Fine Fuel Moisture Code Research Articles

Simulation of Fire Occurrence Based on Historical Data in Future Climate Scenarios and Its Practical Verification

Forest fire is one of the dominant disturbances in the forests of Heilongjiang Province, China, and is one of the most rapid response predictors that indicate the impact of climate change on forests. This study calculated the Canadian FWI (Fire Weather Index) and its components from meteorological record over past years, and a linear model was built from the monthly mean FWI and monthly fire numbers. The significance test showed that fire numbers and FWI had a very pronounced correlation, and monthly mean FWI was suitable for predicting the monthly fire numbers in this region. Then FWI and its components were calculated from the SRES (IPCC Special Report on Emission Scenarios) A2 and B2 climatic scenarios, and the linear model was rebuilt to be suitable for the climatic scenarios. The results indicated that fire numbers would increase by 2.98–129.97% and −2.86–103.30% in the A2 and B2 climatic scenarios during 2020–2090, respectively. The monthly variation tendency of the FWI components is similar in the A2 and B2 climatic scenarios. The increasing fire risk is uneven across months in these two climatic scenarios. The monthly analysis showed that the FFMC (Fine Fuel Moisture Code) would increase dramatically in summer, and the decreasing precipitation in summer would contribute greatly to this tendency. The FWI would increase rapidly from the spring fire season to the autumn fire season, and the FWI would have the most rapid increase in speed in the spring fire season. DMC (Duff Moisture Code) and DC (Drought Code) have relatively balanced rates of increasing from spring to autumn. The change in the FWI in this region is uneven in space as well. In early 21st century, the FWI of the north of Heilongjiang Province would increase more rapidly than the south, whereas the FWI of the middle and south of Heilongjiang Province would gradually catch up with the increasing speed of the north from the middle of 21st century. The changes in the FWI across seasons and space would influence the fire management policy in this region, and the increasing fire numbers and variations in the FWI scross season and space suggest that suitable development of the management of fire sources and forest fuel should be conducted.

The Role of Field Measurements of Fine Dead Fuel Moisture Content in the Canadian Fire Weather Index System—A Study Case in the Central Region of Portugal

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content (mf) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of mf based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region.

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.

Effects of interaction between forest structure and precipitation event characteristics on fuel moisture conditions

Estimating forest fire danger is of primary concern for the Austrian forest fire management. The fine fuel moisture code (FFMC) of the Canadian Fire Weather Index (CFWI) is used for determining ignition danger. The FFMC is calculated by using the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which provides interpolated weather parameters, available at 1 km2 spatial resolution. Automated fuel sticks were used for measuring microclimate-defined moisture content from 2018 to 2020 in two differently structured sites: closed forest and forest gap. A remotely automated weather station (RAWS) measured the meteorological parameters of the research area. First, the capability of an interpolated large-scale FFMC to capture local moisture conditions was studied. Second, the effects of an interaction between forest structure and precipitation event characteristics on moisture content behaviour were investigated. Bayesian-based techniques in dependence of the three consecutive years were applied. Our results show that the correlations between INCA FFMC and RAWS FFMC are high (0.80–0.95) and INCA FFMC is capable of capturing the local microclimatic variations (0.68–0.78). Correlations ranging from 0.74 to 0.86 were evidenced between the fuel stick FFMC values of the forest and the gap, revealing that INCA FFMC cannot capture the microclimate induced differences between the two forest conditions. We found that: (i) the buffering capability of forest structure slows the absorption rate by 1.8 to 3.4%/h, (ii) absorption rate difference between the forest and the gap was lowest (1.8%/h) in the case of short and heavy rain, (iii) during the desorption phase the moisture content remained similar for both the closed forest and the gap. Overall, the structure induced buffering capability was strongest in the case of short and light rain. Longer and more intensive precipitation events, especially after canopy saturation during throughfall, are likely to lessen the buffering capability of the forest.

PENENTUAN TINGKAT RESIKO KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN METODE INDEKS CUACA KEBAKARAN (FIRE WEATHER INDEX) DAN JUMLAH TITIK PANAS (HOTSPOT) DI KABUPATEN BANJAR PROVINSI KALIMANTAN SELATAN

Various indicators of hotspot occurrence as a cause of forest and land fires (karhutla) in Banjar District are still difficult to determine due to limited information. The analysis of FFMC (Fine Fuel Moisture Code) and DC (Drought Code) as well as the monitoring of the number of hotspots aims to determine the level of risk of forest and land fire hazards and can be an early picture of future forest and land fire disasters in the Banjar District of South Kalimantan Province. The data processing process to obtain the FFMC and DC values and their relationship with the number of hotspots is to calculate the FFMC and DC values of station observations and model observations through the Microsoft Excel Add-In (FWI Add-in) program. The two models will then be verified with a scatter plot and through the Pearson correlation test the relationship between the FFMC and DC of the ERA5 model and the number of hotspots can be found. As a result, the FFMC and DC (station observation and model) showed extreme risk levels for the 2014, 2015 and 2018 forest and land fires. Both models show a positive and linear relationship on the scatter plot. And in the Pearson correlation test, both variables between the FFMC and DC of the ERA5 model and the number of hotspots are moderately to strongly correlated. This condition indicates that an increase in the risk level of forest and land fires will be followed by a significant increase in the incidence of forest and land fires in the Banjar Regency area of South Kalimantan Province.

Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach

Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson’s correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM’s main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires.

Critical fire weather conditions during active fire spread days in Canada

A spread day is defined as a day in which fires grow a substantial amount of area; such days usually occur during high or extreme fire weather conditions. The identification and prediction of a spread day based on fire weather conditions could help both our understanding of fire regimes as well as forecasting and managing fires operationally. This study explores the relationships between fire weather and spread days in the forested areas of Canada by spatially and temporally matching a daily fire growth database to a daily gridded fire weather database that spans from 2001 to 2019. By examining the correlations between spread day fire weather conditions and location, conifer coverage (%), and elevation, we found that a spread day happens under less severe fire weather conditions as latitude increases for the entire study area and as conifer coverage increases within non-mountainous study areas. In the western mountain areas, however, with increasing conifer coverage more severe fire weather conditions are required for a spread day to occur. Using two modeling approaches, we were able to identify spread day indicators (generalized additive model) and to predict the occurrence of spread days (semi-binomial regression model) by Canadian Ecozones both annually and seasonally. Overall, Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI), and Fire Weather Index (FWI) performed the best in all models built for spread day identification and prediction but varied depending on the conditions mentioned above. FFMC was the most consistent across all spatial and temporal scales.

Assessing the Effects of Fuel Moisture Content on the 2018 Megafires in California

In 2018, the megafire episodes on record occurred in California, causing a large number of civilian deaths and damages. As an important part of the “fire environment triangle,” the fuel moisture content (FMC) of both live (LFMC) and dead (DFMC) vegetation were broadly accepted as the important drivers of wildfire ignition and spread, but their effects on the 2018 megafires in California were less explored. Here, we explored and compared the effects of LFMC and DFMC on the 2018 megafire in California, allowing for highlighting the role of different types of FMC in megafire risk assessment. The LFMC was collected from the global LFMC product. We used three indices obtained from the Canadian Forest Service Fire Weather Index Rating System as a proxy of DFMC products, including the fine fuel moisture code, duff moisture code (DMC), and drought code. We analyzed the long-term series (2001–2018) of these four indices in California to test whether these indices were indicative of the occurrence of the megafire, and which of the index was the most powerful driving the 2018 megafires. The results showed that all these indices were correlated with the fires in California. The LFMC showed the highest correlation with the fire occurrence between 2001 and 2018, whereas the DMC performed a major role in driving the 2018 megafire in California. This study presented the insights that the LFMC and DMC should be carefully considered in future operational fire risk assessments for megafire prescription, suppression, and response.

Regional Variability and Driving Forces behind Forest Fires in Sweden

Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA’s FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application.

Predicting the fine fuel moisture content in Dalmatian black pine needle litter

During the last three decades, there has been an increase in the incidence of severe crown fires affecting black pine forests in the sub-Mediterranean area. The objective of this study was to develop and test a model to predict fine fuel moisture content for Dalmatian black pine (Pinus nigra J.F. Arnold subsp. dalmatica (Vis.) Franco) needle litter. We performed laboratory measurements of equilibrium moisture content and response time of dead black pine needles to modify the hourly fine fuel moisture code (FFMC) model, and we compared the predictive ability of the hourly FFMC model with that of modified model (PnFFMC). Field tests showed that although the hourly FFMC model reproduced trends in moisture content of black pine needles quite well, it consistently overestimated moisture content (mean absolute error 3.9%). The PnFFMC model performed better than the hourly FFMC model and was closer to the line of equivalence (mean absolute error 0.9%). This study indicates that the hourly FFMC can easily be modified by incorporating appropriate species-specific equilibrium moisture content and response time values, leading to more accurate predictions of fine fuel moisture content. The need to use recently fallen needles in fuel moisture modelling for Mediterranean pine species is highlighted.

Assessing forest fire properties in Northeastern Asia and Southern China with satellite microwave Emissivity Difference Vegetation Index (EDVI)

In the context of global warming, forest fires are expected to occur more frequently and intensively, and impose more significant impacts on human society, terrestrial ecosystems, and atmosphere. Most of the existing methods in monitoring large-scale forest fire are based either on satellite visible and infrared observations or weather-based indices. This work explored the advantages of a new satellite microwave-based vegetation index in monitoring forest fire occurrence and fire intensity in Northeastern Asia and Southern China. Specifically, we used satellite observations during 2002–2011 to investigate the correlation at different temporal scales between forest fire properties (fire count, FC; fire radiative power, FRP) and the vegetation water content proxy of the Microwave Emissivity Difference Vegetation Index (EDVI) derived from the Moderate Resolution Imaging Spectroradiometer and the Advanced Microwave Scanning Radiometer-EOS on Aqua satellite. The correlations were compared to that with three weather-based indices including the Fine Fuel Moisture Code, Initial Spread Index and Fire Weather Index (FWI) to determine whether EDVI provides new independent information of forest fires. Finally, EDVI and the weather-based indices FWI were combined to establish multivariate linear regression models to estimate FC and FRP. Results show that: 1) the temporal variations of FC and total FRP are negatively correlated with EDVI using the daily and monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between FC (and total FRP) and EDVI are observed in Northeastern Asia and Southern China; 2) compared to the weather-based indices, EDVI shows higher correlation with the temperate forest fire properties in Southern China while shows weaker correlation with the forest fire properties in Northeastern Asia; and a combination of the two kind of indices is found to improve the explained variance for fire properties in both regions; 3) multivariate linear regression models based on EDVI and FWI provide better estimation of FC and FRP compared to the linear regression models based on FWI alone. To our knowledge, this is the first work that comprehensively investigates the potential application of the microwave-based vegetation water content index in forest fire count and fire intensity.

ESTIMATING FINE DEAD FUEL MOISTURE CONTENT UNDER EQUATORIAL CLIMATE CONDITIONS

The measurement of the fine dead fuel moisture content (FDFMC) is extremely important for forest fire prevention and suppression activities, as it has a great influence on the ignition probability and fire behavior. The Fine Fuel Moisture Code (FFMC) from the Fire Weather Index (FWI), is one of the most used models to estimate the FDFMC. Nevertheless, studies that assess the efficiency of this model in Brazil or in low latitude regions are rare. The present study aimed to evaluate the efficiency of the FFMC in an equatorial climate area and to develop a new model capable of estimating the FDFMC with greater precision. For this purpose, 861 random samples of fine dead fuel had their moisture content determined through oven drying. The obtained values were compared with those estimated by the FFMC and correlated with meteorological parameters to build a regression model. The results obtained show that the FDFMC was overestimated by the FFMC. The independent variables with the greatest influence on the FDFMC were, in decreasing order of significance: air relative humidity, air temperature, amount of rainfall in the last 24 hours and number of days without rainfall. The developed model presented good statistical parameters (r2 = 0.86; p <0.0001; RMSE = 0.22) and can be used, in areas with similar characteristics of the study area, to estimate the daily fire risk and to determine ideal conditions for prescribed burns.

Characterisation of initial fire weather conditions for large spring wildfires in Alberta, Canada

We evaluated surface and 500-hPa synoptic weather patterns, and fire weather indices from the Canadian Forest Fire Danger Rating System for 80 large wildfires during 1990–2019 in Alberta that started in May and grew to over 1000 ha. Spread days were identified during the first 4 days of wildfire activity. We observed two distinct synoptic weather patterns on these days. Pre-frontal and frontal passage activity was the predominant feature associated with 48% of the calendar spread days. Strong south–south-east winds from a surface high centred east of Alberta (west of Hudson Bay) and supported by an upper ridge, and a surface low located south-west of the ridge occurred on 26% of the calendar spread days. Surface analysis indicates the spring wildfire season in Alberta is driven by very high to extreme Initial Spread Index, a rating of the expected wildfire rate of spread based on Fine Fuel Moisture Code and wind. Very high to extreme values of Buildup Index, a rating of the amount of fuel available for consumption, are not a prerequisite for large wildfires in May. For Alberta, this means large wildfires in May can occur after only a few days of dry, windy weather.

Influence of climatic factor of changes in forest fire danger and fire season length in Turkey.

In contrast to the expectations of an increase in annual fire activity and the severity of fire season due to climate change and large fires, which have been occurring in recent years, a downtrend has been identified in fire activity in many studies conducted for the whole of Europe in recent years. Similarly, in Turkey, according to the General Directorate of Forestry statistics, while there is an increase in the number of annual fires, the burnt area has a downtrend pattern. In this study, fire activity and climate data statistics for Turkey were examined along with the fire season length and severity. The results obtained conform with the studies conducted in places from Spain at the westernmost part of Mediterranean Europe to Israel at the easternmost part of the Mediterranean. Considering the changes in temperatures, temperature rise of 2 to 3 °C was detected at all stations in the study area. No decrease was observed in the average temperatures at any of the stations within the study period between 1940 and 2018. On the other hand, the precipitation trend varied according to the stations. Although there have been increases in precipitation in Fethiye, Isparta, and Marmaris since 1960, the decrease in precipitation by 132 mm in Afyon since 1970 and the decrease in precipitation by 137 mm in Bodrum since 1940 are attention-grabbing. These stations are followed by Izmir station with 66 mm and Cesme station with 37 mm of decrease, despite being smaller decreases. In the study, the long-term (1940-2018) data of the meteorological stations discussed within the study, the Canadian Fire Weather Index (FWI) and the Fine Fuel Moisture Code (FFMC) values were calculated. According to the FWI results used in determining the severity and length of fire season on the coastline of Turkey from the northern Aegean to Antalya, the likelihood of large fires decreased by about 52% in 2018 compared to 1970. This decrease in FWI value indicates that the fire severity is reduced. The specified decrease in fire severity also explains the reason of the decrease in the burnt area that occurred over the years in Turkey. No significant change was observed in the FFMC values indicating the possibility of human-induced fires between 1970 and 2018.

Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.

The Canadian versus the National Forest Fire Danger Rating Systems tested in Mediterranean forests fire Crete, Greece

The Canadian Forest Fire Danger Rating System (CFFDRS) and the National Forest Fire Danger Rating System (NFFDRS) were exercised under the Mediterranean forest type conditions of Crete Island, Greece in 2008–2009. The CFFDRS shows a high association between the assessed values of the duff moisture content on one hand and the Fine Fuel Moisture Code and the Duff Moisture Code on the other hand. During the designated fire seasons, there was an insignificant association between the Fire Weather Index and the wild forest fires which are contrary associated with wildfire occurrences. Meanwhile, the NFFDRS showed monitoring capabilities but with the insufficient prediction of the daily fire danger in the Mediterranean forests. During the tested forest fire seasons, there was an unstable association between the burnt area and the fire occurrences. Consequently, there was no significant correlation between the CFFDRS and the litter soil content. Although, CFFDRS has a higher prediction accuracy of moisture content of some shallow roots annual plants within the studied area. The findings of the current research emphasized that there is no single system that is adequate enough for both soil moisture content and the annuals moisture content simultaneously.

Evaluation of Gridded Precipitation Data and Interpolation Methods for Forest Fire Danger Rating in Alberta, Canada

AbstractThe Canadian Forest Fire Weather Index System is the primary measurement of wildfire danger in Canada. Interpolating daily precipitation, one of the inputs for the Fire Weather Index System is a key challenge in areas without sufficient weather stations. This work evaluates the performance of gridded precipitation from the Canadian Precipitation Analysis (CaPA) System and six interpolation methods to achieve the best fire danger rating in Alberta, Canada. Results show that the CaPA System has only average performance due to limited radar coverage (10%) in the forested region; however, using the CaPA System as a covariate with regression kriging generates significantly better precipitation estimates. Ordinary kriging, regression kriging with elevation as a covariate, and the thin‐plate smoothed spline are methods with similar performance. Fuel moisture codes of the Fire Weather Index System respond differently to precipitation amounts due to differences in their time constants for drying. Fine fuels with a short drying time (Fine Fuel Moisture Code) are best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. Compacted organic fuels with longer drying times (Duff Moisture Code and Drought Code) are best estimated by regression kriging with CaPA because it better predicts significant precipitation events. The dense fire weather station network in our study area (~3.0 stations/10,000 km2) allows us to perform a sensitivity analysis, and we find that a threshold of >0.5 stations/10,000 km2 is needed for regression kriging with CaPA to become appreciably better than the CaPA System.

MODELOS MATEMÁTICOS DE PREVISÃO DO TEOR DE UMIDADE DOS MATERIAIS COMBUSTÍVEIS FLORESTAIS FINOS E MORTOS

O presente artigo busca descrever, por meio de uma revisão da literatura, os principais modelos matemáticos existentes para estimar o teor de umidade dos materiais combustíveis florestais finos e mortos, ou seja, os materiais da classe de 1-h de timelag, com base em variáveis meteorológicas. A determinação desses valores compreende uma importante informação para o delineamento de ações de prevenção e combate a incêndios florestais, e de realização de queimadas controladas, já que respondem pela probabilidade de ignição e comportamento do fogo. Com base na análise realizada, percebe-se que o Fine Fuel Moisture Code (FFMC), um dos componentes do Fire Weather Index (FWI) canadense, constitui o modelo de previsão do teor de umidade mais utilizado no mundo. Porém, considerando-se que trabalhos na literatura relatam limitações e imprecisão tanto no FFMC quanto nos demais modelos analisados nesse artigo, é essencial a validação dos mesmos antes de serem utilizados de forma operacional. Em função da pequena quantidade de estudos envolvendo essa temática no Brasil, recomenda-se a validação ou desenvolvimento de novos modelos, a fim de se aprimorar os programas de prevenção e de delineamento de risco de incêndios florestais em nível nacional.

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.

Hourly fine fuel moisture model for Pinus halepensis (Mill.) litter

Pinus halepensis (Mill.) is the most widely distributed and abundant Mediterranean pine, as well as one of the species most affected by wildfires in Europe. The objective of this study was to develop and test an operationally useful fine fuel moisture prediction model for P. halepensis needle litter below the fibre saturation point, where flammability increases rapidly with decreasing moisture content. The predictive ability of the hourly Fine Fuel Moisture Code (FFMC) was also evaluated. Even though the hourly FFMC model is designed for pine litter, it consistently over-predicted moisture content of P. halepensis needle litter and could not track the rapid moisture change during the day. Experimental measurements of equilibrium moisture content and response time of dead P. halepensis needles were used to modify the hourly FFMC model. The modified model performed better than the hourly FFMC model on all evaluation parameters, with a mean absolute error of 1% moisture content. Our results suggest that fine fuel moisture prediction models should be species-specific in order to achieve the accuracy needed for precise prediction of fire behaviour.