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Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty

机译:气候变化下农药淋洗模型:参数与气候输入不确定性的关系

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Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in southwestern Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM), greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set (1970-1999) for an important agricultural production area in south-western Sweden based on monthly change factors for 2070-2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.
机译:评估气候变化对农药浸出的影响需要仔细考虑各种不确定性来源。我们调查了与气候情景输入有关的不确定性及其相对于农药浸出模型的参数不确定性的重要性。农药归宿模型MACRO已针对瑞典西南部结构良好的粘土的一年综合田间数据集进行了校准。我们获得了代表参数不确定性的56个可接受参数集的集合。在全球气候模型(GCM),温室气体排放情景和GCM初始状态的不同组合的驱动下,可获得区域气候模型RCA3的九种不同气候模型预测。根据2070-2099年的月度变化因子,通过缩放瑞典西南部重要农业产区的参考气候数据集(1970-1999),生成了用于驱动MACRO模型的未来天气数据时间序列。针对农药特性和施用季节的不同组合进行了30年的模拟。我们的分析表明,从现在到将来,农药浸出的预测变化的幅度和方向都很大程度上取决于特定的气候情景。参数不确定性的影响对于模拟农药绝对损失至关重要,而气候不确定性对于预测农药从现在到未来的变化相对来说更为重要。应通过应用各种不同的气候方案来解决气候不确定性问题。基于可接受的参数化和不同气候情景的总体集合预测有可能提供对未来农药损失的可靠概率估计。

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