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.

MAROS 是首个基于Windows 平台利用Microsoft Access 和Microsoft Excel 开发出来的用于地下水污染监测网优化设计的决策支持系统软件。该软件通过统计分析监测区域有关数据在时空上的相关分析结果，来确定监测网在时间上的取样频率和空间上的分布密度，从而保证以最省的监测费用获得地下水污染物随时间和空间变化的最全面信息。本文简单地介绍了MAROS 的设计原理和模块组成，并对该软件的优缺点进行了评述。

This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5.

随机水文地质学方法，较传统的确定性方法而言，是解决非均质含水层中水流和溶质运移问题的一种更为合理的手段。据以往研究，假设渗透系数场遵循对数正态分布，利用直接傅立叶变换方法来生成渗透系数随机场。应用基于随机理论的蒙特卡罗方法，来研究渗透系数的空间变异性对污染物运移结果的影响。实例研究表明，污染物在含水层中运移过程中污染羽的展布范围（二阶矩） 随着渗透系数空间变异方差的增大而扩大，而污染羽在空间上的质心位置（一阶矩） 基本不受方差的影响，仅取决于渗透系数随机场的均值大小。另外还分析了污染羽在各点的浓度变化方差和变异系数分别随渗透系数变异方差的变化状况。

长期过量开采地下水所引发的地面沉降是一个复杂的非线性变化过程，对其进行数值模拟时，如不考虑沉降发展过程中模型参数的非线性变化，会给预测带来很大误差。本文依据苏锡常地区实际资料，通过典型钻孔土力学试验，确定出土体在不同荷载条件下的弹塑性和蠕变特征方程以及土体渗透系数随孔隙度变化的关系式。在此基础上建立了研究区三维变系数地下水流模型及考虑土体垂向不同变化特征（弹塑变或蠕变） 的一维区域沉降模型，并采用多尺度有限单元法对模型进行了真正意义上的耦合求解。

分别将禁忌搜索和遗传算法与地下水流模型MODFLOW 和地下水溶质运移模型MT3DMS相耦合，并将其应用于求解地下水资源优化管理模型。在概述两种智能算法基本原理和地下水管理模型组成的基础上，结合两个理想的应用实例，从优化结果和计算效率两个方面对禁忌搜索和遗传算法进行了对比分析。在两个实例中，禁忌搜索分别以高于遗传算法10 倍和27 倍的计算效率得到了减少抽水流量约160m3/d和节约治理成本约47万元的治理方案。结果表明，禁忌搜索在求解地下水管理模型中具有较好的应用前景。