A fracture-karst aquifer is a karst aquifer with afractured rock matrix, and its parameters are difficult to determine. Two sequential pumping tests in a fracturekarst aquifer system at the Zhangji well field of China are considered, one carried out before (in 2000) and one after (in 2005) the operation of a pumping station in the well field (2003-2005). The sequential tests serve multiple purposes. First, they provide a cross check of the parameters obtained. Second, they can be used to assess the effect of long-term groundwater exploitation of the aquifer. A three-dimensional finite-element transient flow model has been developed to simulate groundwater flow at the site. Generally good agreement has been found between the simulated and observed hydraulic heads for both tests. The hydraulic parameters obtained from the 2005 test are generally consistent with their counterparts from the 2000 test. However, a small but steady increase of hydraulic conductivities from 2000 to 2005 at the site has been observed. A 10-year prediction of groundwater resources has been made and indicates that the well field can accommodate the proposed 8.0×104m3/day exploitation rate under relative drought conditions without causinga steady decline of groundwater levels.

Gaoqing Plain is a major agriculture center of Shandong Province in northern China. Over the last 30 years, the diversion of Yellow River water for intensive irrigation in Gaoqing Plain has led to elevation of the water table and increased evaporation, and subsequently, adramatic increase in salt content in soil and rapid degradation of crop productivity. Optimal strategies have been explored, that will balance the need to extract sufficient groundwater for irrigation (to ease the pressure on diverting Yellow River water) with the need to improve the local environment by appropriately lowering the water table. Two simulation-optimization models have been formulated and a genetic algorithm (GA) is applied to search for the optimal groundwater development strategies in Gaoqing Plain, while keeping the adverse environmental impacts in check. Compared with the trial-and-error approach of previous studies, the optimization results demonstrate that using an optimization model coupled with a GA search is both effective and efficient. The optimal solutions identified by the GA will provide Gaoqing Plain with the blueprints for developing sustainable groundwater abstraction plans to support local economic development and improve its environmental quality.ción con las técnicas de ensayo-y-error de estudios anteriores, los resultados de esta optimización demuestran que usando un modelo de optimización acoplado con una búsqueda mediante GA, es tanto eficaz como eficiente. Las soluciones óptimas identificadas por el GA, proporcionarán las bases para desarrollar los planes de extracción sustentable delagua subterránea en las llanuras de Gaoqing, los cuales darán apoyo al desarrollo económico localy mejorarán su calidad medioambiental.

This paper presents the design, architecture and implementation of a general parallel computing platform, termed PGO, based on the Genetic Algorithm (GA) for global optimization. PGO provides an efficient and easy-to-use framework for parallelizing the global optimization procedure for general scientific modeling and simulation processes. Along with a core optimization kernel built on a GA, PGO also includes a general input generator and an output extractor that can facilitateits easy integration with various scientific computing tasks. In this paper, we demonstrate the efficiency and versatility of PGO with two different applications: (1) the parallelization of a large scale parameter estimation problem associated with modeling water flow in a heterogeneous deep vadose zone; (2) the parallelization of a complex simulation-optimization procedure for searching for an optimal groundwater remediation design. PGO is developed as an open source code, and is independent of the computer operating system. It has been tested in a heterogeneous computing environment consisting of Solaris 9, Fedora Core 2 Linux, and Microsoft Windows machines, and is freely available for download from.

应用地质统计方法研究渗透系数场的空间变异性。利用MMR 含水层场地实测数据，通过去类分析、特异值处理、 正态变换，逐步逼近研究区渗透系数的稳健变差函数，得到三维渗透系数场的几何各向异性套合模型。在此基础上，采用普通克里格法和指示克里格法、高斯序列模拟法和指示序列模拟法分别对数据进行插值和条件模拟。最后结合具体的地质条件，对四种方法在渗透系数场生成中的应用进行对比分析和评价。

A new simulation–optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and aglobal mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OKbased method and results in more potential cost savings. However, the OK-based method leads tomore accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model.