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Copernicus Publications, Geoscientific Model Development Discussions, 6(7), p. 7887-7935

DOI: 10.5194/gmdd-7-7887-2014

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A simulation model to assess groundwater recharge over Europe's karst regions

Journal article published in 2014 by A. Hartmann ORCID, T. Gleeson ORCID, R. Rosolem ORCID, F. Pianosi ORCID, Y. Wada ORCID, T. Wagener ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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 4 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 ranges from 20–50% of precipitation and are sensitive to sub-annual 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 underestimate 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.