Wildland Fire Modeling and Prediction

RAL scientists and engineers are involved in a long-term project to develop useful methods to predict wildfires and their coincident weather.  To safely manage wildland fires, decision makers need, reliable, accurate, frequently updated, easily accessible, geo-referenced current and predicted weather and fire behavior information. Timely information allows decision makers to better assess current conditions and future trends. Reliable information about the potential for rapid rate of fire spread and extreme fire behaviors is essential to saving life and property. 

Figure 1: Photo of Cold Springs fire near Nederland, Colorado, in July 2016.
Figure 1: Photo of Cold Springs fire near Nederland, Colorado, in July 2016.

Currently, operational wildland fire spread prediction systems are not coupled to numerical weather prediction (NWP) models. These systems often rely on wind fields coarsely resolved in space and time. However, when flows are rapidly evolving due to storm outflows, density currents, frontal passages, and other weather features, or are spatially variable due to complex terrain effects, highly resolved wind fields in time and space are needed to accurately predict fire spread. Furthermore, large wildfires result in significant surface heat fluxes generating strong updrafts and consequently intensifying local winds, which in turn cause more rapid fire-spread rates. Large wildfires also result in significant smoke plumes that can affect radiative transfer, while lofted particulate matter and moisture can result in the formation of pyrocumulus clouds. All these phenomena can be predicted only using coupled models. Therefore, developing an operational coupled wildland fire spread capability is essential for accurate wildland fire spread prediction. To achieve this goal RAL researchers are extending capabilities of the Weather Research and Forecasting (WRF) NWP model based on the Coupled Atmosphere Wildland Fire Environment (CAWFE) model. The new developments initially focused on improvements to the fire spread model and the level-set based fire perimeter tracking algorithm. In addition to fire perimeter, the modeling system, which runs at a resolution of 100 m on its inner domain, produces predictions of a variety of fire and weather variables, including rate-of-spread, flame length, and smoke. These developments are being incorporated into the community WRF-Fire model.

FY2017 Accomplishments

Figure 2:  Predicted (red) and observed (black) fire perimeters associated with the Cold Springs fire, which occurred in July 2016 near Nederland, Colorado.
Figure 2:  Predicted (red) and observed (black) fire perimeters associated with the Cold Springs fire, which occurred in July 2016 near Nederland, Colorado.

During the last year, the system was applied to a variety of fires in the State of Colorado, and used experimentally to provide predictions of weather and fire behavior for a prescribed burn in Oregon in collaboration with the National Forest Service Fire Science Laboratory.  An extensive evaluation was undertaken for predictions of fires in Colorado during the 2016 fire season.  In part because fire suppression activities are not incorporated into the modeling system, the model tends to over-predict the size of most fires.  An example of a predicted fire perimeter vs. an observed fire perimeter is shown in Figure 2 for the Cold Springs Fire, which occurred in July of 2016 near Nederland, Colorado.  Enhancements of the modeling system included improvements to the level-set method for advancing the fire perimeter and implementation of an alternate fuel model – the Scott and Burgan system.  In addition, experiments were undertaken to determine the feasibility of operating the system in a cloud computing environment. 

FY2018 Plans

During the next year, the fire modeling system will be further enhanced and extensively tested.  Fuel moisture and terrain improvements will both be a focus of the team efforts.  In addition, the model will be extensively evaluated, including many more fire cases and assessments of the sensitivity of the system to variations in underlying parameters.  The feasibility of applying the model in a cloud computing environment will be further evaluated as an approach to allow simultaneous simulations for multiple fires.