Estimating impact assessment and adaptation strategies under climate change scenarios for crops at EU27 scale
Estimating impact assessment and adaptation strategies under climate change scenarios for crops at EU27 scale
复制标题
估算气候变化情景下欧盟27国规模作物的影响评估和适应策略
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Niemeyer
中科院分区：
文献类型：
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作者：
M. Donatelli;Amit Kumar Srivastava;G. Duveiller;S. Niemeyer
欧洲和国家一级的决策者需要对农业生产的潜在脆弱性进行估计。所要求的估计数是具体到省一级的，并对作物加以说明。这种估计的基础是作物在不适应条件下的生物物理反应，以及探索农民可以自主采取行动的适应水平。然而，由于气候输入对模拟模式的可用性、数据的可靠性和完整性、所选择的抽象水平以及技术方面的原因，产生这种估计带来了重大挑战。本研究以2000年为基准，以2020年和2030年为重点，对欧盟27国的气候变化情景对农业的影响进行了评估。利用欧盟委员会的BioMA建模平台中实施的CropSyst模型，模拟了3种优先作物（小麦、油菜籽和向日葵）在25 × 25公里网格上的潜在和限水产量。输入的天气数据是由随机天气发生器产生的，这些天气发生器参数化了来自ENSEMBLES项目的RCM-GCM缩小模拟，这些数据已经过统计偏差校正。基于HadCM3和ECHAM5 GCMs，在ENSEMBLES中使用了A1B排放情景的两种实现，它们分别代表了整体包络中关于气温趋势的“温暖”和“寒冷”极端，并且在降雨模式方面有所不同。通过模拟农民可采取的技术操作，探讨减轻不利天气模式的后果，突出自主适应的局限性，从而估计潜在的脆弱性热点。所提供的数据侧重于所选基线与2020年和2030年时间范围之间的差异。数据（可通过web服务访问）和仿真平台均可用于非商业用途。
Policy makers at European and national level demand for estimates of potential vulnerability of agricultural production. Estimates are requested specific to province level, and articulated for crops. The base of such estimates is the biophysical representation of crop responses both under conditions of no adaptation, and exploring the level of adaptation which could be acted on autonomously by farmers. However, producing such estimates poses significant challenges due to the usability of climate inputs to simulation models, to reliability and completeness of data, to the level of abstraction to be chosen, and to technological aspects. This study provides an impact assessment of climate change scenarios on agriculture over EU27 focused on the time horizons of 2020 and 2030 with respect to a baseline centered on the year 2000. Potential and water-limited yields are simulated for 3 priority crops (wheat, rapeseed and sunflower) over a 25 by 25 km grid using the CropSyst model implemented within the BioMA modelling platform of the European Commission. Input weather data are generated with a stochastic weather generator parameterized over RCM-GCM downscaled simulation from the ENSEMBLES project, which have been statistically bias-corrected. Two realizations of the A1B emission scenario within ENSEMBLES are used, based on the HadCM3 and ECHAM5 GCMs, which respectively represent the “warmer” and “colder” extremes in the envelope of the ensemble with regard to the air temperature trends, and different with respect to rainfall patterns. Alleviating the consequences of unfavorable weather patterns is explored by simulating technical operations which can be acted on by farmers, highlighting the limits of autonomous adaptation, hence estimating potential vulnerability hotspots. Data are presented focusing on the difference between the baseline chosen and the 2020 and 2030 time horizons. Both data (accessible via web services) and the simulation platform are available for non-commercial use.