How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems

摘要

Live Fuel Moisture Content (LFMC) is a critical variable affecting fire ignition, behavior and severity in many ecosystems. Although the use of meteorological drought indices as proxies for LFMC is a straightforward and widespread approach, it is largely unknown whether it can provide reliable estimates of LFMC, either for local or spatial applications. We address this issue by evaluating the capacity of drought indices to predict LFMC quantitative variations and critical values. LFMC observations used for reference were measured on six different Mediterranean shrub species for 15 years in 20 different sites in Southern France. Six drought indices were evaluated: the Duff Moisture Code (DMC) and Drought Code (DC) of the Canadian Forest Fire Weather Index System, the Keetch-Byram Drought Index (KBDI), the Nesterov Index (NI) and the Relative Water Content (RWC) of the soil derived from a forest water balance model for low (80 mm) and high (160 mm) field capacities. The species were classified in two groups according to their seasonal variability: high and low responding species. We found large differences in the capacity of drought indices to predict LFMC, with indices that simulate long-term drought dynamics (DC, RWC and KBDI) generally performing better than others (NI and DMC). Once calibrated at stand scale, drought indices showed a good potential for predicting LFMC of high responding species, although large variations between sites were observed. In contrast, spatial predictability was limited with a RMSE and 11 on the order of 20% and 0.3, respectively (for high responding species). Our results suggest that drought indices should therefore be used with caution for spatial applications in wildfire research and operational fire management. Because they can explicitly consider environmental (soil, climate) and biological (species traits related to dehydration) factors, mechanistic indices have a great potential to improve LFMC predictions.