Validation of numerical ground water models used to guide decision making

Many sites of ground water contamination rely heavily on complex numerical models of flow and transport to develop closure plans. This complexity has created a need for tools and approaches that can build confidence in model predictions and provide evidence that these predictions are sufficient for decision making. Confidence building is a long-term, iterative process and the author believes that this process should be termed model validation. Model validation is a process, not an end result. That is, the process of model validation cannot ensure acceptable prediction or quality of the model. Rather, it provides an important safeguard against faulty models or inadequately developed and tested models. If model results become the basis for decision making, then the validation process provides evidence that the model is valid for making decisions (not necessarily a true representation of reality). Validation, verification, and confirmation are concepts associated with ground water numerical models that not only do not represent established and generally accepted practices, but there is not even widespread agreement on the meaning of the terms as applied to models. This paper presents a review of model validation studies that pertain to ground water flow and transport modeling. Definitions, literature debates, previously proposed validation strategies, and conferences and symposia that focused on subsurface model validation are reviewed and discussed. The review is general and focuses on site-specific, predictive ground water models used for making decisions regarding remediation activities and site closure. The aim is to provide a reasonable starting point for hydrogeologists facing model validation for ground water systems, thus saving a significant amount of time, effort, and cost. This review is also aimed at reviving the issue of model validation in the hydrogeologic community and stimulating the thinking of researchers and practitioners to develop practical and efficient tools for evaluating and refining ground water predictive models.     

Developing a long-term monitoring network under uncertain flowpaths

A long-term monitoring well network is developed using complementary and simple approaches in conjunction with a stochastic ground water flow and transport model. The development is illustrated for a case study of a U.S. nuclear testing site (Shoal) that is undergoing environmental restoration. The network design builds on three different, yet complementary, tools for locating the monitoring wells with a main objective of detection monitoring. The first tool is applied to select potential siting horizons where monitoring wells could be located. The second tool is used to place monitoring wells in locations with high success probability. The success here is defined as the detection of migrating stochastic plumes before a certain mass percentage reaches a compliance boundary. The third tool is used to analyze detection efficiency of multiple combinations of three wells. Seventy-six different three-well networks are selected from 20 candidate locations and are evaluated for detection efficiency. From the 76 networks analyzed, 28 attain detection efficiency close to or above 70%. The results of the different analyses provide multiple alternatives for the locations of the three wells, which will become part of the long-term monitoring network at Shoal. A number of combinations are equally good, and the final choice will depend on practical considerations and future agreements between model sponsor and regulators.     

Station-keeping for a translunar communication station

A translunar communication station is to be kept close to a nominal unstable periodic ‘Halo’ orbit, visible at all times from Earth. The analytically computed nominal orbit is not perfect, requiring an average control acceleration of about 10^?6 g's for tight control. An adjustable quadratic combination of position deviation and control acceleration is minimized to provide an (adjustable) control law with period feedback gains and a periodic bias. The average control acceleration can be reduced to less than 10^?8 g's with an error settling time of less than 21/2 months. The resulting limiting motion provides, in turn, an improved nominal, permitting the same low control cost with much tighter control, corresponding to settling times of the order of one day.     

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Theoretical and Applied Climatology - Snow is a key element for many socioeconomic activities in mountainous regions. Due to the sensitivity of the snow cover to variations of temperature and...     

DHI evaluation by combining rock physics simulation and statistical techniques for fluid identification of Cambrian-to-Cretaceous clastic reservoirs in Pakistan

The use of seismic direct hydrocarbon indicators is very common in exploration and reservoir development to minimise exploration risk and to optimise the location of production wells. DHIs can be enhanced using AVO methods to calculate seismic attributes that approximate relative elastic properties. In this study, we analyse the sensitivity to pore fluid changes of a range of elastic properties by combining rock physics studies and statistical techniques and determine which provide the best basis for DHIs. Gassmann fluid substitution is applied to the well log data and various elastic properties are evaluated by measuring the degree of separation that they achieve between gas sands and wet sands. The method has been applied successfully to well log data from proven reservoirs in three different siliciclastic environments of Cambrian, Jurassic, and Cretaceous ages. We have quantified the sensitivity of various elastic properties such as acoustic and extended elastic (EEI) impedances, elastic moduli (K _sat and K _sat–μ), lambda–mu–rho method (λρ and μρ), P-to-S-wave velocity ratio (V _P/V _S), and Poisson’s ratio (σ) at fully gas/water saturation scenarios. The results are strongly dependent on the local geological settings and our modeling demonstrates that for Cambrian and Cretaceous reservoirs, K _sat–μ, EEI, V _P/V _S, and σ are more sensitive to pore fluids (gas/water). For the Jurassic reservoir, the sensitivity of all elastic and seismic properties to pore fluid reduces due to high overburden pressure and the resultant low porosity. Fluid indicators are evaluated using two metrics: a fluid indicator coefficient based on a Gaussian model and an overlap coefficient which makes no assumptions about a distribution model. This study will provide a potential way to identify gas sand zones in future exploration.     

Compositional time series analysis for Air Pollution Index data

The increasing importance of understanding the structure of Air Pollution Index (API) makes it necessary to come out with a compositional of API based on its pollutants. This will be more comprehensible for the public and easier to cooperate with authorities in reducing the causes of air pollution. Since five pollutants contribute in determining the API values, API can be shown as a compositional data. This study is conducted based on the data of API and its pollutants collected from Klang city in Malaysia for the period of January 2005 to December 2014. The proportion of each pollutant in API is considered as a component with five components in a compositional API. The existence of zero components in some pollutants, that have no effect on API, is a serious problem that prevents the application of log-ratio transformation. Thus, the approach of amalgamation has been used to combine the components with zero in order to reduce the number of zeros. Also, a multiplicative replacement has been utilized to eliminate the zero components and replace them with a small value that maintains the ratios of nonzero components. Transforming the compositional data to log-ratio coordinates has been done using the additive log ratio transformation, and the transformed series is then modeled by using a VAR model. Four criteria are used to determine the number of lags p of VAR(p) and these are: the Akaike Information, the Schwartz, the Hannan–Quinn and the Final Prediction Error criteria. Based on the results, A VAR (1) model with no constants or trend is considered as the best fitted model and it is used to forecast 12 months ahead. In addition, API values are mainly determined by PM₁₀ that has a proportion close to one most of the time during study period. Therefore, authorities and researchers need to study the sources of PM₁₀ and provide the public with useful information and alternatives in term of reducing the air pollution.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

A Genetic Programming–Based Model for Colloid Retention in Fractures

Understanding the behavior of colloids in groundwater is critical as some are pathogenic while others may facilitate or inhibit the transport of dissolved contaminants. Colloid behavior in saturated fractured aquifers is governed by the physical and chemical properties of the groundwater-particle-fracture system. The interaction between these properties is nonlinear, and there is a need for a mathematical model describing the relationship between them to advance the mechanistic understanding of colloid transport in fractures and facilitate modeling in fractured environments. This paper coupled genetic programming and linear regression within a multigene genetic programming framework to develop a robust mathematical model describing the relationship between colloid retention in fractures and the physical and chemical parameters that describe the system. The data employed for model development and validation were collected from a series of 75 laboratory-scale colloid tracer experiments conducted under a range of conditions in three laboratory-induced discrete dolomite fractures and their epoxy replicas. The model sufficiently reproduced the observed data with coefficients of determination (R²) of 0.92 and 0.80 for model development and validation, respectively. A cross-validation demonstrated the model generality to 86% of the observed data. A variance-based global sensitivity analysis confirmed that attachment is the primary retention mechanism in the systems employed in this work. The model developed in this study provides a tool describing colloid retention in factures, which furthers the understanding of groundwater-particle-fracture system conditions contributing to the retention of colloids and can aid in the design of groundwater remediation strategies and development of groundwater management plans.     

Influence of Relative Density on Static Soil–Structure Frictional Resistance of Dry and Saturated Sand

Soil–structure frictional resistance is required while designing foundation systems and retaining walls. Although much more attention has been paid in recent years regarding soil–structure interaction for dynamic loading, highly conservative values of the static frictional resistance between soil and structure are used in design. Not much emphasis has been given lately to evaluate static frictional resistance between soil and structure. In this study, a well graded sand, as per USCS classification system, was prepared in the laboratory at different relative densities and moisture contents i.e. dry and saturated, and frictional resistances of those soils were measured. Those soil samples were also sheared against wood, concrete, and steel blocks and corresponding soil–structure frictional resistances were measured. Moreover, similar experiments were performed for saturated and loose poorly graded sand (SP), silty sand (SM) and poorly graded sand with silt (SP–SM). The study result shows that the difference between frictional resistance of soil and skin friction depends on the type of soil, relative density and the moisture content. Interestingly, shear envelopes for soil–soil and soil–structure shearing resistance exhibited curvature. The traditionally adopted soil–structure frictional resistance values adopted by various geotechnical manuals were found to be highly conservative.     

Identifying Groundwater Potential in Crystalline Basement Rocks Using Remote Sensing and Electromagnetic Sounding Techniques in Central Western Mozambique

Exploring for groundwater in crystalline rocks in semiarid areas is a challenge because of their complex hydrogeology and low potential yields. An integrated approach was applied in central western Mozambique, in an area covered by Precambrian crystalline basement rocks. The approach combined a digital elevation model (DEM), remote sensing, and a ground-based geophysical survey. The aim was to identify groundwater zones with high potential and to identify geological structures controlling that potential. Lineaments were extracted from the DEM that had been enhanced using an adaptive-tilt, multi-directional, shading technique and a non-filtering technique to characterize the regional fracture system. The shallowness and amount of stored groundwater in the fracture zones was assessed using vegetation indices derived from Landsat 8 OLI images. Then, 14 transient electromagnetic (TEM) survey profiles were taken in different geological settings across continuous lineaments that were considered to be aligned along inferred faults. In the central lineament zones, the TEM soundings gave resistivity values of less than 300 Ωm at a depth of 20–80 m. The values varied with location. Conversely, values greater than 400 Ωm were observed at the sites away from the central zones. This contrast is probably caused by the differences in permeability and degree of weathering along the fractured zones. These differences could be key factors in determining groundwater occurrence. By integrating five water-related factors (lineament density, slope, geology, vegetation index, and proximity to lineaments), high groundwater potential zones were located in the vicinity of the lineaments. In these zones, vegetation remains active regardless of the season.