TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation

能量、水和碳在土壤、植被和大气中的循环影响着地球上生命的分布和质量。随着世界人口及其需求的迅速增长，对我们的自然资源进行可持续和有效的管理变得比以往任何时候都更加重要。TR32的重点是更好地了解土壤、植被和大气内部及其之间的过程和相互依赖关系，这将导致更可靠的天气和气候模型以及更准确的水和二氧化碳运输预测，以支持改进自然资源管理。假设土壤-植被-大气连续体的时空格局起关键作用。例如，农业土地利用——小麦挨着甜菜，或者土豆和玉米——影响土壤和大气之间的水、二氧化碳和热量交换。所有过程都不可分割地交织在一起，在不同的时空尺度上产生复杂的反馈和系统响应。TR32的总体目标是利用创新的监测和建模方法，阐明土壤-植被-大气系统中每个单一组分的时空格局的起源和相互关系。可以在所有尺度上观察到物理参数（例如，土壤水力导电性）、状态变量（例如，土壤湿度或空气温度）和过程（例如，二氧化碳、水和热的通量）的时空结构。这些模式的检测和所涉及的相互作用的理解需要在数值模型中表示不同的空间和时间尺度。

The cycling of energy, water and carbon through soil, vegetation and atmosphere influences the distribution and quality of life on Earth. With the rapid growth of the world population and its needs, the sustainable and efficient management of our natural resources becomes more important than ever. The TR32 focuses on a better understanding of the processes and interdependencies within and between soil, vegetation and the atmosphere, which will lead to more reliable weather and climate models and more accurate predictions for water and CO2 transport to support an improved management of natural resources. Spatial and temporal patterns in the soil-vegetation-atmosphere continuum are postulated to play a key role. For example, agricultural land-use - wheat next to beet, or potatoes and corn - influences the exchange of water, CO2 and heat between soil and atmosphere. All processes are inseparably intertwined, resulting in complex feedbacks and system responses on different spatial and temporal scales.The overall goal of the TR32 is to clarify the origins of and the interrelations between spatial and temporal patterns of each single component within the soil-vegetation-atmosphere system using innovative monitoring and modelling approaches. Spatial and temporal structures of physical parameters (e.g., soil hydraulic conductivity) state variables (such as soil moisture or air temperature) and processes (for example fluxes of CO2, water and heat) can be observed on all scales. The detection of these patterns and the understanding of the interactions involved are required to represent the different spatial and temporal scales in numerical models.