Wildland Fire Modeling and Prediction

Background

RAL is developing a useful suite of methods to predict wildfires and their coincident weather. This is a long-term project engaging the expertise of several scientists and engineers across the organization.  Decision makers who deploy resources and strategize effective targets for fire fighting need reliable, accurate, frequently updated, readily 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 rate of fire spread and extreme fire behaviors is essential to saving lives 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.

Current 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 begin 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 essential to accurately predict fire spread rate and direction. 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 produce significant smoke plumes that can affect radiative transfer, while lofted particulate matter and moisture can form pyrocumulus clouds. All these phenomena can be predicted 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. In addition to gauging 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.

FY2018 Accomplishments

During the last year, the wildland fire prediction model was enhanced by updating the original Anderson (1982) fuel model (consisting of 13 fuel categories) with the Scott and Burgan (2005) fuel model comprising 40 fuel categories (Muñoz Esparza et al. 2018). Better representation of complex terrain was achieved by applying a local terrain-smoothing approach. We also improved the smoke-dispersion prediction capability so the smoke emitted on the inner wildland fire resolving domain is also advected and dispersed on the outer domain covering a larger area by two orders of magnitude. The wildland fire-prediction system was ported to a cloud-computing platform, which allows multiple simultaneous wildland fire simulations, greater flexibility, and potentially faster model execution. The performance of the updated prediction system was assessed using observations from wildland fires in the State of Colorado during 2016 and 2017 fire seasons. The model currently tends to overpredict the size of most fires. To determine which parameters in the rate-of-spread model that may be responsible for overprediction, we carried out an extensive sensitivity study. The sensitivity study focused on the representation of terrain and fuel moisture content. The sensitivity study demonstrated significant model sensitivity to the exact value of fuel moisture content.

Figure 2. Numerical simulation of the Cold Springs fire near Nederland, Colorado, in July 2016 using WRF-Fire coupled atmosphere – wildland fire behavior model.
Figure 2. Numerical simulation of the Cold Springs fire near Nederland, Colorado in July 2016 using WRF-Fire coupled atmosphere – wildland fire behavior model.

Because fuel moisture content is one of the significant parameters controlling the spread rate of wildland fires, having accurate estimates of the fuel moisture content are critical to firefighting managers. However, estimates of the fuel moisture content are currently based on spatially sparse observations. Furthermore, live fuel moisture is sampled infrequently because it has to be done manually. To address this lack, we are developing a real-time system for high-resolution, gridded fuel moisture-content data over conterminous United States (CONUS). Satellite reflectance data from MODIS Terra and Aqua platforms are used together with surface observations in machine-learning models to provide estimates of the dead and live fuel moisture content.

FY2019 Plans

Throughout next year, the wildland fire modeling system will be augmented by adding a spot-fire prediction capability. Spotting often causes the rapid rate of wildland fire spread (Jimenez et al. 2018). Projections about the likelihood of spotting can therefore be critical for more effective wildland fire management. The spotting likelihood will be computed based on the likelihood of firebrand generation, transport, burnout, and deposition upon fuels that can be ignited. Extensive evaluation of the system performance will continue based on wildland fires observed in Colorado. 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.

The real-time system for fuel moisture content estimation will be developed, implemented, and tested using observations from recent fire seasons in Colorado.

References

Jimenez, P. A., D. Muñoz-Esparza, and B. Kosović, 2018: A high resolution coupled fire-atmosphere forecasting system to minimize the impacts of wildland fires: Applications to the Chimney Tops II wildland event. Accepted for publication in Atmosphere.

Muñoz-Esparza, D., B. Kosović, P. A. Jiménez, and J. L. Coen, 2018: An accurate fire-spread algorithm in the Weather Research and Forecasting model using the level-set method. Journal of Advances in Modeling Earth Systems, 10, 908-926. doi: 10.1002/2017MS001108.