The Role of Relative Submergence on Cluster Microtopography and Bedload Predictions in Mountain Streams

1. THE ROLE OF RELATIVE SUBMERGENCE ON CLUSTER MICROTOPOGRAPHY AND BEDLOAD PREDICTIONS IN MOUNTAIN STREAMS AN Thanos Papanicolaou and Casey Kramer IIHR Hydroscience and Engineering University of Iowa, USA Presented at the RCEM meeting, Urbana-Champaign 2005

2. What Is Missing? Introduction • Several authors (Robert et al., 1992, 1996; Robert, 1993; Buffin-Belenger and Roy, 1998; Shamloo et al., 2001) have demonstrated that the most important momentum exchange mechanism in gravel-bed rivers is associated with vortex shedding around large protruding roughness elements (i.e. pebble clusters and clasts) • The role of relative submergence is a significant parameter when protruding roughness is present. • Few to none have studied the role of relative submergence, despite the fact that large roughness elements are ubiquitous features in gravel-bed streams.

3. Definition of Relative Submergence Introduction High Relative Submergence Low Relative Submergence

4. The overarching objective of this investigation was to evaluate the role of relative submergence on the formation and evolution of cluster microforms in gravel bed streams and its implications to bedload transport. • Specific Objectives: • A quantitative description of the geometric features of clusters during a hydrological cycle for high and low relative submergences (HRS and LRS) • A comparison of the geometric features of clusters for high and low relative submergences under the same shearing action of the flow • An improved understanding of the mean flow patterns around clast/cluster obstacles Objectives

5. 1. A setback in the study of bed microtopography and bedload transport in natural streams is bed evolution occurs during high flow events, making it difficult to make real-time measurements and morphological observations. • 2. It is difficult to account for different parameters when making measurements in the field, such as: • Discharge • Stage • Topography • The sediment’s angle of repose • Determining roughness height, etc. Methodology Why Lab Work?

6. Bedload Size Distribution of Feeding Sediment MethodologyTheme 1: Geomorphological Features The size and fraction of bedload material was selected following data from Oak Creek, Oregon, USA

7. Clast Size MethodologyTheme 1: Geomorphological Features The clast size is defined as, dclasts≈ 3darmor, where darmor = d50 = 1.9 cm thus, dclasts = 5.5 cm due to the size of the median particle matching the size of the armored bed (Reid et al., 1992).

8. Spacing of clasts MethodologyTheme 1: Geomorphological Features de Jong (1995) Papanicolaou and Kramer (submitted)

9. Incipient Conditions MethodologyTheme 1: Geomorphological Features To identify the incipient conditions for individual particles, the concept of the probability of entrainment, PE, was employed rather than the Shields diagram where:

10. Incipient Conditions MethodologyTheme 1: Geomorphological Features t = 0 min t = 1.5 min

11. Bedload Feeding rate MethodologyTheme 1: Geomorphological Features Once a critical probability of entrainment is set (PE=0.02), a correspondence between PE and the dimensionless shear stress, tcr* (the Shields parameter) was developed where: A dimensionless bedload, qb*, formula based on t* from Paintal (1971) and calibrated by Strom et al. (2004) for spherical particles was employed where:

12. Bedload Feeding rate MethodologyTheme 1: Geomorphological Features From dimensional analysis the bedload feeding rate [kg/m/s], qb, can be determined from This feeding rate was provided via feeding, well upstream of the clast section, so that the exiting bedload size distribution and transport rate was independent of initial conditions (Parker and Wilcock, 1993)

13. Flume Experimental Setup

14. Clast Section Experimental Setup

15. 0Dx 1Dx 2Dx 3Dx 4Dx 5Dx 6Dx 7Dx 8Dx 9Dx 10Dx Flow Measuring Devices • Acoustic Doppler Velocimeter (10 MHz ADV) • 40 Measuring Locations with 15 point measurements per profile (Total of 600 point measurements ) • 3000 measurements per point, for obtaining velocity measurements of statistical significance (e.g., Nikora and Goring 1998, Papanicolaou and Hilldale 2002). Experimental Setup -3Dy -2Dy -1Dy 0Dy

16. Flow Measuring Devices • Large Scale Particle Image Velocimetry (LSPIV), developed by Japanese researchers and also by American and International researchers here at IIHR • Provided a whole-field (plane, multipoint) of flow velocities • Fully non-intrusive technique • Much quicker means of data collection in comparison to the ADV Experimental Setup LRS LSPIV Test HRS LSPIV Test

17. Experimental conditions

18. Results Bedload Analysis Qualitative Observations for H/dclast = 3.5

19. Results Test B1b Test B2a Test B3b Test B4a

20. Results • Majority of clusters are deposited on the wake region of the clasts • Some few …particles are deposited randomly throughout the test section if not located in the vicinity of a clast Bedload Analysis Qualitative Observations for H/dclast = 3.5

21. Results Test B1b Test B2a Test B3b Test B4a

22. Results Test B1b Test B2a Test B3b Test B4a

23. Results

24. Results Bedload Analysis Qualitative Observations for H/dclast = 0.8

25. Bedload Analysis Qualitative Observations for H/dclast = 0.8 Results • Flow is transcritical around the clast for runs A1a and A2a Depression

26. Bedload Analysis Qualitative Observations for H/dclast = 0.8 Results • Supercritical flow with the presence of surface waves become pronounced for runs A3b and A4a Surface Wave

27. Bedload Analysis Qualitative Observations for H/dclast = 0.8 Results • Two different types of bed topography were observed for the low relative submergence runs: • In-line clusters • Cluster–deposits • In-line clusters (i.e. “streaks”) form during the transcritical flow runs, A1a and A2a • In-line clusters spacing is dictated by clast spacing • Cluster–deposits (known in the literature as dump-deposits, Billi, 1988) are generated during runs A3b and A4a • Cluster-deposits spacing is dictated by surface waves

28. Results Figure 17. Test A1a Figure 18. Test A2a Figure 19. Test A3b Figure 20. Test A4b

29. Results Bedload Analysis Quantitative Observations for H/dclast = 0.8

30. Results Bedload Analysis Quantitative Observations for H/dclast = 0.8 • For the low relative submergence, clusters exhibit a similar effect on bedload to clusters in the high relative submergence runs • The only notable difference between the LRS and the HRS is that the depositional patterns are larger and distinguishable • Incoming and exiting bedload are in phase with one another • The larger percentage of particles are deposited in the stoss region with the most populated fractions being the emerald and amber

31. Results A1a A2a A4b A3b

32. Results A1a A2a A4b A3b

33. Results

34. Flow AnalysisQuantitative Observations for H/dclast = 3.5 and t*= 2.5 t*crQuantitative Observations for H/dclast = 0.8 and t *= 2.5 t *cr

35. 0Dx 1Dx 2Dx 3Dx 4Dx 5Dx 6Dx 7Dx 8Dx 9Dx 10Dx Flow Analysis Quantitative Observations for H/dclast = 3.5 and t*= 2.5 t*cr Results • Analysis of the mean flow measurements performed during a run for t*=2.5t*cr on the specified grid shown below aimed to provide an improved understanding of the flow structures around the clasts with the intent to complement the sediment observations of run B2a (H/dclast = 3.5 and t*= 2.5 t*cr) -3Dy -2Dy -1Dy 0Dy

36. 0 50 100 Velocity (cm/s) Transect 0Dy

37. Flow Analysis Quantitative Observations for H/dclast = 3.5 and t*= 2.5 t*cr Results Log Law Transect -1Dy Log Law Transect -3Dy

38. Flow Analysis Quantitative Observations for H/dclast = 3.5 and t*= 2.5 t*cr Results Transect 0Dy (Side View) Transect -1Dy (Side View) Transect -2Dy (Side View) Transect -3Dy (Side View)

39. Flow Analysis Quantitative Observations for H/dclast = 3.5 and t*= 2.5 t*cr Results Plan View of the Streamwise Velocity at z = 0.6 cm Plan View of the Streamwise Velocity at z = 3.85 cm Plan View of the Streamwise Velocity at z = 17.6 cm

40. Flow Analysis Comparison of ADV and LSPIV Results Plan View of the Streamwise Velocity at z = 17.6 cm LSPIV Streamwise Velocity for H/dclast = 3.5 and t*= 2.5 t*cr

41. Results Flow Analysis Quantitative Observations for H/dclast = 0.8 and t*= 2.5 t*cr • For the low relative submergence experiments, LSPIV measurements were utilized due to ADV measurements not being feasible at low flow depths • The effects of roughness to the flow are well depicted at the free surface • To link flow characteristics around clasts with cluster depositional patterns, the plan view LSPIV images were superimposed with plan view images of depositional patterns

42. Flow Analysis Quantitative Observations for H/dclast = 0.8 and t*= 2.5 t*cr Results LSPIV Streamwise Velocity for H/dclast = 0.8 and t*= 2.5 t*cr

43. Flow Analysis Superimposition Observation for H/dclast = 0.8 and t*= 2.5 t*cr Results LSPIV Particles Overlayed with Bedload for H/dclast = 0.8 and t*= 2.5 t*cr

44. Conclusions • This research examined the effects of relative submergence on cluster formation. • In the laboratory flume, spherical clasts were placed in a fixed grid atop a well-packed glass bead bed. • Two relative submergences were investigated, namely the high and low relative submergences • In the high relative submergence, sediment motion is governed by the particle Reynolds number. • In the low relative submergence, the importance of the Reynolds number on sediment motion diminishes, and the Froude number becomes the governing parameter.

45. Conclusions • The results of this study focused on: • The qualitative evaluation of the bed microtopography for the high and low relative submergence • A quantitative description of the bedload transport rates and their statistical properties • A detailed analysis of the flow characteristics around a clast • The coupling of flow with bed microtopography observations around a clast

46. Conclusions • The following specific conclusions can be drawn from this investigation: • Clasts placed at specified distances regulated the depositional dynamics atop the flume bed, thus minimizing the random formation of microstructures • For the high and low relative submergence experiments clasts/clusters worked as a sink for the incoming sediment • Cluster formation occurred randomly in space for the high relative submergence, while for the low relative submergence clasts appeared to control the areas where cluster formation occurred • For the high relative submergence, a larger percentage of incoming particles were deposited in the wake region. For the low relative submergence and for stress less than t* = 2.5t*cr, particles were mainly deposited in the stoss region of the clasts.

47. Conclusions • For the high relative submergence, the plots of the velocity profiles indicated that the effects of the clasts were not felt significantly at the free surface of the flow where the log law in the outer layer appeared to adequately describe the measured observations. • For the high relative submergence, the effects of clasts on the flow were present within the zone of influence of clasts, which was typically found to be at 0.3-0.5 times the clast diameter in the vertical direction and 2 to 4 times the clast diameter in the streamwise direction

48. Conclusions • 7. For the high relative submergence tests, several factors contributed to the generation of secondary currents of Prandtl’s second kind, including • The low aspect ratio (B/H < 5) • The presence of the fixed grid of clasts, • The feedback process between flow and clasts • The ADV measurements provided improved insight about the governing flow mechanisms for the high relative submergence runs. These mechanisms were described with • flow upwelling at the center of the flume • downwelling occurring along the flume walls

49. Conclusions • The following specific conclusions can be drawn from this investigation: • For the low relative submergence experimental runs, the near-bed flow structures (HS vortices, “Froudian” wakes, and hydraulic jumps) controlled the depositional patterns of the incoming sediment.