Datasets of Tasmanian re weather observations are analysed to investigate the existence of an anecdotal springtime bump, an early re weather peak in the Tasmanian re season. Such a phenomenon is not well-documented in comparison to the usual summer to early autumn peak. The existence of a springtime re danger peak is conrmed for eastern and southeastern Tasmania, approximately one year in two. It is also shown that there has been a substantial increase over recent decades in the number of springtime re weather events in southeast Tasmania with peak McArthur Forest Fire Danger Index (FFDI) in excess of 40. Diurnal variations in re danger between regions of Tasmania and between high- and low- elevation sites are examined, highlighting dierences in typical diurnal re danger behaviour in dierent regimes. For example, re danger peaks are generally experienced during the morning at the (high-level, southeastern) summit of Mt Wellington, around noon on the west coast, and later in the day at low level southeastern sites. High temporal resolution observations illustrate that the peaks of re danger can be quite short-lived and frequently occur at times other than mid-afternoon, however. Thus, 50% of daily re danger peaks at Hobart Airport (in the southeast) occur at times other than 1500 Local Time (LT). This suggests that many climate studies which use widely available 1500 LT observations may substantially underestimate the level of re danger in their areas of study. Case studies of two individual springtime re weather events indicate that at least two mechanisms operate in the generation of severe re weather. Hot, dry air can be advected from continental Australia and/or dry, high-momentum air can be transported from high in the troposphere to the surface through front/trough vertical circulations resulting in abrupt increases in re danger during already severe events. In both cases, there is evidence of a foehn eect contributing to the warmth of the airmasses passing over the central Tasmanian topography. Further study of dangerous re weather days throughout the re season, using high temporal resolution re weather observations, shows that the dierences evident between the two case studies occur more generally and that there are distinct synoptic dierences between the two
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Bushfires is a common feature in Australian and accurate prediction of forest fire danger is essential to predict potential fire and control the fire when it happened. McArthur Forest Fire Danger is widely used in Australia to predict fire danger. Fire danger index is defined as a resultant of both variables and constants fire danger factors which influence the start of fire, rate of spread and the difficulty of manage and control of the bushfire. The integration of these elements can be converted into one or more numerical index. This index called fire danger index which is depends on four main elements, drought factor, relative humidity, temperature and wind speed. Accurate data of weather elements are very important to get accurate assessment of fire danger level and to successful bushfires operations.
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Due to the availability of meteorological data from a mid- slope and a valley station, as well as additional data from a network of temperature and relative humidity data loggers and atmospheric soundings, we managed to analyse how it is resolved by fire danger rating indices. In order to capture the relevant atmospheric conditions, meteorological stations in the valleys and at higher elevations (where forests or other vegetation still occur and a potential thermal belt could de- velop) or high-resolution meteorological model data are nec- essary. Note that contrary to most conventional weather sta- tions, which are usually found in the valleys, the “RAWS” stations used for NFDRS calculations in the US are typ- ically located at mid-elevation south-facing slopes (Cohen and Deeming, 1985; Holden and Jolly, 2011). Fire danger rating indices to be used in such areas and situations should be able to correctly account for variations in the daily cy- cle between the individual stations or grid points. Potential indices are components of the National Fire-Danger Rating System (Bradshaw et al., 1983), hourly calculated Angstrom and FFWI indices, the hourly versions of the McArthur For- est Fire Danger Index (Noble et al., 1980), and the hourly Fine Fuel Moisture Code (Van Wagner, 1977). Further in- vestigation as to which of these or other additional indices are best suited for fire danger rating purposes under the con- ditions described in this paper are necessary, as our current assessment is limited to qualitative comparisons.
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A brief description of the ranked percentile curve approach of Eastaugh et al. (2012) is provided here. For fire danger indices with a daily time resolution, all index values are first converted to percentiles, and the percentiles on days on which fires occurred (“fire days”) are extracted and plotted by ascending rank to create a ranked percentile curve. A non- parametric regression model is then fit to this curve using the Theil–Sen method (Theil, 1950a, b, c; Sen, 1968), se- lected because it is more resistant to outliers than other re- gression techniques (due to the fact that the slope and inter- cept are determined using a median-based approach; Helsel and Hirsch, 2002; Granato, 2006). This resistance to outliers is well suited to the evaluation of meteorological fire danger indices, since the causes of wildfires extend well beyond the meteorological factors that are the only factors accounted for by the FWI components (e.g. variations in human activities – caused for example by weekend vs. weekday activities – might tend to lead to many more ignitions on particular days of the year). For illustrative purposes, Fig. 1 shows Theil– Sen models for three hypothetical fire danger indices: a “per- fect” index (i.e. the highest index percentile possible occurs on each fire day) where slope = 0 and intercept = 100; a fire danger index with no predictive skill (i.e. the distribution of percentiles on fire days is the same as on non-fire days) where slope = the maximum observed percentile value di- vided by the total number of fire days and intercept = 0; and an index with some predictive skill, where slope and inter- cept values fall between the “perfect” and “no skill” indices. Accordingly, intercept and slope values from the Theil–Sen model fits to the fire danger index data can be used to assess index skill and allow comparison between different indices.
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The performance of fire indices based on weather variables was analyzed with a special focus on the Czech Republic. Three fire weather danger indices that are the basis of fire danger rating systems used in different parts of the world were assessed: the Canadian Fire Weather Index (FWI), Australian Forest Fire Danger Index (FFDI) and Finnish Forest Fire Index (FFI). The performance of the three fire danger indices was investigated at different scales and compared with actual fire events. First, the fire danger indices were analyzed for different land use types during the period 1956–2015. In addition, in the analysis, the three fire danger indices were compared with wildfire events during the period 2001–2015. The fire danger indices were also analyzed for the specific locality of the Bzenec area where a large forest fire event occurred in May 2012. The study also focused on the relationship between fire danger indices and forest fires during 2018 across the area of the Jihomoravský region. Comparison of the index values with real fires showed that the index values corresponded well with the occurrence of forest fires. The analysis of the year 2018 showed that the highest index values were reached on days with the greater fire occurrence. On days with 5 or 7 reported fires per day, the fire danger indices reached values between 3 and 4.
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The role played by meteorological factors in the occur- rence of severe fire episodes is conveniently assessed by means of indices of meteorological fire danger that rate the likelihood of a fire event (Finney, 2005). Early examples in- clude the Nesterov index for use in the former Soviet Union (Nesterov, 1949), the Forest Fire Danger Index (FFDI) for eastern Australia (McArthur, 1967) and the National Fire Danger Rating System for the USA (Deeming et al., 1977). One of the most reliable and globally applied fire rating methodologies is the Canadian Forest Fire Weather Index System (CFFWIS). The system consists of six components that account for the effects of fuel moisture and wind on fire behaviour (Van Wagner, 1974). The first five components are based on empirically derived relationships between meteo- rological variables and the stress of different components of typical fuels that are present in jack pine forests of Canada (Stocks et al., 1989). The last component, the Fire Weather Index (FWI), results from the combination of the preceding five (Van Wagner 1987). FWI provides a numeric rating of fire intensity and is particularly suitable as a general index of meteorological fire danger, namely for the ecosystems of Mediterranean Europe (Viegas et al., 1999). Currently FWI operates on the basis of the Fire Danger Forecast module of the European Forest Fire Information System (EFFIS), which is one of the components of the emergency management ser- vices in the EU Copernicus programme (San-Miguel-Ayanz et al., 2012), as well as of the Fire Risk Map (FRM) product disseminated by the Satellite Application Facility for Land Surface Analysis (LSA SAF), which is part of the EUMET- SAT application ground segment (Trigo et al., 2011).
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Recently much effort has been put into finding the “best” fire danger indices for particular regions. Indices with greater skill may allow for more efficient allocation of firefighting re- sources, more appropriate public warning systems and more precise research studies. Regional differences in index per- formance may be apparent at relatively small geographical scales (e.g., Padilla and Vega-Garc´ıa, 2011), and it is un- likely that there will ever be a “one size fits all” approach. The United States Forest Service maintains the “FireFamily Plus” computer programme, one component of which can be used to analyse the performance of a range of fire danger in- dices (Andrews et al., 2003), and similar efforts to systemat- ically compare fire danger index performance are underway
Abstract. Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur’s Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire dan- ger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to veg- etation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction mod- els based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant rela- tionships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and VOD) with an r 2 up to 0.67. DOC levels estimated us- ing satellite observations were then evaluated using field measurements with an r 2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is compa- rable to the existing NDVI-based DOC estimation models.
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Within the marl prairie grasslands of the Everglades, the combined effects of fire and hydrology can lead to significant shifts in tree island composition, structure, and function. Depending on soil moisture (i.e., the elevation of the water table), fire severity, and post-fire hydroperiods, these effects can vary spatially and temporally throughout the landscape, pushing tree island ecosystems from one state (forested) to another (unvegetated, or covered by herbaceous vegetation). Furthermore, fre- quent fire under drought conditions may exac- erbate soil loss within tree islands, leading to lower tree island elevations and thus making tree islands much more susceptible to the ef- fects of post-fire flooding, (e.g., soil anoxia, which may inhibit or prevent the re-sprouting of top-killed trees from basal or root apical meristems). This negative feedback mecha- nism may ultimately contribute to the degrada- tion, contraction, and permanent alteration of tree islands under frequent and severe pertur- bation cycles.
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The list of soft parameters and the weighting scheme are then presented to ten experts. These experts are chosen from the academics and the professionals who are directly involved with fire safety or disaster management. They include four academics involved in disaster management and fire safety design of buildings, three officials from BFSCDA, one official from Bangladesh Army in charge of fire protection, an engineer from National Housing Authority, and an urban risk reduction specialist from the Comprehensive Disaster Management Program. The experts provided their grades for the selected soft parameters as per Table 1 using their own judgment about the parameters and their importance. Advanced techniques, such as Analytical Hierarchy Process, Fuzzy theory, Reliability Interval Method, Grey Relational Model or simulation approach have been applied in the literature in order to generate reliable weights for the parameters from expert judgments (Lo et al. 2005, Zhao et al. 2004, Lo 1999). However, given the lack of resources to generate such measures and noting Watt's (1991) third axiom of fire risk 'a totally objective or scientific way to measure fire risk does not exist' we follow the simple process of averaging the weights for each parameter. In order to ensure that the weights are not significantly influenced by the extreme values offered by one or two experts, we remove the maximum and minimum weights for each parameter and then average the weights by the remaining eight experts. Table 2 presents the list of the soft parameters and their associated weights from the expert opinions.
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In the analysis carried out in the previous section the temporal distances in the past d1 and d2 have been chosen in order to obtain significant results. However the question is how to choose the value of these temporal dis- tances in the past and how long time it is necessary to obtain these values. As it was shown in the previous sec- tion the delay to obtain one value is very short. In order to analyse this problem, other calculation were also made in the same time with in addition three temporal distances in the past, d1 = −15 s, d2 = −30 s, d3 = −1 min, d4 = −5 min, and d5 = −10 min. Figure 4 shows the results of these comparisons with the two used in the pre- vious section. It is clear that these latter two moving averages give the best results. Moreover they are calculated with the two shortest times in the past, thus they are useful and completely compatible with the characteristic time of a compartment fire, about 10 minutes to 1 hour.
Spatial heterogeneity in the habitat is a consequence of fires as they burn across landscapes, resulting in patches of varied fire severity. We examined several spatial variables: patch squareness, total patch perimeter, and distance to fire edge. From the center point of each trapping area, we used GIS to determine the distance to the nearest unburned edge. To investigate how fire severity patch size diversity influences small mammal captures, we calculated total patch perimeter and patch squareness for each small mammal trapping area using FRAGSTATS (McGarigal et al. 2002) and GIS. Total patch perimeter is a measure of patch heterogeneity calculated by summing the perimeter of each fire severity patch contained within the boundaries of the trapping area. Because all of the small mammal trapping sites were equal in size, the site with the highest total patch perimeter indicates a site with more patches of differing fire severities, and therefore a site with higher burn heterogeneity. Patch squareness describes patch shape complexity and is an index that ranges from 0 (square, less edge, minimum complexity) to 1 (least square- like, more edge, high complexity) (Frohn 1998). Unlike contagion and fractal dimension, total patch perimeter and patch squareness are optimized for use with data arranged in rasters, as with Landsat data (Frohn 1998).
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The specific temporal patterns of an- tecedent conditions associated with fire occurrence in the Great Basin and Up- per Colorado River Basin are poorly understood. Using 25 years of com- bined fire and climate data, we identi- fied unique antecedent patterns of cli- mate conditions prior to fires in the Great Basin and Upper Colorado River Basin. Five distinct antecedent pat- terns of climate related to fire were found within the region; with these an- tecedent patterns we were able to con- struct models of fire danger. The oc- currence of these antecedent patterns varies both spatially and temporally, and appears to be driven by drought se- verity. We used a Maximum Entropy approach to model the spatial extent and strength of these fire-climate pat- terns, and the associated fire danger. This approach provides land managers with a practical way to assess fire dan- ger at a relatively fine spatial scale and also gives researchers a tool for assess- ing future fire danger.
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For thousands of years, fire has shaped coniferous forests of the western United States. In more recent time, land use practices have altered the role fire plays in the Sierra Nevada. By understanding the past, land managers can design better fuel treatments today. This research explores the fire regimes of Sagehen Experimental Forest in the eastern Sierra Nevada, California, through a fire scar reconstruction of lower elevation Jeffrey pine (Pinus jeffreyi Balf.) and Jeffrey pine-mixed conifer stands. Prehistoric and historic land use practices, fuel accumulation, and climate influenced the fire regime over three periods of time: pre-settlement (1700 to 1859), settlement (1860 to 1925), and suppression (1925 to 2006). Over the period of analysis, 293 fire scars were assigned calendar years. The mean composite fire return interval for all samples in the study area was two years. The mean composite fire return interval was significantly longer for the suppression period than both the pre-settlement and settlement periods. The lack of sufficient fire intervals for analysis using a filter that included fires that scarred at least three or more trees and 25 % of the total sample indicates that fires in the study area are small in spatial extent. The proportion of dormant season fires increased from pre-settlement through the sup- pression period. No fires were recorded as middle and early earlywood during the sup- pression period. A superposed epoch analysis found significant correlation to warmer (Pacific Decadal Oscillation) and wetter (Palmer Drought Severity Index) conditions three years prior to large fire events during the pre-settlement period. During the post-settle- ment era, large fire years were correlated to El Niño conditions for two consecutive years prior. Little synchrony of fire events was recorded between fire scarred tree clusters. These findings suggest that small frequent prescribed burns would best mimic the pre-set- tlement fire regime if fire is reintroduced into the ecosystem.
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proteins also with potential to be a DAMP, interact with RAGE to induce a specific inflammatory pattern with increased vascularity in endothelial cells and a pro- thrombotic effect, although these effector functions pre- sence after HMGB1 activtion is yet to be determined . The same group has demonstrated that administer- ing HMGB1 blocking agents, such as ethyl pyruvate or anti-RAGE, can significantly reduce serum levels of HMGB1 and restore the expression levels of IL-2 and IL-2a, which mediate expansion of T-cells, key players in the adaptive response. In this study the expression levels of CD152 and Foxp3 were elevated on splenic reg- ulatory T cells, but expression levels of both markers were reduced in groups that were administered ethyl pyruvate or anti-RAGE . With respect to the early inflammatory response Tsung et al.  demonstrated that tissue damage caused by hepatic ischemia was decreased when mice were treated with neutralizing antibodies against HMGB1. Endogeneous tissue damage was worsened when additional exogenous HMGB1, in the form of recombinant HMGB1, was administered to mice after hepatic ischemic injuries . This demon- strates that HMGB1 acts as an early danger/alarmin sig- nal and mediator of tissue injury and trauma in liver ischemia . This could be extrapolated out to surgical trauma but this has yet to be demonstrated in pre-clini- cal models. HMGB1 has already been proven to be a successful therapeutic target in experimental models of infectious and inflammatory disorders including sepsis, cancer and theumatoid arthritis , we are getting clo- ser to isolating this molecule as a target for therapeutic manipulation in trauma.
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The strengths of this study were large sample size, the ques- tionnaire of BPCR index was developed from JHPIEGO and the participants were interviewed by a well-trained research assistant. However, this study had many limitations, it was a cross-sectional study, hence the relationship between vari- ables could not be proven. The answers were self-reported with no means of verification and thus subject to bias. More- over, participants were interviewed while currently pregnant, rather than after completing their pregnancies; they may not yet have had the opportunity or need to decide on BPCR. Another limitation of this study includes factors that might affect BPCR status, such as attitudes and beliefs about birth
In this context, characterized by growing global uncertainty, scientific dissemination takes on a central role for the correct inter- pretation of risks in order to avoid misinformation. Furthermore, I remember how in 1982 the socio-cultural theory of risk, proposed by Mary Douglas and Aaron Wildavsky, allowed to show how the attitude towards a danger is influenced by that set of norms, values, belief systems and social behaviours that they constitute the cul- ture and organization of a community Douglas, et al. . An inter- pretation that allows us to explain how different source judgments exist at a risk. The controversies about risk, therefore, would not only be the result of a distorted perception, but also the result of a social comparison between judgments influenced by moral values and political orientations. According to Douglas and Wildavsky, the understanding of risk depends on the socio-cultural context and can not only be discerned by the results and technical analyzes, and since the risk decisions are also the result of a continuous process of social negotiation, it is not said at all that risks are always accept- ed as actual. Risk and Culture (1982) begins with the proposition that «total knowledge» would be necessary for us to understand the risks we face. The number of possible dangers is infinite, and «[s]ince no one can attend to everything, some sort of priority must be established among danger» Douglas, et al. .
The requirements of risk assessment have become more important particularly after the fatal explosions at Petronas oil terminal at Pasir Gudang Industrial Estate. The incidents occurred when two storage tanks containing petrol and a natural gas tank caught fire by lightning strike during a thunderstorm on 28 April 2006 (Bernama, 2006). Even though no injuries reported, this incident increases awareness among the people about the importance and purpose of risk assessment. Besides that, two incidents are also reported at methanol plant in Seberang Perai, Penang (The Star, 2004) and Labuan (The Star, 2007) on 2004 and 2007. The latest incident is at Tanjung Langsat on 24 August 2008 where a fire broke out at one of the eight oil storage tanks (Bernama, 2008).
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Stainback and Alavalapati  suggest that in even-aged plantation forestry, risk from natural disturbance reduces the incentive that a carbon market would provide to land- owners for increasing rotation age and thus carbon stocks. Previous research has suggested that for forests historically characterized by frequent, low severity fire, thinning the forest can reduce the risk of carbon loss from wildfire [11,12]. Thus, FRCC_DEP can be altered, making this car- bon valuation method robust to site-specific management actions, providing incentive in terms of increased carbon market value for landowners to engage in high severity fire risk reduction measures.
Several improvements to the proposed prognostic model (with covariate ψ) are currently being considered. First, no advantage was taken of the fact that covariates ψ and χ are positively correlated (when ψ is larger than the median). This information may be added by setting up a model of the dis- tribution of χ using information about meteorological con- ditions along the pre-fire season and then incorporating this information in the prognostic model. Second, the model was developed for the entire territory of Portugal, not taking into account regional characteristics of climate, land cover and fire regime. It is therefore worth setting up regional mod- els over areas with distinct pyrogeographical characteristics. Finally, the outlooks with respect to the months of July and August and given the observed trend to have a longer fire sea- son it would be very useful to extend them until October. For instance, as pointed out by Beighley and Hyde (2018), 36 % of the total area burned in 2009–2017 was outside the pe- riod from July to September, a fraction that is 3 times larger than the value of 12 % observed when considering the period 2001–2008.
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