TY - JOUR
T1 - Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
AU - Zhao, Gang
AU - Hoffmann, Holger
AU - Van Bussel, L.G.J.
AU - Enders, Andreas
AU - Specka, Xenia
AU - Sosa, Carmen
AU - Yeluripati, Jagadeesh
AU - Tao, Fulu
AU - Constantin, Julie
AU - Raynal, Helene
AU - Teixeira, Edmar
AU - Grosz, Balázs
AU - Doro, Luca
AU - Zhao, Zhigan
AU - Nendel, Claas
AU - Kiese, Ralf
AU - Eckersten, Henrik
AU - Haas, Edwin
AU - Vanuytrecht, Eline
AU - Wang, Enli
AU - Kuhnert, Matthias
AU - Trombi, Giacomo
AU - Moriondo, Marco
AU - Bindi, Marco
AU - Lewan, Elisabet
AU - Bach, Michaela
AU - Kersebaum, Kurt Christian
AU - Rötter, Reimund
AU - Roggero, Pier Paolo
AU - Wallach, Daniel
AU - Cammarano, Davide
AU - Asseng, Senthold
AU - Krauss, Gunther
AU - Siebert, Stefan
PY - 2015
Y1 - 2015
N2 - We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 processbased crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. The spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
AB - We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 processbased crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. The spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
KW - Crop model
KW - Data aggregation
KW - Model comparison
KW - Scaling
KW - Spatial heterogeneity
KW - Spatial resolution
U2 - 10.3354/cr01301
DO - 10.3354/cr01301
M3 - Article
AN - SCOPUS:84940513077
SN - 0936-577X
VL - 65
SP - 141
EP - 157
JO - Climate Research
JF - Climate Research
ER -