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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-161
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 16 Aug 2018

Model description paper | 16 Aug 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Simulating migration in dynamic vegetation models efficiently with LPJ-GM

Veiko Lehsten1,2, Michael Mischurow2, Erik Lindström3, Dörte Lehsten2, and Heike Lischke1 Veiko Lehsten et al.
  • 1Dynamic Macroecology/Landscape dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
  • 2Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 3Centre for Mathematical Sciences, Department for Mathematics Lund University, Lund, Sweden

Abstract. Dynamic vegetation models are a common tool to assess the effect of climate and land use change on vegetation. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. A number of species have been shown to lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive and establish at the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed simulating either only small areas or have been implemented as post process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for continental simulations. Both distribute seeds between grid cells leading to individual establishment. The first method uses an approach based on Fast Fourier transform while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally marginally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows.

We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1 km, all dispersal simulations need to be performed at least at that scale. However, with the current available computational power it is not feasible to simulate the vegetation dynamics of a whole continent at that scale. We present an option to decrease the required computational costs, reducing the number of grid cells where the local dynamics is computed by simulating it only along migration transects. Evaluation of species patterns and migration speeds shows that although the simulation along transects reduces the migration speed slightly, both methods are reliable. Furthermore, both methods are sufficiently computationally efficient to allow large scale DGVM simulations with migration on entire continents.

Veiko Lehsten et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Veiko Lehsten et al.
Model code and software

Seed_dispersal_runtime_test.m V. Lehsten https://doi.org/10.18161/seed_disp_code_2018

Veiko Lehsten et al.
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Latest update: 13 Nov 2018
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Short summary
To assess the effect of climate on vegetation dynamic vegetation models simulate their response e.g. to climate change. Most currently used dynamic vegetation models ignore that for a colonization of a new area not only the climatic conditions have to be suitable but seeds need to arrive at the site to allow the species to migrate there. In this paper we are developing a novel method which allows to simulate migration within dynamic vegetation models even at continental scale.
To assess the effect of climate on vegetation dynamic vegetation models simulate their response...
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