Robert R. Holmes
United States Geological Survey
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Featured researches published by Robert R. Holmes.
Journal of Hydraulic Research | 2008
Robert R. Holmes; Marcelo H. Garcia
An experimental field study of flows over bedforms was conducted on the Missouri River near St. Charles, Missouri. Detailed velocity data were collected under two different flow conditions along bedforms in this sand-bed river. The large river-scale data reflect flow characteristics similar to those of laboratory-scale flows, with flow separation occurring downstream of the bedform crest and flow reattachment on the stoss side of the next downstream bedform. Wave-like responses of the flow to the bedforms were detected, with the velocity decreasing throughout the flow depth over bedform troughs, and the velocity increasing over bedform crests. Local and spatially averaged velocity distributions were logarithmic for both datasets. The reach-wise spatially averaged vertical-velocity profile from the standard velocity-defect model was evaluated. The vertically averaged mean flow velocities for the velocity-defect model were within 5% of the measured values and estimated spatially averaged point velocities were within 10% for the upper 90% of the flow depth. The velocity-defect model, neglecting the wake function, was evaluated and found to estimate the vertically averaged mean velocity within 1% of the measured values
Environmental Science & Technology | 2014
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Regional change in the variability and magnitude of flooding could be a major consequence of future global climate change. Extreme floods have the capacity to rapidly transform landscapes and expose landscape vulnerabilities through highly variable spatial patterns of inundation, erosion, and deposition. We use the historic activation of the Birds Point-New Madrid Floodway during the Mississippi and Ohio River Flooding of 2011 as a scientifically unique stress experiment to analyze indicators of floodplain vulnerability. We use pre- and postflood airborne Light Detection and Ranging data sets to locate erosional and depositional hotspots over the 540 km(2) agricultural Floodway. While riparian vegetation between the river and the main levee breach likely prevented widespread deposition, localized scour and deposition occurred near the levee breaches. Eroded gullies nearly 1 km in length were observed at a low ridge of a relict meander scar of the Mississippi River. Our flow modeling and spatial mapping analysis attributes this vulnerability to a combination of erodible soils, flow acceleration associated with legacy fluvial landforms, and a lack of woody vegetation to anchor soil and enhance flow resistance. Results from this study could guide future mitigation and adaptation measures in cases of extreme flooding.
Hydraulic Measurements and Experimental Methods Specialty Conference (HMEM) 2002 | 2002
Robert R. Holmes; Marcelo H. Garcia
Bedforms often are present on the bed of alluvial sand-bed rivers. Bedforms, such as dunes, impact the flow field. In this field st udy, velocity and suspended-sediment concentration measurements were made longitudinally along a dune field in large (5-15 meters deep) alluvial sand-bed rivers. The velocity and suspended-sediment concentration data was collected using an acoustic Doppler current profiler, acoustic Doppler velocimeters, an optical backscatter sensor, and two sediment intakes. This paper presents a description of these measurement devices and techniques for the collection of this data. Some preliminary results observed at the Missouri River at St. Charles, Missouri are presented.
Flood Forecasting#R##N#A Global Perspective | 2016
Gregg J. Wiche; Robert R. Holmes
Streamflow data are vital for a variety of water-resources issues, from flood warning to water supply planning. The collection of streamflow data is usually an involved and complicated process. This chapter serves as an overview of the streamflow data collection process. Readers with the need for the detailed information on the streamflow data collection process are referred to the many references noted in this chapter.
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson
Archive | 2015
Allison E. Goodwell; Zhenduo Zhu; Debsunder Dutta; Jonathan A. Greenberg; Praveen Kumar; Marcelo H. Garcia; Bruce L. Rhoads; Robert R. Holmes; Gary Parker; David P. Berretta; Robert B. Jacobson