Improving wildfire simulation accuracy using satellite active fire data for interval reinitialization and rate of spread adjustment

Abstract

Wildfires are reoccurring events that burn millions of hectares over the world every year resulting in ecosystem and economic damage and loss of life, and they are becoming more severe and frequent due to climate changes and global warming. Wildfire simulators are fire behavior prediction tools that can be used to manage fires. However, many factors affect the accuracy of simulations and the results are prone to uncertainties. Rate of spread (ROS) adjustment is a method that improves the accuracy of fire spread models by using data on the location and arrival time of actual fires. However, this task used to be time-intensive, prone to errors, and data in remote fire areas were scarce or inconsistent. The required fire arrival time control data points are obtained through ground or aerial operations. Earth observations (EO) data offer valuable, reliable, easily accessible, and freely available means that can be used to bridge this gap. Satellite active fire data is an EO product that presents the spread of fire near real-time and is an effective way to assess and analyze the accuracy of simulations and improve them. This work develops the innovative method of combining data-driven simulation reinitialization using Visible Infrared Imager Radiometer Suite (VIIRS) active fire data with the Wildfire Analyst's (WFA) automated ROS adjustment algorithm to improve the accuracy of simulations. To avoid accumulated errors in wildfire modeling, which increases drastically when fires last long, this method simulates the fire for 12-h intervals aligned with the VIIRS data production, then adjusts the ROS based on the provided satellite data. Five case studies in Portugal were chosen to include a variety of burn durations and fuel type models to assess this method. This approach significantly improved in reducing error and matching the simulated fire ROS to the actual fire, which also led to more accurate simulations for subsequent burning periods. The mean absolute percentage error (MAPE) in the unadjusted simulations was improved from an average of 71.43 % in 5 case studies to 13.99 %. The mean biased percentage error (MBPE) was decreased from 59.12 % on average for case studies to 7.38 %. The accuracy of satellite data and resolution, overpass interval time, affects of environmental factors on the adjustment, and fuel up ahead of the fire that remain unadjusted are the main limitations of this method. This method can be used as a practical approach in real-life incidents for battling and managing fires to increase the accuracy of operation, resource allocation, and decision-making in real time.