Observation And Simulation Of Ecological Processes In The Typical Region Of Daya Bay

Daya Bay is one of the important sub-tropical waters in China. Along with rapid economic development, Daya Bay is undergoing significant change due to increasing environmental press. Based on water quality and ecological investigation that lasted more than one year, ecological status of the typical region in Daya Bay mouth is analysised, the internal responding relationships within ecosystem are researched, an artificial neural network for phytoplankton biomass prediction is established, and the initial attempt on simulation of Daya Bay ecosystem by kinetic model is presented. The results are concluded as follows:1. During the survey period, water quality status of Daya Bay mouth was oligotrophic, whereas in some months the concentration of phosphate would exceed limit of third-class state seawater quality standard, which caused water quality status changing into mesotrophic or potential eutrophication with nitrogen as the major limiting nutrient factor. Diatoms and copepods were dominant groups of phytoplankton and zooplankton respectively. Biomass of phytoplankton-reached peak value in autumn, followed by winter, summer and spring. Vertically the highest value of phytoplankton biomass mainly appeared in surface or middle layers. Biomass of zooplankton reached climax in autumn, followed by summer, winter and spring. Vertically the maximum value of zooplankton biomass mostly emerged in middle or bottom layers.2. Primary component analysis demonstrates the distinct seasonal variation of ecological envrionment in the typical region of Daya Bay. Stepwise regression analysis reveals that there is negative correlation between surface chl-a and salinity, negative correlation between middle chl-a and ammonia, and correlations between bottom chl-a and DIC, pH with nitrite.3. Using the back-propagation (BP) algorithm, an artificial neural network for forecasting of phytoplankton biomass is established, taking temperature, salinity, pH, phosphate, and silicate as input paremeters. The average relative error of forecast sample is 0.59%, while the average absolute error of forecast sample is 6.37%. It's presented that established neural network model could meet general requirement of prediction accuracy.4. ERSEM model is applied to South China Sea for the first time. Based on collected data, a one-dimensional coupled ecosystem dynamic model (ERSEM-GOTM) is employed to simulate phytoplankton biomass of Daya Bay. Simulated chl-a content ranges between 0.66 and 3.76 mg/m3, with average value of 1.46 mg/m3. Chl-a content emerges crest values in spring and autumn, and its vertical peak values basically appear in surface or middle layer. Nitrogen is the major impacting nutrient element of phytoplankton growth in Daya Bay.