Errors in modelling carbon turnover induced by temporal temperature aggregation

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Title:Main Title: Errors in modelling carbon turnover induced by temporal temperature aggregation
Description:Abstract: Modeling of carbon turnover is a widely accepted tool for the prediction of carbon stocks in soils and the CO2 efflux into the atmosphere. It is well recognized that the choice of the input data (e.g. variable carbon pool sizes, soil hydraulic parameters, atmospheric boundary conditions) determines the outcome of these carbon turnover predictions to a large extent. Temperature is known to be one of the most important driving factors and it varies on a range of temporal scales. Typically, the time discretization of most carbon turnover models is flexible, and can range from minutes to months. However, the implications of variable time discretization for predicted soil carbon turnover are seldomly discussed or reported. In this study, we first demonstrate that averaging of input temperature data will lead to changes in predicted carbon turnover in terms of daily amplitude and the impact of extreme temperatures. The results indicate that averaging from hourly to daily or monthly temperatures will lead to relative errors larger than 4% per year for cumulative CO2 efflux, which is similar to the measurement error for carbon stocks or chamber measurements. Instantaneous CO2 fluxes are even more affected by temperature averaging. Daily and monthly averaging will lead to estimation errors exceeding 20% and 25.8%, respectively. Deviations in predicted instantaneous CO2 efflux using aggregated and reference temperature time series were larger than 10% for 23% and 55% of the time for daily and monthly averaging, respectively. It is also shown that a constant or daily variable temperature amplitude for rescaling daily average temperature did not decrease the error in the predicted CO2 fluxes when using daily or monthly mean temperature instead of hourly data. Therefore, instantaneous fluxes are only accurately predicted when hourly temperature input is used. For long term modelling (e.g. years to centuries), the relative error in cumulative efflux, and therefore, in carbon stocks loss is reasonably low (~ 4 to 5 % annual error). Of course, the absolute error in carbon loss will accumulate over time, and therefore, the predictive error for a 100 year time period will be large again.
Identifier:10.2136/vzj2009.0157 (DOI)
Responsible Party
Creators:Lutz Weihermüller (Author), Johan A. Huisman (Author), Alexander Graf (Author), Michael Herbst (Author), Harry Vereecken (Author)
Publisher:Soil Science Society of America
Publication Year:2013
Topic
TR32 Topic:Other
Related Subproject:B1
Subjects:Keywords: SOC, Soil Respiration, Temperature, Numerical Simulation
File Details
Filename:2011_Weihermueller_VZJ.pdf
Data Type:Text - Article
Size:11 Pages
File Size:4.8 MB
Date:Available: 01.02.2011
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Download Permission:Only Project Members
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:[TR32DB] Data policy agreement
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Specific Information - Publication
Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Vadose Zone Journal
Source Website:www.soils.org
Issue:1
Volume:10
Number of Pages:11 (195 - 205)
Metadata Details
Metadata Creator:Johann Alexander (Sander) Huisman
Metadata Created:05.12.2013
Metadata Last Updated:05.12.2013
Subproject:B1
Funding Phase:2
Metadata Language:English
Metadata Version:V50
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