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

Wednesday, 17 December 2014
Clemens Simmer, University of Bonn, Bonn, Germany
The spatio-temporal dynamics of states, flow and transport within the groundwater-soil-vegetation-atmosphere (GSVA) system lead to complex, scale-dependent patterns, which make predictions of terrestrial systems challenging to both scientists and policymakers. Studying how patterns influence fluxes and state variables across scales is a key goal of the Collaborative Research Centre TR32, which approaches this challenge by monitoring, modelling and data assimilation using the Rur catchment (Germany) as its study area.
The evolution of system state variables across scales is monitored using two dual-polarized X-band Doppler radars as well as the atmospheric boundary layer, cloud and precipitation monitoring site JOYCE (Jülich ObservatorY for Cloud Evolution), and measurements from eddy covariance stations, an extensive soil moisture network including cosmic-ray probes. Monitoring is complemented by a suite of geophysical methods such as Nuclear Magnetic Resonance, Spectral Induced Polarization, Electromagnetic Induction, and Ground-Penetrating Radar, as well as a rhizotrone facility set up to monitor root development in conjunction with plant growth.
The TR32 employs multi-compartment modelling to upscale the water, CO2 and energy fluxes from the local to the catchment scale. The analysis of the simulations with grids that honor the respective scales reveal the role of patterns on the fluxes and helps to design a general upscaling framework that quantifies information transfer between scales.
Model development centers around the coupled model platform TerrSysMP, which considers mutual fluxes from the groundwater to the atmosphere by combining the atmospheric model COSMO, the land surface model CLM, and the hydrological model ParFlow in a scale-consistent way using the OASIS coupler. Processes down to the root scale are modelled at high resolution in order to obtain improved parameterizations for TerrSysMP. State variable assimilation and parameter estimation methods are extended to the complete terrestrial system. We will present selected results and challenges for the prediction of states and fluxes of water, CO2 and energy in terrestrial systems across scales.