A High-Resolution Model for the Assessment and Forecasting of Wildfire Susceptibility

During the last decade, wildfires in the Aburra Valley watershed, located in northwestern Colombia, have caused significant forest and ecosystem losses, health issues in nearby communities associated with aerosols from biomass burning, and increases in the CO2 emissions. Human activities, along with weather variability, modulate the occurrence of forest fires during the dry seasons, and the efforts to reduce them have shown limited success, highlighting the need for the development of holistic prevention strategies. We implemented a general strategy involving real-time monitoring, modeling, and warning based on a distributed Bayesian model coupled with a distributed hydrological model and a regional weather model (WRF) to estimate wildfire susceptibility in the basin. The model operates with a spatial resolution of 60m and an hourly temporal resolution. The model uses static and time-dependent (dynamic) information. Static variables include land use, urban-rural fringe area, historical fire occurrence, an...