title: Using atmospheric tracers to reduce uncertainty in groundwater recharge areas.
abstract: A Monte Carlo-based approach to assess uncertainty in recharge areas shows that incorporation of atmospheric tracer observations (in this case, tritium concentration) and prior information on model parameters leads to more precise predictions of recharge areas. Variance-covariance matrices, from model calibration and calculation of sensitivities, were used to generate parameter sets that account for parameter correlation and uncertainty. Constraining parameter sets to those that met acceptance criteria, which included a standard error criterion, did not appear to bias model results. Although the addition of atmospheric tracer observations and prior information produced similar changes in the extent of predicted recharge areas, prior information had the effect of increasing probabilities within the recharge area to a greater extent than atmospheric tracer observations. Uncertainty in the recharge area propagates into predictions that directly affect water quality, such as land cover in the recharge area associated with a well and the residence time associated with the well. Assessments of well vulnerability that depend on these factors should include an assessment of model parameter uncertainty. A formal simulation of parameter uncertainty can be used to delineate probabilistic recharge areas, and the results can be expressed in ways that can be useful to water-resource managers. Although no one model is the correct model, the results of multiple models can be evaluated in terms of the decision being made and the probability of a given outcome from each model.
authors.affiliation: U.S. Geological Survey, 101 Pitkin Street, East Hartford, CT 06108, USA. jjstarn@usgs.gov
authors.forename: J Jeffrey
authors.initials: JJ
authors.lastname: Starn
authors.forename: Amvrossios C
authors.initials: AC
authors.lastname: Bagtzoglou
authors.forename: Gary A
authors.initials: GA
authors.lastname: Robbins
mesh_terms: D001272:Atmosphere / Q000737:chemistry; D002138:Calibration; D003237:Connecticut; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D014316:Tritium / Q000032:analysis; D035501:Uncertainty; D014872:Water Movements; D014881:Water Supply / Q000032:analysis*
chemical_list: D014316:Tritium