My core research interests include multivariate non-linear spatial dependence structures, associated parameter estimation methods, spatial interpolation and simulation techniques, inversion, as well as quantifying the effects of non-linear spatial dependence structures on dependent properties, such as solute transport behaviour. I apply geostatistical concepts for a variety of environmental parameters such as precipitation, hydraulic conductivity, solute concentrations in river sediments, soil moisture, and many more. These parameters affect the hydrological cycle in hydrosystems such as irrigated agricultural watersheds in arid environments, groundwater quality on regional scales, as well as the inclusion of secondary and censored (“measurements below detection limit”) information for hydrogeological assessments.
More generally, I believe water is an essential natural resource. For meaningful management of this vital resource quantification of its states in all compartments is necessary which is an inherently collaborative endeavour.
I was the Outstanding Student Paper Award Winner at the American Geophysical Union’s Fall Meeting (hydrology, 2009).
My Erdős number is likely 5 (via MathSciNet).
I am an environmental engineer and hydrogeologist. Since my dissertation, my research focuses on multidimensional spatial statistics, using copulae and their effects on physical-, chemical- or other dependent parameters. I think the link between real world data and effective models that describe data is the most important aspect of my research.
A key aspect of my research revolves around the experience that spatial dependence is not linear, in the sense that a) different quantiles can have a different degree of dependence for a given separation distance and b) that this can also change with separation distance. Both aspects effect the characteristics of dependent parameters: For example, two spatially distributed fields of saturated hydraulic conductivity, both with identical linear spatial dependence but with different non-linear dependence, can exhibit largely differing solute transport characteristics. This can and should have impact on design decisions.
As spatially distributed data is always sparse, particularly in the subsurface, it is mandatory to look at other kinds of data that are more frequently available, easier to measure, and / or cheaper. For example, I have successfully used locally averaged land-use information to enhance the spatial dependence model of anthropogenic groundwater quality parameters, which leads to improved and more realistic maps and to a much improved uncertainty, particularly an improved spatial structure of uncertainty.
The kind of spatially distributed data I am working with has been, but is not limited to hydraulic conductivity, groundwater quality, soil moisture, and precipitation.
Based on my master thesis work I have experience in field based hydrogeology and numerical modelling: I collected field data (e.g. drilling, well installation, coring, aqueous geochemistry, electrical resistivity, EM, tile drain sampling), analyzed and interpreted that data, and used it to build a numerical model in FeFlow.
The interplay of data and models, ideally with reasonable and innovative representations of heterogeneity in space and time is still of interest to me, for example related to water resources management in arid environments or in improved modelling strategies for evapotranspiration.