Simulation and dynamical analysis of wildfire propagation using high resolution computational tools
Team
- Principal Investigator
Adrián Navas Montilla, Universidad de Zaragoza - Collaborators
Pablo Diaz Benito, Universidad de Zaragoza
Cordula Reisch, Technische Universität Braunschweig
Ilhan Özgen-Xian, Technische Universität Braunschweig
Duration
August 2023 to September 2026
Summary
Spain is one of the European countries most affected by forest fires. The vulnerability of the Spanish national territory to this problem is increasing because of climate change. According to the latest report of the Intergovernmental Panel on Climate Change (IPCC), temperatures are expected to rise and the weather conducive to wildfires (hot, dry and windy) is becoming more frequent over the next decades. Consequently, forest fires will increase, both in terms of area burned and probability of occurrence.
Our goal is to develop mathematical and computational tools to analyze and simulate wildfire events, that improve the understanding of the non-linear dynamical processes involved both in the surface propagation and in the atmospheric dynamics of a wildfire.
The outcome of this research will involve the derivation of new simplified mathematical models for wildfires (including surface-atmosphere interaction), the generation and development of cutting-edge open-source simulation tools, as well as some novel insights on the interplay between the main drivers of the fire spread. We will analyze the conditions for the system to be most susceptible to tipping points due to changes in the external ambient conditions, providing valuable insights for wildfire management and mitigation strategies in a framework of climate change. The scientific results of this research include novel mathematical, numerical and computational tools, e.g. the derivation of novel high-order numerical solvers, which can be of interest to a broad public working on computational fluid dynamics, earth-system modelling, applied mathematics and dynamical system analysis.
Funding information
This research is funded by the Ministerio de Ciencia e Innovación, Spain, under project-nr. PID2022-141051NA-I00.
