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Geology topics

B. T. Nolan

Publications and source records attributed to B. T. Nolan.

13 recordsLinked to original sources
USGS2013

Selenium in irrigated agricultural areas of the western United States

Selenium was recognized as an important aquatic contaminant following the identification of widespread deformities in waterfowl at the agricultural drainage evaporation ponds of the Kesterson Reservoir (California) in 1983. Since then, California has been the focal point for global research and management of Se contamination. We analyzed the history and current developments in science, policy, and management of irrigation-induced Se contamination in California. In terms of management, we evaluated the effects of improvements in the design of local attenuation methods (drainage reuse and evaporation ponds) in conjunction with the development of programs for Se load reductions at the regional scale (namely the Grassland Bypass Project). In terms of policy, the USEPA is currently working on site-specific water quality criteria for the San Francisco Bay Delta that may be a landmark for future legislation on Se in natural water bodies. We provide a critical analysis of this approach and discuss challenges and opportunities in expanding it to other locations such as the Salton Sea. Management lessons learned in California and the novel policy approach may help prevent future events of Se contamination.

Journal of Environmental Quality↗
USGS2012

The interactive effects of excess reactive nitrogen and climate change on aquatic ecosystems and water resources of the United States

Nearly all freshwaters and coastal zones of the US are degraded from inputs of excess reactive nitrogen (Nr), sources of which are runoff, atmospheric N deposition, and imported food and feed. Some major adverse effects include harmful algal blooms, hypoxia of fresh and coastal waters, ocean acidification, long-term harm to human health, and increased emissions of greenhouse gases. Nitrogen fluxes to coastal areas and emissions of nitrous oxide from waters have increased in response to N inputs. Denitrification and sedimentation of organic N to sediments are important processes that divert N from downstream transport. Aquatic ecosystems are particularly important denitrification hotspots. Carbon storage in sediments is enhanced by Nr, but whether carbon is permanently buried is unknown. The effect of climate change on N transport and processing in fresh and coastal waters will be felt most strongly through changes to the hydrologic cycle, whereas N loading is mostly climate-independent. Alterations in precipitation amount and dynamics will alter runoff, thereby influencing both rates of Nr inputs to aquatic ecosystems and groundwater and the water residence times that affect Nr removal within aquatic systems. Both infrastructure and climate change alter the landscape connectivity and hydrologic residence time that are essential to denitrification. While Nr inputs to and removal rates from aquatic systems are influenced by climate and management, reduction of N inputs from their source will be the most effective means to prevent or to minimize environmental and economic impacts of excess Nr to the nation’s water resources.

Biogeochemistry↗
USGS2012

Root zone water quality model (RZWQM2): Model use, calibration and validation

The Root Zone Water Quality Model (RZWQM2) has been used widely for simulating agricultural management effects on crop production and soil and water quality. Although it is a one-dimensional model, it has many desirable features for the modeling community. This article outlines the principles of calibrating the model component by component with one or more datasets and validating the model with independent datasets. Users should consult the RZWQM2 user manual distributed along with the model and a more detailed protocol on how to calibrate RZWQM2 provided in a book chapter. Two case studies (or examples) are included in this article. One is from an irrigated maize study in Colorado to illustrate the use of field and laboratory measured soil hydraulic properties on simulated soil water and crop production. It also demonstrates the interaction between soil and plant parameters in simulated plant responses to water stresses. The other is from a maize-soybean rotation study in Iowa to show a manual calibration of the model for crop yield, soil water, and N leaching in tile-drained soils. Although the commonly used trial-and-error calibration method works well for experienced users, as shown in the second example, an automated calibration procedure is more objective, as shown in the first example. Furthermore, the incorporation of the Parameter Estimation Software (PEST) into RZWQM2 made the calibration of the model more efficient than a grid (ordered) search of model parameters. In addition, PEST provides sensitivity and uncertainty analyses that should help users in selecting the right parameters to calibrate.

Transactions of the ASABE↗
USGS2010

Soil-test N recommendations augmented with PEST-optimized RZWQM simulations

Improved understanding of year-to-year late-spring soil nitrate test (LSNT) variability could help make it more attractive to producers. We test the ability of the Root Zone Water Quality Model (RZWQM) to simulate watershed-scale variability due to the LSNT, and we use the optimized model to simulate long-term field N dynamics under related conditions. Autoregressive techniques and the automatic parameter calibration program PEST were used to show that RZWQM simulates significantly lower nitrate concentration in discharge from LSNT treatments compared with areas receiving fall N fertilizer applications within the tile-drained Walnut Creek, Iowa, watershed (>5 mg N L-1 difference for the third year of the treatment, 1999). This result is similar to field-measured data from a paired watershed experiment. A statistical model we developed using RZWQM simulations from 1970 to 2005 shows that early-season precipitation and early-season temperature account for 90% of the interannual variation in LSNT-based fertilizer N rates. Long-term simulations with similar average N application rates for corn (Zea mays L.) (151 kg N ha-1) show annual average N loss in tile flow of 20.4, 22.2, and 27.3 kg N ha -1 for LSNT, single spring, and single fall N applications. These results suggest that (i) RZWQM is a promising tool to accurately estimate the water quality effects of LSNT; (ii) the majority of N loss difference between LSNT and fall applications is because more N remains in the root zone for crop uptake; and (iii) year-to-year LSNT-based N rate differences are mainly due to variation in early-season precipitation and temperature. Copyright ?? 2010 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Journal of Environmental Quality↗
USGS2010

Local sensitivity analysis for inverse problems solved by singular value decomposition

Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by regression based on the range of singular values. Identifiability statistic results varied based on the number of SVD parameters included. Identifiability statistics calculated for four SVD parameters indicate the same three most important process-model parameters as CSS/PCC (WFC1, WFC2, and BD2), but the order differed. Additionally, the identifiability statistic showed that BD1 was almost as dominant as WFC1. The CSS/PCC analysis showed that this results from its high correlation with WCF1 (-0.94), and not its individual sensitivity. Such distinctions, combined with analysis of how high correlations and(or) sensitivities result from the constructed model, can produce important insights into, for example, the use of sensitivity analysis to design monitoring networks. In conclusion, the statistics considered identified similar important parameters. They differ because (1) with CSS/PCC can be more awkward because sensitivity and interdependence are considered separately and (2) identifiability requires consideration of how many SVD parameters to include. A continuing challenge is to understand how these computationally efficient methods compare with computationally demanding global methods like Markov-Chain Monte Carlo given common nonlinear processes and the often even more nonlinear models.

Book↗
USGS2007

Factors influencing ground-water recharge in the eastern United States

Ground-water recharge estimates for selected locations in the eastern half of the United States were obtained by Darcian and chloride-tracer methods and compared using statistical analyses. Recharge estimates derived from unsaturated-zone (RUZC) and saturated-zone (RSZC) chloride mass balance methods are less variable (interquartile ranges or IQRs are 9.5 and 16.1 cm/yr, respectively) and more strongly correlated with climatic, hydrologic, land use, and sediment variables than Darcian estimates (IQR = 22.8 cm/yr). The unit-gradient Darcian estimates are a nonlinear function of moisture content and also reflect the uncertainty of pedotransfer functions used to estimate hydraulic parameters. Significance level is <0.001 for nearly all explanatory variables having correlations with RUZC of <-0.3 or >0.3. Estimates of RSZC were evaluated using analysis of variance, multiple comparison tests, and an exploratory nonlinear regression (NLR) model. Recharge generally is greater in coastal plain surficial aquifers, fractured crystalline rocks, and carbonate rocks, or in areas with high sand content. Westernmost portions of the study area have low recharge, receive somewhat less precipitation, and contain fine-grained sediment. The NLR model simulates water input to the land surface followed by transport to ground water, depending on factors that either promote or inhibit water infiltration. The model explains a moderate amount of variation in the data set (coefficient of determination = 0.61). Model sensitivity analysis indicates that mean annual runoff, air temperature, and precipitation, and an index of ground-water exfiltration potential most influence estimates of recharge at sampled sites in the region. Soil characteristics and land use have less influence on the recharge estimates, but nonetheless are significant in the NLR model. ?? 2006 Elsevier B.V. All rights reserved.

Journal of Hydrology↗
USGS2006

Modeling the probability of arsenic in groundwater in New England as a tool for exposure assessment

We developed a process-based model to predict the probability of arsenic exceeding 5 ??g/L in drinking water wells in New England bedrock aquifers. The model is being used for exposure assessment in an epidemiologic study of bladder cancer. One important study hypothesis that may explain increased bladder cancer risk is elevated concentrations of inorganic arsenic in drinking water. In eastern New England, 20-30% of private wells exceed the arsenic drinking water standard of 10 micrograms per liter. Our predictive model significantly improves the understanding of factors associated with arsenic contamination in New England. Specific rock types, high arsenic concentrations in stream sediments, geochemical factors related to areas of Pleistocene marine inundation and proximity to intrusive granitic plutons, and hydrologic and landscape variables relating to groundwater residence time increase the probability of arsenic occurrence in groundwater. Previous studies suggest that arsenic in bedrock groundwater may be partly from past arsenical pesticide use. Variables representing historic agricultural inputs do not improve the model, indicating that this source does not significantly contribute to current arsenic concentrations. Due to the complexity of the fractured bedrock aquifers in the region, well depth and related variables also are not significant predictors. ?? 2006 American Chemical Society.

Environmental Science & Technology↗
USGS2005

Relating nitrogen sources and aquifer susceptibility to nitrate in shallow ground waters of the United States

Characteristics of nitrogen loading and aquifer susceptibility to contamination were evaluated to determine their influence on contamination of shallow ground water by nitrate. A set of 13 explanatory variables was derived from these characteristics, and variables that have a significant influence were identified using logistic regression (LR). Multivariate LR models based on more than 900 sampled wells predicted the probability of exceeding 4 mg/L of nitrate in ground water. The final LR model consists of the following variables: (1) nitrogen fertilizer loading (p-value = 0.012); (2) percent cropland-pasture (p < 0.001); (3) natural log of population density (p < 0.001); (4) percent well-drained soils (p = 0.002); (5) depth to the seasonally high water table (p = 0.001); and (6) presence or absence of a fracture zone within an aquifer (p = 0.002). Variables 1–3 were compiled within circular, 500 m radius areas surrounding sampled wells, and variables 4–6 were compiled within larger areas representing targeted land use and aquifers of interest. Fitting criteria indicate that the full logistic-regression model is highly significant (p < 0.001), compared with an intercept-only model that contains none of the explanatory variables. A goodness-of-fit test indicates that the model fits the data well, and observed and predicted probabilities of exceeding 4 mg/L nitrate in ground water are strongly correlated (r 2 = 0.971). Based on the multivariate LR model, vulnerability of ground water to contamination by nitrate depends not on any single factor but on the combined, simultaneous influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics.

Ground Water↗
USGS2005

Workgroup report: Drinking-water nitrate and health - Recent findings and research needs

Human alteration of the nitrogen cycle has resulted in steadily accumulating nitrate in our water resources. The U.S. maximum contaminant level and World Health Organization guidelines for nitrate in drinking water were promulgated to protect infants from developing methemoglobinemia, an acute condition. Some scientists have recently suggested that the regulatory limit for nitrate is overly conservative; however, they have not thoroughly considered chronic health outcomes. In August 2004, a symposium on drinking-water nitrate and health was held at the International Society for Environmental Epidemiology meeting to evaluate nitrate exposures and associated health effects in relation to the current regulatory limit. The contribution of drinking-water nitrate toward endogenous formation of N-nitroso compounds was evaluated with a focus toward identifying subpopulations with increased rates of nitrosation. Adverse health effects may be the result of a complex interaction of the amount of nitrate ingested, the concomitant ingestion of nitrosation cofactors and precursors, and specific medical conditions that increase nitrosation. Workshop participants concluded that more experimental studies are needed and that a particularly fruitful approach may be to conduct epidemiologic studies among susceptible subgroups with increased endogenous nitrosation. The few epidemiologic studies that have evaluated intake of nitrosation precursors and/or nitrosation inhibitors have observed elevated risks for colon cancer and neural tube defects associated with drinking-water nitrate concentrations below the regulatory limit. The role of drinking-water nitrate exposure as a risk factor for specific cancers, reproductive outcomes, and other chronic health effects must be studied more thoroughly before changes to the regulatory level for nitrate in drinking water can be considered.

Environmental Health Perspectives↗
USGS2002

VOCs, pesticides, nitrate, and their mixtures in groundwater used for drinking water in the United States

Samples of untreated groundwater from 1255 domestic drinking-water wells and 242 public supply wells were analyzed as part of the National Water-Quality Assessment Program of the U.S. Geological Survey between 1992 and 1999. Wells were sampled to define the regional quality of the groundwater resource and, thus, were distributed geographically across large aquifers, primarily in rural areas. For each sample, as many as 60 volatile organic compounds (VOCs), 83 pesticides, and nitrate were analyzed. On the basis of previous studies, nitrate concentrations as nitrogen &ge;3 mg/L were considered to have an anthropogenic origin. VOCs were detected more frequently (44%) than pesticides (38%) or anthropogenic nitrate (28%). Seventy percent of the samples contained at least one VOC, pesticide, or anthropogenic nitrate; 47% contained at least two compounds; and 33% contained at least three compounds. The combined concentrations of VOCs and pesticides ranged from about 0.001 to 100 &mu;g/L, with a median of 0.02 &mu;g/L. Water from about 12% of the wells contained one or more compounds that exceeded U.S. Environmental Protection Agency drinking-water standards or human health criteria, primarily because of nitrate concentrations exceeding the maximum contaminant level in domestic wells. A mixture is defined as a unique combination of two or more particular compounds, regardless of the presence of other compounds that may occur in the same sample. There were 100 mixtures (significantly associated with agricultural land use) that had a detection frequency between 2% and 19%. There were 302 mixtures (significantly associated with urban land use) that had a detection frequency between 1% and <2%. Only 14 compounds (seven VOCs, six pesticides, and nitrate) contributed over 95% of the detections in these 402 mixtures; however, most samples with these mixtures also contain a variety of other compounds.

Environmental Science & Technology↗
USGS1997

Risk of nitrate in groundwaters of the United States - A national perspective

Nitrate contamination of groundwater occurs in predictable patterns, based on findings of the U.S. Geological Survey's (USGS) National Water Quality Assessment (NAWQA) Program. The NAWQA Program was begun in 1991 to describe the quality of the Nation's water resources, using nationally consistent methods. Variables affecting nitrate concentration in groundwater were grouped as 'input' factors (population density end the amount of nitrogen contributed by fertilizer, manure, and atmospheric sources) and 'aquifer vulnerability' factors (soil drainage characteristic and the ratio of woodland acres to cropland acres in agricultural areas) and compiled in a national map that shows patterns of risk for nitrate contamination of groundwater. Areas with high nitrogen input, well-drained soils, and low woodland to cropland ratio have the highest potential for contamination of shallow groundwater by nitrate. Groundwater nitrate data collected through 1992 from wells less than 100 ft deep generally verified the risk patterns shown on the national map. Median nitrate concentration was 0.2 mg/L in wells representing the low-risk group, and the maximum contaminant level (MCL) was exceeded in 3% of the wells. In contrast, median nitrate concentration was 4.8 mg/L in wells representing the high-risk group, and the MCL was exceeded in 25% of the wells.Nitrate contamination of groundwater occurs in predictable patterns, based on findings of the U.S. Geological Survey's (USGS) National Water Quality Assessment (NAWQA) Program. The NAWQA Program was begun in 1991 to describe the quality of the Nation's water resources, using nationally consistent methods. Variables affecting nitrate concentration in groundwater were grouped as `input' factors (population density and the amount of nitrogen contributed by fertilizer, manure, and atmospheric sources) and `aquifer vulnerability' factors (soil drainage characteristic and the ratio of woodland acres to cropland acres in agricultural areas) and compiled in a national map that shows patterns of risk for nitrate contamination of groundwater. Areas with high nitrogen input, well-drained soils, and low woodland to cropland ratio have the highest potential for contamination of shallow groundwater by nitrate. Groundwater nitrate data collected through 1992 from wells less than 100 ft deep generally verified the risk patterns shown on the national map. Median nitrate concentration was 0.2 mg/L in wells representing the low-risk group, and the maximum contaminant level (MCL) was exceeded in 3% of the wells. In contrast, median nitrate concentration was 4.8 mg/L in wells representing the high-risk group, and the MCL was exceeded in 25% of the wells.

Environmental Science & Technology↗
USGS1995

Water resources of Teton County, Wyoming, exclusive of Yellowstone National Park

Surface- and ground-water data were collected and analyzed to describe the water resources of that part of Teton County, Wyoming located south of Yellowstone National Park. Wells and springs inventoried in the Teton County study area most commonly were completed in or issued from Quaternary unconsolidated deposits and Tertiary, Mesozoic, and Paleozoic rocks. The largest measured, reported, or estimated discharges were from Quaternary uncon- solidated deposits (3,000 gallons per minute), the Bacon Ridge Sandstone of Cretaceous age (800 gallons per minute), and the Madison Limestone of Mississippian age (800 gallons per minute). Dissolved-solids concentrations in water samples from Quaternary unconsolidated deposits and Tertiary, Mesozoic, and Paleozoic rocks ranged from 80 to 1,060 milligrams per liter. A time-domain electromagnetic survey of Jackson Hole indicated that the depth of Quaternary unconsolidated deposits ranged from about 380 feet in the northern part of Antelope Flats to about 2,400 feet near the Potholes area in Grand Teton National Park. A streamflow gain-and-loss study indicated that the ground-water discharge to the Snake River between gaging stations near Moran and south of the Flat Creek confluence, near Jackson, was 395 cubic feet per second. Water level contours generated from 137 water-level measurements and 118 stream altitudes indicated that water in Quaternary unconsolidated deposits flows southwest in the general direction of the Snake River.

Water-Resources Investigations Report↗