DOI: 10.1007/s10584-014-1264-3
Scopus记录号: 2-s2.0-84940654541
论文题名: Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model
作者: Watson J. ; Challinor A.J. ; Fricker T.E. ; Ferro C.A.T.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2015
卷: 132, 期: 1 起始页码: 93
结束页码: 109
语种: 英语
Scopus关键词: Agriculture
; Calibration
; Crops
; Errors
; Food supply
; Calibration data
; Crop productivity
; Data collection
; Food production
; High resolution
; Process-based modeling
; Statistical modeling
; Temperature data
; Climate models
; agricultural modeling
; agrometeorology
; calibration
; climate modeling
; crop production
; crop yield
; data acquisition
; food security
; statistical analysis
英文摘要: Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve. © 2014, The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84531
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, United Kingdom; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
Recommended Citation:
Watson J.,Challinor A.J.,Fricker T.E.,et al. Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model[J]. Climatic Change,2015-01-01,132(1)