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Enhancing Regional Digital Preparedness on Natural Hazards ‐ The Application of Science and
Technology in DRR Decision‐Making
Hongey Chen, DirectorWei‐Sen Li, Secretary General
National Sceince and Technology Center for Disaster ReductionChinese Taipei
APEC Workshop on Big Data and Open Data, 29‐30 October 2015, Taipei, Chinese Taipei
Future riskMinority Report (1956,2002)
Answer vs. SolutionData to action and end‐to‐end
On text book, only one answerFor a solution, like hazard maps,
overall understanding of risks is basic
4
Regional collaboration cross‐cutting knowledge sharing
Key issues of using big data and open data
• Use to big or open data Data archives Cloud system Data format Exchange protocols Official sites or social
media
• Inclusive stakeholders Governments Research institutes NGOs, NPOs Media, social media Citizens
• Information intelligence Data Organizing Data Analyzing Data warehousing Data Presenting “Extract”, “Transform”
and “Load”
• Basic type of data sets Physical
vulnerabilities Social vulnerabilities Historical events Numerical models Observations
The major challenges of using data
• In order to apply “Big data and Open data” for better emergencypreparedness, the major challenges to overcome
1. Volume: overwhelming amount of data sets, how to identifyrelationship for integration
2. Velocity: during urgent moments, pop-up situations andinformation could hamper decision making
3. Varity: different and diverse data sets are required to deliveredinformation or maps by request
4. Verification: duplications or rumors from difference sources needrules and synergy to focus real issues
“new normal”?
Outlines – Try to answer the three questions
• How science, technology and research address “new normal”?• How scientific innovations are used in disaster risk reduction?• How can science, technology and research be applied to facilitate
DRR collaboration between and among economies, the private sector, and international organizations?
Home elevation after Superstorm Sandy in New Jersey
“new normal”7
8
Observations of “New normal” and its impacts‐ “unprecedented” becomes “normal”
• “New normal” could be found “increasing” in– Intensity of rainfall– Strength of typhoons– Occurrence of extreme weather events ( floods, droughts)
• The impact would be amplified by– Increasing population– Rapid and unplanned urbanization– Poor land use– Climate change– Vulnerable global supply chain– Economic activities exposed to natural hazards
1hr(mm)
3hrs(mm)
6hrs(mm)
12hrs(mm)
In total(mm)
Turbidity(NTU)
2015 Soudelor 95 253 442 655 792 39,3002012 Saola 79 156 238 367 752 12,0002008 Jangmi 61 132 203 334 574 10,5002008 Sinlaku 51 120 169 271 955 (Nanshi River)
2015 Soudelor 2012 Saola 2008 Jangmi 2008 Sinlaku
Historical records of Fushan St. since 2008‐ new normal, an increasing tendency of rainfall
Fushan St.
By comparisons, “new normal", "record‐breaking” rainfall, seems to be a fact9
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Science and technology provide evidence to DRR‐ help local government with better information
Too much or too little information during emergency response• Channel to acquire useful information• System of systems to integrate information
Lack of common operating picture to coordinate actions• Potential risk maps for planning• Situation maps for operation
When and how to make timely decisions• No well‐defined plans in advance• No experienced staff to make suggestions
Facts observed from 2009 Typhoon Marokot in Chinese Taipei
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Aggregating big data for open data–“Cross‐cutting Synergy” , “Information sharing”, “Actionable”
Portal to accessinformation
InformationExchange service
RegistrationCategorization
Maintenance operationsMaintenance operations
Information Platform for Disaster Management AuthorizationIntegration
• Collect 120 big data sets from 20 agencies
• Categories: basic, monitoring, models and historical
• Adopt advanced model to process for early warning
DataDatabase
InformationActions
• Produce common operating pictures under decision supporting system
Common Operating Picture through Web‐GIS platform to bridge information gap at local level
OverlappedGeo-spatialinformation
Situationalinformation
Real-timeData display
Bookmarks forhighlights
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蘭陽大橋水位站Water level gauge reading
太平山Rain gauge reading
壯圍
冬山
礁溪五結
Situation report about flood risk potential‐ to identify location, situation and estimation
寒溪CCTV
1. Numerical simulation of floods along a river basin2. Real‐time data of gauges to monitor developing situation3. CCTV video to visualize understanding
13
尖石鄉
大同鄉
台9線
台8線169K
巴陵
秀林鄉
南澳鄉
復興鄉
Situation report about landslide risk potential‐ to identify location, situation and estimation
1. Locations of high potential risk of landslides based on history2. Real‐time gauge data to assist in decision making – road closure3. CCTV video to collect current situations
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Scientific evidence to foresee the impacts‐Make a full use of data and models
Base on multiple models plus observed data• In the early morning of Aug. 8th, flood could happened in Wulai District
Estimate of flood risk Real‐time reading of rain gauge Numerical estimation of intensity by hour
Threshold value of flood: 70mm
8/8 00:00 8/8 12:008/7 12:00
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Evidence‐based emergency operation – To decide timing of early evacuation
Potential Risk Map of debris flowat township level
Threshold value of debris flow200 mm accumulated rainfall in 24hrs
Forecast of rainfall
Intensity of rainfall (Model)
Critical happens point at midnight
Red alert (Historical data)
Observed data
Historical data
Observed data
Numerical models
TakeAction!
Typhoon Kong‐Rey in 2013
The ideal criteria to conduct early evacuations1. Day time: less danger to evacuees and emergency responders2. Arranged transportation: to provide convenience
16
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Case of successful early evacuation during Typhoon Fanapi , in Lai‐Yi village, Sep. 2010
照片來源:水保局9/1805:30
9/1908:4014:00 15:00 23:00
Issue landwarning
Early warning of risk
Evacuation operation
Typhoonlandfall time
Landside in Lai‐Yi
32 hours ahead
1. Buried houses: 502. Causality: 03. 400 residents evacuated
2009 after Typhoon Morakot
2009 after Typhoon Morakot 2010
Case of successful early evacuation during Typhoon Soudelor in Her‐liu village, Aug. 7, 2015
Pro‐active evacuation
Red alert
Yellow alert
Evacuation completed
Mud flow happened
Lead time 16 hrs
Local residents voluntarily took action• All 32 residents safely evacuated on 8/7• Rainfall reached the threshold value of
red alter on 8/8, 5:00 am• 10 of 15 houses buried on 8/8, 7:40 am 18
• Imitation of Open Data in 2013, through services of Google CrisisMap and Google Public Alerts to disseminate typhoon warningmessages.
– Typhoon Soulik (7/10‐14) : number of system access about1.3 million
Google Crisis MapGoogle Public Alerts
• In 2014, the total number of accessing Google services is around 12 million– Typhoon Matmo (7/21‐23): 4.5 million– Typhoon Fung‐wong (9/19‐22): 4.9 million
Public‐private partnership on enhancing information coverage (with Google service)
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How to motivate the whole society with DRR‐ from concepts to actions
Sceince and technology
Understandableknowledge
People’smindset
Takeactions
Transformation
Interpretation
Perception
Digest scientific outcomes as becoming feasible and applicable‐ Risk Communication
To explain the relevance and importance related to daily life‐ Enroot culture of DRR
To empower the capability and capacity on when and how‐ Conduct DRR lifecycle
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Conclusions: Roles of S&T to reduce impacts‐ From science to decision making and actions
ScientificPrediction Scientific
Prediction Rea-time
MonitoringRea-time
MonitoringIn-time
OperationIn-time
OperationKey elements to
succeed
• Provide forecasting based on scientific models
• Tool for pre‐disaster deployment
• Reference for decision support
• Limited by technology development
• Provide updated data based on gauges
• Tool for pinpointing blind areas by forecast
• Reference for revisingdecision support
• Limited by number, location, transmission
• Provide reaction based on well‐defined plan
• Tool for saving more time before it’s too late
• Reference for allocating emergency support
• Limited by determination of all‐level administrators
I
An integration of• Natural science• Social science• Engineering• ICT, Social media• Emergency
management• Multiple key
stakeholders• Public‐private
partnership
• Data• ……….
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Thanks for your attention