Combining precipitation forecasts and vegetation health to predict fire risk at subseasonal timescale in the Amazon

Abstract

Current forecast systems provide reliable deterministic forecasts at the scale of weather (1-7 days) and probabilistic outcomes at the scale of seasons (1-9 months). Only in recent years research has begun transitioning to operational settings to provide numerical predictions for a lead time of 2-4 weeks, a timescale known as subseasonal. The Subseasonal Experiment (SubX) multi-model ensemble mean precipitation forecast (2017-2021) for days 8-14 (week-2 forecast) is used as a covariate in logistic regression models to predict fire risk in the Amazon. In a complementary experiment, a vegetation health index (VHI) is added to SubX precipitation forecasts as a predictor of fires. We find that fire risk can be skillfully assessed in most of the Amazon where fires occur regularly. In some sectors, SubX week-2 precipitation alone is a reliable predictor of fire risk, but the addition of VHI as a predictor results both in (a) a larger portion of the Amazon domain with skillful forecasts and; (b) higher skill in some sectors. By comparing two sectors of the Amazon, we find that the added information provided by VHI is most relevant where the mosaic of land covers includes savannas and grassland, whereas SubX precipitation can be used as the sole predictor for week-2 fire risk forecast in areas where the mosaic of land cover is dominated by forests. Our results illustrate the potential for using numerical model forecasts, at the subseasonal timescale, in combination with satellite remote sensing of vegetation to obtain skillful fire risk forecasts in the Amazon. The operationalization of the methods presented in this study could allow for better preparedness and fire risk reduction in the Amazon with a lead time greater than a week.