Rain Project - European Transport Conference 2016

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 608166. The contents of this presentation are the author's views. The European Union is not liable for any use that may be made of the information contained therein.

RAIN - Risk Analysis of Infrastructure Networks in

Response to Extreme WeatherEuropean Transport Conference 2016Barcelona, Spain7th October 2016

Dr. Milenko HalatR&D, Aplicaciones en Informática Avanzada SLBarcelona, [email protected]

http://www.rain-project.eu/

Outline• Motivation• RAIN

– What– Who– What for– How

• Outcomes• Topics for discussion

RAIN Project – European Transport Conference 2016 – 7th October 2016

Motivationmore events, more severe


In recent years, the complex interdependencies of the European/International infrastructure networks have been highlighted through multiple failures during extreme

weather events. These failures have been the driver for this project.


Sandy (2012)


“…that the destruction caused by the storm was the worst disaster in the 108-year history of the New

York City subway system”.

Sandy (2012)– Total 193 deaths– damage >$20 billion (USD) losses (including

business interruption) > $50 billion– Jamaica: 70% of residents without electricity,


• Haiti: food shortages, 200,000 homeless,• USA

– Flooding streets, tunnels and subway lines and cutting power in and around the city.

– New York Stock Exchange remaining closed for trading for two days.

– 7 subway tunnels under the East River were flooded,

– Gas shortages throughout the region.

• Deaths ~150 people and an estimated €150 Billion worth of damage,

• In Germany and the Czech Republic, the worst affected areas– electricity failures, disconnected telecom

links, damage to approximately 250 roads and 256 bridge structures,

– disruption to the Gas service due to damaged pipelines and contamination of clean water with flood water.

– restoration of important services to full capacity took approximately 1 month for electricity, 2 months for Gas and 3 months for telephone communications.


Europe: Flash floods 2012

• Deaths of 25 people and €12bn worth of damage

• Germany, Hungary and the Czech Republic, the worst affected areas

• 23,000 people leave homes in German city of Magdeburg. Elbe river waters rose to 7.4m (normal 2m)!!

Central Europe: Flash floods 2013

• Deaths of 20 people and >€1bn (Bavaria alone) worth of damage

• Mostly Germany and France, but also Austria, Belgium, Romania, Moldova, Netherlands and the United Kingdom


Europe: Flash floods 2016

• This kind of extreme events– Cost billions € in direct and indirect damage– Dozens of deaths– Impact on residential buildings and infrastructures– Several countries involved– Long recovery times– Infrastructures interdependencies– Cascading effects [Electricity -> communications | Road | Water supply |…]

• Relevant problems:– How much should be invested on protection for SEVERE and RARE events

(preparedness vs mitigation)– “Where” the investment will have highest impact– How to measure the social cost more effectively– How to involve / coordinate different governments and stakeholders


Summarizing…

What is RAIN?

networks: the distributed and vulnerable part of infrastructures


RAIN• Risk Analysis of Infrastructure Networks in Response to Extreme Weather

• FP7 Theme 10 - Security Activity - 10.2 Security of Infrastructures and Utilities SEC-2013.2.1-2 - Impact of Extreme Weather on Critical Infrastructure'.

'Activities will concentrate on targets of an incident or disaster of transnational importance…, significant sites of political or symbolic value and utilities being those for energy (including oil, electricity, gas), water, transport (including air, sea, land), communication (including broadcasting), financial, administrative, public health, etc.A series of capabilities are required to cope with this mission area, many of which primarily relate to the phases "protect" but also "prepare”.


Who?

industry + academiaheterogeneous skillsdifferent countries


What for?

decision supportresilience

mitigation strategiespolicies recommendation


• Improve the robustness of Infrastructure Networks so that they will not experience disproportionate damage or disruption in the case of extreme events

• Minimize impact– novel early warning systems, – decision support tools and – engineering solutions

• Recovery Develop systems that will accelerate re-establishing infrastructure links post an extreme event.


Objectives

HOW

identificationassessmentmeasures


1. Meteorological: – Hazard identification (Types & Thresholds)– Geographical dependence– Frequency/return time, mid/ long-term forecasting in Future scenarios

2. Critical Infrastructures– Critical component identification– Vulnerability matrix Component vs Threat– Protection (Prevention and mitigation) measures for each the matrix

3. Framework: Risk assessment and decision support4. Validation: use cases5. Mitigation measures. Benchmarking, effectiveness vs cost6. Policy recommendations


Steps

1. Meteorological: – Hazard identification (Types & Thresholds)– Geographical dependence– Frequency, mid/long-term forecasting in Future scenarios

2. Critical Infrastructures– Critical component identification– Vulnerability matrix Component v/s Threat– Protection (Prevention and mitigation) measures for each the matrix

3. Framework: Risk assessment and decision support4. Validation: use cases5. Mitigation measures. Benchmark, effectiveness vs cost6. Policy recommendations.


Steps

Hazard Identification1. To identify the extreme weather events to be analyzed in detail in RAIN, including

defining appropriate intensity thresholds, taking into account regional differences in vulnerability and climate.

2. To assess the present state-of-the-art forecast systems for extreme weather and their characteristics, and to address and estimate their predictive skill.

3. To assess the frequency of weather hazards throughout Europe for both the present and future climate; by applying state-of-the-art methods to regional (CORDEX) and global (CMIP5) climate model data until the year 2100.


Hazard Identification

images (cc-by-sa license): thunderstorm gusts: John Kerstholtsnow storms: Sebastian Ballard, lightning: Timo Newton-Syms

snow storms freezing rain

tornadoes

wind storms

river floods

lightning, hail,thunderstorm winds

wildfires

heavy rain

coastal floods


River & Coastal Flooding: Data

River dischargesStations with at least three full decades of continuous records (1951-2000)

Country Stations

France 163

Sweden 142

United Kingdom 136

Germany 125

Norway 101

Switzerland 75

Other 277

Total 1019


River & Coastal Flooding: Simulation

Bayesian NetworkMethodology


River & Coastal Flooding: Results 1/3

% Variation in river discharges (100-years)



Flood Extent



Storm SurgesOccurence of storm surges(100-year return period) in Europe in present climate (TU Delft)

Sea level rise up to 2100IPCC projections by emission scenario.


1. Meteorological: – Hazard identification (Types & Thresholds)– Geographical dependence– Frequency, mid, long-term forecasting in Future scenarios




Steps


Vulnerability taxonomyBridge / flood


Vulnerability taxonomy Windstorm /electric

Vulnerability taxonomyTransmission towercharacterization


Vulnerability Matrix ExampleELECTRICITY

LightningWindstorm

sIce/snow

stormsFlash floods

Extreme cold

Extreme heat Wild fires

Sand storms

Seasonal drought

Generators (housed)

Generators (wind / PV)

Lines

Xformers

Sw / Breakers

Relays

SCADA & telecom

Voltage control devs

Protection [prevention]


Scheme for Bayesian Analysis

Impact assessment (E&TC)

Electrical

Telco

Community service

Business

Individual

access to emergency call centers

shutdown and restart

costs

life support systems

opportunity costs of idle resources

elderly people &

people with health

concerns

loss of leisure time

work

looting and vandalism

loss of goods

attention vulnerable

people

financial transactions

coordination among affected

parties

public transportation

other utility providers

1. Meteorological: – Hazard identification (Types & Thresholds)– Geographical dependence– Frequency, mid, long-term forecasting in Future scenarios




Steps

Enumerate all possible states that might arise

Quantify how likely each state is for a given

action (state probability

distribution).

Quantify consequences that each state entails for

a given action in an appropriate metric.

Enumerate all possible actions

Construct Outcome Probability Distributions (outcome metric inherited

from consequence quantification)

Choose that action that maximizes the position of

the Utility Probability Distributions.

Use laws of probability theory:

Consequence analysis:

Map consequences to state probability distributions:

Apply criterion of choice:

Construct Utility Probability Distributions

Transform outcome metric to a metric that takes into

account any ‘wealth’ constraints in play:

Enumeration: Enumeration:

Inference Phase

Decision Phase

Risk Based Decision Framework



Enumerate all possible states that might arise

Quantify how likely each state is for a given

action (state probability

distribution).

Quantify consequences that each state entails for

a given action in an appropriate metric.

Enumerate all possible actions

Construct Outcome Probability Distributions (outcome metric inherited

from consequence quantification)

Choose that action that maximizes the position of

the Utility Probability Distributions.

Use laws of probability theory:

Consequence analysis:

Map consequences to state probability distributions:

Apply criterion of choice:

Construct Utility Probability Distributions

Transform outcome metric to a metric that takes into

account any ‘wealth’ constraints in play:

Enumeration: Enumeration:

Inference Phase

Decision Phase


Validation

Malborghetto case (Italy)Loviisa Nuclear plant (Finland)



Italian Case StudyAlpine Region, August 2003• Torrential rainfall, Flash flooding,

Debris flow• 600 residents were evacuated, 2

deaths• Estimated damage of €190 million

• Critical infrastructure failure(s)– Land transport infrastructure failure(s)

• Bridges• Tunnel• Roads• Railway station


A23

E55

TrasEuropean Network -Transport


DEBRIS FLOWS TRIGGERED BY THE 29 AUGUST 2003CLOUDBURST IN VAL CANALE, EASTERN ITALIAN ALPS.


Rivers and landslides areas

Susceptibility map

SUMMARY

SECTORIAL CRITERIA CROSS-CUTTING CRITERIA (CCC) CRITICALRef Description Road/line K1 K2 K3 K4 K5 K6 K7 K8 K Causalit. Economic ef. Public ef. CCC

B1 Road bridge A23 2.5 5 5 5 5 4 3 2 31.5 NO NO NO NO NO

B2 Road bridge SS13 1.5 5 5 5 1 2 3 2 24.5 NO NO NO NO NO


B4 Railway bridge main 5 5 5 1 3 3 1 0 23 NO NO NO NO NO





B9 Road bridge A23 2.5 5 5 5 5 4 3 2 31.5 NO YES NO YES YES

B9.2 Road bridge SS13 1.5 5 5 5 3 2 3 2 26.5 NO NO NO NO NO


B11 Railway bridge main 5 5 5 5 3 3 1 0 27 NO NO NO NO NO




B15 Railway bridge main 5 5 5 5 5 3 3 2 33 NO YES NO YES YES

T1 Road tunnel A23 2.5 4 4.5 4 5 5 1 0 26 NO NO NO NO NO



T5 Railway tunnel main 5 5 5 4 3 4 1 0 27 NO NO NO NO NO

T6 Road tunnel SS13 1.5 4 5 4 1 3 3 2 23.5 NO NO NO NO NO

T7 Railway tunnel main 5 5 5 4 3 4 1 0 27 NO NO NO NO NO


T9 Road tunnel SS13 1.5 3 4.5 3 1 3 1 0 17 NO NO NO NO NO

T10 Road tunnel SS13 1.5 4 5 3 1 3 3 2 22.5 NO NO NO NO NO


S1 Train Station main 5 3 5 2 3 1 3 2 24 NO NO NO NO NO

S2 Train Station main 5 3 5 2 3 1 3 2 24 NO NO NO NO NO

Component evaluation

Component failure dependencesRoad

Road

Rail


Bayesian network for transportation


Risk profile before & after intervention

Posterior failure probabilities of the bridges given system failure

• In 2005, Europe was exposed to a potential risk of a nuclear disaster caused by the flooding of the Loviisa nuclear power plant in Finland. Sea levels rose by 1.73 meter above normal levels, due to a storm.

• Estimated damage of €20 million in costs to theinsurance company

Validation: Loviisa [On progress]


Outcomes

weather databasearticles

software toolframework

recommendations



https://www.youtube.com/watch?v=mXBftjvyXQs

https://www.youtube.com/watch?v=mXBftjvyXQs

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RAIN Project

www.rain-project.euDr. Milenko Halat

[email protected]


http://www.rain-project.eu/

Topics for discussion


Concepts as collective intelligence or collaborative economy (~Uber, Airbnb…) can be of great help in disasters: people is getting used to them, no big hardware investment (mobiles), etc. Cons: would be used few times.

New trends in the use of technology


These disasters pose infrastructures in a exceptional and vulnerable situation. Intentional attacks could take place (physical and/or cyber). How to be prepared?

Combined attacks

Bulk citizen movements and activity could be inferred on real time through data analysis from sources like phone activity, triangulation traffic cameras / sensors. What’s the price? Is it really useful?

Big data tools

Crisis management systems should consider carefully the personal information they expose, directly or indirectly (inference crossing data sources) How to reach a useful tradeoff between safety and privacy?

Privacy concerns

BRAIN STORMING

Engineering

Rain Project - European Transport Conference 2016