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

Journal article
Authors S. Stoll
H. J. Hendricks Franssen
Roland Barthel
W. Kinzelbach
Published in Hydrology and Earth System Sciences
Volume 15
Issue 12
Pages 3861-3875
ISSN 10275606 (ISSN)
Publication year 2011
Published at Department of Earth Sciences
Pages 3861-3875
Language en
Keywords Anthropogenic cause, Atmospheric circulation patterns, Climate change impact, Data sets, Dominant mechanism, Down-scaling, Downscaling methods, Feedback mechanisms, Global change, Groundwater dynamics, Groundwater system, Hydrological models, Land-use change, Meteorological condition, Southern Germany, Spatiotemporal correlation, Switzerland, Water demand, Climate models, Computer simulation, Dynamics, Groundwater, Groundwater resources, Land use, Time series, Climate change, anthropogenic effect, climate effect, data set, downscaling, feedback mechanism, groundwater resource, hydrodynamics, hydrological modeling, land use change, meteorology, pumping, uncertainty analysis, Germany
Subject categories Oceanography, Hydrology, Water Resources, Hydrology, Water in nature and society


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. © Author(s) 2011.

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