Search

Seawater level changes are affected by natural and anthropogenic impacts. While climate changes are considered to be a cause for all significant recent variations in meteorological and hydrological parameters, there is still a need for the analysis at the smaller regional scale, especially of the seawater level changes. A regional analysis is essential for early warning of upcoming changes that could, firstly, affect islands and coastal areas and, subsequently, expand on larger areas. The determined regional changes could affect the salinity of drinking water sources, increase the presence of natural flooding, and impact land degradation. In this paper, an analysis of local seawater level fluctuations is provided for three available locations in Croatia distributed along the Adriatic Sea's coast. The rescaled adjusted partial sums (RAPS) method was used and applied on time series of the average daily seawater levels for each location. Visual interpretation of the RAPS method indicated the appearance of common regularities of the observed quantities, in this case, averaged daily seawater level changes. Also, it was shown that the regional shape and indentation of the coast did not have a strong effect on the seawater level's rise. Seasonal changes in the sea level are mostly periodic and, therefore, have symmetry visible in its behavior. Fluctuations in the dynamics of sea level studied in this paper were not regular and predictable with simple linear equations, but the symmetry was also found to be present in the irregularities identified with the RAPS method. [ABSTRACT FROM AUTHOR]

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change. [ABSTRACT FROM AUTHOR]

The interest in the study of record statistics has been increasing in recent years in the context of predicting stock markets and addressing global warming and climate change problems such as cyclones and floods. However, because record values are mostly rare observed, its probability distribution may be skewed or asymmetric. In this case, the Bayesian approach with a reasonable choice of the prior distribution can be a good alternative. This paper presents an objective Bayesian method for predicting future record values when observed record values have a two-parameter exponentiated Gumbel distribution with the scale and shape parameters. For objective Bayesian analysis, objective priors such as the Jeffreys and reference priors are first derived from the Fisher information matrix for the scale and shape parameters, and an analysis of the resulting posterior distribution is then performed to examine its properness and validity. In addition, under the derived objective prior distributions, a simple algorithm using a pivotal quantity is proposed to predict future record values. To validate the proposed approach, it was applied to a real dataset. For a closer examination and demonstration of the superiority of the proposed predictive method, it was compared to time-series models such as the autoregressive integrated moving average and dynamic linear model in an analysis of real data that can be observed from an infinite time series comprising independent sample values. [ABSTRACT FROM AUTHOR]