Fire Danger Index Research Articles

A new wildland fire danger index for a Mediterranean region and some validation aspects

Wildland fires are the main cause of tree mortality in Mediterranean Europe and a major threat to Spanish forests. This paper focuses on the design and validation of a new wildland fire index especially adapted to a Mediterranean Spanish region. The index considers ignition and spread danger components. Indicators of natural and human ignition agents, historical occurrence, fuel conditions and fire spread make up the hierarchical structure of the index. Multi-criteria methods were used to incorporate experts’ opinion in the process of weighting the indicators and to carry out the aggregation of components into the final index, which is used to map the probability of daily fire occurrence on a 0.5-km grid. Generalised estimating equation models, which account for possible correlated responses, were used to validate the index, accommodating its values onto a larger scale because historical records of daily fire occurrence, which constitute the dependent variable, are referred to cells on a 10-km grid. Validation results showed good index performance, good fit of the logistic model and acceptable discrimination power. Therefore, the index will improve the ability of fire prevention services in daily allocation of resources.

Forecasting Methodology of National-level Forest Fire Risk Rating

The risk level of forest fire depends not only on weather,topography,human activities,socio-economic conditions,but also closely related to the types,growth,moisture content,and quantity of forest fuel on the ground.How to timely acquire information on the dynamics of growth and moisture content of forest fuel and climate in the whole country is critical to national-level forest fire risk forecasting.The development and application of Remote Sensing(RS),geographic information system(GIS),databases,Internet,and other modern information technologies has provided an important technical means for macro-regional forest fire risk forecasting.Quantified forecasting of national-level forest fire risk was studied using Fuel State Index(FSI) and Background Composite Index(BCI).The FSI was estimated using MODerate resolution Imaging Spectroradiaometer(MODIS) data.National meteorological data and other basic data on distribution of fuel types and forest fire risk rating were standardized in ArcGIS platform to calculate BCI.The FSI and the BCI were used to calculate the Forest Fire Danger Index(FFDI),which is regarded as a quantitative indicator for national forest fire risk forecasting and forest fire risk rating,shifting from qualitative description to quantitative estimation.The major forest fires occurred in recent years was taken as examples to validate the above method,and results indicated that the method can be used for quantitative forecasting of national-level forest fire risks.

Study on forest fire danger over Northern China during the recent 50 years

Daily meteorological data at 263 stations in northern China from 1956 to 2005 were used to calculate various forest fire danger weather (FFDW) indices, such as Nesterov Index (NI), Modified Nesterov Index (MNI), Keetch-Byram Drought Index (KBDI), and Forest Fire Danger Index (FFDI), at different time scales. The relationship between each index and forest fire was analyzed. MNI and FFDI were then selected to study the impact on forest fire danger due to climatic change in northern China in the recent 50 years. Results show that forest fire danger has significantly increased in Northeast China where there is the richest forest resource in China, and also increased in North China. However, it has not changed much in eastern part of Northwest China, and the forest fire danger has even significantly decreased in northern Xinjiang. Significant rise of forest fire danger in Northeast China mainly results from the co-effect of increase of temperature, and decreases of humidity and precipitation. Relative humidity change dominants forest fire danger trends in the four forest regions in northern China.

Impact of More Frequent Observations on the Understanding of Tasmanian Fire Danger

AbstractHalf-hourly airport weather observations have been used to construct high-temporal-resolution datasets of McArthur Mark V forest fire danger index (FFDI) values for three locations in Tasmania, Australia, enabling a more complete understanding of the range and diurnal variability of fire weather. Such an understanding is important for fire management and planning to account for the possibility of weather-related fire flare ups—in particular, early in a day and during rapidly changing situations. In addition, climate studies have hitherto generally been able to access only daily or at best 3-hourly weather data to generate fire-weather index values. Comparison of FFDI values calculated from frequent (subhourly) observations with those derived from 3-hourly synoptic observations suggests that large numbers of significant fire-weather events are missed, even by a synoptic observation schedule, and, in particular, by observations made at 1500 LT only, suggesting that many climate studies may underestimate the frequencies of occurrence of fire-weather events. At Hobart, in southeastern Tasmania, only one-half of diurnal FFDI peaks over a critical warning level occur at 1500 LT, with the remainder occurring across a broad range of times. The study reinforces a perception of pronounced differences in the character of fire weather across Tasmania, with differences in diurnal patterns of variability evident between locations, in addition to well-known differences in the ranges of peak values observed.

Climate input parameters for real-time online risk assessment

Risk assessment of natural hazards is often based on the actual or forecast weather situation. For estimating such climate-related risks, it is important to obtain weather data as frequently as possible. One commonly used climate interpolation routine is DAYMET, which in its current form is not able to update its database for periods of less than a year. In this paper, we report the construction of a new climate database with a standard interface and implement a framework for providing daily updated weather data for online daily weather interpolations across regions. We re-implement the interpolation routines from DAYMET to be compliant with the data handling in the new framework. We determine the optimal number of stations used in two possible interpolation routines, assess the error bounds using an independent validation dataset and compare the results with a previous validation study based on the original DAYMET implementation. Mean absolute errors are 1°C for maximum and minimum temperature, 28 mm for precipitation, 3.2 MJ/m² for solar radiation and 1 hPa for vapour pressure deficit, which is in the range of the original DAYMET routine. Finally, we provide an example application of the methodology and derive a fire danger index for a 1 km grid over Austria.

Decision support system for forest fire protection in the Euro-Mediterranean region

This paper describes the development of a decision support system (DSS) for prevention planning and emergency management of forest fire events that incorporates weather data management, a geographical data viewer, a priori danger forecasting and fire propagation modeling, automatic fire detection, and optimal resource dispatching. Collection, input, storage, management, and analysis of the information rely on advanced and automated methodologies using remote sensing, GPS, digital mapping, and geographic information systems. The results included short-term dynamic fire danger indices developed for improved and realistic prevention and pre-suppression planning. An automatic fire detection technology based on infrared video was developed and successfully tested on site. Several models for understanding fire propagation on forest fires have been proposed for practical application. Additionally, a DSS was developed with the innovation of covering wildland fire hazard management entirely, providing a complete coverage of technical and administrative activities that support decision makers in real time. The DSS was tested for high fire seasons in two different sites in South Europe.

(A227) Tension between Emergency Management Policy Decisions and Aged Care Facilities in Australia: A Case Study

This paper considers the impact that a number of Australian emergency management policy and operational decisions are having on residential aged care facilities located in the community. For example, all residential aged care facilities applying for new federal government funded aged care places are required to demonstrate a plan for environmental disaster threats such as bushfires and floods. Another example is the adoption of new fire danger rating scale, with the inclusion of an extreme level called “catastrophic”-code red. This inclusion requires all services and community members, living in bushfire-prone areas to decide whether or not to evacuate the day before or morning of a Bureau of Meteorology fire danger index indicating a code red. There is evidence that these policy and operational decisions have been made without fully examining the practical implications, particularly for aged care facilities. While many of the facilities on which these decisions impact see the rational for such decisions, they argue that these decisions have serious implications for their services and patients. Many residential aged care facilities, which are privately operated, historically have not been involved in any state or local government emergency management planning. Therefore, the whole concept of risk assessment, preparation, and planning to increase the absorbing, buffering, and response capacity of their facilities against extreme weather events has become quite overwhelming for some. This paper presents a case study that demonstrates the tension between emergency management policy decisions on an aged care facility, and outlines their issues and response.

Behind the flaming zone: Predicting woody fuel consumption in eucalypt forest fires in southern Australia

Pre-fire woody fuel (diameter > 0.6 cm) structure and its consumption by fire were measured at experimental/prescribed fires and high intensity wildfires in eucalypt forests in southern Australia in order to better understand and model the dynamics of woody fuel consumption. Two approaches were used in model development: (1) a fire or plot level analysis, based on a dataset which includes the proportion of the pre-fire woody fuel load consumed at each fire; and (2) a stage level analysis, based on a dataset where woody fuel consumption was measured at a woody fuel particle level (i.e. pre-fire and post-fire diameter). For the plot level analysis a generalised linear model (GLM) approach identified the Forest Fire Danger Index (FFDI) as the best predictor of the proportion of woody fuel consumed, with an R 2 of 0.58 and mean absolute error of 10%. The stage level analysis recognised the various combustion stages through which a burning woody particle would pass, but failed to develop an accurate model that predicted the ignition, partial and full consumption of woody fuels based on fuel, fire behaviour and environmental variables. Analysis showed that consumption of woody fuel particles is highly variable and that variation in fire behaviour potentially has a greater impact on woody fuel consumption, than does variation in fuel characteristics (e.g. state of decay, fuel suspension and interactions with other fuel particles). The FFDI GLM provides forest and fire managers with a tool to manage woody fuel consumption objectives and may assist fire managers with forecasting post-frontal fire behaviour. The FFDI GLM may also assist forest and fire managers to better meet land management goals and to comply with air quality and emission targets.

A comparison of statistical downscaling methods suited for wildfire applications

AbstractPlace‐based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour. However, a majority of downscaling methods are inappropriate for wildfire application due to the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA) that directly incorporate daily data from global climate models, were validated over the western US using global reanalysis data. While both methods outperformed results obtained from direct interpolation from reanalysis, MACA exhibited additional skill in temperature, humidity, wind, and precipitation due to its ability to jointly downscale temperature and dew point temperature, and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, MACA outperformed the daily BCSD due to its ability to more accurately capture relative humidity and winds. Copyright © 2011 Royal Meteorological Society

Fire risk in Austrian pine (Pinus nigra) plantations under various temperature and wind conditions

Fire risk in Austrian pine (Pinus nigra) plantations under various temperature and wind conditions The Austrian pine (Pinus nigra), an introduced conifer in Hungary, forms a highly flammable vegetation type. The fire risk of such stands was examined using McArthur's empirical forest fire danger model. Our study focused on the effects of temperature and wind speed on fire behaviour. By keeping the input parameters of the model constant while changing temperature andwind speed within a specified interval the resulting fire danger index (FDI) and fire behaviour were examined. The applied fixed parameters were: 30 °C temperature, 30% relative humidity, 30 km h-1 wind speed, 30 degree of slope and drought factor value 10. The annual trends of the Byram-Keetch drought index (BKDI) and the drought factor were also calculated. Our results show that increasing temperature and wind speed raises the FDI, flame height, rate of fire spread (ROS) and spotting distance. The amount of fuel does not influence the FDI, but increasing the amount promotes the ROS and raises the flame height. Wind speed was the most important factor in the ROS. A serious fire risk of these plantations was determined. The reliability of McArthur's model was proved by comparison of our results with experimental laboratory data based on literature.

Regional signatures of future fire weather over eastern Australia from global climate models

Skill-selected global climate models were used to explore the effect of future climate change on regional bushfire weather in eastern Australia. Daily Forest Fire Danger Index (FFDI) was calculated in four regions of differing rainfall seasonality for the 20th century, 2050 and 2100 using the A2 scenario from the Special Report on Emissions Scenarios. Projected changes in FFDI vary along a latitudinal gradient. In summer rainfall-dominated tropical north-east Australia, mean and extreme FFDI are projected to decrease or remain close to 20th century levels. In the uniform and winter rainfall regions, which occupy south-east continental Australia, FFDI is projected to increase strongly by 2100. Projections fall between these two extremes for the summer rainfall region, which lies between the uniform and summer tropical rainfall zones. Based on these changes in fire weather, the fire season is projected to start earlier in the uniform and winter rainfall regions, potentially leading to a longer overall fire season.

Modelling burned area in Africa

Abstract. The simulation of current and projected wildfires is essential for predicting crucial aspects of vegetation patterns, biogeochemical cycling as well as pyrogenic emissions across the African continent. This study uses a data-driven approach to parameterize two burned area models applicable to dynamic vegetation models (DVMs) and Earth system models (ESMs). We restricted our analysis to variables for which either projections based on climate scenarios are available, or that are calculated by DVMs, and we consider a spatial scale of one degree as the scale typical for DVMs and ESMs. By using the African continent here as an example, an analogue approach could in principle be adopted for other regions, for global scale dynamic burned area modelling. We used 9 years of data (2000–2008) for the variables: precipitation over the last dry season, the last wet season and averaged over the last 2 years, a fire-danger index (the Nesterov index), population density, and annual proportion of area burned derived from the MODIS MCD45A1 product. Two further variables, tree and herb cover were only available for 2001 as a remote sensing product. Since the effect of fires on vegetation depends strongly on burning conditions, the timing of wildfires is of high interest too, and we were able to relate the seasonal occurrence of wildfires to the daily Nesterov index. We parameterized two generalized linear models (GLMs), one with the full variable set (model VC) and one considering only climate variables (model C). All introduced variables resulted in an increase in model performance. Model VC correctly predicts the spatial distribution and extent of fire prone areas though the total variability is underrepresented. Model VC has a much lower performance in both aspects (correlation coefficient of predicted and observed ratio of burned area: 0.71 for model VC and 0.58 for model C). We expect the remaining variability to be attributed to additional variables which are not available at a global scale and thus not incorporated in this study as well as its coarse resolution. An application of the models using climate hindcasts and projections ranging from 1980 to 2060 resulted in a strong decrease of burned area of ca. 20–25%. Since wildfires are an integral part of land use practices in Africa, their occurrence is an indicator of areas favourable for food production. In absence of other compensating land use changes, their projected decrease can hence be interpreted as a indicator for future loss of such areas.

Using Monte Carlo simulations to estimate relative fire ignition danger in a low-to-medium fire-prone region

A comprehensive assessment of fire ignition danger is nowadays a basic step towards the prioritization of fire management measures. In this study we propose performing a fire selectivity analysis using Monte Carlo simulations to statistically estimate the relative fire ignition danger in a low-to-intermediate fire-prone region such as Canton Ticino, Switzerland. We define fire ignition danger as the likelihood that at a given place a fire will be ignited. For each 25 m × 25 m pixel of the study area, landscape characteristics that may be related to the probability of fire ignition such as vegetation type, elevation, aspect, slope, urban–forest interface were first split into 9–12 categories. The selectivity of each category with respect to fire ignition was then statistically tested by means of Monte Carlo simulations. Finally, we proposed two different approaches for calculating the ignition danger index: cumulating the scores of the Monte Carlo simulations to a final index or producing synthetic scores by performing a principal component analysis of the Monte Carlo results. The validation of the resulting fire danger indices highlights the suitability of both proposed approaches. The PCA-option allows a slightly better discrimination between ignition and non-ignition points and may be of more general application.

NCEP - ECPC monthly to seasonal US fire danger forecasts

Five National Fire Danger Rating System indices (including the Ignition Component, Energy Release Component, Burning Index, Spread Component, and the Keetch–Byram Drought Index) and the Fosberg Fire Weather Index are used to characterise US fire danger. These fire danger indices and input meteorological variables, including temperature, relative humidity, precipitation, cloud cover and wind speed, can be skilfully predicted at weekly to seasonal time scales by a global to regional dynamical prediction system modified from the National Centers for Environmental Prediction’s Coupled Forecast System. The System generates global and regional spectral model ensemble forecasts, which in turn provide required input meteorological variables for fire danger. Seven-month US regional forecasts were generated every month from 1982 to 2007. This study shows that coarse-scale global predictions were more skilful than persistence, and fine-scale regional model predictions were more skilful than global predictions. The fire indices were better related to fire counts and area burned than meteorological variables, although relative humidity and temperature were useful predictors of fire characteristics.

Meteorological conditions and wildfire-related houseloss in Australia

Wildland fires or bushfires occurring under very severe weather conditions are likely to be destructive to infrastructure. This paper reports an analysis of the statistical relationship between house loss and the fire weather under which it occurred. A dataset was derived from 54 bushfires that occurred in Australia between 1957 and 2009, which resulted in the destruction of 8256 houses. The dataset was statistically compared with relevant local meteorological conditions, and a standardised calculation of the McArthur Forest Fire Danger Index (FFDI) applied. The analysis highlights how house loss statistics in Australia are dominated by a few iconic events that have occurred during very intense fire weather with the majority of losses occurring on days when the FFDI exceeds 100. Virtually all of the house loss has occurred above the 99.5th percentile level in the distribution of daily FFDI for each of the regions considered. Regulatory tools will need to focus on the most appropriate fire weather potential of a local area in order to ensure that infrastructure is adequately designed. In Australia, little house loss has occurred on days where the FFDI did not exceed 50, suggesting that historic building practices may be maintained in regions where this level is not likely to be exceeded.

Australian grassland fire danger using inputs from the GRAZPLAN grassland simulation model

Assessing and broadcasting the Fire Danger Rating each day of the fire season is an important activity in fire-prone nations. For grasslands in Australia, grass curing and biomass are biological variables that are not usually archived yet as inputs, along with weather data, to the calculation of Grassland Fire Danger Index (GFDI) and potential fire intensity. To assess past changes in the index, the biological inputs for GFDI for Canberra in south-eastern Australia were obtained using a pasture simulator, GRAZPLAN. Shoot biomass (including leaf litter) and grass curing were modelled using three contrasting pasture models (exotic annual, exotic perennial and native perennial) in order to calculate two variants of McArthur’s GFDI Mark 4 (the original and a modified version which includes fuel load); values were either capped at 100 as in the original (the ‘worst possible’ condition) or left open-ended. GFDI, and the potential fire intensity for fires burning with the wind each afternoon during a 54-year period were calculated. The native perennial grass model gave contrasting results to those from the exotic perennial grass model, whereas the annual grass model usually was intermediate in behaviour. GRAZPLAN outputs allow not only retrospective examination, but also provide a basis for predicting potential fire danger and behaviour as a result of climate change.

Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index

A number of different methodologies are developed for examining the sensitivities of an index. These methodologies are applied to examine the characteristics of the Canadian Fire Weather Index (FWI) and the McArthur Forest Fire Danger Index (FFDI) using 8 years of gridded data throughout Australia. Percentile changes in input conditions show that the indices are similar to each other in that they are both most sensitive to wind speed, then secondly to relative humidity and thirdly to temperature. On a finer scale, a combination of the relationship between the indices and their partial derivatives shows that the FFDI is relatively less sensitive to wind speed and rainfall, and more sensitive to temperature and relative humidity, than the FWI. A method based on equilibrium values of the indices shows that the FFDI has a temperature threshold set by recent rainfall above which its sensitivity increases, resulting in some non-linearity in its relationship with the FWI. The sensitivity differences between the indices mean that the indices are complementary in that they each respond to a somewhat different set of conditions, as is shown by examining a number of recent fire events. The fire events also reveal that index values associated with dangerous fire behaviour can vary greatly between different regions. Methods to reduce the consequences of this variation are examined, including the use of index percentiles. Copyright © 2009 Royal Meteorological Society

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index – an index that incorporates satellite and surface observations to map fire potential at a national scale – in forecasting distributions of large fires.

Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather

The probability of large-fire (≥1000 ha) ignition days, in the Sydney region, was examined using historical records. Relative influences of the ambient and drought components of the Forest Fire Danger Index (FFDI) on large fire ignition probability were explored using Bayesian logistic regression. The preferred models for two areas (Blue Mountains and Central Coast) were composed of the sum of FFDI (Drought Factor, DF = 1) (ambient component) and DF as predictors. Both drought and ambient weather positively affected the chance of large fire ignitions, with large fires more probable on the Central Coast than in the Blue Mountains. The preferred, additive combination of drought and ambient weather had a marked threshold effect on large-fire ignition and total area burned in both localities. This may be due to a landscape-scale increase in the connectivity of available fuel at high values of the index. Higher probability of large fires on the Central Coast may be due to more subdued terrain or higher population density and ignitions. Climate scenarios for 2050 yielded predictions of a 20–84% increase in potential large-fire ignitions days, using the preferred model.

A simple index for assessing fire danger rating

Fire danger rating systems are used to assess the potential for bushfire occurrence, fire spread and difficulty of fire suppression. Typically, fire danger rating systems combine meteorological information with estimates of the moisture content of the fuel to produce a fire danger index. Fire danger indices are used to declare fire bans and to schedule prescribed burns, among other applications. In this paper a simple fire danger index F that is intuitive and easy to calculate is introduced and compared to a number of fire danger indices pertaining to different fuel types that are used in an operational setting in Australia and the United States. The comparisons suggest that F provides a plausible measure of fire danger rating and that it may be a useful pedagogical tool in the context of fire danger and fire weather.