Probabilistic Modeling of Long-term Peatland Carbon Dynamics

长期泥炭地碳动力学的概率模型

• 批准号: 468648567
• 负责人: Professor Dr. Klaus-Holger Knorr
• 金额: --
• 依托单位: Institut für Geoinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/468648567?language=en
• 关键词: Probabilistic; Modeling; Long; term; Peatland

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.

泥炭地调节大气中温室气体的浓度，从而调节全球气候。它们形成了最大的陆地碳库之一，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威胁。动态泥炭地模型（ESTA）需要获得一个机械的理解过程中的相互作用，预测碳积累率的长期变化，并综合对比结果的个别研究。大约40年来，通过包括额外的过程、时间动态和空间异质性来不断改进。敏感性分析的结果表明，不确定性一般都很大，但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经证明是对以前确定性模型的有益扩展。然而，不确定性几乎没有被量化和分析的概率分布。为了使可预测性更有用，我们建议开发一个概率可预测性和量化，分析，并减少其输入数据和参数的不确定性，使用不确定性分析和数据同化。我们预计，数据同化可以减少不确定性，特别是长期分解率，如果一个综合不同的现有数据源（泥炭芯数据和垃圾袋数据）和多个泥炭属性在同一时间（如C和N含量）提供的信息。有了这个框架，我们的目标是评估影响的不确定性，我们的理解的温度，降水和氮沉降对泥炭地碳积累的影响，确定实验条件下产生对比结果，并提供有效的未来不确定性减少的策略。