, the estimation of surface dead FFMC plays an important role in forest fire behaviour, fire management and fire danger assessment. However, there are some challenges with current FFMC measurement methods. Modelling using meteorological variables may be a very potential method to achieve remote real-time FFMC estimation. A surface dead FFMC estimation method based on wireless sensor network (WSN) and back-propagation (BP) neural network was proposed. The WSN can realise the acquisition of microclimate data. The BP neural network can use these data to establish multiple FFMC estimation models for different terrain conditions and dead fuel types. The ability of these models to estimate FFMC at four different terrain sampling sites was evaluated. The results suggested that the dead FFMC can be estimated with some degree of accuracy. The correlation coefficients of the estimation results at the four sampling sites were all greater than 0.9, and the mean square errors were all less than 1. The method can be well applied to forest surface dead FFMC estimation and early fire danger assessment, which has practical significance for the rational allocation of fire fighting resources.

A case-study of wildland fire management knowledge exchange: the barriers and facilitators in the development and integration of the Canadian Forest Fire Danger Rating System in Ontario, Canada

Background Among the most successful examples of Knowledge Exchange (KE) between researchers and practitioners in Canadian wildland fire management is the development and integration of the Canadian Forest Fire Danger Rating System (CFFDRS) into operational use. Aims Our aim was to identify key factors for this success. Methods Through a case study, we investigated historical KE of two CFFDRS components in Ontario, Canada. We held semi-structured interviews with principal Canadian Forest Service researchers and Ontario fire management practitioners active in development and implementation of CFFDRS from the late 1960s to 2010s. Key results The importance of both formal and informal facilitators to support KE was emphasised. Conclusion Participants were most likely to associate successful implementation with informal facilitators such as personal relationships, shared field-based experiences, and opportunities for dialogue between researchers and practitioners. Critical to success were the credibility and soft skills of the knowledge brokers, early engagement, and consideration of training needs for end users in the design of products. Implications This identification of factors that facilitated or hindered the development and implementation of CFFDRS can enhance the impact of research that will help wildland fire management deal with its present and future challenges.

Лесоэкологические последствия ландшафтных пожаров в Забайкалье

Лесоэкологические последствия ландшафтных пожаров в Забайкалье

Reconstructing seasonal fire danger in southeastern Australia using tree rings

Climate projections indicate that dangerous fire weather will become more common over the coming century. We examine the potential of a network of temperature- and moisture-sensitive tree-ring sites in southeastern Australia to reconstruct the number of high fire-danger days for the January–March season. Using the Forest Fire Danger Index (FFDI), we show that modestly statistically skilful reconstructions for the far southeast of Australia (western Tasmania), where the majority of tree-ring predictors are located, can be developed. According to the averaged reconstructions for the 1590–2008 period, there have been 16 years prior to the start of the FFDI records (1950), and 7 years since 1950, with >48 (mean + 1σ) high fire-danger days in the 3-month season. The western Tasmanian reconstructions indicate extended relatively high fire-danger periods in the 1650s–1660s and 1880s–1890s. Fire danger has also been relatively high since 2000 CE. A persistent increase in the number of high fire-danger days over the past four decades has not been matched over the previous 390 years. This work indicates it is possible to produce statistically useful reconstructions of high seasonal fire danger – as opposed to fire occurrence – but that availability of local proxy records is key.

Effect of recent fuel reduction treatments on wildfire severity in southeast Australian Eucalyptus sieberi forests

Climate change is increasing the area of seasonally dry forests burnt by wildfire in many parts of the world, compelling a revision of management strategies to reduce risk and improve the resilience of forest ecosystems. While prescribed burning is a common strategy used to reduce bushfire risk, mechanical thinning has not been as widely applied in Australia’s temperate forests in comparison with the USA and Europe. In this study we report results from a prescribed burning and mechanical thinning experiment in a 37-year-old naturally regenerated Eucalyptus open forest that was burnt through by wildfire in January 2020, providing an opportunity to determine how pre-wildfire fuel status and burning and thinning treatments influenced wildfire severity.Fuel hazard assessments in the experimental forest, made prior to the wildfire, indicated that untreated forest, and thinned forest, had very high to extreme fuel hazard scores, while forests treated with prescribed burning, or with thinning plus burning, had low fuel hazard scores. These fuel hazard scores explained more than 70% of the variation in wildfire severity, indicating that the fire was largely fuel driven and responsive to fuel reduction measures.Forest areas treated with thinning only recorded extreme fuel hazard, largely due to the increase in forest floor residues, which were ignited by the wildfire resulting in a high fire severity ranking. Forest areas treated with thinning and prescribed burning, or with high intensity prescribed burning, did not ignite from the advancing front of the wildfire, clearly demonstrating the effectiveness of fuel reduction management under low to moderate forest fire danger weather.Fuel hazard reduction benefits extend for at least three years after the application of fuel modification treatments in Eucalyptus sieberi forests. The results from this forest experiment demonstrate that the active management of fuels, including thinning with prescribed burning, can reduce wildfire risk and fire severity.

Forecasting Fire Risk in Forest Ecosystems Using Solar Activity Index

Improving forest ecosystems protection management based on a long-term fire hazard forecasting has been studied. Fires long-term dynamics in terms of quantity in various forests has been investigated. Fire risk patterns in forest ecosystems have been established conditional to regional characteristics due to solar period. Fire dynamics and solar periods analytics over multi-year period of time have brought to light that conditions contributing to a high fire frequency in Leningrad and Tver regional forest ecosystems arise in years with maximum and minimum solar activity. A long-term forecast of high forest fire danger periods for the next solar cycle has been performed through parameters of solar activity – Wolf numbers.

Increased extreme fire weather occurrence in southeast Australia and related atmospheric drivers

There is evidence suggesting that fire activity in southeast Australia has increased in the past two decades. There is also anecdotal evidence that pyrocumulonimbus events have become more common, although the observed record is short. We explore the extent to which possible changes in fire and pyrocumulonimbus occurrence in southeast Australia can be explained by changes in the number of extreme (above the 95th percentile) days per year of two fire-weather indices, the Forest Fire Danger Index (FFDI) and the continuous Haines index (C-Haines). For the period 1958 through 2020, we show that there is a dependence between the number of extreme FFDI days per year and burned area for forested parts of southeast Australia. To a lesser extent, there is a relationship between the FFDI and pyrocumulonimbus occurrences. We find an increase in the occurrence of extreme FFDI days from the late 1990s, with up to 24 more extreme days per year. In the southwest of the state of Victoria, there are concomitant increases in temperature and wind speed, and decreases in relative humidity. We show that the structure of C-Haines has not changed, but extreme days more frequently coincide with extreme FFDI days. This suggests that possible observed increases in pyrocumulonimbus occurrences may partly be explained as a consequence of the increases in co-occurring extreme FFDI and C-Haines days, rather than due to changes in the atmospheric stability. We also find that changes in the atmospheric circulation are consistent with those seen in the fire weather indices. Increased surface and mid-tropospheric pressure since 1998 are consistent with a poleward shift of the storm track. In particular, an increase in anticyclonic anomalies in the Tasman Sea is indicative of enhanced blocking activity. Although blocking frequency in these longitudes has decreased overall, the proportion of extreme FFDI days that are considered blocked has increased.

Analysis of drought index to asses land and forest fire season in Maros Regency

Forest and land fires occur almost every year, so they are a concern and priority in their control efforts. One of the important factors in the effort to control forest and land fires is knowing the times and locations that are prone to forest and land fires. This study aims to describe the level of drought and areas prone to forest and land fires in Maros Regency, South Sulawesi. This research was conducted in January to August 2017. The data collection was obtained from the Meteorology, Climatology and Geophysics Agency, the Climate Change Control Center, and the Bantimurung Bulusaraung National Park. The data were analyzed using the Polygon Thiessen method, the Keecth Byram Drought Index (KBDI) method, and the spatial analysis method. The results showed that extreme drought conditions in Maros Regency occurred from September to October based on observations of maximum rainfall and temperature. The forest area classified as moderate forest fire danger rating dominates the Maros Regency area, namely 73418.67 ha (45.77%). Maros Regency which is included in the area with a very high forest fire danger rating is Tompobulu and Cenrana Districts.

Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020.

China's forest cover has increased by approximately 10% as a result of sustainable forest management since the late 1970s. The forest ecosystem area affected by fire is increasing at an alarming rate of approximately 600,000 ha per year. The northeastern part of China, with a forest cover of 41.6%, has the greatest percentage of acres affected by forest fires. This study combines field and satellite weather data to determine factors that influence dead fuel moisture content (FMC). It assesses the use of the Canadian forest fire weather index to determine the daily forest fire danger in a typical temperate forest in Northeastern China during autumn. Based on the Wilcoxon test for paired samples, the observed and predicted values of FMC showed similar variation in eight of eleven sampling sites (72.7%), with a p value > 0.05. Three sampling plots presented lower predicted values of FMC than observed values (27.3%), with a p value < 0.05. The calculation of fire risk using the Canadian Forest Fire Weather Rating System (CFFDRS) in Maoer Mountain forest ecosystems presented low, medium or high risk; thus, the CFFDRS is suitable for determining fire danger in our study region. Along with these results, this study served to compare the use of FMC-metre field data and China Weather Station data to evaluate fire danger. The results of this study led us to suggest the multiplication of meteorological stations in fire-prone regions.