South America
- Belo Horizonte
- Buenos Aires
- Ciudad del Este
- Coquimbo
- Guayaquil
- Manaus
- Medellin
- Rosario
- Sao Paulo
- Lima
The floods of 2015 described as the worst in 50 years, forced 150,000 people across four South American countries to flee their homes. Heavy rains, caused by El Nino weather patterns swelled three major rivers affecting Argentina, Brazil, Paraguay and Uruguay. Total economic losses from flooding in 2016 were in excess of USD$1 billion, with only a small portion insured.
Data for the following cities will be available soon: Belo Horizonte, Buenos Aires, Ciudad del Este, Coquimbo, Guayaquil, Manaus, Medellin, Rosario, Sao Paulo, Villa El Salvador.
If you would like data for a city that isn't listed, please let us know here.
Belo Horizonte
On 16th March 2018 74.6 mm of rain fell in just 20 minutes on Belo Horizonte, around half the average for the month. The fire service received hundreds of calls to deal with flooding, wall collapses and falling trees, many cars were dragged along by the raging flood.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Buenos Aires
The City of Buenos Aires frequently suffers from serious flooding that damages property, the economy, and affects the bulk of its population. Over 50 deaths have been recorded since 2010.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Ciudad del Este
7th January 2016 the River Paraguay rose by 7.84 metres, well above the “critical stage” of 5.5 metres. The Parana has increased by 70cm in the 24 hours in Ciudad Del Este, a city expected to be one of the fastest growing in Latin America by 2030. However, a lack of urban planning makes this city susceptible to climate related impacts, with low levels of preparedness and response.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Coquimbo
130mm of rain fell on Coquimboin mid-May 2017. 1500 people were evacuated and 50,000 were left without drinking water. Roads were damaged or blocked leaving over 11,000 people isolated.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Guayaquil
Guayaquil is the largest city in Ecuador, with a population of 3 million inhabitants. As a coastal delta city, it is prone to urban flooding caused by intensive rainfall and high sea levels. During the wet season typically from late December to late April or early May the city can suffer from multiple flood events per week.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Manaus
Manaus, the capital of Amazonas was subjected to the worst flooding for over a century in 2014. The Rio Madeira, a tributary of the Amazon river reached emergency levels on 22 May, remaining at this level for most of June. The flooding of the Rio Negro caused losses of more than USD$91m ($200 million real), much worse than the flooding of 2012. Homes inundated with contaminated water witnessed deaths through bacterial infections such as leptospirosis.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Medellin
On March 26th, 2018 heavy rain caused flooding in the city of Medellin, over 1oo homes were damaged and thousands were left without electricity. A landslide was also reported in the San Javier neighbourhood.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Rosario
Argentina is no stranger to flooding. Heavy rain hit the central northern region in mid-January 2017. Up to 100 millimetres of rain fell on this “bread-basket” of Argentina, the city of Rosario seeing a 251% increase over the seasonal norm.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Sao Paulo
March 20th and 21st 2018 saw Sao Paulo hit by a heavy rainstorm. 3 people were killed, as buildings collapsed and trees fell, following 50mm of rainfall in 24 hours, over 1,000 people were displaced. Also in March, two years earlier 87.2mm of rain caused severe flooding, landslides and major damages, displacing thousands of people causing the closure of São Paulo-Guarulhos airport for 6 hours.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
Lima
March 20th, 2017, Lima was hit by a spell of heavy rain driven by El Niño conditions, which helped drench Peru in 10 times the amount of normal rainfall. Rivers overflowed causing flooding and mudslides, destroying roads and farmland. More than 70 people died, which also isolated hundreds and displaced thousands.
Both datasets contain fluvial and pluvial undefended data.
Standard data contains 3 layers for return periods of 20, 50 and 100 years.
Extended data contains 10 layers. Everything in standard data, and return periods for 5, 10, 75, 200, 250, 500, 1000 years.
