Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)
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.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2462828
- Journal Information:
- Fire, Journal Name: Fire Journal Issue: 10 Vol. 7; ISSN 2571-6255; ISSN FBSIB9
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- Switzerland
- Language:
- English
Similar Records
Dead but Not Forgotten: How Extracellular DNA, Moisture, and Space Modulate the Horizontal Transfer of Extracellular Antibiotic Resistance Genes in Soil
Effects of particle size and moisture content on the emanation of Rn from coal ash