Fuel Moisture Model Research Articles

An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment

Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-hours fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

A national-scale sampled temperate fuel moisture database

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK.

Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations

The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated (r2=0.629 with defaults and r2=0.693 when species and location optimized). We also consolidated fuel models to five fuel types that eliminated significant index cross-correlation. Index seasonality compared between old (V2) and new USNFDRS models (v4) across six US National Forests was very similar (ρ= 0.97). V4 was as good or better than V2 at predicting fire days in 92% of the cases tested and V4 effectively predicted wildfire days and large fire ignition days (AUCs 0.647 to 0.915). USNFDRS V4 can adequately depict spatial and temporal wildland fire potential and it can be adapted for worldwide use.

Climate-based prediction of carbon fluxes from deadwood in Australia

Abstract. Deadwood is an important yet understudied carbon pool in tropical ecosystems. Deadwood degradation to CO2 through decomposer (microbial, termite) activities is driven by wood moisture and temperature, which are in turn strongly influenced by local climate. Thus, climate data could be used to predict CO2 fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow for the rapid estimation of deadwood-related CO2 fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g., air temperature, precipitation, solar radiation) to simulate wood moisture and temperature along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between wood moisture and temperature and CO2 flux from pine (Pinus radiata) blocks and combined this relationship with our simulations to predict CO2 fluxes from deadwood at 1 h temporal resolution. We compared our pine-based simulations to the moisture–CO2 relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO2 fluxes and wood moisture and temperature. Comparing cumulative CO2 with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO2 regardless of the precipitation regime. At the dry savanna, only about 20 % of the wood mass loss was decomposed within 48 months, compared to almost 100 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in situ as CO2 is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss–flux relationships of decaying native stems deviated from those of pine blocks. Our results indicate that wood moisture and temperature are necessary but not sufficient for predicting CO2 fluxes from deadwood degradation. Other factors, such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes, should be considered in future modeling efforts.

Direct effect of orbital-insolation variation on long-term wildfire activity in central Japan demonstrated using a fuel moisture model

Direct effect of orbital-insolation variation on long-term wildfire activity in central Japan demonstrated using a fuel moisture model

Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece

The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models’ performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from −0.3% to −7.3%. Moreover, in all of the cases examined, apart from one (Sartis’ fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach.

Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest

The moisture content of live fuels is an important determinant of forest flammability. Current approaches for modelling live fuel moisture content typically focus on the use of drought indices. However, these have mixed success partly because of species-specific differences in drought responses. Here we seek to understand the physiological mechanisms driving changes in live fuel moisture content, and to investigate the potential for incorporating plant physiological traits into live fuel moisture models. We measured the dynamics of leaf moisture content, access to water resources (through stable isotope analyses) and physiological traits (including leaf water potential, stomatal conductance, and cellular osmotic and elastic adjustments) across a fire season in a Mediterranean mixed forest in Catalonia, NE Spain. We found that differences in both seasonal variation and minimum values of live fuel moisture content were a function of access to water resources and plant physiological traits. Specifically, those species with the lowest minimum moisture content and largest seasonal variation in moisture (Cistus albidus: 49–137% and Rosmarinus officinalis: 47–144%) were most reliant on shallow soil water and had the lowest values of predawn leaf water potential. Species with the smallest variation in live fuel moisture content (Pinus nigra: 96–116% and Quercus ilex: 56–91%) exhibited isohydric behaviour (little variation in midday leaf water potential, and relatively tight regulation of stomata in response to soil drying). Of the traits measured, predawn leaf water potential provided the strongest predictor of live fuel moisture content (R2 = 0.63, AIC = 249), outperforming two commonly used drought indices (both with R2 = 0.49, AIC = 258). This is the first study to explicitly link fuel moisture with plant physiology and our findings demonstrate the potential and importance of incorporating ecophysiological plant traits to investigating seasonal changes in fuel moisture and, more broadly, forest flammability.

Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire

AbstractChanging frequencies of extreme weather events and shifting fire seasons call for enhanced capability to forecast where and when forested landscapes switch from a nonflammable (i.e., wet fuel) state to the highly flammable (i.e., dry fuel) state required for catastrophic forest fires. Current forest fire danger indices used in Europe, North America, and Australia rate potential fire behavior by combining numerical indices of fuel moisture content, potential rate of fire spread, and fire intensity. These numerical rating systems lack the physical basis required to reliably quantify forest flammability outside the environments of their development or under novel climate conditions. Here, we argue that exceedance of critical forest flammability thresholds is a prerequisite for major forest fires and therefore early warning systems should be based on a reliable prediction of fuel moisture content plus a regionally calibrated model of how forest fire activity responds to variation in fuel moisture content. We demonstrate the potential of this approach through a case study in Portugal. We use a physically based fuel moisture model with historical weather and fire records to identify critical fuel moisture thresholds for forest fire activity and then show that the catastrophic June 2017 forest fires in central Portugal erupted shortly after fuels in the region dried out to historically unprecedented levels.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

Data assimilation of dead fuel moisture observations from remote automated weather stations

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands

The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.

Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data

Abstract Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129–1404 mm year− 1) and leaf area index (0.1–5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE

Assessing the probability of sustained flaming in masticated fuel beds

Mechanical mastication is increasingly used as a fuel management treatment to reduce fire risk at the wildland–urban interface, although ignition and fire behaviour in these novel fuel beds are poorly understood. We investigated the influence of observed fuel moisture content, wind speed, and firebrand size on the probability of sustained flaming of masticated fuel beds under both laboratory and field settings. Logistic regression techniques were applied to assess the probability of sustained flaming in both datasets. Models for the field were also developed using estimated moisture from three sets of weather-based models: (i) the hourly Fine Fuel Moisture Code (FFMC) from the Canadian Forest Fire Weather Index System, (ii) the National Fire Danger Rating System (NFDRS) moisture estimates for 1 h and 10 h fuels, and (iii) a masticated surface fuel moisture model (MAST). In both laboratory and field testing, the likelihood of a successful ignition increased with decreasing moisture content and increasing wind speed; the effect of firebrand size was only apparent in laboratory testing. The FFMC, NFDRS, and MAST predictions had somewhat reduced discriminative power relative to direct moisture in predicting the probability of sustained flaming based on our field observations. Our results speak to the disparity between the fire behaviour modeling that occurs in the laboratory and the fire behavior modeling that occurs in the field, as the methodology permitted comparison of predictions from sustained flaming models that were developed for one experimental setting and applied to the other.

Recent advances and applications of WRF–SFIRE

Abstract. Coupled atmosphere–fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF–SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF–SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF–Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and data-management tools, is presented.

Assimilation of Perimeter Data and Coupling with Fuel Moisture in a Wildland Fire–Atmosphere DDDAS

We present a methodology to change the state of the Weather Research Forecasting (WRF) model coupled with the fire spread code SFIRE, based on Rothermel's formula and the level set method, and with a fuel moisture model. The fire perimeter in the model changes in response to data while the model is running. However, the atmosphere state takes time to develop in response to the forcing by the heat flux from the fire. Therefore, an artificial fire history is created from an earlier fire perimeter to the new perimeter, and replayed with the proper heat fluxes to allow the atmosphere state to adjust. The method is an extension of an earlier method to start the coupled fire model from a developed fire perimeter rather than an ignition point. The level set method can be also used to identify parameters of the simulation, such as the fire spread rate. The coupled model is available from openwfm.org, and it extends the WRF-Fire code in WRF release.

Modelling drying processes of fuelbeds of Scots pine needles with initial moisture content above the fibre saturation point by two-phase models

Modelling the drying process of fuel moisture with initial moisture content above the fibre saturation point can be used to determine when fuel will become sufficiently dry (after precipitation) to burn and provide a more accurate prediction of fire potential. Based on analysis of the mechanism by which the drying process occurs, we propose a model comprising two phases distinguished by a moisture threshold of 0.35 g g–1, the fibre saturation point; one phase is controlled by evaporation and the other by diffusion. Each phase has a distinct equation with a different timelag. We compared our two-phase model with a one-phase model (one-timelag model) and another two-phase model by estimating drying of 15 Scots pine (Pinus sylvestris var. mongolica) needle fuelbeds. The results indicate that the two-timelag model improves moisture modelling, thereby reducing mean absolute error by more than 30%, i.e. from 0.0047 g g–1 (one-phase model) to 0.0030 g g–1. The model yields consistent results, further suggesting its potential for improving fuel moisture prediction of fire danger rating systems. The first timelag of the model is affected by fuelbed properties. Equations based on variables that represent fuelbed properties were established, thus saving time when estimating parameters for stand-specific fuel moisture models.

大兴安岭林区10小时时滞可燃物湿度的模拟

随着森林防火预报精细化的需求,小时尺度可燃物湿度的准确模拟成为火险预报的关键。利用2010年8月连续无降雨天气条件下我国大兴安岭林区10h时滞可燃物湿度和相应气象因子的半小时动态观测资料,从可燃物的失水和吸水过程对目前广泛使用的Fosberg模型和Van Wagner模型进行评估,进而发展了准确模拟10h时滞可燃物失水和吸水过程的可燃物湿度模型。结果表明:Fosberg模型对10h时滞可燃物的失水过程模拟较好(<em>R</em>²=0.96,<em>P</em><0.01),而Van Wagner模型对10h时滞可燃物的吸水过程模拟较好(<em>R</em>²=0.83,<em>P</em><0.01),但均不能独立地准确模拟10h时滞可燃物的湿度变化。通过分析可燃物失水与吸水过程,考虑可燃物在静风条件下的水汽交换,优化了Van Wagner模型参数,建立了综合反映可燃物失水与吸水过程的10h时滞可燃物湿度模型。据比较,该模型可准确地模拟10h时滞可燃物的湿度变化(<em>R</em>²=0.88,<em>P</em><0.01),可为精细化火险预报提供技术支撑。;Wildfire is a necessary disturbance for forest ecosystem's regeneration and succession, but frequent and large fires threaten human life and activities. Forecasting forest fires is a useful method for fire disaster prevention and emergency operation. The accurate simulation of moisture content of dead fuels is critical for forest fire danger forecast because it's strongly related to the ignition and fire spread. Daxinganling provides many forestry productions to China but it was also one of the highly fire-prone areas in China. Developing an accurate model for estimating hour scale dead fuel moisture content in the Daxinganling is very necessary and important. A fire remote automatic weather station was settled in Huzhong National Nature Reserve which is located in the Daxinganling larch forest to collect 10-hour time-lag fuel moisture content and meteorological factors (temperature, relative humidity and wind speed). The record frequency is half-hour per time. In this research, Fosberg model and Van Wagner model were evaluated, since they were widely used in forest fire danger forecast system and based on the equilibrium moisture content (EMC) that is easy to use. Desorption and absorption processes are the key to simulate fuel moisture content. So, this research chooses continuous observation data during August 25-28, 2010 without any precipitation to validate these two processes. The results show that both Fosberg model and Van Wagner model can simulate when desorption or absorption process begins, but all of them could not simulate well for whole process independently. Fosberg model could give better simulation at desorption stage (<em>R</em>²=0.96, <em>P</em><0.01), and Van Wagner model does better at absorption stage (<em>R</em>²=0.83, <em>P</em><0.01). Through analysis the potential actual variation of fuel moisture content in the desorption and absorption process, the vapour exchange coefficient is nearly to be a constant in the desorption which is coincide with Fosberg model's and the absorption's the vapour exchange coefficient seem to be variable which is similar to the Van Wagner model's. Thus, a new hour scale fuel moisture model is developed considering the difference of desorption and absorption processes: the desorption process based on Fosberg model with the Simard's EMC model and the absorption process based on Van Wagner model with the Van Wagner's EMC model. The new model use the difference between fuel moisture content and Van Wagner's absorption EMC to determine the vapor exchange process, and it could give better simulation (<em>R</em>²=0.77, <em>P</em><0.01). But the absorption processes was underestimated in the calm weather, and the modified model simulated better (<em>R</em>²=0.88, <em>P</em><0.01).This research could provide the technical supports for the forest fire danger forecast in Daxinganling.

Simple models for predicting dead fuel moisture in eucalyptus forests

Fire behaviour prediction requires models of dead fuel moisture that are both accurate and suitable for use for operational applications. The paper investigates two methods of developing a simple operational fine fuel moisture model from a more complex process-based model. The first simple model is a table of fuel moisture predictions for values of air temperature, relative humidity, wind speed and solar radiation. The second model reduces the original model to a single differential equation, which may be used on low-powered computers. The simple models are tested against the output of the original model and against observations from two case studies in dry eucalyptus forest in south-western Australia. The single differential equation model was capable of reproducing the prediction of the process-based model at all times of the day, with mean error (ME) in predictions of –0.1% and mean absolute error (MAE) of 0.6%. The table model performed less well, with ME = –0.7% and MAE = 1.1% at 1500 hours, and ME = –1.2% and MAE = 3.0% at other times of the day.

Development and application of a system for dynamic wildfire risk assessment in Italy

In this paper, the architecture and the application of a system designed for the assessment of the distribution of dynamic wildland fire risk over the whole Italian territory are presented. Such an assessment takes place on the basis of static information concerning the vegetation cover and the topography of the territory and of dynamic information consisting of the meteorological forecast, over a certain time horizon, provided by a Limited Area Model. The risk distribution assessment is obtained through the use of two different models, namely the fuel moisture model and the potential fire spread model, which are applied, at each time interval, for each of the cells in which the area of study is discretized. In this paper, the structure of such models is presented in detail, and similarities and differences with respect to other existing models are discussed. As the performance of the overall system is heavily dependent on the choice of the values of the parameters appearing in such models, a careful calibration of such parameters is needed. In this respect, the results provided by a preliminary calibration are presented, carried out with the objective of maximizing the accordance between the outputs of the presented risk assessment system and the information relevant to really occurred fires.