Sensitivity of an ADCIRC Tide and Storm Surge Model to Manning's n

2012 ◽  
Author(s):  
Davina Passeri ◽  
Scott C. Hagen ◽  
Daina Smar ◽  
Negin Alimohammadi ◽  
Allyson Risner ◽  
...  
Keyword(s):  
Storm Surge ◽  
Manning’S N ◽  
Manning's N

A Measure-Theoretic Algorithm for Estimating Bottom Friction in a Coastal Inlet: Case Study of Bay St. Louis during Hurricane Gustav (2008)

2017 ◽  
Vol 145 (3) ◽  
pp. 929-954 ◽  
Author(s):  
Lindley Graham ◽  
Troy Butler ◽  
Scott Walsh ◽  
Clint Dawson ◽  
Joannes J. Westerink
Keyword(s):  
Inverse Problem ◽  
Storm Surge ◽  
Structural Damage ◽  
Measure Theory ◽  
Computational Cost ◽  
Manning’S N ◽  
Using Data ◽  
Coastal Features ◽  
Manning's N

The majority of structural damage and loss of life during a hurricane is due to storm surge, thus it is important for communities in hurricane-prone regions to understand their risk due to surge. Storm surge in particular is largely influenced by coastal features such as topography/bathymetry and bottom roughness. Bottom roughness determines how much resistance there is to the flow. Manning’s formula can be used to model the bottom stress with the Manning’s n coefficient, a spatially dependent field. Given a storm surge model and a set of model outputs, an inverse problem may be solved to determine probable Manning’s n fields to use for predictive simulations. The inverse problem is formulated and solved in a measure-theoretic framework using the state-of-the-art Advanced Circulation (ADCIRC) storm surge model. The use of measure theory requires minimal assumptions and involves the direct inversion of the physics-based map from model inputs to output data determined by the ADCIRC model. Thus, key geometric relationships in this map are preserved and exploited. By using a recently available subdomain implementation of ADCIRC that significantly reduces the computational cost of forward model solves, the authors demonstrate the method on a case study using data obtained from an ADCIRC hindcast study of Hurricane Gustav (2008) to quantify uncertainties in Manning’s n within Bay St. Louis. However, the methodology is general and could be applied to any inverse problem that involves a map from model input to output quantities of interest.

Field Determination of Manning's n Value for Shrubs and Woody Vegetation

1998 ◽  
Author(s):  
Gary E. Freeman ◽  
Ronald R. Copeland ◽  
William Rahmeyer ◽  
David L. Derrick
Keyword(s):  
Woody Vegetation ◽  
N Value ◽  
Manning’S N ◽  
Manning's N

Manning’s N Value of Bridge-in-Backpack

2014 ◽  
Vol 638-640 ◽  
pp. 965-968
Author(s):  
Jing Ma ◽  
Ling Qiang Yang
Keyword(s):  
Water Flow ◽  
Roughness Coefficient ◽  
N Value ◽  
New Type ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

Bridge-in-a-Backpack is a new type bridge. this study will investigate the interaction of flow under the bridge with the tubes and decking, and recommend Manning’s roughness coefficient for water flow under the composite backbridge system.

MANNING'S 'n' FOR STAGGERED FLEXIBLE EMERGENT VEGETATION

2013 ◽  
Vol 07 (05) ◽  
pp. 1250029 ◽  
Author(s):  
L. NOARAYANAN ◽  
K. MURALI ◽  
V. SUNDAR
Keyword(s):  
Flow Resistance ◽  
Empirical Relationship ◽  
Vegetation Density ◽  
Flow Conditions ◽  
Property A ◽  
Steady Current ◽  
Physical Tests ◽  
Manning’S N ◽  
The Individual ◽  
Manning's N

Vegetation along a coastline could offer significant protection of the adjoining land area against natural hazards such as storm surge and tsunami. In this context, the flexibility of the individual that stems within the green belt is understood to play an important role in the attenuation of momentum of the incoming waves. The physics of which, is yet to be understood completely. Difficulty in modeling the rigidity of the plantations, both numerically and experimentally, is the main cause for this lack of understanding. In the present work, a detailed laboratory study is taken up to examine the resistance characteristics of a group of model slender flexible cylinders. The individual cylinders of the group were fixed to the bed in a staggered configuration. The size, vegetation density and the elastic modulus of the individual stems were chosen such that the tests covered the practical ranges of vegetation in coastal forestry. The Manning's n for different flow conditions as well as for vegetative parameters was obtained from the physical tests in uniform steady current. The results clearly bring out the variation of flow resistance in terms of flow velocity, density of plantation, individual stem diameter and its elastic property. A new empirical relationship is proposed for estimation of Manning's n for staggered flexible emerging plantations which is valid for depths of flow greater than 0.8 times the undeflected plant height.

scholarly journals Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1505 ◽  
Author(s):  
Yuval Sadeh ◽  
Hai Cohen ◽  
Shimrit Maman ◽  
Dan Blumberg
Keyword(s):  
Surface Roughness ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Roughness Coefficient ◽  
Arid Environments ◽  
Hydrological Models ◽  
Radar Backscatter ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

An Evaluation of Methods for Estimating Manning's n in Small Mountain Streams

1992 ◽  
Vol 12 (3) ◽  
pp. 227 ◽  
Author(s):  
W. Andrew Marcus ◽  
Keith Roberts ◽  
Leslie Harvey ◽  
Gary Tackman
Keyword(s):  
Mountain Streams ◽  
Manning’S N ◽  
Manning's N ◽  
Evaluation Of Methods

scholarly journals Evaluation of a New Observationally Based Channel Parameterization for the National Water Model

2021 ◽  
Author(s):  
Aaron Heldmyer ◽  
Ben Livneh ◽  
James McCreight ◽  
Laura Read ◽  
Joseph Kasprzyk ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Model ◽  
Surface Model ◽  
Modeling Framework ◽  
Channel Geometry ◽  
Channel Parameter ◽  
Manning’S N ◽  
Manning's N ◽  
Channel Properties ◽  
National Water

Abstract. Accurate representation of channel properties is important for forecasting in hydrologic models, as it affects height, celerity, and attenuation of flood waves. Yet, considerable uncertainty in the parameterization of channel geometry and hydraulic roughness (Manning’s n) exists within the NOAA National Water Model (NWM), due largely to data scarcity: only ~2,800 out of the 2.7 million river reach segments in the NWM have measured channel properties. In this study, we seek to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry (HyG) dataset of approximately 48,000 gages. We begin with a Sobol’ sensitivity analysis of channel geometry parameters for 12 small semi-natural basins across the continental U.S., which reveals an outsized sensitivity of simulated flow to Manning’s n relative to channel geometry parameters. We then develop and evaluate a set of regression-based regionalizations of channel parameters estimated using the HyG dataset. Finally, we compare the model output generated from updated channel parameter sets to observations and the current NWM v2.1 parameterization. We find that, while the NWM land surface model holds the most influence over flow given its control over total volume, the updated channel parameterization leads to improvements in simulated streamflow performance relative to observed flows, with a statistically significant mean R2 increase from 0.479 to 0.494 across approximately 7,400 gage locations. HyG-based channel geometry and roughness provide a substantial overall improvement in channel representation over the default parameterization, updating the previous set value for most reaches of Manning’s n = 0.060 to a new range between 0.006 and 0.537 (median 0.077). This research provides a more representative, observationally based channel parameter dataset for the NWM routing module, as well as new insight into the influence of the routing module within the overall modeling framework.

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