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A Parallel Flood Forecasting and Warning Platform Based on HPC Clusters

Author(s): Ronghua Liu; Liang Guo; Yali Wang; Xiaolei Zhang; Qi Liu; Yizi Shang; Xiaoyan Zhai; Jiyang Tian; Dayong Huang

Linked Author(s): Ronghua Liu, Xiaolei Zhang

Keywords: China Flash Flood Hydrological Model; Flood forecasting and warning; High performance computing clusters; Parallel computing

Abstract: As floods could be effectively forecasted by distributed hydrological model, their study and application became the key points of flood forecasting and early warning. Based on high performance computing clusters, a parallel flood forecasting and warning platform with the characteristics of partition, classification, and complicated process coupled was established to forecast and warn flood across China, especially for flash flood in China. In addition, the platform was based on China Flash Flood Hydrological Model (CNFF-HM). It used files (not MPI), which based on shared a hierarchical storage system, to pass message to control the start and stop of simulation processes, and the rapid communication among simulation processes was realized; preallocation and dynamic allocation methods was together applied to manage the resource of the high performance computing clusters; the automatic switch among different time scale models was realized by simulation driven strategy based on rainfall events; the reboot framework was designed to deal with the process crash and delayed rainfall data. The effectiveness and stability of the platform was test by the flood events of 2017. Finally, a case of Weishui catchment in Hunan Province was shown.


Year: 2018

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