Transregional Collaborative Research Centre 32: Patterns in Soil-Vegetation-Atmosphere-Systems

The Collaborative Research Centre TR32 has the goal to perform pattern-based prediction of states and fluxes of water, CO2 and energy in terrestrial systems across scales. For this, the TR32 set up the following three elements during the past nine years:

measurement techniques that allow us to characterize and monitor the spatiotemporal dynamics and evolution of system properties across scales, a cross-scale, multi-compartment terrestrial system modeling approach that includes all relevant processes using the terrestrial model platform TerrSysMP and state variable assimilation and parameter estimation methods. We will present examples of how the TR32 utilizes these three elements to improve our understanding of the water cycle. The available soil moisture monitoring network consisting of e.g. cosmic-ray sensors or an in situ NMR slim-line logging tool has been helpful in understanding the interactions of plant growth and soil moisture dynamics. New algorithms derive soil moisture from satellite based SAR systems, which showed potential for the derivation of surface roughness and vegetation information. For surface precipitation, a radar composite using observations from two dual-polarized X-band Doppler radars provides nearly 100% coverage of the Rur catchment. To also be able to include other precipitation observations which occur at different temporal and spatial resolutions, such as rain gauges, a high resolution space-time precipitation model is being developed. Commercial microwave links used for cell phone communication have also been experimented with to improve polarimetric quantitative precipitation estimation. In addition, uncertainty plays a major role with respect to the central goal of the TR32 and is taken into account in various ways. For example, model uncertainty in the Rur catchment results in large parts from anthropogenic activities such as e.g. drainage patterns in fields, the control of the Rur discharge, groundwater pumping, storage lakes, etc. Other approaches to quantify uncertainty include sensitivity experiments to evaluate the effects of parameter lumping.