Continental-scale prediction of live fuel moisture content using soil moisture information

Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil (SM) one variables known to influence plant water use. The present study analyses LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted 60 selected sites shows strength between varies from site and, general, moderately strong (median ~0.5). However, changes with type also soil profile depth. At all sites, found be leading indicator LFMC. lag location range days months. Based on location-based correlation analysis, we identify 0-35 cm (SM0-35cm) best predictor We developed simple model predict daily LFMC, where it hypothesised variations its annual cycle can predicted deviations SM0-35cm. cycles SM0-35cm are modelled Fourier cosine series. averaged (over sites) obtained for validation period 0.74 when time-lag 14 assumed at locations. When applied nationally 5 km grid, normalised root mean squared error less than 25% general. results highlight modelling strategy used address critical gap forecast spatially temporally continuous regional scales advance operational fire management applications.