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    Hydrologic Evaluation of Low Impact Development Using a Continuous, Spatially-Distributed Model

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    Bosley_MS_Thesis.pdf (3.373Mb)
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    Date
    2008-07-11
    Author
    Bosley II, Eugene Kern
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    Abstract
    Low Impact Development (LID) is gaining popularity as a solution to erosion, flooding, and water quality problems that stormwater ponds partially address. LID analysis takes a spatially lumped approach, based on maintaining the predevelopment Curve Number and time of concentration, precluding consideration of the spatial distribution of impervious areas and Integrated Management Practices (IMP's), runoff-runon processes, and the effects of land grading. Success is thus dependent on the accuracy of the assumption of watershed uniformity, applied to both land cover distribution and flow path length. Considering the cost of long-term paired watershed monitoring, continuous, spatially-distributed hydrologic modeling was judged a better method to compare the response of LID, forest, and conventional development. Review of available models revealed EPA-SWMM 4.4H as the most applicable to the task. A 4.3-acre subwatershed of a local subdivision was adapted to LID using impervious surface disconnection, forest retention, and IMPs. SWMM was applied to the LID development at a fine spatial scale, yielding an 80-element SWMM model. The LID model was modified to reflect conventional development, with gutters, storm sewer, and detention. A predevelopment forest model was also developed. Two parameter sets were used, representing a range of assumptions characterized as favorable or unfavorable toward a particular development form. Modeled scenarios included favorable and unfavorable versions of Forest, LID, uncontrolled Conventional Development, and Conventional Development with Stormwater Management. SWMM was run in continuous mode using local rainfall data, and event mode using NRCS design storms. Runoff volumes, peak flows, and flow duration curves were compared.
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    http://hdl.handle.net/10919/34263
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    • Masters Theses [20942]

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