Annual Litter Fuel Load Estimation from Optimality-Derived Litterfall and Decomposition Dynamics

Fine fuel loads ignite easily because they dry rapidly and are therefore an important driver of wildfire occurrence and spread. Accurate modelling of fine fuel load dynamics is crucial not only for current and future wildfire prediction, but also carbon cycling. Current fire-enabled dynamic global vegetation models simulate fine fuel accumulation and decomposition, but using parameters that vary with plant functional types (PFTs). Observationally derived models from satellite products provide good estimates of fine fuel loads but cannot be used to predict how these will change in response to ongoing climate changes. We have combined an eco-evolutionary modelling approach to simulate litterfall with a simple empirical model of decomposition rate to predict fine litter loads. The litterfall model predicts the amount of leaf mass that is shed using leaf economics principles and predictions of optimal leaf area index to predict litterfall for evergreen and broadleaf trees and C₃ and C₄ grasses. The model of decomposition rate uses a generalised linear mixed model to fit a large available dataset of decomposition rate to three variables: C:N ratio representing the litter quality and growing degree days and dry days representing local climate. Both models were independently validated using field observations collated from the literature. We show that the combined model predicts the spatial and temporal variation in fine fuel loads reasonably well when compared to field observations and existing products. This new approach provides a robust framework to derive environmentally driven changes in fine fuel loads in the context of prognostic modelling of wildfires.