Bayesian Data Fusion (BDF) of Monitoring Data with a Statistical Groundwater Contamination Model to Map Groundwater Quality at the Regional Scale — Oak Academic Publishing
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Bayesian Data Fusion (BDF) of Monitoring Data with a Statistical Groundwater Contamination Model to Map Groundwater Quality at the Regional Scale
Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
1 Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
2 Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
3 Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
4 Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
5 Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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.
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