Estimating the impacts of climate change on groundwater represents one of the most difficult challenges faced by water resources specialists. One difficulty is that simplifying the representation of the hydrological system, or using too simple climate change scenarios often leads to discrepancies in projections. Additionally, these projections are affected by uncertainties from various sources, and these uncertainties are not evaluated in previous studies. In this context, the objective of this study is to provide an improved methodology for the estimation of climate change impact on groundwater reserves, including the evaluation of uncertainties. This methodology is applied to the case of the Geer basin catchment (480 km²) in Belgium. A physically-based surface-subsurface flow model has been developed for the Geer basin with the finite element model HydroGeoSphere. The simultaneous solution of surface and subsurface flow equations in HydroGeoSphere, as well as the internal calculation of the actual evapotranspiration as a function of the soil moisture at each node of the defined evaporative zone, improve the representation and calibration of interdependent processes like recharge, which is crucial in the context of climate change. Fully-integrated surface-subsurface flow models have recently gained attention, but have not been used in the context of climate change impact studies. This surface-subsurface flow model is combined with advanced climate change scenarios for the Geer basin. Climate change simulations were obtained from six regional climate model (RCM) scenarios assuming the SRES A2 greenhouse gases emission (medium-high) scenario. These RCM scenarios were statistically downscaled using two different methods: the 'Quantile Mapping Biased Correction' technique and a 'Weather Generator' technique. Both of them are part of the most advanced downscaling techniques. They are able to apply corrections not only to the mean of climatic variables, but also across the statistical distributions of these variables. This is important as these distributions are expected to change in the future, with more violent rainfall events, separated by longer dry periods. The 'quantile mapping bias-correction' technique generate climate change time series representative of a stationary climate for the periods 2011-2040, 2041-2070 and 2071-2100. The 'CRU' weather generator is used to generate a large number of equiprobable scenarios simulating full transient climate change between 2010 and 2085. All these scenarios are applied as input of the Geer basin model. The uncertainty is evaluated from different possible sources. Using a multi-model ensemble of RCMs and GCMs enables to evaluate the uncertainty linked to climatic models. The application of a large number of equiprobable climate change scenarios, generated with the 'weather generator', as input of the hydrological model allows assessing the uncertainty linked to the natural variability of the weather. Finally, the uncertainty linked to the calibration of the hydrological model is evaluated using the computer code 'UCODE_2005'. The climate change scenarios for the Geer basin model predict hotter and drier summers and warmer and wetter winters. Considering the results of this study, it is very likely that groundwater levels and surface flow rates in the Geer basin will decrease. This is of concern because it also means that groundwater quantities available for abstraction will also decrease. However, this study also shows that the uncertainty surrounding these projections is relatively large and that it remains difficult to state on the intensity of the decrease.