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| Titel |
A large-scale simulation model to assess karstic groundwater recharge over Europe and the Mediterranean |
| VerfasserIn |
A. Hartmann, T. Gleeson, R. Rosolem, F. Pianosi, Y. Wada, T. Wagener |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 6 ; Nr. 8, no. 6 (2015-06-11), S.1729-1746 |
| Datensatznummer |
250116405
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| Publikation (Nr.) |
 copernicus.org/gmd-8-1729-2015.pdf |
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| Zusammenfassung |
| Karst develops through the dissolution of carbonate rock and is a major
source of groundwater contributing up to half of the total drinking water
supply in some European countries. Previous approaches to model future water
availability in Europe are either too-small scale or do not incorporate
karst processes, i.e. preferential flow paths. This study presents the first
simulations of groundwater recharge in all karst regions in Europe with a
parsimonious karst hydrology model. A novel parameter confinement strategy
combines a priori information with recharge-related observations (actual
evapotranspiration and soil moisture) at locations across Europe while
explicitly identifying uncertainty in the model parameters. Europe's karst
regions are divided into four typical karst landscapes (humid, mountain,
Mediterranean and desert) by cluster analysis and recharge is simulated from
2002 to 2012 for each karst landscape. Mean annual recharge ranges from
negligible in deserts to > 1 m a−1 in humid regions. The majority
of recharge rates range from 20 to 50% of precipitation and are
sensitive to subannual climate variability. Simulation results are
consistent with independent observations of mean annual recharge and
significantly better than other global hydrology models that do not consider
karst processes (PCR-GLOBWB, WaterGAP). Global hydrology models
systematically under-estimate karst recharge implying that they over-estimate
actual evapotranspiration and surface runoff. Karst water budgets and thus
information to support management decisions regarding drinking water supply
and flood risk are significantly improved by our model. |
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