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Urban Water Interfaces (UWI) - DFG Reasearch Training Group

Urban Water Interfaces (UWI) 

Teilprojekt: N2  Heat and vapor transport at the soil-atmosphere interface in urban areas 
Deutsche Forschungsgemeinschaft (DFG) 
Project partner of the UWI research training group 
2015 - 2018
M.Sc. Kyle Pipkins
Prof. Dr. Birgit Kleinschmit


The Urban Water Interfaces (UWI) research training group is a collaboration between the Technische Universität Berlin and the Leibniz-Institute of Freshwater Ecology and Inland Fisheries, along with many other associated partners and research institutions. Its aims are to educate graduate students while increasing the understanding of natural and technical interfaces in urban water systems. Interfaces in this context refer to the boundaries between hydrological system components, including those between wastewater and gas in sewers, groundwater and surface water, runoff and surface water, and water and the atmosphere. These interfaces are characterized by steep physical or biogeochemical gradients as well as high heterogeneity, especially within urban environments. Due to this complexity, there are many research gaps regarding water interfaces in urban areas.

The research program consists of a combination of controlled laboratory experiments, field experiments, and the development of models for the various interfaces. The program is also designed to encourage collaboration between engineers and natural scientists. Thus, the research will include a focus on commonalities between the various interfaces. In particular, collaborations will be pursued between measurement, modelling, and scaling methods.

The UWI research training group is divided into twelve focus areas, divided between natural and technical interfaces. The research project N2 will focus on heat and vapor transport at the soil-atmosphere interface in urban areas. The first aim is to understand the relationship between evapotranspiration and soil sealing using in-situ measurements. The next aim is to model evapotranspiration on different scales over urban regions, using a combination of remote sensing and ancillary datasets. To accomplish this, high resolution remote sensing datasets will be used to determine local scale urban evapotranspiration rates. Following this, the resulting datasets will be upscaled from the local to regional level using machine learning methods, in order to identify new interactions between urban structure, temperature, and evapotranspiration.



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