| Summary: | International audience Groundwater level (GWL) variations can be expressed over a wide range of timescales. As aquifers act as low-pass filters, low-frequency variability (from interannual to decadal timescales) originating from large-scale climate variability represents a significant part of GWL variance. This is typically the case of aquifers in the Seine River basin for which extreme GWL appears largely dependent on such variations. In addition to expected trend patterns (e.g., increase/decrease of seasonal precipitation amounts), which may be induced by climate change, GWL could indeed be modulated by internal modes of climate variability, such as El Nino Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). How GWL variability, including extremes, may respond to such changes and variations in climate however remains an open question. To tackle this issue, we implemented an empirical numerical approach allowing to assess the sensitivity of aquifers to changes in large-scale climate variability, using the whole Seine hydrosystem as a case study. The approach consisted in: i) identifying and modifying the spectral content of precipitation, originating from large-scale climate variability, using signal processing; ii) injecting perturbed precipitation fields as input in a physically-based hydrological/hydrogeological model (the CaWaQS software) for the Seine river basin. We used the Safran precipitation field for calibration and validation over the period 1970-2018. GWL data for the Seine basin is a subset of a database of climate-sensitive time series (i.e. low anthropogenic influence) recently set up at the BRGM and University of Rouen Normandy. First, the Safran reanalysis and observed GWL time series were analyzed using continuous wavelet transform to identify the different timescales of variability: interannual (2-4yr), multiannual (5-8yr) and decadal (~15yr). Then, the different timescale of precipitation time series were extracted using maximum overlap discrete wavelet transform. For ...
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