The PARFLOOD model

We develop our in-house parallel numerical models for flood numerical simulations.

The PARFLOOD model is developed by the HyLab research group (Dept. of Engineering and Architecture, University of Parma), in collaboration with the Dept. of Mathematical, Physical and Computer Sciences.

The model exploits the Graphics Processor Unit (GPU) computational capability under the NVIDIA™’s Compute Unified Device Architecture (CUDA). A state-of-the-art Finite Volume explicit discretization technique is adopted, which is well balanced, second-order accurate and based on positive depth reconstruction. This led to speedups of two orders of magnitude compared to a single-core CPU allowing for much faster high–resolution simulations.

Applications:

  • Creating accurate flood inundation maps
  • Tsunami simulation
  • Flood risk assessment
  • Analyzing the impact of river infrastructures, and testing solution for their optimal managements
  • Increasing the resilience and the preparedness by developing emergency planning based on high-resolution simulations
  • Maximizing the efficiency of flood control reservoirs.
figure2dnumerical

Figure: 2D numerical simulation of the Thacker test case of oscillating planar water surface: comparison between analytical and numerical results.
figure-interaction-flux

Figure: Iteration flux diagram, each phase is handled by a different GPU kernel.

Model availability:

The model is currently shared only for scientific collaboration (contact: renato.vacondio@unipr.it).

 

Developers:

Dr. Renato Vacondio, Prof. Paolo Mignosa, Dr. Alessia Ferrari, Dr. Susanna Dazzi, Dr. Federico Prost, Prof. Francesca Aureli (Department of Engineering and Architecture)

Prof. Alessandro Dal Palù (Department of Mathematical, Physical and Computer Sciences)

 

Main references:

  • Vacondio, R., Dal Palù, A., & Mignosa, P. (2014). GPU-enhanced Finite Volume Shallow Water solver for fast flood simulations. Environmental Modelling & Software, 57, 60-75. https://doi.org/10.1016/j.envsoft.2014.02.003
  • Vacondio, R., Dal Palù, A., Ferrari, A., Mignosa, P., Aureli, F., & Dazzi, S. (2017). A non- uniform efficient grid type for GPU-parallel shallow water equations models. Environmental Modelling & Software, 88, 119-137. https://doi.org/10.1016/j.envsoft.2016.11.012

 

Other publications:

  • Vacondio, R., Aureli, F., Ferrari, A., Mignosa, P., & Dal Palù, A. (2016). Simulation of the January 2014 flood on the Secchia river using a fast and high-resolution 2D parallel shallow-water numerical scheme. Natural Hazards, 80 (1), 103-125.
  • Dazzi, S., Vacondio, R., Dal Palù, A., & Mignosa, P. (2018). A local time stepping algorithm for GPU-accelerated 2D shallow water models. Advances in Water Resources, 111, 274-288.
  • Ferrari, A., D’Oria, M., Vacondio, R., Palù, A. D., Mignosa, P., & Tanda, M. G. (2018). Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model. Hydrology and Earth System Sciences, 22(10), 5299-5316.
  • Mignosa, P., Vacondio, R., Aureli, F., Dazzi, S., Ferrari, A., & Prost, F. (2018). “High resolution 2D modelling of rapidly varying flows: some case studies.” Italian Journal of Engineering Geology and Environment, Special Issue 2018. DOI: 10.4408/IJEGE.2018-01.S-14.
  • Dazzi, S., Vacondio, R., & Mignosa, P. (2019). Integration of a levee breach erosion model in a GPU-accelerated 2D shallow water equations code. Water Resources Research, 55 (1), 682-702.
  • Ferrari, A., Viero, D. P., Vacondio, R., Defina, A., & Mignosa, P. (2019). Flood inundation modeling in urbanized areas: A mesh-independent porosity approach with anisotropic friction. Advances in water resources, 125, 98-113.
  • Dazzi, S., Vacondio, R., & Mignosa, P. (2020). Internal boundary conditions for a GPU-accelerated 2D shallow water model: Implementation and applications. Advances in Water Resources, 137, 103525.
  • Ferrari, A., Dazzi, S., Vacondio, R., & Mignosa, P. (2020). Enhancing the resilience to flooding induced by levee breaches in lowland areas: a methodology based on numerical modelling. Natural Hazards & Earth System Sciences, 20(1).
  • Aureli, F., Prost, F., Vacondio, R., Dazzi, S., & Ferrari, A. (2020). A GPU-Accelerated Shallow-Water Scheme for Surface Runoff Simulations. Water, 12(3), 637.