An artificial oyster shell reef was deployed in Rongcheng Bay, East China. However, the effects of this reef on the surrounding macrobenthic communities were unknown. We compared sedimentary factors, macrobenthic biomass, abundance, and community composition and ecological indicators between the reef and non-reef areas over a one year period. The mean values for chlorophyll a (Chl a), total organic matter (TOM), total organic carbon (TOC), and total nitrogen (TN) content in surface sediments in the reef area were slightly higher than those in the non-reef area. The Chl a levels differed significantly between the two areas, but the TOM, TOC, and TN were not significantly different. The abundance of crustaceans was significantly different between the two areas, but the abundance and biomass ofpolychaetes, echinoderms, mollusk did not differ significantly. The permutational multivariate analysis of variance (PERMANOVA) revealed that the macrobenthic community differed significantly through time and analysis of similarity multivariate analyses (ANOSIM) revealed that the macrobenthic community differed significantly in some months. The ecological indicators revealed that the environmental quality of the reef area was slightly better than that of the non-reef area. Overall, our results suggest that the artificial oyster shell reef may change the macrobenthic community and the quality of the environment. Despite the lack of an effect in the short term, long-term monitoring is still needed to evaluate the effects of artificial oyster shell reefs on macrobenthic communities. 相似文献
For the Xiamen coast where typhoon frequently occurs, beaches are subject to severe erosion during typhoons. To investigate storm-induced beach profile changes at Xiamen coast, four inner XBeach models were applied using typhoon Dan as a case study. These numerical simulations utilized hydrodynamic and wave conditions determined from larger-scale outer and middle coupled Delft3D-FLOW and SWAN models. The models were validated against historic measurements of tidal level, storm tide, storm surge and beach profiles, thus showing the accuracy of outer and middle models to provide boundary conditions and the reliability of inner models to reflect beach profile changes during a typhoon process. The applicability of this modeling approach to Xiamen coast was verified. The results also demonstrated that an enormous amount of dune face erosion occurred at the selected beaches during the typhoon Dan process and the slopes in the vicinity of zero elevation for the chosen four beach profiles all turned out to be gentler after typhoon Dan. Nevertheless, these beaches suffered different impact degrees and processes during the typhoon influence period. Compared to swash and collision regimes, overwash and inundation regimes have the ability to alter beach profile rapidly in short time. Post-storm beach profile with and without vegetation indicated that vegetation is capable of protecting coastal beaches to some extent. By running the nested models, the simulated results can be employed in the management of the beach system and the design of beach nourishment projects at Xiamen coast.
In this study, a 91-year data set at the Wusong Station near Shanghai City in Yangtze Estuary has been used to estimate the 100-year Annual Maximum Water Level (AMWL). The performances of four common distribution models have been evaluated. The GEV model provides the best estimates of an AMWL. It results in the minimum difference (0.04 m) compared to the observed 92-year AMWL, with the high correlation coefficient (0.99) and minimum root-mean-square-error (0.045 m) value. Predictions from other distribution models cause non-negligible deviations, underestimating the 92-year AMWL by 0.57, 0.38, and 0.15 m for Weibell, Lognormal, and Gumbel distribution models, respectively. In order to examine the effects of a shorter data set, a 59-year data set was investigated. Model predictions using 59-year data set underestimates the observed 60-year AMWLs. By comparing to the 100-year AMWL estimated by the GEV distribution, using the 91-year data set, results using the shorter 59-year data set lead to underestimates of the 100-year AMWL by 0.78 m for Weibull, 0.58 m for Lognormal, 0.38 m for Gumbel, and 0.39 m for GEV distributions. Therefore, one should be cautious when estimating the 100-year AMWL if the data set covers a period much shorter than 100 years. Selecting an appropriate distribution model can improve prediction accuracy. 相似文献
The prediction of high extremes in sustained water level is very important for coastal engineering design and planning. The recorded historical water level datasets in Colombo, Sri Lanka, are not long enough for the traditional frequency analysis in predicting extreme water levels, such as 50-, 100- and 200-year extreme water levels. In this study, the integrated ADCIRC + SWAN hydrodynamic model and Monte Carlo model have been applied to predict extreme water level in Colombo station of Sri Lanka. The meteorological driving forces of cyclone storm surge are simulated by Monte Carlo stochastic model. The calibrated ADCIRC model with SWAN wave model is used to simulate the potential surge setups with the driving forces generated by Monte Carlo model. By ranking the maximum high water levels in each storm surge procedure, the estimation on extreme high water levels for the desired return period is proposed in this study. The estimated extreme high water levels with return period of 50, 100 and 200 years are 1.28, 1.40 and 1.50 m correspondingly. The estimated extreme high water levels are recommended for engineering design and planning. 相似文献
Natural Hazards - The central part of Khyber Pakhtunkhwa, Pakistan, is a highly flood-prone area of the province. The lives and assets of local communities are deeply vulnerable, attributed to the... 相似文献
Natural Hazards - Continuing to enhance the understanding of occurrence probabilities of spatial extreme sea levels is a fundamental requirement of coastal hazard mitigation and prevention. To... 相似文献
The three-parameter generalized-extreme-value (GEV) model has been recommended by FEMA [FEMA (Federal Emergency Management Agency of the United States), 2004. Final Draft Guidelines for Coastal Flood Hazard Analysis and Mapping for the Pacific Coast of the United States. http://www.fema.gov/library/viewRecord.do?id=2188] for frequency analysis of annual maximum water levels in the Pacific coast of the United States. Yet, the GEV model's performance in other coastal areas still needs to be evaluated. The GEV model combines three types of probability distributions into one expression. The probability distributions can be defined by one of the three parameters of the GEV model. In this study, annual maximum water levels at nine water-level stations with long history data (more than 70 years) were chosen for analysis in five coastal areas: Pacific, Northeast Atlantic, East Atlantic, Southeast Atlantic, and Gulf of Mexico coasts. Parameters of the GEV model are estimated by the maximum likelihood estimation (MLE) method. Results indicate that probability distributions are characterized by the GEV Type III model at stations in the Pacific, Northeast, and East Atlantic coastal areas, while they are described by GEV Type II in stations of the Southeast Atlantic and Gulf of Mexico coastal areas. GEV model predictions of extreme water levels show good correlation to observations with correlation coefficients of 0.89 to 0.99. For predictions of 10% annual maximum water levels, the GEV model predictions are very good with errors equal to or less than 5% for all nine stations. Comparison of observations and GEV model estimations of annual maximum water levels for the longest recorded return periods, close to 100 years, revealed errors equal to or less than 5% for stations in the Pacific and Northeast Atlantic coastal areas. However, the errors range from 10% to 28% for other stations located in the East and Southeast Atlantic coasts as well as Gulf of Mexico coastal areas. Findings from this study suggest caution regarding the magnitudes of errors in applying the GEV model to the East and Southeast Atlantic coasts and Gulf of Mexico coast for estimating 100-year annual maximum water levels for coastal flood analysis. 相似文献