Space–time changes in hydrological processes in response to human activities and climatic change in the south China

2011 ◽  
Vol 26 (6) ◽  
pp. 823-834 ◽  
Author(s):  
Xinjun Tu ◽  
Qiang Zhang ◽  
Vijay P. Singh ◽  
Xiaohong Chen ◽  
Chun-Ling Liu ◽  
...  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xianzhe Zhang ◽  
Gang Chen ◽  
Jiechen Wang ◽  
Manchun Li ◽  
Liang Cheng

Research on the forecasting of marine traffic flows can provide a basis for port planning, planning the water area layout, and ship navigation management and provides a practical background for sustainable development evaluation of shipping. Most of the traditional marine traffic volume forecasting studies focus on the variation of the traffic volume of a single port or section in time dimension and less research on traffic correlation of associated ports in shipping networks. To reveal the spatial-temporal autocorrelation characteristics of the shipping network and to establish a suitable space-time forecasting model for marine traffic volume, this paper uses the AIS data from 2011 to 2016 for the South China Sea to construct a regional shipping network. The adjacent discrimination rule based on network correlation is proposed, and the traffic demand between ports is estimated based on the gravity model. On this basis, STARMA (space-time autoregressive moving average) model was introduced for deducing the interaction between he traffic volumes of adjacent ports in shipping network. The experimental results show that (1) there is a significant positive correlation between time and space in the South China Sea shipping network, and this spatial-temporal correlation has the characteristics of time dynamics and spatial heterogeneity; (2) the forecasting accuracy of the marine traffic volume based on the spatial-temporal model is better than the traditional time-series-based forecasting model, and the spatial-temporal model can better portray the spatial-temporal autocorrelation of maritime traffic.


1999 ◽  
Vol 156 (1-4) ◽  
pp. 109-121 ◽  
Author(s):  
Carles Pelejero ◽  
Joan O Grimalt ◽  
Michael Sarnthein ◽  
Luejiang Wang ◽  
José-Abel Flores

2019 ◽  
Author(s):  
Jiayuan Liang ◽  
Kefu Yu ◽  
Yinghui Wang ◽  
Xueyong Huang ◽  
Wen Huang ◽  
...  

Abstract Background: Coral reef ecosystems cannot operate normally without an effective nitrogen cycle. For oligotrophic coral reef areas, coral-associated diazotrophs are indispensable participants in the nitrogen cycle. How coral-associated diazotrophs will change in order to adapt to environmental changes resulting from global warming and human activities is a topic of concern for researchers. To this end, 68 colonies of scleractinian coral were collected from 6 coral reefs areas with different environmental variables in the South China Sea to investigate the composition of associated diazotrophs based on nifH gene amplification using high-throughput sequencing. The six coral reefs can be clearly divided into two types (fringing reefs and island reefs), are affected by varying degrees of human activities and are located at different latitudes from 9°20′06′′N to 22°34′55′′N with different seawater temperatures. Results: Alpha- and beta-diversity analyses showed that the distribution of diazotrophs among coral reefs exhibited greater geographical fluctuations than interspecific fluctuations. The predominant bacterial phyla included Proteobacteria, Chlorobi, Cyanobacteria, and two unclassified phyla. Chlorobi exhibited an abundance of 47–96% in coral samples from the high-latitude Daya Bay fringing reef affected by eutrophication. Unclassified bacteria II, with an abundance of 28–87%, was found in all coral samples from the midlatitude Luhuitou fringing reef affected by eutrophication. However, unclassified bacteria I and Proteobacteria dominated (> 80% abundance) in most of the coral samples from the Weizhou Island fringing reef, which is far from land, and three island reefs (Huangyan Island, Xinyi Reef, and Sanjiao Reef) at relatively low latitudes. At the genus level, some core diazotrophs were found in different coral sample groups. In addition, the correlation analysis with various environmental variables revealed that the variables correlated positively or negatively with different diazotrophic genera. Conclusions: We found that coral-associated diazotrophs were common among coral individuals. The presence of these diazotrophs was not affected by the external environment, but their population abundances were closely related to the different environmental variables. These results provide insights into the ecological characteristics of coral-associated diazotrophs and their relationships with critical environmental variables in the South China Sea.


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