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The aim of this work is to understand the exchange of water between the Serra Geral aquifer system (SGAS) and Guarani aquifer system (GAS). The objectives are two-fold. First, introduce the capability of the modified self-organizing maps (MSOM) as an unbiased nonlinear approach to estimate missing values of hydrochemistry and hydraulic transmissivity associated with the SGAS, a transboundary groundwater system spanning parts of four South American countries. Second, identify areas with potential connectivity of the SGAS with the GAS based on analysis of the spatial variability of key elements and comparison with current conceptual models of hydraulic connectivity. The MSOM is employed to calculate correlations (trends) between 27 variables from 1,132 wells. Hydraulic transmissivity is calculated from specific capacity values from well-pump tests in 157 locations. Hydrochemical facies estimates appear unbiased and consistent with current conceptual-connectivity models indicating that vertical fluxes from GAS are influenced by geological structure. The MSOM provides additional spatial estimates revealing new areas with likely connections between the two aquifer systems.

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibiraci, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively...

Numerical models provide a way to evaluate groundwater systems, but determining the hydrostratigraphic units (HSUs) used in constructing these models remains subjective, nonunique, and uncertain. A three-step machine-learning approach is proposed in which fusion, estimation, and clustering operations are performed on different data sets to arrive at HSUs at different scales. In step one, data fusion is performed by training a self-organizing map (SOM) with sparse borehole hydrogeologic (lithology, hydraulic conductivity, aqueous field parameters, dissolved constituents) and geophysical (gamma, spontaneous potential, and resistivity) measurements. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Application of the Davies-Bouldin criteria to k-means clustering of SOM nodes is used to determine the number and location of discontinuous borehole HSUs with low lateral density (based on borehole spacing at 100 s m) and high vertical density (based on cm-scale logging). In step two, a scaling network is trained using the estimated borehole HSUs, airborne electromagnetic measurements, and numerically inverted resistivity profiles. In step three, independent airborne electromagnetic measurements are applied to the scaling network, and the estimation performed to arrive at a set of continuous HSUs with high lateral density (based on sounding locations at meter (m) spacing) and medium vertical density (based on m-layer modeled structure). Performance metrics are used to evaluate each step of the approach. Efficacy of the proposed approach is demonstrated to map local-to-regional scale HSUs using hydrogeophysical data collected at a heterogeneous surficial aquifer in northwestern Nebraska, USA.

The objectives of this research were to determine the role of preferential flow paths in the transport of atrazine (2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine) and dicamba (3-6-dichloro-2-methoxybenzoic acid) through silt and loam soils overlying the High Plains aquifer in Nebraska. In a previous study, 3 of 6 study areas demonstrated high percentages of macropores; those three areas were used in this study for analysis of chemical transport. As a subsequent part of the study, 12 intact soil cores (30-cm diameter by 40-cm height), were excavated sequentially, two from each of the following depths: 0-40cm and 40-80cm. These cores were used to study preferential flow characteristics using dye staining and to determine hydraulic properties. Two undisturbed experimental field plots, each with a 3-m2 surface area, were installed in three study areas in Nebraska. Each was instrumented with suction lysimeters and tensiometers at depths of 10cm to 80cm in 10-cm increments. Additionally, each plot was planted with corn (Zea mays). A neutron probe access tube was installed in each plot to determine soil water content at 15-cm intervals. All plots were enclosed with a raised frame (of 8-cm height) to prevent surface runoff. All suction lysimeters were purged monthly for three months and were sampled immediately prior to pre-plant herbicide application to obtain background chemical concentrations. Atrazine and dicamba moved rapidly through the soil, but only after a heavy rainfall event, probably owing to the presence of preferential flow paths and lack of microbial degradation in these soil areas. Staining of laboratory cores showed a positive correlation between the percent area stained by depth and the subsequent breakthrough of Br- in the laboratory and leaching of field-applied herbicides owing to large rainfall events. Suction lysimeter samples in the field showed increases in concentrations of herbicides at depths where laboratory data indicated greater percentages of what appeared to be preferential flow paths. Concentrations of atrazine and dicamba exceeding 0.30 and 0.05µg m1-1 were observed at depths of 10-30cm and 50-70cm after two months following heavy rainfall events. It appears from the laboratory experiment that preferential flow paths were a significant factor in transport of atrazine and dicamba. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government or GNS.

A hybrid modeling approach is proposed for near real-time three-dimensional (3D) mapping of surficial aquifers. First, airborne frequency-domain electromagnetic (FDEM) measurements are numerically inverted to obtain subsurface resistivities. Second, a machine-learning (ML) algorithm is trained using the FDEM measurements and inverted resistivity profiles, and borehole geophysical and hydrogeologic data. Third, the trained ML algorithm is used together with independent FDEM measurements to map the spatial distribution of the aquifer system. Efficacy of the hybrid approach is demonstrated for mapping a heterogeneous surficial aquifer and confining unit in northwestern Nebraska, USA. For this case, independent performance testing reveals that aquifer mapping is unbiased with a strong correlation (0.94) among numerically inverted and ML-estimated binary (clay-silt or sand-gravel) layer resistivities (5–20 ohm-m or 21–5,000 ohm-m), and an intermediate correlation (0.74) for heterogeneous (clay, silt, sand, gravel) layer resistivities (5–5,000 ohm-m). Reduced correlation for the heterogeneous model is attributed to over-estimating the under-sampled high-resistivity gravels (about 0.5 % of the training data), and when removed the correlation increases (0.87). Independent analysis of the numerically inverted and ML-estimated resistivities finds that the hybrid procedure preserves both univariate and spatial statistics for each layer. Following training, the algorithms can map 3D surficial aquifers as fast as leveled FDEM measurements are presented to the ML network.

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009-2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.

The joint inversion of receiver function (RF) and surface-wave dispersion data is popular because it reduces the nonuniqueness of the modeled subsurface- seismic velocities. Whereas various inverse procedures have been used in joint subsur- face imaging, the evaluation of uncertainties in the estimated parameter distribution is usually overlooked or considered qualitatively. We present a quantitative method for determination of uncertainty in velocity models estimated by the joint inversion of RF and surface-wave data by using the prediction of each observation sample to map the objective-function surface and create a statistical distribution of estimated model parameters. The proposed methodology is evaluated in a controlled test using syn- thetic data simulating a realistic shear-wave velocity model. We then apply the method to field data recorded at the POPB seismic station in the Parana Basin, southeast Brazil. The respective range of uncertainty for modeled S-wave velocity distribu- tions in the synthetic and field tests were 0:111-0:412 km=s (2.5%-9.2%), and 0:110-0:341 km=s (2.5%-7.9%).

A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km^2 Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km^2 fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km^2 transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.

The objective of this research was to determine the process of atrazine transport compared to bromide and δO18 transport in sands near Denver. Three 1.5 × 2 × 1.5-m plots were installed and allowed to equilibrate for 2 years before research initiation and were instrumented with 1.5 × 2-m zero-tension pan lysimeters installed at 1.5-m depths. Additionally, each plot was instrumented with suction lysimeters, tensiometers, time domain reflectometry (TDR) moisture probes, and thermocouples (to measure soil temperature) at 15-cm depth increments. All plots were enclosed with a raised frame (of 8-cm height) to prevent surface runoff. During the 2-year period before research began, all suction and pan lysimeters were purged monthly and were sampled for fluids immediately prior to atrazine and KBr application to obtain background concentrations. Atrazine illustrated little movement until after a significant rainfall event, which peaked concentrations at depths of about 90 to 135 cm. Both Br− and δO18 moved rapidly through the soil, probably owing to soil porosity and anion exclusion for Br−. Concentrations of atrazine exceeding 5.0 μL−1 were observed with depth (90 to 150 cm) after several months. It appears that significant rainfall events were a key factor in the movement of atrazine in the sand, which allowed the chemicals to move to greater depths and thus avoid generally found biodegradation processes.

SUMMARY The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self-organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

It is not possible, using numerical methods, to model groundwater flow and transport in the fractured crystalline rock of northeastern Brazil. As an alternative, the usefulness of self-organizing map (SOM), k-means clustering, and Davies-Bouldin techniques to conceptualize the hydrogeology was evaluated. Also estimated was the well yield and groundwater quality across the Jua region. This process relies on relations in the underlying multivariate density function associated with a sparse local set of hydrogeologic (electrical conductivity, geology, temperature, and well yield) and a complete regional set of airborne geophysical (electromagnetic, magnetic, and radiometric) and satellite spectrometric measurements. Resampling of the regional well yield and electrical conductivity estimates provides sufficient resolution to construct variograms for stochastic modeling of the hydrogeologic variables. The combination of these stochastic maps provides a way to identify potential drilling targets for future groundwater development. The data-driven estimation approach, when applied to available airborne electromagnetic and water-well hydrogeologic measurements, provides a low-cost alternative to numerical groundwater flow modeling. In addition to fractured rock environments, the alternative modeling framework can provide spatial parameter estimates and associated variograms for constraints to improve the traditional calibration of equivalent groundwater-porous-media models.

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave–convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75 cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave–convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data.

article i nfo Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km 2 ) and regional (~50,000 km 2 ) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Parana. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave-convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady- state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

The Australian machine-learning workflows apply fusion, clustering, and estimation operations to hydrogeophysical data for deriving hydrostratigraphic units (HSUs). Data fusion is performed by training a self-organizing map (SOM) with these data. The application of Davies-Bouldin criteria to K-means clustering of SOM nodes determines the number and location of HSUs. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Two workflows provide 3D characterization of HSUs (and related attributes) from different hydrogeophysical data (measured, derived, interpolated, and estimated values) sets. In Workflow 1, the SOM learns to recognize relationships among a subset of borehole geophysical and hydrogeologic data. Using the data-fusion approach described above, the missing hydrological data are estimated using these learned relationships and HSUs determined at borehole sample locations resulting in a low lateral density and high vertical density spatial distribution. Variogram modeling of the regional field data and HSU estimates is undertaken to evaluate the spatial statistical structure of selected attributes. In workflow 2, the learned relationships between borehole data and the more spatially extensive AEM conductivity model are used to estimate the key attributes and HSUs at a number of locations away from the borehole. The AEM conductivity profile at a number of random locations are mapped to the SOM network and estimation performed to arrive at a set of continuous HSUs with high lateral density and medium vertical density (based on m-layer modelled structure). Performance metrics and validation are used to test each step of both workflows.

A regionalization procedure is presented and used to predict probable flooding in four ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. The flood-prediction problem is sequentially solved for two regions: upstream mountains and downstream alluvial plains. In the upstream mountains, a set of rainfall-runoff parameter values and recurrent peak-flow discharge hydrographs are simultaneously estimated for 20 tributary-basin models. Application of dissimilarity equations among tributary basins (soft prior information) permitted development of a parsimonious parameter structure subject to information content in the recurrent peak-flow discharge values derived using regression equations based on measurements recorded outside the ungauged study basins. The estimated joint set of parameter values formed the basis from which probable minimum and maximum peak-flow discharge limits were then estimated revealing that prediction uncertainty increases with basin size. In the ...

A regularized joint inverse procedure is presented and used to estimate the magnitude of extreme rainfall events in ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. Since streamflow measurements reflect temporal and spatial rainfall information, peak-flow discharge is hypothesized to represent a similarity measure suitable for regionalization. To test this hypothesis, peak-flow discharge values determined from streamflow recurrence information (10-year, 25-year, and 100-year) collected outside the study basins are used to develop regional (country-wide) regression equations. Peak-flow discharge derived from these equations together with preferred spatial parameter relations as soft prior information are used to constrain the simultaneous calibration of 20 tributary basin models. The nonlinear range of uncertainty in estimated parameter values (1 curve number and 3 recurrent rainfall amounts for each model) is determined using an inverse calibration-constrained Monte Carlo approach. Cumulative probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase in magnitude and range among basins with increasing return interval. Comparison of the estimated median rainfall amounts for all return periods were reasonable but larger (3.2–26%) than rainfall estimates computed using the frequency-duration (traditional) approach and individual rain gauge data. The observed 25-year recurrence rainfall amount at La Hachadura in the Paz River basin during Hurricane Mitch (1998) is similar in value to, but outside and slightly less than, the estimated rainfall confidence limits. The similarity in joint inverse and traditionally computed rainfall events, however, suggests that the rainfall observation may likely be due to under-catch and not model bias.

The representativeness of soil pore water extracted by suction lysimeters in ground-water monitoring studies is a problem that often confounds interpretation of measured data. Current soil water sampling techniques cannot delineate from which soil volume a pore water sample is extracted, neither macroscopic, microscopic, or preferential flowpath. This research was undertaken to compare δ18O and Br− values of extracted suction lysimeters samples from intact soil cores with samples obtained by the direct extraction methods of centrifugation and azeotropic distillation. Also, the study was concerned with determining what portion of soil pore water is sampled by each method and explaining differences in concentrations of the extracted water from each method to allow a determination of the accuracy and viability of the three methods of extraction. Intact soil cores (30 cm diameter by 40 cm height) were extracted from two different sites. Site 1 was rapid infiltration basin number 50, near Altamonte Springs in Seminole County, Florida. Site 2 was the Missouri Management System Evaluation Area (MSEA) near Centralia in Boone County, Missouri. Isotopically (18Oδ) labeled water and bromide concentrations within water samples taken by suction lysimeters was compared with samples obtained by methods of centrifugation and azeotropic distillation. The 18Oδ water was analyzed by mass spectrometry while bromide concentration, applied in the form of KBr was measured using standard IC procedures. Water collected by centrifugation and azeotropic distillation data were about 0.25‰ more negative than that collected by suction lysimeter values from a sandy soil and about 2–7‰ more negative from a well structured soil. Results indicate that the majority of soil water in well-structured soil is strongly bound to soil grain surfaces and is not easily sampled by suction lysimeters. In cases where a sufficient volume of water has passed through the soil profile and displaced previous pore water, suction lysimeters will collect a representative sample of soil pore water from the sampled depth interval. It is suggested that for stable isotope studies monitoring precipitation and soil water, suction lysimeter be installed at shallow depths (10 cm). Samples should also be coordinated with precipitation events. The data also suggest that each extraction method samples a separate component of soil-pore water. Centrifugation can be used with success, particularly for efficient sampling of large areas. Azeotropic distillation is more appropriate when strict qualitative and quantitative data on sorption desorption, and various types of kinetic studies may be needed.

Results from a multi-year, pilot-scale land treatment project for PAHs and PCBs biodegradation were evaluated. A mathematical model, capable of describing sorption, sequestration, and biodegradation in soil/water systems, is applied to interpret the efficacy of a sequential active–passive biotreatment process of organic chemicals on remediation sites. To account for the recalcitrance of PAHs and PCBs in soils and sludges during long-term biotreatment, this model comprises a kinetic equation for organic chemical intraparticle sequestration process. Model responses were verified by comparison to measurements of biodegradation of PAHs and PCBs in land treatment units; a favorable match was found between them. Model simulations were performed to predict on-going biodegradation behavior of PAHs and PCBs in land treatment units. Simulation results indicate that complete biostabilization will be achieved when the concentration of reversibly sorbed chemical (SRA) reduces to undetectable levels, with a certain amount of irreversibly sequestrated residual chemical (SIA) remaining within the soil particle solid phase. The residual fraction (SIA) tends to lose its original chemical and biological activity, and hence, is much less available, toxic, and mobile than the “free” compounds. Therefore, little or no PAHs and PCBs will leach from the treatment site and constitutes no threat to human health or the environment. Biotreatment of PAHs and PCBs can be terminated accordingly. Results from the pilot-scale testing data and model calculations also suggest that a significant fraction (10–30%) of high-molecular-weight PAHs and PCBs could be sequestrated and become unavailable for biodegradation. Bioavailability (large Kd, i.e., slow desorption rate) is the key factor limiting the PAHs degradation. However, both bioavailability and bioactivity (K in Monod kinetics, i.e., number of microbes, nutrients, and electron acceptor, etc.) regulate PCBs biodegradation. The sequential active–passive biotreatment can be a cost-effective approach for remediation of highly hydrophobic organic contaminants. The mathematical model proposed here would be useful in the design and operation of such organic chemical biodegradation processes on remediation sites.

The chemical interactions of hydrophobic organic contaminants with soils and sediments may result in strong binding and slow subsequent release rates that significantly affect remediation rates and endpoints. In order to illustrate the recalcitrance of chemical to degradation on sites, a sorption mechanism of intraparticle sequestration was postulated to operate on chemical remediation sites. Pseudo-first order sequestration kinetics is used in the study with the hypothesis that sequestration is an irreversibly surface-mediated process. A mathematical model based on mass balance equations was developed to describe the fate of chemical degradation in soil/water microcosm systems. In the model, diffusion was represented by Fick’s second law, local sorption-desorption by a linear isotherm, irreversible sequestration by a pseudo-first order kinetics and biodegradation by Monod kinetics. Solutions were obtained to provide estimates of chemical concentrations. The mathematical model was applied to a benzene biodegradation batch test and simulated model responses correlated well compared to measurements of biodegradation of benzene in the batch soil/water microcosm system. A sensitivity analysis was performed to assess the effects of several parameters on model behavior. Overall chemical removal rate decreased and sequestration increased quickly with an increase in the sorption partition coefficient. When soil particle radius, a, was greater than 1 mm, an increase in radius produced a significant decrease in overall chemical removal rate as well as an increase in sequestration. However, when soil particle radius was less than 0.1 mm, an increase in radius resulted in small changes in the removal rate and sequestration. As pseudo-first order sequestration rate increased, both chemical removal rate and sequestration increased slightly. Model simulation results showed that desorption resistance played an important role in the bioavailability of organic chemicals in porous media. Complete biostabilization of chemicals on remediation sites can be achieved when the concentration of the reversibly sorbed chemical reduces to zero (i.e., undetectable), with a certain amount of irreversibly sequestrated chemical left inside the soil particle solid phase.

Reliance on basin models by hydrologists to study the effects of changing landscapes on sustainability of water resources is increasing. The success of basin models to provide reliable predictions depends on adequacy of the calibrated model and residual model uncertainty. In this paper, reductions in predictive streamflow uncertainty are demonstrated using streamflow quantities integrated over different sample frequencies as calibration constraints and iterative updating of prior information. Respective model‐quality results in terms of coefficient of model‐fit efficiencies for cases using published parameter values and no calibration, traditional calibration constrained by daily streamflow measurements, and calibration constrained by various derived streamflow quantities were ‐0.44 (very poor), 0.66 (fair), and 0.66 (fair) to 0.91 (excellent). Incrementally reducing the number of active excellent‐quality‐model parameters (from 130 to 6 parameters) reduced the range of uncertainty (from 287.7 m3 ...

The objectives of the laboratory study described in this paper were (1) to determine the effectiveness of four nutrient solutions and a control in stimulating the microbial degradation of toluene in the unsaturated zone as an alternative to bioremediation methodologies such as air sparging, in situ vitrification, or others (Part I), and (2) to compare the effectiveness of the addition of the most effective nutrient solution from Part I (modified Hoagland type, nitrate-rich) and hydrogen peroxide (H2O2) on microbial degradation of toluene for repeated, simulated spills in the unsaturated zone (Part II). For Part 1, fifteen columns (30-cm diameter by 150-cm height), packed with air-dried, 0.25-mm, medium-fine sand, were prepared to simulate shallow unconfined aquifer conditions. Toluene (10 mL) was added to the surface of each column, and soil solution and soil gas samples were collected from the columns every third day for 21 days. On day 21, a second application of toluene (10 mL) was made, and the experiment was run for another 21 days. Solution 4 was the most effective for microbial degradation in Part I. For Part II, three columns were designated nutrient-rich 3-day toluene columns and received toluene injections every 3 days; three columns were designated as nutrient-rich 7-day columns and received toluene injections every 7 days; and two columns were used as controls to which no nutrient was added. As measured by CO2 respiration, the initial benefits for aerobic organisms from the O2 enhancement were sustained by the bacteria for only a short period of time (about 8 days). Degradation benefits from the nutrient solution were sustained throughout the experiment. The O2 and nutrient-enhanced columns degraded significantly more toluene than the control columns when simulating repeated spills onto the unsaturated zone, and demonstrated a potentially effective in situ bioremediation technology when used immediately or within days after a spill. The combined usage of H2O2 and nitrate-rich nutrients served to effectively maximize natural aerobic and anaerobic metabolic processes that biodegrade hydrocarbons in petroleum-contaminated media. Applications of this technology in the field may offer economical advantages to other, more intrusive abatement technologies.

The objective of this study was to test the effectiveness of a nitrate-rich nutrient solution and hydrogen peroxide (H2O2) to enhance in-situ microbial remediation of toluene in the unsaturated zone. Three sand-filled plots were tested in three phases (each phase lasting approximately 2 weeks). During the control phase, toluene was applied uniformly via sprinkler irrigation. Passive remediation was allowed to occur during this phase. A modified Hoagland nutrient solution, concentrated in 150 L of water, was tested during the second phase. The final phase involved addition of 230 moles of H2O2 in 150 L of water to increase the available oxygen needed for aerobic biodegradation. During the first phase, measured toluene concentrations in soil gas were reduced from 120 ppm to 25 ppm in 14 days. After the addition of nutrients during the second phase, concentrations were reduced from 90 ppm to about 8 ppm within 14 days, and for the third phase (H2O2), toluene concentrations were about 1 ppm after only 5 days. Initial results suggest that this method could be an effective means of remediating a contaminated site, directly after a BTEX spill, without the intrusiveness and high cost of other abatement technologies such as bioventing or soil-vapor extraction. However, further tests need to be completed to determine the effect of each of the BTEX components.

Coal-bed methane (CBM) co-produced discharge waters in the Powder River Basin of Wyoming, resulting from extraction of methane from coal seams, have become a priority for chemical, hydrological and biological research during the last few years. Soil and vegetation samples were taken from affected and reference sites (upland elevations and wetted gully) in Juniper Draw to investigate the effects of CBM discharge waters on soil physical and chemical properties and on native and introduced vegetation density and diversity. Results indicate an increase of salinity and sodicity within local soil ecosystems at sites directly exposed to CBM discharge waters. Elevated concentrations of sodium in the soil are correlated with consistent exposure to CBM waters. Clay-loam soils in the study area have a much larger specific surface area than the sandy soils and facilitate a greater sodium adsorption. However, there was no significant relation between increasing water sodium adsorption ratio (SAR) values and increasing sediment SAR values downstream; however, soils exposed to the CBM water ranged from the moderate to severe SAR hazard index. Native vegetation species density was highest at the reference (upland and gully) and CBM affected upland sites. The affected gully had the greatest percent composition of introduced vegetation species. Salt-tolerant species had the greatest richness at the affected gully, implying a potential threat of invasion and competition to established native vegetation. These findings suggest that CBM waters could affect agricultural production operations and long-term water quality.

In this study, an inverse methodology is presented and used to evaluate the effect that calibration of a synthetic artificial recharge model, constrained by different combinations of measurements (pressure head, temperature, and concentration), has on estimated vadose zone model parameter-value nonuniqueness and predictive water, heat, and solute transport uncertainty. Several findings are arrived at following model calibration and predictive analysis. First, composite scaled sensitivities revealed that all calibration measurement combinations contributed to the estimation of 30 water, heat, and solute transport parameters by inverting a set of vadose zone transport equations that were coupled explicitly through dependent variables and implicitly through parameters and fluid properties. Second, despite excellent model quality and perfect match of simulated-to-measured dependent field variables, the limitations in information content of field measurements used to constrain the calibration process promoted correlation among parameters; correlation among parameters promoted parameter nonuniqueness; and parameter nonuniqueness promoted predictive uncertainty. Consequently, simulations by transport models calibrated against field information represent a single realization associated with some quantifiable range of predictive uncertainty. Third, a primary reduction in uncertainty was achieved by increasing the number of calibration-constraint measurements, but reductions in uncertainty appeared restricted implying a practical limit to parameterization detail. Fourth, for a fixed number of measurements, a less prominent reduction in the range of predictive uncertainty could be realized through selective use of measurement types to constrain the calibration process. Therefore, field measurement types used to constrain the calibration process should be matched to target predictions. Fifth, because correlation among parameters contributes to predictive uncertainty, it may be possible to further reduce predictive uncertainty by estimating parameters that also minimize the largest eigenvalue in the normalized eigenvector matrix.

In an effort aimed at locating highly stressed and destressed areas that may influence rockbursts, the US Bureau of Mines conducted an active 3-D seismic tomographic investigation between the 7100 and 7250 levels (2165 m to 2210 m depth) of the Homestake gold mine, Lead, South Dakota. Existing rock bolts were used to mount geophones and as strike points for introducing seismic energy (nominally 750 Hz) using an 8-lb sledge hammer. Approximately 2900 compressional wave travel time measurements were recorded spanning up to 155 m between drifts at several levels. Eight different reconstructions, computed using different uniform starting models, were averaged together to produce a robust final velocity model. The average velocity tomogram portrayed a heterogeneous structure (3–6.8 km/s). Regions of low-velocity correlated with known locations of drifts, stopes, ore shoots, and rockburst damage, while high-velocity regions were indicative of elevated levels of compressional stress. 3-D isoprobability surfaces were used to assess the degree of risk associated with anomalous regions; significant risk zones appeared adjacent to backfilled slopes and at the center of pillars indicating the likelihood of concentrated stress.

The U.S. Bureau of Mines (USBM) investigated the use of seismic refraction tomography (SRT) for identifying changes in pillar loading at the Foidel Creek coal mine near Steamboat Springs, Colo. The inherent three-dimensional problem of directly imaging coal pillar conditions is reduced to a single plane by considering only the load transmitted from the pillar to the adjacent floor rock. Floor strata velocity tomograms (at two pillar locations) illustrate that loading conditions are nonuniform and nonunique. Also, time sequence monitoring, as the longwall face advanced, demonstrated that SRT can be used to image spatial changes in loading conditions. SRT offers the mining engineer a simple and flexible technique for evaluating pillar conditions and, thus, both present and alternative mine designs.

Mining-induced stress-field changes pose both safety and economic hazards. In an effort aimed at developing technology for mitigating such hazards, the Bureau of Mines together with Hecla Mining Company conducted an active 3-D seismic tomographic investigation of anomalous rockmass conditions in a large underground, high-grade, remnant ore pillar, at the Lucky Friday mine near Mullan, ID. Roughly 2400 P-wave traveltime measurements, were simultaneously inverted to obtain a velocity distribution. The resulting velocity structure appears extremely heterogeneous (1.5–6.0 km/s) and well correlated with mechanical models indicating the transfer of stress in direct response to mining. Regions of anomalous ground (intense fracturing or high-stress) were identified using threshold probabilities; the minimum velocity regions surrounding drifts indicate a zone of stress relief that extends up to three drift diameters into the rockmass, while regions of maximum velocity above and below mined-out portions of veins indicate the likelihood of concentrated stress. Active tomographic imaging provides the engineer with a flexible tool for routine underground 3-D monitoring of mechanical conditions in large mine structures.

Artigos relacionados com as alterações climáticas e seus possíveis efeitos nos recursos hídricos, nomeadamente os recursos hídricos subterrâneos / Papers related with climatic changes and its possible effects in water resources, namelly in groundwater.

Climate change is one possible external driver of ecosystem services. In the tropical Pacific, short-term climate change is influenced by oceanic Kelvin waves that induce remote temperatures to rise (El Nino event) or decrease (La Nina event). This teleconnection is not globally uniform; in the United States (U.S.) drought conditions induced by El Nino commonly appear in the northern latitudes, whereas drought induced by La Nina occurs in the southern latitudes. Should natural or anthropogenic climate forcing influence the frequency or intensity of drought, there is a potential for catastrophic events to occur placing our national and global security at risk. Because climate forcing interacts with ecosystems characterized by nonlinear and multivariate processes over local-to-global and immediate-to-long-term scales, their assessment and prediction are challenging. This study demonstrates the efficacy of an alternative modeling paradigm based on using a self-organizing map to examine and predict the role that climatic change has on water-resource related ecosystem services. Examples include: (1) hindcasting 2,000 years of temperature and precipitation across states in the south-central and southwestern U.S.; (2) forecasting climate-induced groundwater recharge variability across subbasins in mid-western U.S.; and, (3) forecasting climate-change effects on post-fire hydrology and geomorphology in the western U.S.

This article summarizes a field experiment to compare the effectiveness of selected geophysical electromagnetic methods for locating a conductive solution 160 m below the surface. The conductive solution simulated leach solution for in situ mining in order to examine alternatives for monitoring leach geophysically. The technology could also be applied to many other types of environmental monitoring. The experiment was part of a broad US Bureau of Mines research program on in situ mining.

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

Mauritania anticipates an increase in mining activities throughout the country and into the foreseeable future. Because mining-induced changes in the landscape are likely to affect their limited ground-water resources and sensitive aquatic ecosystems, a water-quality assessment program was designed for Mauritania that is based on a nationally consistent environmental stratification framework. The primary objectives of this program are to ensure that the environmental monitoring systems can quantify near real-time changes in surface-water chemistry at a local scale, and quantify intermediateto long-term changes in groundwater and aquatic ecosystems over multiple scales.