GreenhousePeat: a model linking CO 2 emissions from subsiding peatlands to changing groundwater levels
Oxidation of organic matter in peat above the phreatic groundwater table causes subsidence and carbon dioxide (CO2) emissions. Because 25 % of the Netherlands has shallow peat layers in its subsurface, it is essential for Dutch policy makers and stakeholders to have reliable information on present d...
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Veröffentlicht in: | Proceedings of the International Association of Hydrological Sciences 2020-04, Vol.382, p.609-614 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Oxidation of organic matter in peat above the phreatic groundwater table
causes subsidence and carbon dioxide (CO2) emissions. Because 25 % of
the Netherlands has shallow peat layers in its subsurface, it is essential
for Dutch policy makers and stakeholders to have reliable information on
present day and near future CO2 emissions under changes in groundwater
levels. Furthermore, it is important to reduce greenhouse gas emissions in
view of international agreements. We are developing GreenhousePeat: a nationwide model that synthesizes
information on peat organic carbon content, land subsidence, and CO2
emission monitoring to model present-day and future CO2 emissions from
subsiding peatlands. Here, we discuss the approach and input data of GreenhousePeat.
GreenhousePeat is based on a UNFCCC approved model to predict CO2
emissions, albeit based on new input data: 3-D organic matter maps,
nationwide subsidence rates, and ranges in oxidation fraction. We validate
model outcomes with previously documented CO2 emissions measured at
four different locations. We found that for one site the upper bound of the
model reproduces the measured CO2 emissions. The modelled emissions at
two sites have a relative deviation of approximately 73 % to 29 % from the
measured emissions. Whereas one site is a net CO2 sink, although low
emissions were modelled. Finally, we conclude on the suitability of the
model for CO2 emission forecasting and suggest improvements by
incorporating groundwater level information and land use type. |
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ISSN: | 2199-899X 2199-899X |
DOI: | 10.5194/piahs-382-609-2020 |