Canadian Fire Weather Index System Research Articles

Factors affecting severity of wildfires in Scottish heathlands and blanket bogs

Temperate heathlands and blanket bogs are globally rare and face growing wildfire threats. Ecosystem impacts differ between low and high severity fires, where severity reflects immediate fuel consumption. This study assessed factors influencing fire severity in Scottish heathlands and blanket bogs, including the efficacy of the Canadian Fire Weather Index System (CFWIS). Using remote sensing, we measured the differenced Normalised Burn Ratio at 92 wildfire sites from 2015 to 2021. We used Generalised Additive Mixed Models to investigate the impact of topography, habitat wetness, CFWIS components and 30-day weather on severity. Dry heath exhibited higher severity than wet heath and blanket bog, and slope, elevation and south facing aspect were positively correlated to severity. Weather effects were less clear due to data scale differences, yet still indicated weather's significant role in severity. Rainfall had an increasingly negative effect from approximately 15 days before the fire, whilst temperature had an increasingly positive effect. Vapour Pressure Deficit (VPD) was the weather variable with highest explanatory value, and predicted severity better than any CFWIS component. The best-explained fire severity model (R2 = 0.25) incorporated topography, habitat wetness wind and VPD on the day of the fire. The Drought Code (DC), predicting organic matter flammability at ≥10 cm soil depth, was the CFWIS component with the highest predictive effect across habitats. Our findings suggest that wildfires in wet heath and blanket bogs are typically characterised by low severity, but that warmer, drier weather may increase the risk of severe, smouldering fires which threaten peatland carbon stores.

Forecasting Hourly Wildfire Risk: Enhancing Fire Danger Assessment using Numerical Weather Prediction

Abstract Wildfire agencies use Fire Danger Rating Systems (FDRS) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian Fire Weather Index (FWI) System, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly Fire Radiative Power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI’s ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system’s value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior.

Improving the fire weather index system for peatlands using peat-specific hydrological input data

Abstract. The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWIpeat, was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWIpeat should enable improved monitoring of fire risk in boreal peatlands.

Evaluating the Drought Code for lowland taiga of Interior Alaska using eddy covariance measurements

Background The Drought Code (DC) of the Canadian Fire Weather Index System (CFWIS) has been intuitively regarded by fire managers in Alaska, USA, as poorly representing the moisture content in the forest floor in lowland taiga forests on permafrost soils. Aims The aim of this study was to evaluate the DC using its own framework of water balance as cumulative additions of daily precipitation and substractions of actual evaporation. Methods We used eddy covariance measurements (EC) from three flux towers in Interior Alaska as a benchmark of natural evaporation. Key results The DC water balance model overpredicted drought for all 14 site-years that we analysed. Errors in water balance cumulated to 109 mm by the end of the season, which was 54% of the soil water storage capacity of the DC model. Median daily water balance was 6.3 times lower than that measured by EC. Conclusions About half the error in the model was due to correction of precipitation for canopy effects. The other half was due to dependence of the actual evaporation rate on the proportional ‘fullness’ of soil water storage in the DC model. Implications Fire danger situational awareness is improved by ignoring the DC in the CFWIS for boreal forests occurring on permafrost.

Assessment of lake-level fluctuation as an indicator of fire activity in boreal Canada

Fuel moisture content is a highly variable property that dictates the ignition and combustion potentials of wildfire fuels. The Canadian Fire Weather Index (FWI) System—a subset of the established Canadian Forest Fire Danger Rating System—relies solely on weather observations to track fuel moisture and compute indices of fire potential. Ancillary environmental measures, such as lake levels in non-regulated lake systems, may integrate climatic and landscape factors that better reflect environmental moisture deficits and thereby complement the FWI System. Accordingly, we investigated whether inter-annual variation in lake levels has potential to serve as an indicator of regional fire activity using a quasi-binomial regression approach. This study combines annual measurements of fire activity, lake level, and other climatic variables from the Boreal Shield and Boreal Plain ecozones of Canada from 1980 to 2019 to determine whether variation in regional area burned can be explained by lake-level fluctuation, and whether this explanatory power differs by ecozone. Results indicated that lake-level fluctuation is a significant predictor of regional area burned for many lakes (28–48%). It often performs as well as or better than traditional fire-weather metrics (such as temperature and precipitation), and this relationship is stronger on the Boreal Shield compared to the Boreal Plain. Lake-level fluctuation provides information on fuel availability unaccounted for by traditional fire-weather metrics, suggesting that this variable could be added to the suite of environmental indicators of regional fire activity in Canada.

Summer and Fall Extreme Fire Weather Projected to Occur More Often and Affect a Growing Portion of California throughout the 21st Century

Annual burned area has increased in California over the past three decades as a result of rising temperatures and a greater atmospheric demand for moisture, a trend that is projected to continue throughout the 21st century as a result of climate change. Here, we implement a bias-correction and statistical downscaling technique to obtain high resolution, daily meteorological conditions for input into two fire weather indices: vapor pressure deficit (VPD) and the Canadian Fire Weather Index System (FWI). We focus our analysis on 10 ecoregions that together account for the diverse range of climates, ecosystems, topographies, and vegetation types found across the state of California. Our results provide evidence that fire weather conditions will become more extreme and extend into the spring and fall seasons in most areas of California by 2100, extending the amount of time vegetation is exposed to increased atmospheric demand for moisture, and heightening the overall risk for the ignition and spread of large wildfire. The ecoregion-level spatial scale adopted for this study increases the spatial specificity of fire weather information, as well as the resolution with which fire and land managers can implement strategies and counter-measures when addressing issues related to climate change.

Global increase in wildfire risk due to climate-driven declines in fuel moisture.

There is mounting concern that global wildfire activity is shifting in frequency, intensity, and seasonality in response to climate change. Fuel moisture provides a powerful means of detecting changing fire potential. Here, we use global burned area, weather reanalysis data, and the Canadian fire weather index system to calculate fuel moisture trends for multiscale biogeographic regions across a gradient in vegetation productivity. We quantify the proportion of days in the local fire season between 1979 and 2019, where fuel moisture content is below a critical threshold indicating extreme fire potential. We then associate fuel moisture trends over that period to vegetation productivity and comment on its implications for projected anthropogenic climate change. Overall, there is a strong drying trend across realms, biomes, and the productivity gradient. Even where a wetting trend is observed, this often indicates a trend toward increasing fire activity due to an expected increase in fuel production. The detected trends across the productivity gradient lead us to conclude global fire activity will increase with anthropogenic climate change.

Fire prediction with logistic regression on territory of Bosnia and Herzegovina

The Canadian Fire Weather Index system [1] has been used worldwide by many countries as classic approach in fire prediction. It represents system that account for the effects of fuel moisture and weather conditions on fire behaviour. It numerical outputs are based on calculation of four meteorological elements: air temperature, relative humidity, wind speed and precipitation in last 24h. In this paper meteorological data in combination with Canadian Fire Weather Index system (CFWI) components is used as input to predict fire occurrence using logistic regression model. As logistic regression is a supervised machine learning method it’s based on user input in the form of dataset. Dataset is collected using NASA GES DISC Giovanni web-based application in the form of daily area-averaged time series in period of 31.7.2010 to 31.7.2020, it’s analysed and pre-processed before it is used as input for logit model. CFWI components values are not imported but calculated in run-time based on pre-processed meteorological data. As a result of this research windows application was developed to assist fire managers and all those involved in studying the fire behaviour.

Methodology for the Study of Near-Future Changes of Fire Weather Patterns with Emphasis on Archaeological and Protected Touristic Areas in Greece

This work introduces a methodology for assessing near-future fire weather pattern changes based on the Canadian Fire Weather Index system components (Fire Weather Index (FWI), Initial Spread Index (ISI), Fire Severity Rating (FSR)), applied in touristic areas in Greece. Four series of daily raster-based datasets for the fire seasons (May–October), concerning a historic (2006 to 2015) and a future climatology period (2036–2045), were created for the areas under consideration, based on high-resolution climate modelling with the Representative Concentration Pathway (RCP), PCR 4.5 and RCP 8.5 scenarios. The climate model data were obtained from the European Coordinated Downscaling Experiment (EURO-CORDEX) climate database and consisted of atmospheric variables as required by the FWI system, at 12.5 km spatial resolution. The final datasets of the abovementioned variables used for the study were processed at 5 km spatial resolution for the domain of interest after applying regridding based on the nearest neighbour interpolating process. Geographic Information Systems (GIS) spatial operations, including spatial statistics and zonal analyses, were applied on the series of the derived daily raster maps in order to provide a number of output thematic layers. Moreover, historic FWI percentile values, which were estimated for Greece in the frame of a past research study of the Environmental Research Laboratory (EREL), were used as reference data for further evaluation of future fire weather changes. The straightforward methodology for the assessment of the evolution of spatial and temporal distribution of Fire weather Danger due to climate change presented herewith is an essential tool for enhancing the knowledge for the decision support process for forest fire prevention, planning and management policies in areas where the fire risk both in terms of fire hazard likelihood and expected impact is quite important due to human presence and cultural prestige, such as archaeological and tourist protected areas.

A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code

Abstract. We present a global high-resolution calculation of the Canadian Fire Weather Index (FWI) System indices using surface meteorology from the ERA5 HRES reanalysis for 1979–2018. ERA5 HRES represents an improved dataset compared to several other reanalyses in terms of accuracy, as well as spatial and temporal coverage. The FWI calculation is performed using two different procedures for setting the start-up value of the Drought Code (DC) at the beginning of the fire season. The first procedure, which accounts for the effects of inter-seasonal drought, overwinters the DC by adjusting the fall DC value with a fraction of accumulated overwinter precipitation. The second procedure sets the DC to its default start-up value (i.e. 15) at the start of each fire season. We validate the FWI values over Canada using station observations from Environment and Climate Change Canada and find generally good agreement (mean Spearman correlation of 0.77). We also show that significant differences in early season DC and FWI values can occur when the FWI System calculation is started using the overwintered versus default DC values, as is highlighted by an example from 2016 over North America. The FWI System moisture codes and fire behaviour indices are made available for both versions of the calculation at https://doi.org/10.5281/zenodo.3626193 (McElhinny et al., 2020), although we recommend using codes and indices calculated with the overwintered DC, unless specific research requirements dictate otherwise.

Fire Behaviour Observation in Shrublands in Nova Scotia, Canada and Assessment of Aids to Operational Fire Behaviour Prediction

Parks Canada, in collaboration with Nova Scotia Lands and Forests and Natural Resources Canada, documented shrub fire behaviour in multiple plots burned over two periods: a spring period in June 2014 and a summer period in July 2017. The study area, located within Cape Breton Highlands National Park, comprised fifteen burn units (20 m by 20 m in size). Each unit was ignited by line ignition and burned under a wide range of conditions. Pre-burn fuel characteristics were measured across the site and used to estimate pre-fire fuel load and post-fire fuel consumption. This fuel complex was similar to many flammable shrub types around the world, results show that this shrub fuel type had high elevated fuel loads (3.17 ± 0.84 kg/m2) composed of exposed live and dead stunted black spruce as well as ericaceous shrubs, mainly Kalmia angustifolia (evergreen) and Rhodora canadensis (deciduous). Data show that the dead moisture content in this fuel complex is systematically lower than expected from the traditional relationship between FFMC and moisture content in the Canadian Fire Weather Index System but was statistically correlated with Equilibrium Moisture Content. A significant inverse relationship between bulk density and fire rate of spread was observed as well as a clear seasonal effect between the spring burns and the summer burns, which is likely attributable to the increase in bulk density in the summer. Unlike most shrub research, wind and dead moisture content did not have a statistically significant association with fire spread rates. However, we believe this to be due to noise in wind data and small dataset. Rate of spread as high as 14 m/min and flame lengths over 4 m were recorded under Initial Spread Index values of 6.4 and relative humidity of 54%. A comparison with a number of well-known shrubland spread rate prediction models was made. An aid to operational fire prediction behaviour is proposed, using a fuel type from the Canadian Fire Prediction System (O-1b) and a modified estimate of fuel moisture of the elevated fuel in the fuel complex.

ERA5-based global meteorological wildfire danger maps

Forest fires are an integral part of the natural Earth system dynamics, however they are becoming more devastating and less predictable as anthropogenic climate change exacerbates their impacts. In order to advance fire science, fire danger reanalysis products can be used as proxy for fire weather observations with the advantage of being homogeneously distributed both in space and time. This manuscript describes a reanalysis dataset of fire danger indices based on the Canadian Fire Weather Index system and the ECMWF ERA5 reanalysis dataset, which supersedes the previous dataset based on ERA-Interim. The new fire danger reanalysis dataset provides a number of benefits compared to the one based on ERA-Interim: it relies on better estimates of precipitation, evaporation and soil moisture, it is available in a deterministic form as well as a probabilistic ensemble and it is characterised by a considerably higher spatial resolution. It is a valuable resource for forestry agencies and scientists in the field of wildfire danger modeling and beyond. The global dataset is produced by ECMWF, as the computational centre of the European Forest Fire information System (EFFIS) of the Copernicus Emergency Management Service, and it is made available free of charge through the Climate Data Store.

Preceding Fall Drought Conditions and Overwinter Precipitation Effects on Spring Wildland Fire Activity in Canada

Spring fire activity has increased in parts of Canada, particularly in the west, prompting fire managers to seek indicators of potential activity before the fire season starts. The overwintering adjustment of the Canadian Fire Weather Index System’s Drought Code (DC) is a method to adjust and carry-over the previous season’s drought conditions into the spring and potentially point to what lies ahead. The occurrence of spring fires is most strongly influenced by moisture in fine fuels. We used a zero-inflated Poisson regression model to examine the impact of the previous end of season Drought Code (DCf) and overwinter precipitation (Pow) while accounting for the day-to-day variation in fine fuel moisture that drives ignition potential. Impacts of DCf and Pow on area burned and fire suppression effectiveness were also explored using linear and logistic regression frameworks. Eight fire management regions across the boreal forests were analyzed using data from 1979 to 2018. For the majority of regions, drier fall conditions resulted in more human-caused spring fires, but not in greater area burned or reduced suppression effectiveness. The influence of Pow was much more variable pointing to the conclusion that Pow alone is not a good indicator of spring drought conditions.

Weather-related thresholds for wildfire danger in a Mediterranean region: The case of Greece

The objective of the following paper is to identify weather-related thresholds for wildfire danger, i.e., the potential extent of fire, based on local weather conditions. The target area is Greece, a wildfire prone Mediterranean country which experienced on average 2000 wildfires annually over the last two decades. Initially, the Fire Weather Index (FWI) component of the Canadian Fire Weather Index (FWI) System (FWI System) adopted by the European Forest Fire Information System (EFFIS), is evaluated with respect to its wildfire danger predictive ability. Hence, weather and wildfire data at municipality level and on a daily basis, for the period 2000–2016 are exploited. The analysis showed that the FWI thresholds proposed by EFFIS for assessing the level of fire danger in Europe are too low for the case of Greece and, therefore, are not representative of the country's fire weather conditions. Two statistical approaches, cluster analysis and non-linear least-squares regression, are subsequently applied to determine the most appropriate FWI thresholds for discrete levels of wildfire danger. The results are presented in 4 sets of FWI thresholds and they are further evaluated on the basis of verification measures. All sets of FWI thresholds were found to significantly improve the predictability of wildfire danger compared to the EFFIS fire danger class thresholds. In particular, two of them were found to meet selected performance requirements for a balanced predictive performance, namely a reduced overestimation of wildfire danger and increased reliability in danger classification. The results are expected to have significant practical implications for wildfire prevention and risk mitigation strategies implemented by the forest fire control agencies of the country.

Fire Activity and Their Relationship with the Global Fire Weather Index Database Components in Guinea

The relationships between the Canadian Fire Weather Index (FWI) System components and the monthly burned area as well as the number of active fire which has taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua/TERRA were investigated in 32 Guinean stations between 2003 and 2013. A statistical analysis based on a multi-linear regression model was used to estimate the skills of FWI components on the predictability of burned area and active fire. This statistical analysis gave performances explaining between 16 to 79% of the variance for the burned areas and between 29 and 82% of the variance for the number of fires (P<0.0001) at lag 0. Respectively 16 to 79 % and 29 to 82 % of the variance of the burned areas and variance for the number of fires (P<0.0001) at lag0 can be explained based on the same statistical analysis. All the combinations used gave significant performances to predict the burned areas and active fire on the monthly timescale in all stations excepted Fria and Yomou where the predictability of the burned areas was not obvious. We obtained a significant correlation between the average over all of the stations of burned areas, active fires and FWI composites with percentage of variance between (75 to 84% and 29 to 77%) for active fires and burned areas at lag0 respectively. While for burned area peak (January), the skill of the predictability remains significant only one month in advance, for the active fires, the model remains skilful 1 to 3 months in advance. Results also showed that active fires are more related to fire behavior indices while the burned areas are related to the fine fuel moisture codes. These outcomes have implications for seasonal forecasting of active fire events and burned areas based on FWI components, as significant predictability is found from 1 to 3 months and one month before respectively.

FIRE BEHAVIOR VARIABLES AND HAZARD INDICES OF FOREST FIRES

ABSTRACT The objective of this study was to evaluate the performance of different fire hazard indices (FWI, FMA, FMA+, Telicyn, Nesterov, P-EVAP and EVAP/P), taking into account the fire behavior variables and the susceptibility to fire expressed by the moisture of the combustible material. For this purpose, controlled burnings were performed at different times and information was recorded in relation to the meteorological conditions, characteristics of the combustible material and fire behavior variables. In general, all the indices presented significant correlations with both the moisture of the combustible material and the behavior of the fire. However, in general, a higher linear correlation of components of the Canadian Fire Weather Index (FWI) system was observed in predicting fire behavior and EVAP / P index in fuel moisture. The consistency of the correlations between the various indices and the analyzed variables makes the methodology possible to be used in any place, facilitating the decision making in regions where records of occurrences of forest fires are absent or unreliable.

Comparison of methods for spatial interpolation of fire weather in Alberta, Canada

Spatial interpolation of fire weather variables from station data allow fire danger indices to be mapped continuously across the landscape. This information is crucial to fire management agencies, particularly in areas where weather data are sparse. We compare the performance of several standard interpolation methods (inverse distance weighting, spline, and geostatistical interpolation methods) for estimating output from the Canadian Fire Weather Index (FWI) system at unmonitored locations. We find that geostatistical methods (kriging) generally outperform the other methods, particularly when elevation is used as a covariate. We also find that interpolation of the input meteorological variables and the previous day’s moisture codes to unmonitored locations followed by calculation of the FWI output variables is preferable to first calculating the FWI output variables and then interpolating, in contrast to previous studies. Alternatively, when the previous day’s moisture codes are estimated from interpolated weather, rather than directly interpolated, errors can accumulate and become large. This effect is particularly evident for the duff moisture code and drought moisture code due to their significant autocorrelation.

Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada

Wildfires burn an average of 2 million hectares per year in Canada, most of which can be attributed to only a few days of severe fire weather. These “spread days” are often associated with large-scale weather systems. We used extreme threshold values of three Canadian Fire Weather Index System (CFWIS) variables — the fine fuel moisture code (FFMC), initial spread index (ISI), and fire weather index (FWI) — as a proxy for spread days. Then we used self-organizing maps (SOMs) to predict spread days, with sea-level pressure and 500 hPa geopotential height as predictors. SOMs require many input parameters, and we performed an experiment to optimize six key parameters. For each month of the fire season (May–August), we also tested whether SOMs performed better when trained with only one month or with neighbouring months as well. Good performance (AUC of 0.8) was achieved for FFMC and ISI, while nearly good performance was achieved for FWI. To our knowledge, this is the first study to develop a machine-learning model for extreme fire weather that could be deployed in real time.

Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015

We have constructed a fire weather climatology over North America from 1979 to 2015 using the North American Regional Reanalysis dataset and the Canadian Fire Weather Index (FWI) System. We tested for the presence of trends in potential fire season length, based on a meteorological definition, and extreme fire weather using the non-parametric Theil–Sen slope estimator and Mann–Kendall test. Applying field significance testing (i.e. joint significance of multiple tests) allowed the identification of the locations of significant trends, taking into account spatial correlations. Fire season length was found to be increasing over large areas of North America, especially in eastern Canada and the south-western US, which is consistent with a later fire season end and an earlier fire season start. Both positive and negative trends in potential fire spread days and the 99th percentile of FWI occurred in Canada and the contiguous United States, although the trends of largest magnitude and statistical significance were mostly positive. In contrast, the proportion of trends with significant decreases in these variables were much lower, indicating an overall increase in extreme fire weather. The smaller proportion of significant positive trends found over Canada reflects the truncation of the time series, necessary because assimilation of precipitation observations over Canada ceased in the reanalysis post-2002.

Regional variation in fire weather controls the reported occurrence of Scottish wildfires.

Fire is widely used as a traditional habitat management tool in Scotland, but wildfires pose a significant and growing threat. The financial costs of fighting wildfires are significant and severe wildfires can have substantial environmental impacts. Due to the intermittent occurrence of severe fire seasons, Scotland, and the UK as a whole, remain somewhat unprepared. Scotland currently lacks any form of Fire Danger Rating system that could inform managers and the Fire and Rescue Services (FRS) of periods when there is a risk of increased of fire activity. We aimed evaluate the potential to use outputs from the Canadian Fire Weather Index system (FWI system) to forecast periods of increased fire risk and the potential for ignitions to turn into large wildfires. We collated four and a half years of wildfire data from the Scottish FRS and examined patterns in wildfire occurrence within different regions, seasons, between urban and rural locations and according to FWI system outputs. We used a variety of techniques, including Mahalanobis distances, percentile analysis and Thiel-Sen regression, to scope the best performing FWI system codes and indices. Logistic regression showed significant differences in fire activity between regions, seasons and between urban and rural locations. The Fine Fuel Moisture Code and the Initial Spread Index did a tolerable job of modelling the probability of fire occurrence but further research on fuel moisture dynamics may provide substantial improvements. Overall our results suggest it would be prudent to ready resources and avoid managed burning when FFMC > 75 and/or ISI > 2.