In various regions along the east and southeast coast of China and on the outer shelf of the East China Sea, series of cheniers have been found and studied. Their geomorphological and sedimentological features have been described and the main conditions for their formation established.
The development of cheniers in China can be divided into three periods: 24,000-15,000 yrs B.P., 7000-5000 yrs B.P. and 5000 yrs B.P. onwards. These periods have been based on 14C datings of 80 shell samples. The chenier groups of the three periods are distributed throughout various geomorphological locations.
Using the geomorphological locations of the cheniers in combination with their age of formation, the palaeopositions of the Chinese coastline in the various periods since 24,000 yrs B.P. are described. The history of the position of the east coast of China, including the features attesting to sealevel changes during this era, is briefly discussed. 相似文献
The null distribution of the lag-k sample serial correlation coefficient (rk, k=1,2,3) was investigated by Monte Carlo simulation. For a time series with normal, exponential, Pearson 3, EV1 (Gumbel), or
generalized Pareto (GP) distribution type, the null distribution of its rk can be approximated by the normal distribution with mean −1/(n−k) and variance 1/(n−1). But for a time series with the lognormal, EV2 or EV3 (Weibull) distribution type, the null distribution of rk is skewed distributed. In such cases, a simulation technique is suggested to construct percentile confidence intervals at
a given significance level. 相似文献
The non-parametric Mann–Whitney (MW) statistic test has been popularly used to assess the significance of a shift in median
or mean of hydro-meteorological time series. It has been considered that the test is more suitable for non-normally distributed
data and it may be not sensitive to the distribution type of sample data. However, no evidence has been provided to demonstrate
these. This study investigates the power of the test in various circumstances by means of Monte Carlo simulation. Simulation
results demonstrate that the power of the test is very sensitive to various properties of sample data. The power depends on
the pre-assigned significance level, magnitude of a shift, sample size, and its occurrence position within a time series;
and it is also strongly affected by the variation, skewness, and distribution type of a time series. The bigger the magnitude
of a shift, the more powerful the test is; the larger the sample size, the more powerful the test is; and the bigger the variation
within a time series, the less power the test has. The test has the highest power if a shift occurs at the midpoint of a time
series. For the samples with different distribution types, the power of the test is dramatically different. The test has the
highest power for time series with the extreme value type III (EV3) distribution while it indicates the lowest power for time
series with the lognormal distribution. 相似文献