DOI: 10.1002/2016MS000625
Scopus记录号: 2-s2.0-84983486825
论文题名: Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield
作者: Xiong W ; , Skalský R ; , Porter C ; H ; , Balkovič J ; , Jones J ; W ; , Yang D
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 3 起始页码: 1358
结束页码: 1375
语种: 英语
英文关键词: Agriculture
; Biology
; Calibration
; Climate models
; Crops
; Decision making
; Environmental protection
; Input output programs
; Uncertainty analysis
; Adaptation strategies
; Agricultural productions
; Climate change impact
; Climate scenarios
; Environmental policy integrated climate models
; EPIC
; Geographical data
; Sources of uncertainty
; Climate change
; calibration
; climate change
; climate effect
; climate modeling
; crop production
; crop yield
; data set
; environmental policy
; global change
; maize
; phenology
; uncertainty analysis
; yield response
; Zea mays
英文摘要: Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75886
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Climate Change Division, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China; Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States; International Institute of Applied Systems Analysis, Ecosystem Services and Management Program, Laxenburg, Austria; National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Bratislava, Slovakia; Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovakia
Recommended Citation:
Xiong W,, Skalský R,, Porter C,et al. Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(3)