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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Research Foundation; Meteorology and Climate Science

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