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A Parallel Flood Forecasting and Warning Platform Based on HPC Clusters

8 pagesPublished: September 20, 2018


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 a shared hierarchical storage system, to pass message to control the start and stop of simulation processes, and the rapid communication among simulation processes was realized; pre-allocation 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 has been tested by the flood events of 2017. Finally, a case of Weishui catchment in Hunan Province was shown.

Keyphrases: China Flash Flood Hydrological Model, Flood Forecasting and Warning, High performance computing clusters, parallel computing

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1232--1239

BibTeX entry
  author    = {Ronghua Liu and Liang Guo and Yali Wang and Xiaolei Zhang and Qi Liu and Yizi Shang and Xiaoyan Zhai and Jiyang Tian and Dayong Huang},
  title     = {A Parallel Flood Forecasting and Warning Platform Based on HPC Clusters},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {1232--1239},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2516-2330},
  url       = {},
  doi       = {10.29007/5vfl}}
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