Pyro-Ecophysiology: Shifting the Paradigm of Live Wildland Fuel Research

Jolly, W. Matt; Johnson, Daniel M.

doi:10.3390/fire1010008

Cited by 70 publications

(63 citation statements)

References 29 publications

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“…In recent years, increasing attention has been paid to the understanding of the physiological drivers of live fuel moisture content, opening a field that has been called the "pyro-ecophysiology" by (Jolly and Johnson 2018), which should lead to the development of processbased approaches to predict fuel moisture content that should be valid under drought. This idea is supported by empirical findings showing the close relationships between LFMC and drought indices derived from functional approaches (Nolan et al 2018;Pivovaroff et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Although water and carbon cycles in plants both dictate the variation of LFMC over the year (Jolly et al 2014;Jolly and Johnson 2018), the mechanisms associated to the water cycle are the most influential drivers of live fuel moisture content dynamics over the course of a seasonal drought, whereas carbon cycles processes are particularly crucial during organ growth and development. Carbon-cycle processes influence LFMC through the phenological and physiological mechanisms that drive dry matter accumulation during growth, including photosynthesis, respiration and carbon allocation.…”

Section: Introductionmentioning

confidence: 99%

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Modelling live fuel moisture content at leaf and canopy scale under extreme drought using a lumped plant hydraulic model

Martin‐StPaul¹,

Ruffault²,

Blackmann³

et al. 2020

Preprint

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Ø Water content in living vegetation (or live fuel moisture content, LFMC), is increasingly recognized as a key factor linked to vegetation mortality and wildfire ignition and spread. Most often, empirical indices are used as surrogates for direct LFMC measurements. Ø In this paper, we explore the functional and ecophysiological drivers of LFMC during drought at the leaf and canopy scale using the SurEau-Ecos model, and a three years dataset of leaf and canopy scale measurements on a mature Quercus ilex forest, including an extreme drought. The model is based on forest hydrology and plant hydraulics and allows to simulate temporal variations of water potential and content at a daily time step. At leaf level, it simulates the relationship between water potential and water content by separating the apoplasm and the symplasm. Symplasm water content is modeled using the pressure volume curve theory, and apoplasm water content is modelled using the xylem vulnerability to cavitation. Fuel moisture content was upscaled to the canopy level by accounting for foliage mortality estimated from drought induced cavitation. Ø The model was parameterized either with site-measured traits or using a calibration procedure, and compared with water potential and LFMC measured at leaf level, and NDVI variation measured at canopy level and taken as a surrogate for foliage mortality. Ø At leaf level, LFMC prediction using measured hydraulic traits could be improved by considering year-to-year osmotic adjustments. At canopy level, foliage mortality due to drought induced cavitation was a key driver of LFMC decline during the most extreme drought. Ø A sensitivity analysis showed that parameters driving soil water balance (leaf area index, soil water capacity, and regulation of transpiration) and parameters determining pressure volume curves are key traits driving LFMC dynamics at leaf level. At the canopy level, parameters that drives hydraulic failure were the most sensitive and included, both soil water balance parameters and hydraulic traits (the leaf vulnerability to cavitation) were the main drivers of LFMC decline during extreme drought. Ø We also showed that under normal historic weather conditions, most variation of LFMC are linked to reversible symplasm dehydration, however under future, hotter and dryer conditions, most variations are due to the decline canopy of LFMC driven by foliage mortality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling live fuel moisture content at leaf and canopy scale under extreme drought using a lumped plant hydraulic model

Martin‐StPaul¹,

Ruffault²,

Blackmann³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Moisture variation within complex matrices of live and dead fuel particles across both temporal and spatial scales governs fire spread (Nelson 2001;Loudermilk et al 2018). Similarly, seasonal variation in phenology of living plant material leads to variation in plant flammability (Jolly and Johnson 2018) and rate of particulate matter emitted per biomass consumed (Robertson et al 2014). Fuel moisture patterns at fine scales further complicate prescribed fire operations in ways not planned for on wildfire operations due to the sequence of ignitions and prescription constraints.…”

Section: Fuels: Toward 3-and 4-dimensional Characterizationmentioning

confidence: 99%

Prescribed fire science: the case for a refined research agenda

et al. 2020

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The realm of wildland fire science encompasses both wild and prescribed fires. Most of the research in the broader field has focused on wildfires, however, despite the prevalence of prescribed fires and demonstrated need for science to guide its application. We argue that prescribed fire science requires a fundamentally different approach to connecting related disciplines of physical, natural, and social sciences. We also posit that research aimed at questions relevant to prescribed fire will improve overall wildland fire science and stimulate the development of useful knowledge about managed wildfires. Because prescribed fires are increasingly promoted and applied for wildfire management and are intentionally ignited to meet policy and land manager objectives, a broader research agenda incorporating the unique features of prescribed fire is needed. We highlight the primary differences between prescribed fire science and wildfire science in the study of fuels, fire behavior, fire weather, fire effects, and fire social science. Wildfires managed for resource benefits ("managed wildfires") offer a bridge for linking these science frameworks. A recognition of the unique science needs related to prescribed fire will be key to addressing the global challenge of managing wildland fire for long-term sustainability of natural resources.Keywords: fire behavior, fire effects, fire weather, fireline interactions, fuels characterization, post-fire tree mortality, prescribed burning, wildland fire research Resumen El ámbito de la ciencia del fuego comprende tanto a los incendios de vegetación no controlados como a las quemas prescriptas. La mayoría de las investigaciones en este amplio campo se han enfocado en los incendios de vegetación, a pesar de la prevalencia de las quemas prescriptas y la probada necesidad de que la ciencia guíe su aplicación. Argüimos que la ciencia de las quemas prescriptas requiere de un enfoque fundamentalmente diferente para conectarse con las disciplinas relacionadas de la ciencias físicas, sociales y naturales. También postulamos que la investigación enfocada a preguntas relevantes para las quemas prescriptas va a mejorar la ciencia de fuegos de vegetación en general y estimular el desarrollo del conocimiento útil sobre el manejo de fuegos de vegetación. Dado que las quemas prescriptas son propuestas y aplicadas de manera incremental para para el manejo de fuegos (Continued on next page) de vegetación, y que son intencionalmente iniciadas para lograr metas y objetivos de manejo de tierras, una agenda más amplia de investigación, incorporando aspectos únicos de las quemas prescriptas, se hace necesaria. Ilustramos las diferencias primarias entre la ciencia de las quemas prescriptas y la de la ciencia de fuegos naturales de vegetación en lo que hace al estudio de los combustibles, el comportamiento del fuego, la meteorología, los efectos del fuego, y las ciencias sociales relacionadas con el fuego. Los incendios manejados para beneficio de los recursos ("fuegos manejados") ofrecen un puente para li...

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“…Pimont et al 2019). Fire danger rating could be improved by the use of more mechanistic models for fuel moisture (Jolly andJohnson 2018, Martin StPaul et al 2020 in prep). Finally, the most important limitation of the FWI was probably suggested by the failure of our model to simulate observed burned area in 2003.…”

Section: Factors Of Fire Activitymentioning

confidence: 99%

Prediction of regional wildfire activity with a probabilistic Bayesian framework

Pimont

Fargeon

Opitz

et al. 2020

Preprint

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Modelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events. • Here, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria. • A set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires (≥1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy.• Regarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves. • Our study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change.summer following a prolonged drought (Trigo et al. 2005). We finally discuss the strength and weaknesses of the current model and its potential applications for wildfire-related research avenues and the improvement of operational fire suppression and management. METHODS Data and site descriptionStudy site and fire activity. The study area consists of 15 NUTS3-level French administrative units located in southeastern France (Fig. 1A, 75,560 km 2 ), which concentrate the vast majority of burnt area during the summer season in France. The climate of this area is mostly Mediterranean, characterized by cool and moist winters and hot and dry summers, but exhibits strong variations with orography,...

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Pyro-Ecophysiology: Shifting the Paradigm of Live Wildland Fuel Research

Cited by 70 publications

References 29 publications

Modelling live fuel moisture content at leaf and canopy scale under extreme drought using a lumped plant hydraulic model

Modelling live fuel moisture content at leaf and canopy scale under extreme drought using a lumped plant hydraulic model

Prescribed fire science: the case for a refined research agenda

Prediction of regional wildfire activity with a probabilistic Bayesian framework

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