Analysis of stream restoration design approaches through man-made drained ponds, using the Stream Quantification Tool

Abstract

Manner, Catherine. Master of Natural Resources- Assessment and Analysis Technical Option. Title: Analysis of Stream Restoration Design Approaches through Man-Made Drained Ponds using, the Stream Quantification Tool Every stream restoration design is different from the rest, no two restoration projects are the same. No design method is proven the best method in all scenarios. In the mitigation/restoration field, more and more stream restoration projects are being completed to restore historical stream channels through drained man-made ponds. Yet, providers of mitigation have attempted many different ways to design this specific kind of stream. The field has yet to agree on the best technical design approach for designing a channel through a drained pond. A survey was conducted through use of Google Forums to assess an industry wide opinion concerning different design approaches through drained ponds. The questions were designed to identify people’s backgrounds in the industry and to gage if there place of work or experience level influenced there choice in preferred design approaches. In order to gain an understanding about which design approaches produced the most successful restoration projects one assessment method was used on all case study sites. The three (Site A, Site B, and Site C) stream restoration sites were assessed using the Stream Quantification Tool (SQT). Each of these three sites where stream restoration was conducted was located where a pond once existed. At each site a different design approach was used. The SQT was used at each site to determine which design approach provided the most functional uplift to the stream system. In North Carolina, the Stream Quantification Tool (SQT), created by Will Harman, is the latest assessment method to be developed. The SQT focuses on stream restoration and how it is necessary to understand how stream functions work together. The tool is built around function based parameters, which are interrelated and build upon each other. Assessing a stream’s function allows for a more successful stream restoration project.