Probabilistic Modeling of Long-term Peatland Carbon Dynamics

泥炭地调节着大气中温室气体的浓度，从而调节着全球气候。它们形成了最大的陆地碳储量之一，目前和预计的温度、降水和氮沉降的长期变化对这些功能构成了潜在的威胁。动态泥炭地模型（DPM）需要获得对过程相互作用的机制理解，预测碳积累速率的长期变化，并综合个体研究的对比结果。近40年来，DPM不断得到改进，包括附加过程、时间动态和空间异质性。DPM的敏感性分析表明，不确定性通常很大，但对过程相互作用的正确解释至关重要。在其他学科中，概率模型、不确定性分析和通过数据同化减少不确定性的应用已被证明是以前确定性模型的有用扩展。然而，概率DPM的不确定性很少被量化和分析。为了使DPM更有用，我们建议开发一个概率DPM，并使用不确定性分析和数据同化来量化、分析和减少其输入数据和参数中的不确定性。我们预计，如果综合不同的现有数据源（泥炭核心数据和凋殖质袋数据）和多种泥炭性质同时提供的信息（如碳和氮含量），数据同化可以减少不确定性，特别是长期分解率的不确定性。在此框架下，我们的目标是评估不确定性对我们理解温度、降水和氮沉降对泥炭地碳积累的影响的影响，确定实验产生对比结果的条件，并提供有效降低DPM未来不确定性的策略。

Peatlands regulate atmospheric greenhouse gas concentrations and thus the global climate. They form one of the largest terrestrial C stores and current and projected long-term shifts in temperature, precipitation, and nitrogen deposition represent a potential threat to these functions. Dynamic peatland models (DPM) are needed to acquire a mechanistic understanding of process interactions, to predict long-term changes in C accumulation rates, and to synthesize contrasting results of individual studies. For about 40 years DPM have continuously been improved by including additional processes, temporal dynamics, and spatial heterogeneity. Sensitivity analyses of DPM have revealed that uncertainties are generally large yet crucial for a correct interpretation of process interactions. In other disciplines, the application of probabilistic models, uncertainty analysis, and uncertainty reduction via data assimilation has proven useful extensions of former deterministic models. However, uncertainty has barely been quantified and analyzed for probabilistic DPM. To make DPM more useful, we suggest to develop a probabilistic DPM and to quantify, analyze, and reduce uncertainties in its input data and parameters, using uncertainty analysis and data assimilation. We expect that data assimilation can reduce uncertainties especially for long-term decomposition rates if one synthesizes different existing data sources (peat core data and litter bag data) and the information provided by multiple peat properties at the same time (e.g. C and N content). With this framework, we aim to assess the impact uncertainties have for our understanding of the effects of temperature, precipitation, and nitrogen deposition on peatland C accumulation, identify conditions under which experiments yield contrasting results, and provide strategies for efficient future uncertainty reduction in DPM.