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首页> 外文期刊>Agriculture, Ecosystems & Environment: An International Journal for Scientific Research on the Relationship of Agriculture and Food Production to the Biosphere >Uncertainty propagation in soil greenhouse gas emission models: an experiment using the DNDC model and at the Oensingen cropland site.
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Uncertainty propagation in soil greenhouse gas emission models: an experiment using the DNDC model and at the Oensingen cropland site.

机译：土壤温室气体排放模型中的不确定性传播：使用DNDC模型并在Oensingen农田进行的实验。

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The increase of green house gas (GHG) concentrations in the atmosphere is predominantly caused by the anthropogenic activities of fossil fuel burning and land use change. The flux of GHGs from soils and ecosystems to the atmosphere is large, and any errors in estimating these fluxes have a significant impact on our quantification of the relative importance of land use in contributing to global warming. Numerical models have been developed to estimate the net flux of the biogenic GHGs: CO2, N2O and CH4, for various agricultural management practices. These models have been developed using data from many different experimental sites around the world, encompassing different crops, farm management systems, soil and climatic conditions. Crop experiments and GHG flux measurements are expensive and last several years if not decades so these models are often used to test hypothesis about the effect of future conditions, land use scenarios and also to predict the effect of novel land management scenarios to reduce emissions. However, uncertainties in the input soil parameters and meteorological data that drive these models propagates through them, resulting in uncertainties in the predictions of biogenic GHG emissions. This paper describes an experiment that investigates how well the commonly used de-nitrification de-composition (DNDC) soil model performs when used to predict the eddy-covariance CO2 fluxes and crop yields measured in the first full year of the Oensingen cropland site in Switzerland. DNDC N2O predictions are compared to the IPCC emissions factors for arable land. This study includes an estimation of the uncertainty of soil input parameters, a sensitivity study as to their effect on predicted GHG emissions and the propagation of their uncertainty through the model. This study considers uncertainty in meteorological measurements and the impact of using subsets of this data in the model. In particular the effect of using monthly meteorological parameters to generate daily time series for input into the model is investigated and the error propagation quantified. The overall impact of uncertainty in input parameters on predicted biogenic GHG emissions is relatively small with the PDF of the uncertainties indicating that the NEE is over estimated by 3.6% and has a SD of 3.6% of the actual NEE. Nitrous oxide emissions are not biased but have a larger SD of 23% of emissions, which when the global warming impact is considered is only 3% of net flux. DNDC can therefore be used with confidence to predict emissions, with the caveat that the biomass production needs to be match to local conditions.
机译：大气中温室气体（GHG）浓度的增加主要是由化石燃料燃烧和土地利用变化的人为活动引起的。从土壤和生态系统到大气的温室气体排放量很大，估计这些排放量的任何错误都会对我们量化土地利用对全球变暖的相对重要性的量化产生重大影响。已经开发了用于估算生物源温室气体净通量的数学模型：CO 2 ，N 2 O和CH 4 ，用于各种农业管理实践。这些模型是使用来自世界各地许多不同实验点的数据开发的，涵盖了不同的农作物，农场管理系统，土壤和气候条件。作物实验和温室气体排放量测量很昂贵，并且要持续数年甚至数十年，因此这些模型通常用于检验关于未来条件，土地利用情景的假设，并预测新型土地管理情景减少排放的影响。但是，驱动这些模型的输入土壤参数和气象数据的不确定性会通过它们传播，从而导致生物温室气体排放量预测的不确定性。本文描述了一个实验，该实验研究了常用的反硝化分解（DNDC）土壤模型在预测第一个全程中测得的涡旋协方差CO 2 通量和农作物产量时的表现如何瑞士Oensingen农田的一年。将DNDC N 2 O的预测值与IPCC耕地的排放因子进行比较。这项研究包括对土壤输入参数不确定性的估计，关于其对预计温室气体排放的影响以及其不确定性在模型中的传播的敏感性研究。这项研究考虑了气象测量的不确定性以及在模型中使用此数据子集的影响。特别是，研究了使用每月气象参数生成每日时间序列以输入模型的影响，并对误差传播进行了量化。输入参数不确定性对预测的生物源温室气体排放的总体影响相对较小，不确定性的PDF表示NEE被高估了3.6％，SD为实际NEE的3.6％。一氧化二氮的排放没有偏见，但具有23％的较大SD，当考虑到全球变暖影响时，其仅为净通量的3％。因此，可以放心地使用DNDC来预测排放量，但需要注意的是，生物量的生产需要与当地条件相匹配。

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