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基于湖泊水动力的太湖藻类短期预测模型
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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
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