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.     

Seasonal Changes in Gonad Index,Biochemical Composition and Heavy Metal Determination of Sea Urchin Paracentrotus lividus Gonads from the South Coast of Morocco

Ocean Science Journal - Seasonal variations of the gonad index (GI), the biochemical composition, and the concentration of heavy metals in the gonads of the sea urchin, Paracentrotus lividus...     

Integration of multiple soft data sets in MPS thru multinomial logistic regression: a case study of gas hydrates

A new approach is described to allow conditioning to both hard data (HD) and soft data for a patch- and distance-based multiple-point geostatistical simulation. The multinomial logistic regression is used to quantify the link between HD and soft data. The soft data is converted by the logistic regression classifier into as many probability fields as there are categories. The local category proportions are used and compared to the average category probabilities within the patch. The conditioning to HD is obtained using alternative training images and by imposing large relative weights to HD. The conditioning to soft data is obtained by measuring the probability–proportion patch distance. Both 2D and 3D cases are considered. Synthetic cases show that a stationary TI can generate non-stationary realizations reproducing the HD, keeping the texture indicated by the TI and following the trends identified in probability maps obtained from soft data. A real case study, the Mallik methane-hydrate field, shows perfect reproduction of HD while keeping a good reproduction of the TI texture and probability trends.     

Development of functionalized carrageenan,chitosan and alginate as polymeric chelating ligands for water softening

Chitosan, carrageenan and alginate are among the most abundant biopolymers in nature. They were prepared in uniform beads shape with a diameter of 2 mm ± 10%, using the encapsulator for removal of calcium, magnesium and iron cations from hard water. Solutions of 100–500 mg/L were prepared from each cation, and the detection of cations was carried out using atomic absorption spectrometer. Carrageenan and chitosan were able to chelate the three cations without further modification. However, alginate beads succeeded to chelate iron and magnesium and failed to chelate any calcium ions; in contrast, it increased the initial calcium concentration! That could be due to the pre-cross-linking of alginate beads using calcium chloride solution, which may be leaked back to the solution. However, grafting the alginate beads with polyethyleneimine and bromoacetic acid rectified this problem and the new functional group, –COOH, has been proved using the FT-IR. Optimization of the results in terms of beads weight (0.25–3.0 g) and cations concentrations (100–500 mg/L) has shown that most biopolymeric beads can chelate 85–100% of the cations in concentrations up to 500 mg/L. According to our finding, we came up with the recommendation to use chitosan for chelation of calcium and iron as it showed 100% chelation efficiency of both cations, whereas carrageenan is highly recommended for chelation of iron and magnesium, as it showed 100 and 98% chelation efficiency, respectively. Further work can be done on the reusability of the beads and scale up for the industrial use.     

The Relationship between Fe Mineralization and Magnetic Basement Faults using Multifractal Modeling in the Esfordi and Behabad Areas (BMD), Central Iran

Multifractal modeling is a mathematical method for the separation of a high potential mineralized background from a non-mineralized background. The Concentration-Distance to Fault structures (C-DF) fractal model and the distribution of the known iron (Fe) deposits/mines seen in the Esfordi and Behabad 1:100,000 sheets from the Bafq region of central Iran are used to distinguish Fe mineralization based on their distance to magnetic basement structures and surface faults, separately, using airborne geophysical data and field surveys. Application of the C-DF fractal model for the classification of Fe mineralizations in the Esfordi and Behabad areas reveals that the main ones show a correlation with their distance from magnetic basement structures. Accordingly, the distances of Fe mineralizations with grades of Fe higher than 55% )43% < Fe ≤ 60%) are located at a distance of less than 1 km, whereas for surfacial faults with grades of 43% ≤ Fe ≤ 60%, the distances are 3162< DF ≤ 4365 m from the faults. Thus, there is a positive relationship between Fe mineralization and magnetic basement structures. Also, the proximity evidence of Precambrian high-grade Fe mineralization related to magnetic basement structures indicates syn-rifting tectonic events. Finally, this C-DF fractal model can be used for exploration of magmatic and hydrothermal ore deposits.     

Geology and petrography of the Nagar Parkar igneous complex,southeastern Sindh,Pakistan: The Kharsar body

Kharsar hill is one of many granitic plutons comprising the Nagar Parkar igneous complex. The eastern part of the hill is occupied by grey-pink granite (earlier) and the western part by pink granite (later). They are composed of perthite, quartz, and plagioclase, with minor opaque oxide, biotite, titanite, local amphibole, and secondary chlorite, epidote, leucoxene/titanite. The pink granite is characterized by the presence of mafic clots. Both the granitoids are intruded by microgranite/aplite, and porphyritic mafic and rhyolite dykes, locally in swarms. These are abundant in a NE trending 200 m wide zone cutting the entire granite hill. The dykes may extend over 1 km in length and >10 m in thickness, but most are < 100 m in length. The felsic dykes are of several generations; some are associated with the two varieties of granite, others are contemporaneous with the rhyolite and mafic dykes. The mafic dykes can be grouped into two types one of which contains hornblende and the other augite as the principal mafic mineral. Major element analyses suggest that the granitic rocks are metaluminous. The Kharsar granites, like the others in Nagar Parkar, may be an extension of the Malani igneous suite of Rajasthan. The occurrence of bimodal mafic-felsic dykes and petrographic variation in the mafic dykes are briefly discussed.     

Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh

Natural Hazards - Flooding is one of the natural disasters that affect the livelihood of the people living in the floodplains, like Bangladesh. Here, we proposed to employ SAR satellite images in...     

Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran

Globally, landslides cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year. A landslide susceptibility map describes areas where landslides are likely to occur in the future by correlating some of the principal factors that contribute to landslides with the past distribution of landslides. A case study is conducted in the mountainous northern Iran. In this study, a landslide susceptibility map of the study area was prepared using bivariate method with the help of the geographic information system. Area density (bivariate) method was used to weight landslide-influencing data layers. An overlay analysis is carried out by evaluating the layers obtained according to their weight and the landslide susceptibility map is produced. The study area was classified into five hazard classes: very low, low, moderate, high, and very high. The percentage distribution of landslide susceptibility degrees was calculated. It was found that about 26% of the study area is classified as very high and high hazard classes.