Red River Valley Flooding Response
The Red River of the North has a long history of spring flooding events, many of which have stretched for miles outside its banks. There are two prime reasons for this phenomenon. First, it is one of few major rivers in the United States that flows north into Canada. As such, during the spring melt in the upper Midwest, the waters of the Red River move north in a channel that is often choked off by debris and ice in areas that have not yet thawed. Second, the Red River cuts through an area that was previously the bottom of a glacial lake. Consequently, the terrain is exceptional flat, allowing water to flood for great distances once it comes out of the banks of the river.
Since the Red River forms the border that separates Minnesota from North and South Dakota, an effective response to a flooding disaster along its course requires detailed and ongoing coordination involving many federal, tribal, state and local units of government. Key to such multi-agency collaborative efforts is a near real-time understanding of what is taking place – geographically – through use of maps and Geographic Information Systems (GIS).
It’s against this backdrop that in March 2009, the MnGeo Emergency Preparedness Committee “Go Team” mobilized for the first time in response to a natural disaster. Using software, web services and hosting donated by SharedGeo, as well as services provided by several units of government, an online node was created that allowed the download of highly accurate and current maps for disaster response workers and the general public. These map products were continuously updated throughout this flood of record dimensions, with some select areas receiving daily updates. As shown by the example screen shot below, this product is believed to have been a first in the nation effort.
In February 2010, SharedGeo subsequently provided assistance to open-source mapping initiatives for the Haiti Earthquake Response based on the techniques it developed for the 2009 Red River Valley flooding disaster (efficient and continuous indexing of large volumes of image files).