fires and floods: new models for disaster prediction swlgema hilton 16 9 2.pdf · fires and floods:...

Fires and floods: New models for disaster prediction SWLGEMA Conference, Bunbury, August 2015

DIGITAL PRODUCTIVITY FLAGSHIP

James Hilton, Raymond Cohen, Yunze Wang and Mahesh Prakash

ENIAC (1943) iPhone 6 A8 (2014) 150,000,000

times faster.. • What can we do with this new computing power?

Introduction Natural Hazard Modelling

CSIRO | SWLGEMA 2015

Introduction Natural Hazard Modelling • Ability to model natural hazards • Digitally represent natural systems • Movement of water or fire can be predicted

• Greater resolution • More physics • Not necessarily more accurate

More computing power gives:


• Fire propagation • Arrival time • Fire intensity • Risk assessment

Introduction Natural hazard modelling

Swift - floods • Flash flooding • Coastal inundation • Mitigation modelling • Integrated hydraulic model

Spark – bushfires:

Goal: Open and reliable disaster modelling software for decision makers


Analysis • Risk identification (Manual analysis) • Visualisation (Impact/communication) • Automated data gathering

Generation • Data conversion/generation (Clean-up) • Parameter space

Synthesis Synthesis Computation • Run computer models • Multiple simulations

Scenario • Requirements (Risk assessment, validation) • Gather data (Terrestrial, bathymetric, vegetation)

Introduction Anatomy of prediction


Introduction Workflow Architecture

• Workflow execution system • GUI workflow editor • Freely available at: http://research.csiro.au/workspace

CSIRO Workspace

Workspace GUI editor


• Collection of parts for modelling natural hazards • Each part is represented by a block

• These parts can simply be plugged together



• The parts build up a system

1. Solver 2. Execution 3. Input 4. Output



External linkage


Introduction Overview • Integrated software • Multiple OS

• Can be run on a cloud server


Introduction Overview

Bushfire Model Spark • Tracks evolution of fire perimeter • Front speed from empirical models • Uses fuel, wind, moisture parameters • Tile- based level set method

• Arrival time • Fire intensity

Front speed


• Track fire front • Front speed from empirical models • Dependant on wind, fuel, topography… • Many different empirical models

Fire front 1 2 3

Computational level set method

• Natural for interfaces • Front represented by level set • Handles topological changes • Computationally efficient

New fire front

Front speed


Bushfire Model Computational Model

Solver

Input layers

Log

Fire model

OpenLayers display Processor

Bushfire Model Spark


Bushfire Model Spark


• System can be behind a user interface


Bushfire Model Spark framework

• Fire behaviour models can easily be added • For example, including a spotting model

Spotting demo in workspace


Bushfire Model Spark framework

• One result isn’t useful for random conditions, such as spotting • Need to run ensemble simulations

• Ensemble/analysis functionality built into framework

Run 1 Run 2 Run 3 Run 4 1000 runs

Bushfire Model Ensemble analysis


• Input conditions from image analysis of from real fires using OpenCV

Bushfire Model Image analysis


• Is curvature crucial to bushfire propagation?

• Curvature is not incorporated in any conventional bushfire models

• Radiation or convection?

• Current study to look into curvature effects

Tim

e

Experiment Conventional model

Curvature incorporation

Bushfire Model Fire dynamics


Automate input: fire locations, precipitation predictions, storm surge Up-to date land classification: remote sensing data analysis

Bushfire Model Operational Systems • Input data must be as recent as possible:

• Output data must clearly quantify impact and risk

• Simulation data must be generated as fast as possible

• System fulfils all of these requirements


• Water depth << width • 2D formulation • Predicts water height, momentum • Shallow water finite volume model

Hydrodynamic Model Swift

width

depth

• Dam breaks • Flash flooding • Coastal inundation


Solver

Visualisation sub-workflow

Processor: Hydraulic model


Display


• NOAA standard validation test cases • ‘Simple beach’ test case • Wave run-up and inundation

Initial wave

Hydrodynamic Model Validation

• All NOAA standard validation cases • Historical dam breaks • Historical flash flood events


Hydrodynamic Model Townsville storm surge

http://hardenup.org

• Risk assessment for large-scale inundation event


Hydrodynamic Model Kakadu Saline Inundation


Structural mitigation options: • Levees, basins and dams • Drainage networks

Elwood Swamp 1886

Hydrodynamic Model City of Port Phillip


Drainage flow direction

Normal conditions

• Head-based network model • Assume sound speed >> gravity wave speed

= Storm tide Drainage flow direction

Drainage flow reversal

• Drainage reversal



• Extensive urban drainage network (~13k pipes) • Model effects of 1.3 m storm surge

0.1

m3/s

0

m3/s

Flo

w rate

N



• Present day sea level +80 cm



• Storm surge and flooding scenarios • Range of mitigation options • Included one-way valves

Without mitigation With mitigation

24 hrs 2 hrs Retention time



Present day sea level

Without mitigation With mitigation

24 hrs 2 hrs Retention time


Hydrodynamic Model City of Port Phillip • Storm surge and flooding scenarios • Range of mitigation options • Included one-way valves

0.4 m SLR


Hydrodynamic Model City of Bunbury

• Coastal region susceptible to flooding • Look at both storm surge and rainfall events • Preston River and the Leschenault Inlet areas historically worst hit

• WA National Disaster Resilience Program (WA-NDRP) project

• Study flooding for future planning and mitigation options

• Investigate extreme present and future (with SLR) flooding events

• Impact of potential mitigations including

• Retention/detention schemes

• Changes to the drainage network

• Changes to pumping stations


Hydrodynamic Model City of Bunbury

Lidar, bathymetry, land usage Pipe networks, pumps,

retention systems

• Input data sets Rainfall

Tides

Joint probability analysis

Statistical extreme tide & rainfall scenarios

Summary Modular solver architecture


• Efficient models for natural hazards • Open and configurable • Platform for risk modelling • Flexible, adaptable framework • Embedded data analysis • Interactive applications

DIGITAL PRODUCTIVITY FLAGSHIP

CSIRO Digital Productivity James Hilton

t +61 3 9545 8002 e [email protected] w http://www.csiro.au

CSIRO Workspace http://research.csiro.au/workspace

Thank you

fires and floods: new models for disaster prediction swlgema hilton 16 9 2.pdf · fires and floods:...

Documents