TY  - JOUR
AU  - Hoffmann, Holger
AU  - Zhao, Gang
AU  - Asseng, Senthold
AU  - Bindi, Marco
AU  - Biernath, Christian
AU  - Constantin, Julie
AU  - Coucheney, Elsa
AU  - Dechow, Rene
AU  - Doro, Luca
AU  - Eckersten, Henrik
AU  - Gaiser, Thomas
AU  - Grosz, Balázs
AU  - Heinlein, Florian
AU  - Kassie, Belay T.
AU  - Kersebaum, Kurt-Christian
AU  - Klein, Christian
AU  - Kuhnert, Matthias
AU  - Lewan, Elisabet
AU  - Moriondo, Marco
AU  - Nendel, Claas
AU  - Priesack, Eckart
AU  - Raynal, Helene
AU  - Roggero, Pier P.
AU  - Rötter, Reimund P.
AU  - Siebert, Stefan
AU  - Specka, Xenia
AU  - Tao, Fulu
AU  - Teixeira, Edmar
AU  - Trombi, Giacomo
AU  - Wallach, Daniel
AU  - Weihermüller, Lutz
AU  - Yeluripati, Jagadeesh
AU  - Ewert, Frank
TI  - Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations
JO  - PLoS one
VL  - 11
IS  - 4
SN  - 1932-6203
CY  - Lawrence, Kan.
PB  - PLoS
M1  - FZJ-2016-06190
SP  - e0151782 -
PY  - 2016
AB  - We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000373608000007
C6  - pmid:27055028
DO  - DOI:10.1371/journal.pone.0151782
UR  - https://juser.fz-juelich.de/record/820926
ER  -