Project Team: DEVELOP Tech Team
Team Location: NASA Langley Research Center, Hampton, Virginia
This image shows the flow direction across La Mosca Watershed. This data was used to determine the shape of the watershed and the drainage basins. Image Credit: DEVELOP Tech Team.
Authors:
Stephanie Rushley, Project Lead (North Carolina State University)
Matthew Carter (U.S. Air Force)
Charles Chiou (Old Dominion University)
Rick Farmer (Mathews High School)
Kevin Haywood (U.S. Air Force)
Anthony Pototzky (Old Dominion University)
Adam White (Christopher Newport University)
Daniel Winker (University of Virginia)
Mentors/Advisors:
Lauren Childs-Gleason (NASA DEVELOP, National Operations Lead)
Dr. Kenton Ross (NASA DEVELOP, National Science Advisor)
Past/Other Contributors:
Angelica Gutierrez (National Oceanic and Atmospheric Administration)
Pilar Galindo (Colombia Instituto de HidrologÌ_a, MeteorologÌ_a y Estudios Ambientales)
Ricardo Quiroga (Colombia Instituto de HidrologÌ_a, MeteorologÌ_a y Estudios Ambientales)
Abstract:
Colombia’s terrain is highly variable, ranging from the Andes Mountains’ peaks to low-lying coastal areas, with many areas at risk of flooding. To identify risk, NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study region. Precipitation data from the Tropical Rainfall Measuring Mission’s (TRMM) near-real-time rainfall products and precipitation data from the Colombian Instituto de HidrologÌ_a, MeteorologÌ_a y Estudios Ambientales (Institute of Hydrology, Meteorology and Environmental Studies [IDEAM]) and stations in the La Mosca River Basin were used to create rainfall distribution maps for the region. The preliminary risk assessment was applied to a pilot study area of the La Mosca River basin. Using the precipitation data and the ASTER DEM, the Web application, Mi PronÌ_stico, run by IDEAM, was updated to include an interactive map which allows users to search for a location and view the vulnerability and current conditions in rainfall and flood warnings. The geospatial information was linked to an early warning system in Mi PronÌ_stico that can alert the public of flood warnings and identify locations of nearby shelters.