Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level

Southern European countries are severally affected by forest fires every year, which lead to very large environmental damages and great economic investments to recover affected areas. All affected countries invest lots of resources to minimize fire damages. Emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more efficient use of resources in fire fighting. In this case of trans-boundary fires, the European Forest Fire Information System (EFFIS) works as a complementary system to a national and regional systems in the countries, providing information required for international collaboration on forest fires prevention and fighting. In this work, we describe a way of exploiting all the available information in the system to feed a Dynamic Data Driven wildfire behavior prediction model that can deliver results to support operational decision. The model is able to calibrate the unknown parameters based on the real observed data, such as wind condition and fuel moisture using a steering loop. Since this process is computational intensive, we exploit multi-core platforms using a hybrid MPI-OpenMP programming paradigm.