Can fluvial-hydraulic models accurately predict bed load transport in gravel bed streams?

Transcription

1 Can fluvial-hydraulic models accurately predict bed load transport in gravel bed streams? Scott B. Katz 1,2, Catalina Segura 1,2 1 Water Resources Graduate Program, 2 Department of Forest Engineering, Resources and Management Oregon State University

2 Introduction Understanding bed-load transport rates (Q b ) is important for a wide range of river restoration applications including:

3 Introduction Understanding bed-load transport rates (Q b ) is important for a wide range of river restoration applications including: Stability of engineered structures Engineered Riffle (River Design Group)

4 Introduction Understanding bed-load transport rates (Q b ) is important for a wide range of river restoration applications including: Stability of engineered structures Large wood addition Large Wood Placement Mill Creek, OR

5 Introduction Understanding bed-load transport rates (Q b ) is important for a wide range of river restoration applications including: Stability of engineered structures Large wood addition Environmental flow determination Detroit Dam Spillway Santiam River, OR (pbase.com)

6 Introduction Understanding bed-load transport rates (Qb) is important for a wide range of river restoration applications including: Stability of engineered structures Large wood addition Environmental flow determination Measuring Qb is expensive and dangerous Dec 2015 Flood on the Siletz River, OR

7 Introduction Understanding bed-load transport rates (Qb) is important for a wide range of river restoration applications including: Stability of engineered structures Large wood addition Environmental flow determination Measuring Qb is expensive and dangerous Modeling Qb is a convenient strategy Dec 2015 Flood on the Siletz River, OR

8 Introduction Understanding bed-load transport rates (Qb) is important for a wide range of river restoration applications including: Stability of engineered structures Large wood addition Environmental flow determination Measuring Qb is expensive and dangerous Modeling Qb is a convenient strategy Current estimation methods do not incorporate spatial variability of the flow field Dec 2015 Flood on the Siletz River, OR

9 Introduction This study asks: Can we use 2-dimensional flow models coupled with detailed grain size measurements to make accurate estimates of Qb? Dec 2015 Flood on the Siletz River, OR

10 Study Site Study conducted in Oak Creek, OR.

11 Study Site Study conducted in Oak Creek, OR. Located directly upstream from historical sediment sampling facility

12 Study Site Study conducted in Oak Creek, OR. Located directly upstream from historical sediment sampling facility Historical Facility

13 Study Site Study conducted in Oak Creek, OR. Located directly upstream from historical sediment sampling facility Sediment research conducted between late 1960 s-1990 s (P.I. Dr. Peter Klingeman, Milhous 1973) Vortex sampler in action (Photo Credit : P. Klingeman)

14 Study Site Study conducted in Oak Creek, OR. Located directly upstream from historical sediment sampling facility Sediment research conducted between late 1960 s-1990 s (P.I. Dr. Peter Klingeman, Milhous 1973) One of the most comprehensive sediment transport datasets to date Vortex sampler in action (Photo Credit : P. Klingeman)

15 Study Site Study conducted in Oak Creek, OR. Located directly upstream from historical sediment sampling facility Sediment research conducted between late 1960 s-1990 s (P.I. Dr. Peter Klingeman, Milhous 1973) One of the most comprehensive sediment transport datasets to date Sediment Sample Collection (Photo Credit : P. Klingeman)

16 Method Validation Historical Data Vortex Sampler Location Compare modeled Q b against historical data

17 Method Validation Historical Data Vortex Sampler Location Vortex Sampler Location 1970 s Compare modeled Q b against historical data

18 Method Validation Field Data Compare model results to 5 bedload samples collected in 2016 Validate use of historical data Collected with 3 Helley-Smith bedload sampler Bed-load Sampling

19 Motivation Shear stress (τ) = driving force of sediment transport

20 Motivation Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations

21 Motivation Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product)

22 Motivation Critical shear stress for D 50 = 40.3 (N/m 2 ) Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product)

23 Motivation Critical shear stress for D 50 = 40.3 (N/m 2 ) Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product) Transport unlikely

24 Motivation Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product) Transport unlikely Critical shear stress for D 50 = 40.3 (N/m 2 ) Transport Threshold Immobile Mobile τ (N/m 2 ) Shear stress distribution calculated using a 2-D model

25 Motivation Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product) Transport unlikely 2-D: 10% of bed capable of transport Critical shear stress for D 50 = 40.3 (N/m 2 ) Transport Threshold Immobile τ (N/m 2 ) Mobile Shear stress distribution calculated using a 2-D model

26 Motivation Shear stress (τ) = driving force of sediment transport Shear stress can be modeled using both 1-D and 2-D approximations For a 0.4 Q bf flow: 1-D: τ = 32.3 (N/m 2 ) (depth-slope product) Transport unlikely 2-D: 10% of bed capable of transport

27 Motivation Compare how these two different methods of estimating shear stress perform in making accurate estimates of Q b

28 Method Model flows 0.1Q bf 1.2Q bf (110 total flows) using quasi-unsteady model FaSTMECH (USGS)

29 Method Model flows 0.1Q bf Q bf (110 total flows) using quasi-unsteady model FaSTMECH (USGS) τ (N/m 2 ) Model output for each flow level

30 Method Calculations Based on Segura and Pitlick 2015 τ (N/m 2 )

31 Method Calculations Based on Segura and Pitlick τ (N/m 2 )

32 Method Calculations Based on Segura and Pitlick τ (N/m 2 ) Perform sediment transport calculations using entire shear stress and grain size distributions

33 Method Calculations Based on Segura and Pitlick τ (N/m 2 ) Sediment Transport Equation Parker and Klingeman 1982 (Subsurface) - PK

34 Method Calculations Based on Segura and Pitlick τ (N/m 2 ) For each value of shear stress τ j Sediment Transport Rates (kg/s) For each grain size D i

35 Results

36 Results

37 Results

38 Results Unpublished data is consistent with Milhous 1973

39 Results Unpublished data is consistent with Milhous 1973

40 Results Unpublished data is consistent with Milhous 1973 Recent field samples match historical data

41 Results Unpublished data is consistent with Milhous 1973 Bed-load transport dynamics have remained stable since original study Recent field samples match historical data

42 Results Unpublished data is consistent with Milhous 1973 Bed-load transport dynamics have remained stable since original study Recent field samples match historical data Confident to compare modeled Q b against historic data

43 Results

44 Results

45 Results Strong agreement of 2-D model with flows > ~0.3 Q bf

46 Results 2-D model over predicts at low flows Strong agreement of 2-D model with flows > ~0.3 Q bf

47 Results 2-D model over predicts at low flows Strong agreement of 2-D model with flows > ~0.3 Q bf

48 Results 2-D model over predicts at low flows Strong agreement of 2-D model with flows > ~0.3 Q bf 1-D model under predicts at low flows

49 Results 2-D model over predicts at low flows 1-D model over predicts at high flows Strong agreement of 2-D model with flows > ~0.3 Q bf 1-D model under predicts at low flows

50 Results 2-D model over predicts at low flows Strong agreement of 2-D model with flows > ~0.3 Q bf 1-D model over predicts at high flows Strong agreement of 1-D model at moderate flows 1-D model under predicts at low flows

51 Results 2-D model over predicts at low flows Strong agreement of 2-D model with flows > ~0.3 Q bf 1-D model over predicts at high flows Strong agreement of 1-D model at moderate flows 1-D model under predicts at low flows

52 Results Summary 2-D model able to accurately estimate Q b at moderate-high flows (>0.3Q bf )

53 Results Summary 2-D model able to accurately estimate Q b at moderate-high flows (>0.3Q bf ) 1-D model under-predicts at low flows and over-predicts at high flows

54 Results Summary 2-D model able to accurately estimate Q b at moderate-high flows (>0.3Q bf ) 1-D model under-predicts at low flows and over-predicts at high flows Recent field samples match model estimates and historical data

55 Results Summary 2-D model able to accurately estimate Q b at moderate-high flows (>0.3Q bf ) 1-D model under-predicts at low flows and over-predicts at high flows Recent field samples match model estimates and historical data Future work needed to validate model using grain size information

56 Potential Restoration Applications Gravel bars on Rough and Ready Creek SW Oregon

57 Potential Restoration Applications This method (once validated) can be used as a tool to better inform design decisions Gravel bars on Rough and Ready Creek SW Oregon

58 Potential Restoration Applications This method (once validated) can be used as a tool to better inform design decisions Incorporate spatial and temporal variability of Q b Gravel bars on Rough and Ready Creek SW Oregon

59 Potential Restoration Applications This method (once validated) can be used as a tool to better inform design decisions Incorporate spatial and temporal variability of Q b Prevent failure of structures due to aggradation and erosion Gravel bars on Rough and Ready Creek SW Oregon

60 Potential Restoration Applications This method (once validated) can be used as a tool to better inform design decisions Incorporate spatial and temporal variability of Q b Prevent failure of structures due to aggradation and erosion Understand how design impacts sediment transport dynamics Gravel bars on Rough and Ready Creek SW Oregon

61 Potential Restoration Applications This method (once validated) can be used as a tool to better inform design decisions Incorporate spatial and temporal variability of Q b Prevent failure of structures due to aggradation and erosion Understand how design impacts sediment transport dynamics Utilizes commonly collected data and open-source hydraulic model Gravel bars on Rough and Ready Creek SW Oregon

62 Take home messages 2-D fluvial-hydraulic models can be used to make accurate estimates of bed-load transport rates

63 Take home messages 2-D fluvial-hydraulic models can be used to make accurate estimates of bed-load transport rates Further validation of method with grain size distribution is needed

64 Take home messages 2-D fluvial-hydraulic models can be used to make accurate estimates of bed-load transport rates Further validation of method with grain size distribution is needed Provides a tool to make better informed design decisions on river restoration projects

65 Questions? Acknowledgements Data and equipment Dr. Peter Klingeman Field Crew Joey Tinker, Michael Griffith, Catalina Seufert Scientific and Moral Support Russell Bair, Kira Puntenney, and the rest of the OSU Hydrophiles

66 Some Definitions Qbf = Bankfull discharge (m 3 /s) Qb = Bed-load transport rate (kg/s) ττ = shear stress (N/m 2 ) -Driving Force behind Sediment transport Driving Force ττ = shields stress (dimensionless) ~ Resisting Force ττ cc = critical shields stress amount of force needed to move particle if ττ /ττ cc > 1 motion likely if ττ /ττ cc < 1 motion unlikely

67 Method Flow model Model inputs include: Surveyed topography Discharge Boundary condition Downstream stage Model calibrated for roughness with water surface elevation (WSE) at: 0.1 Q bf 0.4 Q bf Q bf RMSE ranged from m