Abstract
A quantitative estimation of the sustainability of groundwater resources is a challenge for water supply in most regions of the world. Sustainability can be estimated through numerical simulation, but uncertainty of models somehow limits the reliability of models' predictions. To overcome this limitation, we explore how parametric and predictive uncertainties is reduced by adding complementary groundwater and surface water dynamics data to a lumped parameter model. This is illustrated with the example of a Mediterranean karst hydrosystem pumped at high flow rate (∼1 m3 s-1) to supply water to about 350,000 habitants. A lumped parameter model was set up to simulate spring discharge, water table levels and surface water discharge measured within this karst hydrosystem over a 40 year period. Then, a parameter estimation and uncertainty quantification, based on groundwater discharge, water table levels and surface water discharge observations, was conducted with a Bayesian approach. Our study illustrates how the consideration of complementary hydrological data allows reducing parametric and predictive uncertainty and improving model performance. The model is then applied to assess the impact of an increasing groundwater abstraction according to different prospective scenarios, with a focus on the storage and dynamic of flux between the different compartments of the karst hydrosystem. Based on simulation results and related predictive uncertainty, a groundwater abstraction threshold, which must not be exceeded for a sustainable management of the groundwater resource, is determined.