Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

The prediction of wildfire occurrence is an important component of fire management. We have developed probabilistic daily fire prediction models for a Mediterranean region of Europe (Cyprus) at the mesoscale, based on Poisson regression. The models use only readily available spatio-temporal data, which enables their use in an operational setting. Influencing factors included in the models are weather conditions, land cover and human presence. We found that the influence of weather conditions on fire danger in the studied area can be expressed through the FWI component of the Canadian Forest Fire Weather Index System. However, the prediction ability of FWI alone was limited. A model that additionally includes land cover types, population density and road density was found to provide significantly improved predictions. We validated the probabilistic prediction provided by the model with a test data set and illustrate it with maps for selected days.