Enhancing Regional Digital Preparedness on Natural Hazards The Application of Science and Technology in DRR Decision Making

Transcription

1 Enhancing Regional Digital Preparedness on Natural Hazards The Application of Science and Technology in DRR Decision Making Hongey Chen, Director Wei Sen Li, Secretary General National Sceince and Technology Center for Disaster Reduction Chinese Taipei APEC Workshop on Big Data and Open Data, October 2015, Taipei, Chinese Taipei Future risk Minority Report (1956,2002)

2 Answer vs. Solution Data to action and end to end On text book, only one answer For a solution, like hazard maps, overall understanding of risks is basic Regional collaboration cross cutting knowledge sharing 4

3 Key issues of using big data and open data 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 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 The major challenges of using data In order to apply Big data and Open data for better emergency preparedness, the major challenges to overcome 1. Volume: overwhelming amount of data sets, how to identify relationship for integration 2. Velocity: during urgent moments, pop-up situations and information could hamper decision making 3. Varity: different and diverse data sets are required to delivered information or maps by request 4. Verification: duplications or rumors from difference sources need rules and synergy to focus real issues

4 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? new normal 7 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 8

5 Historical records of Fushan St. since 2008 new normal, an increasing tendency of rainfall 2015 Soudelor 2012 Saola 2008 Jangmi 2008 Sinlaku Fushan St. 1hr (mm) 3hrs (mm) 6hrs (mm) 12hrs (mm) In total (mm) Turbidity (NTU) 2015 Soudelor , Saola , Jangmi , Sinlaku (Nanshi River) By comparisons, new normal", "record breaking rainfall, seems to be a fact 9 Science and technology provide evidence to DRR help local government with better information Facts observed from 2009 Typhoon Marokot in Chinese Taipei 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 10

6 Aggregating big data for open data Cross cutting Synergy, Information sharing, Actionable Adopt advanced model to process for early warning Produce common operating pictures under decision supporting system Collect 120 big data sets from 20 agencies Categories: basic, monitoring, models and historical Data Database Information Actions Portal to access information Information Platform for Disaster Management Registration Authorization Categorization Integration Maintenance operations Information Exchange service 11 Common Operating Picture through Web GIS platform to bridge information gap at local level Bookmarks for highlights Situational information Overlapped Geo-spatial information Real-time Data display 12

7 Situation report about flood risk potential to identify location, situation and estimation 太平山 Rain gauge reading 蘭陽大橋水位站 Water level gauge reading 冬山 寒溪 CCTV 五結 壯圍 礁溪 1. Numerical simulation of floods along a river basin 2. Real time data of gauges to monitor developing situation 3. CCTV video to visualize understanding 13 Situation report about landslide risk potential to identify location, situation and estimation 巴陵 復興鄉 台 9 線 尖石鄉 台 8 線 169K 大同鄉 南澳鄉 秀林鄉 1. Locations of high potential risk of landslides based on history 2. Real time gauge data to assist in decision making road closure 3. CCTV video to collect current situations 14

8 Scientific evidence to foresee the impacts Make a full use of data and models Threshold value of flood: 70mm 8/7 12:00 Real time reading of rain gauge Estimate of flood risk 8/8 00:00 8/8 12:00 Numerical estimation of intensity by hour Base on multiple models plus observed data In the early morning of Aug. 8 th, flood could happened in Wulai District 15 Evidence based emergency operation To decide timing of early evacuation The ideal criteria to conduct early evacuations 1. Day time: less danger to evacuees and emergency responders 2. Arranged transportation: to provide convenience Potential Risk Map of debris flow at township level Observed data Threshold value of debris flow 200 mm accumulated rainfall in 24hrs Red alert (Historical data) Forecast of rainfall Critical happens point at midnight Intensity of rainfall (Model) Typhoon Kong Rey in 2013 Historical data Numerical models Observed data Take Action! 16

9 Case of successful early evacuation during Typhoon Fanapi, in Lai Yi village, Sep Buried houses: Causality: residents evacuated 2009 after Typhoon Morakot 9/18 9/19 照片來源 : 水保局 05:30 14:00 15:00 08:40 23:00 Issue land warning Early warning of risk Evacuation operation Typhoon landfall time Landside in Lai Yi 32 hours ahead 2009 after Typhoon Morakot Case of successful early evacuation during Typhoon Soudelor in Her liu village, Aug. 7, 2015 Red alert Mud flow happened Yellow alert Pro active evacuation Evacuation completed 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

10 Public private partnership on enhancing information coverage (with Google service) Imitation of Open Data in 2013, through services of Google Crisis Map and Google Public Alerts to disseminate typhoon warning messages. Typhoon Soulik (7/10 14) : number of system access about 1.3 million 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 Google Public Alerts Google Crisis Map 19 How to motivate the whole society with DRR from concepts to actions Sceince and technology Understandable knowledge People s mindset Take actions 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 20

11 Conclusions: Roles of S&T to reduce impacts From science to decision making and actions Scientific Prediction Rea-time Monitoring In-time Operation Key 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 revising decision support Limited by number, location, transmission Provide reaction based on welldefined plan Tool for saving more time before it s too late Reference for allocating emergency support Limited by determination of alllevel administrators An integration of Natural science Social science Engineering ICT, Social media Emergency management Multiple key I stakeholders Public private partnership Data. 21 Thanks for your attention