Paper No. 12
Presentation Time: 4:45 PM


GOLDADE, Melissa M.1, LEE, Eunmok2, KASTENS, Jude2, JOHNSON, William C.1 and MACPHERSON, G.L.3, (1)Dept. of Geography, University of Kansas, 1475 Jayhawk Blvd, Rm. 213, Lawrence, KS 66045, (2)Kansas Biological Survey, Lawrence, KS 66045, (3)Dept. of Geology, Univ of Kansas, 1475 Jayhawk Blvd, 120 Lindley Hall, Lawrence, KS 66045,

In 2012 alone, catastrophic flooding in Ghor, Sari Pul, and Takhar provinces of northern Afghanistan killed scores and forced over 10,000 from their homes. Extreme flood events are not unusual in these semi-arid to hyper-arid regions of the world, and Afghanistan is no exception, especially given its mountainous terrain. Flood-water inundation estimation using available satellite imagery can be accomplished remotely, thereby providing a means to reduce the impact of such flood events by improving shared situational awareness during major flood events. Timely acquisition of appropriate imagery for flood extent estimation, however, is often complicated by satellite orbital considerations, weather, cost, data licensing restrictions, and other issues. Recognizing the need to develop tools to supplement imagery where not available, complement imagery when it is available, and bridge the gap between imagery based flood mapping and traditional hydrodynamic modeling approaches, we have developed a topographic floodplain model (FLDPLN) that has been used to identify and map river valley floodplains using elevation data ranging from 90-m SRTM to 1-m LiDAR. Resulting floodplain “depth to flood” (DTF) databases are completely seamless and modular. Consequently, FLDPLN can be used for river valley identification, flood estimation, hydrologic connectivity indexing, and scenario modeling (e.g., a particular flood event or impacts of adding or removing levees or other structures). Model outputs have applications for river valley morphology assessment, archaeological explorations, ecological modeling, wetland identification and delineation, and flood disaster response mitigation and damage assessment. FLDPLN is being applied in Afghanistan to flood-prone areas along the southern and northern flanks of the Hindu Kush mountain range to generate a continuum of flood-event models up to 10m in depth. Elevation data used in this application of FLDPLN included high-resolution, drone-acquired LiDAR (1 m) and IFSAR (5m; INTERMAP). Results provide a quantitative approach to evaluating the potential risk to urban/village infrastructure as well as to irrigation systems, agricultural fields and archaeological sites.