Download PDFOpen PDF in browserA Parallel Flood Forecasting and Warning Platform Based on HPC Clusters8 pages•Published: September 20, 2018AbstractAs 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.
|