DOI: 10.1016/j.watres.2018.01.046
Scopus记录号: 2-s2.0-85041480919
论文题名: Adaptive forecasting of phytoplankton communities
作者: Page T. ; Smith P.J. ; Beven K.J. ; Jones I.D. ; Elliott J.A. ; Maberly S.C. ; Mackay E.B. ; De Ville M. ; Feuchtmayr H.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 134 起始页码: 74
结束页码: 85
语种: 英语
英文关键词: Cyanobacteria
; Data assimilation
; Ensemble Kalman Filter
; Forecasting
; Phytoplankton model
; PROTECH
Scopus关键词: Kalman filters
; Lakes
; Phytoplankton
; Adaptive forecasting
; Cyanobacteria
; Data assimilation
; Ensemble Kalman Filter
; Harmful algal blooms
; Management decisions
; Phytoplankton community
; PROTECH
; Forecasting
; chlorophyll a
; chlorophyll
; community structure
; cyanobacterium
; data assimilation
; decision making
; ecological modeling
; ensemble forecasting
; mesotrophic environment
; phytoplankton
; water management
; algal bloom
; Article
; community structure
; cyanobacterium
; forecasting
; nonhuman
; nutrient
; phytoplankton
; priority journal
; forecasting
; lake
; microbiology
; theoretical model
; Cumbria
; England
; Lake District
; United Kingdom
; algae
; Cyanobacteria
; Chlorophyll
; Forecasting
; Harmful Algal Bloom
; Lakes
; Models, Theoretical
; Phytoplankton
英文摘要: The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time. This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties and model nonlinearities to be propagated to forecast outputs. Tests were made on two mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting success was shown for chlorophyll a, but not all forecasts were able to perform better than a persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but forecasts for up to four or five days showed noticeably greater promise than those for longer periods. Associated forecasts of phytoplankton community structure were broadly consistent with observations but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of simulated functional species. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/112901
Appears in Collections: 气候减缓与适应
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作者单位: Lancaster Environment Centre, Library Avenue, Lancaster University, Lancaster, LA1 4YQ, United Kingdom; Lake Ecosystems Group, Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, LA1 4AP, United Kingdom; ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom
Recommended Citation:
Page T.,Smith P.J.,Beven K.J.,et al. Adaptive forecasting of phytoplankton communities[J]. Water Research,2018-01-01,134