1. Modelisation of suspended sediment transport in rivers Master thesis Véronique Briguet 2011 Alain Recking, Oldrich Navratil, Nicolle Mathys

2. Contents 2 1. Introduction 2. Data set 3. Data analysis 4. Modelling 5. Perspectives 6. Conclusion

3. 3 Suspension versus bedload Data set

4. 4 Fraser River at Yale Lochsa River 2 mm d50 du lit GBRSBR Sand Bed River versus Gravel Bed River

5. 5 Connexion with hillslopes processes: Head Water Streams HWS versus Lowland Rivers LR Erosion on the Draix Catchment Data set

6. 0.03 mg/L < C < 29 g/L; 0.01% < S < 18%; 0.012 <Q< 3770 m 3 /s ; 0.08 < A < 31313 km². MeasurementNbr of value Rivers Source 1Instantaneous Bedload + susp 3186 88 reaches, 76 rivers, 15 SBR, 62 GBR USGS, USDA … 2Instantaneous susp 150 5 SBR Brownlie 1985.. 3Annual load139 1 SBR and 8 GBR, 139 years ORE Draix, McLean, Church et al 1999 … 4Event load213 5 HWS ORE Draix

7. Log(A)Log(C)Log(d50)Log(H)Log(P)Log(q)Log(qb)Log(qs)Log(S)Log(U)Log(W) Log(C) 0.1851.000-0.1740.3020.3640.4500.6300.859-0.1680.5180.106 Log(qs) 0.5530.8590.0360.7080.7290.8310.7111.000-0.5130.8380.471 Correlation coefficient R A: Watersherd area D50: median diameter H: depth P: power  QS U: Velocity C: Suspended load concentration q s : Suspended load / unit width q b : bedload / unit width q: Q/W W: Width S: Slope q s =Cq => autocorrelation Correlation between q b and C Data Analysis 7

8. 8 Dispersion du ratio instantané Qs / QT Suspension versus bedload in the total load Data Analysis Instantaneous measurements

9. 9 Event scale Data Analysis Bedload (t) Suspension (t)

10. 10 Data Analysis Annual load Bedload (t) Suspension (t)

11. 11 Suspension – bedload interactions: some hypothesis  1 : bedload  2 >  1 : progressive suspension concentration SBR  1 : weak suspension  2 >  : Sharp suspension concentration with bedload GBR Data Analysis HWSInconsistencies in Qs/Qt between event and volumes Possible bias in instantaneous measurements with bedload absent for the flood conditions considered

12. Modelling Deterministics : Bagnold (1966), Einstein (1950), Celik et Rodi (1991)… Empirical models : Lefort (1990), Abrahams (2001)… 12 Equations of fluid mechanics Constants calibration with experimental data Deterministic Model Identification of representative variables Fit equations with experimental data Empirical model SUSPENSION Qs OR TOTAL TRANSPORT QT

13. 13 Limits of deterministics and empirical models: Most of them calibrated in flume With uniform materials Fine sands Modelling Correlation analyses with field data Fit equations « black box » models Statistical models qs=f(q): Turowski & Rickenmann (2010) Use generally limited to the river used to built the data set (Prosser & Rustomji 2000)

14. 14 Confluence Galabre - Bès Haute Bléone à Prads Bès à Sivan Modelling Width? Bed diameter? Transported diameter? Fall velocity?...

15. 20/06/2011 15 Modelling Discrepancy ratio r = q s calculated q s measured Scores = % of r values obtained in a given interval Tested in the interval [0.1 – 10] Ex: a scores of 30% significates that 30% of the predictions are correct within plus or minus one order of magnitude

16. 20/06/2011 16 Modelling

17. 20/06/2011 17 Modèle de Bagnold (1966) Modèle de Celik et Rodi (1991) Modelling Auto correlation ! q s =Cq

18. 20/06/2011 18 Modelling q s =Cq with C randomly choosen

19. 20/06/2011 19 => An accurate concentration model is required Modèle de Celik et Rodi (1991) Modelling

20. 20/06/2011 20 Perspectives ?

21. Tested with a new bedload model specifically developed for gravel beds (Recking 2010) 21 C calculated with q b measured C calculated with q b computed Perspectives ?

22. 22 Perspectives ?

23. Conclusion 23 No tool is really efficient in the field, especially in gravel bed rivers Strong correlation between suspended load and bedload, especially in gravel bed rivers Necessity to develop a new concentration model

24. 20/06/201124 Thank you for your attention