Data-driven wildfire spread modeling

1. Towards predictive simulations of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position M. Rochoux, C. Emery, S. Ricci, B. Cuenot, A. Trouvé 11th International Symposium on Fire Safety Science U. Canterbury, February 9-14, 2014

2. INTRODUCTION ● ● ● ● Regional-scale wildfire spread 2 Smoke plume Flame region Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 2009 Black Saturday bushfires 300 km © NASA Wildfire complexity • Wide range of length-scales • Poorly-defined biomass fuels • Atmospheric external forcing Need for computer model tools to forecast fire spread • Scenarios/Real-time • Change of behavior with climate change (megafires)

3. 3 3INTRODUCTION ● ● ● ● Regional-scale modeling viewpoint 3 Favone wildfire (30 ha) • Objective: predict the time-evolving position of the fire front FOREFIRE Coupled Observed Semi-empirical models: Rothermel [1] , Balbi [2] [1] Rothermel, Tech. Rep. USDA Forest Service (1972) [2] Balbi et al. Comb. Flame (2004) [3] Filippi et al. Proc. Comb. Inst. (2013) Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury FOREFIRE/MESO-NH [3] Two-way fire/atmosphere coupling • Surface fire spread model • Surface/atmosphere fluxes • Atmospheric transport/chemistry

4. 4INTRODUCTION ● ● ● ● Scope of the thesis 4 Sources of epistemic uncertainty • Environmental conditions • Physical modeling inaccuracies Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury • OBJECTIVE: Quantify and reduce uncertainty in ROS modeling for wildfire spread forecasting Data assimilation Measurements Front-tracking Environmental conditions Sparse due to the opacity of the thermal plume ➔ Optimal combination of measurements and model Improved forecast of the fire front position Weather forecast Atm. chemistry Hydrology Biomechanics

5. ①➀ An eye onto data assimilation ②➁ Strategy for wildfire spread forecast • Data assimilation components • Estimation of error statistics ➂ Data assimilation experiments • Synthetic cases • Real-case study ➃ Concluding remarks 5Outline 5 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury

6. reality Model forecast Diagnostic Measurements Analysis Time Sequential approach • Estimation of modeling/observation error statistics: distance to reality • Explicit formulation ➔ Ensemble Kalman filter (EnKF) ➔ Correction of the forecast control variables = + K [ - ] Distance to observationsKalman gain matrix Stochastic characterization • Ensemble of simulations of the forward model • Statistical evaluation of error covariance matrices ➀ DATA ASSIMILATION ● Kalman filter and non-linear extension 6 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury

8. 8 ➁ STRATEGY ● ● Data assimilation components 8 Observations Observed front x y Uncertainty for each front marker Type of observations Front-tracking simulator Control variables Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury

9. Observations Model counterpartFIREFLY Front distance • Rothermel ROS model • Level-set-based solver Simulated front Observed front (x1 , y1 ) (x2 , y2 ) (x3 ,y3 ) (x4 , y4 ) (x1 O ,y1 O ) (x2 O ,y2 O ) ROS model parameters Initial condition • Wind magnitude/direction • Fuel moisture content • Fuel surface/volume Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 9 ➁ STRATEGY ● ● Data assimilation components 9 Type of observations Front-tracking simulator Control variables

10. Observations Model counterpartFIREFLY Front distance Simulated front Observed front (x1 , y1 ) (x2 , y2 ) (x3 ,y3 ) (x4 , y4 ) (x1 O ,y1 O ) (x2 O ,y2 O ) ROS model parameters Initial condition Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 10 ➁ STRATEGY ● ● Data assimilation components 10 Type of observations Front-tracking simulator Control variables AnalysisCurrently: model state estimation • Correction of the fire front location • Spatial deformations of the fire front Ensemble Kalman filter Parameter estimation Model state estimation Previously: parameter estimation • Sequential approach • Mean correction along the fireline

11. 11 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 11 ➁ STRATEGY ● ● Estimation of error statistics 11 ISOTROPIC Uncertainty: ignition location • Generation of the forecast ensemble • Representation of the sources of uncertainties • Description of error correlations along the fireline Analysis True Observation Forecast True Observation Distance to simulated front marker [m] Errorcorrelation Correlations y-y Rochoux et al., NHESS Special Issue, part II, in preparation

12. ANISOTROPIC Uncertainty: wind magnitude and direction, fuel moisture content, fuel surface/volume, fuel depth, ignition location 12 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 12 ➁ STRATEGY ● ● Estimation of error statistics 12 • Generation of the forecast ensemble • Representation of the sources of uncertainties • Description of error correlations along the fireline Analysis True Forecast True Observation Distance to simulated front marker [m] Errorcorrelation Correlations y-y Rochoux et al., NHESS Special Issue, part II, in preparation ISSUE: How to accurately estimate error correlations?

14. 1414 ③ EXPERIMENTS ● ● Synthetic cases 14 • Sensitivity to the location of observed markers True Forecast Analysis True Observation IDEAL CASE Improved performance in the non-informed section with data assimilation PRACTICAL CASE Opacity of the fire thermal plume Observation Analysis True Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury

15. 1515 ③ EXPERIMENTS ● ● Synthetic cases 15 • Forecast performance: limited persistence of the initial condition • Anisotropic case with temporally-varying wind conditions • Multiple assimilation cycles at 150 s intervals consequence of stopping the analysis analysis every cycle Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury State estimation needs to be renewed frequently in accordance to the persistence of the initial condition

16. 1616 ③ EXPERIMENTS ● ● Real-case study 16 • Controlled grassland fire experiment • Quasi-homogeneous short grass ➔ Moisture content: 22 % • Mean ROS = 2 cm/s ➔ Max. ROS = 5 cm/s Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 1min32s50s 1min46s1min04s 1min28s Time Wind 1m/s x [m] y [m] Extraction of the 600 K iso-temperature at 14 s intervals Estimated error: 5 cm (1 % burned area) Front

17. Free runAnalysis MeasurementForecast 1717 ③ EXPERIMENTS ● ● Real-case study 17 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury 1min32s50s 1min46s1min04s 1min28s Time Free runAnalysis Measurement IC • 100 members • Uncertain parameters • Grass moisture content • Grass surface/volume • Wind magnitude • Wind direction • Fuel depth in 4 zones • Ignition location Free run Analysis Measurement Forecast

19. 1919 ④ CONCLUSION ● ● Contributions of this work 19 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury Data-driven fire modeling is capable of • Correcting inaccurate predictions of the fire front position • Providing optimized forecast of the wildfire behavior State estimation approach • Non-uniform correction of the fire front location • Limited persistence of the initial condition FORECAST State Parameter State Parameter ANALYSIS Rochoux et al., PROCI, submitted for publication.

20. 2020 ④ CONCLUSION ● ● Contributions of this work 20 Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury Data-driven fire modeling is a promising novel approach to • Reduce modeling uncertainties by integrating fire modeling and fire sensing technologies • Take advantage in recent progress made in sensor technology (in-situ/airborne/spaceborne) Application to real-time emergency response management • Monitoring of fire growth • Forecasting of fire location/size and smoke transport

21. Mélanie ROCHOUX 14 février 2014IAFSS conference, U. Canterbury Thank you! Any question? Contact: melanie.rochoux@graduates.centraliens.net Ph.D. thesis (soon available online) Towards a more comprehensive monitoring of wildfire spread: Contributions of model evaluation and data assimilation strategies.

Data-driven wildfire spread modeling

Mélanie Rochoux

Data-driven wildfire spread modeling