A 32-year species-specific live fuel moisture content dataset for southern California chaparral

TL;DR

This paper creates a 32-year dataset of live fuel moisture content in southern California chaparral to improve wildfire modeling.

Contribution

The study introduces species-specific random forest models to generate a long-term, high-resolution LFMC dataset for chaparral vegetation.

Findings

Species-specific random forest models predicted LFMC with a mean absolute error of 9.68% for chamise.

The dataset captures annual variability, spatial differences, and interspecies variation in LFMC.

Quantile mapping bias correction improved the accuracy of the chamise LFMC predictions.

Abstract

Live fuel moisture content (LFMC) strongly affects the behavior of wildland fire, resulting in its incorporation into wildfire spread models and danger ratings. In this study, over ten thousand LFMC observations are combined with predictor variables from Landsat imagery and the Weather Research and Forecasting model to train species-specific random forest models that predict the LFMC of four fuel types—chamise, old growth chamise, black sage, and bigpod ceanothus. These models are then utilized to create a historical, 32-year long, LFMC dataset in southern California chaparral. Additionally, the high spatial and temporal sampling frequency of chamise allowed for quantile mapping bias correction to be applied. The final chamise output, which is the most robust, has a mean absolute error of 9.68% and an R2 value of 0.76. The LFMC dataset successfully captures the variability in the annual cycle, the spatial heterogeneity, and the interspecies differences, which makes it applicable for better understanding varying fire season characteristics and landscape level flammability.

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