Fire Danger Forecast Research Articles

Australian Fire Danger Rating System: implementing fire behaviour calculations to forecast fire danger in a research prototype†

Background The Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed fire behaviour models. The system is modular and allows for ongoing incorporation of new scientific research and improved datasets. Aims Prior to operational implementation of the AFDRS, a Research Prototype (AFDRSRP), described here, was built to test the input data and systems and evaluate the performance and potential outputs. Methods Fire spread models were selected and aligned with fuel types in a process that captured bioregional variation in fuel characteristics. National spatial datasets were created to identify fuel types and fire history in alignment with existing spatial weather forecast layers. Key results The AFDRSRP demonstrated improvements over the McArthur Forest and Grass Fire Danger systems due to its use of improved fire behaviour models, as well as more accurately reflecting the variation in fuels. Conclusions The system design was robust and allowed for the incorporation of updates to the models and datasets prior to implementation of the AFDRS.

Introduction to the Australian Fire Danger Rating System†

Background Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years.

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

Global seasonal prediction of fire danger

The European Centre for Medium range weather forecast (ECMWF) on behalf of the Copernicus Emergency Management Service (CEMS) has recently widened the fire danger data offering in the Climate Data Store (CDS) to include a set of fire danger forecasts with lead times up to 7 months. The dataset incorporates fire danger indices for three different models developed in Canada, United States and Australia. The indices are calculated using ECMWF Seasonal Forecasting System 5 (SEAS5) and verified against the relevant reanalysis of fire danger based on the ECMWF Re-Analysis (ERA5). The data set is made openly available for the period 1981 to 2023 and will be updated regularly providing a resource to assess the predictability of fire weather at the seasonal time scale. The data set complements the availability of seasonal forecast provided by the Copernicus Emergency Management Service in real time.A preliminary analysis shows that globally anomalous conditions for fire weather can be predicted with confidence 1 month ahead. In some regions the prediction can extend to 2 months ahead. In most situations beyond this horizon, forecasts do not show more skill than climatology. However an extended predictability window, up to 6-7 months ahead is possible when anomalous fire weather is the results of large scale phenomena such as the El Niño Southern Oscillation and the Indian Ocean Dipole, often conducive of extensive fire burning in regions such as Indonesia and Australia.

Accounting for fuel in fire danger forecasts: the fire occurrence probability index (FOPI)

A new fire danger index is proposed to overcome one of the most important limitations of current fire danger metrics. The fire occurrence probability index (FOPI) combines the Canadian fire weather index (FWI) with remote observations of vegetation characteristics to better predict landscape flammability. The FOPI is designed to improve fire danger predictions in all fuel-limited environments where fire is driven by the short-term drying of intermittently-available fuel. The FOPI considerably outperforms the FWI in arid biomes while remaining comparable to the FWI where fuel is abundant.

Downscaled subseasonal fire danger forecast skill across the contiguous United States

AbstractThe increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

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.

Experimental evaluation of fire weather conditions during periods of wildfires in Ukraine in 2020

Introduction. Almost every year, the territory of Ukraine is affected by drought of different intensity and duration, what is a favorable condition for wildfires. In the forest areas of the country arose about 106.8 thousand fires with a total area of 139.2 thousand hectares during 1990-2017. An analysis of the long-term dynamics shows that wildfires in Ukraine are a sustainable phenomenon. In 2020, under the severe drought conditions, 209 forest fires occurred, the most significant of them were in the Chernobyl zone and in the Lugansk region. Formulation of the problem. There is still no universally accepted method to describe all drought related processes due to complexity of drought phenomena. Although drought indices and fire weather indices usually have different time scales, their combination can be a basis in the prediction scheme in which the drought index acts as a background indicator, which enhances or decreases the current fire weather index. The purpose of this study is evaluate informativeness of a new fire weather index HDW (Hot-Dry-Windy Index) for the territory of Ukraine, which takes into account the main meteorological parameters necessary to describe the degree of wildfire danger due to weather conditions, so this index may become a component of the drought and fire monitoring system. Data and methods. The HDW index has a physical justification as the atmosphere affects the fire, namely, the combination of three atmospheric parameters characterizes the current conditions for the ignition and distribution of fire: wind, temperature, humidity. For the calculation of the HDW index, the data of GFS global numerical model with grid step 0.25 degrees is used. The calculation of the daily fields of the HDW index was carried out for the territory of Ukraine and for local areas. To localize areas of wildfires, in the study were used daily hotspots data provided by the web-resource Firms (https://firms2.modaps.eosdis.nasa.gov/), which are determined using satellite instruments MODIS (AQUA and Terra Satellites) and VIIRS (S-NPP and NOAA-20 satellites). Research results. Comparison of the calculated HDW fields in Ukraine with the position of hotspots showed that the maximum values of the HDW index are generally concentrated in fire areas. Comparison with the synoptic situation showed that the maximum values of the HDW are located in the zones of advection of warm and dry air in the lower troposphere and zones of strong surface winds, which usually correspond to the periphery of anticyclone or the warm sector of cyclone. The analysis of the time courses of the HDW index showed the typical feature, which expressed in the fact that in all cases there was an increase in the index values in the period before the wildfire, maximum of the HDW is fixed at the time of ignition and after that the index values sharply decreases. Scientific novelty and practical significance. The fields of new HDW index reflects the weather conditions typical for wildfires periods in the territory of Ukraine. The presence of specific features and extreme values in the index time course can be used to track the process of increase of fire danger under changing weather conditions to make short- and medium-range fire danger forecasts with evaluation of fire danger classes.

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

• Live Fuel Moisture Content (LFMC) is an essential constraint of wildfire activity. • LFMC estimated from weather/vegetation indices lacks species-specific predictions. • We use a water balance model to predict species-specific LFMC from water potential. • Our model shows an improvement in predictive performance over existing approaches. • Our approach can be implemented within large-scale fire danger forecast systems. Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates. Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from different functional types by adjusting a soil water balance model which estimates predawn leaf water potential (Ψ pd ). Our overarching goal was to balance the trade-off between biological realism, which enhances model applicability, and parameterization complexity, which may limit its value within operational settings. After calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of fit for our model ( R 2 = 0.5) was better than that obtained by an existing models based on drought indices ( R 2 = 0.3) or spectral vegetation indices ( R 2 = 0.1). We observed the best predictive performance for seeding shrubs ( R 2 = 0.6) followed by trees ( R 2 = 0.5) and resprouting shrubs ( R 2 = 0.4). Through its relatively simple parameterization, the approach developed here may pave the way for a new generation of process-based models that can be used for operational purposes within fire risk mitigation scenarios.

Arctic Oscillation and Pacific-North American pattern dominated-modulation of fire danger and wildfire occurrence

Based on statistical analyses and Arctic Oscillation (AO) and the Pacific-North American pattern (PNA) induced climate anomalies in the 2001–2020 interval, it has been found that these climate modes drastically influence the fire danger (PFIv2) in differing ways across coastal and inland regions. The AO induces higher fire risk in northern Eurasia and central North America, whereas the PNA increases the fire danger across southern Asia and western North America. Moreover, fires have been predominantly identified, up to 70%, during the positive phases of AO and PNA northward of 50°N, in particular over Alaska, Baltic States and eastern Asia. For coincident positive AO and negative PNA days, a large number of fires have been identified over northwestern North America and northern Eurasia. Spectral analyses demonstrate that weather anomalies related to AO and PNA lead fire danger by 10–20 days, and both modes are significantly correlated to PFIv2 over north America and most of Eurasia. Despite some drawbacks related to the fire danger methods currently applied (PFI and FWI), it is demonstrated that the influence of AO and PNA on potential environmental driven-fires can be anticipated, in some locations on almost 90% of days. Fire danger forecasts are urgently needed and the understanding of factors and conditions, which are able to modify the environmental susceptibility to fire development, are crucial for adequate management to reduce the harmful effects of fire. In this sense, our results reveal that a better prediction of the fire season can be achieved by advanced assessment of the PNA and AO behavior, and shed light on the need to investigate the impact of other modes of climate variability upon wildfire frequency and severity.

Implementation of Wildfire Risk Evaluation Elements into the Hungarian Forest Fire Prevention System

Nowadays, wildfires are an increasing challenge for the defence sector. The fire risk of a given area depends only in part on human factors and the number of registered fires.A fire occurs when the moisture content of dead biomass drops to a level, where the fire can already spread between the individual pieces of fuel. Daily fire danger forecast examines the constant and changing components of the fire environment. This determines the flammability of the biomass; the rate of fire spread makes firefighting moredifficult. The fire danger forecast identifies the fire hazard periods when fires can occur. Fire Risk Assessment Systems have been developed in many countries around the world.In addition to the daily fire risk, these include parameters describing the vulnerability of the areas affected by the fire. National risk assessments are available in many countries around the world using several methodologies. The Joint Research Centre of the European Commission has developed a community-wide approach to forest fire risk assessment, using scientific results and studying good practices. In this approach, the risk of a forest fire is made up of the effects of daily fire hazards and vulnerabilities. The risk of fire due to weather conditions is associated with ignition and the spread of fire. The authors examine in the paper the basic criteria to assess wildfire risk at the pan-European level. The authors analyse external and internal risk factors in an observation plot and examine how international recommendations can be utilised in Hungary.

Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model

Due to climate change and associated longer and more frequent droughts, the risk of forest fires increases. To address this, the Institute of Meteorology and Water Management implemented a system for forecasting fire weather in Poland. The Fire Weather Index (FWI) system, developed in Canada, has been adapted to work with meteorological fields derived from the high-resolution (2.5 km) Weather Research and Forecasting (WRF) model. Forecasts are made with 24- and 48-h lead times. The purpose of this work is to present the validation of the implemented system. First, the results of the WRF model were validated using in situ observations from ~70 synoptic stations. Second, we used the correlation method and Eastaugh’s percentile analysis to assess the quality of the FWI index. The data covered the 2019 fire season and were analysed for the whole forest area in Poland. Based on the presented results, it can be concluded that the FWI index (calculated based on the WRF model) has a very high predictive ability of fire risk. However, the results vary by region, distance from human habitats, and size of fire.

Modeling Drying of Degenerated Calluna vulgaris for Wildfire and Prescribed Burning Risk Assessment

Research highlights: Moisture diffusion coefficients for stems and branches of degenerated Calluna vulgaris L. have been obtained and a mathematical model for the drying process has been developed and validated as an input to future fire danger modeling. Background and objectives: In Norway, several recent wildland–urban interface (WUI) fires have been attributed to climate changes and accumulation of elevated live and dead biomass in degenerated Calluna stands due to changes in agricultural activities, i.e., in particular abandonment of prescribed burning for sheep grazing. Prescribed burning is now being reintroduced in these currently fire prone landscapes. While available wildfire danger rating models fail to predict the rapidly changing fire hazard in such heathlands, there is an increasing need for an adapted fire danger model. The present study aims at determining water diffusion coefficients and develops a numerical model for the drying process, paving the road for future fire danger forecasts and prediction of safe and efficient conditions for prescribed burning. Materials and methods: Test specimens (3–6 mm diameter) of dead Calluna stems and branches were rain wetted 48 h and subsequently placed in a climate chamber at 20 °C and 50% relative humidity for mass loss recordings during natural convection drying. Based on the diameter and recorded mass versus time, diffusion coefficients were obtained. A numerical model was developed and verified against recoded mass loss. Results: Diffusion coefficients were obtained in the range 1.66–10.4 × 10−11 m2/s. This is quite low and may be explained by the very hard Calluna “wood”. The large span may be explained by different growth conditions, insect attacks and a varying number of years of exposure to the elements after dying. The mathematical model described the drying process well for the specimens with known diffusion coefficient. Conclusions: The established range of diffusion coefficients and the developed model may likely be extended for forecasting moisture content of degenerated Calluna as a proxy for fire danger and/or conditions for efficient and safe prescribed burning. This may help mitigate the emerging fire risk associated with degenerated Calluna stands in a changing climate.

Impacts of ENSO events on forest fire weather of China

The ENSO events affect climate and fire danger of China. It would be helpful for improving fire danger forecast to understand the impacts of ENSO events on fire weather for various ecological zones in the country. We calculated the fire weather index (FWI) using the daily climatic dataset (V3.0) of international exchange weather stations in China during 1951-2016. The burned areas in forests for each ecological zone in 2001-2016 were derived from MODIS fire products. Temperature, precipitation, FWI and burned areas in fire season were estimated for each ecological zone by ENSO events (weak, medium, strong, and super strong El Niño events and weak, medium, and strong La Niña events). The results showed that there were 19 El Niño events and 14 La Niña events during 1950-2016. The average daily maximum temperature of the spring fire season increased significantly in the northwestern region with the influence of strong or super strong El Niño event, while the temperature reduced significantly in the medium El Niño event for mid-temperate semi-arid grassland. Precipitation in fire season generally increased in El Niño events in southern and southwestern forest regions. It would be reduced in most areas affected by the low and medium intensity La Niña event, but be increased during the strong La Nina event. The fire weather indices of southern forest regions decreased due to the weak El Niño event. The FWI of the northern forest regions increased with the strong or super strong El Niño event, and reduced in the southern and southwestern forest areas. There was a significant spatial difference on the FWI for some ecological zones with the impacts of the El Niño/La Niña events. The burned areas showed a consistent change trend with seasonal severity rating (SSR) during 2001-2016 when the SSR changed significantly for the regions of deciduous broad-leaved forest in humid/semihumid areas of warm temperate zone, broad-leaved forest in the middle north subtropical humid areas, and broad-leaved forest in tropical and subtropical humid areas. The burned areas in the rest regions were not affected by the ENSO events.

A 1980\u20132018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices

This data descriptor documents a dataset containing over 38 years of global reanalysis of wildfire danger. It consists of seven fields to assess fuel moisture as well as fire behavior. The methodology employed to generate these data is based on the Canadian Forest Fire Weather Danger Rating and utilizes weather forcing from ERA-Interim, a global reanalysis dataset produced by the European Centre for Medium-range Weather Forecasts. Global fire danger reanalysis data are used to quantify the climatological expectation of fire danger at a certain time of the year and for any location on the globe. It can be regarded as a complementary product to the fire danger forecasts issued daily by the Global Wildfire Information System (GWIS) under the umbrella of the European Copernicus program.

An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data

Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.

Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat

Abstract. We present a procedure that allows the operational generation of daily forecasts of fire danger over Mediterranean Europe. The procedure combines historical information about radiative energy released by fire events with daily meteorological forecasts, as provided by the Satellite Application Facility for Land Surface Analysis (LSA SAF) and the European Centre for Medium-Range Weather Forecasts (ECMWF). Fire danger is estimated based on daily probabilities of exceedance of daily energy released by fires occurring at the pixel level. Daily probability considers meteorological factors by means of the Canadian Fire Weather Index (FWI) and is estimated using a daily model based on a generalized Pareto distribution. Five classes of fire danger are then associated with daily probability estimated by the daily model. The model is calibrated using 13 years of data (2004–2016) and validated against the period of January–September 2017. Results obtained show that about 72 % of events releasing daily energy above 10 000 GJ belong to the “extreme” class of fire danger, a considerably high fraction that is more than 1.5 times the values obtained when using the currently operational Fire Danger Forecast module of the European Forest Fire Information System (EFFIS) or the Fire Risk Map (FRM) product disseminated by the LSA SAF. Besides assisting in wildfire management, the procedure is expected to help in decision making on prescribed burning within the framework of agricultural and forest management practices.

Developing Lebanon's fire danger forecast

Lebanon lacks a system for forecasting wildfire danger with the combined use of dynamic weather forecasts and comprehensive fire risk information (i.e., hazard and vulnerability). Accordingly, the aim of this study was to develop a new national fire danger forecast system for use as an operational fire management tool in Lebanon. The specific objectives were to 1) produce an advanced fire risk map of Lebanon, 2) customize an index for use in fire danger forecasting, and 3) develop an automated system for daily fire danger forecasts. Producing an advanced fire risk map involved the combined use of collected fire risk data and predicted wildfire occurrence. Forward stepwise binary logistic regression analysis of 24 socio-economic and biophysical variables was initially adopted to predict wildfire occurrence. Geographic object-based image analysis (GEOBIA) approach was employed to map the different classes of fire risk. Consequently, the customization of an index for fire danger forecasting involved the use of both fire risk and Fire Weather Index (FWI) data using also the GEOBIA approach. Overall, modeling socio-economic and biophysical factors correctly predicted an average of 85.7% of fire occurrences between 1998 and 2012 across the country. In addition, a comparison of FWI forecasts and fire occurrence (i.e., observed fire events) during the fire season of 2015 resulted in sensitivity (76.2%) and specificity (18.6%) estimates. Finally, automated daily fire danger forecasts are expected to serve as essential tools for both scientific and operational purposes.

Spatial Pattern of the Seasonal Drought/Burned Area Relationship across Brazilian Biomes: Sensitivity to Drought Metrics and Global Remote-Sensing Fire Products

Fires are complex processes having important impacts on biosphere/atmosphere interactions. The spatial and temporal pattern of fire activity is determined by complex feedbacks between climate and plant functioning through and biomass desiccation, usually estimated by fire danger indices (FDI) in official fire risk prevention services. Contrasted vegetation types from fire-prone Brazilian biomes may respond differently to soil water deficit during the fire season. Then, we propose to evaluate the burned area (BA)/FDI relationship across Brazil using most common FDIs and the main BA products from global remote sensing. We computed 12 standard FDIs- at 0.5° resolution from 2002 to 2011 and used the monthly BA from four BA datasets—from the MODIS sensor (MCD45A1), the MERIS sensor (MERIS FIRE_CCI), the Global Fire Emission Database version 4 (GFED4) and version 4s including small fires (GFED4s). We performed a Principal Component Analysis (PCA) on the coefficients of determination (R2) of the FDI/BA relationship to investigate the biome specificities of Brazilian biomes and the sensitivity to BA datasets. Good relationships (R2 > 0.8) were observed for all BA datasets, except SPEI (R2 < 0.2). We showed that FDIs computed from empirical water balances considering a lower soil capacity are more correlated to the seasonal pattern of fire occurrence in the Cerrado biome with contrasted adjustments between the western (early drying) and eastern part (late drying), while the fine fuel moisture index is more correlated to the fire seasonal pattern in Amazonia. The biome specificities of the FDI/BA relationship was evaluated with a general linear model. High accuracies in the biome distribution according to the FDI/BA relationship (>50%, p < 0.001) was observed in Amazonia and Cerrado, with lower accuracy (<32%, p < 0.001) in the Atlantic Forest and Caatinga. These results suggest that the FDI/BA relationship are biome-specific to explain the seasonal course of burned in Brazilian biomes, independently of the global BA product used. Selected FDIs should be used for fire danger forecast in each Brazilian biome.