A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams.

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dc.contributor.author Cresto-Aleina, Fabio
dc.contributor.author Brovkin, Victor
dc.contributor.author Muster, Sina
dc.contributor.author Boike, Julia
dc.contributor.author Kutzbach, Lars
dc.contributor.author Sachs, Torsten
dc.contributor.author Zuyev, Sergey
dc.coverage.spatial LATITUDE: 72.373760 * LONGITUDE: 126.488430
dc.date.accessioned 2019-11-23T02:00:46Z
dc.date.available 2019-11-23T02:00:46Z
dc.date.issued 2013-08-22
dc.identifier https://doi.pangaea.de/10.1594/PANGAEA.818292
dc.identifier https://doi.org/10.1594/PANGAEA.818292
dc.identifier.citation Cresto-Aleina, Fabio; Brovkin, Victor; Muster, Sina; Boike, Julia; Kutzbach, Lars; Sachs, Torsten; Zuyev, Sergey (2013): A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams. Earth System Dynamics, 4(2), 187-198, https://doi.org/10.5194/esd-4-187-2013
dc.identifier.uri https://repository.geologyscience.ru/handle/123456789/7028
dc.description.abstract Subgrid processes occur in various ecosystems and landscapes but, because of their small scale, they are not represented or poorly parameterized in climate models. These local heterogeneities are often important or even fundamental for energy and carbon balances. This is especially true for northern peatlands and in particular for the polygonal tundra, where methane emissions are strongly influenced by spatial soil heterogeneities. We present a stochastic model for the surface topography of polygonal tundra using Poisson–Voronoi diagrams and we compare the results with available recent field studies. We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system to the main small-scale processes within the single polygons.
dc.format text/tab-separated-values, 4 data points
dc.language.iso en
dc.publisher PANGAEA
dc.relation Overview image, distance between polygon centroids (URI: hdl:10013/epic.41957.d003)
dc.rights CC-BY-3.0: Creative Commons Attribution 3.0 Unported
dc.rights Access constraints: unrestricted
dc.source Supplement to: Cresto-Aleina, Fabio; Brovkin, Victor; Muster, Sina; Boike, Julia; Kutzbach, Lars; Sachs, Torsten; Zuyev, Sergey (2013): A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams. Earth System Dynamics, 4(2), 187-198, https://doi.org/10.5194/esd-4-187-2013
dc.subject AWI_PerDyn
dc.subject File content
dc.subject MULT
dc.subject Permafrost Research (Periglacial Dynamics) @ AWI
dc.subject Samoylov_Island_Cresto
dc.subject Samoylov Island, Lena Delta, Siberia
dc.subject Uniform resource locator/link to file
dc.title A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams.
dc.title.alternative Wet tundra polygon centers and distance between polygon centroids on Samoylov Island, Lena Delta, Siberia, Russia, with links to shape files
dc.type Dataset


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