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Titel |
What can we learn from long-term groundwater data to improve climate change impact studies? |
VerfasserIn |
S. Stoll, H. J. Hendricks Franssen, R. Barthel, W. Kinzelbach |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1027-5606
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Digitales Dokument |
URL |
Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 12 ; Nr. 15, no. 12 (2011-12-21), S.3861-3875 |
Datensatznummer |
250013061
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Publikation (Nr.) |
copernicus.org/hess-15-3861-2011.pdf |
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Zusammenfassung |
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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