Probabilistic mapping of groundwater nitrate pollution using a Bayesian Gaussian process model

Nitrate groundwater pollution threatens the quality of drinking water and is directly linked to intensive fertiliser inputs on agricultural fields. To reduce pollution from agricultural sources, areas with, or at risk of, elevated nitrate concentrations must be designated as Nitrate Vulnerable Zones (NVZs) under the European Nitrates Directive. In Germany, as elsewhere in Europe, the designation of NVZs follows a binary classification scheme that does not account for uncertainties in the underlying data and interpolation method. We present an alternative geostatistical framework that explicitly introduces uncertainties into the established designation framework, enabling a more accurate assessment of nitrate groundwater pollution. Using a Bayesian Gaussian process model, nitrate concentrations in groundwater were predicted across the federal state of Brandenburg, Germany, where nitrate pollution is an acute problem. Our model specifically incorporates measurement errors as well as systematic biases from different observation types. The model allows for the calculation of exceedance probabilities which provides a continuous representation of nitrate pollution risk across space, relative to the legal nitrate limit of 50 mg/L. We show that the majority of agricultural land in the study area has at least a 50% probability of exceeding this limit. Additionally, measurement errors were identified as the main source of uncertainty in estimated nitrate concentrations, leading to relatively wide posterior predictive distributions. The results indicate that areas with high exceedance probability extend beyond currently designated NVZs. Unlike the established designation workflow, the proposed approach accounts for the complex reality and uncertainty of nitrate pollution in groundwater and can be readily extended to other countries in the EU and beyond. This enables a more robust and transparent designation of NVZs, and demonstrates the value of explicitly incorporating uncertainty into environmental modelling in high-profile policy settings.