Key environmental parameters and numerical prediction model of jellyfish bloom in Qingchuan Bay Nuclear Power Plant, China

Abstract

In recent years, there has been a notable increase in cooling water intake blockage caused by marine organism blooms at coastal nuclear power plants worldwide, resulting in shutdowns of nuclear power plants and large economic losses. A sizable portion of these incidents were caused by blooms from jellyfish, a planktonic invertebrate with a unique growth pattern. Suitable external conditions are conducive to the rapid growth of jellyfish, and blooms can occur within a few days. In order to better predict jellyfish bloom and enable nuclear power plants to prepare for it in advance, this study explores the numerical relationship between jellyfish biomass and environmental parameters. A series of time windows (evaluation intervals) were defined and constructed by a time-series recursive approach, which solved the problem of poor correlation between jellyfish biomass and environmental parameters at non-bloom points. The optimal time window length D = 10 was obtained, and the key environmental parameters affecting jellyfish biomass were screened as sea surface temperature, salinity, voltage value, dissolved oxygen, and chlorophyll. According to the ADF and KPSS tests, the key parameters have no significant time dependence, which ensures the stability and reliability of the subsequent predictions. The jellyfish bloom prediction model was derived by calculating the score F through the recursive Principal Component Analysis of the key environmental parameter in the time interval preceding the prediction point. The sudden change moments of score F correspond well to the jellyfish bloom moments, and the sudden change moments are all advanced for a period of time compared to the bloom time, which can provide valuable time for the nuclear power plant to organize manpower to deal with the blockage. Finally, a maximum score F threshold model was proposed to be coupled with the jellyfish bloom prediction model to provide a more robust basis for early warning of jellyfish at nuclear power plants.