Live Fuel Moisture Content Research Articles

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.

Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species.

Temperature Is Likely an Important Omission in Interpreting Vegetation Optical Depth

AbstractVegetation optical depth (VOD) satellite microwave retrievals provide significant insights into vegetation water content and responses to hydroclimatic changes. While VOD variations are commonly linked to dry biomass and live fuel moisture content (LFMC), the impact of canopy temperature (Tc) remains overlooked in large‐scale studies. Here, we investigated the impact of Tc on L‐band (1.4 GHz) and X‐band (10.7 GHz) VOD at diurnal and seasonal timescales. Synthetic benchmark VOD was created using realistic fields of Tc, LFMC, and biomass in an electromagnetic model. Perturbation experiments revealed that Tc strongly affects diurnal VOD variations at both L‐band and X‐band. Seasonally, while biomass emerges as the largest contributor to VOD variations in 70% (at X‐band) and 90% (at L‐band) of our study region, Tc and LFMC still play substantial roles. The findings stress the importance of refining retrieval algorithms to distinguish Tc, LFMC, and biomass effects for future VOD applications in ecohydrology.

Exploring the role of plant hydraulics in canopy fuel moisture content: insights from an experimental drought study on Pinus halepensis Mill. and Quercus ilex L.

Key MessageUnderstanding the impact of extreme drought on the canopy fuel moisture content (CFMC) is crucial to anticipate the effects of climate change on wildfires. Our study demonstrates that foliage mortality, caused by leaf embolism, can substantially diminish CFMC during drought on Pinus halepensis Mill. and Quercus ilex L. It emphasizes the importance of considering plant hydraulics to improve wildfire predictions.ContextCanopy fuel moisture content (CFMC), which represents the water-to-dry mass ratio in leaves and fine twigs within the canopy, is a major factor of fire danger across ecosystems worldwide. CFMC results from the fuel moisture content of living foliage (live fuel moisture content, LFMC) and dead foliage (dead fuel moisture content, DFMC) weighted by the proportion of foliage mortality in the canopy (αDead). Understanding how LFMC, αDead, and ultimately CFMC are affected during extreme drought is essential for effective wildfire planning.AimsWe aimed to understand how plant hydraulics affect CFMC for different levels of soil water deficit, examining its influence on both LFMC and αDead.MethodsWe conducted a drought experiment on seedlings of two Mediterranean species: Aleppo pine (Pinus halepensis Mill.) and Holm oak (Quercus ilex L.). Throughout the drought experiment and after rewatering, we monitored CFMC, LFMC, and αDead along with other ecophysiological variables.ResultsLFMC exhibited a significant decrease during drought, and as leaf water potentials reached low levels, αDead increased in both species, thereby reducing CFMC. Distinct water use strategies resulted in species-specific variations in dehydration dynamics.ConclusionOur findings demonstrate that as drought conditions intensify, foliage mortality might become a critical physiological factor driving the decline in CFMC.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

A global fuel characteristic model and dataset for wildfire prediction

Abstract. Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting fire occurrence, and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into earth system models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling, and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (∼9 km, daily), is the first of its kind and will be regularly updated.

Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics

Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics

Modelling of live fuel moisture content in different vegetation scenarios during dry periods using meteorological data and spectral indices

Wildfires are of increasing concern to society, the scientific community and managers. Each year the forest fire season is becoming longer or deseasonalised. One of the main factors in fire behaviour is the live fuel moisture content. Climatological variables such as air humidity, temperature and precipitation directly affect the fuel that is likely to burn. Soil moisture also contributes to fire spread and severity. For these reasons, an analysis of fuel moisture is carried out at 3 sampling sites in the interior of Galicia (Spain), through multiple linear regression models. The 3 plots represent different characteristics for shrub growth: one plot is composed of pine trees, another plot contains a high density of undergrowth and the last plot was burned in 2020 and is regenerating undergrowth. A specific model was generated for each plot from field samples of live fuel and soil field samples collected from September to May, meteorological variables obtained from the stations and Sentinel-2 satellite spectral indices for the field sampling days.The best resulting spectral index for plots without trees was the NDI45, but with a low correlation (R2 < 0.2), while in the plot with pine trees it was the SAVI index (R2 = 0.85). On the other hand, specific model equations were estimated combining these spectral indices with the soil moisture samples and the average temperature of the last 7 days. In all cases the result was considerably improved (R2 between 0.72 and 0.91). These models can be helpful in estimating the probability of fire danger, outside the summer season, with long periods of drought, enabling decision making by managers in each region.

Analyzing Independent LFMC Empirical Models in the Mid-Mediterranean Region of Spain Attending to Vegetation Types and Bioclimatic Zones

This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and vegetation types (trees and shrubs). We also applied a species-specific LFMC model for Rosmarinus officinalis in plots with this dominant species. Spectral indices extracted from Sentinel-2 images and their averages over the study time period in each plot with a spatial resolution of 10 m were used as predictors, together with interpolated meteorological, topographic, and seasonal variables. The models achieved adjusted R2 values ranging between 52.1% and 74.4%. Spatial and temporal variations of LFMC in shrub areas were represented on a map. The results highlight the feasibility of developing satellite-derived LFMC operational empirical models in areas with various vegetation types and taking into account bioclimatic zones. The adjustment of data through GAM (generalized additive models) is also addressed in this study. The different error metrics obtained reflect that these models provided a better fit (most adjusted R2 values ranged between 65% and 74.1%) than the linear models, due to GAMs being more versatile and suitable for addressing complex problems such as LFMC behavior.

Vegetation fuel characterization using machine learning approach over southern Portugal

Understanding the role of fire in the water and carbon cycles is crucial for understanding the Earth's system. Remote sensing is a valuable tool for this purpose as it covers large areas consistently over time. Furthermore, fire propagation models use vegetation parameters to gather information about wildfire conditions, thus reinforcing the need for vegetation dynamics comprehension. Hence, the study aims to improve vegetation representation of fuel load and moisture content, through remote sensing and in-situ data in Southern Portugal. In this study, three above-ground biomass (AGB) datasets and, for live fuel moisture content (LFMC), biweekly samples over two field sites (Herdade da Mitra and Serra de Ossa) were collected during the period between April and October 2022, counting 246 field samples. These samples combined with satellite data information derived from Sentinel-2 (spectral bands and indices) were used in a machine learning approach, the Random Forest (RF) classifier, considering 30 variables to predict the AGB and LFMC. Results showed reasonable agreement between predicted and observed values, with r2 and RSME values of 0.56 (0.69) and 17.56 ton ha−1 (6.47%) for AGB (LFMC). Finally, the RF model generated wall-to-wall dynamic AGB and LFMC maps. This study allowed a product derived from remote sensing data combined with an RF model to produce reliable information about vegetation conditions essential for wildfire risk assessment and atmosphere fire modelling in Southern Portugal.

Exploring low order fractional derivative spectra indices for estimating leaf fuel moisture content across a variety of plant species

ABSTRACT Accurate estimation of fuel moisture content (FMC) is essential for determining the physiological status of vegetation and assessing fire risk. As FMC is not directly or physically linked to light absorption processes, its accurate estimation from hyperspectral data remains a challenge. Inspired by the recent boosting of applying fractional order derivative (FOD) spectra–based indices into leaf mass per area estimation, we investigated the potential of using FOD indices to retrieve FMC across a range of plant species of different vegetation types. We developed fractional indices to retrieve FMC based on a composite dataset consisting of 2891 leaf samples from different species. The evaluation results showed that the newly identified ND(1900, 2095) index based on the 0.8-order fractional derivative spectra had a high prospect, with an overall R2 value of 0.90, to capture a wide range (42–1321%) of FMCs in different plant species. Although the R2 values dropped when applied to each specific vegetation type, the regressions remained highly significant. Our results demonstrated the great potential of FOD-derived indices to estimate a wide range of live FMCs from hyperspectral reflected information.

Specific leaf area and vapour pressure deficit control live fuel moisture content

Abstract The live fuel moisture content (LFMC) is an important precondition for wildfire activity, yet it remains challenging to predict LFMC due to the dynamic interplay between atmospheric and hydrological conditions that determine the plant's access to, and loss of water. We monitored LFMC and a range of plant water‐use traits (predawn and midday leaf water potentials [Ψleaf]), leaf traits (specific leaf area [SLA]), hydrological status (soil water content [SWC] in the shallow layer and full profile) and atmospheric variables (air temperature, vapour pressure deficit [VPD], CO2 concentrations) in a mature eucalypt woodland at the Eucalyptus Free‐Air CO2 Enrichment (EucFACE) facility during a drought. We combined plant traits, hydrological status and atmospheric variables into a biophysical model to predict LFMC dynamics, and compared these with predictions of LFMC based on a satellite model and established relationships between Ψleaf and LFMC from pressure–volume curves. Predawn Ψleaf could be well predicted from changes in SWC, but variation in midday Ψleaf and LFMC were more responsive to atmospheric than hydrological variables. The biophysical model explained up to 89% of variability in LFMC and outperformed established approaches to predict LFMC. SLA was the single most important variable to predict LFMC, followed by VPD, which explained 33% of the remaining variability in LFMC. Our study demonstrates that the co‐variation of plant traits and atmospheric and hydrological conditions affect LFMC during drought, suggesting a new way forward for predicting LFMC by combining biophysical and satellite‐based models of LFMC with seasonal forecasts of meteorological and hydrological variables. Read the free Plain Language Summary for this article on the Journal blog.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

Projecting live fuel moisture content via deep learning

Background Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models). Accurate modelling of LFMC in advance (projection models) would provide wildfire managers with more timely information for assessing and preparing for wildfire risk. Aims The aim of this study was to investigate the potential for deep learning models to predict LFMC across the continental United States 3 months in advance. Method Temporal convolutional networks were trained and evaluated using a large database of field measured samples, as well as year-long time series of MODerate resolution Imaging Spectroradiometer (MODIS) reflectance data and Parameter-elevation Relationships on Independent Slopes Model (PRISM) meteorological data. Key results The proposed 3-month projection model achieved an accuracy (root mean squared error (RMSE) 27.52%; R2 0.47) close to that of the nowcasting model (RMSE 26.52%; R2 0.51). Conclusions The study is the first to predict LFMC with a 3-month lead-time, demonstrating the potential for deep learning models to make reliable LFMC projections. Implications These findings are beneficial for wildfire management and risk assessment, showing proof-of-concept for providing advance information useful to help mitigate the effect of catastrophic wildfires.

Estimation of Live Fuel Moisture Content From Multiple Sources of Remotely Sensed Data

Estimation of Live Fuel Moisture Content From Multiple Sources of Remotely Sensed Data

Remotely sensed dry matter productivity and soil moisture content as potential predictors of arid rangeland wildfires: A case study of Kgalagadi District, Botswana

Fire is a critical tool for managing rangeland ecosystems; however, the increasing wildfire occurrence poses a considerable danger to rangeland ecosystem continuity. Predicting fire occurrence and mapping wildfire danger is critical in managing highly flammable rangelands. To identify potential remotely sensed variables for wildfire prediction, this study employed a Random Forest (RF) classifier using selected environmental variables to assess their possible use for wildfire prediction in Kgalagadi District, Botswana. The study used 107,883 active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor from 2015 to 2021. Datasets of remotely sensed Dry Matter Productivity (DMP), Soil Moisture (SM), Land Surface Temperature (LST), Live Fuel Moisture Content (LFMC), and Dead Fuel Moisture Content (DMFC) were analysed in ArcMap 10.7 Esri©. The RF model developed gave an Out of Bag (OOB) error of 9.91% and an overall accuracy of 90.15% for classifying fires and non-fire points using the test dataset. The results also showed a Kappa coefficient of 0.803, with 88.25% and 91.76% producer and user accuracies, respectively, for classifying fire points. The DMP was the most significant variable with Mean Decrease Accuracy (MDA)= 1,055.20 and Mean Decrease Gini (MDG)= 9.328.62), followed by SM (MDA= 828.39 and MDG= 15,745). The LFMC and DMFC were found to be weak in detecting fires. It is recommended that field studies be carried out in the study area to calibrate these to improve their contribution to accurate fire prediction, as most literature shows that they are significant in fire prediction.

Microclimate and species composition shape the contribution of fuel moisture to positive fire-vegetation feedbacks

Ecosystems with positive fire-vegetation feedbacks are highly sensitive to extreme fire events, which are expected to increase in frequency with climate change. Understanding mechanisms maintaining flammability differences between alternative vegetation states is key to elucidate possible ecosystems shifts related to altered fire regimes. Here we assessed whether microclimatic differences between pyrophobic forests and pyrophilic shrublands in northwestern Patagonia can affect the fuel moisture content and partially explain their contrasting flammability. We measured dead and live fuel moisture content and recorded air temperature and relative humidity during one fire season in 10 sites corresponding to either forest or shrubland physiognomy. To disentangle effects from microclimate and species composition on live/dead fuel moisture content we used similar-composition fuels (wood sticks and single-species live leaves) and fuel mixtures (litter and a mixture of live leaves). We found that forests’ microclimate is cooler and moister than shrublands’, harbouring dead fuels with higher moisture content, irrespective of species composition. This suggests that dead fuel moisture content is contributing to make forests less flammable than shrublands. Live fuels tended to show higher moisture content under the forest microclimate, but only when single-species samples were considered. Contrarily, the species mixtures showed higher moisture content in shrublands, the most desiccant communities, suggesting that live fuel moisture content does not contribute to make shrublands more flammable.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.