Author(s): Reinaldo Garcia; Pedro Restrepo; Mike Deweese; Mark Ziemer; Justin Palmer; Jonathon Thornburg; Javier Murillo; Mario Morales Hernández; Pilar Garcia-Navarro; Asier Lacasta

Linked Author(s): Pilar García-Navarro

Keywords: 2D Modeling; GPU Parallelization; Flood Forecasting; Red River of the North

Abstract: One-dimensional (1D) models have traditionally been used for river flood prediction in large river reaches, mainly because they run fast due to the simplified flow equations used. However, in complex and relatively flat floodplains or where the flow is unconfined, 1D models may not provide an adequate solution due to the limitations of uniform water velocity and constant water surface elevation on each cross section. This warrants the use of more accurate two-dimensional (2D) models. Yet, the numerical solution of the 2D dynamic equations and the requirement of flexible meshes to resolve complex terrain characteristics until recently had made 2D models considerably more demanding in computer times than 1D models. However, recent advances in massive parallelization techniques for 2D hydraulic models are able to reduce computer times by orders of magnitude making 2D applications competitive and practical for operational flood prediction in large river reaches. Moreover, high performance code development can take advantage of general purpose and inexpensive Graphical Processing Units (GPU) ,allowing to run 2D simulations more than 100 times faster than old generation 2D codes, in some cases. This work describes the application of the River Flow2D GPU model to a 670-km reach of the Red River in North Dakota and Minnesota, USA. This project is a collaborative effort between NOAA, Hydronia, LLC and the University of Zaragoza to assess the accuracy and performance the River Flow2D GPU model at NOAA North Central River Forecast Center as a 2D operational model for river flood forecasting. Test runs and preliminary results shown here indicate that routing a 50-day hydrograph using a 593, 109-cell mesh can run in as little 36 minutes on a NVIDIA GTX Titan Black 2, 880 GPU-core hardware. This performance is even better than the existing 1D model currently used as a forecast tool in the same river reach. Ongoing efforts include tests on other advanced GPU hardware such as the NVIDIA TESLA K20 and K40. Results suggest that 2D GPU models such as River Flow2D are able to achieve the performance required in hydrological forecasting such as that of NOAA River Forecast Centers.

DOI:

Year: 2015