Programs for calibration-based Monte Carlo simulation of recharge areas.

One use of groundwater flow models is to simulate contributing recharge areas to wells or springs. Particle tracking can be used to simulate these recharge areas, but in many cases the modeler is not sure how accurate these recharge areas are because parameters such as hydraulic conductivity and recharge have errors associated with them. The scripts described in this article (GEN_LHS and MCDRIVER_LHS) use the Python scripting language to run a Monte Carlo simulation with Latin hypercube sampling where model parameters such as hydraulic conductivity and recharge are randomly varied for a large number of model simulations, and the probability of a particle being in the contributing area of a well is calculated based on the results of multiple simulations. Monte Carlo simulation provides one useful measure of the variability in modeled particles. The Monte Carlo method described here is unique in that it uses parameter sets derived from the optimal parameters, their standard deviations, and their correlation matrix, all of which are calculated during nonlinear regression model calibration. In addition, this method uses a set of acceptance criteria to eliminate unrealistic parameter sets.

Using atmospheric tracers to reduce uncertainty in groundwater recharge areas.

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.