Reducing Uncertainty of Groundwater Redox Condition Predictions at National Scale, for Decision Making and Policy.

Abstract

Understanding hydrogeochemical heterogeneity, associated with natural nitrate attenuation, is an integral part of implementing integrated land and water management on a regional or national scale. Redox conditions are a key indicator of naturally occurring denitrification in the groundwater environment, and often used to inform spatial planning and targeted regulation. This work describes the development of a statistical redox condition model for the groundwater environment at a national scale, using spatially variable physiochemical descriptors as predictors. The proposed approach builds on previous work, by complementing the available data with expert knowledge, in the form of synthetic data. Special care is given so that the synthetic data do not overfit and create further imbalances to the training dataset. The predictor dataset is further complemented by the results of a data driven model of the water table developed for this study, which is used both as a predictive parameter and a reference level for groundwater redox condition predictions at different depths. The developed model predicted the redox class for 84% of the samples in the out-of-bag datasets. We also propose an alternative approach for the communication of prediction uncertainty. We use the concept of a discriminate function to identify model classifications that may be ambiguous. Our results show a marked reduction in prediction uncertainty at shallow depths, with uncertainty in reduced environments decreasing from 76 to 12%, and overall uncertainty reduced by approximately 20%, though improvements at greater depths are less pronounced. We conclude that this approach can highlight robust model predictions that are defendable for decision making and can identify areas where monitoring or sampling efforts can be focused for improved outcomes.