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Geomorphological analysis of North Fork Toutle River, Washington, 1980-1984 /Pearson, Monte L. January 1985 (has links)
Thesis (Ph. D.)--Oregon State University, 1986. / Typescript (photocopy). Includes bibliographical references (leaves 159-167). Also available online.
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Fluvial Biogeomorphic Evolution of the Upper South Fork Toutle River, WA After the 1980 Eruption of Mount St. HelensProctor, Sarah 01 May 2017 (has links)
The eruption of Mount St. Helens in 1980 severely impacted the woody vegetation within the geomorphic floodplain as well as the morphology of the Upper South Fork Toutle River. Historic aerial imagery and LiDAR data were used in combination to create snapshots of the channel and vegetation in 1980, 1983, 1996, 2003, and 2014. This data was mapped and analyzed using GIS, with the primary focus on 2D channel change, vegetation change, and channel-vegetation interactions from 1980 to 2014. No vegetation was discernable in 1980-83 but the vegetation present in 1996 increased in area and in density from 1996 to 2014. The number of channels locations were dependent on vegetation density and presence while vegetation growth occurred predominately in areas previously occupied by the channel.
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State space approach to flood stage estimationJones, Gregory Allen 01 January 1984 (has links)
A flood routing and stage prediction model is developed using the techniques of State Space and Kalman filtering. The governing equation is the physically based hydrologic method of flood routing with the output being an optimal estimate of stage given known inputs of streamflow. These equations are developed in state space and the Kalman filter is employed to estimate the flow and river stage.
The model is applied to the Toutle and Cowlitz Rivers in the State of Washington, where the stage is affected by a shifting bed elevation. With the deterministic inputs at Mayfield Dam on the Cowlitz and Tower Road on the Toutle, the optimal output of stage is predicted at Castle Rock on the Cowlitz River. Addition of the Kalman Filter improves stage prediction based on an application to an observed storm event.
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