Recent work by researchers from U.C. Berkeley and the U.S. Forest Service has produced a spatially-explicit predictive model that can be used to forecast where regeneration of (non-serotinous) conifers is most likely to occur after wildfire. This predictive model combines seed availability with climatic, topographic, and burn severity data to forecast the spatial patterns of post-fire conifer regenerationRead More
From PSW website: "An efficient raster fire-spread model named HFire is introduced. HFire can simulate single-fire events or long-term fire regimes, using the same fire-spread algorithm. This paper describes the HFire algorithm, benchmarks the model using a standard set of tests developed for FARSITE, and compares historical and predicted fire spread perimeters for three southern California fires. HFire is available for download at http://firecenter.berkeley.edu/hfire/."
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This research project assessed the accuracy of three different smoke models (CALPUFF, DAYSMOKE, and CMAQ) at predicting PM2.5 emissions from prescrubed burn and wildfire events in the southeastern United States. Past fire events were modeled, and models were compared to observed data from the actual events.
Visit Firescience.gov to download full text PDFs >