Former Working Groups
Ecohydology - Projects:
Systems Biology - Projects:
Short description of former projects
Improved modelling of root water uptake - the role of root architectures for plant transpiration
In this project, we applied a bulk soil water model (OpenGeoSys) relying on precise estimates of the sink term distribution provided by the root architecture based water uptake model aRoot. Our overall aim is to investigate the role that root architectures plays for root water uptake in different regimes/phases as well as to define potential constraints when neglecting the root network. Therefore we compared root water uptake behaviours for different root system realizations belonging to the same species. We found that there is an increasing uncertainty in relating the root water uptake to root abundace when dealing with a range of different root system realizations.
Further Information: Modeling root water uptake using aRoot
In addition, we extend this approach (aRoot) to the scale of plant communities (multiRoots) where several plant individuals compete for belowground water resources. Within this framework, we again will stress the validity of relating root abundance to root water uptake as it is done in state-of-the-art models of root water uptake, now at the community scale.
Contact: Christoph Scheider, Anke Hildebrandt,
Modelling feedbacks between the riparian vegetation and the water cylce of a semiarid ephemeral river basin
In a former PhD project we developed an ecohydrological modelling framework, which was applied to an ephemeral river in Namibia. We identified mechanisms that enhance the coexistence in the model as observed in reality. Further, we applied several model versions to assess different management strategies regarding their ability to exploit the ground water sustainably while preserving the natural vegetation structure.
Contact: Sven Arnold, Anke Hildebrandt,,
The role of horizontal precipitation for vegetation and groundwater recharge
Contact: Abdullah Bawain, Anke Hildebrandt,
Temporal vegetation dynamics as a tool for ecosystem monitoring and land cover change detection
Vegetation coverage is often a highly dynamic process. Vegetation indices as provided by the Moderate Resolution Satellite (MODIS) is now available for ten years in daily or 16-day temporal steps. Especially in semi-arid regions the additional temporal vegetation signal yields information about vegetation type (deep/ shallow rooting plants) as well as growing season and duration of transpiration periods that static land cover maps do not provide. The temporal signal component of the vegetation cover is currently applied in the Batinah and the Dhofar region of Oman.
This project is part of the IWAS Oman project.
Contact:, Anke Hildebrandt,
Validating rainfall estimates from satellite data through data redundancies with auxiliary spatial data
Spatially distributed rainfall data is one of the main input parameters for groundwater recharge models in arid and semi-arid regions. Limited availability of ground observations as well as the often high spatial and temproal variability of rainfall events renders interpolation of station data nearly impossible.
Rainfall estimates from satellite data provide spatial rainfall data for large regions but are often flawed with high uncertainties. Combining station data and large scale satellite data is challenging as the point scale has to be compared to a pixel size of 25km or more. To improve satellite rainfall estimates and overcome the spatial gap between the different data sources a methodology is developed that utilizes data redundancies between rainfall events, vegetation fields, and land surface temperature. First results for desert regions in the Arabian Peninsula show data redundancies between large rainfall events and subsequent emergence of vegetation fields.
This project is part of the IWAS Oman and IWAS Q1 projects.
Global determination of diurnal differences in ERS Scatterometer backscatter data
Soil moisture estimated through satellites is an important parameter in hydrological modeling.
ESA's recently launched SMOS and NASA’s upcoming SMAP missions are the first satellite missions directly dedicated to soil moisture.
Satellite derived soil moisture is based on algorithms that mainly use satellite backscatter data as input. Friesen et al. (2005) showed for the Volta Basin, that the spatial patterns of the diurnal backscatter differences do not correspond with the natural moisture distribution in the Volta Basin. In this study a global analysis of ESA's ERS Scatterometer (ESCAT) data, looking at diurnal backscatter differences by comparing data from descending (10:30 am) and ascending (10:30 pm) satellite tracks, is presented.
Weigthing Trees - tree rainfall interpection measured through stem compression
A method for measuring whole-tree interception of precipitation is presented which employs mechanical displacement sensors to measure trunk compression caused by the water captured by the tree canopy. Next to measuring changes in canopy water storage the presented technique offers a wide range of observations linked to mass change of the tree canopy. Mass change observations can be applied in the field of nondestructive seasonal biomass changes, direct measurements of wind load on live trees, and measurements of the moduli of elasticity of living wood. This direct and nondestructive method is demonstrated to be sensitive to less than 5 kg of canopy interception water.
Water relations and biodiversity
This project is part of the DFG Forschergruppe Biodiversity. In this subproject we aim at (1) quantifying the soil water balance based on measurements with high resolution soil moisture sensors and inverse modelling. (2) using those results for investigating the role of biodiversity for below ground water relations of ecosystems.
We combine experimental and modelling methods to understand if and how diverse ecosystems allocate the available soil water resource efficiently, particularly under stress conditions in summer.
Contact: Yao Hu, Guido Schwichtenberg, Anke Hildebrandt,
Modeling the impact of environmental chemicals on immune cell differentiation
Differentiation processes of the immune system's cells have been shown to be affected from several environmental factors. Yet the underlying mechanism behind the differentiation processes of the T lymphocytes cells remain elusive.
Here we combine a mathematical model based on ordinary differential equations with experiments in order to investigate the influence of parameters in biological models. We perform a global sensitivity analysis to identify the parameters which have a major impact on the model. We find the feasible ranges of each parameter discovering those that influence the model most.
The present findings allows to underlying the importance of a prior sensitivity analysis in order to increase the efficiency and reliability of a parameter inversion of complex models and therefore reduce the experimental efforts.
Contact: Giovanni Dalmasso,,
Parameter inversion and sensitivity analysis of biological systems
Fluorescence recovery after photobleaching (FRAP) is a widespread technique used to determine intracellular reaction and diffusion parameters. In recent years there was a resurging interest in FRAP applications, due to technical advances and an increasing number of mathematical models for analysis. However, care has to be taken when inverting parameters from such data. We study potential influences on FRAP acquisition and analysis like initial fluorescence distribution, membrane passage, and geometrical aspects.
Monte-Carlo simulations are employed for the investigation of reaction- diffusion processes to additionally include cases in which no analytical description is available. To assess the importance of influencing factors we apply a sensitivity method based on Elementary Effects providing an esti- mate for the global parameter space.
The combination of simulations and sensitivity measure helps to predict ranges of parameters used in acquisition and analysis for which a reliably inversion of reaction-diffusion parameters is possible. Using this approach we show that FRAP data are highly susceptible to misinterpretation. However, by identifying the parameters of susceptibility, our analysis provides the means for taking measures to significantly improve FRAP data interpretation and analysis.
Modeling distribution and interaction of contaminants in cellular environments
Polycyclic aromatic hydrocarbons (PAHs), such as benzo[a]pyrene (BaP) represent an important class of environmental contaminants. PAHs are ubiquitous contaminants derived from tobacco smoke, automobile exhaust or incomplete combustion of organic matter in general. They exert a wide range of toxic effects including carcinogenic, immunosuppressive or pro-inflammatory responses. PAHs are known ligands of the aryl hydrocarbon receptor (AhR) signalling pathway. AhR is a ligand activated transcription factor important for detoxification of environmental agents, toxicology, the induction of inflammatory signals or the oxidative stress response.
Some central aspects of the AhR signalling pathway are understood: contaminants enter the cell, distribute in the cytosol and different cellular organelles and interact with the cytoplasmic AhR. Binding to the AhR triggers translocation of the receptor/ligand complex to the nucleus, the association with the AhR nuclear translocator and the interaction with xenobiotic responsive elements (XREs) at the DNA. This usually leads to enhanced expression of a number of genes, which are presumed to play a major role in deleterious effects of PAHs. In spite of this general knowledge of AhR-mediated signalling, little is known about the dynamic behaviour of AhR upon activation.
We observe the behavior of the cytoplasmic AhR as well as the AhR-complex inside the nucleus in living cells. We applied the Fluorescence Recovery After Photobleaching (FRAP) method which is a well known and widely used experiment to investigate parameters of motion and interaction. Within this work we present a general method to analyse FRAP data without using pre-assumptions. We applied the method to artificial datasets as well as real measurements on AhR. The analysis of the AhR measurements e.g. lead to predictions on concentration- and time-dependent binding of AhR to the XREs and might help to understand the quantitative relationship betweeen DNA bound AhR and the transcriptional response. Deduced parameters were used to set up a 3D simulation of AhR distribution upon activation based on realistic cell geometries.
We introduce a general approach to determine parameters of dynamic processes inside the cell. This approach is applicable to various cellular systems expressing an fluorescent molecule of interest. The parameters deduced can be used for various applications to gain knowledge on the behaviour of cellular systems in different microenvironments.
From contaminants to cellular response - Gene- and Protein network modeling
Hepa1C1C7 cells exposed to selected BaP concentration and time periods, show a differential behavior in terms of gene and protein expression. In order to develop an understanding of the underlying gene/protein network, a modeling method has been developed that reverse engineers the gene/protein networks given the expression data. The method bridges the continuous and discrete modeling approaches, through the so called Zhegalkin Polynomials that are continuous representations of Boolean functions. The method computationally comprises of solving a discrete optimization problem and renders a Zhegalkin Polynomial model corresponding to each gene/protein. These predictive models are very helpful in providing an insight into the connectivity structure of the network.
Contact: Saadia Faisal,