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Geofluids
Volume 2019, Article ID 5246307, 11 pages
https://doi.org/10.1155/2019/5246307
Research Article

Estimation of Groundwater Temperatures in Paris, France

1Ingolstadt University of Applied Sciences, Institute of New Energy Systems (InES), Ingolstadt 85049, Germany
2Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences (AGW), Karlsruhe 76131, Germany
3University of California San Diego, School of Global Policy and Strategy (GPS), La Jolla, CA 92093, USA
4Cerema, Direction Centre-Est, 46 rue Saint Théobald, F-38081 L’Isle d’Abeau, France

Correspondence should be addressed to Peter Bayer; ed.iht@reyab.retep

Received 29 December 2018; Revised 23 April 2019; Accepted 26 May 2019; Published 17 June 2019

Guest Editor: Antonio Galgaro

Copyright © 2019 Hannes Hemmerle et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Subsurface temperature data is usually only accessible as point information with a very limited number of observations. To spatialize these isolated insights underground, we usually rely on interpolation methods. Unfortunately, these conventional tools are in many cases not suitable to be applied to areas with high local variability, like densely populated areas, and in addition are very vulnerable to uneven distributions of wells. Since thermal conditions of the surface and shallow subsurface are coupled, we can utilize this relationship to estimate shallow groundwater temperatures from satellite-derived land surface temperatures. Here, we propose an estimation approach that provides spatial groundwater temperature data and can be applied to natural, urban, and mixed environments. To achieve this, we combine land surface temperatures with anthropogenic and natural processes, such as downward heat transfer from buildings, insulation through snow coverage, and latent heat flux in the form of evapotranspiration. This is demonstrated for the city of Paris, where measurements from as early as 1977 reveal the existence of a substantial subsurface urban heat island (SUHI) with a maximum groundwater temperature anomaly of around 7 K. It is demonstrated that groundwater temperatures in Paris can be well predicted with a root mean squared error of below 1 K by means of satellite-derived land surface images. This combined approach is shown to improve existing estimation procedures that are focused either on rural or on urban conditions. While they do not detect local hotspots caused by small-scaled heat sources located underground (e.g., sewage systems and tunnels), the findings for the city of Paris for the estimation of large-scale thermal anomalies in the subsurface are promising. Thus, the new estimation procedure may also be suitable for other cities to obtain a more reliable insight into the spatial distribution of urban ground and groundwater temperatures.