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

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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

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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

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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

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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