Karst aquifers provide large parts of the water supply for Mediterranean countries, though climate change is expected to have a significant negative impact on water availability. Recharge is therefore a key variable that has to be known for sustainable groundwater use. In this study, we present a new approach that combines two independent methods for karst recharge estimation. The first method derives spatially distributed information of mean annual recharge patterns through GIS analysis. The second is a process-based karst model that provides spatially lumped but temporally distributed information about recharge. By combining both methods, we add a spatial reference to the lumped simulations of the process-based model. In this way, we are able to provide spatiotemporal information of recharge and subsurface flow dynamics also during varying hydroclimatic conditions. We find that there is a nonlinear relationship between precipitation and recharge rates resulting in strong decreases of recharge following even moderate decreases of precipitation. This is primarily due to almost constant actual evapotranspiration amounts despite varying hydroclimatic conditions. During the driest year in the record, almost the entire precipitation was consumed as actual evapotranspiration and only little diffuse recharge took place at the high altitudes of our study site. During wettest year, recharge constituted a much larger fraction of precipitation and occurred at the entire study site. Our new method and our findings are significant for decision makers in similar regions that want to prepare for possible changes of hydroclimatic conditions in the future.

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Karst aquifers provide drinking water for 10% of the world's population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset's coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries. WoKaS database will ensure easy access to a large-sample of good quality datasets suitable for a wide range of applications: comparative studies, trend analysis and model evaluation. This database will largely contribute to research advancement in karst hydrology, supports karst groundwater management, and promotes international and interdisciplinary collaborations. Background & Summary Karst aquifers are essential sources of drinking water to about 10% of the world's population 1. In many regions across the globe, karst groundwater is also an indispensable resource for ecosystems, agriculture and, economic activities, as well as for tourism and recreation 2,3. For example, in Europe, 21.6% of the land surface is underlain by carbonate rock 4 which contributes up to 50% of supplied drinking water in some countries 5-7. However, groundwater flow in karst aquifers is characterised by a complex interplay of fast-flowing conduit and slow-flowing matrix systems 8,9. Hence, the storage capacity of karst aquifers is variable and systems are extremely vulnerable to climatic pressures, human impacts and contamination 10. In order to ensure adequate protection of karst water sources, in-depth hydrogeological knowledge is necessary. Large-scale modelling and comparative water resource research have shown the great value of large datasets in hydrology 11. Numerous studies have applied these large datasets for several purposes such as model evaluation, global parameter estimations, impact studies, statistical and comparative analyses. For instance, large-scale hydrological models such as WaterGAP 12 used discharge data from the Global Runoff Data Centre (https://www. bafg.de/GRDC) for parameter estimation. Likewise, streamflow data from the Model Parameter Estimation Experiment (MOPEX) 13 and the Global Runoff Data Centre (GDRC) were combined to derive global base flow indexes and recession constants 14. Streamflow observations of near-natural catchments obtained from UNESCO's European Water Archive (EWA) were used to investigate the streamflow trends across Europe and differentiated the impacts from climatic variability and anthropogenic dri...

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