A combined numerical model of wind, wave, tide, and storm surges was built on the basis of the “wind field model in limited
sea surface areas”. When used to forecast the sea surface wind, wave height and water level, it can describe them very well.
Contribution No. 4108 from the Institute of Oceanology, Chinese Academy of Sciences.
This work supported by Stress Project (KZ952-S1-420), Chinese Academy of Sciences; 863 Project (863-818-06-05), and (863-818-Q-07) 相似文献
Mariculture has rapidly grown worldwide, which might greatly influence the biogeochemical cycle of organic carbon in coastal seas. In this study, several geochemical parameters, including grain size composition, sedimentary total organic carbon(TOC), total nitrogen(TN), stable carbon(δ13 C) and nitrogen(δ15 N) isotopic compositions, were analyzed for surface sediments collected from different mariculture zones of Sanggou Bay and in different seasons. We investigated the composition and distribution of organic matter in surface sediments and further evaluated the contribution of mariculture activities to TOC sources. The TOC and TN contents(mass percentage) in the bay were in the range of 0.14% to 1.45% and 0.03% to 0.20%, respectively. The spatial distribution indicated that sedimentary TOC and TN contents in shellfish monoculture and shellfish-kelp polyculture zones were higher than in other mariculture zones, which might be related to grain size composition and mariculture organisms. Seasonal variations of TOC contents were observed in different mariculture zones. The TOC/TN atomic ratio(C/N), δ13 C and δ15 N were in the ranges of 5.97 to 10.97,-21.76‰ to-13.14‰ and 2.13‰ to 8.08‰, respectively, implying that sedimentary organic matter in Sanggou Bay was the mixture of marine phytoplankton, terrestrial and maricultural sources. A simple mixing model based on δ13 C was applied and the results indicated that the relative contributions of organic carbon sources in Sanggou Bay followed the order kelp(36.6%) ? marine phytoplankton(28.7%) ? shellfish bio-deposition(23.8%) ? terrestrial input(10.9%). Surface sediments in Sanggou Bay were dominated by mariculture-derived organic carbon, which on average accounted for 60.4% of TOC. 相似文献
The knowledge of prey small fish stock, distribution and abundance is necessary to guide stocking of piscivorous fish for the biomanipulation in domestic tap water lakes. This study describes the current status of small fish community in Lake Kuilei (China), and examines the spatial and seasonal variations of the community in relation to key environmental factors. Based on submerged macrophyte cover and water depth, the lake was divided into five major habitats: (1) macrophyte covered shallow habitat of water depth < 2.00 m, (2) uncovered or less-covered shallow habitat (2.00 m–3.50 m), (3) uncovered medium shallow habitat (3.50 m–5.00 m), (4) uncovered medium deep habitat (5.00 m–6.50 m) and (5) uncovered deep habitat (6.50 m–8.50 m). The abundance and composition of small fish were monitored by benthic fykenet sampling from April 2013 to January 2014. A total of 2881 individuals belonging to 5 families and 21 species were collected. Based on their abundance (accounted for 88.96% of the total) and occurrence (more than 33.33%), Acheilognathus chankaensis, Acheilognathus macropterus, Microphysogobio microstomus, Pseudorasbora parva and Rhinogobius giurinus were recognized as dominant small fish species. The results of correlation analysis identified that species richness ( Sr ), Shannon-Wiener diversity index ( H′ ) and Margalef′s richness index ( D ) were significantly negatively correlated with water depth, but positively correlated with biomass of submerged macrophytes.Redundancy analysis (RDA) revealed that the spatial distributions of most small fishes were negatively associated with water depth. The details of these findings are beneficial to understanding the adaptation of the small fishes in degraded environments, and to developing suitable biomanipulation strategies for the management of fish resources and water quality in the lakes along the lower reach of the Changjiang (Yangtze) River basin.
Natural Resources Research - This study tested and compared the mineral potential mapping capabilities of the random forest (RF) and maximum entropy (MaxEnt) algorithms using gold deposit... 相似文献
Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献