Towards global monitoring and seasonal forecasting of wildfire hazards based on a global hydrological model

Over the last decades, the frequency and magnitude of wildfires increased worldwide, posing a risk not only for people and infrastructure, but also for the environment. To enable preventive and protective measures it is crucial to monitor and forecast wildfire hazards. Existing systems derive wildfire indices from meteorological data or include remote-sensing-based observations, such as soil moisture anomalies and the state of vegetation health. However, their ability to forecast wildfire hazards into the future is very limited.

Therefore, we here suggest utilizing the potential of a global hydrological model to not only monitor, but also provide seasonal forecasts of wildfire hazards globally. To do so, we force the global water resources and use model WaterGAP by meteorological data from ERA5 reanalysis and SEAS5 seasonal ensemble forecasts. Model output, including for example soil moisture anomalies, are combined with meteorological data to derive indicators for wildfire hazards at a global scale. We assess the capability of such indicators to reflect spatio-temporal pattern of wildfire hazards during the year 2018 by performing a regional analysis.

Eventually, derived wildfire hazard indicators can be made available for stakeholders via the operational multi-sectoral global drought monitoring and seasonal forecasting system (OUTLAST) on WMO’s HydroSOS web portal.