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At least 411 million people affected by natural disasters in 2016

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In 2016, EM-DAT preliminary data indicates that 301 country level disasters occurred, affecting 102 countries. The impact of which sums up to a total of 7,628 deaths, 411 million affected people, and US$97 billion of economic damages.

China was the most disaster-affected country, with a total of 29 events, of which 52% were hydrological and 41% meteorological (Fig. 1). Those disasters killed a total of 1,151 people, which brings China to the TOP10 of countries ranked by number of death, and resulted in 13 million affected people (Fig. 2). It was followed by the U.S. where 20 events (Fig. 1) caused the death of 250 people and affected 360,000 others (Fig. 2). In India, 17 natural disasters (Fig. 1) caused 884 deaths and 331 million affected, which brings it to the first place when countries are ranked by number of affected people (Fig. 2).

This result is mainly explained by a severe drought that affected 330 million Indians (80% of the 2016 affected people). The two deadliest events in 2016 occurred on the American continent. The 7.8 magnitude Ecuador earthquake, on April 16th, killed 676 people and affected 1.23 million others. The total economic losses were estimated at $US3.3 billion. This was followed in Septembre-October by Hurricane Matthew which was responsible for the death of at least 546 people in Haiti and 49 in the USA. In the Democratic People’s Republic of Korea, 538 people lost their lives because of intense flooding events in August-September.

The analysis by disaster type (Fig. 3) suggests that 50% of 2016 events are related to flooding, and storms represent 22% of all natural events reported this year. Together, those two types of disasters are responsible for 71% of all natural disasters related deaths, followed by earthquakes (17% related deaths). Droughts account for a tremendous part of the disaster affected population (94%). On the other hand, the reported number of people affected by floods and storms is really low (5%) but this might be due to data reporting issues resulting from the difficulty in defining and measuring the “affected” variable