(Table B2) Relative abundances of diatom species used in the Canoco analysis at sediment surface samples from the Chinese inshore waters

Canonical correspondence analysis has been used to analyze and to visualize the relationships between the main species and selected environmental variables in a study of diatoms from surface sediment samples in Chinese inshore waters. The result shows that the diatom distribution in Chinese inshore waters is closely correlated with the environmental variables and that the measured environmental variables account for the major changes of the diatom composition. Winter sea-surface temperature (WST), winter sea-surface salinity (WSS), water depth and summer sea-surface salinity (SSS) play an important role for the diatom distribution. Among the environmental factors, winter sea-surface temperature is the most important, controlling the distribution of diatoms in the surface sediments in Chinese inshore waters, and therefore, it may be potentially reconstructed in palaeoceanographic studies. Three diatom assemblages are distinguished, representing environments with different hydrological characteristics. The temperate-water diatom assemblage may be used as an indicator of the coastal circulation system of Bohai Sea and Yellow Sea. While the warm-temperate water diatom assemblage is closely related to Shanghai-Zhejiang-Fujian coastal currents and Northern Bay coastal currents of South China Sea. The deep water diatom assemblage is a response to that the waters are less controlled by coastal currents, but are more influenced by open sea currents, such as Kuroshio.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.