Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change
Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change
复制标题
DOI：
10.1088/1748-9326/ab5ebb
复制
发表时间：
2019-12
影响因子：
6.7
通讯作者：
Ben Parkes;Thomas P. Higginbottom;K. Hufkens;Francisco Ceballos;B. Kramer;T. Foster
中科院分区：
文献类型：
--
作者：
Ben Parkes;Thomas P. Higginbottom;K. Hufkens;Francisco Ceballos;B. Kramer;T. Foster
热浪、干旱和降雨过剩等天气冲击是全球作物产量损失和粮食不安全的主要原因。统计或基于过程的作物模型可用于量化产量对这些事件和未来气候变化的反应。然而，从作物模型得出的天气-产量关系的准确性，无论是基于统计的还是基于过程的，都取决于用于运行这些模型的基础输入数据的质量。在这种情况下，许多发展中国家面临的一个重大挑战是缺乏可获取和可靠的气象数据集。由现场测量仪、遥感和气候模式组合而成的网格化天气数据集提供了填补这一空白的解决方案，并已被广泛用于评估全球数据稀缺地区的气候对农业的影响。然而，众所周知，这些参考数据集也包含重要的偏差和不确定性。迄今为止，很少有研究评估参考数据集的选择如何影响作物产量对天气的预测敏感性。我们比较了多个免费的网格数据集，这些数据集提供了1983-2005年期间印度次大陆的每日天气数据，并探讨了它们对估计该地区主要作物（小麦和水稻）对天气变化的产量响应的影响。我们的研究结果表明，单个网格化天气数据集对印度各地历史时空温度和降水模式的表现有所不同。我们表明，这些差异在作物产量响应和生长季节天气变化的估计中造成了很大的不确定性，这反过来又强调了在探索气候变率和变化对农业的影响的统计研究中需要改进对输入数据不确定性的考虑。
Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983–2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.