Linking tree hydrodynamic traits, ecohydrological processes, and wildfire propagation: Exploratory modelling for theory development

• Applicant: Professor Dr. Ilhan Özgen, since 3/2026
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 560668181

Under changing climate, wildfires have become a significant threat to ecosystems worldwide. The prediction of wildfire propagation is hindered by our current limited understanding of it. Here, we propose a model-based study of the influence of tree hydrodynamic traits on the propagation of wildfire. Tree hydrodynamic traits are defined as ecological traits that affect the hydraulic behaviour of trees, for example, plant conductance, stomatal conductance, and plant capacitance. We know that these traits are linked to plant hydraulics, and thus, the fuel moisture in the region, which in turn influences the wildfire propagation. Understanding how these traits interact with other environmental factors to affect the way wildfire propagates would enable us to improve the representation of biological effects in predictive wildfire propagation models. In order to achieve this, we propose to build a model chain that uses the output of a trait-based plant hydraulics model as input to a mechanistic wildfire propagation model that we have developed in the past year. Using this modelling tool, we will run exploratory simulations of ecohydrology and wildfire propagation in an idealised temperate forest site. We will explore the behaviour of different tree species that are characterised by different hydrodynamic traits under varying environmental conditions. This will allow us to understand a hierarchy of controls on wildfire propagation. Using insights obtained from these simulations, we will derive a minimal trait-based wildfire model that can link hydrodynamic traits to burned area. The aim of this minimal model is testing the hypothesis that burned area can indeed be predicted on the basis of hydrodynamic traits. We will test our model chain and our minimal model on a real world wildfire event that occurred in Nonaspe, Aragon, Spain, in the summer of 2022. Differences between both modelling approaches will help us to either support or falsify our assumption that hydrodynamic traits can be used to predict wildfire propagation.

• DFG Programme: Research Grants
• International Connection: Spain, USA
• Co-Investigators: Dr.-Ing. Matthias Beyer; Dr. Gökben Demir; Professor Dr. Dirk Langemann
• Cooperation Partners: Dr. Octavia Crompton; Dr. Miquel de Cáceres Ainsa; Dr. Adrián Navas Montilla
• Ehemalige Antragstellerin: Dr. Cordula Reisch, until 3/2026