Resilience of a statistical sampling scheme

Abstract Most statistical sampling algorithms on hydrologic random fields assume that the new measurements will agree reasonably well with their predicted values. This in turn implies the stationarity of the estimated covariance function. In order to test the reliability of one such statistical algorithm (i.e., variance reduction analysis), noisy input data are generated, and results of sampling from these data are compared to the case of sampling with the unperturbed data. These comparisons and a related regret analysis reveal that the effects of the noisy data are primarily accommodated by adjustments to the covariance function parameters, while selected sets show a high degree of resilience. Variance reduction analysis seems to be a reliable method for maximizing information by sampling random fields with an unstable parameter space but a resilient action space.