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Determining a wellhead protection area (WHPA) is a basic and important step in protecting groundwater from contamination. Based on availability of data and complexity of hydrogeological conditions, different delineation methods can be adopted. This study's objective was to examine the relation and difference of the results calculated using six WHPA delineation methods, which ranged from simplified shapes to stochastic models with superior sampling means. All the methods were applied to a well field in Beijing, China. The WHPA probability distribution from a stochastic simulation was used as reference for comparison with other methods. WHPAs calculated using simplified and analytical approaches turned out to be smaller than reference WHPA. Those calculated using the numerical model provided better results but still cannot include a considerable area of reference WHPA. The stochastic approach based on efficient orthogonal Latin hypercube sampling provided an estimation of the probability distribution of WHPA delineation with different degrees of uncertainty, which played an important role in the WHPA delineation of the well field in Beijing. To attain a realistic WHPA delineation in a complex aquifer system, various delineation approaches can be adopted and all results that validate each other must be considered. [ABSTRACT FROM AUTHOR]

Stochastic modeling is a rapidly evolving, popular approach to the study of the uncertainty and heterogeneity of groundwater systems. However, the use of Monte Carlo-type simulations to solve practical groundwater problems often encounters computational bottlenecks that hinder the acquisition of meaningful results. To improve the computational efficiency, a system that combines stochastic model generation with MODFLOW-related programs and distributed parallel processing is investigated. The distributed computing framework, called the Java Parallel Processing Framework, is integrated into the system to allow the batch processing of stochastic models in distributed and parallel systems. As an example, the system is applied to the stochastic delineation of well capture zones in the Pinggu Basin in Beijing. Through the use of 50 processing threads on a cluster with 10 multicore nodes, the execution times of 500 realizations are reduced to 3% compared with those of a serial execution. Through this application, the system demonstrates its potential in solving difficult computational problems in practical stochastic modeling. [ABSTRACT FROM AUTHOR]