Search

Marine biogrowth infestation of a seawater intake system was investigated. A digital camera fixed onto a skid was used to record the biogrowth at intervals of 5 m up to a depth of 55 m. Divers inspected the intake shaft and collected the biogrowth samples for biomass estimation. A biomass density of 7.5 kg m−2 and 28.2 kg m−2 was recorded at 5 and 30 m depths respectively. Inspection by the divers revealed that hard-shelled organisms such as oysters and brown and green mussels were observed in plenty up to a thickness of 15 cm and bryozoans grew as epibionts. At lower depths ( m), hydroids grew on the shells of green mussels along with silt accumulation. The biofouling community was composed of 46 organisms, exhibiting variation in distribution and abundance. The study explains the extent and type of marine biogrowth phenomena with depth and describes biofouling preventive methods. Supplemental data for this article is available online at https://doi.org/10.1080/08927014.2021.1933457 .

The covariance function and the variogram play very important roles in modelling and in prediction of spatial and spatio-temporal data. The assumption of second order stationarity, in space and time, is often made in the analysis of spatial data and the spatio-temporal data. Several times the assumption of stationarity is considered to be very restrictive, and therefore, a weaker assumption that the data is Intrinsically stationary both in space and time is often made and used, mainly by the geo-statisticians and other environmental scientists. In this paper we consider the data to be intrinsically stationary. Because of the inclusion of time dimension,the estimation and derivation of the sampling properties of various estimators related to spatio-temporal data become complicated. In this paper our object is to present an alternative way, based on Frequency Domain methods for modelling the data. Here we consider Discrete Fourier Transforms (DFT) defined for the (Intrinsic) time series data observed at several locations as our data, and then consider the estimation of the parameters of spatio-temporal covariance function, estimation of Frequency Variogram, tests of independence etc. We use the well known property that the Discrete Fourier Transforms of stationary time series evaluated at distinct Fourier Frequencies are asymptotically independent and distributed as complex normal in deriving many results considered in this paper., Comment: 25 pages