21 September 2018
Super Typhoon Mangkhut made landfall in Guangdong Province, China around 5 PM Sunday (16 September 2018) as a Category 2 hurricane (in Saffir–Simpson hurricane wind scale). This blog summarises a number of rapid analyses we performed and some of our existing information products that can be used to help assess the impact of this major event. New public datasets on global elevation and population have been explored.
1. Landfall information
It’s well known that various agencies in the region (in mainland China, Hong Kong, Japan, etc.) develop and release such information, but we sourced the official landfall information about this event through the National Meteorological Center, China.
A comparison about the landfall between Super Typhoon Mangkhut and Hurricane Florence (the event that devastated the US a couple days earlier) was made.
As far far the event track is concerned, agencies in the region usually do not publicly release such data in a readily accessible geospatial format, so we sourced it through the Joint Typhoon Warning Center in the US.
#TyphoonMangkhut devastates southern coast of China: Some rapid risk analyses
1. Landfall Information: Comparison between #TyphoonMangkhut & #HurricaneFlorence (in the US)
2. In terms of wind speed, it’s the 9th most powerful typhoon hitting Guangdong Province since records began pic.twitter.com/TOoBuTTNT0— BigData Earth (@BigDataEarth) September 17, 2018
2. Aggregate exposure concentration and site-level location profile report
The Guangdong Province is one of the most developed regions in China, and the Pearl River Delta has seen the most rapid population increase in the world over the past three decades. This time Super Typhoon Mangkhut directly impacted the whole region and posed serious threats to all industrial assets, infrastructure and commercial activities.
Aggregate exposure information on population (2017) and GDP (2017) was collected. We also calculated population surges for the region using a global population dataset; our recently developed APIs for innovative exposure analytics have made this task very handy, and other forms of exposure data (e.g. building stocks and sum insured) can be analysed similarly.
For assessing risk at individual locations, we provide two web pages (Link 1 and Link 2, with Google Maps embedded) to allow users to request site-level location profile report automatically. We also offer APIs for independent developers to integrate such resources.
#TyphoonMangkhut wreaks havoc in southern China, incl. #Guangdong #HongKong #Macau
1. Stats on aggregate exposure at risk (2017 population & GDP)
2. Rapid population increase in China’s Pearl River Delta from 2000 to 2015
3. Site-level location profile report enabled by web APIs pic.twitter.com/J0sy6Wy2dV— BigData Earth (@BigDataEarth) September 17, 2018
3. Impact in northern Philippines
Impact of the Super Typhoon #TyphoonMangkhut in northern #Philippines
– Severe flooding in #Luzon Island seen by today’s (2018-09-18) #sentinel2 satellite imageryCredit: EU, contains modified Copernicus Sentinel data (2018)@CopernicusEU @CopernicusEMS @sentinel_hub pic.twitter.com/cB2oKPadxt
— BigData Earth (@BigDataEarth) September 18, 2018