TY  - JOUR
AU  - Grosz, Balázs
AU  - Dechow, Rene
AU  - Gebbert, Sören
AU  - Hoffmann, Holger
AU  - Zhao, Gang
AU  - Constantin, Julie
AU  - Raynal, Helene
AU  - Wallach, Daniel
AU  - Coucheney, Elsa
AU  - Lewan, Elisabet
AU  - Eckersten, Henrik
AU  - Specka, Xenia
AU  - Kersebaum, Kurt-Christian
AU  - Nendel, Claas
AU  - Kuhnert, Matthias
AU  - Yeluripati, Jagadeesh
AU  - Haas, Edwin
AU  - Teixeira, Edmar
AU  - Bindi, Marco
AU  - Trombi, Giacomo
AU  - Moriondo, Marco
AU  - Doro, Luca
AU  - Roggero, Pier Paolo
AU  - Zhao, Zhigan
AU  - Wang, Enli
AU  - Tao, Fulu
AU  - Rötter, Reimund
AU  - Kassie, Belay
AU  - Cammarano, Davide
AU  - Asseng, Senthold
AU  - Weihermüller, Lutz
AU  - Siebert, Stefan
AU  - Gaiser, Thomas
AU  - Ewert, Frank
TI  - The implication of input data aggregation on up-scaling soil organic carbon changes
JO  - Environmental modelling & software
VL  - 96
SN  - 1364-8152
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2017-05895
SP  - 361 - 377
PY  - 2017
AB  - In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000408356600029
DO  - DOI:10.1016/j.envsoft.2017.06.046
UR  - https://juser.fz-juelich.de/record/836859
ER  -