Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains
Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains
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DOI：
10.1016/j.agee.2008.09.012
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Xianzeng Niu;W. Easterling;C. J. Hays;A. Jacobs;L. Mearns
中科院分区：
文献类型：
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作者：
Xianzeng Niu;W. Easterling;C. J. Hays;A. Jacobs;L. Mearns
作物模拟模型经常用于估计气候变化对作物生产的影响。然而，很少有研究以大多数研究人员在气候影响研究中实践的方式评估模型的性能。在本文中，我们研究了 EPIC 模型在不同气候情景（即正常或极端温度和降水年份）下模拟美国大平原谷物高粱 (Sorghum bicolor (L.) Moench) 产量的可靠性。我们还调查了由输入数据引入的模型不确定性，这些数据不是特定地点的，但通常用于或可用于气候变化研究。内布拉斯加州米德实验中心的高粱历史田间试验数据用于模型评估。结果显示，模型总体可靠性约为 56%。平均绝对相对误差 (absRE) 约为 29%。准确度和可靠性随气候等级和氮 (N) 处理的不同而变化。最大的偏差发生在干旱年份（RE=-25%），最不可靠的结果出现在 N-0 处理中（可靠性=32%）。输入数据引起的不确定性被限制在absRE的20%以下的可能性超过69%。我们的结果支持 EPIC 模型在美国大平原气候变化影响研究中的应用。然而，需要努力提高模拟作物对极端水和氮胁迫条件的反应的准确性。
Crop simulation models are frequently used to estimate the impact of climate change on crop production. However, few studies have evaluated the model performance in ways that most researchers practiced in climate impact studies. In this article, we examined the reliability of the EPIC model in simulating grain sorghum (Sorghum bicolor (L.) Moench) yields in the U.S. Great Plains under different climate scenarios, namely in years with normal or extreme temperature and precipitation. We also investigated model uncertainties introduced by input data that are not site-specific but commonly used or available for climate change studies. Historical field trial data of sorghum at the Mead Experimental Center, NE, were used for model evaluations. The results showed that overall model reliability was about 56%. The mean absolute relative error (absRE) was about 29%. The degree of accuracy and reliability varied with climate-classes and nitrogen (N)-treatments. The largest bias occurred in drought years (RE=−25%) and the most unreliable results were found in N-0 treatment (reliability=32%). There was more than 69% probability that input-data-induced uncertainties were limited to less than 20% of absRE. Our results support the application of the EPIC model to climate change impact studies in the U.S. Great Plains. However, efforts are needed to improve the accuracy in simulating crop responses to extreme water- and nitrogen-stressed conditions.