UNCERTAINTY ASSESSMENT OF GROUNDWATER QUALITY INDEX USING SEQUENTIAL INDICATOR SIMULATION

A probabilistic approach, utilizing geostatistics, is proposed to assess groundwater contamination. The study case was a monitoring network of 40 wells located in an about 1300 km2 area of a pilot basin in southern Italy. Sequential Indicator Simulation (SIS) was applied to spatialise the groundwater quality data, using categorical lithological data as auxiliary variable. Standardized entropy was calculated to assess prediction uncertainty. Post-processing of one thousand simulations produced two kinds of maps: groundwater quality class with the largest probability of occurrence and prediction uncertainty. The results showed a general poor quality of the groundwater, mostly in the southern part of the study area, characterized by intensive farming practices. The two maps, jointly used, could effectively support water managers' activities.