Publication Date
10-1-2024
Document Type
Article
Publication Title
Fire
Volume
7
Issue
10
DOI
10.3390/fire7100358
Abstract
This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.
Funding Number
DE-AC52-07NA27344
Funding Sponsor
Pacific Gas and Electric Company
Keywords
data assimilation, dead fuel moisture content, reanalysis dataset
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Research Foundation; Meteorology and Climate Science
Recommended Citation
Angel Farguell, Jack Ryan Drucker, Jeffrey Mirocha, Philip Cameron-Smith, and Adam Krzysztof Kochanski. "Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)" Fire (2024). https://doi.org/10.3390/fire7100358
