A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content
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Date
2022
Authors
Balaguer-Romano, Rodrigo
Díaz-Sierra, Rubén
De Cáceres, Miquel
Cunill-Camprubí, Àngel
Nolan, Rachael H.
Boer, Matthias M.
Voltas Velasco, Jordi
Resco de Dios, Víctor
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Abstract
Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the
availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or
inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into
species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates.
Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from
different functional types by adjusting a soil water balance model which estimates predawn leaf water potential
(Ψpd). Our overarching goal was to balance the trade-off between biological realism, which enhances model
applicability, and parameterization complexity, which may limit its value within operational settings. After
calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across
peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of
fit for our model (R2 = 0.5) was better than that obtained by an existing models based on drought indices (R2 =
0.3) or spectral vegetation indices (R2 = 0.1). We observed the best predictive performance for seeding shrubs
(R2 = 0.6) followed by trees (R2 = 0.5) and resprouting shrubs (R2 = 0.4). Through its relatively simple
parameterization, the approach developed here may pave the way for a new generation of process-based models
that can be used for operational purposes within fire risk mitigation scenarios.
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Agricultural and forest meteorology, 2022, vol. 323, núm. 109022, p. 1-8