Modelling chamise fuel moisture content across California: a machine learning approach
Scott B. Capps A C , Wei Zhuang A , Rui Liu A , Tom Rolinski B and Xin Qu A
+ Author Affiliations
- Author Affiliations
A Atmospheric Data Solutions, LLC, 15275 South Wagon Road, #59, Jackson, WY 83001, USA.
B Southern California Edison, 6000 Irwindale Avenue, Irwindale, CA 91702, USA.
C Corresponding author. Email: scapps@atmosdatasolutions.com
International Journal of Wildland Fire 31(2) 136-148 https://doi.org/10.1071/WF21061
Submitted: 11 May 2021 Accepted: 23 November 2021 Published: 9 December 2021
Journal Compilation © IAWF 2022 Open Access CC BY-NC-ND
Abstract
Live fuel moisture content plays a significant and complex role in wildfire propagation. However, in situ historical and near real-time live fuel moisture measurements are temporally and spatially sparse within wildfire-prone regions. Routine bi-weekly sampling intervals are sometimes exceeded if the weather is unfavourable and/or field personnel are unavailable. To fill these spatial and temporal gaps, we have developed a daily gridded chamise (Adenostoma fasciculatum) live fuel moisture product that can be used, in conjunction with other predictors, to assess current and historical wildfire danger/behaviour. Chamise observations for 52 new- and 41 old-growth California sites from the National Fuel Moisture Database were statistically related to dynamically downscaled high-resolution weather predictors using a random forest machine learning model. This model captures reasonably well the temporal and spatial variability of chamise live fuel moisture content within California. Compared with observations, model-predicted live fuel moisture values have an overall R2, root mean squared error (RMSE) and bias of 0.79, 15.34% and 0.26%, respectively, for new growth and 0.63, 8.81% and 0.11% for old growth. Given the success of the model, we have begun to use it to produce daily forecasts of chamise live fuel moisture content for California utilities.
Keywords: Adenostoma, chamise, live fuel moisture content, new growth, old growth, wildfire, machine learning, California, live fuel moisture, numerical weather modelling, WRF, random forest, LFMC.
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