DOI: 10.1016/j.watres.2018.04.018
Scopus记录号: 2-s2.0-85047447218
论文题名: How physiological and physical processes contribute to the phenology of cyanobacterial blooms in large shallow lakes: A new Euler-Lagrangian coupled model
作者: Feng T. ; Wang C. ; Wang P. ; Qian J. ; Wang X.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 140 起始页码: 34
结束页码: 43
语种: 英语
英文关键词: Agent-based model
; Eulerian model
; Microcystis blooms
; Physical process
; Physiological process
Scopus关键词: Autonomous agents
; Buoyancy
; Computational methods
; Financial data processing
; Forecasting
; Lagrange multipliers
; Lakes
; Mixing
; Physiology
; Principal component analysis
; Turbulence
; Agent-based model
; Eulerian models
; Microcystis blooms
; Physical process
; Physiological process
; Physiological models
; algal bloom
; cyanobacterium
; Eulerian analysis
; Lagrangian analysis
; lake water
; MODIS
; phenology
; physiology
; population outbreak
; principal component analysis
; shallow water
; turbulence
; algal bloom
; algal community
; aquatic environment
; Article
; correlation analysis
; cyanobacterium
; Euler Lagrangian coupled model
; fungal life cycle stage
; lake
; microbial biomass
; nonhuman
; phenology
; physical phenomena
; physiological process
; principal component analysis
; priority journal
; process model
; simulation
; wind
; China
; Taihu Lake
; algae
; Cyanobacteria
; Microcystis
英文摘要: Cyanobacterial blooms have emerged as one of the most severe ecological problems affecting large and shallow freshwater lakes. To improve our understanding of the factors that influence, and could be used to predict, surface blooms, this study developed a novel Euler-Lagrangian coupled approach combining the Eulerian model with agent-based modelling (ABM). The approach was subsequently verified based on monitoring datasets and MODIS data in a large shallow lake (Lake Taihu, China). The Eulerian model solves the Eulerian variables and physiological parameters, whereas ABM generates the complete life cycle and transport processes of cyanobacterial colonies. This model ensemble performed well in fitting historical data and predicting the dynamics of cyanobacterial biomass, bloom distribution, and area. Based on the calculated physical and physiological characteristics of surface blooms, principal component analysis (PCA) captured the major processes influencing surface bloom formation at different stages (two bloom clusters). Early bloom outbreaks were influenced by physical processes (horizontal transport and vertical turbulence-induced mixing), whereas buoyancy-controlling strategies were essential for mature bloom outbreaks. Canonical correlation analysis (CCA) revealed the combined actions of multiple environment variables on different bloom clusters. The effects of buoyancy-controlling strategies (ISP), vertical turbulence-induced mixing velocity of colony (VMT) and horizontal drift velocity of colony (HDT) were quantitatively compared using scenario simulations in the coupled model. VMT accounted for 52.9% of bloom formations and maintained blooms over long periods, thus demonstrating the importance of wind-induced turbulence in shallow lakes. In comparison, HDT and buoyancy controlling strategies influenced blooms at different stages. In conclusion, the approach developed here presents a promising tool for understanding the processes of onshore/offshore algal blooms formation and subsequent predicting. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/112705
Appears in Collections: 气候减缓与适应
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作者单位: Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China
Recommended Citation:
Feng T.,Wang C.,Wang P.,et al. How physiological and physical processes contribute to the phenology of cyanobacterial blooms in large shallow lakes: A new Euler-Lagrangian coupled model[J]. Water Research,2018-01-01,140