Canadian Forest Fire Weather Index Research Articles

Synoptic-scale drivers of fire weather in Greece

The identification of the large-scale atmospheric circulation patterns which are associated with extreme fire weather is of great importance for developing early warning systems, management strategies, and for increasing awareness and preparedness of all the involved entities, including both the public and practitioners. Such a forecasting approach is currently missing in Greece and many other countries. Furthermore, considering climate projections over the Mediterranean, which indicate an environment more conducive to wildfire activity, the need for timely forecasting of extreme fire weather becomes increasingly urgent. Here, we present an alternative fire weather forecasting framework using ERA5 reanalysis data of atmospheric variables and fire weather indices of the Canadian Forest Fire Weather Index System (CFFWIS) during the period June–October from 1979 to 2019. Within this framework, we define the critical fire weather patterns (CFWPs) of Greece associated with different levels of fire weather severity by applying Self-Organizing-Maps (SOMs) on mid-tropospheric geopotential height. We quantify the fire weather conditions associated with each CFWP. Using a set of CFFWIS indices and key fire weather variables, our SOM-based analysis reveals five distinct CFWPs linked to different levels and characteristics of fire weather severity. The lowest fire weather severity is associated with lower than average geopotential heights, and anomalous cold and moist weather. The highest fire weather severity is associated with higher than average geopotential heights, and anomalous hot, dry, and windy conditions, suggesting the potential for wind-driven wildfires. Our analysis yields elevated fire weather severity linked to a CFWP, when hot and dry conditions are accompanied by atmospheric instability, suggesting the potential for plume-driven wildfires and the potential for pyroconvection. The main advantage of this forecasting framework is that it could be used for providing valuable information regarding the upcoming fire weather conditions even up to 7–12 days in advance depending on the atmospheric predictability.

Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe

Abstract. Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate.

Satellite observed response of fire dynamics to vegetation water content and weather conditions in Southeast Asia

Fires greatly affect terrestrial ecosystems, climate, human health, and economy. Under the threat of global warming, assessing fire occurrence frequency and fire danger is critical to mitigate fire’s negative impacts. The state-of-the-art satellite microwave observation provides the unique opportunity to monitor vegetation water content (VWC) for multiple vegetation strata, yet still lacks associated investigation in fire dynamics. Here, we applied the multiple microwave Emissivity Difference Vegetation Indexes (EDVIs) as VWC proxies of different vegetation strata to explore their influence on fire occurrence frequency (given by the number of fires, NOF) and the cumulative fire radiant energy (FRE) in mainland Southeast Asia during 2015–2019; and compared to that with the commonly used fire weather indices from the Canadian Forest Fire Weather Index system. Then, we identified the optimal regression models to depict the relationship of fires with EDVIs and fire weather indices, respectively; and incorporated both kinds of indices in models to retrospectively predict NOF and FRE. Results show that: (1) the monthly mean NOF and FRE nonlinearly decrease with EDVI in mainland Southeast Asia, and 40–60% of the variances are explained by EDVI, which further improve to 60–70% by incorporating EDVIs of two different vegetation layers. (2) In comparison, NOF and FRE nonlinearly increase with fire weather indices, and connections are stronger than that with EDVI; nevertheless, combing fire weather indices with EDVI, especially with EDVIs of two vegetation layers provides almost the highest explanation for fires. (3) The log-linear and/or log-quadratic models are identified to outperform the other models in depicting the fire-EDVI and fire-fire weather indices relationships; and incorporating EDVI and fire weather indices in models generally improves the prediction accuracy. These findings reveal the differentiated VWC effects on NOF and FRE among vegetation strata and quantify the strong nonlinear response of fires to VWC and weather conditions in mainland Southeast Asia, which could provide new insight for fire prediction and fire danger assessments with the utility of microwave observations.

Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

Abstract. Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean. Weather and climate are among the main factors influencing forest fire potential. As fire danger is expected to increase under changing climate, seasonal forecasting of meteorological conditions conductive to fires is of paramount importance for implementing effective fire prevention policies. The aim of the current study is to provide high-resolution (∼9 km) probabilistic seasonal fire danger forecasts, utilizing the Canadian Forest Fire Weather Index (FWI) for the Attica region, one of the most fire-prone regions in Greece and the Mediterranean, employing the fifth-generation ECMWF seasonal forecasting system (SEAS5). Results indicate that, depending on the lead time of the forecast, both the FWI and ISI (Initial Spread Index) present statistically significant high discrimination scores and can be considered reliable in predicting above-normal fire danger conditions. When comparing the year-by-year fire danger predictions with the historical fire occurrence recorded by the Hellenic Fire Service database, both seasonal FWI and ISI forecasts are skilful in identifying years with a high number of fire occurrences. Overall, fire danger and its subcomponents can potentially be exploited by regional authorities in fire prevention management regarding preparedness and resources allocation.

A data-driven model for Fennoscandian wildfire danger

Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

The Performance of ECMWF Ensemble Prediction System for European Extreme Fires: Portugal/Monchique in 2018

At the beginning of August 2018, Portugal experienced a severe heat episode over a few days that consequently increased the probability of wildfire events. Due to the advection of an anomalous very hot and dry air mass, severe fire-prone meteorological conditions were forecasted mainly over southern Portugal, in the Monchique region. Together with the significant fuel amount accumulated since the last extreme wildfire in August 2003, all the unfavorable conditions were set to drive a severe fire over this region. The Monchique fire started on 3 August 2018, being very hard to suppress and lasting for seven days, with a burnt area of 27,000 ha. Regarding the need to have operational early warning tools, this work aims to evaluate the reliability of fire probabilistic products, up to 72 h ahead, together with the use of fire radiative power products, as support tools in fire monitoring and resource activities. To accomplish this goal, we used the fire probabilistic products of the Ensemble Prediction System, provided by the Copernicus Atmosphere Monitoring Service. Among available fire danger rating systems, the Fire Weather Index and the Fine Fuels Moisture Code of the Canadian Forest Fire Weather Index System were selected to assess the meteorological fire danger. The assessment of the fire intensity was based on the Fire Radiative Energy released, considering the Fire Radiative Power, delivered in near real-time, by EUMETSAT Land Surface Analysis Satellite Applications Facility. The exceptional fire danger over southern Portugal that favors the ignition of the Monchique fire and its severity was essential driven by two important factors: (i) the anomalous fire weather danger, before and during the event; (ii) the accumulated fuel amount, since the last severe event occurred in 2003, over the region. Results show that the selected fire probabilistic products described the meteorological fire danger observed well, and the LSA-SAF products revealed the huge amount of fire energy emitted, in line with the difficulties faced by authorities to suppress the Monchique fire.

Projections of wildfire weather danger in the Canary Islands

Climate change is expected to enhance weather conditions prone to wildfires. Climate regionalized projections for the Canary Islands were performed, using as boundary conditions some of the results provided by the Coupled Model Intercomparison Project (CMIP5) initiative, and covering the recent past (1980–2009) and future (2070–2099) periods, under two Representative Concentration Pathways, 4.5 and 8.5. All fire risk indicators derived from the Canadian Forest Fire Weather Index (FWI) are foreseen to worsen by the end of the century. The fire season could increase its length up to 75 days per year, being more noticeable as altitude increases. The extreme risk days (FWI > 60) show an average increase of 58%, reaching 12 days a year, and the area with high risk could increase by 44%. Analyzing the contribution of the different meteorological variables, it is observed that the main parameter in the fire danger index result is the temperature (currently weights 46%). However, in the future, the importance of precipitation will increase, since the rainfall reduction in some areas could reach 41%. The high dependence of the expected changes on land height, and the small size of the islands, demonstrates the necessity of using high-resolution climate regionalizations.

Optimal Cross‐Validation Strategies for Selection of Spatial Interpolation Models for the Canadian Forest Fire Weather Index System

AbstractThe Canadian forest fire weather index (FWI) system requires spatially continuous, gridded weather data for temperature, relative humidity, wind speed, and precipitation. Reliable estimates of the Canadian FWI system components are needed to ensure the safety of communities, resources, and ecosystems. The quality of the interpolated input weather variables are typically evaluated using error estimates from cross‐validation. These error estimates are used for selecting between spatial interpolation methods for generating the continuous weather surfaces. Leave‐one‐out cross‐validation (LOOCV) is the most commonly used method, but it is biased in spatially clustered weather station networks. Accurate error estimation is important for selecting the optimal interpolation method and evaluating how well an interpolated surface represents true patterns in a weather variable. Other cross‐validation methods may better account for bias relating to clustered weather station networks. We present a comparison of cross‐validation methods for evaluating spatial interpolation models of weather variables for generating the inputs to the Canadian FWI system with the objective of determining whether they identify the same spatial interpolation model as having the lowest error. We found that LOOCV, shuffle‐split, stratified shuffle‐split, and a modified buffered leave‐one‐out procedure generally identified the same spatial interpolation models as having the lowest error. Spatial k‐fold favored spatial interpolation models with extrapolation ability. Our findings indicate that the most computationally efficient cross‐validation approach can be used for automatically selecting spatial interpolation models for weather surface generation, which will improve the quality of historical daily FWI maps.

Crown fuel consumption in Canadian boreal forest fires

Predictive crown fuel consumption models were developed using empirical data from experimental burning projects. Crown fuel load for foliage, bark, branchwood and stemwood were calculated for live overstorey and understorey trees in each plot using nationally derived tree biomass algorithms. Standing dead tree branchwood and stemwood biomass were similarly calculated. Crown bulk density values were calculated for all non-stemwood fuel components. Factors that affect the initiation and spread of crown fires (live crown base height, foliar moisture content, surface fuel consumption, critical surface fire spread rate, critical surface fire intensity) and components of the Canadian Forest Fire Weather Index System were not statistically significant variables. Crown bulk density was moderately correlated with crown fuel consumption but was not an influential factor. A new crown fuel consumption model was developed by regression analysis using fuel load of overstorey tree foliage and standing dead tree branchwood, and fire rate of spread through crown fraction burned. A simpler model was developed using only overstorey tree foliage fuel load and fire rate of spread. Both models provide forest and fire management agencies with enhanced ability to determine crown fuel consumption, fire behaviour and carbon emissions in boreal fires using basic forest inventory or biomass/carbon datasets.

Automatic forest fire danger rating calibration: Exploring clustering techniques for regionally customizable fire danger classification

Effective wildfire management begins with fire prevention and the assessment of the local fire danger. Typical approaches to fire danger classification rely heavily on manual analysis for specific regions based on expert knowledge. In this paper, a novel approach is proposed for the automatic calibration of fire danger classes based on the Canadian Forest Fire Weather Index System (CFFWIS) applied for specific regions. The proposed automatic calibration method is based on clustering algorithms, namely k-means, fuzzy c-means, Gaussian mixture models, and data-clouds, which are used to identify clusters in datasets composed of elements from CFFWIS and wildfire historical records. The clusters are associated with fire danger classes which are separated by proposed thresholds based on the fire weather index values contained within each cluster. Exhaustive experiments ensured an accurate comparison of performance with the analysis of our fire danger classes against the classes defined by the European Forest Fire Information System (EFFIS), also based on the CFFWIS. These experiments consider individual information from each of the selected European regions from a total of 769 regions with available data, with validation aimed at the analysis of the large fires in a general context, and a case study for Portuguese regions.

Development of Google Earth Engine Fire Weather Index Calculator for Indonesian Fire Danger Rating System

Tropical peatlands make up almost ten percent of the land surface in Indonesia, making peat fires detrimental not only for global atmospheric carbon levels, but also to public health and socioeconomic activities in the region. Indonesian Fire Danger Rating System (FDRS) was developed based on the Canadian Forest Fire Weather Index System (CFFWIS), using three different fuel codes and three indices representing fire behaviour. Daily Fire Weather Index (FWI) calculation is done by the Meteorological Climatological and Geophysical Agency (BMKG) with data from its synoptic weather stations network. Distribution of such weather stations are sparse, therefore this paper reports on the development of Fire Weather Index calculator on Google Earth Engine, using high resolution weather data, provided by weather model and remote-sensing open datasets. The resulting application is capable of generating daily maps of FWI components to be used by the Indonesian Fire Danger Rating System.

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.

Combining multi-spectral and thermal remote sensing to predict forest fire characteristics

Forest fires preparedness strategies require the assessment of spatial and temporal variability of fire danger. While several tools have been developed to predict fire occurrence and behaviour from weather data, it is acknowledged that fire danger models may benefit from direct assessment of live fuel condition, as allowed by Earth Observation technologies. In this study, the performance of pre-fire observations of land surface temperature (LST) anomaly and of the Perpendicular Moisture Index (PMI) in predicting fire characteristics was evaluated against the Canadian Forest Fire Weather Index (FWI) System, a fire danger model adopted in several areas worldwide. To this purpose, a database of forest fires recorded in Campania (13,595 km2), Italy, was combined with MODIS retrievals of LST anomaly and PMI, and with FWI maps from NASA’s Global Fire Weather Database. Fires were grouped in decile bins of LST anomaly, PMI and FWI System components, and probability distribution functions of burned area, fire duration and rate of spread were fitted in each bin. The dependence of probability model parameters on LST anomaly, PMI and FWI System components was assessed by means of trend analysis (coefficient of determination and p-value of the linear fit, Sen’s slope and Mann-Kendall test) and likelihood ratio test versus the corresponding unconditional probability model. Finally, the probability of an extreme event, conditional to ignition, was modelled as a function of LST anomaly and PMI. Results show that the probability distribution function of burned area has a strong dependence on both LST anomaly and PMI, that the probability distribution function of fire duration has a strong dependence on LST anomaly but not on PMI, and that the probability distribution function of rate of spread has a weak dependence on LST anomaly and a strong dependence on PMI. These results are in line with expectations from models of the combustion and flames propagation processes. Trend analyses and likelihood ratio tests showed that the FWI System components are good predictors of burned area and fire duration, but not of rate of spread. They also confirmed that, where LST anomaly and PMI are covariates of the considered fire characteristic, their performance is similar or better than the FWI System components. Finally, the probability of an extreme event in terms of burned area as a joint function of LST anomaly and PMI shows a wider dynamic range than the same probability modelled as a function of these remote sensing variables individually.

Will land use land cover change drive atmospheric conditions to become more conducive to wildfires in the United States?

AbstractThe increase in wildfire risk in the United States in recent decades has been linked to rapid growth of the wildland‐urban interface and to changing climate. While there have been numerous studies on wildfires and climate change, few have separately assessed the impact of climate response to land‐use‐land‐cover change (LULCC) on wildfires. In this study, we analyse two 10‐year regional climate simulations driven by the current (2011) and future (2100) land‐use‐land‐cover patterns to assess modifications by the projected LULCC to the frequency and severity of fire‐prone atmospheric conditions described by two fire weather indices, the Canadian Forest Fire Weather Index and the Hot‐Dry‐Windy Index. The simulation corresponding to future land‐use‐land‐cover pattern yields higher surface temperature and vapour pressure deficit and lower precipitation compared to the simulation with the current pattern in areas where urbanized landscapes replace forests and grasslands, such as along the Piedmont and outside the Chicagoland region, while in areas where croplands replace forests, such as the southeast Coastal Plains, the results are reversed. These changes to local and regional atmospheric conditions lead to longer fire seasons and more extreme fire‐weather conditions in much of the eastern United States, specifically in the Southeast and Ohio River Valley where significant urban expansion is projected by the end of the century. Whereas in Southern California where some highly flammable shrublands will be replaced by urban or crop lands, fire‐prone atmospheric conditions are likely to be less frequent and less extreme in the future. However, much of California moves towards a year‐round fire season under the projected LULCC. The results suggest that by altering atmospheric conditions, LULCC may play an important role in determining fire regime, but the effects are highly heterogeneous and regionalized.

Potential Hazard of Open Space Fire in Black Pine Stands (Pinus nigra J.F. Arnold) in Regard to Fire Severity

Black pine (Pinus nigra J.F. Arnold) is one of the most important reforestation species for dry and rocky terrain in the sub-Mediterranean zone. Fire is an important factor in black pine stands that largely defines the distribution range of black pine and the floristic composition of its stands. Fire causes less damage during autumn or early spring when tree cones contain ripe seeds that can partially reforest burnt surfaces. Every fire is a potentially significant threat to forest habitats. There are many damages, from direct damages to wood mass and reforestation costs, to indirect ecological, edaphic, protective damages and the loss of biological diversity. The aim of prevention works in forestry is to reduce the number of fires and reduce burnt surface area per fire. Research was based on vegetation analysis performed on each experimental plot in a black pine stand. The assessment of fire severity was performed using the Canadian Forest Fire Weather Index (FWI). The assessment of the severity of fire indicates the suitability of conditions for the occurrence of forest fires, and enables sound and effective protective methods beginning with silvicultural works. The results indicate better quality stands in the higher parts of the sub-Mediterranean area. Taking into account the climatic parameter, the areas on the border with the continental climate have better habitat conditions for black pine. Also, attention should be focused on 2012 and 2015. High values (Figure 13 – 16.22, Figure 11 – 6.99, Figure 8 – 8.38, Figure 7 – 12.02 and 12.25, Figure 5 – 8.32) indicate the overlapping of extremely dry periods and extremely high summer temperatures, and probably a strong wind influence that further increases the index. Fire severity assessments indicate the suitability of conditions for the onset of fire.

Assessing impacts of future climate change on extreme fire weather and pyro-regions in Iberian Peninsula

Weather conditions play an important role in wildfire activity. In many regions, future climate could lead to different fire weather, with impacts on the ignition, behaviour, and suppression of wildfires, which may, therefore, force new fire regimes. This study aimed to assess the evolution of fire weather indices and the Number of Extreme Days (NED) in the context of climate change. We estimated the impact of these changes on monthly Normalized Burnt Area (NBA) and in the spatial distribution of Pyro-Regions (PR), using a recently identified relationship between NED and NBA intra-annual patterns. The components of the Canadian Forest Fire Weather Index System (CFFWIS) in the Iberian Peninsula were analysed for present-day conditions and future climate scenarios, using daily data from ERA-Interim (1980–2014) and an ensemble of simulations from 11 EURO-CORDEX high spatial resolution models, for two future periods (2041–2070 and 2071–2100) and scenarios (RCP4.5 and RCP8.5).Results suggest a significant increase in future fire weather risk, especially in late spring and early autumn, and also in southern and eastern Iberian Peninsula. NED is expected to strongly increase in summer months in the four PRs, but also to decrease in March and April in the northwestern and southwestern PR. This could change the spatial distribution of PRs, with a general northwards movement: the northern PR is expected to disappear except north of the Cantabrian Mountains, being replaced by the northwestern PR; the southwestern PR is expected to grow and occupy part of the area currently in the northwestern PR; and a new PR could appear in parts of the current eastern PR. These PR changes follow the projected modifications in the major climate regions. Results suggest different fire regimes in the future, with higher fire weather risk, and a longer and harsher fire season.

Evaluating the Drought Code Using In Situ Drying Timelags of Feathermoss Duff in Interior Alaska

The Drought Code (DC) is a moisture code of the Canadian Forest Fire Weather Index System underlain by a hydrological water balance model in which drying occurs in a negative exponential pattern with a relatively long timelag. The model derives from measurements from an evaporimeter and no soil parameters are specified, leaving its physical nature uncertain. One way to approximate the attributes of a “DC equivalent soil” is to compare its drying timelag with measurements of known soils. In situ measurements of timelag were made over the course of a fire season in a black spruce-feathermoss forest floor underlain by permafrost in Interior Alaska, USA. On a seasonally averaged basis, timelag was 28 d. The corresponding timelag of the DC water balance model was 60 d. Water storage capacity in a whole duff column 200 mm deep was 31 mm. Using these figures and a relationship between timelag, water storage capacity, and the potential evaporation rate, a “DC equivalent soil” was determined to be capable of storing 66 mm of water. This amount of water would require a soil 366 mm deep, suggesting a revision of the way fire managers in Alaska regard the correspondence between soil and the moisture codes of the FWI. Nearly half of the soil depth would be mineral rather than organic. Much of the soil water necessary to maintain a 60 d timelag characteristic of a “DC equivalent soil” is frozen until after the solstice. Unavailability of frozen water, coupled with a June peak in the potential evaporation rate, appears to shorten in situ timelags early in the season.

A Conceptual Interpretation of the Drought Code of the Canadian Forest Fire Weather Index System

The Drought Code (DC) was developed as part of the Canadian Forest Fire Weather Index System in the early 1970s to represent a deep column of soil that dries relatively slowly. Unlike most other fire danger indices or codes that operate on gravimetric moisture content and use the logarithmic drying equation to represent diffusion, the DC is based on a model that balances daily precipitation and evaporation. This conceptually simple water balance model was ultimately implemented using a “shortcut” equation that facilitated ledgering by hand but also mixed the water balance model with the abstraction equation, obscuring the logic of the model and concealing two important variables. An alternative interpretation of the DC is presented that returns the algorithm to an equivalent but conceptual form that offers several advantages: The simplicity of the underlying water balance model is retained with fewer variables, constants, and equations. Two key variables, daily depth of water storage and actual evaporation, are exposed. The English system of units is eliminated and two terms associated with precipitation are no longer needed. The reduced model does not include or depend on any soil attributes, confirming that the nature of the “DC equivalent soil” cannot be precisely known. While the “Conceptual Algorithm” presented here makes it easier to interpret and understand the logic of the DC, users may continue to use the equivalent “Implemented Algorithm” operationally if they wish.

Recent evolution of spatial and temporal patterns of burnt areas and fire weather risk in the Iberian Peninsula

The weather is an important driver of the wildfire incidence because it strongly influences fuel availability and flammability. In the Mediterranean climate, the spatial and temporal patterns of fire weather present high variability, which help to understand the variable fire regimes. To assess this link in the Iberian Peninsula and the role of fire weather on fire incidence variability, this work identified pyro-regions, characterized the fire regimes in these regions and compared them with the spatial and seasonal distribution of several components of the Canadian Forest Fire Weather Index (FWI) System. A cluster analysis was performed on monthly normalized burnt area (NBA) series for the 1980 – 2015 period in each administrative division of the Iberian Peninsula (Portuguese districts and Spanish provinces), which revealed four pyro-regions (Northwest, North, Southwest and East) as well as significant spatial and temporal variability of NBA patterns. A separate analysis for 1980 – 1997 and 1998 – 2015 revealed noteworthy changes in the border of pyro-regions caused by changes in NBA seasonal patterns in some administrative regions. The analysis of the fire weather risk distribution for 1980 – 2017 included the assessment of extreme fire weather days, defined as days with Daily Severity Rating (DSR) above the 95th percentile, for the summer fire season, and as days with Drought Code above the 95th percentile, for the winter-spring fire season. The distribution of the monthly extreme fire weather days is highly correlated with the NBA seasonal variability and explains the differences between seasonal wildfire characteristics and the recent changes in the border administrative regions. The analysis of the fire weather patterns for large wildfires disclosed that these events were linked with extreme DSR days, especially in the NW pyro-region. These findings highlight the strong link between climate variability and fire regimes in the Iberian Peninsula, and can therefore help assess the impacts of climate change and to project future burnt area patterns.

Fuel characteristics, loads and consumption in Scots pine forests of central Siberia

Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21.0 kg/m2. Stand biomass was higher in plots in the southern taiga, while ground fuel loads were higher in the central taiga. We developed equations for fuel biomass (both aerial and ground) that could be applicable to similar pine forest sites of Central Siberia. Fuel loading variability found among plots is related to the impact and recovery time since the last wildfire and the mosaic distribution of living vegetation. Fuel consumption due to surface fires of low to high-intensities ranged from 0.95 to 3.08 kg/m2, that is, 18–74% from prefire values. The total amount of fuels available to burn in case of fire was up to 4.5–6.5 kg/m2. Moisture content of fuels (litter, lichen, feather moss) was related to weather conditions characterized by the Russian Fire Danger Index (PV-1) and FWI code of the Canadian Forest Fire Weather Index System. The data obtained provide a strong foundation for understanding and modeling fire behavior, emissions, and fire effects on ecosystem processes and carbon stocks and could be used to improve existing global and regional models that incorporate biomass and fuel characteristics.