Title page for ETD etd-05302006-140944


Type of Document Master's Thesis
Author Bernard, Aaron Michael
URN etd-05302006-140944
Title Geospatial Modeling of Forest Road Networks and Their Effect on Stream Macroinvertebrate Communities
Degree Master of Science
Department Forestry
Advisory Committee
Advisor Name Title
Prisley, Stephen P. Committee Chair
Aust, Wallace Michael Committee Member
Heatwole, Conrad D. Committee Member
Keywords
  • Geographic Information Systems
  • Sedimentation
  • Benthic Macroinvertebrates
  • Road Impacts
Date of Defense 2006-05-03
Availability unrestricted
Abstract
Road construction and maintenance throughout the country continues to be one of the largest contributors of sediment pollution to aquatic systems. Though impacts of road networks on aquatic systems can be potentially severe, little work has been performed to evaluate the effect that road spatial location within a watershed has on water quality. To address this issue from a quantitative perspective, a “Road Impact Factor” protocol was designed to identify potential erosion-prone segments of road networks based on road gradient, spatial location based on hydrologic flow length, surface composition, and water control installations. The protocol was developed for two regions in Central Idaho and Eastern Oregon. We then used the hydrologic travel time procedure, developed for use in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) runoff and routing model, in order to characterize the spatial distribution of potential road runoff impacts within the study areas. Ten macroinvertebrate metrics sensitive to sedimentation (i.e. % Intolerant Taxa, Hilsenhoff Biotic Index, etc.) were analyzed to test the significance of the spatial distribution of Road Impact Factors. These 10 metrics were analyzed under the hypothesis that values will be lower for those study areas that have a higher degree of road impact and a lower distance between the road segments and stream reaches. Results of a quadrant analysis and hierarchical clustering analysis showed hypothesized trends for several metrics in Idaho, though the trends were not strong. No trends were observed in Oregon. The variability in results is likely due to limitations of the input datasets.
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