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Travis A. Dahl

Researcher at Engineer Research and Development Center

Publications -  30
Citations -  120

Travis A. Dahl is an academic researcher from Engineer Research and Development Center. The author has contributed to research in topics: Geology & Sediment. The author has an hindex of 5, co-authored 22 publications receiving 104 citations. Previous affiliations of Travis A. Dahl include Rensselaer Polytechnic Institute & United States Army Corps of Engineers.

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Estimates of sediment trapping rates for two reservoirs in the Lake Erie watershed: Past and present scenarios

TL;DR: In this paper, the authors investigated the historical function of dams as sediment storage points and assessed the effect of anthropogenic influences including land use change and dam construction on sediment yield and accumulation within the Lake Rockwell and Ballville Dam watershed.
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Impacts of projected climate change on sediment yield and dredging costs

TL;DR: In this article, the authors applied a hydrologic model driven by a large suite of climate change scenarios to simulate both historical and future sediment yield and transport in two large adjacent watersheds in the Great Lakes region.
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Quantifying beaver dam dynamics and sediment retention using aerial imagery, habitat characteristics, and economic drivers

TL;DR: In this paper, the authors used aerial photos from >70 years along with GIS analysis to quantify counts, sizes, and distributions of beaver dams and impoundments over time, including site recurrence.
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Growth-Climate Relationships of Pinus strobus In the Floodway Versus Terrace Forest Along the Banks of the Red Cedar River, Michigan

TL;DR: The authors examined the effects of climate (i.e. temperature, precipitation, moisture index, river discharge) on the radial growth of eastern white pine (Pinus strobus) in two contrasting microhabitats (floodway versus terrace) in relation to an elevational and moisture gradient along the banks of the Red Cedar River, Michigan.
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The effect of long-memory arrivals on queue performance

TL;DR: A factorial simulation experiment is used to relate four performance measures to degree of long memory, server utilization, number of customers, and their interactions and shows a clear relationship between long memory and queue performance.