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Data-Driven Wildland Fire Spread Modeling

Cong Zhang (Ph.D.), Maria Theodori (M.S.)

Advisor: 
Sponsor: 
NSF (OCI), UMD/ConE
Collaborators: M. Rochoux (Environnement Canada), S. Ricci (CERFACS, France),
I. Altintas, J. Block, R. de Callafon (UCSD); E. Ellicott, M.J. Gollner, K. Ide (UMD)

 

The challenges found on the route to developing quantitative fire models are two-fold. First, there is the classical modeling challenge associated with providing accurate mathematical representations of the multi-physics phenomena that determine the fire dynamics. Second, there is the less common data challenge associated with providing accurate estimates of the input parameters required by the models. Current fire models are limited in scope because of the large uncertainties associated with the accuracy of physical models, because also of the large uncertainties associated with many of the input parameters to the fire problem. A possible approach to overcome the limitations found in numerical simulations of fires is data assimilation (DA): DA consists in combining computer simulation tools with sensor observations, or more precisely in using observations to correct and optimize computer model predictions. While still original in the field of fire and combustion, DA is an established approach in several scientific areas, for instance in the field of numerical weather predictions.

The objective of this project is to develop a prototype data-driven wildland fire simulation capability capable of forecasting the fire spread dynamics. The prototype simulator features the following main components: a level-set-based fire propagation solver that adopts a regional scale viewpoint, treats wildland fires as propagating fronts, and uses a description of the local rate of spread (ROS) of the fire as a function of vegetation, topographical and meteorological properties based on Rothermel's model; a series of observations of the fire front position; and a data assimilation algorithm based on a Kalman filter (the Ensemble Kalman Filter – EnKF). The DA algorithm features a choice between a parameter estimation approach in which the estimation targets (the control variables) are the input parameters of the ROS model and a state estimation approach in which the estimation targets are the spatial coordinates of the discretized fire front. The prototype data-driven wildland fire spread simulator has been previously evaluated in a series of verification tests using synthetically-generated observations; the tests include representative cases with spatially-varying vegetation properties and temporally-varying wind conditions. The prototype simulator has also been evaluated in a preliminary validation study corresponding to a small-scale controlled grassland fire experiment. Early results have been very encouraging and demonstrate that data-driven simulations are capable of successfully correcting inaccurate predictions of the fire front position and of subsequently providing an optimized forecast of the wildland fire behavior. The prototype simulator is currently being evaluated in a series of validation studies corresponding to both field-scale controlled fire experiments and accidental wildfires.

Test simulation of wildland fire propagation (regional scale). Mean forecast (blue line): mean of statistical ensemble of predicted fire front position without data assimilation. Mean analysis (red line): mean of statistical ensemble of predicted fire front position with data assimilation. The good agreement between the mean analysis and the observations demonstrate the performance of data assimilation.

To learn more:

Rochoux, M.C., Emery, C., Ricci, S., Cuenot, B. and Trouvé, A. (2015) “Towards predictive data-driven simulations of wildfire spread. Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread,” Nat. Hazards Earth Syst. Sci., 15:1721-2015.

Rochoux, M.C., Ricci, S., Lucor, D., Cuenot, B. and Trouvé, A. (2014) “Towards predictive data-driven simulations of wildfire spread. Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation,” Nat. Hazards Earth Syst. Sci., 14:2951-2973.

Rochoux, M.C., Emery, C., Ricci, S., Cuenot, B. and Trouvé, A. (2014) “Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position”, Fire Safety Science – Proc. Eleventh International Symposium, International Association for Fire Safety Science, 1443-1456.

Rochoux, M.C., Delmotte, B., Cuenot, B., Ricci*, S. and Trouvé, A. (2013) “Regional-scale simulations of wildland fire spread informed by real-time flame front observations,” Proc. Combust. Inst., 34:2641-2647.

cong and maria

Cong Zhang (left) is a Doctorate Student in the Department of Mechanical Engineering. For further information about his research, Cong can be contacted at: cong0129@umd.edu.

Maria Theodori (right) is a Master of Science Student in the Department of Fire Protection Engineering. For further information about her research, Maria can be contacted at: mft@terpmail.umd.edu .