GRAMP API

GET /api/models/11/?format=api

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{
    "url": "/models/11",
    "family": "DNDC",
    "title": "Landscape-DNDC",
    "description": "<p><p>LandscapeDNDC is partly based on the DNDC\r\nmodel and contains a series of new features \"with regard to process\r\ndescriptions, model structure and data I/O functionality.\" (Haas et al.,\r\n2012).&nbsp; LandscapeDNDC incorporates\r\nfunctions of DNDC, PnET-N-DNDC/Forest-DNDC and was further developed from the\r\nMoBiLE model framework (Rahn et al.,2012).&nbsp;\r\nLandscapeDNDC simulates carbon (C), nitrogen (N) and water related\r\nbiospehere-atmosphere-hydrospehere fluxes for forest, arable and grassland\r\necosystems.&nbsp; It incorporates different\r\nmanagement practises and vegetation types and allows dynamic simulation of\r\nlanduse change.&nbsp; Ecosystems are divided\r\ninto six substates: canopy air chemistry, microclimate, physiology, water\r\ncycle, vegetation structure and soil biogeochemistry (Haas et al., 2012).&nbsp; Modules, derived from physical and chemical\r\nprinciples, that describe soil environmental conditions, soil-chemistry\r\nintegrating microbial C and N turnover processes and vegetation dynamics are\r\nintegrated within the model (Rahn et al., 2012).</p>\r\n<p>&nbsp;The\r\nmodel can be applied at site scale and three-dimensional region\r\nsimulations.&nbsp; &nbsp;The integration of &nbsp;all grid cells synchronously forward in time\r\nfor regional simulations, enables efficient two-way exchange of states and easy\r\ncoupling to other spatially distributed models.</p>\r\n<p>Comparison of simulated and measured flux\r\ndata by Rahn et al. (2012) showed high agreement, however, freeze-thaw\r\nprocesses (potential strong impact of N2O emission) could not be reproduced as\r\nthe underlying processes are not included in the Landscape-DNDC.</p></p>",
    "keywords": "DNDC, Greenhouse gas emissions, Inventory, LandscapeDNDC, Model Coupling, Regionalization",
    "principal_authors": "",
    "contact_name": "E. Haas ",
    "contact_email": "edwin.haas@kit.edu",
    "organization": "Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, Grmisch-Partenkirchen",
    "latest_version": "",
    "website": "",
    "language": "",
    "systems_supported": "",
    "source_code_available": "",
    "model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; RICE-DNDC;",
    "sectors": "",
    "submitted_by": "",
    "reference_url": "",
    "published_on": "2012-07-01",
    "lft": 156,
    "rght": 161,
    "tree_id": 1,
    "level": 1,
    "parent": "http://gramp.ags.io/api/models/2/?format=api"
}

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Title

Description

LandscapeDNDC is partly based on the DNDC
model and contains a series of new features "with regard to process
descriptions, model structure and data I/O functionality." (Haas et al.,
2012).&nbsp; LandscapeDNDC incorporates
functions of DNDC, PnET-N-DNDC/Forest-DNDC and was further developed from the
MoBiLE model framework (Rahn et al.,2012).&nbsp;
LandscapeDNDC simulates carbon (C), nitrogen (N) and water related
biospehere-atmosphere-hydrospehere fluxes for forest, arable and grassland
ecosystems.&nbsp; It incorporates different
management practises and vegetation types and allows dynamic simulation of
landuse change.&nbsp; Ecosystems are divided
into six substates: canopy air chemistry, microclimate, physiology, water
cycle, vegetation structure and soil biogeochemistry (Haas et al., 2012).&nbsp; Modules, derived from physical and chemical
principles, that describe soil environmental conditions, soil-chemistry
integrating microbial C and N turnover processes and vegetation dynamics are
integrated within the model (Rahn et al., 2012).
&nbsp;The
model can be applied at site scale and three-dimensional region
simulations.&nbsp; &nbsp;The integration of &nbsp;all grid cells synchronously forward in time
for regional simulations, enables efficient two-way exchange of states and easy
coupling to other spatially distributed models.
Comparison of simulated and measured flux
data by Rahn et al. (2012) showed high agreement, however, freeze-thaw
processes (potential strong impact of N2O emission) could not be reproduced as
the underlying processes are not included in the Landscape-DNDC.

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Latest version

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Content:

{
 "url": "/models/11",
 "family": "DNDC",
 "title": "Landscape-DNDC",
 "description": "LandscapeDNDC is partly based on the DNDC\r\nmodel and contains a series of new features \"with regard to process\r\ndescriptions, model structure and data I/O functionality.\" (Haas et al.,\r\n2012).&nbsp; LandscapeDNDC incorporates\r\nfunctions of DNDC, PnET-N-DNDC/Forest-DNDC and was further developed from the\r\nMoBiLE model framework (Rahn et al.,2012).&nbsp;\r\nLandscapeDNDC simulates carbon (C), nitrogen (N) and water related\r\nbiospehere-atmosphere-hydrospehere fluxes for forest, arable and grassland\r\necosystems.&nbsp; It incorporates different\r\nmanagement practises and vegetation types and allows dynamic simulation of\r\nlanduse change.&nbsp; Ecosystems are divided\r\ninto six substates: canopy air chemistry, microclimate, physiology, water\r\ncycle, vegetation structure and soil biogeochemistry (Haas et al., 2012).&nbsp; Modules, derived from physical and chemical\r\nprinciples, that describe soil environmental conditions, soil-chemistry\r\nintegrating microbial C and N turnover processes and vegetation dynamics are\r\nintegrated within the model (Rahn et al., 2012).\r\n&nbsp;The\r\nmodel can be applied at site scale and three-dimensional region\r\nsimulations.&nbsp; &nbsp;The integration of &nbsp;all grid cells synchronously forward in time\r\nfor regional simulations, enables efficient two-way exchange of states and easy\r\ncoupling to other spatially distributed models.\r\nComparison of simulated and measured flux\r\ndata by Rahn et al. (2012) showed high agreement, however, freeze-thaw\r\nprocesses (potential strong impact of N2O emission) could not be reproduced as\r\nthe underlying processes are not included in the Landscape-DNDC.",
 "keywords": "DNDC, Greenhouse gas emissions, Inventory, LandscapeDNDC, Model Coupling, Regionalization",
 "principal_authors": "",
 "contact_name": "E. Haas ",
 "contact_email": "edwin.haas@kit.edu",
 "organization": "Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, Grmisch-Partenkirchen",
 "latest_version": "",
 "website": "",
 "language": "",
 "systems_supported": "",
 "source_code_available": "",
 "model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; RICE-DNDC;",
 "sectors": "",
 "submitted_by": "",
 "reference_url": "",
 "published_on": "2012-07-01",
 "lft": 156,
 "rght": 161,
 "tree_id": 1,
 "level": 1,
 "parent": "http://gramp.ags.io/api/models/2/?format=api"
}

Gramp Model Instance