Uncertainty of predicted hydro-biogeochemical fluxes and trace gas emissions on the landscape scale under climate and land use change

水、碳和氮是所有生态系统周转过程中的关键要素，它们与各种环境问题有关，包括富营养化、温室气体排放或碳固存。需要深入了解水、碳和氮在景观尺度上的相互作用，以改善土地利用和管理，同时减轻环境影响。在“气候和土地利用变化下景观尺度水文地球化学通量和痕量气体排放预测的不确定性”的框架内，我们主张发展完全耦合的、面向过程的模型，明确模拟景观尺度上水、碳和氮周转过程的动态相互作用。我们将使用集水区建模框架CMF，一个模块化的工具箱来实现和测试水文行为的假设，并将其耦合到土壤地球化学景观DNDC模型，一个基于过程的动态模型，用于模拟土壤温室气体排放及其相关的周转过程。由于所使用的模型的内在复杂性，耦合模型的预测不确定性是未知的。这种预测（全球）的不确定性是由随机和结构组件。随机不确定性是由参数估计中的误差、模型的初始状态知之甚少、边界条件不匹配或模型输入和验证数据不准确造成的。结构不确定性与模型中对自然过程的错误或简化描述有关，因此，本建议的目标是量化耦合水文地球化学模型的全局不确定性，并通过建立CMF和LandscapeDNDC的不同组合，研究从参数不确定性到强迫数据不确定性的结构模型不确定性链。一个全面的工作计划已被开发成4个工作包，包括（1）模式建立，校准和不确定性评估的网站规模，其次是（2）应用和不确定性评估的耦合模式结构的区域尺度，（3）全球变化情景分析，最后（4）评估模式结果的集合方式。该提案的另一个动机是提供项目成果，以支持在不确定情况下的规划和决策，因为该提案是关于“处理景观规划和相关建模中不确定性的方法”的一揽子提案的一部分。

Water, carbon and nitrogen are key elements in all ecosystem turnover processes and they are related to a variety of environmental problems, including eutrophication, greenhouse gas emissions or carbon sequestration. An in-depth knowledge of the interaction of water, carbon and nitrogen on the landscape scale is required to improve land use and management while at the same time mitigating environmental impact. This is even more important under the light of future climate and land use changes.In the frame of the proposal “Uncertainty of predicted hydro-biogeochemical fluxes and trace gas emissions on the landscape scale under climate and land use change“ we advocate the development of fully coupled, process-oriented models that explicitly simulate the dynamic interaction of water, carbon and nitrogen turnover processes on the landscape scale. We will use the Catchment Modelling Framework CMF, a modular toolbox to implement and test hypothesis of hydrologic behaviour and couple this to the biogeochemical LandscapeDNDC model, a process-based dynamic model for the simulation of greenhouse gas emissions from soils and their associated turnover processes.Due to the intrinsic complexity of the models in use, the predictive uncertainty of the coupled models is unknown. This predictive (global) uncertainty is composed of stochastic and structural components. Stochastic uncertainty results from errors in parameter estimation, poorly known initial states of the model, mismatching boundary conditions or inaccuracies in model input and validation data. Structural uncertainty is related to the flawed or simplified description of natural processes in a model.The objective of this proposal is therefore to quantify the global uncertainty of the coupled hydro-biogeochemical models and investigate the uncertainty chain from parameter uncertainty over forcing data uncertainty up the structural model uncertainty be setting up different combinations of CMF and LandscapeDNDC. A comprehensive work program has been developed structured in 4 work packages, that consist of (1) model set up, calibration and uncertainty assessment on site scale followed by (2) an application and uncertainty assessment of the coupled model structures on regional scale, (3) global change scenario analyses and finally (4) evaluating model results in an ensemble fashion.Last but not least, a further motivation of this proposal is to provide project results in a manner that they support planning and decision taking under uncertainty, as this proposal is part of the package proposal on “Methodologies for dealing with uncertainties in landscape planning and related modelling”.