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.
Highlights
Assessing the quality of groundwater is a prerequisite for designing sustainable water management strategies e.g. when implementing the Water Framework Directive [1]
The first presented mapping method relies on the interpolation of the measured nitrate concentrations through ordinary kriging
About 66.5% of the study area has kriged nitrate concentrations lower than the standard of 50 mg/L, while nitrate concentrations exceeding 80 mg/L are observed in some parts of the study area (Figure 2(a))
Summary
Assessing the quality of groundwater is a prerequisite for designing sustainable water management strategies e.g. when implementing the Water Framework Directive [1]. Groundwater quality is, a spatially distributed attribute. It is generally accepted that knowledge of the spatial distribution of this attribute allows the design of site specific protection and remediation measures. Robust and validated techniques are needed to map the spatial distribution of groundwater quality within the groundwater body continuum. Properties related to groundwater quality, such as nitrate concentration, can only directly be measured at the local scale. The mapping of groundwater quality within the water body continuum will often build on data interpolation or prediction of properties related to groundwater quality through deterministic or stochastic models, and the adopted technique will impact the results of the final assessment
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