Problem

Existing fire behaviour models lack real-time updating capabilities, hindering accurate prediction and dynamic adaptation to changing conditions during Extreme Wildfire Events. This limitation compromises the effectiveness of emergency response efforts, including resource allocation, evacuation planning, and firefighting strategies. 

Why the Problem exists?

The problem of insufficient real-time updating in fire behaviour modelling arises from several factors. Firstly, traditional fire behaviour models rely on static inputs and assumptions, such as weather data and fuel conditions, often based on historical averages or pre-event predictions. However, these inputs can rapidly change during Extreme Wildfire Events, rendering the initial model outputs inaccurate and inadequate for effective decision-making. Additionally, the lack of real-time data integration, technological constraints, and limited computational power hinder the ability to update and refine fire behaviour models in real time, impeding their applicability in dynamic emergency situations. 

Looking for solutions that completely or partially solve the following:

• Improve accuracy in predicting fire behaviour in EWE. 
• Enable continuous data assimilation to update model inputs dynamically. 
• Reflect changing fire conditions and ensure accurate predictions throughout the event. 
• Enable emergency responders and fire management agencies to interpret and utilise real-time fire behaviour modelling outputs effectively. 
• Improve fire predictions by integrating previous days’ weather information and/or real-time fire front (provided by drones or any other remote sensing method) in existing tools used by firefighters (e.g., PROMETHEUS). 
• Enable projections of different models under different climate change scenarios for the future (for example, vegetation dynamics, configuration and structure of forests, agriculture, and demographic patterns, among others).

Requirements

• Considering the inherent uncertainties and complexities associated with Extreme Wildfire Events 
• Enable the seamless integration of diverse data sources and technologies. 
• Ensure scalability to accommodate varying fire sizes, complexities, and computational resources. 
• Ensure that the real-time fire behaviour modelling solutions are accessible to a wide range of stakeholders. 

Limitation(s)

• Constraints due to data availability, quality, and consistency. 
• Limited real-time data sources or subject to uncertainties. 
• Accuracy and reliability of inputs used for real-time updating. 
• Availability of computational infrastructure. 
• Financial limitations. 

Fire Management Phase(s) Detection & Response

Living Labs

Portugal Living Lab; Catalonia-Spain Living Lab; Nouvelle Aquitaine – France Living Lab.

Voice of the Living Lab(s)