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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. [ABSTRACT FROM AUTHOR]

With more frequent and intense fires expected under future climate conditions, it is important to understand the mechanisms that control flammability in Australian forests. We followed a systematic review approach to determine which physical traits make eucalypts leaves more or less flammable. Specifically, we reviewed 20 studies that covered 35 eucalypt species across five countries and found that leaf water content, leaf area (LA), and specific leaf area (SLA) are the main drivers of leaf flammability. These traits are easy and straightforward to measure, while more laborious traits (e.g., volatile organic compounds and structural carbohydrates) are seldom measured and reported. Leaf flammability also varies with species, and, while the biochemistry plays a role in how leaves burn, it plays a minor role in fire behaviour at landscape scales. This review highlights the range of different protocols used to measure flammability and leaf water content, warranting caution when comparing traits and results between studies. As a result, we propose a standardised protocol to measure leaf water content and advocate for long-term measurements of leaf traits and flammability. This study not only contributes to the understanding of how and why eucalypt leaves burn but also encourages research into the relative importance of traits in influencing flammability and provides a guide for selecting traits that can be monitored using satellite images to inform fire management policies and strategies. [ABSTRACT FROM AUTHOR]

Live fuel moisture content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal inference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology. We show that in California when LFMC is below a critical flammability threshold, the likelihood of fires is 1.8 times as high statewide (2.25% vs. 1.27%) and 2.5 times as high in shrubs, compared to when LFMC is greater than the threshold. This risk ratio is >2 times when LFMC is 10% less than the threshold. Between 2016 and 2021, the risk ratio was highest in 2020 (2.3 times), potentially contributing to the record‐breaking wildfire activity in 2020. Our estimates can inform several wildfire prediction and management applications, including wildfire suppression, prescribed burn planning, and public safety power shutoff implementation. Plain Language Summary: Wildfires are complex phenomena and can occur under a range of conditions, making it difficult to determine how different drivers affect wildfire occurrence. For example, dense contiguous vegetation, dry litter, dry vegetation, high winds, and warm air all contribute to high fire likelihood. Under such co‐occurring conditions, how can we determine each driver's effect on fire likelihood? Here, we present a framework to attribute an individual driver's effect on fire occurrence, while controlling for other correlated factors that might provide a false signal. We consider lightning‐based ignitions, assuming they are randomly distributed, and compare the fire outcomes for different vegetation dryness levels, all else held constant. We test our framework in California and find that when vegetation is dryer than the critical flammability threshold, wildfire likelihood increases by almost a factor of two. These results help clarify the uncertainty in the effect of vegetation dryness on fire occurrence, and may also help improve wildfire preparedness, management, and response strategies. Key Points: We provide a causal inference framework to understand the contribution of natural drivers to wildfire occurrenceWhen live fuels are extremely flammable, lightning strikes are about twice as likely to cause wildfiresThe increase in fire likelihood is highest in shrubs and lowest in grass [ABSTRACT FROM AUTHOR]

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk. [ABSTRACT FROM AUTHOR]

Many fire management agencies aim to detect and suppress all ignitions within their jurisdiction and may benefit from understanding the causes of year-to-year variation. Ignition variation is likely to be associated with climatically driven changes in fuel quantity or moisture in landscapes where ignition sources vary little over similar temporal scales. We assessed how monthly ignitions varied in south-eastern Australia in response to climate anomalies over approximately nine years. Once accounting for seasonal effects, ignitions increased with temperature and vapour pressure deficit anomalies and decreased with precipitation anomalies. These findings indicate that climatic conditions play a role in limiting ignition frequency via effects on fuel moisture. Our models predicted that warmer and drier conditions during our study caused considerable increases in ignition frequency compared to the long-term average reference period (1961–1990). Like many fire-prone landscapes around the world, our study area is projected to become hotter and drier because of climate change. Fire management agencies will need to improve fire detection and suppression capabilities under a future climate. [ABSTRACT FROM AUTHOR]

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%; R 2 0.47) close to that of the nowcasting model (RMSE 26.52%; R 2 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. We have proposed a deep learning model that makes advance predictions of fuel (vegetation) moisture content, a major driver of wildfire ignition and propagation. This proof-of-concept model produces accurate predictions with a 3-month lead time, demonstrating a potentially useful addition to wildfire management tools, facilitating early warning of high wildfire risk. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic. [ABSTRACT FROM AUTHOR]