TR32-Database: Database of Transregio 32

[676] - Errors in modelling carbon turnover induced by temporal temperature aggregation

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Weihermüller, L., Huisman, J. A., Graf, A., Herbst, M., Vereecken, H., 2011. Errors in modelling carbon turnover induced by temporal temperature aggregation. Vadose Zone Journal, 10 (1), 195 - 205. DOI: 10.2136/vzj2009.0157.
Identification
Title(s):Main Title: Errors in modelling carbon turnover induced by temporal temperature aggregation
Description(s):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(s):DOI: 10.2136/vzj2009.0157
Responsible Party
Creator(s):Author: Lutz Weihermüller
Author: Johan A. Huisman
Author: Alexander Graf
Author: Michael Herbst
Author: Harry Vereecken
Publisher:Soil Science Society of America
Topic
TR32 Topic:Other
Subject(s):CRC/TR32 Keywords: SOC, Soil Respiration, Temperature, Numerical Simulation
File Details
File Name:2011_Weihermueller_VZJ.pdf
Data Type:Text
Size(s):11 Pages
File Size:4925 kB (4.81 MB)
Date(s):Available: 2011-02-01
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:None
Measurement Location:--None--
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2011
Type:Article
Article Type:Journal
Source:Vadose Zone Journal
Issue:1
Volume:10
Number Of Pages:11
Page Range:195 - 205
Metadata Details
Metadata Creator:Johann Alexander (Sander) Huisman
Metadata Created:2013-12-05
Metadata Last Updated:2013-12-05
Subproject:B1
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:147
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity
Features