Improving model prediction of soil N2O emissions through Bayesian calibration.
Improving model prediction of soil N2O emissions through Bayesian calibration.
复制标题
通过贝叶斯校准改进土壤 N2O 排放的模型预测。
DOI：
10.1016/j.scitotenv.2017.12.202
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Myrgiotis V
中科院分区：
文献类型：
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作者：
Myrgiotis V
农田土壤中N2O产生的生态地球化学过程受时间和空间变化的驱动因素控制。对土壤N2O排放跨尺度预测的需要意味着农业生态系统生态地球化学模型被广泛用于模拟N2O排放。由于农业生态系统模型参数密集的性质，必须根据预定应用地区的土壤和气候条件校准其参数。贝叶斯校准被认为是完成这项任务的最先进的方法之一。在这项研究中，我们校准9个参数的景观DNDC过程为基础的农业生态系统模型，这是关键的N2O预测。在四个单独的实施，以估计参数后验分布在英国的四个耕地的大都会黑斯廷斯算法。这个过程的结果是可视化的，总结和评估对测量的N2O数据从10个独立的耕地。研究表明，在许多情况下，土壤N2O排放峰值，没有预测与默认的模型参数进行了预测后，校准。总体而言，预测土壤N2O通量在所有被认为是提高了33%，当使用校准参数。
The biogeochemical processes that lead to the production of N2O in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil N2O emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate N2O emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its N2O prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured N2O data from ten independent arable sites. The study shows that, in many cases, soil N2O emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil N2O fluxes across all the sites that were considered was improved by 33% when using the calibrated parameters.