A. James Clark School of Engineering

Permanent URI for this communityhttp://hdl.handle.net/1903/1654

The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Predicting Low Probability Streamflow Using Lidar Data and Hydraulic Geometry
(2019) Mardones, Javier; Brubaker, Kaye L; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Predicting stream flow is essential for safe and economic planning and design of hydraulic structures. This study uses the observed channel cross-section from LiDAR data and physical concepts of shear stress to estimate bankfull discharge (Qbf). Assuming that Qbf is the median of the annual peak flow distribution, a 2-parameter Extreme Value Type I distribution was fitted to predict discharge to a 200-year return period. The method was compared with gauged sites in low-order streams (less than 90-meter bankfull width) resulting in SE/SY=1.31 for Qbf and SE/SY=1.90 for the 200-year return period discharge; model precision is poor. However, the relative bias (-15\% to +15\%) demonstrates that on average results are similar to gauged data. Relationships between flow and channel geometry assure a quick way to estimate stream data and can serve as a tool used prior to applying conventional hydrologic methods such as flow routing and regional regression equations.