PengGuiotWuEtAl2011

Référence

Peng, C., Guiot, J., Wu, H., Jiang, H., Luo, Y. (2011) Integrating models with data in ecology and palaeoecology: Advances towards a model-data fusion approach. Ecology Letters, 14(5):522-536. (Scopus )

Résumé

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e. palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services. © 2011 Blackwell Publishing Ltd/CNRS.

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@ARTICLE { PengGuiotWuEtAl2011,
    AUTHOR = { Peng, C. and Guiot, J. and Wu, H. and Jiang, H. and Luo, Y. },
    TITLE = { Integrating models with data in ecology and palaeoecology: Advances towards a model-data fusion approach },
    JOURNAL = { Ecology Letters },
    YEAR = { 2011 },
    VOLUME = { 14 },
    PAGES = { 522-536 },
    NUMBER = { 5 },
    ABSTRACT = { It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e. palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services. © 2011 Blackwell Publishing Ltd/CNRS. },
    COMMENT = { Cited By (since 1996): 3 Export Date: 16 May 2012 Source: Scopus CODEN: ECLEF doi: 10.1111/j.1461-0248.2011.01603.x },
    ISSN = { 1461023X (ISSN) },
    KEYWORDS = { Carbon cycle, Data assimilation, Earth system modelling, Ecological forecasting, Global climate change, Inverse modelling, Palaeoclimatic reconstruction, Sequential data assimilation, Variational data assimilation, carbon, carbon cycle, climate change, data assimilation, ecological approach, ecological modeling, ecosystem structure, empirical analysis, error analysis, estimation method, global climate, identification method, integrated approach, numerical model, paleoclimate, paleoecology, paleoenvironment, quantitative analysis, remote sensing, research work, uncertainty analysis, vegetation type, biological model, climate change, ecology, ecosystem, forecasting, methodology, review, statistical analysis, statistical model, Carbon, Climate Change, Data Interpretation, Statistical, Ecology, Ecosystem, Forecasting, Models, Biological, Models, Statistical },
    OWNER = { Luc },
    TIMESTAMP = { 2012.05.16 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-79953836175&partnerID=40&md5=778176669997acc5a6baf20a8cc017d0 },
}

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