Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC
Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC
复制标题
DOI：
10.5194/gmd-11-1199-2018
复制
发表时间：
2018-03
影响因子：
5.1
通讯作者：
J. Susiluoto;M. Raivonen;L. Backman;M. Laine;J. Mäkelä;O. Peltola;T. Vesala;T. Aalto
中科院分区：
文献类型：
--
作者：
J. Susiluoto;M. Raivonen;L. Backman;M. Laine;J. Mäkelä;O. Peltola;T. Vesala;T. Aalto
摘要。估算天然湿地的甲烷（CH4）排放量是复杂的，并且估算包含很大的不确定性。用于该任务的模型通常是大量参数化的，并且参数值不是众所周知的。在本研究中，我们对一个新的湿地CH4排放模型进行了贝叶斯模型校准，以提高预测质量，并了解该模型的局限性。我们分析的详细过程模型包含了对厌氧呼吸产生CH4、CH4氧化以及通过扩散、沸腾和维管植物的通气细胞进行气体输送的描述。这些过程由几个可调参数控制。采用分层统计模型对参数进行描述，并结合自适应马尔可夫链蒙特卡罗（MCMC）、重要性重采样和时间序列分析技术，得到了过程中参数和不确定性的后验分布。对于估算，分析利用了芬兰南部Siikaneva通量测量站的测量数据。定量地描述了与参数和模型过程有关的不确定性。在工艺水平上，通量测量数据能够约束CH4生产过程、甲烷氧化过程和不同的气体输送过程。后验协方差结构解释了参数和过程之间的关系。此外，在年和日两个水平上分析了通量和通量分量的不确定性。得到的参数后置密度提供了不同过程重要性的信息，这也有助于开发湿地甲烷排放模型，而不是泥炭地甲烷积累和排放的平方根赫尔辛基模型（sqHIMMELI）。分层建模使我们能够在每年的基础上评估一些参数的影响。校正和交叉验证结果表明，早春净初级产量可用于预测影响年甲烷产量的参数。尽管该校准是Siikaneva站点特有的，但分层建模方法非常适合于更大规模的研究，并且估计结果为区域或全球尺度的湿地排放模型贝叶斯校准铺平了道路。
Abstract. Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models. The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.