We estimate the travel time of percolating water through a deep vadose zone at the regional scale using a transfer function model and a physical based conceptual flow model (Hydrus-1D), thereby exploiting the time series of precipitation, actual evapotranspiration and groundwater piezometry and generic vadose zone data. With the transfer function model we observe a high variability of estimated travel time varying from 0.9 to 3.1 years, corresponding to estimated vertical water flux velocities varying from 6.6 to 28.0 m/year. These results were compared with the travel time estimated from the physical based conceptual model. With the flow model, estimated travel time varies between 4.7 and 15.5 years, corresponding to water flux velocities varying between 1.7 and 4.1 m/year. The estimated travel time calculated with the flow model were therefore about five times larger than those estimated with the transfer function model. This could be explained by the fact that the transfer function model considers heterogeneous recharge from the vadose zone as well as from the vicinity of the piezometer through the so called "pushing effect". In addition, the flow model requires various hydrogeological and hydrodynamic parameters which were estimated using generic parametrisation approaches, that are largely affected by uncertainty and may not reflect the local conditions. In contrast, the transfer function model only exploits available measurable time series and has the advantage of being site-specific.

No abstract

Groundwater contamination by nitrate within an unconfined sandy aquifer was mapped using a Bayesian Data Fusion (BDF) framework. Groundwater monitoring data was therefore combined with a statistical groundwater contamination model. In a first step, nitrate concentrations, measured at 99 monitoring stations irregularly distributed within the study area, were spatialized using ordinary kriging. Secondly, a statistical regression tree model of nitrate contamination in groundwater was constructed using land use, depth to the water table, altitude and slope as predictor variables. This allowed the construction of a regression tree based contamination map. In a third step, BDF was used to combine optimally the kriged nitrate contamination map with the regression tree based model into one single map, thereby weighing the kriged and regression tree based contamination maps in terms of their estimation uncertainty. It is shown that BDF allows integrating different sources of information about contamination in a final map, allowing quantifying the expected value and variance of the nitrate contamination estimation. It is also shown that the uncertainty in the final map is smaller than the uncertainty from the kriged or regression tree based contamination map

Isotopic fingerprinting is an advanced technique allowing the classification of the nitrate source pollution of groundwater, but needs further development and validation. In this study, we performed measurements of natural stable isotopic composition of nitrate ((15)N and (18)O) in the groundwater body of the Brussels sands (Belgium) and studied the spatial and temporal dynamics of the isotope signature of this aquifer. Potential nitrogen sources sampled in the region had isotopic signatures that fell within the corresponding typical ranges found in the literature. For a few monitoring stations, the isotopic data strongly suggest that the sources of nitrate are from mineral fertiliser origin, as used in agriculture and golf courses. Other stations suggest that manure leaching from unprotected stockpiles in farms, domestic gardening practices, septic tanks and probably cemeteries contribute to the nitrate pollution of this groundwater body. For most monitoring stations, nitrate originates from a mixing of several nitrogen sources. The isotopic signature of the groundwater body was poorly structured in space, but exhibited a clear temporal structure. This temporal structure could be explained by groundwater recharge dynamics and cycling process of nitrogen in the soil-nitrogen pool.

No abstract