The condition of surface water storage in lakes and impoundments is used as an index of drought in the Massachusetts drought management plan. The U.S. Geological Survey visited 28 of these lakes and impoundments at 14 single and multiple waterbody systems to evaluate their appropriateness for characterizing drought. The data collection and computation methods at each system were then reviewed and checked for consistency. The types of historical monthly data available varied by system and included water surface elevation, depth of water below the spillway, volume, or reservoir capacity (percent full). For this analysis, water surface elevations and reservoir capacities were converted to volumes to assess the interannual variability in lake volumes. As a second level of assessment, analysis was also done on water surface elevation variability. Systems that did not have enough differentiation in monthly values between lake volume or water surface elevations to clearly demarcate drought levels were identified as unsuitable for use in the drought index for that month. This report discusses the limitations of using the reviewed lakes and impoundments as a drought index, as well as a list of best practices for data collection techniques to improve the confidence and reliability of the data collected.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Secchi depth measurements in Lake Tahoe have been collected at a Long-Term Profile (LTP) monitoring site since 1968. Periodic updates in Secchi trend analysis are needed to understand changes in the long-term record, changes in seasonal pattern, and to provide insight into the progress of restoration efforts in improving lake clarity. As such, this analysis is intended to evaluate the long-term and seasonal clarity conditions by updating the analysis by Jassby and others, (1999) and to demonstrate the use of statistical measures as metrics for comparing ongoing monitoring data (Watanabe, 2024).
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Performance metrics used to assess the accuracy of the MOVE.1 models indicated that most models provided reasonable estimates of groundwater levels. Root mean square error values ranged from 0.097 to 2.292 feet, with a median of 0.536 foot. Nash-Sutcliffe efficiency coefficient values ranged from 0.623 to 0.996, with a median value of 0.759. Generally, study wells in close geographical proximity to their index well resulted in stronger model performance.
The average length of groundwater level records was extended by 14.1 years to a new average of 18.1 years. The estimated groundwater level records from the MOVE.1 models resulted in an increase in the range of highest and lowest groundwater levels at 23 of 27 wells. The increase in range of groundwater levels was between 0.08 to 7.95 feet. Monthly percentiles for State drought indices were computed from the estimated MOVE.1 records and observed records through December 31, 2021. Percentiles computed from estimated records show an average groundwater level about 1.0 foot lower than observed data at the 2d percentile and 0.1 foot lower at the 30th percentile.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Introduction: White Sturgeon Acipenser transmontanus in the Kootenai River basin is listed as endangered in the United States and Canada. Declines have been mainly attributed to poor recruitment exacerbated by the environmental effects of Libby Dam in Montana. Reduced primary production downstream of Libby Dam has been identified as one factor limiting growth of White Sturgeon, thereby limiting natural reproduction through delayed sexual maturity. However, estimating changes in growth over time without knowledge of fish location (i.e., Kootenai River vs. Kootenay Lake) is difficult. The objective of this project was to use microchemistry analysis to describe the movement of White Sturgeon within the Kootenai River basin.
Methods: Water samples (n = 27) and White Sturgeon fin rays (n = 162) collected in the Kootenai River basin were measured for strontium isotope (87Sr:86Sr) ratios using laser ablation multicollector inductively coupled plasma mass spectrometry. All water samples and a subset of fin rays (n = 8) were also analyzed for trace elements (e.g., Sr, Ba, Mg). Fin ray annuli were measured and tested using knowledge of known age; known locations from physical capture events were assigned to age at capture.
Results: Strontium isotope analysis was unable to detect differences in the Kootenai River and Kootenay Lake using water or fin ray samples. The Kootenai River and Kootenay Lake were distinguishable using trace element data from water samples, but not fin rays. The discrepancy with trace elements appears to be associated with the physiology of fin ray growth.
Discussion: Although this study did not meet its original objective of describing the movement of White Sturgeon in the Kootenai River basin from fin ray microchemistry, our results provide insight into the potential influence of physiology on microchemistry analysis. In particular, fin ray microchemistry of slow-growing fishes may be possible in freshwater systems with further investigation into the physiological processes associated with growth and the incorporation of elements into calcified structures.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2024.1475115","usgsCitation":"Ghere, C., Quist, M.C., Hardy, R.S., Willmes, M., Lewis, L., Wilson, S., and Smith, T., 2024, An evaluation of fin ray microchemistry to describe movement of White Sturgeon in the Kootenai River basin: Insights and limitations: Frontiers in Freshwater Science, v. 2, 1475115, 16 p., https://doi.org/10.3389/ffwsc.2024.1475115.","productDescription":"1475115, 16 p.","ipdsId":"IP-167840","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffwsc.2024.1475115","text":"Publisher Index Page"},{"id":485211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, Idaho, Montana","otherGeospatial":"Kootenai River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.58401652080497,\n 50.69930521150616\n ],\n [\n -117.49821629131017,\n 49.509131619753205\n ],\n [\n -116.48726648061708,\n 48.128537941603184\n ],\n [\n -115.02431190642167,\n 48.23747385319575\n ],\n [\n -115.07423825028928,\n 48.982857626149\n ],\n [\n -115.7490124116938,\n 50.94774297789954\n ],\n [\n -117.58401652080497,\n 50.69930521150616\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2024-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghere, Courtnie L.","contributorId":352032,"corporation":false,"usgs":false,"family":"Ghere","given":"Courtnie","middleInitial":"L.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":934915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hardy, Ryan S.","contributorId":167032,"corporation":false,"usgs":false,"family":"Hardy","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":6764,"text":"Idaho Department of Fish and Game, Nampa, Idaho","active":true,"usgs":false}],"preferred":false,"id":934917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Willmes, Malte","contributorId":198500,"corporation":false,"usgs":false,"family":"Willmes","given":"Malte","email":"","affiliations":[],"preferred":false,"id":934918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Levi","contributorId":313579,"corporation":false,"usgs":false,"family":"Lewis","given":"Levi","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":934919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Sean","contributorId":352033,"corporation":false,"usgs":false,"family":"Wilson","given":"Sean","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":934920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Troy","contributorId":353368,"corporation":false,"usgs":false,"family":"Smith","given":"Troy","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":934921,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259385,"text":"70259385 - 2024 - Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning","interactions":[],"lastModifiedDate":"2024-10-07T14:39:04.576768","indexId":"70259385","displayToPublicDate":"2024-09-27T09:34:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning","docAbstract":"Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies, and this imbalance is expected to increase as the world transitions away from fossil fuel energy sources. High concentrations of lithium in brines have been observed in the Smackover Formation in southern Arkansas (>400 milligrams per liter). We used published and newly collected brine lithium concentration data to train a random forest machine-learning model using geologic, geochemical, and temperature explanatory variables and create a map of predicted lithium concentrations in Smackover Formation brines across southern Arkansas. Using these predicted lithium maps with reservoir parameters and geologic information, we calculated that there are 5.1 to 19 million tons of lithium in Smackover Formation brines in southern Arkansas, which represents 35 to 136% of the current US lithium resource estimate. Based on these calculations, in 2022, 5000 tons of dissolved lithium were brought to the surface within brines as waste streams of the oil, gas, and bromine industries.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adp8149","usgsCitation":"Knierim, K.J., Blondes, M., Masterson, A., Freeman, P., McDevitt, B., Herzberg, A., Li, P., Mills, C., Doolan, C.A., Jubb, A., Ausbrooks, S., and Chenault, J., 2024, Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning: Science Advances, v. 10, no. 39, eadp8149, 11 p., https://doi.org/10.1126/sciadv.adp8149.","productDescription":"eadp8149, 11 p.","ipdsId":"IP-153803","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":466893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adp8149","text":"Publisher Index Page"},{"id":462662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Smackover 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Center","active":true,"usgs":true}],"preferred":true,"id":915106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masterson, Andrew Laurence 0000-0002-3422-2985","orcid":"https://orcid.org/0000-0002-3422-2985","contributorId":343951,"corporation":false,"usgs":true,"family":"Masterson","given":"Andrew Laurence","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":224150,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzberg, Amanda Sha 0000-0003-0343-9425","orcid":"https://orcid.org/0000-0003-0343-9425","contributorId":333089,"corporation":false,"usgs":true,"family":"Herzberg","given":"Amanda Sha","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915111,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Peng","contributorId":344957,"corporation":false,"usgs":false,"family":"Li","given":"Peng","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State Geologist","active":true,"usgs":false}],"preferred":false,"id":915112,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mills, Ciara","contributorId":344958,"corporation":false,"usgs":false,"family":"Mills","given":"Ciara","email":"","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State Geologist","active":true,"usgs":false}],"preferred":false,"id":915113,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915114,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915115,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ausbrooks, Scott","contributorId":344959,"corporation":false,"usgs":false,"family":"Ausbrooks","given":"Scott","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State 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Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Framework for mapping liquefaction hazard–Targeted design ground motions","docAbstract":"Vegetation communities in restored bottomland hardwood forests in northeast Indiana were studied 6–21 years after restoration to assess progress toward restoration objectives. The study focused on four sites that were restored to compensate for resource injuries after contaminant releases. The restored sites were compared with four reference-site conditions, including crops (prerestoration condition), old field communities representing a no-management alternative, locally sampled second-growth mature forests, and forest community types described by the US National Vegetation Classification (USNVC), which represent ideal or defining conditions of recognized vegetation communities. Fixed-area plots provided data on field-sampled environmental variables, vegetation, soil, and hydrological conditions for crops, old fields, restored areas, and mature forests. The USNVC database provided quantitative data for three historically and geographically relevant reference forest community types for comparison with the sampled communities. Results of nonmetric multidimensional scaling based on species cover revealed clear gradients relating to site age and canopy development. Along those gradients, restored areas demonstrated increasing similarity to mature forest reference communities in terms of floristic composition. Specifically, the floristic quality of restored areas was significantly greater than that of crops and old fields. Furthermore, soil health measurements of physical, chemical, and hydrological conditions indicated significant improvements in restored site soils compared with prerestoration conditions represented by cropland soils. Descriptions and data from the USNVC provided ecological context for restoration target conditions and facilitated the assessment of restoration recovery along a trajectory from starting conditions to those target conditions. Descriptions by USNVC also helped identify deviations from the intended restoration objectives (e.g., invasive species recruitment) and potential adaptive management actions to return sites to their intended trajectories. Integr Environ Assess Manag 2024;00:1–22. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.
Elevated nitrogen loads are pervasive in the Long Island Sound, an estuary that receives freshwater and nutrients from both surface-water and groundwater discharge. Surface-water nitrogen loads to the Long Island Sound are relatively well characterized, but less is known about groundwater-transported nitrogen loads. Prior work on the northern shore of Long Island Sound (Connecticut and areas of New York and Rhode Island) suggested that groundwater travel times are relatively short (median less than 2 years) and that decade-long nutrient legacies are not widespread. Because the travel times are short, groundwater flow and nutrient loads likely vary substantially between months. In the current study, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection, developed a set of models to better characterize spatial and temporal patterns of groundwater-transported nitrogen loading from atmospheric deposition, septic systems, and fertilizers within the study area. The models provide an estimate, with uncertainty, of groundwater-transported nitrogen loads in the study area, filling a key gap in the nitrogen budget for Long Island Sound. The models also highlight the spatial and temporal variation in nitrogen loading throughout the study area.
The modeling workflow involved four models. (1) A soil-water-balance model was developed by using the Soil-Water-Balance software to simulate groundwater recharge across the study area for water years 2005 through 2022. The simulated mean monthly recharge from the soil-water-balance model was used as input into a groundwater-flow model. (2) The groundwater-flow model was developed by using the MODFLOW 6 software and data for water years 1993 through 2022 and simulates average monthly hydrologic conditions. The groundwater-flow model was calibrated by using the Iterative Ensemble Smoother method within the PEST++ software. The Iterative Ensemble Smoother method generates an ensemble of sets of parameter values, with each set producing reasonable simulated hydrologic parameter values. (3) An ensemble of MODPATH particle-tracking simulations were run to generate particle flow paths and travel times, with each simulation using a different set of the flow model parameters. (4) A nitrogen load model uses the MODPATH simulation outputs to track nitrogen from the land surface through multiple attenuation zones until it discharges into fresh or saline surface water. As with the groundwater-flow model, the nitrogen model simulated average monthly groundwater-transported nitrogen loads for water years 1993 through 2022. One novel aspect of the nitrogen load model is that the nitrogen attenuation parameters were calibrated to observed nitrogen loads.
Across the ensemble of simulated nitrogen loads, the median study-area-wide monthly simulated nitrogen loads from the aquifer to Long Island Sound throughout the year ranged from 900 to 18,600 kilograms of nitrogen per day, with a median load of 5,100 kilograms of nitrogen per day. The simulated loads were based on average monthly conditions for water years 1993 through 2022. Loads were highest during the winter and early spring and lowest during the late summer. However, simulated travel times for groundwater and nitrogen loads discharged to Long Island Sound during summer were longer than travel times for groundwater and loads discharged during the winter, indicating that, on average, groundwater discharged during summer traveled along different, and longer, flow paths, than groundwater discharged during winter. This indicates that summer loads would respond more slowly to changes in nitrogen inputs at the water table than winter loads. Over the entire study area, approximately 15 percent of the simulated load is from atmospheric deposition sources, 30 to 40 percent is from fertilizer, and 50 to 60 percent is from septic systems.
The final analysis of the study involved simulating the change in groundwater-transported nitrogen load in response to upgrading septic systems or reducing fertilizing inputs to areas of turf grass. Both management interventions reduced the groundwater-transported nitrogen load, and reductions were greater in areas with greater loads from septic systems or turf-grass fertilizers. The delay between management actions and substantial reductions in groundwater-transported nitrogen loads varied seasonally; loads during the late summer months remained elevated longer than the winter loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., Holland, M.J., and Mullaney, J.R., 2024, Simulated mean monthly groundwater-transported nitrogen loads in watersheds on the north shore of Long Island Sound, 1993–2022: U.S. Geological Survey Scientific Investigations Report 2024–5090, 63 p., https://doi.org/10.3133/sir20245090.","productDescription":"Report: xi, 63; 3 Data Releases","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150246","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":462294,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HKENGV","text":"USGS data release","linkHelpText":"MODFLOW6 groundwater flow model, MODPATH particle-tracking simulation, and groundwater-transported nitrogen load model of average monthly conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, 1993–2022"},{"id":497952,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117502.htm","linkFileType":{"id":5,"text":"html"}},{"id":462296,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215116","text":"Scientific Investigations Report 2021–5116","linkHelpText":"- Simulation of Groundwater Budgets and Travel Times for Watersheds on the North Shore of Long Island Sound, With Implications for Nitrogen-Transport Studies"},{"id":462295,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1GUC7FE","text":"USGS data release","linkHelpText":"Soil-Water-Balance model developed to simulate net infiltration in watersheds on the north shore of the Long Island Sound"},{"id":462293,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XEN74S","text":"USGS data release","linkHelpText":"Summary simulated groundwater-transported nitrogen loads on the north shore of Long Island Sound and associated data"},{"id":462292,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5090 XML"},{"id":462291,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5090/images/"},{"id":462290,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245090/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5090 HTML"},{"id":462289,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.pdf","text":"Report","size":"24.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5090 PDF"},{"id":462288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5090/coverthb.jpg"}],"country":"United States","state":"Connecticut, Rhode Island","otherGeospatial":"Long Island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.60521599677895,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 40.99974278286342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Groundwater-quality data and potential fluid-migration pathways near the Placerita Oil Field in Los Angeles County, California, were examined by the U.S. Geological Survey to determine if oil-field fluids (water and gas from oil-producing and non-producing zones) have mixed with groundwater resources. Six of the 13 new groundwater samples collected for this study contained petroleum hydrocarbons, thermogenic gas, inorganic chemical signatures, and (or) isotopic values consistent with potential mixing with fluids from hydrocarbon-bearing formations.
For historical groundwater samples, benzene was the most detected petroleum hydrocarbon. The historical groundwater samples with a benzene concentration greater than 0.5 micrograms per liter were from environmental monitoring wells at industrial or commercial facilities unrelated to oil and gas development that, in many cases, have identified soil or groundwater contamination and were not typically analyzed for other constituents that could provide additional lines of evidence for potential mixing with oil-field fluids. Methane was not detected in any of the 12 historical samples with a reported measurement.
Reviewing historical data revealed factors that could potentially adversely affect groundwater quality in the study area. These factors include modified hydraulic gradients caused by large volumes of water extracted from the main production area and reinjected downgradient into nonproducing zones, well-barrier failures in wells constructed in the northern part of the oil field before the 1970s, well-barrier failures in produced-water disposal wells downgradient from the main production area, and naturally occurring hydrocarbons at shallow intervals. The groundwater samples most geochemically similar to samples from hydrocarbon-bearing formations were in areas where hydrocarbons are naturally occurring at shallow intervals and where oil development is at shallow depths. Additional data for hydraulic heads, water quality, and formation temperatures at multiple depths in areas with large injection volumes and well-integrity issues are needed to evaluate whether those factors have contributed to mixing between fluids from oil-producing or injection formations and groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245042","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Stanton, J.S., Landon, M.K., Shimabukuro, D.H., Kulongoski, J.T., Hunt, A.G., McMahon, P.B., Cozzarelli, I.M., Anders, R., and Sowers, T.A., 2024, Groundwater quality near the Placerita Oil Field, California, 2018: U.S. Geological Survey Scientific Investigations Report 2024–5042, 65 p., https://doi.org/10.3133/sir20245042.","productDescription":"Report: ix, 65 p.; 2 Data Releases; 7 Tables","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-152098","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":462192,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042_app2_csv.zip","text":"Appendix 2, Tables 2.1–2.7","size":"10.4 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- CSV files"},{"id":462187,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5042/images/"},{"id":462188,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5042 XML"},{"id":462186,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245042/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5042 HTML"},{"id":462191,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042_app2.xlsx","text":"Appendix 2, Tables 2.1–2.7","size":"56.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Supplemental Tables for the Placerita Oil Field Study Area, California, 2018"},{"id":462190,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G5UD7J","text":"USGS data release","linkHelpText":"Water chemistry data for samples collected at groundwater sites in the Placerita Oil Field study area, June 2018—November 2018, Los Angeles County, California"},{"id":462189,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KFFR8","text":"USGS data release","linkHelpText":"Produced water chemistry data collected from the Oxnard Oil Field, Ventura County, and the Placerita Oil Field, Los Angeles County, 2018, California"},{"id":462185,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5042 PDF"},{"id":462184,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5042/coverthb.jpg"},{"id":497953,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117501.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Placerita Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5667,\n 34.4333\n ],\n [\n -118.5667,\n 34.3333\n ],\n [\n -118.4,\n 34.3333\n ],\n [\n -118.4,\n 34.4333\n ],\n [\n -118.5667,\n 34.4333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
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6000 J Street, Placer Hall
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The U.S. Geological Survey studied groundwater near the Placerita Oil Field in Los Angeles County to see if oil-field fluids have mixed with groundwater. Six out of 13 new samples showed signs of mixing with fluids from hydrocarbon-bearing formations. Historical data revealed factors that could affect groundwater quality, including modified hydraulic gradients, well-barrier failures, and naturally occurring hydrocarbons. More data are needed to evaluate the effects of these factors.
","publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":913789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":913791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913792,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":913793,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anders, Robert 0000-0002-2363-9072 randers@usgs.gov","orcid":"https://orcid.org/0000-0002-2363-9072","contributorId":1210,"corporation":false,"usgs":true,"family":"Anders","given":"Robert","email":"randers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913794,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sowers, Theron A. 0000-0002-3208-5411","orcid":"https://orcid.org/0000-0002-3208-5411","contributorId":211482,"corporation":false,"usgs":false,"family":"Sowers","given":"Theron","email":"","middleInitial":"A.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":913795,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259191,"text":"70259191 - 2024 - Factors contributing to pesticide contamination in riverine systems: The role of wastewater and landscape sources","interactions":[],"lastModifiedDate":"2024-10-01T11:49:20.686522","indexId":"70259191","displayToPublicDate":"2024-09-26T06:44:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Factors contributing to pesticide contamination in riverine systems: The role of wastewater and landscape sources","docAbstract":"This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggae353","usgsCitation":"Minson, S.E., 2024, Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems: Geophysical Journal International, v. 239, no. 3, p. 1629-1649, https://doi.org/10.1093/gji/ggae353.","productDescription":"21 p.","startPage":"1629","endPage":"1649","ipdsId":"IP-158117","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":466901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggae353","text":"Publisher Index Page"},{"id":463412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"239","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":917419,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70259465,"text":"70259465 - 2024 - Spatial variation of eDNA detection across an invasion gradient for invasive species monitoring programs","interactions":[],"lastModifiedDate":"2025-03-25T15:45:28.873478","indexId":"70259465","displayToPublicDate":"2024-09-25T09:40:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variation of eDNA detection across an invasion gradient for invasive species monitoring programs","docAbstract":"Spatial and temporal distribution data provide critical information for invasive species management. For example, distribution data can help managers with early detections and guiding other response actions. Environmental DNA (eDNA)-based sampling exists as one tool for monitoring invasive species. As part of bigheaded carp Hypophthalmichthys spp. monitoring efforts in the Illinois River, USA, we compared eDNA-based sampling results at multiple habitats across an invasion gradient in 2015. Greater densities of carp occurred downriver in the Illinois River and lower densities occurred upriver. We sampled from five locations along this gradient and from three habitat types (backwater, main channel, and shoreline) within each location. We sampled each location in April and June. A priori, we hypothesized that more eDNA detections would occur downriver, where higher densities of carp occur; that more eDNA detections would occur in backwater habitats compared to areas of the river with more fish movement; and that more eDNA detections would occur in April, because bigheaded carps are thought to use our sampling areas more during the spring. We compared the proportion of samples positive across this gradient, the habitat type, and the two sampling time periods. The most downriver location had the highest proportion of samples with eDNA detections, the backwater habitats had the highest proportion of samples with eDNA detections, and April had more positive detections than June. Our results highlight the importance of sampling across multiple habitat types and across time to gain a clear understanding of distribution when using eDNA-based sampling. Thus, being cognizant of the interactions between seasonal habitat use and eDNA-based detections is important for managers who rely upon eDNA-based monitoring.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-23-038","usgsCitation":"Peterman, L.L., Tuttle-Lau, M.T., DeHaan, P.W., Coulter, D.P., Spear, S.F., and Erickson, R.A., 2024, Spatial variation of eDNA detection across an invasion gradient for invasive species monitoring programs: Journal of Fish and Wildlife Management, v. 15, no. 2, p. 350-360, https://doi.org/10.3996/JFWM-23-038.","productDescription":"11 p.","startPage":"350","endPage":"360","ipdsId":"IP-138781","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":466902,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-038","text":"Publisher Index Page"},{"id":462743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.97996788684303,\n 41.96009875147479\n ],\n [\n -89.36825568969475,\n 41.96009875147479\n ],\n [\n -89.36825568969475,\n 40.943421835218516\n ],\n [\n -87.97996788684303,\n 40.943421835218516\n ],\n [\n -87.97996788684303,\n 41.96009875147479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterman, Laura Lynne 0000-0001-6976-4138","orcid":"https://orcid.org/0000-0001-6976-4138","contributorId":328472,"corporation":false,"usgs":true,"family":"Peterman","given":"Laura","email":"","middleInitial":"Lynne","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tuttle-Lau, Maren T.","contributorId":146196,"corporation":false,"usgs":false,"family":"Tuttle-Lau","given":"Maren","email":"","middleInitial":"T.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":915402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeHaan, Patrick W.","contributorId":145918,"corporation":false,"usgs":false,"family":"DeHaan","given":"Patrick","email":"","middleInitial":"W.","affiliations":[{"id":16297,"text":"USFWS Abernathy Fish Technology Center, Longview, WA 98632","active":true,"usgs":false}],"preferred":false,"id":915403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coulter, David P.","contributorId":205629,"corporation":false,"usgs":false,"family":"Coulter","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":915404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spear, Stephen Frank 0000-0001-8351-9382","orcid":"https://orcid.org/0000-0001-8351-9382","contributorId":293162,"corporation":false,"usgs":true,"family":"Spear","given":"Stephen","email":"","middleInitial":"Frank","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915406,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259609,"text":"70259609 - 2024 - Groundwater-Surface water interactions research: Past trends and future directions","interactions":[],"lastModifiedDate":"2024-10-17T11:58:22.178363","indexId":"70259609","displayToPublicDate":"2024-09-25T06:55:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater-Surface water interactions research: Past trends and future directions","docAbstract":"The U.S. Geological Survey (USGS) in partnership with the International Joint Commission installed and operated continuous water-quality monitors at three sites on the Souris River from May 2019 to October 2023. Continuously recorded data included dissolved oxygen (DO), water temperature, and specific conductance at the Souris River near Sherwood, North Dakota (USGS station 05114000), Souris River above Minot, N. Dak. (USGS station 05117500), and Souris River near Westhope, N. Dak (USGS station 05124000). The three sites on the Souris River were chosen for additional DO monitoring because they provided the best opportunity to capture potential effects on DO in areas downstream from major flow control structures and because identifying the connection of streamflow to DO at the international border is a focus of the International Souris River Board (ISRB).
The continuous water-quality monitoring at three sites on the Souris River from May 16, 2019, to October 1, 2023, indicated different patterns in DO among the three sites, and the different patterns indicate different factors affect DO concentrations among the sites. DO concentrations near Sherwood indicated the strong effect of algal dynamics at lower streamflow conditions with large diurnal fluctuations in DO concentration and indicated that streamflow does seem to affect DO concentrations when the streamflow is greater than about 100 cubic feet per second. DO concentrations were also frequently less than the water-quality objective (WQO) of 5 milligrams per liter in the summer and winter months, particularly during relatively low streamflow conditions in 2020 and 2021. DO concentrations above Minot had a different pattern with considerably fewer diurnal fluctuations than near Sherwood, high DO concentrations most winters except for the winter of 2021–22, and fewer instances when the DO was less than the WQO compared to Sherwood. The pattern of DO concentrations near Westhope seemed to be mainly influenced by the water chemistry coming out of J. Clark Salyer Pool 357 rather than streamflow and channel conditions at the site. The Westhope site also had the most days with daily minimum DO concentrations less than the WQO among the three sites, mainly in the winter when concentrations were consistently at or near 0 milligrams per liter for most of the winter months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241043","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Galloway, J.M., 2024, Dissolved oxygen monitoring on the Souris River, 2019–23: U.S. Geological Survey Open-File Report 2024–1043, 13 p., https://doi.org/10.3133/ofr20241043.","productDescription":"Report: iv, 13 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167787","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497955,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117500.htm","linkFileType":{"id":5,"text":"html"}},{"id":462183,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241043/full"},{"id":462182,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":462181,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1043/images/"},{"id":462180,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1043/ofr20241043.XML"},{"id":462179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1043/ofr20241043.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1043"},{"id":462178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1043/coverthb.jpg"}],"country":"Canada, United States","state":"North Dakota","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.03302625208204,\n 50.79350866915286\n ],\n [\n -105.03302625208204,\n 47.533599370512235\n ],\n [\n -98.11163953333218,\n 47.533599370512235\n ],\n [\n -98.11163953333218,\n 50.79350866915286\n ],\n [\n -105.03302625208204,\n 50.79350866915286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
The U.S. Geological Survey, in cooperation with Colorado Springs Utilities and Colorado Springs Stormwater Enterprise, synthesized previous studies and evaluated recent monitoring data to understand the distribution of fish and invertebrates in the Fountain Creek Basin and documented response to streamflow, water temperature, and water quality. The goal was to identify opportunities for aligning data collection to help maximize information gained from additional monitoring. Fifty-two publications were compiled from the literature that were completed within the study area between 1964 and 2022. Of these publications, 19 were fish and invertebrate focused. Overall, the distribution of fish and invertebrates in the Fountain Creek Basin has changed since the early 1900s. The occurrence of several fish species has increased or decreased since 2003, and a few species have not been collected in more than 100 years. Several mayfly, stonefly, and caddisfly taxa once common at several locations before 2000 are now rarely encountered, and those that now occur more frequently are associated with warmer-water streams. Decreasing invertebrate multimetric index values were noted at six locations, and the invasive Potamopyrgus antipodarum (New Zealand mud snail) is now established at two locations and occurs at several others, but in low numbers. Various streamflow characteristics were frequently noted to affect spatial and temporal patterns in fish and invertebrate communities, including early development and recruitment of Platygobio gracilis (flathead chub). Water quality and temperature contributed to patterns in aquatic communities, but less is known about the direct effects as these data were inconsistently available. Reach-scale habitat also contributed to patterns in aquatic communities, especially measures associated with the streambank, stream channel, and composition of streambed substrate. Moving forward, aligning consistent streamflow, water temperature, water quality, and geomorphic data collection at fish-, invertebrate-, and habitat-monitoring locations could maximize information gained from monitoring efforts and potentially inform evolving management activities and interests within the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245074","collaboration":"Prepared in cooperation with Colorado Springs Utilities and Colorado Springs Stormwater Enterprise","usgsCitation":"Zuellig, R.E., Wahl, C.F., Hennessy, E.K., Jouney, A., and Foutz, P., 2024, Evaluation and review of ecology-focused stream studies to support cooperative monitoring, Fountain Creek Basin, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5074, 30 p., https://doi.org/10.3133/sir20245074.","productDescription":"Report: vii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158983","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":497958,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117504.htm","linkFileType":{"id":5,"text":"html"}},{"id":462209,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245074/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5074"},{"id":462156,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5074/images"},{"id":462112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91QQ6GT","text":"USGS data release","linkHelpText":"Datasets for Evaluation and Review of Ecology-Focused Stream Studies, Fountain Creek Basin, Colorado"},{"id":462111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5074/sir20245074.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5074"},{"id":462110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5074/coverthb.jpg"},{"id":462157,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5074/sir20245074.xml"}],"country":"United States","state":"Colorado","otherGeospatial":"Fountain Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.45322123672925,\n 39.26331261795599\n ],\n [\n -105.45322123672925,\n 38.0924698636114\n ],\n [\n -104.3145085834951,\n 38.0924698636114\n ],\n [\n -104.3145085834951,\n 39.26331261795599\n ],\n [\n -105.45322123672925,\n 39.26331261795599\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 1 (January–March), 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website:
https://earthexplorer.usgs.gov.
This quarterly report is the third to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
This quarterly report is the first to not include analysis results for Landsat 7 because Enhanced Thematic Mapper Plus imaging was suspended on January 19, 2024, after the satellite transitioned into full sunlight. The satellite has been drifting since early 2022 after being lowered from the nominal orbit altitude, and the transition into full sunlight is a result of the satellite operating in its extended science mission. Additional information about the imaging suspension is available at https://www.usgs.gov/landsat-missions/news/landsat-7-imaging-suspended. Additional information about the Landsat 7 extended science mission is available at https://www.usgs.gov/landsat-missions/landsat-7-extended-science-mission.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241058","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Barsi, J., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2024 (ver. 1.1, December 2024): U.S. Geological Survey Open-File Report 2024–1058, 57 p., https://doi.org/10.3133/ofr20241058.","productDescription":"Report: viii, 57 p.; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-165326","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":439140,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1058/images/"},{"id":439137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1058/coverthb2.jpg"},{"id":439138,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1058"},{"id":439139,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.XML"},{"id":439141,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241058/full"},{"id":439142,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":464902,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1058/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: September 24, 2024; Version 1.1: December 11, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Well integrity monitoring has always been a critical component of subsurface oil and gas operations. Distributed fiber-optic sensing is an emerging technology that shows great promise for monitoring processes, both in boreholes and in other settings. In this study, we present a case study of using distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope (ANS). The well was drilled as part of a long-term gas hydrate study, and the downhole DTS data were recorded over a period of approximately 2 years. By applying a temporal gradient and removing instrument instability noise, we reveal subtle (<0.001°C/h) thermal anomalies, which are characterized by brief warming periods followed by longer cooling periods at discrete depths along the borehole. The observed coherent events show an upward trajectory from deeper formations into the overlying permafrost interval, with the thermal anomalies concentrated in relatively coarse-grained sandstone layers. We also observe that the upward migration rate of the DTS anomalies varies with formation lithology and that there is a spatial and temporal correlation between the subsurface events and measured wellhead annular pressures. We interpret that the observed warming events represent the exothermic process of gas hydrate formation that is occurring in association with the upward migration of gas outside the well casing, and this interpretation is confirmed by numerical simulations. These observations demonstrate the ability of suitably processed DTS data to detect subtle processes and highlight the value of DTS technologies for wellbore integrity monitoring.
","language":"English","publisher":"Society of Petroleum Engineers","doi":"10.2118/223111-PA","usgsCitation":"Garcia-Ceballos, A., Jin, G., Collett, T., Merey, S., and Haines, S.S., 2024, Long-term distributed temperature sensing monitoring for near-wellbore gas migration and gas hydrate formation: SPE Journal, v. 29, no. 11, p. 5804-5819, https://doi.org/10.2118/223111-PA.","productDescription":"16 p.","startPage":"5804","endPage":"5819","ipdsId":"IP-162957","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":498024,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2118/223111-pa","text":"Publisher Index Page"},{"id":464441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151,\n 71\n ],\n [\n -151,\n 70\n ],\n [\n -148,\n 70\n ],\n [\n -148,\n 71\n ],\n [\n -151,\n 71\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Ceballos, Ana","contributorId":333715,"corporation":false,"usgs":false,"family":"Garcia-Ceballos","given":"Ana","email":"","affiliations":[],"preferred":false,"id":914769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Ge","contributorId":333716,"corporation":false,"usgs":false,"family":"Jin","given":"Ge","email":"","affiliations":[],"preferred":false,"id":914770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220812,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":914771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merey, Sukru","contributorId":344807,"corporation":false,"usgs":false,"family":"Merey","given":"Sukru","email":"","affiliations":[{"id":82412,"text":"Batman University","active":true,"usgs":false}],"preferred":false,"id":914772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":914773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258081,"text":"70258081 - 2024 - The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning","interactions":[],"lastModifiedDate":"2024-11-22T16:08:59.048285","indexId":"70258081","displayToPublicDate":"2024-09-23T17:17:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning","docAbstract":"The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland-specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non-wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5985","usgsCitation":"Soulard, C.E., Walker, J., Smith, B.W., and Kreitler, J.R., 2024, The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning: Earth Surface Processes and Landforms, v. 49, no. 14, p. 4632-4649, https://doi.org/10.1002/esp.5985.","productDescription":"18 p.","startPage":"4632","endPage":"4649","ipdsId":"IP-158309","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":462417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"49","issue":"14","noUsgsAuthors":false,"publicationDate":"2024-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Britt Windsor 0000-0003-1556-2383","orcid":"https://orcid.org/0000-0003-1556-2383","contributorId":287481,"corporation":false,"usgs":true,"family":"Smith","given":"Britt","email":"","middleInitial":"Windsor","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912042,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70258161,"text":"cir1525 - 2024 - Remote sensing for monitoring mine lands and recovery efforts","interactions":[],"lastModifiedDate":"2024-09-23T14:10:34.799856","indexId":"cir1525","displayToPublicDate":"2024-09-23T09:35:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1525","displayTitle":"Remote Sensing for Monitoring Mine Lands and Recovery Efforts","title":"Remote sensing for monitoring mine lands and recovery efforts","docAbstract":"Under the Bipartisan Infrastructure Law Ecosystem Restoration Program, the U.S. Department of the Interior has invested in assessing and recovering degraded ecosystems to promote healthy human communities and wildlife habitats. One priority established by the program is the need to address degraded ecosystems associated with mine lands, including active, inactive, and abandoned mines. Mine lands occur in every State of the United States and present a range of environmental hazards and safety risks to human communities and wildlife habitats. However, limited information compiled across the United States exists on the whereabouts of mining activities and their potential environmental effects on landscapes. Remote sensing is the process of acquiring information about the landscape from ground platforms, aircraft, or satellites to assess surface characteristics such as topography, vegetation, and soil properties and can therefore provide important cost-effective methods for identifying mining sites and assessing their environmental effects. Based on a literature review of remote sensing applications that assessed land health conditions and monitoring of mine lands, this report highlights important approaches, capabilities, considerations, and case studies using a breadth of techniques. The report identifies considerations for setting appropriate vegetation recovery targets and demonstrates how remote sensing can inform the prioritization, recovery design, and long-term assessments of recovery to help support decision makers and land managers. Applications of remote sensing to mine recovery will be most effective when recovery targets are clearly defined and quantifiable from data collected before mining activity, per the Surface Mining Control and Reclamation Act of 1977 and similar laws. Additionally, the collected data would need to be accessible and maintained in databases for baseline references used to establish these recovery targets.
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U.S. Geological Survey
2150 Centre Ave., Bldg. C
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The U.S. Geological Survey (USGS), in cooperation with the Washington State Department of Ecology (Ecology), conducted a study to describe the current understanding of the regional groundwater system of the lower Duwamish River valley and groundwater and surface-water interactions in the lower Duwamish Waterway. The lower Duwamish Waterway is the final 5-mile (mi) reach of the Duwamish River before it empties into Elliott Bay in Puget Sound near Seattle, Washington. A nearshore site (hereinafter referred to as “Nearshore Site” to distinguish the particular site from general discussions of nearshore areas) along the western shoreline of the Duwamish River, about 1.5 mi upstream from the river mouth, was selected for focused groundwater data collection by USGS. Data loggers were deployed in seven groundwater wells and one stilling well in the Duwamish River to measure specific conductance, temperature, and depth at 15-minute intervals for a period of about 2 years.
At the Nearshore Site during 2020–22, water levels in the shallow wells were 3–8 feet (ft) higher than water levels in the deep wells, providing evidence for a low-permeability layer between the shallow and deep aquifers in this area. The shallow wells had a pronounced seasonal variability, with high water levels in winter and low water levels in summer. Data from the deep wells showed far less seasonal variability, with slight increases in winter and a near-constant water level from spring to autumn. The deep wells had a strong hydraulic connection to the Duwamish River, as evidenced by the synchronous water-level variability during the tidal cycle, whereas the shallow wells had minimal to no tidal response. The potentiometric maps developed for the Nearshore Site and surrounding areas indicate large differences in groundwater-flow directions for the shallow and deep aquifers at low and high tides. For the shallow aquifer, flow is toward the lower Duwamish Waterway near the Nearshore Site, regardless of the tidal condition. For the deep aquifer, a potentiometric trough forms parallel to the shoreline during high tide, indicating that groundwater flow converges from the uplands to the west and the Duwamish River to the east. The geometry of the potentiometric surfaces between the nearshore-most well and the shoreline is complex and is further confounded by intermittent shoreline armoring and other buried infrastructure, which could serve as either a barrier or a conduit to flow.
Groundwater and surface-water interactions in the lower Duwamish Waterway are inherently complex as a result of three overarching factors. First, water levels in the lower reaches of the Duwamish River vary daily by 11–16 ft because of tides from Puget Sound, which create large swings in the hydraulic gradient in the nearshore groundwater system. Second, the density and chemical composition of water in the Duwamish River change daily with the tides and seasonally, which constrains how river water entering the nearshore sediments interacts with discharging groundwater. Third, the nearshore subsurface and shoreline conditions are heterogenous because of extensive shoreline armoring over the past century, which governs the flow of groundwater and infiltrating river water. These unique features of groundwater and surface-water interactions in the lower Duwamish Waterway thus govern the transport of terrestrial contaminants to the lower Duwamish Waterway. Furthermore, the heterogenous aquifer properties in the lower Duwamish Waterway contribute to spatially and temporally dynamic contaminant-transport processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245046","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Mitchell, J.N., and Conn, K.E., 2024, Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5046, 43 p., https://doi.org/10.3133/sir20245046.","productDescription":"Report: vi, 43 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158073","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":497961,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117497.htm","linkFileType":{"id":5,"text":"html"}},{"id":434877,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.XML"},{"id":434876,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5046/images"},{"id":434875,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1F7QX42","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of groundwater- and surface-water-interactions report— Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington"},{"id":434874,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245046/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5046"},{"id":434873,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.pdf","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5046"},{"id":434872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.jpg"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45687695827122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.28\n ],\n [\n -122,\n 47.28\n ],\n [\n -122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.691936104689205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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934 Broadway, Suite 300
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Although Mars today does not have a core dynamo, magnetizations in the Martian crust and in meteorites suggest a magnetic field was present prior to 3.7 billion years (Ga) ago. However, the lack of ancient, oriented Martian bedrock samples available on Earth has prevented accurate estimates of the dynamo's intensity, lifetime, and direction. Constraining the nature and lifetime of the dynamo are vital to understanding the evolution of the Martian interior and the potential habitability of the planet. The Perseverance rover, which is exploring Jezero crater, is providing an unprecedented opportunity to address this gap by acquiring absolutely oriented bedrock samples with estimated ages from ∼2.3 to >4.1 Ga. As a first step in establishing whether these samples could contain records of Martian paleomagnetism, it is important to determine their ferromagnetic mineralogy, the grain sizes of the phases, and the forms of any natural remanent magnetization. Here, we synthesize data from various Perseverance instruments to achieve those goals and discuss the implications for future laboratory paleomagnetic analyses. Using the rover's instrument payload, we find that cored samples likely contain iron oxides enriched in Cr and Ti. The relative proportions of Fe, Ti, and Cr indicate that the phases may be titanomagnetite or Fe-Ti-Cr spinels that are ferromagnetic at room temperature, but we cannot rule out the presence of non-ferromagnetic ulvöspinel, ilmenite, and chromite due to signal mixing. Importantly, the inferred abundance of iron oxides in the samples suggests that even <1 mm-sized samples will be easily measurable by present-day magnetometers.