Inverse modeling of different stimuli and hydraulic tomography: A laboratory sandbox investigation

•HT survey underscores its superiority to inverse modeling of other natural events.•Conditional Monte Carlo simulation addresses uncertainty in validating results.•Conditional effective K from natural gradient flow as prior improves HT results. The limited area of influence in highly permeable aquifers hampers hydraulic tomography (HT) surveys with traditional pumping tests. Few have suggested that flow data under natural stimuli could complement HT. Verifying this conjecture in controlled laboratory sandbox experiments does not exist. Similarly, many have employed independent pumping events to validate inverse modeling results, but few have explored the validation uncertainty. This study first conducted sandbox experiments to investigate the effectiveness of head data from HT, natural gradient (NG), and precipitation/infiltration (PI) events for estimating hydraulic conductivity (K) field. Conditional Monte Carlo simulation of independent pumping tests then addresses the uncertainty of validating these estimated K fields. The effectiveness of the estimates from NG and PI as prior information for HT was investigated next. Cross-correlation analysis, exploring the relationship between the observed heads and K heterogeneity under different stimuli, then assesses the usefulness of flow data under different stimuli and their possibility for complementing HT as prior information. Estimates from NG and PI events as the mean of the prior probability distribution for HT then corroborate the cross-correlation analysis. Conditional Monte Carlo simulation of twelve independent pumping tests further confirms that a decent NG's K estimate as the mean for the prior probability distribution for HT inversion yields the highest resolution of K estimates.