www.crs4.it

An integrated system for the forecasting of wildfire behavior An integrated system for the forecasting of wildfire behavior

based on cloud and virtualization technologiesbased on cloud and virtualization technologies

Antioco VargiuAntioco Vargiu11 M. Maroccu1, L. Massidda1, M. Cabianca1, C. Impagliazzo1 L. Carrogu2, E.

Usai2

1. CRS4 srl, Pula (Ca), 1. NICE srl, Pula (Ca) Project Project ““Cloud for Remote ViewingCloud for Remote Viewing””

funded by the Autonomous Region of Sardinia, action “Pacchetti Integrati di Agevolazioni "Industria, Artigianato e Servizi"

(Annualita' 2010), Programmazione Unitaria 2007/2013 P.O. FESR 2007/2013

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TopicsTopics

Scheme of the Presentation:Scheme of the Presentation:

• Introduction to the WildFire Cloud ServiceIntroduction to the WildFire Cloud Service

• Data and Methods of the WildFire Cloud ServiceData and Methods of the WildFire Cloud Service

• Test on relevant wildfire cases in SardiniaTest on relevant wildfire cases in Sardinia

• Conclusions and future developmentsConclusions and future developments

Keywords:Keywords:

Rothermel's fire modeling, mass-consistent wind downscaling, cloud computing

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Fire Behavior Modeling Fire Behavior Modeling

PBLmodelig

Meteorology

1m

10m

100m

1km

10km

100km

1000km

Land modeling

Forest fire

Principal issue: Principal issue: fire development and spread is driven by fire development and spread is driven by physical processes taking place at physical processes taking place at different scales.different scales.

In order to run wildfire simulations In order to run wildfire simulations is required the modeling (at the landscape is required the modeling (at the landscape scale) of: scale) of: • Surface Weather Conditions (PBL).Surface Weather Conditions (PBL).• Topography. Topography. • Land Cover and Fuel Representation. Land Cover and Fuel Representation.

To obtain realtime wildfire simulations, Rothermel’s semi-To obtain realtime wildfire simulations, Rothermel’s semi-empirical model is empirical model is the most common approachthe most common approach..

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Layout of the Cloud WildFire Forecasting System

• Modular design coded in Python with precompiled libs.

• Easily changeable functional elements.

• Custom-tailored setup.

• Simple user interface based on the Cloud Computing paradigms:

“Software as a Service” “Remote Desktop Virtualization” with support of 3D GPU accelation

Suitable for: • Scenario analysis• Personnel training• Emergency management

The WildFire Cloud ServiceThe WildFire Cloud Service

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REMOTEUSERS

WEB INTERFACE

VIRTUAL RESOURCES PHYSICAL

HARDWARE

Cloud Layers

The Cloud Computing: the conceptual schemaThe Cloud Computing: the conceptual schema

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BOLAM_Father:nlon=290, nlat=170, nlev=42 Dlon=0.3° (30 km)

MOLOCH:nlon=722, nlat=674, nlev=51 Dlon=0.03° (3km)

BOLAM_Son:nlon=560, nlat=362, nlev=46 Dlon=0.1° (10 km)

Bolam-Moloch Weather Forecast

Chain

Scheduled daily runs.No user interaction

WindPotentialFoam;nx=300, ny=300, nz=35dx=35m

Run “on demand”: users can select position and time.

Lam-Mass-Consistent:nlon=861, nlat=1341, nlev=41 Dlon=0.0021° (200m)

A Weather Forecast system for Wildfire modeling A Weather Forecast system for Wildfire modeling

Mass-Consistent Wind Model

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WindPotentialFoam,WindPotentialFoam, a Mass-Consistent Wind Modela Mass-Consistent Wind Modelbased on the CFD software package OpenFOAM

ν0

202

0,

v

v n v

Lagrange Multiplier Approach

λ→ Scalar Potential Field 2

0

0

( )

( ) min( ( ))

s

s s

s

v

E v v v dxdydz

E v E v

Variational Formulation

1

20v = v + λ

Physically adjusted wind

ν

Issue: interpolated winds field show a flat unrealistic behavior near the terrain.

Effective solution: MCM enforces a "divergence free” flow, computing an optimal mass-correction.

A more realistic flow is obtained near the surface.

Very High Resolution Wind Modeling Very High Resolution Wind Modeling Wind is one of the environmental variables which greatly influence the spread and intensity of wildland fires (Rothermel, 1972).

Equations soved on aTerrain-Following Mesh

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PROS

Fast Wind Model, suitable for real-time applications

Vertical corrections linked to the atmosphere stability

Build in an advanced open source CFD platform.

Very High Resolution Wind Modeling Very High Resolution Wind Modeling

CONS

Diagnostic Model: Steady State, No dynamics, No turbulence

Slight deviations from the initial wind V0

openFoam: some libraries and/or tools too much resource-hungry, “ad hoc” modifications are required to speed-up realtime applications.

WindPotentialFoam,WindPotentialFoam, a Mass-Consistent Wind Modela Mass-Consistent Wind Modelbased on the CFD software package OpenFOAM

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DTM

Raster resolution=10m

Wildfire modeling: Land Dataset Wildfire modeling: Land Dataset

Corine Land Cover 2006Raster resolution=20m

Land Cover

Alternative Global Dataset:

SRTM Digital Elevation Database v4.1 ESA GlobCover2009 resolution: 3-arc seconds (~ 90m at the equator) 10-arc seconds (~ 300m)

Landscape Dataset (from www.sardegnageoportale.it)

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Scott, Joe H. and Burgan, Robert E. (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model.General Technical Report RMRS-GTR-153. USDA Forest Service, Rocky Mountain Research Station, Fort Collins,

Wildfire modeling: Fuel ModelsWildfire modeling: Fuel Models

Assessment of the Fuel Moisture Content Assessment of the Fuel Moisture Content

• Fine Fuel Moisture (1h) evaluated with the Viney model (Viney 1991).Fine Fuel Moisture (1h) evaluated with the Viney model (Viney 1991).

• Moisture Scenario for Thicker Fuel, (according to Scott 2005),Moisture Scenario for Thicker Fuel, (according to Scott 2005), scenario chosen by means of the 1h value.scenario chosen by means of the 1h value.

• Seasonal adjustment factor for live and dead fuel.Seasonal adjustment factor for live and dead fuel.

• 13 NFFL standard fuel models

• A custom model for the Mediterranean Maquis (Arca 2007)

Rothermel's fuel

Arca B, Duce P, Laconi M, Pellizzaro G, Salis M, Spano D, 2007. Evaluation of FARSITE simulator in Mediterranean maquis. International Journal of Wildland Fire 16, 563–572

Viney (1991), A Review of Fine Fuel Moisture Modelling, International Journal of Wildland Fire 1(4) 215 - 234

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The Fire Perimeter evolution:The Fire Perimeter evolution:

To propagate the fire front the Fast Marching Method has been implemented.

This method can automatically deal with topological changes of the burned area.

It is a special case of the well known Level Set Method.

Wildfire modeling: The spread model and Fire Front TrackingWildfire modeling: The spread model and Fire Front Tracking

• LSM solves the time evolution of the “zero isoline” over the NB subset.

• FMM solves an equivalent “boundary value” problem over the unburned area

≤ 0 > 1

≤ 0

(B U NB)

The Surface Spread Model:The Surface Spread Model:

We have developed specific Python Interfaces for the Rothermel libraries: • FireLib, (1996, Collin D. Bevins).• Fire Behavior SDK (2006, Collin D. Bevins). (http://www.fire.org)

ROS(x,t)

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Fast Marching MethodFast Marching Method

Optimal “One-Shot” method to compute the front evolution.

The “Arrival Time” of the fire is estimated solving an linearized Eikonal equation over the unburned area (NB):

Wildfire modeling: Fire Front TrackingWildfire modeling: Fire Front Tracking

with V(x) ≥0: Rate Of Spread T(x): Arrival Time (or Travel Time) and auxiliary variable : (x)=0, →T(x)=0: Initial Burned area, B (x)=1,: Unburned area, NB

NBxxT

NBx

xTxV

,0

1|)(|)(

T > 3

T = 1

T = 3

T = 0

T = 2

(B U NB)

In order to update B.C. and correct nonlinear terms, the FMM is called inside a specific time loop. In order to update B.C. and correct nonlinear terms, the FMM is called inside a specific time loop.

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Typical synoptic conditions that lead severe wildfires in Sardinia

A blocked Atlantic circulation (slow east-ward motion, strong meridional heath exchange:

a) Persistent warm winds from North Africa to the Mediterranean basin.

b) After the low-pressure area has moved to the East-Central Europe, Mistral winds break into the Mediterranean basin.

Consequences:

•Typical fuels (grass, pasture, maquis) almost dry.

•Gusty Winds

High Rate of SpreadHigh Rate of Spread

Testing of the WildFire forecast systemTesting of the WildFire forecast system

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Ignition Point Oniferi (Nu), at ~ 10.00 A.M. (GMT) The wildfire lasted longer than 15 hrBurned area: > 9000 ha

SIGRI, Progetto Pilota Protezione Civile dagli Incendi Boschivi:Workshop Finale 6 Novembre 2012, Roma.

Grid 447*447, dx=35mFire Front plotted over 9hr, dt=1h

Weather forcing from the Moloch Model + WPF,conditions updated from 10.00 A.M. to 9.00 P.M

! Fire fighting actions not simulated !

Performance of the WildFire forecast systemPerformance of the WildFire forecast system

Test Case Nuoro 2007-07-23Test Case Nuoro 2007-07-23

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Test Case Lochiri 2011-07-13Test Case Lochiri 2011-07-13

Grid 1001*1001, dx=30mFire Front plotted over 9hr, dt=1h

Weather forcing from the Bolam Model +WPF,with fixed conditions (frozen time) at 10.00 A.M.Land Cover: Corine

Grid 395*395, dx=30mFire Front plotted over 9hr, dt=1h

Weather forcing from the Moloch Model +WPF,conditions updated from 10.00 A.M. to 8.00 P.M.Land Cover: GlobCover2009

Ignition Point Lochiri (OT)Burned area ~2500 ha, from 10.45 to 17.30 GMT;

Gusty winds from the west and dry fuel have determinated a fast front spread towards the territory of Berchidda and Monti.

Four helicopters of the Forest Service and a Elitanker were used to fight the wildfire.

! The black line is the real perimeter. Fire fighting actions not simulated !

Performance of the WildFire forecast systemPerformance of the WildFire forecast system

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In this project we developed and tested a Cloud Service for the modeling of wildfire on Sardinia’s vegetation.

The Cloud Service consists of: • a Virtual HPC session dedicated to the numerical modeling.• a Remote Visualization session with full 3D GPU acceleration.

Conclusions and future developments

Virtualization technologies are mature enough to be used effectively in environmental applications, but with some limitations on massive computational task.

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Our WildFire Cloud Service has proved to be adequately realistic and fast in simulating the general behavior of a wildfire propagation.

The system can be widely improved, with (e.g.):

1) A new weather forecast chain with a final resolution under 1km.

2) A more sophisticated solver for the wind downscaling.

3) A better classification of fuel models.

4) A better assessment of fuel moisture.

5) Models optimization and validation with well documented cases.

6) Ensemble simulations to handle uncertainties.

7) A more interactive user interface.

Conclusions and future developments

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RAS (Sardinian Regional Authority)

CNR-IBIMET, Institute of Biometeorology, Sassari, Italy

UNISS (UNIVERSITY OF SASSARI)

Forest Guard Cagliari, Civil Protection Rome

Acknowledgements and Credits

Credits to: CNR-ISAC Institute Bologna openFoam (www.openfoam.com)Corine Land Cover, European Environment AgencyGlobCover 2009 ProjectFirelib project (C. D. Bevins)