An approach to aquifer vulnerability including uncertainty in a spatial random function framework

- Current vulnerability assessment methods do not include uncertainty quantification.
- DRASTIC indices are amenable to be treated in a geostatistical framework.
- The suite of methods proposed is parameter, type and amount of data dependent.
- The method is extended to include uncertainty assessment and degree of confidence.
- Application is straightforward combining geostatistical methods in a GIS environment.

SummaryWe introduce a methodology to include uncertainty into DRASTIC (Aller et al., 1987), the most commonly used method to define aquifer vulnerability. For this purpose, we explore separately all seven DRASTIC indices, and analyze how uncertainty can be introduced into each one of them, by proposing a simple decision tree to select the most appropriate geostatistical methods depending on the amount and type of available data. We further analyzed the implications of introducing the concept of degree of confidence, based on the estimated point variances, into the estimates of some of the indices. The methodology is illustrated by mapping the DRASTIC indices in an aquifer located in northeast Spain. It is concluded that the final vulnerability index may vary substantially depending on the degree of confidence accepted in the analysis, indicating the possibility of extending the deterministic version of DRASTIC to a stochastic one, where uncertainty is directly accounted for.