Our main research areas
Thermodynamics, self-organisation and functional similarity in the water cycle
Self-organisation of a hillslope
The water cycle is shaped by the landscape and in turn affects its spatial appearance. We study
Data-based learning, data infrastructures and measurement procedures
Multivariate probability distribution
We apply methods from information theory, machine learning, geostatistics and multivariate statistics to investigate the information content of measurement data and how well hydrological models use this information. On this basis we develop methods for optimising measurement networks for research and practice, as well as modelling concepts which combine information from local data and global knowledge in an optimal way (Ehret et al. 2018, Loritz et al. 2018).
We also develop innovative measurement approaches for hydrologically relevant data such as precipitation (Neuper et al. 2018), snow (Reusser and Zehe 2011) and soil moisture dynamics (Jackisch et al. 2017), as well as a virtual research environment for the management and analysis of environmental data (www.vforwater.de).
Hydrologal modelling and prediction uncertainty
Ecohydrology and environmental quality
Biotic controls play a key role in water and substance turnover and consequently also for environmental quality: Vegetation controls a large proportion of terrestrial water and carbon cycling via gas exchange. Root channels and burrows of small mammals substantially influence water flow paths and therefore also contaminant transport in the soil. Bacteria and fungi control the degradation of contaminants in the environment, however they also contribute to environmental pollution, for example by transforming nitrogen into the greenhouse gas (N2O) (Köhler et al. 2012). Within our group, we are interested in determining the influence of earthworm burrows and other preferential flow paths on the relocation and degradation of pesticides (Klaus et al. 2014) and the influence of transpiration characteristics of vegetation on the water balance in landscapes (Hassler et al. 2018).