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Behnaz Pirzamanbein . Photo

Behnaz Pirzamanbein

Associate senior lecturer

Behnaz Pirzamanbein . Photo

Reconstruction of past human land use from pollen data and anthropogenic land cover changes

Author

  • Behnaz Pirzamanbein
  • Johan Lindström

Summary, in English

Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land-cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human-induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen-based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land-use estimates to decompose the unforested land into natural vegetation and human deforestation. This results in maps of both natural and human-induced vegetation, which can be used by climate modelers to quantify the influence of deforestation on the past climate. The model was applied to five time periods from 1900 CE to 4000 BCE over Europe. The model uses a latent Gaussian Markov random field (GMRF) for the interpolation and Markov chain Monte Carlo for the estimation. The sparse precision matrix of the GMRF, together with an adaptive Metropolis-adjusted Langevin step, allows for rapid inference.

Department/s

  • MERGE: ModElling the Regional and Global Earth system
  • Department of Statistics
  • Mathematical Statistics
  • Centre for Environmental and Climate Science (CEC)
  • eSSENCE: The e-Science Collaboration
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2022-06-12

Language

English

Publication/Series

Environmetrics

Volume

33

Issue

6

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Probability Theory and Statistics

Keywords

  • compositional data
  • dirichlet and beta observations
  • fossil pollen record
  • Gaussian Markov random field
  • Markov chain Monte Carlo
  • spatial statistics

Status

Published

ISBN/ISSN/Other

  • ISSN: 1180-4009