Search

Wildfires are destructive fires over the wildland that can wipe out large areas of vegetation and infrastructure. Such fires are hard to control and manage as they can change directions almost instantly, driven by changing environmental conditions. Effective response to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. The WIFIRE project builds an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction, and visualization of wildfire behavior. One goal of WIFIRE is to provide the tools to predict a more accurate rate of a spreading wildfire. To this end, WIFIRE has developed interfaces for ingesting and visualizing high-density sensor networks to improve fire and weather predictions, and has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feeds for wildfire research and response. This paper presents WIFIRE’s Firemap web platform to make these geospatial data and products accessible. Through a web browser, Firemap enables geospatial information visualization and a unified access to geospatial workflows using Kepler. Using GIS capabilities combined with scalable big data integration and processing, Firemap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. [ABSTRACT FROM AUTHOR]

This paper shows the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter. [ABSTRACT FROM AUTHOR]