Continuous Improvement of a Groundwater Model over a 20‐Year Period: Lessons Learned

Groundwater models developed for specific sites generally become obsolete within a few years due to changes in: (1) modeling technology; (2) site/project personnel; (3) project funding; and (4) modeling objectives. Consequently, new models are sometimes developed for the same sites using the latest technology and data, but without potential knowledge gained from the prior models. When it occurs, this practice is particularly problematic because, although technology, data, and observed conditions change, development of the new numerical model may not consider the conceptual model's underpinnings. As a contrary situation, we present the unique case of a numerical flow and trichloroethylene (TCE) transport model that was first developed in 1993 and since revised and updated annually by the same personnel. The updates are prompted by an increase in the amount of data, exposure to a wider range of hydrologic conditions over increasingly longer timeframes, technological advances, evolving modeling objectives, and revised modeling methodologies. The history of updates shows smooth, incremental changes in the conceptual model and modeled aquifer parameters that result from both increase and decrease in complexity. Myriad modeling objectives have included demonstrating the ineffectiveness of a groundwater extraction/injection system, evaluating potential TCE degradation, locating new monitoring points, and predicting likelihood of exceedance of groundwater standards. The application emphasizes an original tenet of successful groundwater modeling: iterative adjustment of the conceptual model based on observations of actual vs. model response.     

Uncertainty analysis in statistical modeling of extreme hydrological events

With the increase of both magnitude and frequency of hydrological extreme events such as drought and flooding, the significance of adequately modeling hydrological extreme events is fully recognized. Estimation of extreme rainfall/flood for various return periods is of prime importance for hydrological design or risk assessment. However, due to knowledge and data limitation, uncertainty involved in extrapolating beyond available data is huge. In this paper, different sources of uncertainty in statistical modeling of extreme hydrological events are studied in a systematic way. This is done by focusing on several key uncertainty sources using three different case studies. The chosen case studies highlight a number of projects where there have been questions regarding the uncertainty in extreme rainfall/flood estimation. The results show that the uncertainty originated from the methodology is the largest and could be >40% for a return period of 200 years, while the uncertainty caused by ignoring the dependence among multiple hydrological variables seems the smallest. In the end, it is highly recommended that uncertainty in modeling extreme hydrological events be fully recognized and incorporated into a formal hydrological extreme analysis.     

New Ways of Using Well Construction Reports for Hydrostratigraphic Analyses

Hydrostratigraphic models require subsurface data. Those data can be gleaned from Well Construction Reports (WCRs) if the reports are properly reviewed and processed. In the United States, WCRs or similar documents are generally required to be submitted to a state agency. The quality of those reports varies considerably and depends on the water well contractor's level of geologic knowledge. Nevertheless, these types of reports are usually the largest available subsurface data set. We present two examples from Wisconsin, each of which had approximately 2000 WCRs, to demonstrate a variety of new applications of WCRs including obtaining information about the depositional environment; selecting sites to perform surface geophysical measurements; and creating three‐dimensional hydrostratigraphic models for groundwater modeling.     

Reactive-transport modeling of fly ash–water–brines interactions from laboratory-scale column studies

Dynamic leaching tests are important studies that provide more insights into time-dependent leaching mechanisms of any given solid waste. Hydrogeochemical modeling using PHREEQC was applied for column modeling of two ash recipes and brines generated from South African coal utility plants, Sasol and Eskom. The modeling results were part of a larger ash–brine study aimed at acquiring knowledge on (i) quantification and characterization of the products formed when ash is in contact with water–brines in different scenarios, (ii) the mineralogical changes associated with water–brine–ash interactions over time, (iii) species concentration, and (iv) leaching and transport controlling factors. The column modeling was successfully identified and quantified as important reactive mineralogical phases controlling major, minor and trace elements’ release. The pH of the solution was found to be a very important controlling factor in leaching chemistry. The highest mineralogical transformation took place in the first 10 days of ash contact with either water or brines, and within 0.1 m from the column inflow. Many of the major and trace elements Ca, Mg, Na, K, Sr, S(VI), Fe, are leached easily into water systems and their concentration fronts were high at the beginning (within 0.1 m from the column inflow and within the first 10 days) upon contact with the liquid phase. However, their concentration decreased with time until a steady state was reached. Modeling results also revealed that geochemical reactions taking place during ash–water–brine interactions does affect the porosity of the ash, whereas the leaching processes lead to increased porosity. Besides supporting experimental data, modeling results gave predictive insights on leaching of elements which may directly impact on the environment, particularly ground water. These predictions will help develop scenarios and offer potential guide for future sustainable waste management practices as a way of addressing the co-disposal of brines within inland ash dams and heaps.     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

基于自适应区间二型模糊聚类的遥感土地覆盖自动分类

遥感影像土地覆盖分类面临"类别密度差异显著"、"同谱异物"和"同物异谱"等不确定性问题,传统的分类方法(如FCM)因不能描述高阶模糊不确定性,无法完成准确建模,使分类误差较大,而二型模糊集恰是处理此类不确定性的有效工具.在引入二型模糊集新概念和自适应降型新方法的基础上,提出一种自适应二型模糊分类方法(A-IT2FCM):(1)基于样本集模糊距离度量构建面向分类的区间二型模糊集,以尽可能降低对先验知识和预设参数的依赖,从而满足自动分类的要求;(2)给出一种自适应探求等价一型代表(模糊)集合的高效降型方法,在此基础上进行自适应区间二型模糊聚类.实验数据为珠海横琴和北京颐和园的SPOT5影像数据,对比方法有AIT2FCM、基于Karnik-Mendel算法降型和基于Tizhoosh提出的简易降型方法的区间二型模糊C均值聚类以及作者前期研究提出的区间值模糊C-均值算法(IV-FCM).实验结果表明,A-IT2FCM方法分类效果佳,在类别具有较大密度差异和多重模糊性时能得到比FCM及IV-FCM更精确的边界和更连贯的类别,适于处理遥感影像土地覆盖类别的深层不确定性;同时在"光谱混叠"现象严重时,可以获得比对比方法更稳健、精度更高的影像自动分类结果,且时间复杂度明显低于基于Karnik-Mendel方法.     

Novel theoretical insights into geomorphic process–environment relationships using simulated response curves

Robust models of geomorphic process–environment relationships are important to advance theoretical knowledge of geomorphic systems. Here, we examined a generalized additive modeling (GAM) based approach to provide new theoretical insights into process–environment relationships. More precisely, we (i) simulated the shapes of the relationships between geomorphic processes and environmental variables based on GAM and (ii) compared the shapes of the simulated response curves to (a) the hypothetical curves based on theory and (b) the response curves produced by generalized linear modeling (GLM). Hitherto, GLM was the most common technique to study the relationships between environmental gradients and geomorphic processes. The study is based on empirical cryoturbation and solifluction data and environmental variables from subarctic Finland. Our results showed that non‐linear relationships were more common than linear responses and the simulated GAM based response curves coincided more closely with the hypothetical response curves than did the response curves derived from GLM. The simulated response curves showed high potential in geomorphic hypothesis testing. In conclusion, our findings indicate that careful examination of the response curves may provide new insights into theoretical debates in the earth sciences. Copyright © 2010 John Wiley and Sons, Ltd.     

A unified approach to environmental systems modeling

The paper considers the differences between hypothetico-deductive and inductive modeling: between modelers who put their primary trust in their scientific intuition about the nature of an environmental model and tend to produce quite complex computer simulation models; and those who prefer to rely on the analysis of observational data to identify the simplest form of model that can represent these data. The tension that sometimes arises because of the different philosophical outlooks of these two modeling groups can be harmful because it tends to fractionate the effort that goes into the investigation of important environmental problems, such as global warming. In an attempt to improve this situation, the paper will outline a new Data-Based Mechanistic (DBM) approach to modeling that tries to meld together the best aspects of these two modeling philosophies in order to develop a unified approach that combines the hypothetico-deductive virtues of good scientific intuition and simulation modeling with the pragmatism of inductive data-based modeling, where more objective inference from data is the primary driving force. In particular, it demonstrates the feasibility of a new method for complex simulation model emulation, in which the methodological tools of DBM modeling are used to develop a reduced dynamic order model that represents the ‘dominant modes’ of the complex simulation model. In this form, the ‘dynamic emulation’ model can be compared with the DBM model obtained directly from the analysis of real data and any tensions between the two modeling approaches may be relaxed to produce models that suit multiple modeling objectives.     

Assessment of global TEC mapping using a three-dimensional electron density model

Dual-frequency transmissions from the Global Positioning System satellites can be used to measure and map ionospheric total electron content (TEC) on global scales. Using data exclusively from ground-based GPS networks, global ionosphere mapping has been successfully applied using either two- or three-dimensional techniques. Two-dimensional TEC maps retrieve a horizontally-varying distribution of total electron content, assuming a fixed vertical electron density profile. In three-dimensional mapping, both the horizontal and vertical distribution density are adjusted to fit the data. We describe a three-dimensional TEC mapping algorithm that uses three independent constant-density slabs stacked vertically to model the electron density, and compare with a more conventional two-dimensional approach using a single slab. One apparent benefit of the new method is reduction in a level error of the TEC maps, which decreased by 1.7 TECU using the three-dimensional retrieval on simulated data (1 TEC Unit corresponds to 10¹⁶ electrons/m²). Another benefit of the multilayer approach is improved slant TEC modeling. Using actual data from an equatorial site at Cocos Islands (96.8E, 12.2 S), three slab modeling improved estimates of slant TEC by a factor of 2 for elevation angles between 10 and 20° (9 versus 4.4 TECU, root-mean-square). However, the global structure of the vertical TEC retrievals we analyzed did not improve using three-dimensional modeling. This may be due to a critical approximation shared by both techniques that TEC persists unchanged at a given local time. This assumption is required to produce global maps from observations acquired from widely scattered ground receivers. Further improving the retrieval of global TEC structure with ground-based data probably requires improved dynamical models of TEC behavior. New data available from GPS receivers in low Earth orbit is also promising.     

Simulation/optimization modeling for robust pumping strategy design

A new simulation/optimization modeling approach is presented for addressing uncertain knowledge of aquifer parameters. The Robustness Enhancing Optimizer (REO) couples genetic algorithm and tabu search as optimizers and incorporates aquifer parameter sensitivity analysis to guide multiple-realization optimization. The REO maximizes strategy robustness for a pumping strategy that is optimal for a primary objective function (OF), such as cost. The more robust a strategy, the more likely it is to achieve management goals in the field, even if the physical system differs from the model. The REO is applied to trinitrotoluene and Royal Demolition Explosive plumes at Umatilla Chemical Depot in Oregon to develop robust least cost strategies. The REO efficiently develops robust pumping strategies while maintaining the optimal value of the primary OF-differing from the common situation in which a primary OF value degrades as strategy reliability increases. The REO is especially valuable where data to develop realistic probability density functions (PDFs) or statistically derived realizations are unavailable. Because they require much less field data, REO-developed strategies might not achieve as high a mathematical reliability as strategies developed using many realizations based upon real aquifer parameter PDFs. REO-developed strategies might or might not yield a better OF value in the field.     

Optimal Estimation of Two-Dimensional Contaminant Transport

The state-space estimation technique presented herein provides a method for obtaining optimal estimates of concentrations for two-dimensional plumes in ground water. The concentration of a plume was defined as the state variable. The technique uses the Kalman filter and involves combining two independent estimates of plume concentrations. One estimate is called the process modeling data and the other is called the “measurement” data. The process modeling data is obtained from a numerical model. The “measurement” data is obtained from field measurements; however, for illustration in this paper it was generated by a different numerical model than the one used to obtain the process modeling data. The state-space technique produces a distribution of contaminant concentrations that is more accurate than either of the distributions generated by the process modeling or the “measurement” data. An example is presented to show that the technique produces significant improvements in the prediction of plume concentration distributions.     

冲绳海槽南段与中、北段构造活动性对比的热模拟研究

受新生代太平洋板块弧后扩张剧烈活动的地缘特性影响,冲绳海槽构造特征复杂,南、中、北段在热液活动、断裂性质、火成岩特性、扩张时代等方面存在显著不同,因此认识该区各段构造活动性对查清其复杂地质特征具有重要意义.本文依据前人通过磁异常反演得到的居里面深度资料,利用热模拟的方法,对冲绳海槽各段深、浅构造活动性进行了探讨.模拟结果表明,南段软流层构造活动强度约为中、北段的6倍,而岩石圈浅层构造活动却相对较弱.该结论与前人所得到的地质地球物理资料相符,主要表现为:相对于中、北段,南段在海槽总体演化历程上裂陷较深;海底火成岩岩浆源区较深,结晶分异程度较弱,同化混染程度较强;切穿沉积基底的大型断裂较为发育,而沉积层内部的小型断裂分布相对稀疏;沉积层岩浆侵入活动较弱,海底所呈现出的热液活动区数量较少;现代地震活动较多,震源深度较大.根据模拟结果与实际资料的对比分析我们可以推测:(1)冲绳海槽北段可能还有一些热液区没有被探测到,也可能在历史演化进程中失去活力,或者被第四系沉积物覆盖;(2)南段存在孕育更多热液活动区的潜力.     

Relevance of Deterministic Structures for Modeling of Transport: The Lauswiesen Case Study

Knowledge of site‐specific contaminant transport processes is an essential requirement for performing various tasks concerning the protection and management of groundwater resources. However, prediction of their behavior is often difficult, especially in heterogeneous aquifers because of the lack of information about flow‐ and transport‐governing subsurface structures and parameters. Hence, stochastic approaches have been developed and frequently used. However, extensive modeling studies on sedimentary structures have shown that consideration of hydrogeological subunits and their distribution can be essential for transport modeling. A case study from the intensely investigated Lauswiesen site is used to demonstrate that more accurate predictions are possible with improved knowledge of deterministic structures. Results of this case study using direct‐push injection logging (DPIL) provide a more reliable characterization of hydraulic conductivity than sieve and flow meter data.     

Using Stereo Images For Digital Terrain Modeling

More and more applications can benefit from having 3Dinformation or 3D terrain knowledge. After describingthe general principles of digital terrain modeling, weconcentrate on the matching step used in thestereoscopic process. Limitations encouraged us toextend the approach to multiple views, in order toautomate further the digital terrain modeling.But other ways are also possible to produce 3D terraininformation such as using different imagery (videosequences or SAR imagery) or topographic maps. Finally,when dealing with the DTM (Digital Terrain Model) orDEM (Digital Elevation Model) produced, one often hasto cope with fusion and mosaicking problems.     

Multiscale characterization of a heterogeneous aquifer using an ASR operation

Heterogeneity in the physical properties of an aquifer can significantly affect the viability of aquifer storage and recovery (ASR) by reducing the recoverable proportion of low-salinity water where the ambient ground water is brackish or saline. This study investigated the relationship between knowledge of heterogeneity and predictions of solute transport and recovery efficiency by combining permeability and ASR-based tracer testing with modeling. Multiscale permeability testing of a sandy limestone aquifer at an ASR trial site showed that small-scale core data give lower-bound estimates of aquifer hydraulic conductivity (K), intermediate-scale downhole flowmeter data offer valuable information on variations in K with depth, and large-scale pumping test data provide an integrated measure of the effective K that is useful to constrain ground water models. Chloride breakthrough and thermal profiling data measured during two cycles of ASR showed that the movement of injected water is predominantly within two stratigraphic layers identified from the flowmeter data. The behavior of the injectant was reasonably well simulated with a four-layer numerical model that required minimal calibration. Verification in the second cycle achieved acceptable results given the model's simplicity. Without accounting for the aquifer's layered structure, high precision could be achieved on either piezometer breakthrough or recovered water quality, but not both. This study demonstrates the merit of an integrated approach to characterizing aquifers targeted for ASR.     

Estimation of tail dependence coefficient in rainfall accumulation fields

Extreme rainfall events are of particular importance due to their severe impacts on the economy, the environment and the society. Characterization and quantification of extremes and their spatial dependence structure may lead to a better understanding of extreme events. An important concept in statistical modeling is the tail dependence coefficient (TDC) that describes the degree of association between concurrent rainfall extremes at different locations. Accurate knowledge of the spatial characteristics of the TDC can help improve on the existing models of the occurrence probability of extreme storms. In this study, efficient estimation of the TDC in rainfall is investigated using a dense network of rain gauges located in south Louisiana, USA. The inter-gauge distances in this network range from about 1 km to 9 km. Four different nonparametric TDC estimators are implemented on samples of the rain gauge data and their advantages and disadvantages are discussed. Three averaging time-scales are considered: 1 h, 2 h and 3 h. The results indicate that a significant tail dependency may exist that cannot be ignored for realistic modeling of multivariate rainfall fields. Presence of a strong dependence among extremes contradicts with the assumption of joint normality, commonly used in hydrologic applications.     

Groundwater Modeling in Integrated Water Resources Management—Visions for 2020

Groundwater modeling is undergoing a change from traditional stand-alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater-related geophysical data, geologic models, as well as a national groundwater-surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.     

Fast modeling of cross-covariances in the LMC: A tool for data integration

Although modeling of cross-covariances by fitting the linear model of coregionalization (LMC) is considered a cumbersome task, cross-covariances are the key for integration of data for multiple attributes in environmental hydrology, aquifer and reservoir characterizations using multivariate geostatistics. This paper proposes a novel method of modeling cross-covariances in the linear model of coregionalization (LMC). The classic minimum/maximum autocorrelation factors (MAF) method is analyzed and found to be a good tool to discriminate the elementary nested structures of directional sample covariance matrices. Thus, separate modeling of the scalar sample covariance for each MAF factor may allow to obtain the complete LMC model for the original attributes after a back rotation of the diagonal model covariance matrix of directional factors. However, such a back rotation is not computable following the classic MAF formulation. This paper introduces an ambi-rotational minimum/maximum autocorrelation factors (AMAF) method that allows a back and forth double rotation of the directional diagonal model covariance matrix for factors. This approach provides a device for modeling of the full matrix of directional covariance and cross-covariance for the original attributes in the LMC without recurring to iterations. In this way, the use of multivariate geostatistics for data integration is allowed avoiding collocated approaches or rotation and modeling of data factor scores. The method is illustrated with an example for covariances for three attributes.