Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 15, issue 5
Biogeosciences, 15, 1497–1513, 2018
https://doi.org/10.5194/bg-15-1497-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 15, 1497–1513, 2018
https://doi.org/10.5194/bg-15-1497-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Mar 2018

Research article | 14 Mar 2018

Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach

Peter Levy, Marcel van Oijen, Gwen Buys, and Sam Tomlinson Peter Levy et al.
  • Centre for Ecology & Hydrology, Edinburgh, UK

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/.

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We present a new method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with reliable national-scale census data whilst retaining the information available from several other sources. This includes detailed spatial data; further data sources, such as new satellites, could easily be added in future. Uncertainty is propagated appropriately into the output.
We present a new method for estimating land-use change using a Bayesian data assimilation...
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