Kathrin Menberg

Subsurface urban heat islands in German cities.

Little is known about the intensity and extension of subsurface urban heat islands (UHI), and the individual role of the driving factors has not been revealed either. In this study, we compare groundwater temperatures in shallow aquifers beneath six German cities of different size (Berlin, Munich, Cologne, Frankfurt, Karlsruhe and Darmstadt). It is revealed that hotspots of up to +20K often exist, which stem from very local heat sources, such as insufficiently insulated power plants, landfills or open geothermal systems. When visualizing the regional conditions in isotherm maps, mostly a concentric picture is found with the highest temperatures in the city centers. This reflects the long-term accumulation of thermal energy over several centuries and the interplay of various factors, particularly in heat loss from basements, elevated ground surface temperatures (GST) and subsurface infrastructure. As a primary indicator to quantify and compare large-scale UHI intensity the 10-90%-quantile range UHII(10-90) of the temperature distribution is introduced. The latter reveals, in comparison to annual atmospheric UHI intensities, an even more pronounced heating of the shallow subsurface.

Long-Term Evolution of Anthropogenic Heat Fluxes into a Subsurface Urban Heat Island

Anthropogenic alterations in urban areas influence the thermal environment causing elevated atmospheric and subsurface temperatures. The subsurface urban heat island effect is observed in several cities. Often shallow urban aquifers exist with thermal anomalies that spread laterally and vertically, resulting in the long-term accumulation of heat. In this study, we develop an analytical heat flux model to investigate possible drivers such as increased ground surface temperatures (GSTs) at artificial surfaces and heat losses from basements of buildings, sewage systems, subsurface district heating networks, and reinjection of thermal wastewater. By modeling the anthropogenic heat flux into the subsurface of the city of Karlsruhe, Germany, in 1977 and 2011, we evaluate long-term trends in the heat flux processes. It revealed that elevated GST and heat loss from basements are dominant factors in the heat anomalies. The average total urban heat flux into the shallow aquifer in Karlsruhe was found to be ∼759 ± 89 mW/m(2) in 1977 and 828 ± 143 mW/m(2) in 2011, which represents an annual energy gain of around 1.0 × 10(15) J. However, the amount of thermal energy originating from the individual heat flux processes has changed significantly over the past three decades.

Spatial resolution of anthropogenic heat fluxes into urban aquifers.

Urban heat islands in the subsurface contain large quantities of energy in the form of elevated groundwater temperatures caused by anthropogenic heat fluxes (AHFS) into the subsurface. The objective of this study is to quantify these AHFS and the heat flow they generate in two German cities, Karlsruhe and Cologne. Thus, statistical and spatial analytical heat flux models were developed for both cities. The models include the spatial representation of various sources of AHFS: (1) elevated ground surface temperatures, (2) basements, (3) sewage systems, (4) sewage leakage, (5) subway tunnels, and (6) district heating networks. The results show that the district heating networks induce the largest AHFS with values greater than 60 W/m(2) and one order of magnitude higher than fluxes from other sources. A covariance analysis indicates that the spatial distribution of the total flux depends mainly on the thermal gradient in the unsaturated zone. On a citywide scale, basements and elevated ground surface temperatures are the dominant sources of heat flow. Overall, 2.1 PJ/a and 1.0 PJ/a of heat are accumulated on average in Karlsruhe and the western part of Cologne, respectively. Extracting this anthropogenically originated energy could sustainably supply significant parts of the urban heating demand. Furthermore, using this heat could also keep groundwater temperatures from rising further.

A matter of meters: state of the art in the life cycle assessment of enhanced geothermal systems

This work presents a review of the studies applying life-cycle assessment (LCA) methodologies to evaluate the environmental performance of enhanced geothermal systems (EGSs). Due to the scarcity of commercially installed EGS power plants such studies are rare and usually represent very site-specific conditions and plant characteristics. A detailed inspection of the outcome of these studies shows that the major environmental impacts of the investigated EGS plants are caused by the drilling of geothermal wells during construction. However, recent developments in environmentally friendly drilling technologies, which up to now have only marginally been considered in LCA studies, will provide opportunities to reduce the impact by drilling. Our analysis reveals that the use of electricity from a grid instead of diesel to drive the drilling rigs can improve the environmental performance of EGS plants significantly, provided that the employed electricity is supplied by environmentally friendly technologies. The largest share of uncertainty in the LCA of EGS plants is consequently linked to the incalculable number of boreholes that need to be drilled during the life-time of the plant in order to sustain efficient power production. From the LCA perspective, however, the power needed to drill these additional boreholes can be subtracted directly from the power production of the EGS plant. Under this presumption the examination of greenhouse gas emissions of EGS plants as a function of drilled borehole meters shows that even EGS plants with a large number of deep wells can be competitive in terms of environmental effects compared to conventional energy technologies. When predictions of technological improvements in geothermal drilling and plant design are taken into account, future EGS power plants are prone to have the potential to perform environmentally better than most other renewable energy technologies.

Influence of error terms in Bayesian calibration of energy system models

Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinize the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework.