Geology topics

Thomas Harter

Publications and source records attributed to Thomas Harter.

7 recordsLinked to original sources

USGS2021

Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA

The travel time or “age” of groundwater affects catchment responses to hydrologic changes , geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.

Great Lakes basin↗

USGS2019

Adjudicating groundwater: A judge’s guide to understanding groundwater and modeling

Dividing the Waters offers this groundwater science bench book that cannot be matched by any other scientific or judicial publication. Adjudicating Groundwater combines the expertise and experience of academic scientists (UC Davis/Stanford), federal scientists (U.S. Geological Survey), and judicial officers to create a resource that can fulfill the needs of judges tackling the most difficult groundwater conflicts. This bench book explains both the fundamentals of groundwater science (hydrogeology) and groundwater modeling. The bench book received peer review from scientists and judges, with oversight by the U.S. Geological Survey, making this book among the most reliable resources for judges with water cases.

Book↗

USGS2017

Effects of solid-liquid separation and storage on monensin attenuation in dairy waste management systems

Environmental release of veterinary pharmaceuticals has been of regulatory concern for more than a decade. Monensin is a feed additive antibiotic that is prevalent throughout the dairy industry and is excreted in dairy waste. This study investigates the potential of dairy waste management practices to alter the amount of monensin available for release into the environment. Analysis of wastewater and groundwater from two dairy farms in California consistently concluded that monensin is most present in lagoon water and groundwater downgradient of lagoons. Since the lagoons represent a direct source of monensin to groundwater, the effect of waste management, by mechanical screen separation and lagoon aeration, on aqueous monensin concentration was investigated through construction of lagoon microcosms. The results indicate that monensin attenuation is not improved by increased solid-liquid separation prior to storage in lagoons, as monensin is rapidly desorbed after dilution with water. Monensin is also shown to be easily degraded in lagoon microcosms receiving aeration, but is relatively stable and available for leaching under typical anaerobic lagoon conditions.

Journal of Environmental Management↗

USGS2017

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

California↗

USGS2016

Bayesian nitrate source apportionment to individual groundwater wells in the Central Valley by use of elemental and isotopic tracers

Groundwater quality is a concern in alluvial aquifers that underlie agricultural areas, such as in the San Joaquin Valley of California. Shallow domestic wells (less than 150 m deep) in agricultural areas are often contaminated by nitrate. Agricultural and rural nitrate sources include dairy manure, synthetic fertilizers, and septic waste. Knowledge of the relative proportion that each of these sources contributes to nitrate concentration in individual wells can aid future regulatory and land management decisions. We show that nitrogen and oxygen isotopes of nitrate, boron isotopes, and iodine concentrations are a useful, novel combination of groundwater tracers to differentiate between manure, fertilizers, septic waste, and natural sources of nitrate. Furthermore, in this work, we develop a new Bayesian mixing model in which these isotopic and elemental tracers were used to estimate the probability distribution of the fractional contributions of manure, fertilizers, septic waste, and natural sources to the nitrate concentration found in an individual well. The approach was applied to 56 nitrate-impacted private domestic wells located in the San Joaquin Valley. Model analysis found that some domestic wells were clearly dominated by the manure source and suggests evidence for majority contributions from either the septic or fertilizer source for other wells. But, predictions of fractional contributions for septic and fertilizer sources were often of similar magnitude, perhaps because modeled uncertainty about the fraction of each was large. For validation of the Bayesian model, fractional estimates were compared to surrounding land use and estimated source contributions were broadly consistent with nearby land use types.

California↗

USGS2015

Fecal indicator and pathogenic bacteria and their antibiotic resistance in alluvial groundwater of an irrigated agricultural region with dairies

Surveys of microbiological groundwater quality were conducted in a region with intensive animal agriculture in California, USA. The survey included monitoring and domestic wells in eight concentrated animal feeding operations (CAFOs) and 200 small (domestic and community supply district) supply wells across the region. Campylobacter was not detected in groundwater, whereas Escherichia coli O157:H7 and Salmonella were each detected in 2 of 190 CAFO monitoring well samples. Nonpathogenic generic E. coli and Enterococcus spp. were detected in 24.2% (46/190) and 97.4% (185/190) groundwater samples from CAFO monitoring wells and in 4.2% (1/24) and 87.5% (21/24) of CAFO domestic wells, respectively. Concentrations of both generic E. coli and Enterococcus spp. were significantly associated with well depth, season, and the type of adjacent land use in the CAFO. No pathogenic bacteria were detected in groundwater from 200 small supply wells in the extended survey. However, 4.5 to 10.3% groundwater samples were positive for generic E. coli and Enterococcus. Concentrations of generic E. coli were not significantly associated with any factors, but concentrations of Enterococcus were significantly associated with proximity to CAFOs, seasons, and concentrations of potassium in water. Among a subset of E. coli and Enterococcus isolates from both surveys, the majority of E. coli (63.6%) and Enterococcus (86.1%) isolates exhibited resistance to multiple (≥3) antibiotics. Findings confirm significant microbial and antibiotic resistance loading to CAFO groundwater. Results also demonstrate significant attenuative capacity of the unconfined alluvial aquifer system with respect to microbial transport.

California↗

USGS2010

Use and environmental occurrence of pharmaceuticals in freestall dairy farms with manured forage fields

Environmental releases of antibiotics from concentrated animal feeding operations (CAFOs) are of increasing regulatory concern. This study investigates the use and occurrence of antibiotics in dairy CAFOs and their potential transport into first-encountered groundwater. On two dairies we conducted four seasonal sampling campaigns, each across 13 animal production and waste management systems and associated environmental pathways: application to animals, excretion to surfaces, manure collection systems, soils, and shallow groundwater. Concentrations of antibiotics were determined using on line solid phase extraction (OLSPE) and liquid chromatography-tandem mass spectrometry (LC/MS/MS) with electrospray ionization (ESI) for water samples, and accelerated solvent extraction (ASE) LC/MS/MS with ESI for solid samples. A variety of antibiotics were applied at both farms leading to antibiotics excretion of several hundred grams per farm per day. Sulfonamides, tetracyclines, and their epimers/isomers, and lincomycin were most frequently detected. Yet, despite decades of use, antibiotic occurrence appeared constrained to within farm boundaries. The most frequent antibiotic detections were associated with lagoons, hospital pens, and calf hutches. When detected below ground, tetracyclines were mainly found in soils, whereas sulfonamides were found in shallow groundwater reflecting key differences in their physicochemical properties. In manure lagoons, 10 compounds were detected including tetracyclines and trimethoprim. Of these 10, sulfadimethoxine, sulfamethazine, and lincomycin were found in shallow groundwater directly downgradient from the lagoons. Antibiotics were sporadically detected in field surface samples on fields with manure applications, but not in underlying sandy soils. Sulfadimethoxine and sulfamethazine were detected in shallow groundwater near field flood irrigation gates, but at highly attenuated levels.

Environmental Science & Technology↗