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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 12
Hydrol. Earth Syst. Sci., 15, 3861–3875, 2011
https://doi.org/10.5194/hess-15-3861-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 15, 3861–3875, 2011
https://doi.org/10.5194/hess-15-3861-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Dec 2011

Research article | 21 Dec 2011

What can we learn from long-term groundwater data to improve climate change impact studies?

S. Stoll1, H. J. Hendricks Franssen2, R. Barthel3, and W. Kinzelbach1 S. Stoll et al.
  • 1Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
  • 2Agrosphere, IBG-3, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • 3Institute of Hydraulic Engineering, Universität Stuttgart, 70569 Stuttgart, Germany

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

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