Interaction among alluvial cover, bed roughness, and incision rate in purely bedrock and alluvial-bedrock channel

2014 ◽  
Vol 119 (10) ◽  
pp. 2123-2146 ◽  
Author(s):  
Takuya Inoue ◽  
Norihiro Izumi ◽  
Yasuyuki Shimizu ◽  
Gary Parker
Keyword(s):  
Bedrock Channel ◽  
Bed Roughness

scholarly journals Lateral erosion in an experimental bedrock channel: The influence of bed roughness on erosion by bed load impacts

2016 ◽  
Vol 121 (5) ◽  
pp. 1084-1105 ◽  
Author(s):  
Theodore K. Fuller ◽  
Karen B. Gran ◽  
Leonard S. Sklar ◽  
Chris Paola
Keyword(s):  
Bed Load ◽  
Bedrock Channel ◽  
Lateral Erosion ◽  
Bed Roughness

scholarly journals Estimation of Vegetation-Induced Flow Resistance for Hydraulic Computations Using Airborne Laser Scanning Data

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1864
Author(s):  
Peter Mewis
Keyword(s):  
Laser Scanning ◽  
Flow Resistance ◽  
Laser Scanner ◽  
Airborne Laser Scanning ◽  
Density Parameter ◽  
Vegetation Density ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Bed Roughness ◽  
Available Information

The effect of vegetation in hydraulic computations can be significant. This effect is important for flood computations. Today, the necessary terrain information for flood computations is obtained by airborne laser scanning techniques. The quality and density of the airborne laser scanning information allows for more extensive use of these data in flow computations. In this paper, known methods are improved and combined into a new simple and objective procedure to estimate the hydraulic resistance of vegetation on the flow in the field. State-of-the-art airborne laser scanner information is explored to estimate the vegetation density. The laser scanning information provides the base for the calculation of the vegetation density parameter ωp using the Beer–Lambert law. In a second step, the vegetation density is employed in a flow model to appropriately account for vegetation resistance. The use of this vegetation parameter is superior to the common method of accounting for the vegetation resistance in the bed resistance parameter for bed roughness. The proposed procedure utilizes newly available information and is demonstrated in an example. The obtained values fit very well with the values obtained in the literature. Moreover, the obtained information is very detailed. In the results, the effect of vegetation is estimated objectively without the assignment of typical values. Moreover, a more structured flow field is computed with the flood around denser vegetation, such as groups of bushes. A further thorough study based on observed flow resistance is needed.

Four decades of radar-echo sounding: The past, present, and future of radar applications for understanding subglacial environments 

2021 ◽  
Author(s):  
Winnie Chu
Keyword(s):  
Ice Sheets ◽  
Ice Sheet ◽  
Thermal Conditions ◽  
Machine Learning Algorithms ◽  
Polar Ice Sheets ◽  
Radar Echo ◽  
Radar Applications ◽  
Bed Roughness ◽  
Subglacial Environments ◽  
Echo Sounding

<p>Airborne radar sounding observations have been instrumental in understanding subglacial environments and basal processes of ice sheets. Since the advent of analog radar-echo sounding (RES) system in the early 1970s, there have been tremendous innovations in both RES hardware and signal processing techniques. These technological advancements have provided high-resolution ice thickness measurements, improved detection and characterization of subglacial hydrology, as well as improved understanding of basal thermal conditions, bed roughness and geomorphology, and other processes that govern the basal boundary of the polar ice sheets. In this talk, I will provide an overview of the recent developments in radar processing approaches and system designs and highlight some of the new understanding of ice sheet subglacial processes that emerge from these breakthroughs. I will end by discussing areas where future radar applications and discoveries may be possible, including the utilization of machine learning algorithms, space-borne radar missions, and ground-based passive radar platforms to provide long-term monitoring of ice sheet subglacial environments.</p>

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