SpringerLink
Log in

Uncertainty quantification of large-eddy simulation results of riverine flows: a field and numerical study

  • Original Article
  • Published:
Environmental Fluid Mechanics Aims and scope Submit manuscript

Abstract

We present large-eddy simulations (LES) of riverine flow in a study reach in the Sacramento River, California. The riverbed bathymetry was surveyed in high-resolution using a multibeam echosounder to construct the computational model of the study area, while the topographies were defined using aerial photographs taken by an Unmanned Aircraft System (UAS). In a series of field campaigns, we measured the flow field of the river river across multiple transects throughout the field site using an acoustic Doppler current profiler (ADCP) and estimated using large-scale particle velocimetry of the videos taken during the operation UAS. We used the measured data of the river flow field to evaluate the accuracy of the LES-computed hydrodynamics. The propagation of uncertainties in the LES results due to the variations in the riverbed’s effective roughness height and the river’s inflow discharge was studied and showed that both parameters redistributed the flow distribution laterally and vertically in the velocity profile. For the uncertainty quantification (UQ) analyses, the polynomial chaos expansion (PCE) method was used to develop a surrogate model, which was randomly sampled sufficiently by the Monte Carlo Sampling (MCS) method to generate confidence intervals for the LES-computed velocity field. Also, Sobol indices derived from the PCE coefficients were calculated to help understand the relative influence of different input parameters on the global uncertainty of the results. The UQ analysis showed that uncertainties of LES results in the shallow near bank regions of the river were mainly related to the roughness, while the variation of inflow discharge leads to uncertainty in the LES results throughout the river, indiscriminately.

Article highlights

Changes in discharge and bed roughness altered the flow distribution laterally and vertically in the vertical profile.

Polynomial chaos expansions provided a practical approach for characterizing uncertainty in LES results of a natural river.

Uncertainty in simulation results near the bank is mainly due to parameter uncertainty in bed roughness characterization.

Uncertainty related to the inflow discharge dominated the overall uncertainty in LES results of the river.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The code in this paper includes the Virtual Flow Simulator (VFS-Geophysics) model. The data include the bathymetric and topographic data of the Sacramento River and the high-fidelity simulation results of the river.

References

  1. Kang S, Khosronejad A, Hill C, Sotiropoulos F. Mean flow and turbulence characteristics around single-arm instream structures.J Hydraul Res2020:1–17. https://doi.org/10.1080/00221686.2020.1780494

  2. Kara S, Kara MC, Stoesser T, Sturm TW (2015) Free-Surface versus Rigid-Lid LES Computations for Bridge-Abutment Flow. J Hydraul Eng 141:04015019. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001028

    Article  Google Scholar 

  3. Khosronejad A, Flora K, Kang S. Effect of inlet turbulent boundary conditions on scour predictions of coupled LES and morphodynamics in a field-scale river: bankfull flow conditions.J Hydraul Eng2020;146. https://doi.org/10.1061/(asce)hy.1943-7900.0001719

  4. Khosronejad A, Flora K, Zhang Z, Kang S (2020) Large-eddy simulation of flash flood propagation and sediment transport in a dry-bed desert stream. Int J Sediment Res 35:576–586. https://doi.org/10.1016/j.ijsrc.2020.02.002

    Article  Google Scholar 

  5. McCoy A, Constantinescu G, Weber LJ (2008) Numerical Investigation of Flow Hydrodynamics in a Channel with a Series of Groynes, vol 134. J Hydraul Eng, New York, NY, pp 157–172

    Google Scholar 

  6. Fischer-Antze T, Olsen NRB, Gutknecht D (2008) Three-dimensional CFD modeling of morphological bed changes in the Danube River. Water Resour Res 44. https://doi.org/10.1029/2007WR006402

  7. Wilson C, Stoesser T, Olsen NRB, Bates P (2003) Application and validation of numerical codes in the prediction of compound channel flows. Proc Inst Civ Eng - Water Marit Eng 156:117–128. https://doi.org/10.1680/wame.2003.156.2.117

    Article  Google Scholar 

  8. Rüther N, Jacobsen J, Olsen NRB, Vatne G (2010) Prediction of the three-dimensional flow field and bed shear stresses in a regulated river in mid-Norway. Hydrol Res 41:145–152. https://doi.org/10.2166/nh.2010.064

    Article  Google Scholar 

  9. Liu X, García MH (2008) Three-Dimensional Numerical Model with Free Water Surface and Mesh Deformation for Local Sediment Scour. J Waterw Port Coastal Ocean Eng 134:203–217. https://doi.org/10.1061/(asce)0733-950x(2008)134:4(203)

    Article  Google Scholar 

  10. Ge L, Fotis S (2005) 3D Unsteady RANS Modeling of Complex Hydraulic Engineering Flows. I: Numerical Model. J Hydraul Eng 131:800–8. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:9(800)

    Article  Google Scholar 

  11. Constantinescu G, Koken M, Zeng J (2011) The structure of turbulent flow in an open channel bend of strong curvature with deformed bed: Insight provided by detached eddy simulation. Water Resour Res 47. https://doi.org/10.1029/2010WR010114

  12. Khosronejad A, Kang S, Flora K (2019) Fully coupled free-surface flow and sediment transport modelling of flash floods in a desert stream in the Mojave Desert, California. Hydrol Process 33:2772–2791. https://doi.org/10.1002/hyp.13527

    Article  Google Scholar 

  13. Khosronejad A, Kozarek JL, Diplas P, Sotiropoulos F (2015) Simulation-based optimization of in-stream structures design: J-hook vanes. J Hydraul Res 53:588–608. https://doi.org/10.1080/00221686.2015.1093037

    Article  Google Scholar 

  14. Khosronejad A, Le T, DeWall P, Bartelt N, Woldeamlak S, Yang X et al (2016) High-fidelity numerical modeling of the Upper Mississippi River under extreme flood condition. Adv Water Resour 98:97–113. https://doi.org/10.1016/j.advwatres.2016.10.018

    Article  Google Scholar 

  15. Sadrehaghighi I, Computational, Error, Uncertainty Quantification Within CFD V & V Validation (2020) & https://doi.org/10.13140/RG.2.2.33074.43200/1

  16. Celik IB, Ghia U, Roache PJ, Freitas CJ, Coleman H, Raad PE (2008) Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. J Fluids Eng Trans ASME 130:0780011–0780014. https://doi.org/10.1115/1.2960953

    Article  Google Scholar 

  17. Lawless M, Robert A (2001) Scales of boundary resistance in coarse-grained channels: Turbulent velocity profiles and implications. Geomorphology 39:221–238. https://doi.org/10.1016/S0169-555X(01)00029-0

    Article  Google Scholar 

  18. Casas A, Lane SN, Hardy RJ, Benito G, Whiting PJ (2010) Reconstruction of subgrid-scale topographic variability and its effect upon the spatial structure of three-dimensional river flow. Water Resour Res 46:1–17. https://doi.org/10.1029/2009WR007756

    Article  Google Scholar 

  19. Warmink JJ, van der Klis H, Booij MJ, Hulscher S (2011) Identification and Quantification of Uncertainties in a Hydrodynamic River Model Using Expert Opinions. Water Resour Manag 25:601–622. https://doi.org/10.1007/s11269-010-9716-7

    Article  Google Scholar 

  20. Flora K, Khosronejad A. On the impact of bed-bathymetry resolution and bank vegetation on the flood flow field of the American River, California: Insights gained using data-driven large-eddy simulation.J Irrig Drain Eng2021;Forthcomin. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001593

  21. Matahel AG-CJ, Orlin A (2002) Hydraul Meas Exp Methods 2021:1–10. https://doi.org/10.1061/40655(2002)15. K. Comparison of Discharge Estimates from ADCP Transect Data with Estimates from Fixed ADCP Mean Velocity Data

  22. Moore SA, Jamieson EC, Rainville F-M, De, Rennie CD, Mueller DS (2017) Monte Carlo Approach for Uncertainty Analysis of Acoustic Doppler Current Profiler Discharge Measurement by Moving Boat. J Hydraul Eng 143:4016088

    Article  Google Scholar 

  23. Mueller DS (2016) :50

  24. Mueller DS, Wagner CR(2009) Measuring discharge with acoustic Doppler current profilers from a moving boat.

  25. Muste M, Yu K, Spasojevic M (2004) Practical aspects of ADCP data use for quantification of mean river flow characteristics; Part I: Moving-vessel measurements. Flow Meas Instrum 15:1–16. https://doi.org/10.1016/j.flowmeasinst.2003.09.001

    Article  Google Scholar 

  26. Muste M, Kim D, González-Castro JA (2010) Near-Transducer Errors in ADCP Measurements: Experimental Findings. J Hydraul Eng 136:275–289. https://doi.org/10.1061/(asce)hy.1943-7900.0000173

    Article  Google Scholar 

  27. Schmalz S, Hörmann B, Fohrer G (2012) Accuracy, reproducibility and sensitivity of acoustic Doppler technology for velocity and discharge measurements in medium-sized rivers. Hydrol Sci Journal-Journal Des Sci Hydrol 57:1626–1641. https://doi.org/10.1080/02626667.2012.727999

    Article  Google Scholar 

  28. Despax A, Le Coz J, Hauet A, Mueller DS, Engel FL, Blanquart B et al (2019) Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments. Water Resour Res 55:7520–7540. https://doi.org/10.1029/2019WR025296

    Article  Google Scholar 

  29. Legleiter CJ, Kyriakidis PC, McDonald RR, Nelson JM (2011) Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river. Water Resour Res 47:1–24. https://doi.org/10.1029/2010WR009618

    Article  Google Scholar 

  30. Casas A, Benito G, Thorndycraft VR, Rico M (2006) The topographic data source of digital terrain models as a key element in the accuracy of hydraulic flood modelling. Earth Surf Process Landforms 31:444–456. https://doi.org/10.1002/esp.1278

    Article  Google Scholar 

  31. Pasternack GB, Gilbert AT, Wheaton JM, Buckland EM (2006) Error propagation for velocity and shear stress prediction using 2D models for environmental management. J Hydrol 328:227–241. https://doi.org/10.1016/j.jhydrol.2005.12.003

    Article  Google Scholar 

  32. Lane SN, Bradbrook KF, Richards KS, Biron PA, Roy AG (1999) The application of computational fluid dynamics to natural river channels: Three-dimensional versus two-dimensional approaches. Geomorphology 29:1–20. https://doi.org/10.1016/S0169-555X(99)00003-3

    Article  Google Scholar 

  33. Keylock CJ, Constantinescu G, Hardy RJ (2012) The application of computational fluid dynamics to natural river channels: Eddy resolving versus mean flow approaches. Geomorphology 179:1–20. https://doi.org/10.1016/j.geomorph.2012.09.006

    Article  Google Scholar 

  34. Robert A (2003) RIVER PROCESSES: An introduction to fluvial dynamics – 1st Edition - A, 1st edn. Arnold Publishers

  35. Recking A, Frey P, Paquier A, Belleudy P, Champagne JY (2008) Feedback between bed load transport and flow resistance in gravel and cobble bed rivers. Water Resour Res 44:1–21. https://doi.org/10.1029/2007WR006219

    Article  Google Scholar 

  36. Camenen B, Bayram A, Larson M (2006) ;132:1146–58. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:11(1146)

  37. Chanson H (2004) Sediment transport capacity and total sediment transport. Hydraul. Open Channel Flow, Elsevier; p. 218–38. https://doi.org/10.1016/b978-075065978-9/50018-0

  38. Sumer BM, Kozakiewicz A, Fredsøe J, Deigaard R (1996) Velocity and Concentration Profiles in Sheet-Flow Layer of Movable Bed, vol 122. J Hydraul Eng, New York, NY), pp 549–558

    Google Scholar 

  39. van Rijn L (1984) Sediment transport; Part I, Bed load transport. J Hydraul Eng 110:1431–1456

    Article  Google Scholar 

  40. Kang S, Sotiropoulos F (2012) Numerical modeling of 3D turbulent free surface flow in natural waterways. Adv Water Resour 40:23–36. https://doi.org/10.1016/j.advwatres.2012.01.012

    Article  Google Scholar 

  41. Giang LS, Hong TTM (2019) 3D numerical modeling of flow and sediment transport in rivers and open channels. Sci Technol Dev J - Sci Earth Environ 3:23–36. https://doi.org/10.32508/stdjsee.v3i1.508

    Article  Google Scholar 

  42. Ge L, Sotiropoulos F (2007) A numerical method for solving the 3D unsteady incompressible Navier-Stokes equations in curvilinear domains with complex immersed boundaries. J Comput Phys 225:1782–1809. https://doi.org/10.1016/j.jcp.2007.02.017

    Article  Google Scholar 

  43. Khosronejad A, Sotiropoulos F (2014) Numerical simulation of sand waves in a turbulent open channel flow. J Fluid Mech 753:150–216. https://doi.org/10.1017/jfm.2014.335

    Article  Google Scholar 

  44. Smagorinsky J(1963) General circulation experiments wiht the primitive equations I. The basic experiment.Mon Weather Rev;91

  45. Kang S, Lightbody A, Hill C, Sotiropoulos F (2011) High-resolution numerical simulation of turbulence in natural waterways. Adv Water Resour 34:98–113. https://doi.org/10.1016/j.advwatres.2010.09.018

    Article  Google Scholar 

  46. Khosronejad A, Kang S, Borazjani I, Sotiropoulos F (2011) Curvilinear immersed boundary method for simulating coupled flow and bed morphodynamic interactions due to sediment transport phenomena. Adv Water Resour 34:829–843. https://doi.org/10.1016/j.advwatres.2011.02.017

    Article  Google Scholar 

  47. Wilcox DC (1993) Turbulence modeling for CFD.

  48. Fonstad MA, Dietrich JT, Courville BC, Jensen JL, Carbonneau PE (2013) Topographic structure from motion: A new development in photogrammetric measurement. Earth Surf Process Landforms 38:421–430. https://doi.org/10.1002/esp.3366

    Article  Google Scholar 

  49. Drone Map** Software (2021) - OpenDroneMap n.d. https://www.opendronemap.org/

  50. Parsons DR, Jackson PR, Czuba JA, Engel FL, Rhoads BL, Oberg KA et al (2013) Velocity Map** Toolbox (VMT): A processing and visualization suite for moving-vessel ADCP measurements. Earth Surf Process Landforms 38:1244–1260. https://doi.org/10.1002/esp.3367

    Article  Google Scholar 

  51. Khosronejad A, Ghazian Arabi M, Angelidis D, Bagherizadeh E, Flora K, Farhadzadeh A. Comparative hydrodynamic study of rigid-lid and level-set methods for LES of open-channel flow.J Hydraul Eng2019;145. https://doi.org/10.1061/(asce)hy.1943-7900.0001546

  52. Flora K, Santoni C, Khosronejad A (2021) Effect of bank vegetation on the hydrodynamics of the American River under flood conditions: a numerical study. J Hydraul Eng Forthcomin:1–14. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001912

    Article  Google Scholar 

  53. Khosronejad A, Kang S, Sotiropoulos F (2012) Experimental and computational investigation of local scour around bridge piers. Adv Water Resour 37:73–85. https://doi.org/10.1016/j.advwatres.2011.09.013

    Article  Google Scholar 

  54. Khosronejad A, Hill C, Kang S, Sotiropoulos F (2013) Computational and experimental investigation of scour past laboratory models of stream restoration rock structures. Adv Water Resour 54:191–207. https://doi.org/10.1016/j.advwatres.2013.01.008

    Article  Google Scholar 

  55. Khosronejad A, Diplas P, Angelidis D, Zhang Z, Heydari N, Sotiropoulos F (2020) Scour depth prediction at the base of longitudinal walls: a combined experimental, numerical, and field study. Environ Fluid Mech 20:459–478. https://doi.org/10.1007/s10652-019-09704-x

    Article  Google Scholar 

  56. Khosronejad A, Kozarek JL, Palmsten ML, Sotiropoulos F (2015) Numerical simulation of large dunes in meandering streams and rivers with in-stream rock structures. Adv Water Resour 81:45–61. https://doi.org/10.1016/j.advwatres.2014.09.007

    Article  Google Scholar 

  57. Hey RD (1979) Flow Resistance in Gravel-Bed Rivers. ASCE J Hydraul Div 105:365–379. https://doi.org/10.1061/jyceaj.0005178

    Article  Google Scholar 

  58. Ferguson R (2007) Flow resistance equations for gravel- and boulder-bed streams. Water Resour Res 43. https://doi.org/10.1029/2006WR005422

  59. Adams B, Bohnhoff W, Dalbey K, Ebeida M, Eddy J, Eldred M et al(2020) Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User’s Manual.

  60. Eldred MS. Recent advances in non-intrusive polynomial chaos and stochastic collocation methods for uncertainty analysis and design. Collect Tech Pap - AIAA/ASME/ASCE/AHS/ASC Struct Struct Dyn Mater Conf 2009. https://doi.org/10.2514/6.2009-2274

  61. Narayan A, **u D (2012) Stochastic Collocation Methods on Unstructured Grids in High Dimensions via Interpolation. SIAM J Sci Comput 34:A1729–A1752. https://doi.org/10.1137/110854059

    Article  Google Scholar 

  62. Sudret B (2008) Global sensitivity analysis using polynomial chaos expansions. Reliab Eng Syst Saf 93:964–979. https://doi.org/10.1016/j.ress.2007.04.002

    Article  Google Scholar 

  63. Eldred MS, Bohnhoff W, Hart W, DAKOTA(1999) A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Sensitivity Analysis, and Uncertainty Quantification Acknowledgment.

  64. Khosronejad A, Hansen AT, Kozarek JL, Guentzel K, Hondzo M, Guala M et al (2016) Large eddy simulation of turbulence and solute transport in a forested headwater stream. J Geophys Res F Earth Surf 121:146–167. https://doi.org/10.1002/2014JF003423

    Article  Google Scholar 

  65. Le TB, Khosronejad A, Sotiropoulos F, Bartelt N, Woldeamlak S, Dewall P (2019) Large-eddy simulation of the Mississippi River under base-flow condition: hydrodynamics of a natural diffluence-confluence region. J Hydraul Res 57:836–851. https://doi.org/10.1080/00221686.2018.1534282

    Article  Google Scholar 

  66. Buchanan TJ, Somers WP, Survey USG(1976) Discharge measurements at gaging stations.

  67. Le Coz J, Hauet A, Pierrefeu G, Dramais G, Camenen B (2010) Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers. J Hydrol 394:42–52. https://doi.org/10.1016/j.jhydrol.2010.05.049

    Article  Google Scholar 

  68. Welber M, Le Coz J, Laronne JB, Zolezzi G, Zamler D, Dramais G et al (2016) Field assessment of noncontact stream gauging using portable surface velocity radars (SVR). Water Resour Res 52:1108–1126. https://doi.org/10.1002/2015WR017906

    Article  Google Scholar 

  69. Welber M, Le Coz J, Laronne JB, Zolezzi G, Zamler D, Dramais G, et al. Field assessment of noncontact stream gauging using portable surface velocity radars (SVR). Water Resour Res 2016;52:1108–26. https://doi.org/https://doi.org/10.1002/2015WR017906.

Download references

Acknowledgements

Computational resources were provided by the Center for Excellence in Wireless and Information Technology (CEWIT) of the College of Engineering and Applied Sciences at Stony Brook University. The field campaign to measure bathymetric survey, conduct aerial survey, and collect ADCP data were supported by the California Department of Transportation.

Funding

This work was supported by grants from the National Science Foundation (EAR-0120914) and the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Water Power Technologies Office (WPTO) Award Number DE-EE0009450. The views expressed herein do not necessarily represent the view of the U.S. Department of Energy or the United States Government.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering & Applied Sciences, Stony Brook University, 11794, Stony Brook, NY, USA

    Kevin Flora & Ali Khosronejad

Authors
  1. Kevin Flora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Khosronejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Khosronejad.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flora, K., Khosronejad, A. Uncertainty quantification of large-eddy simulation results of riverine flows: a field and numerical study. Environ Fluid Mech 22, 1135–1159 (2022). https://doi.org/10.1007/s10652-022-09882-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10652-022-09882-1

Search

Navigation