STREAM LOCATIONS AND IDENTIFICATION: A COMPARISON AND CASE STUDY FROM THE OZARK MOUNTAINS USING THREE SOURCES OF INFORMATION

Land managers face a unique challenge when utilizing digital information in Geographic Information Systems, the largest part of which is the accuracy, detail and scale of the information available for use. A prime example of this arises through the need for accurate hydrologic feature representation when planning for land management activities. Traditional sources for identifying surface water features, such as topographic maps, possess inherent limitations dependant on the landscape mapped and methods used to derive these maps. New technologies have recently made it possible to augment these traditional sources using either modeled representations of a stream network, or ground based field collection of the actual stream network. These less traditional sources of information have their own limitations and inherent inaccuracies. In addition to this, unique landscapes often demonstrate specific hydrologic characteristics and features which may not be compatible with traditional or contemporary identification and mapping. For this study, hydrologic features were identified using three sources: the National Hydrography Dataset (NHD) 1:24,000 representation for topographic features, a modeled stream network derived from flow accumulation routes performed on a 10 meter DEM coverage, and field collection of stream channel locations using a differential GPS unit on a representative sample area. This study is an attempt to analyze the limitations of these different sources as well as to identify their potential benefits with respect to land management applications in the Boston Mountain Ecoregion of the Ozark Mountain landscape. This study concludes that the NHD information was found to under represent the ephemeral streams of the landscape. The modeled stream network was found to represent the presence and general magnitude of occurrence for these ephemeral drains but did not accurately represent their location. Ground based field collection proved most accurate for identifying these features but was most time consuming and cost limiting.