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Coahuila Disasters -  NASA DEVELOP: Piedras Negras

Tuesday, May 7th 2013


Coahuila, one of the 32 Federal Entities of Mexico, shares a border with the U.S. for a 512-kilometer stretch along the Rio Bravo, also known as the Rio Grande. Flood risk is a geographically sporadic, yet very serious concern in the state. In April 2004, the state experienced a devastating flood in the border city of Piedras Negras, where more than 30 people died and 4,000 citizens lost their homes. In 2010, Coahuila experienced extensive flooding in Cuidad Acuña and other neighboring cities. Hundreds of people had to be evacuated and the border bridge closed due to flooding from major rainfall swelling behind a dam. Around 10,000 people suffered severe damage to their homes or possessions due to these flood events. Also in 2010, Hurricane Alex caused more than a dozen deaths relating to the flood damage, as well as the loss of up to 300,000 hectares of agricultural lands. This fall DEVELOP used NASA Earth observing systems to assist with flood-risk mapping, flood modeling, and flood mitigation. Data from a suite of satellites including Landsat 5, Tropical Rainfall Measuring Mission (TRMM) and the Shuttle Radar Topography Mission (SRTM) were employed to provide a methodology for assessing flood risk before an event and flood extent during and after an event. This project assisted partners from various Mexican agencies by presenting them with a more efficient methodology for flood response and damage assessment during and after a flood, potentially saving lives and resources. The project also analyzed the socio-economic factors that can indicate the susceptibility of a community to floods. By performing a Principal Component Analysis, community vulnerability levels were identified for areas in Coahuila that could be at higher risk after a flood event. Finally, to validate the results, satellite data and socio-economic data were incorporated into the Federal Emergency Management Agency's (FEMA) Hazards-United States (Hazus) Multi Hazard estimation model. The results from this model were able to provide risk estimation maps that included economic losses, infrastructure damage and the hazard intensity of the flood events.

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