Geology topics

Celia Z. Rosecrans

Publications and source records attributed to Celia Z. Rosecrans.

7 recordsLinked to original sources

USGS2024

Evaluation of the characteristics, discharge, and water quality of selected springs at Fort Irwin National Training Center, San Bernardino County, California

Eight springs and seeps at Fort Irwin National Training Center were described and categorized by their general characteristics, discharge, geophysical properties, and water quality between 2015 and 2017. The data collected establish a modern (2017) baseline of hydrologic conditions at the springs. Two types of springs were identified: (1) precipitation-fed upland springs (Cave, Desert King, Devouge, No Name, and Panther Springs) and (2) groundwater discharge-fed basin springs (Garlic, Bitter, and Jack Springs). Comparison of electrical resistivity tomography data collected at groundwater basin springs from 2015 to 2017 indicated that spring discharge and connection to the underlying groundwater system is highly focused, although the springs themselves appear diffuse and are spread out over a large area. Spring discharge was consistently less than reported by Thompson (1929), except at Garlic Spring where discharges and vegetation have increased in recent years. Multiple discrete flume and seepage meter measurements taken between October 2015 and April 2016 indicated that discharge changed predictably on diurnal and seasonal timescales in response to evapotranspiration. These preliminary results and the lush vegetation noted at some of the springs, particularly at Bitter, Garlic, and Jack Springs, indicated plant evapotranspiration accounts for a substantial part of the discharge from these springs. The quality of water ranges from fresh in precipitation-fed upland springs (Cave, Desert King, Devouge, and Panther Springs) to slightly saline (Garlic and Jack Springs) and moderately saline (Bitter Spring) in groundwater-fed discharge springs. Nitrate concentrations from water at most of the springs were less than 3 milligrams per liter, except for samples from Devouge and Desert King Springs and one sample from Jack Spring. An analysis of delta nitrogen-15 in nitrate (δ 15 N-NO 3 ) and delta oxygen-18 in nitrate (δ 18 O-NO 3 ) indicates high nitrate concentrations in excess of the U.S. Environmental Protection Agency maximum contaminant level at Jack Spring and Desert King Spring resulting from the dissolution of nitrate-bearing caliche deposits; nitrate concentrations at Devouge Spring are a result of algal growth within the spring, and the source of nitrate concentrations in Garlic Spring are consistent with a treated wastewater origin from Langford Valley-Irwin subbasin upgradient. The source of water in upland springs, indicated by values of delta oxygen-18 (δ 18 O) and delta deuterium (δD) are consistent with recharge from winter precipitation. In groundwater basin springs, values of δ 18 O and δD are consistent with groundwater sampled from nearby wells. Summer monsoonal precipitation appears to contribute little water to spring flow. Most springs contain low levels of tritium and appear to be primarily older (pre-1950s) groundwater. Groundwater basin springs with detectable tritium may result from occasional streamflow in nearby washes. These springs could be susceptible to decreases in flow during extended dry periods when the localized recharge may be reduced due to the loss of focused recharge through nearby washes. Groundwater samples from Garlic and Bitter Springs contained arsenic concentrations above the U.S. Environmental Protection Agency maximum contaminant level. Groundwater samples from all springs, except Cave, Desert King, and Devouge Springs, exceeded the State of California maximum contaminant level for fluoride. Garlic Spring was the only sampled spring that contained vanadium concentrations that exceeded the State of California notification level. Only a single water sample from Jack Spring contained uranium at a concentration that exceeded the U.S. Environmental Protection Agency maximum contaminant level. Many other constituents of concern were analyzed, including those from anthropogenic sources that may be a result of military activities. Most of these constituents were not detected above their respective reporting levels in spring water; only 15 were detected in spring waters. Diesel and gasoline degradants, many of which also occur naturally, were the most commonly detected compounds. Several other organic compounds, primarily solvents or their degradants, were detected in groundwater basin springs. These constituents, in order of decreasing detection frequency, were carbon disulfide; perchlorate; mercury; acetone; methylnaphthalene; toluene; methyl ethyl ketone; cyanide; and styrene; 4-iso-propyl-toluene; isopropylbenzene; methyl salicylate; and phenol. Except for Garlic Spring, which is affected by discharges of treated wastewater, the quality of water from most springs appears to be relatively unaffected by activities at the Fort Irwin National Training Center.

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USGS2021

Relative risk of groundwater-quality degradation near California (USA) oil fields estimated from 3H, 14C, and 4He

Relative risks of groundwater-quality degradation near selected California oil fields are estimated by examining spatial and temporal patterns in chemical and isotopic data in the context of groundwater-age categories defined by tritium and carbon-14. In the Coastal basins, western San Joaquin Valley (SJV), and eastern SJV; 82, 76, and 0% of samples are premodern (pre-1953 recharge), respectively; and 3, 0, and 31% are modern (recharged during or after 1953), respectively. Carbon-14 and helium-4 data indicate most premodern samples are 1000 to 10,000 (33%) or >10,000 (50%) years old. Organic chemicals that could be associated with deeper hydrocarbon reservoirs (e.g. thermogenic gases and benzene) occur most frequently in premodern groundwater, suggesting premodern groundwater has a higher risk of degradation from upward migration of hydrocarbons than modern and mixed-age groundwater. Low sulfate concentrations in some premodern groundwater containing high thermogenic-methane concentrations (>28 mg/L) indicate methane attenuation associated with sulfate reduction can be limited in premodern groundwater. The more common occurrence of manufactured compounds, like tetrachloroethene, in modern and mixed-age groundwater than in premodern groundwater indicates modern and mixed-age groundwater has a higher risk of degradation from land-surface sources than premodern groundwater. Time-series data for chloride in groundwater affected by disposal of oil-field water in unlined ponds indicate some modern and mixed-age groundwater are susceptible to chemical migration within 2–3 km of surface sources. Timescales for diluting chloride concentrations in groundwater with fresh recharge once disposal ponds are decommissioned are shorter in mixed-age groundwater with large fractions of modern water (9–14 years in one example) than in mixed-age groundwater with large fractions of premodern water (no evidence of dilution after 12 years of monitoring in one example). The presence of predominantly premodern groundwater in the Coastal basins and western SJV indicates these areas have relatively high risk from upward migration of hydrocarbons, reduced methane attenuation capacity, and long dilution times, whereas predominantly modern- and mixed-age groundwater in the eastern SJV indicates this area has relatively high risk from chemical migration from land-surface sources and subsequent extensive spreading. Age-based characterizations of relative risk could inform the design of groundwater-monitoring programs near oil fields in terms of the spatial distribution of monitoring points relative to source areas and monitoring frequency and duration.

California↗

USGS2021

Groundwater quality of aquifers overlying the Oxnard Oil Field, Ventura County, California

Groundwater samples collected from irrigation, monitoring, and municipal supply wells near the Oxnard Oil Field were analyzed for chemical and isotopic tracers to evaluate if thermogenic gas or water from hydrocarbon-bearing formations have mixed with surrounding groundwater. New and historical data show no evidence of water from hydrocarbon-bearing formations in groundwater overlying the field. However, thermogenic gas mixed with microbial methane was detected in 5 wells at concentrations ranging from 0.011–9.1 mg/L. The presence of these gases at concentrations <10 mg/L do not indicate degraded water quality posing a known health risk. Analysis of carbon isotopes (δ 13 C-CH 4 ) and hydrogen isotopes (δ 2 H-CH 4 ) of methane and ratios of methane to heavier hydrocarbon gases were used to differentiate sources of methane between a) microbial, b) thermogenic or c) mixed sources. Results indicate that microbial-sourced methane is widespread in the study area, and concentrations overlap with those from thermogenic sources. The highest concentrations of thermogenic gas were observed in proximity to relatively high density of oil wells, large injection volumes of water disposal and cyclic steam, shallow oil development, and hydrocarbon shows in sediments overlying the producing oil reservoirs. Depths of water wells containing thermogenic gas were within approximately 200 m of the top of the Vaca Tar Sand production zone (approximately 600 m below land surface). Due to the limited sampling density, the source and pathways of thermogenic gas detected in groundwater could not be conclusively determined. Thermogenic gas detected in the absence of co-occurring water from hydrocarbon-bearing formations may result from natural gas migration over geologic time from the Vaca Tar Sand or deeper formations, hydrocarbon shows in sediments overlying producing zones, and/or gas leaking from oil-field infrastructure. Denser sampling of groundwater, potential end-members, and pressure monitoring could help better distinguish pathways of thermogenic gases.

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USGS2020

Water Quality of groundwater used for public supply in principal aquifers of the western United States

Groundwater provides nearly half of the Nation’s drinking water. As the Nation’s population grows, the importance of (and need for) high-quality drinking-water supplies increases. As part of a national-scale effort to assess groundwater quality in principal aquifers (PAs) that supply most of the groundwater used for public supply, the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Project staff sampled six principal aquifers in the western United States between 2013 and 2017: (1) the Basin and Range carbonate-rock aquifers, (2) Basin and Range basin-fill aquifers, (3) Rio Grande aquifer system, (4) High Plains aquifer, (5) Colorado Plateaus aquifers, and (6) Columbia Plateau basaltic-rock aquifers. These six PAs supply a large part of the Nation’s drinking water and cover a large geographic extent of the western conterminous United States. Groundwater samples were analyzed for a large suite of water-quality constituents including major ions, nutrients, trace elements, volatile organic compounds (VOCs), pesticide compounds, radioactive constituents, age tracers, and, in selected PAs, perchlorate. Two types of assessments were made: (1) a status assessment that describes the quality of the groundwater resource at time of collection and (2) an understanding assessment that evaluates relations between groundwater quality and potential explanatory factors that represent characteristics of the aquifer system. The assessments characterize untreated groundwater quality, which might be different than the quality of drinking water delivered to consumers. The assessments are based on water-quality data collected from 352 wells and 6 springs using an equal-area grid sampling design. This sampling approach allows for the estimation of the proportion of high, moderate, or low concentrations relative to federal water-quality benchmarks of selected constituents in the area of each PA. Results were compared to established benchmarks for drinking-water quality to provide context for evaluating the quality of untreated groundwater: Federal regulatory benchmarks for protecting human health, non-regulatory human-health benchmarks, and non-regulatory benchmarks for nuisance chemicals. Not all constituents that were analyzed have benchmarks and thus were not considered for assessments. Concentrations are characterized as high if they are greater than their benchmark. Concentrations are considered moderate if they are greater than one-half their benchmark (for inorganic constituents), or greater than one-tenth their benchmark (for organic constituents). Concentrations are considered low if they are less than moderate or the constituent was not detected. Status assessment results indicated that inorganic constituents more commonly occurred at high and moderate concentrations in the six PAs than organic constituents, and organic constituents predominately occurred at low concentrations. Inorganic constituents that exceeded health-based benchmarks (high concentrations) were present in all six PAs; aquifer-scale proportion were 30 percent in the Rio Grande aquifer system, 22 percent in the Basin and Range basin-fill aquifers, 20 percent in the Basin and Range carbonate-rock aquifers, 19 percent in the High Plains aquifer, 16 percent in the Colorado Plateaus aquifers, and 8 percent in the Columbia Plateau basaltic-rock aquifers. Arsenic, fluoride, manganese, and total dissolved solids were the constituents most commonly present at high concentrations. Organic constituents with human-health benchmarks (pesticide compounds and VOCs) did not occur at high concentrations and moderate concentrations were infrequent; aquifer-scale proportions ranged from 0 to 5 percent. Detections of organic compounds at low concentrations, however, occurred in all six PAs, with detection frequencies ranging from 10 to 26 percent for pesticide compounds and from 10 to 46 percent for VOCs. Specific organic constituents with detection frequencies greater than 10 percent were four herbicides (atrazine, didealkylatrazine, bromoform, and propazine), one insecticide (propoxur), and two VOCs (the trihalomethanes chloroform and bromodichloromethane). Where collected—in the Rio Grande aquifer system and High Plains aquifer—perchlorate did not occur at high concentrations; moderate aquifer-scale proportions were 3 and 11 percent, respectively. The understanding assessment included statistical tests to evaluate relations between constituent concentrations and potential explanatory factors to identify natural and human factors that affect groundwater quality. Potential explanatory factors included depth to bottom of well perforation, groundwater age category, land use, aquifer lithology, hydrologic conditions, and geochemical conditions. Higher concentrations of trace elements, radioactive constituents, and constituents with non-health-based benchmarks generally were associated with unconsolidated sand and gravel aquifer lithologies, premodern groundwater age, greater aridity, and more alkaline pH. Organic constituents with detection frequencies greater than 10 percent generally were associated with urban land use, shallower well depths, and higher total dissolved solids concentrations. The results for the six western PAs provide important insights into the quality of groundwater that is used for drinking water in the western United States, as well as natural and human factors that affect groundwater quality in this region.

Arizona, California, Colorado, Idaho, Kansas, Mont↗

USGS2018

Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California

The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.

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USGS2017

Predicted pH at the domestic and public supply drinking water depths, Central Valley, California

This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R ( http://www.r-project.org/ ). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis ) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml ). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer. Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/ . Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7 .

California↗

USGS2017

Prediction and visualization of redox conditions in the groundwater of Central Valley, California

Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.

California↗