Prediction of the bed friction coefficient using either high resolution bathymetric data or granulometry samples

nicolas.huybrechts@developpement-durable.gouv.fr

Prediction of the bed friction coefficient using either high resolution bathymetric data or granulometry samples

N. Huybrechts

,S. Le Bot

, H. Smaoui

, Y. Ferret

, C. Michel

, A. Ouahsine

and R. Lafite

Laboratoire Roberval (UMR 7337 UTC-CNRS)¹, CNRS UMR 6143 M2C (Univ. Rouen, CNRS, Normandie Université, SFR SCALE) ², CETMEF³

o Located in Eastern English Channel

o Severe sedimentation (1.3 cm/ year, Verger 2005) o Sediment feeding from offshore

o Influences of bedform on sediment fluxes

First step: 30 km offshore the bay mouth (this study)

Second step: in the bay mouth

o Information related to the bed texture => prediction of friction coefficient

SOMME ESTUARY

o Data collected in 2007 and 2008 (MOSAG07 & MOSAG08, Ferret 2011)

 ADCP and water level measurements (C1 & C2)

High resolution bathymetry (1 data/3m)

o Granulometry of the bed material: Shom &M2C dataset, Ferret et al. 2010, Ferret 2011.

o Bed form geometry: ParamDunes (SHOM, Ferret 2011)

o Hydrodynamic: Telemac 2D V6P2 o Mesh size: 37 000 nodes with node distance from 5 km offshore to 10 m near harbours

o Offshore boundary conditons:

European Shell Solution (Oregon State University)

o Flow rates: Somme and Authie AVAILABLE DATA

MODEL SET UP

PREDICTION OF BED FRICTION COEFFICIENT

2 2

2 '

D

R

k k

k k

k



 



 ^

 ^{   }

 0 . 00008 f h 1 e

600

k

With k_s the equivalent bed roughness, k’_s the grain roughness, k_R roughness induced by ripples, k_MR roughness induced by megaripples , k_D roughness induced by dunes, U the flow velocity, h the water depth, D the excess of relative density ,d₅₀ the median diameter, H the dune height and L the dune wavelength Xc set of N measured variables and Yc set of N predicted variables

50 2

gd U

 D



 

 



 



D

H e

k 0 . 77 1

o Accuracy: RMAE < 0.25

o Both approaches can provide a first set of values for a finer calibration

o Set up easiness: GRAIN

o Unsteady bed texture => GRAIN RESULTS

o More detailed map of the dune geometry o Influence of waves

o Bedform at the mouth: shallow and intertidal o 3D Modeling

o Coupling with sediment transport modeling PERSPECTIVES

o This study contribues to the « FORSOM » project funded by EC2CO INSU CNRS

oThanks to T. Garlan from SHOM for providing grain size data

ACKNOWLEDGMENT STUDIED AREA

van Rijn formulae (1984 &2007)

0.00 0.25 0.50 0.75 1.00

12.5 13.5 14.5

Velocity (m/s)

Time (days)

data c1 GRAIN BATHY 0.00

0.25 0.50 0.75 1.00 1.25 1.50

12.5 13.5 14.5

Velocity (m/s)

Time (days)

data c2 GRAIN BATHY

Zones Dune height H (m)

Dune

wavelength L (m)

Equivalent bed

roughness k_s (m)

NW 9.00 900 1.53

SW 6.50 530 1.32

C 4.25 375 0.80

E 6.00 425 1.37

SD 0.25 2.8 0.17

MD 0.6 7.5 0.40

RMAE Velocity C1

Velocity C2

Water level C2

Average

BATHY 0.16 0.25 0.07 0.16

GRAIN 0.17 0.21 0.07 0.15

Relative Mean Absolute Error

“RMAE”

Values

Excellent <0.2

Good 0.2-0.4

Reasonable 0.4-0.7

Poor 0.7-1.0

Bad >1.0

c c c

X X RMAE Y 



Sutherland et al (2004)

QUALITY CRITERIA





i

x

X N

1

1