Stream slope is a critical component in lotic systems research. It is commonly associated with fish and invertebrate distribution, and is prominently used in many stream classification schemes. Stream slope is also required to compute other stream variables, such as stream power, a fundamental component in stream sediment dynamics. Due to its importance, stream slope is regularly estimated remotely using a Geographic Information System (GIS). However, the accuracy of GIS-derived stream slope estimates is not well established, especially in low-slope regions. Additionally, little is known about variables that may influence the accuracy of GIS-derived slope estimates. In this study, the accuracy of eight GIS methods for estimating stream slope was evaluated by comparison to "true" field-surveyed values. Several novel GIS methods for estimating stream slope are presented. Five stream variables were assessed for their contribution to error in GIS-derived stream slope estimates. To demonstrate practical applicability, GIS-derived stream slope estimates were used to calculate stream power. GIS-derived stream slope estimates produced using 1:24,000 USGS topographic maps and Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) were most accurate. Estimates derived from 1- and 1/3-Arc Second National Elevation Dataset DEMs were less accurate. The application of a focal statistics tool to LiDAR-derived DEMs improved stream slope estimate accuracy. Consistent sources of error in GIS stream slope estimates were not identified. The utility of GIS-derived stream slope estimates was demonstrated by presenting an association between stream power and depth of fine sediment.
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University of Minnesota (Minneapolis, Minnesota)
Kocian, Matthew James
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