Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection
Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection
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DOI：
10.1016/j.agrformet.2009.02.015
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发表时间：
2009-08-03
影响因子：
6.2
通讯作者：
Yokozawa, Masayuki
中科院分区：
文献类型：
--
作者：
Tao, Fulu;Zhang, Zhao;Yokozawa, Masayuki
对气候变化影响的估计受到许多物理、生物和社会经济过程的不确定性的困扰。在紧迫的研究优先事项中，需要更全面地评估影响，更好地反映不确定性。本文提出了一种新的基于超集合的概率投影方法，以考虑影响评估模型中来自CO2排放情景、气候变化情景和生物物理过程的不确定性。我们展示的方法，在处理的概率变化，在华北平原玉米产量在未来。新的基于过程的通用作物模型MCWLA [Tao，F.，Yokozawa，M.张志，2009.模拟天气和气候变化对大面积作物生产力的影响：基于新过程的模型开发、优化和不确定性分析。农业。陨石。149，831 - 850），使用。MCWLA解释了气候变率的关键影响机制，在大面积上是准确的。我们使用了10个气候情景，包括5个GCM和2个排放情景的组合，相应的大气CO（2）浓度范围，和60套作物模型参数，代表作物模型的生物物理不确定性。虽然假设日后的作物品种及管理措施与基准期的水平相同，但种植窗期的设定容许在种植窗期内进行种植转移。概率分布结果表明，在2020年代，2050年代和2080年代，河南省玉米种植网格的预期产量变化分别为-9.7%，-15.7%，-24.7%，95%概率区间为（-29.4%，+15.8）。（-45.7，+24.0），（-92.8，+20.3%）。山东省相应值为-9.1、-19.0。-25.5%，95%概率区间为（-28.4，+17.4），（-45.4，+15.9），（-60.1，+12.8）。我们还调查了整个地区玉米产量变化和变异的时空格局。除了华北平原玉米生产力概率变化的新发现外，我们的研究还展示了一种先进的基于超集合的概率预测方法，用于解决气候变率（变化）对区域农业生产的影响和不确定性。(C)2009 Elsevier B.V.保留所有权利。
Estimates of climate change impacts are plague with uncertainties from many physical, biological, and social-economic processes. Among the urgent research priorities, more comprehensive assessments of impacts that better represent the uncertainties are needed. Here, we develop a new super-ensemble-based probabilistic projection approach to account for the uncertainties from CO(2) emission scenarios, climate change scenarios, and biophysical processes in impact assessment model. We demonstrate the approach in addressing the probabilistic changes of maize production in the North China Plain in future. The new process-based general crop model, MCWLA [Tao, F., Yokozawa, M. Zhang, Z., 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149,831-850], is used. MCWLA accounts for the key impact mechanisms of climate variability and is accurate over a large area. We use 10 climate scenarios consisting of the combinations of five GCMs and two emission scenarios, the corresponding atmospheric CO(2) concentration range, and 60 sets of crop model parameters representing the biophysical uncertainties from crop models. The planting window is set to allow planting shift within the window, although the crop cultivar and management practice in future were assumed to be same as the level during the baseline period. The resulting probability distributions indicate expected yield changes of -9.7, -15.7, -24.7% across the maize cultivation grids in Henan province during 2020s, 2050s, and 2080s, with 95% probability intervals of (-29.4,+15.8).(-45.7,+24.0), (-92.8, +20.3) in percent of 1961-1990 yields, respectively. The corresponding value in Shandong province is -9.1, -19.0. -25.5%, with 95% probability intervals of (-28.4, +17.4),(-45.4, +15.9),(-60.1, +12.8). We also investigate the temporal and spatial pattern of changes and variability in maize yield across the region. Besides the new findings on the probabilistic changes of maize productivity in the North China Plain, our study demonstrated an advanced super-ensemble-based probabilistic projection approach in addressing the impacts of climate variability (change) on regional agricultural production and the uncertainties. (C) 2009 Elsevier B.V. All rights reserved.