DOI: 10.3354/cr01326
Scopus记录号: 2-s2.0-84945578174
论文题名: Variability of effects of spatial climate data aggregation on regional yield simulation by crop models
作者: Hoffmann H. ; Zhao G. ; Van Bussel L.G.J. ; Enders A. ; Specka X. ; Sosa C. ; Yeluripati J. ; Tao F. ; Constantin J. ; Raynal H. ; Teixeira E. ; Grosz B. ; Doro L. ; Zhao Z. ; Wang E. ; Nendel C. ; Kersebaum K.C. ; Haas E. ; Kiese R. ; Klatt S. ; Eckersten H. ; Vanuytrecht E. ; Kuhnert M. ; Lewan E. ; Rötter R. ; Roggero P.P. ; Wallach D. ; Cammarano D. ; Asseng S. ; Krauss G. ; Siebert S. ; Gaiser T. ; Ewert F.
刊名: Climate Research
ISSN: 0936577X
出版年: 2015
卷: 65 起始页码: 53
结束页码: 69
语种: 英语
英文关键词: Crop simulation model
; Input data
; Model comparison
; Scaling
; Spatial aggregation effects
; Variability
; Yield simulation
Scopus关键词: arable farming
; climate change
; data interpretation
; maize
; parameterization
; spatial resolution
; Germany
; North Rhine-Westphalia
; Triticum aestivum
; Zea mays
英文摘要: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize ) and 3 production situations (potential, waterlimited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha-1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization. © Inter-Research 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116462
Appears in Collections: 气候减缓与适应
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Recommended Citation:
Hoffmann H.,Zhao G.,Van Bussel L.G.J.,et al. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models[J]. Climate Research,2015-01-01,65