Data-driven wildfire spread modeling - Extension to cases with complex terrain topography

1. Data-driven wildﬁre spread modeling! A comparative study of parameter estimation and state estimation approaches! M. Rochoux, C. Emery, S. Ricci, B. Cuenot, A. Trouvé Department of Fire Protection Engineering! University of Maryland, College Park (USA).! CERFACS, Centre Européen de Recherche et Formation Avancée en Calcul Scientiﬁque, Toulouse (France).! VII International Conference on Forest Fire Research! 17 November 2014

2. PROBLEM STATEMENT! Data-driven wildfire spread modeling! Slide 2 §  Current fire modeling capabilities are far from being predictive •  Modeling challenge: Large uncertainties in physical/numerical modeling of complex multi-physics processes ➙ Compartment/wildland fires: turbulence, combustion, soot, thermal radiation, convective heat transfer, solid fuel sources Favone wildfire (30 ha) simulated by ForeFire, with or without coupling with a 3-D atmospheric model (CNRS, J-B. Filippi) ForeFire! Coupled! Observed!

3. PROBLEM STATEMENT! Data-driven wildfire spread modeling! Slide 3 §  Current fire modeling capabilities are far from being predictive •  Modeling challenge: Large uncertainties in physical/numerical modeling of complex multi-physics processes ➙ Compartment/wildland fires: turbulence, combustion, soot, thermal radiation, convective heat transfer, solid fuel sources •  Data challenge: Large uncertainties in input parameters used in fire models ➙ Compartment fires: solid fuel sources, wall properties ➙ Wildland fires: vegetation properties, meteorological conditions Input parameters of the rate of fire spread model

4. PROBLEM STATEMENT! Data-driven wildfire spread modeling! Slide 4 §  Develop predictive fire modeling capabilities •  Uncertainty estimation: Reduce fire modeling uncertainties by integrating fire modeling and fire sensing technologies ➙ Take advantage of recent progress made in sensor technology and ubiquity of sensor networks •  Data assimilation: Well-established approach in many areas of science ➙ Numerical weather predictions ➙ Land surface predictions •  Application to: detection of incipient fires; post-event forensic investigations; real-time emergency response management ➙ ex. Real-time wildfire spread monitoring

5. ① ➀ Improved forward model! ② ➁ Data-driven wildﬁre spread prototype ! ③ ➂ Data assimilation experiments! OUTLINE! Data-driven wildﬁre spread modeling! Slide 5

6. ① ➀ Improved forward model! ②  ➙ extension to complex terrain topography ③ ➁ Data-driven wildﬁre spread prototype ! ④ ➂ Data assimilation experiments! OUTLINE! Data-driven wildﬁre spread modeling! Slide 6

7. PART I ● ● ●! Improved forward model! Slide 7 §  Regional-scale modeling (10 m < L < 10 km)! •  Surface fires ≠ crown fires, ≠ ground fires, ≠ firebrands •  Front topology: Interface between burning/non-burning areas ➙ Semi-empirical rate of spread (ROS) model due to Rothermel (1972) ➙ Front-tracking solver

8. PART I ● ● ●! Improved forward model! Slide 8 §  Model description of the rate of spread (ROS)! •  Rothermel-based formulation ! ROS1D = f ([δf , !!mf ,ρf ,Σf ,M f ],  Nslope,  uwind ) = ROS0 (1+Φw * +Φsl * ) ➙ correction coefﬁcients for wind and slope effects

9. PART I ● ● ●! Improved forward model! Slide 9 §  Model description of the rate of spread (ROS)! •  Rothermel-based formulation •  Extension to 2-D propagation over complex terrain Horizontal plane! East! Vertical direction ! αa! αsl : slope angle! North! αsl! αa : aspect angle! ROS1D = f ([δf , !!mf ,ρf ,Σf ,M f ],  Nslope,  uwind ) = ROS0 (1+Φw * +Φsl * ) ➙ correction coefﬁcients for wind and slope effects

10. PART I ● ● ●! Improved forward model! Slide 10 §  Model description of the rate of spread (ROS)! •  Rothermel-based formulation •  Extension to 2-D propagation over complex terrain Horizontal plane! East! Vertical direction ! αa! αsl : slope angle! North! αsl! αa : aspect angle! Wind! αw! αw : wind direction! ROS1D = f ([δf , !!mf ,ρf ,Σf ,M f ],  Nslope,  uwind ) = ROS0 (1+Φw * +Φsl * ) ➙ correction coefﬁcients for wind and slope effects

11. PART I ● ● ●! Improved forward model! Slide 11 §  Model description of the rate of spread (ROS)! •  Rothermel-based formulation •  Extension to 2-D propagation over complex terrain ROS1D = f ([δf , !!mf ,ρf ,Σf ,M f ],  Nslope,  uwind ) = ROS0 (1+Φw * +Φsl * ) ➙ correction coefficients for wind and slope effects ➙ new formulation of the rate of spread ROS3D = ROS0 max(1,1+Φw +Φsl ), Φsl = cos αfr −(αa +π)( )Φsl * Φw = cos αfr −(αw +π)( )Φw *➙ with updated correction coefficients depending on the local direction of fire spread: ➙ projection onto horizontal plane: ROS2D = ROS3D 1+ tan2 (αsl )cos2 (αa −αfr )( ) − 1 2 .

12. PART I ● ● ●! Improved forward model! Slide 12 ∂c ∂t = ROS2D ×| ∇c | ➙ Total variation diminishing scheme, Rehm & McDermott (2009) Fire front location! §  Front-tracking solver! •  Fire propagation equation !

13. Growth of the burnt area over1500 s, simulated with FireFly over a complex terrain topography; moderate horizontal wind conditions (0.75 m s-1, 315°). PART I ● ● ●! Improved forward model! Slide 13

14. ① ➀ Improved forward model! ② ➁ Data-driven wildﬁre spread prototype ! ③  ➙ unchanged formulation of the inverse problem ④ ➂ Data assimilation experiments! OUTLINE! Data-driven wildﬁre spread modeling! Slide 14

15. PART II ● ●! Data-driven prototype! Slide 15 §  Observations! •  Data acquisition using Mid-InfraRed Imaging (MIR) ➙ Assumed real-time ﬁre front observations Requirements for data assimilation • High-spatial resolution images (10 m) • High-temporal resolution (10 minutes) • Need to account for measurement errors 0 0.5 1 1.5 2 2.5 3 3.5 0 0.5 1 1.5 2 Airborne monitoring Raw MIR imaging Observation post-processing (extraction of the ﬁreline location)

16. PART II ● ●! Data-driven prototype! Slide 16 §  Observations! •  Distance between simulated and observed ﬁre fronts Simulated front (cfr = 0.5)! Observed ! front! (x1 , y1 ) (x2 , y2 ) (x3 , y3 ) (x4 , y4 ) (x1 O , y1 O ) (x2 O , y2 O ) dt,1 dt,2 c = 0 c =1 ➙ Selection of markers: pairing between observed and simulated front markers ➙ Currently applicable if the topology of the front is not too complex !

17. PART II ● ●! Data-driven prototype! Slide 17 §  Data assimilation! •  Application to wildfire spread ➙ Find best estimate of the system state/parameters (fire front position, ROS model parameters) using observations of the fire front position FIREFLY wildfire spread simulator Parameters Initial condition Boundary conditions Comparison Simulated fronts Observations Ensemble Kalman filter Parameter estimation State estimation Rochoux et al., Proc. Comb. Institute (2013) Rochoux et al., IAFSS Proceedings (2014)

18. PART II ● ●! Data-driven prototype! Slide 18 §  Data assimilation! •  Ensemble Kalman filter (EnKF) ➙ Stochastic representation of the uncertainties and of the model nonlinearities between the estimation targets and the fire front positions Rochoux et al., IAFSS Proceedings (2014) 300 320 340 360 380 400 420 440 280 320 360 400 440 480 520 x [m] y[m] True Forecast! ➙ Improved performance in the non-informed section with data assimilation! 320 330 340 350 360 370 380 330 340 350 360 370 380 x [m] y[m] PRACTICAL CASE Opacity of the fire thermal plume! Observations! Analysis! True! State estimation: Anisotropic test with uncertain ROS model parameters and uncertain ignition location

19. ① ➀ Improved forward model! ② ➁ Data-driven wildfire spread prototype ! ③ ➂ Data assimilation experiments! ①  ➙ synthetic cases ②  ➙ reduced-scale controlled grassland fire OUTLINE! Data-driven wildfire spread modeling! Slide 19

20. PART III ● ●! Experiments! Slide 20 §  Reduced-scale controlled grassland ﬁre experiment! •  Mid-InfraRed imaging ➙ Quasi-homogeneous grass, moderate wind conditions ➙ Max. ROS = 5 cm/s ! Rochoux et al., NHESS (2014, parts I and II)

21. 0 0.5 1 1.5 2 2.5 3 3.5 0 0.5 1 1.5 2 x [m] y[m] 0 0.5 1 1.5 2 2.5 3 3.5 0 0.5 1 1.5 2 x [m]y[m] PART III ● ●! Experiments! Slide 21 §  Reduced-scale controlled grassland fire experiment! •  Parameter estimation vs. State estimation Rochoux et al., NHESS (2014, parts I and II) FORECAST! State ! Parameter! State! Parameter! ANALYSIS!Observations! Observations! •  State estimation: Non-uniform correction of the fire front •  Parameter estimation: Mean correction along the fireline •  State estimation: Persistence of the correction of initial condition (short-term) •  Parameter estimation: Correction of the ROS model (mid- and long-term forecast) Comparison of the average of the EnKF ensemble between parameter estimation and state estimation at time 106 s Free run! Free run!

22. 020406080100120140160180200 020406080100120140160180200 −20 0 20 40 60 y0 [m]x 0 [m] z 0 [m] true! PART III ● ●! Experiments! Slide 22 §  Synthetic test with complex terrain topography! •  Forecast step ➙ Spatially-varying test with uncertain ROS model parameters (fuel moisture content, fuel layer thickness, wind speed and direction) FORECAST! t = 750 s

23. 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 −20 0 20 40 60 y 0 [m]x 0 [m] z 0 [m] mean analysis! observations! mean forecast ! PART III ● ●! Experiments! Slide 23 §  Synthetic test with complex terrain topography! •  Analysis step (state estimation) ➙ Spatially-varying test with uncertain ROS model parameters (fuel moisture content, fuel layer thickness, wind speed and direction) t = 750 s

24. CONCLUSION! Data-driven wildfire spread modeling! Slide 24 §  Promising novel approach to forecast wildfire spread •  Capable of: ➙ correcting inaccurate predictions of the fire front position ➙ providing an optimized forecast of the wildfire behavior •  Parameter estimation vs. State estimation ➙ Long-term forecast: correction of the ROS model parameters ➙ Short-term forecast: correction of the initial condition §  Application to real-time emergency response management Monitoring of fire growth and smoke transport

25. PERSPECTIVES! Data-driven wildfire spread modeling! §  Extension to large-scale wildfire events •  Application to FireFlux experiment ➙ observation requirements for accurate forecast (e.g. assimilation frequency) ➙ estimation of spatially-distributed parameters •  Application to surface/atmosphere coupled system •  Improved integration of Fire Radiative Power measurements ➙ distance between simulated and observed fire fronts ➙ estimation of plume emissions

26. Contact: ! Arnaud Trouvé, atrouve@umd.edu! Mélanie Rochoux, melanie.rochoux@graduates.centraliens.net! Thank you for your attention!!