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Natural Hazards

, Volume 91, Issue 2, pp 567–586 | Cite as

Hydrodynamic modeling of flash flood in mountain watersheds based on high-performance GPU computing

  • Xiaozhang Hu
  • Lixiang Song
Original Paper

Abstract

Numerical accuracy and computational efficiency are the two key factors for flash flood simulation. In this paper, a two-dimensional fully hydrodynamic model is presented for the simulation of flash floods in mountain watersheds. A robust finite volume scheme is adopted to accurately simulate the overland flow with wet/dry fronts on highly irregular topography. A graphics processing unit-based parallel method using OpenACC is adopted to realize high-performance computing and then improve the computational efficiency. Since the finite volume scheme is explicit which involves many computationally intensive loop structures without data dependence, the parallel flash flood model can be easily realized by using OpenACC directives in an incremental developing way based on the serial model codes, except that data structure and transportation should be optimized for parallel algorithm. Model accuracy is validated by benchmark cases with exact solutions and experimental data. To further analyze the performance of the model, we considered a real flash flooding-prone area in China using a NVIDIA Tesla K20c card and three grid division schemes with different resolution. Results show that the proposed model can fast simulate the rainfall−runoff process related to the rapid mountain watersheds response, and a higher speedup ratio can be achieved for finer grids resolution. The proposed model can be used for real-time prediction of large-scale flash flood on high-resolution grids and thus has bright application prospects.

Keywords

Flash flood Mountain watershed Hydrodynamic model Finite volume GPU computing Numerical simulation 

Notes

Acknowledgements

This work was supported by a grant from the Natural Science Foundation of Guangdong Province, China (No. 2014A030310283), a grant from the National Key Research and Development Program of China (No. 2017YFC0405900), a grant from the Open Research Foundation of PRHRI (Project No. 2013KJ01), and a grant from the Special Research Foundation for the Public Welfare Industry of the Ministry of Water Resources (No. 201501030).

References

  1. Begnudelli L, Sanders BF (2006) Unstructured grid finite-volume algorithm for shallow-water flow and scalar transport with wetting and drying. ASCE J Hydraul Eng 132(4):371–384CrossRefGoogle Scholar
  2. Begnudelli L, Sanders BF (2007) Conservative wetting and drying methodology for quadrilateral grid finite-volume models. ASCE J Hydraul Eng 133(3):312–322CrossRefGoogle Scholar
  3. Elfeki A, Masoud M, Niyazi B (2017) Integrated rainfall–runoff and flood inundation modeling for flash flood risk assessment under data scarcity in arid regions: Wadi Fatimah basin case study, Saudi Arabia. Nat Hazards 85:87–109CrossRefGoogle Scholar
  4. Giammarco PD, Tadini E, Lamberti P (1996) A conservative finite element approach to overland flow: the control volume finite element formulation. J Hydrol 175(1–4):267–291CrossRefGoogle Scholar
  5. Guinot V, Sanders BF, Schubert JE (2017) Dual integral porosity shallow water model for urban flood modeling. Adv Water Resour 103:16–31CrossRefGoogle Scholar
  6. Herdman JA, Gaudin WP, Mcintosh-Smith S, et al (2012) Accelerating hydrocodes with OpenACC, OpenCL and CUDA. In: SC companion: high performance computing, networking, storage and analysis. IEEE computer society, pp 465–471Google Scholar
  7. Huang G (2006) Physics based, integrated modeling of hydrology and hydraulics at watershed scales. PhD thesis, The Pennsylvania State UniversityGoogle Scholar
  8. Hubbard ME (1999) Multidimensional slope limiters for MUSCL-type finite volume schemes on unstructured grids. J Comput Phys 155(1):54–74CrossRefGoogle Scholar
  9. Iwagaki Y (1955) Fundamental studies on the runoff by characteristics. Bull Disaster Prev Res Inst Kyoto Univ 10:1–25Google Scholar
  10. Kim B, Sanders BF, Schubert JE et al (2014) Mesh type tradeoffs in 2D hydrodynamic modeling of flooding with a Godunov-based flow solver. Adv Water Resour 68:42–61CrossRefGoogle Scholar
  11. Kourgialas NN, Karatzas GP, Nikolaidis NP (2012) Development of a thresholds approach for real-time flash flood prediction in complex geomorphological river basins. Hydrol Process 26:1478–1494CrossRefGoogle Scholar
  12. Lai W, Khan AA (2016) A parallel two-dimensional discontinuous galerkin method for shallow-water flows using high-resolution unstructured meshes. J Comput Civil Eng 31(3):04016073CrossRefGoogle Scholar
  13. Lian J, Yang W, Xu K et al (2017) Flash flood vulnerability assessment for small catchments with a material flow approach. Nat Hazards 88:699–719CrossRefGoogle Scholar
  14. Liang Q, Borthwick AGL (2009) Adaptive quadtree simulation of shallow flows with wet-dry fronts over complex topography. Comput Fluids 38(2):221–234CrossRefGoogle Scholar
  15. Liang Q, Xia X, Hou J (2016) Catchment-scale high-resolution flash flood simulation using the GPU-based technology. Procedia Eng 154:975–981CrossRefGoogle Scholar
  16. Liu W, Chen W, Hsu M et al (2010) Dynamic routing modeling for flash flood forecast in river system. Nat Hazards 52:519–537CrossRefGoogle Scholar
  17. Pender G, Cao Z, Zhang S et al (2010) Hydrodynamic modelling in support of flash flood warning. Water Manag 163(7):327–340Google Scholar
  18. Sanders BF, Schubert JE, Detwiler RL (2010) ParBreZo: a parallel, unstructured grid, Godunov-type, shallow-water code for high-resolution flood inundation modeling at the regional scale. Adv Water Resour 33(12):1456–1467CrossRefGoogle Scholar
  19. Singh VP (1996) Kinematic wave modeling in water resources: surface-water hydrology. Wiley, New YorkGoogle Scholar
  20. Singh J, Altinakar MS, Ding Y (2015) Numerical modeling of rainfall-generated overland flow using nonlinear shallow-water equations. ASCE J Hydraul Eng.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0001124 Google Scholar
  21. Song L, Zhou J, Guo J et al (2011) A robust well-balanced finite volume model for shallow water flows with wetting and drying over irregular terrain. Adv Water Resour 34(7):915–932CrossRefGoogle Scholar
  22. Tao J, Barros AP (2013) Prospects for flash flood forecasting in mountainous regions-An investigation of Tropical Storm Fay in the Southern Appalachians. J Hydrol 506:69–89CrossRefGoogle Scholar
  23. Toro EF (2001) Shock-capturing methods for free-surface shallow flows. Wiley, Chichester. ISBN 0-471-98766-2Google Scholar
  24. Tsai TL, Yang JC (2005) Kinematic wave modeling of overland flow using characteristics method with cubic-spline interpolation. Adv Water Resour 28(7):661–670CrossRefGoogle Scholar
  25. Wang X, Shangguan Y, Onodera N et al (2014) Direct numerical simulation and large eddy simulation on a turbulent wall-bounded flow using lattice Boltzmann method and multiple GPUs. Math Probl Eng 2014:742432Google Scholar
  26. Yang L, Smith JA, Baeck ML et al (2016) Flash flooding in small urban watersheds: storm event hydrologic response. Water Resour Res 52:4571–4589CrossRefGoogle Scholar
  27. Zeng Z, Tang G, Long D et al (2016) A cascading flash flood guidance system: development and application in Yunnan Province, China. Nat Hazards 84:2071–2093CrossRefGoogle Scholar
  28. Zhang S, Yuan R, Wu Y et al (2016) Implementation and efficiency analysis of parallel computation using OpenACC: a case study using flow field simulations. Int J Comput Fluid Dyn 30(1):79–88CrossRefGoogle Scholar
  29. Zhang S, Yuan R, Wu Y et al (2017) Parallel computation of a dam-break flow model using OpenACC applications. J Hydraul Eng 143(1):04016070CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Water Resources and EnvironmentPearl River Hydraulic Research InstituteGuangzhouChina

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