Abstract
In karst hydrosystems, the heterogeneity and the complexity of the transfer processes within the different compartments (soil, epikarst, unsaturated zone, saturated zone) control the recharge-discharge relationship. The MEDYCYSS observatory (Multi scalE observatory of flooD dYnamiCs and hYdrodynamicS in karSt) has been set up to better understand these transfers in a Mediterranean context. This observatory consists in a large karst hydrosystem located between the Hérault River, the Vidourle River and the Mediterranean Sea (Southern France, north and west from Montpellier). It comprises two main hydrogeological sites and three main hydrological sites where karst/river interactions occur. The challenges of the MEDYCYSS observatory are i) to better characterize the karst/river interactions for the risk assessment of floods and water resource contamination; ii) to discriminate and quantify flow distribution (rapid and slow transfers) between the recharge catchment and the outlets of the hydrosystem; iii) to understand the role of the unsaturated zone on the long term storage of the aquifer and quantify the water storage variation with time; iv) to characterize groundwater hydrodynamics in karst watersheds under climatic and anthropogenic forcing at nested observation scales; and v) to quantify the recharge, which is a key variable for the assessment and management of groundwater resources in karsts. Accordingly, a continuous monitoring of the hydrochemistry and hydrodynamics (wells, karst network, permanent and temporary springs), as well as of the river and spring discharges has been constructed to characterize transfer processes and parameterize local and regional scale models. Meteorological variables are also monitored at various points where measurements of soil humidity and evapotranspiration recently started.KeywordsUnsaturated ZoneFlash FloodKarst AquiferFlood HazardKarst GroundwaterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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