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

TRR 32：土壤-植被-大气系统模式：监测、建模和数据同化

基本信息

批准号：
15232683
负责人：
金额：
--
依托单位：
Universität Mannheim
依托单位国家：
德国
项目类别：
CRC/Transregios
财政年份：
2007
资助国家：
德国
起止时间：
2006-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/15232683?language=en
关键词：
TRR 32 Patterns Soil Vegetation

项目摘要

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.

能量、水和碳通过土壤、植被和大气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快速增长，对自然资源的可持续和有效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、植被和大气内部和之间的过程和相互依赖性，这将带来更可靠的天气和气候模型以及更准确的水和二氧化碳输送预测，以支持改进自然资源管理。土壤-植被-大气连续体的时空模式被认为发挥着关键作用。例如，农业土地利用——小麦仅次于甜菜，或者土豆和玉米——影响土壤和大气之间的水、二氧化碳和热量的交换。所有过程密不可分地交织在一起，导致不同空间和时间尺度上的复杂反馈和系统响应。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成部分的起源以及空间和时间模式之间的相互关系。可以在所有尺度上观察物理参数（例如土壤导水率）、状态变量（例如土壤湿度或空气温度）和过程（例如二氧化碳、水和热量的通量）的时空结构。需要检测这些模式并理解所涉及的相互作用，以表示数值模型中不同的空间和时间尺度。

项目成果

期刊论文数量（38）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Large-Eddy Atmosphere–Land-Surface Modelling over Heterogeneous Surfaces: Model Development and Comparison with Measurements

DOI：
10.1007/s10546-013-9823-0
发表时间：
2013-04
期刊：
Boundary-Layer Meteorology
影响因子：
4.3
作者：
Y. Shao;Shaofeng Liu;J. Schween;S. Crewell
通讯作者：
Y. Shao;Shaofeng Liu;J. Schween;S. Crewell

Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale

小流域尺度土壤湿度空间格局的季节和事件动态

DOI：
10.1029/2011wr011518
发表时间：
2012
期刊：
Water Resources Research
影响因子：
5.4
作者：
Rosenbaum;H.R. Bogena;M. Herbst;J.A. Huisman;T.J. Peterson;A. Weuthen;A.W. Western;H. Vereecken
通讯作者：
H. Vereecken

Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths. Part I: Radar Calibration and Partial Beam Blockage Estimation