Dead Fine Fuel Moisture Content Research Articles

VPD-based models of dead fine fuel moisture provide best estimates in a global dataset

Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/.

A comparison of five models in predicting surface dead fine fuel moisture content of typical forests in Northeast China

IntroductionThe spread and development of wildfires are deeply affected by the fine fuel moisture content (FFMC), which is a key factor in fire risk assessment. At present, there are many new prediction methods based on machine learning, but few people pay attention to their comparison with traditional models, which leads to some limitations in the application of machine learning in predicting FFMC.MethodsTherefore, we made long-term field observations of surface dead FFMC by half-hour time steps of four typical forests in Northeast China, analyzed the dynamic change in FFMC and its driving factors. Five different prediction models were built, and their performances were compared.ResultsBy and large, our results showed that the semi-physical models (Nelson method, MAE from 0.566 to 1.332; Simard method, MAE from 0.457 to 1.250) perform best, the machine learning models (Random Forest model, MAE from 1.666 to 1.933; generalized additive model, MAE from 2.534 to 4.485) perform slightly worse, and the Linear regression model (MAE from 2.798 to 5.048) performs worst.DiscussionThe Simard method, Nelson method and Random Forest model showed great performance, their MAE and RMSE are almost all less than 2%. In addition, it also suggested that machine learning models can also accurately predict FFMC, and they have great potential because it can introduce new variables and data in future to continuously develop. This study provides a basis for the selection and development of FFMC prediction in the future.

Estimating dead fine fuel moisture content of forest surface, based on wireless sensor network and back-propagation neural network

The dead fine fuel moisture content (FFMC) in forest affects the occurrence and spread of forest fires. Therefore, the estimation of surface dead FFMC plays an important role in forest fire behaviour, fire management and fire danger assessment. However, there are some challenges with current FFMC measurement methods. Modelling using meteorological variables may be a very potential method to achieve remote real-time FFMC estimation. A surface dead FFMC estimation method based on wireless sensor network (WSN) and back-propagation (BP) neural network was proposed. The WSN can realise the acquisition of microclimate data. The BP neural network can use these data to establish multiple FFMC estimation models for different terrain conditions and dead fuel types. The ability of these models to estimate FFMC at four different terrain sampling sites was evaluated. The results suggested that the dead FFMC can be estimated with some degree of accuracy. The correlation coefficients of the estimation results at the four sampling sites were all greater than 0.9, and the mean square errors were all less than 1. The method can be well applied to forest surface dead FFMC estimation and early fire danger assessment, which has practical significance for the rational allocation of fire fighting resources.

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.

Modulating influence of drought on the synergy between heatwaves and dead fine fuel moisture content of bushfire fuels in the Southeast Australian region

During the 2019-20 summer season, Australia experienced frequent heatwave events with scorching temperatures and massive bushfires with dense smoke. These catastrophic heatwaves and bushfires resulted in huge socio-economic and ecological losses. The frequency and intensity of both heatwaves and bushfires are projected to increase in the future warming world. While considerable effort has been directed at understanding the physical mechanisms of these individual extreme events, an investigation of their interaction is lacking. We focus on the relationship between heatwaves and bushfire fuels by considering dead fine fuel moisture content, a critical factor that regulates the intensity, spread rate and the likelihood of profuse spotting of fires. We investigate the relationship by exploring the statistical correlations between various heatwave characteristics (frequency, duration, magnitude, and amplitude) and mean dead fine fuel moisture content over southeast Australia in the peak heat and fire season. This relationship varies among different heatwave characteristics as well as with regions. The prolonged duration of a heatwave is well associated with dead fine fuel dryness around the southeastern parts of the Southeast Australian region, whereas the hotter heatwave season favours the lower dead fine fuel moisture content over the Northeast parts of the Southeast Australia and central Victorian region. Results also suggest that dead fine fuel moisture content is significantly decreased on heatwave days compared to non-heatwave days. Lastly, we explored the effects of rainfall deficit on the relationship between heatwave and mean dead fine fuel moisture content by splitting the seasons based on the Standard Precipitation Index (SPI). Results show that the correlation strength is both seasonally and regionally dependent.

PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks—fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.

Exploring the key drivers of forest flammability in wet eucalypt forests using expert-derived conceptual models

Fire behaviour research has largely focused on dry ecosystems that burn frequently, with far less attention on wetter forests. Yet, the impacts of fire in wet forests can be high and therefore understanding the drivers of fire in these systems is vital. We sought to identify and rank by importance the factors plausibly driving flammability in wet eucalypt forests, and describe relationships between them. In doing so, we formulated a set of research priorities. Conceptual models of forest flammability in wet eucalypt forests were elicited from 21 fire experts using a combination of elicitation techniques. Forest flammability was defined using fire occurrence and fireline intensity as measures of ignitability and heat release rate, respectively. There were shared and divergent opinions about the drivers of flammability in wet eucalypt forests. Widely agreed factors were drought, dead fine fuel moisture content, weather and topography. These factors all influence the availability of biomass to burn, albeit their effects and interactions on various dimensions of flammability are poorly understood. Differences between the models related to lesser understood factors (e.g. live and coarse fuel moisture, plant traits, heatwaves) and the links between factors. By documenting alternative conceptual models, we made shared and divergent opinions explicit about flammability in wet forests. We identified four priority research areas: (1) quantifying drought and fuel moisture thresholds for fire occurrence and intensity, (2) modelling microclimate in dense vegetation and rugged terrain, (3) determining the attributes of live vegetation that influence forest flammability, (4) evaluating fire management strategies.

Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

Corrigendum to: Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

An analysis of the effect of aspect and vegetation type on fine fuel moisture content in eucalypt forest

The magnitude of site effect on dead fine fuel moisture content (FMC) in eucalypt forests was investigated for different fuel strata: bark, near-surface, top and profile litter. In addition, the variation across two fire seasons in litter FMC and fraction of fuel available for burning was analysed. Over different fuel strata, the aspect effect ranged from an average moisture content of ~3% in the dry forest to 11% in the moist forest, but the effect in the moist forest could also be attributed to increased vegetation cover. For the top litter (first 1 cm of litter), moisture content averaged ~4% higher than bark, and 3% lower than profile (whole) litter, differences being higher on the wetter sites. Site effects were greatest for profile and least for bark. The percentage of days that the top litter moisture content fell into categories associated with severe fire behaviour and prescribed burning availability was calculated for each site in the different forest types to facilitate discussion of fire management planning.

A semi-mechanistic model for predicting the moisture content of fine litter

The moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter <25.4mm) are particularly important. A variety of indices, as well as empirical and mechanistic models, have been proposed to predict fuel moisture, but these approaches have seldom been validated across temporally extensive datasets, or widely contrasting vegetation types. Here, we describe a semi-mechanistic model, based on the exponential decline of fuel moisture content with atmospheric vapor pressure deficit, that predicts daily minimum fuel moisture content. We calibrated the model at one site in New South Wales, Australia, and validated it at three contrasting ecosystem types in California, USA, where 10-h fuel moisture content was continuously measured every 30min over a year. We found that existing drought indices did not accurately predict fuel moisture, and that empirical and equilibrium models provided biased estimates. The mean absolute error (MAE) of the fuel moisture content predicted by our model across sites and years was 3.7%, which was substantially lower than for other, commonly used models. Our model’s MAE dropped to 2.9% when fuel moisture was below 20%, and to 1.8% when fuel moisture was below 10%. Our model’s MAE was comparable to instrumental MAE (3.1–2.5%), indicating that further improvement may be limited by measurement error. The simplicity, accuracy and precision of our model makes it suitable for a range of applications, such as operational fire management and the prediction of fire risk in vegetation models, without the need for site-specific calibrations.

Evaluating models to predict daily fine fuel moisture content in eucalypt forest

Two models were evaluated for predicting dead fine fuel moisture content on sites in dry and damp eucalypt forests in Tasmania on a daily basis. Models were based on modifications of the Canadian Fine Fuel Moisture Code, and the process-based model of (Matthews, 2006) with and without modifications to better fit the data. All three models predicted well on the dry site, with little to choose between the two modified models. The process-based model performed better on the damp site. Site differences in moisture content could be explained by differences in vegetation and canopy cover. The ability of the models to predict conditions suitable for prescribed burning was tested. The modified Canadian model resulted in more correct predictions of optimal burning conditions than the process-based models. The modified process-based model gave the most predictions that would falsely indicate conditions were suitable for prescribed burning.

Evaluation of a very simple model for predicting the moisture content of eucalypt litter

Operational prediction of wildfire behaviour requires assessment of dead fuel moisture content to an acceptable degree of accuracy. Ideally, the methods of assessment should be simple enough to implement in most operational settings, including those where computational power is a constraining factor. In this short note, we describe a very simple model for estimating dead fine fuel moisture content and compare its predictions with several fuel moisture observations and the predictions of a complex process-based model and two of its simplifications. Remarkably, the very simple model is shown to fit the observational data just as well, if not slightly better, than the more sophisticated models. The result highlights the issues of engineering and parsimony of models for dead fuel moisture content. These issues are briefly discussed.

Predicting the elevated dead fine fuel moisture content in gorse (Ulex europaeus L.) shrub fuels

Methods were developed to predict the moisture content of the elevated dead fine fuel layer in gorse ( Ulex europaeus L.) shrub fuels. This layer has been observed to be important for fire development and spread in these fuels. The accuracy of the Fine Fuel Moisture Code (FFMC) of the Canadian Fire Weather Index System to predict the moisture content of this layer was evaluated. An existing model was used to determine the response time and equilibrium moisture content from field data. This response time was incorporated into a bookkeeping model, combining the FFMC and this response time–equilibrium moisture content model. The FFMC poorly predicted the elevated dead fuel moisture content in gorse fuels, and attempts to improve its accuracy through regression modelling were unsuccessful. The response time of the elevated dead fine fuel layer was very fast (38–77 min) and has important implications for fire danger rating. The bookkeeping approach was the most promising method to predict elevated dead fuel moisture content. A limitation was the inability to model fuel-level meteorology. However, this model warrants further validation and extension to other shrub fuels and could be incorporated into existing fire danger rating systems that can utilize hourly weather data.

Construction of empirical models for predicting Pinus sp. dead fine fuel moisture in NW Spain. I: Response to changes in temperature and relative humidity

A statistical methodology is presented for developing moisture content models from repeated measurements made on non-destructive repeated measurements. Empirical vapour exchange models for dead fine fuels generated in Pinus radiata and P. pinaster stands are developed by using the methodology proposed. Experiments were carried out with five types of fuel particles (surface and aerial fine fuels) of the two species of pine, in Lugo (Galicia, north-west Spain). The samples of each fuel type were collected and placed inside an instrument shelter so that vapour exchange with the atmosphere was the only source of moisture in the fuels. Statistical criteria obtained from the residuals indicated that the fitted models were acceptable. The cross-validation results also confirmed the validity of the fitted models. The model underlined the decisive role played by the time lag in dead fine fuel moisture content variation.