利用趋势化随机参数场的地下水流数值模拟优化方法

水文地质参数场的刻画是建立地下水流数值模拟模型的关键问题和难点问题。通常来讲,参数场合理性程度越高,模型拟合精度越高。本次研究将随机方法和参数空间分布表达进行结合,提出了趋势化随机参数场的构建方法。以渗透系数为研究对象,首先利用MCMC采样和样本数据特征确定水文地质参数的基本数据结构,进而根据样本空间分布特征对其进行趋势化处理,最终形成趋势化的渗透系数场。通过算例分析,利用趋势化处理后的渗透系数场能够大幅提高模拟精度,相比传统赋均值方法其误差可降至原来的1/3。在北京大兴跌隆起地区进行的实例应用说明,趋势化渗透系数场对提升岩性粒径较大(中砂以上)地区模拟精度效果显著,案例中粗砂区域渗透系数经趋势化处理后平均拟合误差由2.76 m下降至0.64 m;而对岩性以细砂及以下粒径为主的区域模拟精度提升并不明显。总体来说,该方法可为地下水流数值模型的优化提供借鉴,提升模型拟合精度,从而更加合理地刻画地下水流系统。     

地下水数值模拟不确定性分析研究进展

地下水数值模拟不确定性分析旨在提高研究区域地下水流的模拟精度。学者们将地下水数值模拟不确定性分析分为:模型的不确定性、参数的不确定性以及资料的不确定性三类,其中参数的不确定性分析在研究中是最为重要的。同时,对模型、参数、资料不确定性分析的研究进展和成果进行归纳总结,补充关于参数不确定性分析过程中的替代模型的一些研究成果以及模型不确定性分析的多模型分析;强调在地下水数值模拟的过程中,重视模型、资料的不确定性分析;展望未来,随着地下水数值模拟不确定性分析研究的深入,方法和应用会更加多样化。     

西辽河平原地下水补给植被的临界埋深

许多地区地下水是生态稳定的重要支撑或补充,确定地下水补给地表植被的临界埋深对地下水管理和生态安全至关重要。以西辽河平原为例,主要研究成果如下:①根据地下水补给植被原理划分包气带水分运动结构,描述地下水补给植被的作用机理和物理过程,定义地下水补给植被的临界埋深及相关物理概念。②根据植被根系吸收潜水蒸发的物理机制,地下水面附近由毛管水上升形成的潜水影响层是临界埋深计算的关键。③推导土壤毛管有效孔径计算推理公式,通过构建土壤微结构模型求解推理公式中的孔隙特征参数,解决毛管水最大上升高度的精确计算难题;④结合不同群落植被根系层厚度,形成地下水补给植被的临界埋深计算模型。⑤通过野外调查和观测试验实证,表明计算结果可靠,研究成果在科尔沁草原得到了及时应用。     

面向生态的干旱半干旱地区区域地下水资源评价的方法体系

为了提高区域地下水资源评价的可靠性、扩展干旱半干旱地区面向生态的区域地下水资源评价方法,对准噶尔盆地和鄂尔多斯盆地地下水与生态环境关系进行调查研究,认为地下水具有重要的生态价值。目前在干旱半干旱地区区域地下水资源评价中对地下水的生态价值考虑不够,是造成地下水资源评价可靠性不高的原因之一。因此,提出了干旱半干旱地区地下水生态价值的概念,围绕可持续利用的地下水资源量与生态环境良性循环的目标,构建了面向生态的区域地下水资源评价方法体系。方法体系的基本思路是突出系统的观点和地表水与地下水统一评价、水质水量并重、地下水资源与其相关生态环境统一评价的3个原则;通过建立水文地质概念模型、集中参数型模型、分布参数型模型和基于地下水变化的生态环境综合评价4个模型,加上引进水量、水位与水质3个约束条件,以及开发基于GIS的地下水资源评价信息系统的平台,对地下水资源、调蓄能力与生态环境效益进行整体评价与预警。方法体系注重了地下水的资源和生态的双重属性,强调地下水资源与相关生态环境整体评价和预警的一体化,突出了模型体系与GIS技术在区域地下水资源评价和相关生态环境评价的有效集成。     

Groundwater recharge zones mapping using GIS: a case study in Southern part of Jordan Valley,Jordan

This paper aims at mapping the potential groundwater recharge zones in the southern part of Jordan Valley (JV). This area is considered as the most important part for agricultural production in Jordan. The methodology adopted in this study is based on utilizing the open ended SLUGGER-DQL score model, which was developed by Raymond et al (2009). Geographic information systems were used in this study to build up the different layers of this model and to create the potential groundwater recharge zones. Based on the generated SLUGGER-DQL potential map, it was found that about 70.8 % of the investigated area was categorized as high potential for groundwater recharge, 18.7 % as moderate, and 10.5 % as low potential for groundwater recharge. To validate the model results, sensitivity analysis was carried out to assess the influence of each model parameter on the obtained results. Based on this analysis, it was found that the slope parameter (S) is the most sensitive parameter among SLUGGER-DQL model parameters, followed by water level in summer (L), well density (D), water quality (Q), runoff availability (R), land use/land cover, geology (GE), whereas the lowest sensitive parameter is the geology parameter (GE). Moreover, the parameters R, D, and Q show the lowest effective weights. The effective weight for each parameter was found to differ from the assigned theoretical weight by SLUGGER-DQL index model.     

Application of differential evolutionary optimization methodology for parameter structure identification in groundwater modeling

Parameter structure identification is formulated in terms of solving an inverse problem, which allows for a determination of an appropriate level of parameter structure complexity, and the identification of its pattern and the associated parameter values. With the increasing complexity of parameter structure identification in groundwater modeling, demand for robust, fast, and accurate optimizers is on the rise among researchers from groundwater hydrology fields. A novel global optimizer, differential evolution (DE), has been proposed to solve the parameter-structure-identification problem. The Voronoi tessellation is adopted for the automatic parameterization. The stepwise regression method and the error covariance matrix are used to determine the optimal structure complexity. Numerical experiments with a continuous hydraulic conductivity distribution are conducted to demonstrate the proposed methodology. The results indicate that the DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters and mutation schemes implemented in the DE is employed to examine their influence on the objective function. The comparison between DE and genetic algorithm shows the advantage of DE in terms of robustness and efficiency. The proposed methodology is also applied to a real groundwater system, Pingtung Plain in Taiwan, and the properties of aquifers are successfully identified.     

无网格法在地下水水位预测中的应用

基于移动最小二乘理论的无网格法是近几年来兴起的一种新的数值计算方法,与有限元法相比,它的主要优点在于无需单元信息,只需节点信息。用无网格法构造了场函数,包括基函数和权函数的选取,形函数及其导数的计算。根据鞍山市首山区水文地质条件,建立了求解双层渗流二维平面系统的数学模型,详细推导了模型求解的无网格方程。应用已识别的参数,用无网格法对该数学模型进行了求解, 并对该区的地下水水位进行了预测,预测的水位与实际水位变化规律基本一致。     

基于MODFLOW参数不确定性的地下水水流数值模拟方法

考虑到模型不确定性引起的地下水数值模拟不确定性对模拟过程的影响,在简要介绍含水层水文地质参数变异性研究进展和地质统计学的基础上,基于常用的确定性地下水流数值模拟软件MODFLOW开发了MODFLOW-Gslib软件,相较于传统的数值模拟方法,将地质统计学与数值模拟结合的方法能够模拟非均质含水层中的参数变异性问题。将MODFLOW-Gslib软件运用于模拟实例中,选择常见的不确定性因素进行模拟,并对其模拟产生的数据进行统计分析,结果表明,软件转化后的参数符合水文地质参数不确定性的相关特征;与原模拟结果进行对比,该软件能够更加真实地刻画含水层参数变异性特征。     

The impact of tunneling construction on the hydrogeological environment of “Tseng-Wen Reservoir Transbasin Diversion Project” in Taiwan

Groundwater flow and the associated surface water flow are potential negative factors on underground tunnels. Early detection of environmental impacts on water resources is of significant importance to planning, design and construction of tunnel projects, as early detection can minimize accidents and project delays during construction. The groundwater modeling software package Groundwater Modeling System (GMS), which supports the groundwater numerical codes MODFLOW and FEMWATER, was utilized to determine the impact of tunneling excavation on the hydrogeological environment in a regional area around the tunnel and a local hot springs area, at the “Tseng-Wen Reservoir Transbasin Diversion Project”, in Taiwan. A hydrogeological conceptual model was first developed to simplify structures related to the site topography, geology and geological structure. The MODFLOW code was then applied to simulate groundwater flow pattern for the hydrogeological conceptual model in the tunnel area. The automated parameter estimation method was applied to calibrate groundwater level fluctuation and hydrogeological parameters in the region. Calibration of the model demonstrated that errors between simulated and monitored results are smaller than allowable errors. The study also observed that tunneling excavation caused groundwater to flow toward the tunnel. No obvious changes in the groundwater flow field due to tunnel construction were observed far away in the surrounding regions. Furthermore, the FEMWATER code for solving 3-D groundwater flow problems, in which hydrogeological characteristics are integrated into a geographic information system (GIS), is applied to evaluate the impact of tunnel construction on an adjacent hot spring. Simulation results indicated that the groundwater drawdown rate is less than the groundwater recharge rate, and the change to the groundwater table after tunnel construction was insignificant for the hot spring area. Finally, the groundwater flow obtained via the GMS indicated that the hydrogeological conceptual model can estimate the possible quantity of tunnel inflow and the impact of tunnel construction on the regional and local groundwater resources regime of the transbasin diversion project.     

利用EARTH模型计算河北栾城地下水垂向补给量

准确计算地下水的垂向入渗补给量是合理评价和利用地下水资源的基础.EARTH模型是一种集中参数的水文模型,可刻画水流在包气带中的运移过程,弥补黑箱模型的不足.以中国科学院栾城农业生态试验站的地下水位观测资料以及气象资料为基础,综合运用降水、蒸发、土壤水、地下水动态观测资料,利用EARTH模型计算了河北平原地下水垂向入渗补给量.计算结果表明,2003年1月1日至2005年8月31日期间,栾城农业生态试验站在降水量1404.1 mm、灌溉量1050.0 mm的条件下地下水入渗补给量为487.2 mm,平均年入渗补给量为182.6 mm, 占降水和灌溉总量(2454.1 mm)的19.9%.在模拟结果的基础上,对不同年份的降水量(含灌溉)和入渗补给量分布的对比分析表明,河北栾城地下水补给滞后现象明显,在研究时间段内峰值滞后18～35 d.     

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation–optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation–optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation–optimization process and also maintained high computation accuracy.     

GIS在地下水流模拟系统中应用研究

本文在研究孔隙地下水流可视化模拟系统框架的基础上,较详细地阐明了基于GIS的孔隙地下水流模拟概念模型的构建、模型的时间与空间离散、计算参数的自动赋值、模型的可视化拟合、模拟结果的可视化表达等的技术路线与实现的具体方法,基本实现了GIS与孔隙地下水流模拟模型的紧密集成以及孔隙地下水流模拟过程的可视化,为其它类型的地下水流可视化模拟系统的研究与开发提供了一些探索性的思路。     

An optimal procedure for identifying parameter structure and application to a confined aquifer

This study applies an optimal procedure to identify the spatial distribution of groundwater hydraulic conductivity for a confined aquifer in north Taiwan. The parameter structure is determined by the number of zones, zonation pattern, and an uniform hydraulic conductivity associated with each zone. The proposed optimal procedure uses the Voronoi diagram in describing zonation and applies simulated annealing algorithm to optimize its pattern and associated hydraulic conductivity. Three criteria are defined to stop the searching process, including the residual error, the parameter uncertainty, and the structure error. Observation hydraulic heads in years 2000 and 2001 and hydraulic conductivity value from pumping tests are used. The results show that the parameter structure with five zones conforms to the three criteria and, thus, is recommended for future groundwater simulation for the study site. Different heuristic algorithms may also play the role of simulated annealing to optimize the parameter structure. However, which optimization algorithm is more efficient is not discussed and requires further study.     

电阻率法和激发极化法在地下水勘查中的应用

依据提供的物性参数的不同,广泛应用于找水工作的电法手段可分为电阻率法和激发极化法两大类,其中电阻率法提供电阻率参数,主要解决与赋水有关的构造问题(如赋水层位或断裂构造)。方法上有常规电法、电磁法,可根据地区赋水部位的不同选择适当的方法。激发极化法则利用含水层的激发极化效应进一步确定目标层位或构造的赋水性。电阻率法和激发极化法相互配合,在找水工作中相得益彰。     

基于区间序列的GM(1.1)地下水水位预测

为探讨基于区间序列的GM（1.1）模型预测地下水水位的准确性和信息量,本文选取界点建模、转换建模和参数建模3种区间方法进行比较和分析,结果表明：界点的相对误差均值都小于0.01%,区间相对误差均值小于7%;区间序列的GM（1.1）模型具有一定的延迟效应和紊乱效应;实际水位值波动幅度较小时模型预测精度较高;参数建模的预测精度较优于界点建模和转换建模。与点预测相比,区间序列的GM（1.1）模型预测的地下水水位变化规律更有利于地下水资源管理与开发利用。     

基于非等间距序列GM(1,1)模型的地下水温度预测

地下水温度是地下水源热泵系统设计中的一个重要参数,它关系到热泵系统换热器的选型计算以及整个系统的优化设计,所以在地热井勘探和使用过程中,通过定期监测各层段地下水的温度,预测地下水温度随深度的变化规律,为地下水源热泵设计、运行、成井工艺等提供参考资料,具有非常重要的实用价值。基于灰色理论基础,应用西安市泾河开发区两口观测井30～108m的实测水温作为原始数据,构建非等间距序列GM(1,1)预测模型,预测其128～150m的水温变化情况。结果表明:模拟结果与实际值拟合好,该模型能够较好地预测地下水温度随埋藏深度的变化趋势,是分析地下水温垂向变化趋势的一种新途径。     

基于高斯过程回归的地下水模型结构不确定性分析与控制

目前针对模型结构不确定性的研究方法主要为贝叶斯模型平均方法,而该方法受到模型权重计算困难等影响,应用受限。基于数据驱动的模型结构误差统计学习方法最近得到关注。研究采用高斯过程回归方法对地下水模型结构误差进行统计模拟,并将DREAMzs算法与高斯过程回归相结合,对地下水模型和统计模型的参数同时进行识别。基于此方法,分别以理想岩溶裂隙海水入侵过程和溶质运移柱体实验为例,进行地下水数值模拟及预测结果的不确定性分析。相对于不考虑模型结构误差条件的不确定性分析,结果表明,考虑结构误差之后,能够明显减少参数识别过程中的参数补偿影响,且能显著提高模型的预测性能。因此,基于高斯过程回归的模型结构不确定性分析可以一定程度控制地下水数值模拟的不确定性,提高模型预测可靠性。     

Sensitivity Analysis of a Combined Groundwater Flow and Solute Transport Model Using Local-Grid Refinement: A Case Study

Combining groundwater flow models with solute transport models represents a common challenge in groundwater resources assessments and contaminant transport modeling. Groundwater flow models are usually constructed at somewhat larger scales (involving a coarser discretization) to include natural boundary conditions. They are commonly calibrated using observed groundwater levels and flows (if available). The groundwater solute transport models may be constructed at a smaller scale with finer discretization than the flow models in order to accurately delineate the solute source and the modeled target, to capture any heterogeneity that may affect contaminant migration, and to minimize numerical dispersion while still maintaining a reasonable computing time. The solution that is explored here is based on defining a finer grid subdomain within a larger coarser domain. The local-grid refinement (LGR) implemented in the Modular 3D finite-difference ground-water flow model (MODFLOW) code has such a provision to simulate groundwater flow in two nested grids: a higher-resolution sub-grid within a coarse grid. Under the premise that the interface between both models was well defined, a comprehensive sensitivity and uncertainty analysis was performed whereby the effect of a parameter perturbation in a coarser-grid model on transport predictions using a higher-resolution grid was quantified. This approach was tested for a groundwater flow and solute transport analysis in support of a safety evaluation of the future Belgian near-surface radioactive waste disposal facility. Our reference coarse-grid groundwater flow model was coupled with a smaller fine sub-grid model in two different ways. While the reference flow model was calibrated using observed groundwater levels at a scale commensurate with that of the coarse-grid model, the fine sub-grid model was used to run a solute transport simulation quantifying concentrations in a hypothetical well nearby the disposal facility. When LGR coupling was compared to a one-way coupling, LGR was found to provide a smoother flow solution resulting in a more CPU-efficient transport solution. Parameter sensitivities performed with the groundwater flow model resulted in sensitivities at the head observation locations. These sensitivities identified the recharge as the most sensitive parameter, with the hydraulic conductivity of the upper aquifer as the second most sensitive parameter in regard to calculated groundwater heads. Based on one-percent sensitivity maps, the spatial distribution of the observations with the highest sensitivities is slightly different for the upper aquifer hydraulic conductivity than for recharge. Sensitivity analyses were further performed to assess the prediction scaled sensitivities for hypothetical contaminant concentrations using the combined groundwater flow and solute transport models. Including all pertinent parameters into the sensitivity analysis identified the hydraulic conductivity of the upper aquifer as the most sensitive parameter with regard to the prediction of contaminant concentrations.     

水文地质概念模型的水动力场研究方法——"流场分析法"

水文地质概念模型建立的正确与否是地下水资源评价计算的关键,水文地质概念模型的水动力场研究方法——“流场分析法”是以地下水动力学和水均衡原理为基础,通过对水头函数的时空分布特征分析,判断出地下水系统的结构和功能,据此,科学概化出地下水系统的水文地质概念模型,为地下水资源的正确评价计算奠定基础。     

DREAM算法分析地下水数值模拟不确定性的影响因素

地下水数值模拟常受到模型不确定性、观测资料不确定性等多种不确定性因素的影响,对这些影响因素进行定量分析十分必要。将差分进化自适应梅特罗波利斯(Differential Evolution Adaptive Metropolis,DREAM)算法与MODFLOW模型结合应用于地下水数值模拟不确定性的定量分析。以模型结构概化、水位观测资料误差这两种重要不确定性来源为例,通过一个理想地下水流模型,系统分析两者对模型参数及模型输出结果不确定性的影响。研究结果表明:模型结构概化及水位观测资料误差共同引起了地下水数值模拟的不确定性,但模型结构概化起到了主控作用。模型结构概化合理时,模型参数及模型输出结果的不确定性较小,并随观测资料误差不确定性的增大而增大;模型结构概化不合理时,模型参数及模型输出结果主要受控于模型结构概化带来的影响,且不确定性显著增大;观测资料误差相同情况下,模型结构概化越接近于真实的水文地质条件,模型参数及模型输出结果的不确定性越小。