基于湖泊水动力的太湖藻类短期预测模型.pdf

H i J a b c a A R R A K H L C M I e m p 2 r 2 s t a m 1 p s p a 1 0 dLimnologica 42 (2012) 7– 18 Contents lists available at SciVerse ScienceDirect Limnologica j ourna l homepage: www.elsev ier .de / l imno ydrodynamic-phytoplankton model for short-term forecasts of phytoplankton n Lake Taihu, China iacong Huanga,b, Junfeng Gaoa,?, Georg H?rmannc Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China Graduate University of the Chinese Academy of Sciences, Beijing 100049, China Department of Hydrology and Water Resources Management, Ecology Centre, Kiel University, Kiel, Germany r t i c l e i n f o rticle history: eceived 4 August 2010 eceived in revised form 29 May 2011 ccepted 27 June 2011 eywords: ydrodynamic-phytoplankton model ake Taihu hlorophyll- odel calibration a b s t r a c t Phytoplankton biomass is an important factor for short-term forecasts of algal blooms. Our new hydrodynamic-phytoplankton model is primarily intended for simulating the spatial and temporal dis- tribution of phytoplankton in Lake Taihu within a time frame of 1–5 days. The model combines two modules: a simple phytoplankton kinetics module for growth and loss; and a mass-transport module, which defines phytoplankton transport horizontally with a two dimensional hydrodynamic model. To adapt field data for model input and calibration, we introduce two simplifications: (a) exclusion of some processes related to phytoplankton dynamics like nutrient dynamics, sediment resuspension, miner- alization and nitrification, and (b) use of monthly measured data of the nutrient state. Chlorophyll- concentration, representing phytoplankton biomass, is the only state variable in the model. A sensitiv- ity analysis was carried out to identify the most sensitive parameter set in the phytoplankto