A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

Balaguer‐Romano, Rodrigo; Díaz‐Sierra, Rubén; Cáceres, Miquel De; Camprubí, Àngel Cunill; Nolan, Rachael H.; Boer, Matthias M.; Voltas, Jordi; Dios, Víctor Resco de

doi:10.1016/j.agrformet.2022.109022

Cited by 12 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The partial dependence of LFMC on LST was similar to that reported in previous studies in that LST only affected LFMC after a certain temperature threshold [28]. LST is related to VPD [60], which is a variable that can also affect plant water content as a primary driver of evapotranspiration [61]. The importance of LST may thus be related to the fact that VPD significantly acts on leaf moisture content after a certain threshold is reached.…”

Section: Selected Predictorssupporting

confidence: 84%

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

2022

Self Cite

View full text Add to dashboard Cite

Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMCRF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMCRF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin.

show abstract

Section: Selected Predictorssupporting

confidence: 84%

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, the high correlation of SLA with LFMC holds great promise for using widely measured traits to overcome gaps in direct observations of LFMC by either linking repeat sampling of SLA to instantaneous LFMC or by using species-specific and/or community means of SLA to determine maximum LFMC and, thus, the upper limit of landscape-scale variability in LFMC. Furthermore, our model comparison suggests that including plant traits and atmospheric variables has great potential to improve existing models for predicting spatiotemporal variation in LFMC, irrespective of whether these models are based on hydrological variables such as soil moisture (Vinodkumar et al, 2021), meteorological drought indices (Pellizzaro et al, 2007;Ruffault et al, 2018;Viegas et al, 2001), process-based models (Balaguer-Romano et al, 2022) or optical remote sensing (Caccamo et al, 2011;Nolan et al, 2016;Yebra et al, 2013Yebra et al, , 2018.…”

Section: Why Does Specific Leaf Area Exert Such Strong Control On Liv...mentioning

confidence: 99%

“…As community compositions vary spatially and plants respond dynamically to changes in growing conditions, vegetation can introduce significant influence on LFMC and speciesspecific calibrations might be needed to predict variation in LFMC from environmental conditions (Jolly et al, 2014;Nolan et al, 2018Nolan et al, , 2020Pivovaroff et al, 2019;Qi et al, 2016;Scarff et al, 2021). For example, key hydraulic traits such as leaf-saturated moisture content and leaf water potentials (particularly at turgor loss point [TLP]) have been identified to introduce large variation in LFMC between woody species in south-eastern Australia (Nolan et al, 2020(Nolan et al, , 2022Scarff et al, 2021) and Spain (Balaguer-Romano et al, 2022). In contrast, only a few studies have assessed the influence of leaf traits such as specific leaf area (SLA; the ratio of leaf area to leaf mass) on spatial and temporal predictions of LFMC and confirmed its influence on instantaneous LFMC across plant functional types (Brown et al, 2022) or on maximum LFMC at the species level (Nolan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Contrasting with the conclusions by (Griebel et al 2023), our results suggest that the decrease in dead fuel moisture content but not in live plant moisture content of living leaves and vegetation mortality is likely to mediate this relationship (Figure 3). The emergence of plant hydraulic approaches to fuel moisture prediction (Balaguer-Romano et al 2022;Ruffault et al 2022a), which can better represent the mechanisms driving both live and dead fuel variations, will help in interpreting and predicting climate change effects on wildfire danger.…”

Section: -Mechanisms Possibly Explaining Why Wildfire Activity Is Exa...mentioning

confidence: 99%

How much does VPD drive tree water stress and forest disturbances?

Martin‐StPaul

Ruffault

Guillemot

et al. 2023

Preprint

View full text Add to dashboard Cite

Vapor Pressure Deficit (VPD, atmospheric drought) and soil water potential (Ψsoil, soil drought) have both been reported to affect terrestrial plant water stress, plant functions (growth, stomatal conductance, transpiration) and vulnerability to ecosystem disturbances (mortality or vulnerability to wildfires). Which of atmospheric drought or soil drought has the greatest influence on these responses is yet an unresolved question. Using a state-of-the-art soil-plant-atmosphere hydraulic model, we conducted an in-silico experiment where VPD and Ψsoil were manipulated one at a time to quantify the relative importance of atmospheric vs soil drought on most critical plant functions. The model simulates the combined effects of soil drought and atmospheric drought on plant water potential (ΨPlant), a physiologically meaningful metric of plant water status driving plant turgor, stomatal conductance, hydraulic conductance or water content, and thus mortality and fire risks. Contrary to expectations, we showed that VPD had a weaker effect than Ψsoil on tree water stress and forest disturbances risk (i.e leaf moisture content). While physiological responses associated with low water stress such as stomatal closure or turgor loss could be driven by both VPD or soil drought, consequences of extreme water stress such as hydraulic failure, leaf desiccation and vulnerability to wildfires were almost exclusively driven by low Ψsoil. Our results therefore suggest that most plant functions are affected by VPD through its cumulative effect on Ψsoil via increased plant transpiration, rather than through a direct instantaneous effect on plant water potential. We argue that plant hydraulics provide a strong foundation for predicting tree and terrestrial ecosystem responses to climate changes and propose a list of explanations and testable hypotheses to reconcile plant hydraulic theory and observations of soil and atmospheric drought effects on plant functions.

show abstract

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

Cited by 12 publications

References 44 publications

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

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

How much does VPD drive tree water stress and forest disturbances?

Contact Info

Product

Resources

About