Deep-learning and process-based models for simulating carbon and water fluxes across scales (C04)

C4.1基于过程的模型模拟我们将开发基于过程的模型模拟和深度学习工具，用于数据分析，以与基于传感器的监测进行交互和优化，并加深我们对时空异质性和动态对整个生态系统水和碳交换的影响的理解。现有的基于过程的2D模型将在现在和预测的3D模式下进行扩展、校准和运行，涵盖小规模过程的时空异质性，并集成用于非线性相互作用的新标度律。C4.2深度学习使用深度学习算法，将有效地评估过多的数据，以区分重要数据和冗余数据。其目的是提供足够的时空分辨率，节省传感器节点能量，减少冗余数据积累。因此，深度学习和过程模拟通过i)来自传感器网络的数据同化和ii)基于模拟输出和预测调整和优化测量设计来与传感器网络交互

C4.1 Process-based model simulationsWe will develop process-based model simulations and deep learning tools for data analysis to interact with and optimize the sensor-based monitoring, as well as to deepen our understanding of impacts of spatio-temporal heterogeneity and dynamics for total ecosystem water and carbon exchange. An existing 2D process-based model will be extended, calibrated and run in a now- and forecasting 3D mode, covering spatio-temporal heterogeneity of small-scale processes and integrating new scaling laws for non-linear interactions.C4.2 Deep learningUsing deep-learning algorithms, the plethora of data will be efficiently evaluated to distinguish between important and redundant data. The aim is to provide sufficient spatio-temporal resolution and save sensor node energy and reduce redundant data accumulation. Thereby, deep learning and process simulations interact with the sensor network through i) data assimilation from the sensor network and ii) adjustment and optimization of the measuring design based on simulated outputs and predictions