Risk Based Planning and Mitigation of Natural Disasters through Network Modelling and Simulation

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1 Risk Based Planning and Mitigation of Natural Disasters through Network Modelling and Simulation Professor Janaka Ruwanpura, PhD, P.ng, PQS, MRICS Vice-Provost International Professor of Civil ngineering/ Project Management University of Calgary, Canada

2 The Restless arth! The history of any part of the arth, like the life of a soldier, consists of long periods of boredom and short periods of terror - Derek V Ager (British Geologist) xtracted from Prof. S.S. Wickramasuriya, University of Moratuwa

3 Disasters 3

4 Network Modeling T DT NT D D D D DT D D TG T D BS NT D CM X

5 RCOVRY Disaster Management PRVNTION MITIGATION DISASTR MANAGMNT PRPARDNSS Pre-disaster Stage Deaths and injuries could be minimized by taking appropriate actions at the pre-touch down phase of a tornado.

6 Stages of a Disaster 6

8 Pre-Natural Disaster/ Hazard Preparedness and Mitigation Vulnerability Assessment Pro-active Planning to reduce the source/exposure Pro-active Planning to reduce the impact of the disaster Develop Communication Strategies Identify the responsibilities and accountabilities Capacity Building

9 Best Practices Modelling Signs of a Possible Disaster Framework Pre-Planning to face the Disaster (More than 24 hours, a few hours, less than one hour) Scientific Verification of the Magnitude of the Disaster vacuation (if any) Identify the vacuation and Protection Methods Action Plan for Alert, Warning, vacuation Develop Communication and Decision making Plan for vacuation or Protection

10 Risks and Uncertainties Uncertainty Certainty Page 10

11 Risk Management! Why? Proactive vs. Reactive (Covey s Time Management Matrix) No Drivers Built in drivers Urgent and Important Urgent but Not Important Not Urgent but Important Not Urgent Not Important Never go here

12 Pro-Active Risk Planning Identification Qualification Quantification Mitigation and Impact Analysis Impact of Response Plan using Simulation Response Plan Page 12

13 Impact Risk Management Serious Sever Moderate Ouch Negligible Rare Unlikely Possible Likely Almost certain Page 13 Probability of Occurrence

14 Impact Impact Impact Serious Risk Sever Moderate Mitigation Strategy 1 Ouch Negligible Rare Unlikely Possible Likely Probability of Occurrence Almost certain Serious Mitigation Strategy 2 Sever Moderate Ouch Serious Negligible Sever Moderate Rare Unlikely Possible Likely Probability of Occurrence Almost certain Ouch Negligible Rare Unlikely Possible Likely Probability of Occurrence Almost certain

15 Impact Impact Impact Serious Risk Sever Moderate Ouch Negligible Serious Rare Unlikely Possible Likely Probability of Occurrence Mitigation Strategy 2 (Cost y, ffort y, Benefit Y) Almost certain Serious Sever Moderate Ouch Negligible Mitigation Strategy 1 (Cost x, ffort x, Benefit X) Risk Sever Moderate Rare Unlikely Possible Likely Probability of Occurrence Almost certain Ouch Negligible Risk Rare Unlikely Possible Likely Probability of Occurrence Almost certain

16 Reduce the Likelihood of Occurrence Source/ xposure Pro-active thinking Positive Long-term planning Dedication Continuous Monitoring Be a detective

17 Reduce the Impact Integrated and sustainable planning Development of accurate and prompt warning systems Design and construction of robust structures Implementation of hazard-reduction methods such as construction and strengthening of sea embankment, drains, conservation and promotion of natural windbreakers Reliable communication system Mass awareness on preparedness and mitigation at all levels

18 Reduce the Impact Proactive Pre-disaster Strategies Communication plan when signs/symptoms are visible arly warning systems Training of personnel for rescue and evacuation Training/ducating different levels of stakeholders Rescue and vacuation Plans (including traffic plans) Arranging inventories of necessary materials, equipment and commodities Procedures and guidelines for better decision making

19 Case Study # 1: Mitigation of Tsunami Threat Through Better Planning From Ph.D. research of Dr. Sanjeewa Wickramaratne (supervised by Drs. Wirasinghe and Ruwanpura)

20 Global Vulnerability

21 Need for Better Tsunami Preparedness Planning Indian Ocean is under a constant threat of tsunamigenic seismo-active zones (Sunda and Makran). No grass root level study completed to evaluate the preparedness for future tsunami in the Indian sub continent. A research performed focussing Sri Lanka revealed that its astern coast can not be successfully evacuated on time.

22 Understanding the Tsunami Warning Processes in Sri Lanka International Component National Component Local Component Detection Transmission Analysis by PTWC etc. Communication Decision Making by: DoM Communication DMC Police Media vacuation

23 xisting Warning System in Sri Lanka Initial Stage Warning & vacuation Network

24 Analysis Dept. of Meteorology

25 Dept. of Meteorology (DoM), Sri Lanka

26 National Warning Process- Sri Lanka

27 Modeling Scenarios for Sri Lanka Tsunami Types + Time of the day Tsunamis from Sunda Trench (Indonesia) Tsunamis from Makran Fault (Pakistan) Day time and night time tsunamis System Failures Malfunction of regional warning centers (PTWC/JMA) Lapses/failures by warning authorities (DoM/ DMC) Lapses in local evacuation (Police, DMC)

28 Triangular Distribution The durations associated with individual activities can be defined by the three-point estimate: minimum time (the fastest possible response) maximum time mode (the slowest possible response) (most likely response time)

29 Monte Carlo Simulation The principle behind Monte Carlo Simulation is that the behaviour of a statistic in random samples can be assessed by the empirical process of actually drawing lots of random samples and observing this behaviour. The system generates a large number of random numbers in the range of zero to unity and assigns them in the relevant probability distribution of each activity. The resulting random variates are used to calculate the overall time consumption of the network.

30 Frequency Probability Density Functions for Activity Durations PDF & CDF for DoM Activities Cumulative Probability Curve Time

31 CPD of vacuation xisting Warning System Sunda Tsunamis (Daytime and Night )

32 CPD of vacuation xisting Warning System Makran Tsunamis (Daytime & Night) 1

33 Local vacuation Network People React to Warning Inform Family Members Cross Check with Media/Neighbours Decide to vacuate Collect Personal Belongings Co-ordinate with Neighbours Receive Guidance from Police/DDMCU Decide Path / Destination Find Transport scape

34 vacuation Systems

35 System Failure Few Scenarios of Failure No Warning Denied vacuation No Awareness

36 What-if Analysis - Detection Mechanisms A- Global Seismic Network B- Global Tidal Gauges Network C- Tsunami Buoys Systems (DART) C A A B B

37 Proposed Improvements A tide gauge system and a buoy system Traditional Tide Gauge Tsunami Buoys 37

38 Tide Gauges in the Indian Ocean 38

39 Tsunami Buoys in the Indian Ocean 39

40 Improvements in Warning Time Sunda Tsunamis Makran Tsunamis Key benefits: arly detection and thus more warning time Reliability improvement via more instruments 40

41 Alternatives: 1. Tide Gauge System 2. Buoy System Stochastic Decision Tree Analysis T DT D D D 3. Combined System 4. Do Nothing NT D D 1. Cost of detection systems DT D 2. Probability of detection 3. Probability of false warnings 4. Probability of successful TG BS T NT D D evacuations CM 5. Cost of false warnings 6. Cost of evacuations X 41

42 Probability (%) Variation of xpected Value (V) Makran Tsunami Scenario V (millions USD) xpected Value computation promotes the xisting System as the preferred choice for Makran tsunami scenario. 42

43 Probability (%) Variation of xpected Value (V) Sunda Tsunami Scenario V (millions USD) The combined system records the highest V and thus is chosen as the preferred option for Sunda tsunami scenario. 43

44 Saving of Warning Time 44

45 Case Study 2: Mitigation of the Impact of Canadian Prairie Tornadoes From Ph.D. research of Dr. Samanthi Durage (supervised by Drs. Wirasinghe and Ruwanpura)

46 Tornadoes in Canada In Canada, during an average year, approximately 80 tornadoes occur and, on average, cause two deaths and 20 injuries, plus tens of millions of dollars in property damage. (nvironment Canada) 46

47 Tornado Prediction and Detection Doppler radar base data Ground truth observations Weather prediction models Scientific prediction and detection processes 47

48 Warning Communication Weatheradio Radio and televisions Private meteorological companies Weather office website mergency response systems Direct observations Local emergency management agencies Directly from storm prediction centres 24/7 service Interrupting normal programming Scrolled text on the bottom of the screen Broadcasting news about tornado warnings.g.: The Weather Network warnings_e.html In public institutions such as universities By the public By storm chasers Information from the public Public 48

49 49

50 Network Simulation There is a 50% chance that tornado detection, warning and communication can be completed within 20.5 minutes or less. 50

51 Curve Fitting Warning issuance to the warning receipt point (Network Simulation) Warning receipt point to the evacuation completion point (Tornado Survey) 51

52 vacuation There is around 25% chance that the evacuation can be completed within 10 minutes from the warning issuance point. Increasing the time by 5 minutes gives a 75% chance for a household to complete evacuation. 52

53 Few Remarks! Future never rely solely on past disasters and its consequences Plan for unthinkable scenarios ngineers, Scientists and Planners MUST play a major role to mitigate the impact of Disasters

MITIGATION OF THE IMPACT OF TORNADOES IN THE CANADIAN PRAIRIES

12th Annual Canadian Risk and Hazards Network Symposium Coast Plaza Hotel, Calgary, Alberta MITIGATION OF THE IMPACT OF TORNADOES IN THE CANADIAN PRAIRIES BY: SAMANTHI W. DURAGE (PhD) PROFESSOR S.C. WIRASINGHE