The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board. During 2019–23 the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped and described the geology and hydrostratigraphy of the rocks composing the Edwards and Trinity aquifers within parts of Bandera and Kendall Counties from field observations of the surficial expressions of the rocks. The thicknesses of the mapped lithostratigraphic and hydrostratigraphic units were also estimated from field observations in the study area.
The Cretaceous rocks in the study area are part of the Trinity Group and Edwards Group. The groups, formations, and members are composed primarily of layers of marls, shales, and limestones. The limestones are composed of mudstone through grainstone, framestone and boundstone, dolomite, and argillaceous and evaporitic rocks.
The principal structural feature in the study area is the Balcones fault zone. The Balcones fault zone is the result of late Oligocene and early Miocene extensional faulting and fracturing that was a result of the eastern Edwards Plateau uplift. In the Balcones fault zone, most of the faults in the study area are high-angle to vertical, en echelon, normal faults that are predominantly downthrown to the southeast.
Hydrostratigraphically, the rocks exposed in the study area are those that contain the Edwards aquifer, the upper zone of the Trinity aquifer, and the middle zone of the Trinity aquifer. Descriptions of the hydrostratigraphic units, thicknesses, hydrologic function, porosity types, and field identification and observations are provided, including those for the informal Bandera and Love Creek hydrostratigraphic units of the Edwards aquifer, which were identified through the mapping for this study.
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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.
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\"}}]}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Abhishek","contributorId":342795,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhishek","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreadis, Konstantinos","contributorId":258831,"corporation":false,"usgs":false,"family":"Andreadis","given":"Konstantinos","affiliations":[{"id":52307,"text":"Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":912754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Caitlyn","contributorId":316779,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912756,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252054,"text":"70252054 - 2024 - Predicting redox conditions in groundwater at a national scale using random forest classification","interactions":[],"lastModifiedDate":"2024-03-26T15:02:02.331974","indexId":"70252054","displayToPublicDate":"2024-03-07T09:58:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting redox conditions in groundwater at a national scale using random forest classification","docAbstract":"Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c07576","usgsCitation":"Tesoriero, A.J., Wherry, S., Dupuy, D., and Johnson, T., 2024, Predicting redox conditions in groundwater at a national scale using random forest classification: Environmental Science and Technology, v. 58, no. 11, p. 5079-5092, https://doi.org/10.1021/acs.est.3c07576.","productDescription":"14 p.","startPage":"5079","endPage":"5092","ipdsId":"IP-154897","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":440191,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c07576","text":"Publisher Index Page"},{"id":435024,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DVPJIX","text":"USGS data release","linkHelpText":"Input and results from a random forest 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]\n}","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dupuy, Danielle 0000-0001-9007-641X","orcid":"https://orcid.org/0000-0001-9007-641X","contributorId":222277,"corporation":false,"usgs":true,"family":"Dupuy","given":"Danielle","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896394,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254184,"text":"70254184 - 2024 - Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","interactions":[],"lastModifiedDate":"2024-05-13T11:57:09.891301","indexId":"70254184","displayToPublicDate":"2024-03-07T06:53:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2321,"text":"Journal of Geophysical Research: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","docAbstract":"Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land-sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF-Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling.
South Fork Quantico Creek (SFQ; 19.8 square kilometre (km2), forested) and Fourmile Run (4MR; 32.4-km2, urban) are small watersheds in northern Virginia, United States. Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation-adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long-term trend in annual peak flow was non-linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation-adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area-normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.
Rising water temperatures in rivers due to climate change are already having observable impacts on river ecosystems. Warming water has both direct and indirect impacts on aquatic life, and further aggravates pervasive issues such as eutrophication, pollution, and the spread of disease. Animals can survive higher temperatures through physiological and/or genetic acclimation, behavioral and phenological change, and range shifts to more suitable locations. As such, those animals that are adapted to cool-water regions typically found in high altitudes and latitudes where there are fewer dispersal opportunities are most at risk of future extinction. However, sub-lethal impacts on animal physiology and phenology, body-size, and trophic interactions could have significant population-level effects elsewhere. Rivers are vulnerable to warming because historic management has typically left them exposed to solar radiation through the removal of riparian shade, and hydrologically disconnected longitudinally, laterally, and vertically. The resilience of riverine ecosystems is also limited by anthropogenic simplification of habitats, with implications for the dispersal and resource use of resident organisms. Due to the complex indirect impacts of warming on ecosystems, and the species-specific physiological and behavioral response of organisms to warming, predicting how river ecosystems will change in the future is challenging. Restoring rivers to provide connectivity and heterogeneity of conditions would provide resilience to a range of expected co-occurring pressures, including warming, and should be considered a priority as part of global strategies for climate adaptation and mitigation.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1724","usgsCitation":"Johnson, M.F., Albertson, L.K., Algar, A.C., Dugdale, S.J., Edwards, P., England, J., Gibbins, C., Kazama, S., Komori, D., Maccoll, A., Scholl, E.A., Wilby, R., de Oliveira Roque, F., and Wood, P., 2024, Rising water temperature in rivers: Ecological impacts and future resilience: WIREs Water, v. 11, no. 4, e1724, 26 p., https://doi.org/10.1002/wat2.1724.","productDescription":"e1724, 26 p.","ipdsId":"IP-151079","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":440222,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1724","text":"Publisher Index Page"},{"id":426633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Matthew F. mjjohnson@usgs.gov","contributorId":334825,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"F.","affiliations":[{"id":80261,"text":"School of Geography, University of Nottingham, NG7 2RD, UK","active":true,"usgs":false}],"preferred":false,"id":896620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albertson, Lindsey K.","contributorId":218803,"corporation":false,"usgs":false,"family":"Albertson","given":"Lindsey","email":"","middleInitial":"K.","affiliations":[{"id":39916,"text":"Montana State University, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":896621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Algar, Adam C.","contributorId":334826,"corporation":false,"usgs":false,"family":"Algar","given":"Adam","email":"","middleInitial":"C.","affiliations":[{"id":80264,"text":"Department of Biology, Lakehead University, Canada.","active":true,"usgs":false}],"preferred":false,"id":896622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugdale, Stephen J.","contributorId":269592,"corporation":false,"usgs":false,"family":"Dugdale","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":56000,"text":"School of Geography, University of Nottingham, University Park, Nottingham, NG7 2RD, UK","active":true,"usgs":false}],"preferred":false,"id":896623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, 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Environmental and Sustainability Science (TESS) and College of Science and Engineering, James Cook University, Cairns, QLD 4878, Australia","active":true,"usgs":false}],"preferred":false,"id":896632,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wood, Paul F.","contributorId":203707,"corporation":false,"usgs":false,"family":"Wood","given":"Paul F.","affiliations":[],"preferred":false,"id":896633,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70252654,"text":"70252654 - 2024 - Exploring landscape and geologic controls on spatial patterning of streambank groundwater discharge in a mixed land use watershed","interactions":[],"lastModifiedDate":"2024-04-02T11:41:50.760863","indexId":"70252654","displayToPublicDate":"2024-03-05T06:37:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Exploring landscape and geologic controls on spatial patterning of streambank groundwater discharge in a mixed land use watershed","docAbstract":"Preferential groundwater discharge features along stream corridors are ecologically important at local and stream network scales, yet we lack quantification of the multiscale controls on the spatial patterning of groundwater discharge. Here we identify physical attributes that best explain variation in the presence and lateral extent of preferential groundwater discharges along two 5th order streams, the Housatonic and Farmington Rivers, and 32 1st to 4th order reaches across the Farmington River network. We mapped locations of preferential groundwater discharge exposed along streambanks using handheld thermal infrared cameras paired with high-resolution topographic and land use land cover datasets, surficial soil characteristic maps, and depth-to-bedrock geophysical measurements. The unconfined Housatonic River, MA, USA (12 km) had fewer discharge locations and less lateral extent (41 discharge locations with 38 m of active discharge/km of river) compared to the partially confined Farmington River, CT, USA (26 km; 169 discharge locations with 129 m of active discharge/km of river). Using a moving window analysis, we found along both rivers that discharge was more likely to occur where bank slopes were steeper, floodplain extent was narrower, and degree of confinement was higher. Along the Farmington River, groundwater discharge was more likely to occur where saturated hydraulic conductivity was higher and depth-to-bedrock was shallower. Among the 32 stream reaches surveyed (33.2 km of total stream length) within the Farmington River watershed, preferential discharge was observed in all but two stream reaches, varied from 0 to 25% of lateral extent along stream banks (mean = 6%), and was more likely to occur where stream reach slopes were steep, saturated hydraulic conductivity was high, and watershed urbanization was low. Our results show that, though both surface (e.g., topographic, land use land cover) and subsurface (e.g., soil characteristics, bedrock depth) factors control the prevalence of streambank preferential groundwater discharge, the dominant controls vary across valley settings and stream sizes.
We evaluated the Virgin, Verde, Salt, and Gila Rivers in the Lower Colorado River Basin. The watersheds have extents in Arizona, Utah, New Mexico, US and Sonora, MX.
We calculated trends in total dissolved solids (TDS) concentrations and fluxes with the Weighted Regressions on Time, Discharge and Season model. The modeling framework leverages daily streamflow and discrete water quality observations at specific monitoring sites. We evaluated trends for a common period (1976–2008) and the whole period of record at each monitoring site in terms of climate and anthropogenic controls.
Three rivers had persistent TDS concentrations exceeding the EPA secondary drinking water standard. All were associated with a geologic source of TDS. We observed increases and decreases in TDS concentrations at our monitoring sites, contrasting with global freshwater salinization and declining TDS concentrations in the Upper Colorado River Basin (UCRB). We attributed concentration variability to wintertime hydroclimatic forcing, with secondary influences of human water use. Reservoirs may decrease TDS concentrations by 50%. Efficiency improvements in irrigation and mining water uses may decrease TDS concentrations, while municipal growth increases TDS concentrations. We observed TDS flux declines at most monitoring sites. We attributed up to 85% of the TDS flux trend to changes in streamflow arising from drought and groundwater use. This study informs salinity dynamics in arid and aridifying locations, including the UCRB.
Fluvial landscape analysis is an essential part of geomorphology, hydrology, ecology, and cartography. It is traditionally focused on the transition between hillslopes and channel domain, in which the network drainage is represented by static flow lines. However, the natural fluctuations of the processes occurring in the watershed induce lateral and longitudinal expansions and contractions in the drainage patterns and variations of stream surface area. These dynamics can be better understood by introducing a two-dimensional (2D) view of catchment hydrography, in which river width and floodplain are included in the analysis.
The novelty introduced in this work is the development of a hydrodynamic hierarchical framework (HHF) to analyse the transitions among geomorphic and hydrographic features of the fluvial landscape, distinguishing hillslope, unchanneled valleys, floodplains, and single/multithreads channels. HHF is based on the estimation of nested inundation pattern domains (IPDs) from digital elevation models and 2D hydrodynamic modeling. IPDs are defined by scaling laws that characterize log–log relations between watershed drainage density and unit discharge thresholds extracted from a 2D direct rainfall method (DRM) under steady state solutions.
The physical significance of the IPDs is analysed within the context of both the physiographic features of the fluvial landscape and the rainfall rates employed as input for the modeling approach. Initially, the spatial heterogeneity of the IPDs is used to derive stream width metrics as a function of the rainfall rate. Then, a spatial index, representative of the IPDs' heterogeneity, is introduced as a measure of the susceptibility of the drainage network surface area to expansion and contraction. Finally, the consistency of the results is assessed in comparison to another hydrodynamic-based method for fluvial landscape analysis recently proposed in the literature.
The proposed approach is analysed using challenging mountain and low-relief environments, characterized by multithread channels, meander cut-offs, oxbow lakes, and extreme landscapes that feature glacial outwash, permafrost, and peatlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.130728","usgsCitation":"Costabile, P., Costanzo, C., Lombardo, M., Shavers, E.J., and Stanislawski, L., 2024, Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks: Journal of Hydrology, v. 632, 130728, 24, https://doi.org/10.1016/j.jhydrol.2024.130728.","productDescription":"130728, 24","ipdsId":"IP-155522","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":489912,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.130728","text":"Publisher Index Page"},{"id":481446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"632","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Costabile, Pierfranco","contributorId":350091,"corporation":false,"usgs":false,"family":"Costabile","given":"Pierfranco","affiliations":[{"id":83681,"text":"University of Calabria","active":true,"usgs":false}],"preferred":false,"id":925350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costanzo, Carmelina","contributorId":350092,"corporation":false,"usgs":false,"family":"Costanzo","given":"Carmelina","affiliations":[{"id":83681,"text":"University of Calabria","active":true,"usgs":false}],"preferred":false,"id":925351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lombardo, Margherita","contributorId":350093,"corporation":false,"usgs":false,"family":"Lombardo","given":"Margherita","affiliations":[{"id":66387,"text":"University of Bari","active":true,"usgs":false}],"preferred":false,"id":925352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":925353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":925354,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252589,"text":"70252589 - 2024 - Seasonal and decadal subsurface thaw dynamics of an Aufeis feature investigated through numerical simulations","interactions":[],"lastModifiedDate":"2024-03-29T11:55:55.292733","indexId":"70252589","displayToPublicDate":"2024-03-03T06:54:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and decadal subsurface thaw dynamics of an Aufeis feature investigated through numerical simulations","docAbstract":"Aufeis (also known as icings) are large sheet-like masses of layered ice that form in river channels in arctic environments in the winter as groundwater discharges to the land surface and subsequently freezes. Aufeis are important sources of water for Arctic river ecosystems, bolstering late summer river discharge and providing habitat for caribou escaping insect harassment. The aim of this research is to use numerical simulations to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis. We used a conceptual model based on geophysical data from the Kuparuk aufeis field on the North Slope of Alaska to develop a two-dimensional heterogeneous vertical profile model of groundwater flow, heat transport, and freeze/thaw dynamics. Modelling results showed that groundwater can flow to the land surface through subvertical high permeability pathways during winter months when the lower permeability soils near the land surface are frozen. The groundwater discharge can freeze on the surface, contributing to aufeis formation throughout the winter. We performed sensitivity analyses on subsurface properties and surface temperature and found that aufeis formation is most sensitive to the volume of unfrozen water available in the subsurface and the rate at which the subsurface water travels to the land surface. Although a trend of warming air temperatures will lead to a greater volume of unfrozen subsurface water, the aufeis volume can be reduced under warming conditions if the period of time for which air temperatures are below freezing is reduced.
Understanding age and growth are important for fisheries science and management; however, age data are not routinely collected for many populations. We propose and test a method of borrowing age–length data across increasingly broader spatiotemporal levels to create a hierarchical age–length key (HALK). We assessed this method by comparing growth and mortality metrics to those estimated from lake–year age–length keys ages using seven common freshwater fish species across the upper Midwestern United States. Levels used for data borrowing began most specifically by borrowing within lake across time and increased in breadth to include data within the Hydrologic Unit Code (HUC) 10 watershed, HUC8 watershed, Level III Ecoregion, and finally a species-wide data ALK using all available data with our study for a species. Median deviation in mean length of age-3 fish was within 1 cm for the most specific HALK levels, and median deviation in total annual mortality was close to 0 for most species when borrowing occurred within HUC10 and HUC8 watersheds. Percent error in growth curves increased with data borrowing, but plateaued—or even decreased—for some species when data borrowing expanded across spatial levels. We present the HALK as a method for gaining age information about a fishery when age data are unavailable.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.11019","usgsCitation":"Frater, P.N., Feiner, Z.S., Hansen, G., Isermann, D.A., Latzka, A.W., and Jensen, O., 2024, The incredible HALK: borrowing data for age assignment: Fisheries Magazine, v. 49, no. 3, p. 117-128, https://doi.org/10.1002/fsh.11019.","productDescription":"12 p.","startPage":"117","endPage":"128","ipdsId":"IP-152013","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440269,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.11019","text":"Publisher Index Page"},{"id":433013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, South Dakota, Wisconsin","otherGeospatial":"upper Midwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.34241140551524,\n 49.05094743128561\n ],\n [\n -104.34241140551524,\n 38.11034629510851\n ],\n [\n -84.84599091399372,\n 38.11034629510851\n ],\n [\n -84.84599091399372,\n 49.05094743128561\n ],\n [\n -104.34241140551524,\n 49.05094743128561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Frater, Paul N.","contributorId":342573,"corporation":false,"usgs":false,"family":"Frater","given":"Paul","email":"","middleInitial":"N.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feiner, Zachary S.","contributorId":342575,"corporation":false,"usgs":false,"family":"Feiner","given":"Zachary","email":"","middleInitial":"S.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Gretchen J.A.","contributorId":342577,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":37643,"text":"University of Minnesota-Twin Cities","active":true,"usgs":false}],"preferred":false,"id":910200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Latzka, Alexander W.","contributorId":342581,"corporation":false,"usgs":false,"family":"Latzka","given":"Alexander","email":"","middleInitial":"W.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910202,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Olaf P.","contributorId":342584,"corporation":false,"usgs":false,"family":"Jensen","given":"Olaf P.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910203,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250564,"text":"sir20235131 - 2024 - Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona","interactions":[],"lastModifiedDate":"2026-01-30T19:31:08.947912","indexId":"sir20235131","displayToPublicDate":"2024-02-29T08:10:40","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5131","displayTitle":"Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona","title":"Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona","docAbstract":"The Las Cienegas National Conservation Area was established by the Las Cienegas National Conservation Area Establishment Act of 1999 (Public Law 106–538) and is managed by the Bureau of Land Management. Located in southeastern Arizona, the conservation area contains more than 45,000 acres of rolling grassland, wetlands, and woodlands surrounded by isolated mountain ranges that are part of the Madrean archipelago. This report describes the surface-water and groundwater resources within, and hydrologically connected to, the conservation area.
Two primary aquifers have been identified within the Las Cienegas National Conservation Area: a Quaternary alluvial aquifer and a Miocene to Pliocene basin-fill aquifer. The Quaternary alluvial aquifer consists of Quaternary saturated stream alluvium along Cienega Creek and its major tributaries. This aquifer provides the water necessary for base flow in the perennial stream reaches that support aquatic life and for wetland and riparian habitat along the stream courses. Wells and piezometers completed in the Quaternary alluvial aquifer show both seasonal and daily water-level fluctuation patterns, as well as responses to flood flows in Cienega Creek. The basin-fill aquifer, in contrast, consists chiefly of Miocene to Pliocene alluvium within a sedimentary basin that is at least 4,800 feet deep. This aquifer is developed for anthropogenic uses more often than the Quaternary alluvial aquifer is developed. Generally, water levels in wells completed in the basin-fill aquifer have gradually declined a few feet between 2011, when measurements began, and 2022, when this report was written. Most water-chemistry samples available from the basin-fill aquifer had either a sodium-bicarbonate or calcium-bicarbonate water type. Previous research has shown that most recharge to the basin-fill aquifer likely comes from mountain-front and mountain-block recharge. Research further shows that this aquifer likely provides most of the recharge to the Quaternary alluvial aquifer. Because no production wells completed in bedrock exist within the conservation area, little is known about the hydraulic properties of the bedrock therein, but usable quantities of water can likely be produced from places where the bedrock has highly developed joint or fracture systems.
During 2006–2021, the average combined length of measured perennial stream reaches within the main part of the Las Cienegas National Conservation Area was 6.35 miles. The average annual base flow of Cienega Creek during 2002–2021, estimated with the Standard Base-Flow Index method using data from a streamgage within the conservation area, was 0.62 cubic feet per second. Monthly mean streamflow measured at this streamgage for the same period ranged from a low of 0.29 cubic feet per second (in June) to a high of 9.8 cubic feet per second (in July). The July average is heavily influenced by a flood that occurred in July 2021; the median July streamflow for 2002–2021 is just 0.84 cubic feet per second. Periods with no daily flow are not uncommon at this gage during late May and June.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235131","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Mason, J.P., 2024, Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2023–5131, 31 p., https://doi.org/10.3133/sir20235131.","productDescription":"vii, 31 p.","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-144415","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":432298,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X94P5B","text":"USGS Data Release","description":"Mason, J.P., 2023, Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9X94P5B.","linkHelpText":"Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona"},{"id":423631,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5131"},{"id":499394,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116143.htm","linkFileType":{"id":5,"text":"html"}},{"id":425761,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5131/covrthb.jpg"},{"id":423634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.xml"},{"id":423633,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5131/images"},{"id":423632,"rank":2,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235131/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Las Cienegas National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.778891765439,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.89756957809155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The U.S. Geological Survey, in cooperation with the City of Crystal Lake, Illinois, started a study to increase understanding of groundwater and surface-water interaction between the glacial aquifer and the city’s namesake lake, Crystal Lake, and the effect of higher and lower precipitation conditions on groundwater and lake levels. The results from this study could be used by the city and others to aid in lake management strategies. This report describes the hydrologic lake budget and each of the budget components, which are then used in the construction, calibration, and application of a regional groundwater flow model. The flow model is used to simulate the shallow groundwater flow system and the lake responses to increased and decreased precipitation under the current weir elevation and the proposed lowered weir elevation.
Using the program groundwater flow analytic element model (GFLOW), a two-dimensional, steady-state model was constructed. The model was calibrated by matching target water levels and stream base flows by adjusting model input parameters. A sensitivity analysis was completed by adjusting the parameters within reasonable ranges and noting the magnitude of changes in model calibration targets. Potential effects of extended wet and dry periods (within historical ranges and published predicted ranges) were evaluated by adjusting precipitation, groundwater recharge, and discharge at Crystal Lake culvert outlet in the model and comparing the resulting simulated lake stage and water budgets to stages and water budgets from the calibrated model.
Model results under average, wet, and dry conditions with a lowered weir of 1 foot at the Crystal Lake culvert outlet indicate minor changes in the simulated lake-water budgets and associated lake levels and groundwater elevation contours; however, simulations with an increased outflow at the Crystal Lake culvert outlet decreased the lake water levels by as much as 1.87 feet and also decreased the groundwater levels surrounding the lake by about 1–2 feet during average and wet conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245007","collaboration":"City of Crystal Lake","usgsCitation":"Gahala, A.M., Bristow, E.L.D., Sharpe, J.B., Metcalf, B.G., and Matson, L.A., 2024, Simulation of groundwater and surface-water interaction and lake resiliency at Crystal Lake, City of Crystal Lake, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5007, 43 p., https://doi.org/10.3133/sir20245007.","productDescription":"Report: vii, 43 p.;3 Data Releases; Database","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137122","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":426065,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MVOLW","text":"USGS data release","linkHelpText":"Seepage Meter Data Collected at Crystal Lake, City of Crystal Lake, Illinois, 2020"},{"id":426064,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97BTQZO","text":"USGS data release","linkHelpText":"GFLOW groundwater flow model of Crystal Lake, City of Crystal Lake, Illinois"},{"id":426060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5007/sir20245007.pdf","text":"Report","size":"3.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5007"},{"id":426070,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245007/full","text":"Report"},{"id":426059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5007/coverthb.jpg"},{"id":426062,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5007/sir20245007.XML","text":"Report","description":"SIR 2024–5007"},{"id":426063,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5007/images"},{"id":426067,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS Water Data for the Nation"},{"id":499421,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116142.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","otherGeospatial":"Crystal Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.40229797795483,\n 42.27567643715733\n ],\n [\n -88.40229797795483,\n 42.20603158225242\n ],\n [\n -88.31028234452793,\n 42.20603158225242\n ],\n [\n -88.31028234452793,\n 42.27567643715733\n ],\n [\n -88.40229797795483,\n 42.27567643715733\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N Goodwin Ave
Urbana, IL 61801
Contact Pubs Warehouse
In support of a preliminary analysis performed by New York State Department of Environmental Conservation that found elevated nutrient levels along selected reaches of the Mohawk River, a one-dimensional, unsteady hydraulic and water-quality model (Hydrologic Engineering Center River Analysis System Nutrient Simulation Module 1 [HEC–RAS NSM I]) was developed by the U.S. Geological Survey for the 127-mile reach of the Mohawk River between Rome and Cohoes, New York. The model was designed to accurately simulate within-channel flow conditions for this highly regulated, control-structure dense river reach. The model was calibrated for the period of May through September 2016 using available streamflow, temperature, and water-quality data. Nitrogen, phosphorus, dissolved oxygen, and water column algae were balanced within the model; however, the nutrient model calibration was focused on phosphorus.
The HEC–RAS hydraulic model simulated streamflow adequately at the calibration locations with observed and simulated daily flows demonstrating coefficient of determination (r2) values ranging from 0.91 to 0.97, mean absolute error ranging from 15–20 percent, and bias ranging from −7 to 16 percent. The water temperature model within HEC–RAS NSM I demonstrated remarkable ability to simulate water temperature: typical water temperature errors were less than 1.0 degree Celsius (°C). Simulated water temperature results closely tracked observed continuous water temperature data at three locations on the Mohawk River, with mean absolute error for the 2016 study period ranging from 0.87 to 0.90 °C, and a root mean square error of 1.00 to 1.07 °C.
Performance criteria for the water-quality (nutrient) model were applied differently than the water temperature model because of the temporally coarse discrete samples collected for the project. The average difference between final simulated concentrations and observed concentrations of organic phosphorus for all sample locations was within 0.01 milligrams per liter (mg/L) and within 0.09 mg/L for orthophosphate using all locations except Rome, which was within 0.25 mg/L.
The calibrated model was used to implement nine phosphorus reduction scenarios by applying reductions to wastewater treatment plant effluent concentrations within the model. Monthly mean differences were computed for five comparison locations. Scenario results were generally linear and predictable; scenarios implementing the highest reductions showed correspondingly larger differences in Mohawk River concentrations downstream from the wastewater treatment plants associated with those reductions. The largest monthly mean differences were realized from reduction scenario nine and ranged from −0.018 to −0.076 mg/L for organic phosphorus and from 0.001 to −0.138 mg/L for orthophosphate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245005","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Suro, T.P., Niemoczynski, M.J., and Boetsma, A., 2024, Development and calibration of HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River, New York: U.S. Geological Survey Scientific Investigations Report 2024–5005, 90 p., https://doi.org/10.3133/sir20245005","productDescription":"Report: xii, 90 p.; Data Release","numberOfPages":"90","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127136","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":425874,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FRAYLT","text":"USGS data release","linkHelpText":"HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River between Rome and Cohoes, New York"},{"id":425872,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5005/images/"},{"id":425873,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.XML"},{"id":425869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5005/coverthb.jpg"},{"id":425870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.pdf","text":"Report","size":"20.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5005"},{"id":425871,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245005/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5005"},{"id":499420,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116141.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.4,\n 42.0\n ],\n [\n -73.2,\n 42.0\n ],\n [\n -73.2,\n 43.4\n ],\n [\n -75.4,\n 43.4\n ],\n [\n -75.4,\n 42.0\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Lawrenceville, NJ 08648
Urbanization can have substantial effects on water quality due to altered hydrology and introduction of constituents to water bodies. In arid and semi-arid environments, streams are further stressed by dewatering as a result of diversions. We conducted a high-resolution synoptic survey of two streams in Colorado, USA that transition abruptly from granitic/metamorphic forested mountains to sedimentary urbanized plains and are both highly managed for water supply, yet differ in degree of urbanization. A synoptic mass balance approach developed for mine drainage applications was adapted to elucidate effects of urbanization, geology, and diversions on stream chemistry during baseflow conditions. Urbanization was a more important driver of stream concentrations than geology. The urban area was a strong source of bromide, calcium, chloride, and manganese, while lanthanum and dissolved organic carbon were primarily sourced from the mountains. A majority of streamflow was removed by diversions near the mountains/plains interface. Groundwater accounted for 31% of the subsequent flow increase to the urbanized stream, and delivered at least 33% of chloride loading. Constituents that were primarily urban-derived (bromide, calcium, chloride, and manganese) were 2–3 times higher in the urban region due to diversions; without diversions, stream water quality would have largely retained characteristics of forested streams through the urban reach. This study provides insights into processes that affect water quality in highly managed streams of the semi-arid western USA.
Climate-induced expansion of invasive hybridization (breeding between invasive and native species) poses a significant threat to the persistence of many native species worldwide. In the northern U.S. Rocky Mountains, hybridization between native cutthroat trout and non-native rainbow trout has increased in recent decades due, in part, to climate-driven increases in water temperature. It has been postulated that invasive hybridization may enhance physiological tolerance to climate-induced thermal stress because laboratory studies indicate that rainbow trout have a higher thermal tolerance than cutthroat trout. Here, we assessed whether invasive hybridization improves cardiac performance response to acute water temperature stress of native wild trout populations. We collected trout from four streams with a wide range of non-native admixture among individuals and with different temperature and streamflow regimes in the upper Flathead River drainage, USA. We measured individual cardiac performance (maximum heart rate, “MaxHR”, and temperature at arrhythmia, “ArrTemp”) during laboratory trials with increasing water temperatures (10–28°C). Across the study populations, we observed substantial variation in cardiac performance of individual trout when exposed to thermal stress. Notably, we found significant differences in the cardiac response to thermal regimes among native cutthroat trout populations, suggesting the importance of genotype-by-environment interactions in shaping the physiological performance of native cutthroat trout. However, rainbow trout admixture had no significant effect on cardiac performance (MaxHR and ArrTemp) within any of the three populations. Our results indicate that invasive hybridization with a warmer-adapted species does not enhance the cardiac performance of native trout under warming conditions. Maintaining numerous populations across thermally and hydrologically diverse stream environments will be crucial for native trout to adapt and persist in a warming climate.
The hydrological effects of climate change are documented in many regions; however, climate-driven impacts to the source and transport of river nutrients remain poorly understood. Understanding the factors controlling nutrient dynamics across river systems is critical to preserve ecosystem function yet challenging given the complexity of landscape and climate interactions. Here, we harness a large regional dataset of nitrate (NO3–) yield, concentration, and isotopic composition (δ15N and δ18O) to evaluate the strength of hydroclimate and landscape variables in controlling the seasonal source and transport of NO3–. We show that hydroclimate strongly influenced the seasonality of river NO3–, producing distinct, source-dependent NO3– regimes across rivers from two mountain ranges. Riverine responses to hydroclimate were also constrained by watershed-scale topographic features, demonstrating that while regional climate strongly influences the timing of river NO3– transport, watershed topography plays a distinct role in mediating the sensitivity of river NO3– dynamics to future change.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s43247-024-01235-8","usgsCitation":"Elmstrom, E., Holtgrieve, G., Scheuerell, M.D., Andrew J. Schauer, and Leazer, K., 2024, Climate and landform interact to control the source and transport of nitrate in Pacific Northwest rivers: Communications Earth and Environment, v. 5, 90, 13 p., https://doi.org/10.1038/s43247-024-01235-8.","productDescription":"90, 13 p.","ipdsId":"IP-149744","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-024-01235-8","text":"Publisher Index Page"},{"id":486521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Washington","otherGeospatial":"British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.87855871265536,\n 34.69978264689745\n ],\n [\n -113.87855871265536,\n 34.0589425188774\n ],\n [\n -113.23223159100554,\n 34.0589425188774\n ],\n [\n -113.23223159100554,\n 34.69978264689745\n ],\n [\n -113.87855871265536,\n 34.69978264689745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.3245432375632,\n 49.988705305763744\n ],\n [\n -122.3245432375632,\n 45.99397202023496\n ],\n [\n -119.56144227480337,\n 45.99397202023496\n ],\n [\n -119.56144227480337,\n 49.988705305763744\n ],\n [\n -122.3245432375632,\n 49.988705305763744\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Elmstrom, Elizabeth J.","contributorId":355859,"corporation":false,"usgs":false,"family":"Elmstrom","given":"Elizabeth J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":938283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holtgrieve, Gordon W.","contributorId":355860,"corporation":false,"usgs":false,"family":"Holtgrieve","given":"Gordon W.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":938284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrew J. Schauer","contributorId":355861,"corporation":false,"usgs":false,"family":"Andrew J. Schauer","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":938286,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leazer, Karrin","contributorId":355862,"corporation":false,"usgs":false,"family":"Leazer","given":"Karrin","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":938287,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251794,"text":"70251794 - 2024 - Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers","interactions":[],"lastModifiedDate":"2024-02-29T13:05:02.300022","indexId":"70251794","displayToPublicDate":"2024-02-19T07:02:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers","docAbstract":"The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to improve model calibration. Using high-resolution rain gauge data, we estimated “real” rainfall errors across the Turkey River watershed at storm and daily scales. Tests on synthetic and real-world scenarios successfully estimated errors correlated with observed values – even at daily scales. However, a bias remained from model parameter compensation, and identifying errors was challenging for low precipitation and snowfall. Despite synthetic results showing good error correlation, the biases in parameter identification masked potential improvements in hydrological calibration. This study highlights the potential of IES to provide additional information on rainfall errors, even only using streamflow observations.
Fluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as well as climate and land use, dictate the amount of Si exported by streams. Understanding Si regimes—the seasonal patterns of Si concentrations—can help identify processes driving Si export. We analyzed Si concentrations from over 200 stream sites across the Northern Hemisphere to establish distinct Si regimes and evaluated how often sites moved among regimes over their period of record. We observed five distinct regimes across diverse stream sites, with nearly 60% of sites exhibiting multiple regime types over time. Our results indicate greater spatial and interannual variability in Si seasonality than previously recognized and highlight the need to characterize the watershed and climate variables that affect Si cycling across diverse ecosystems.
Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process-based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process-based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.
Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel-1 and Sentinel-2 based algorithms to track monthly surface water extent (2017–2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape-based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006–2025 and 2061–2080 using MACA-CMIP5 (MACAv2-METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017–2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature.
Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The “impact” approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The “variability” approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.
In 2001, the New York City Department of Environmental Protection installed 25 wells on the southern embankment of the Hillview Reservoir in Westchester County in an unsuccessful attempt to locate the source of a large seep (seep A) that began flowing continuously in 1999. In 2005, the U.S. Geological Survey began a cooperative study with the NYCDEP to characterize the hydrology of the local groundwater system and identify potential sources of seep A and other seeps on the embankment.
At least two groundwater-flow zones—one shallow and the other deep—overlie the bedrock at the Hillview Reservoir in southern Westchester County, New York. Analyses of slug tests of wells drilled into the southern embankment of the reservoir were used to determine the three-dimensional distribution of hydraulic conductivity of the embankment materials. The wells with the minimum and maximum hydraulic conductivity values are in the deep saturated zone on the southern embankment, where hydraulic conductivity ranges from 0.0012 to 2 feet per day. Hydraulic conductivity ranges from 0.0026 to 1 foot per day in the shallow saturated zone and from 0.021 to 0.27 foot per day in the toe of the embankment. A hydraulic conductivity of 0.016 foot per day was determined for one toe well partially screened in the crystalline-bedrock aquifer. In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment of the Hillview Reservoir in southern Westchester County, New York.
Long-term hydrologic data indicated that water levels trended downward in 29 of 41 sites, including the reservoir basin that was monitored during the 12-year study period; data from a National Weather Service precipitation gage at Central Park indicated annual precipitation also trended downward during the same 12-year period. Of the seven wells in which water levels trended upward during the study, two of the wells are on the west side of the southern embankment, proximal to a major water supply conduit, whereas the five remaining wells are screened in the toe. These data indicate an increasing hydrostatic pressure within the deep system and the toe of the dam, which could result in future seeps on the southern embankment near these wells.
Results of 11 suspended-sediment samples collected from seeps along the southern embankment at 234.1- and 221.6-feet elevation, and another drainage outflow point between 2007 and 2015 indicate a poor correlation between suspended-sediment concentration and discharge. From the flowing seep at 234.1 feet, suspended-sediment concentrations ranged from 1 milligram per liter at a flow of 2.6 gallons per minute (that is, 1 milligram per 0.26 gallons) during March 2008 to 16 milligrams per liter at 12 gallons per minute during July 2014. At about 12 gallons per minute discharge, suspended-sediment concentration from samples collected at that seep during different sampling events, ranged from 3 to 16 milligrams per liter. From the seep at 221.6 feet elevation, the suspended sediment concentration was 2 milligrams per liter at a discharge of 3.4 gallons per minute and 2 milligrams per liter at a discharge of 1.1 gallons per minute. Only one sample was collected at the drainage outflow point, for which the suspended sediment concentration was 2 milligrams per liter at a discharge of 2.4 gallons per minute.
Anomalously high-water levels were recorded in deep-system wells between June 5, 2013, and January 14, 2014. The period for the increase and the decrease back to more typical water-level elevations occurred rapidly during a 13-hour period in each instance. The sudden and rapid changes, in addition to the spatial distribution of magnitude of water-level response indicate that leaky water infrastructure was the source of recharge to the affected wells.
A major water supply conduit was drained for repairs between July 7 and 10, 2010. The seeps indicated an immediate response and a substantial hydraulic connection to the water supply conduit. Approximately 10.5 hours after the water supply conduit was drained, flow from a seep on the southern embankment decreased from about 20 gallons per minute to less than 1 gallon per minute. This seep is located at about the same elevation and within the vicinity of the water supply conduit. A travel-time of about 10.5 hours from the source to the seep at 234.1 feet elevation was estimated from the dewatering timeline. During the 3-month shutdown of the water supply conduit, the previously flowing seeps remained dry until precipitation resulted in discharge of about 0.7 gallon per minute at the higher elevation seep, indicating a minor contribution from precipitation to the total seepage discharge. Discharge from the seeps resumed almost immediately coincident with the refilling of the water supply conduit, supporting the hydraulic connection observations during the drainage stage. In addition, during the refilling of the water supply conduit on September 21, 2010, a new seep (I) was observed on the southern embankment. Discharge from this new seep remained relatively constant until it became inaccessible under construction stone from subsequent embankment repairs by the New York City Department of Environmental Protection. Precipitation after the refilling stage of the shutdown seemed to have induced a rise in water levels in the toe wells and an increase in discharge from the seep at 234.1 feet elevation. The post shutdown discharge was less than 12 gallons per minute, compared to a discharge of about 20 gallons per minute before the repairs. The lower discharge rate measured during the period of historically higher discharge rates for the fall season indicates that the repair of the major water supply conduit may have contributed to a reduced discharge from the seeps. There were no definitive responses to the shutdown in any of the wells near the major water supply conduit.
The more transmissive deep system of the southern embankment near the major water supply conduit and its associated infrastructure seems to be the preferential flow path for leaking infrastructure. The wells screened in this system showed a response during the deep system anomaly and have some of the highest hydraulic conductivities of the tested wells. All the seeps are in the elevation range of the deep system from approximately the crystalline bedrock surface around 200 feet elevation to the contact between the deep and shallow saturated zones of the reservoir at about 250 feet elevation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235123","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Chu, A., Noll, M.L., Capurso, W.D., and Welk, R.J., 2023, Hydrologic analysis of an earthen embankment dam in southern Westchester County, New York: U.S. Geological Survey Scientific Investigations Report 2023–5123, 41 p., https://doi.org/10.3133/sir20235123.","productDescription":"Report: vii, 41 p.; Data Release; Dataset","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099377","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":499388,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116022.htm","linkFileType":{"id":5,"text":"html"}},{"id":425302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5123/coverthb.jpg"},{"id":425303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5123/sir20235123.pdf","text":"Report","size":"8.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5123"},{"id":425304,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235123/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5123"},{"id":425308,"rank":7,"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":425307,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J404KW","text":"USGS data release","linkHelpText":"Data and analytical type-curve match for selected hydraulic tests at an earthen dam site in southern Westchester County, New York"},{"id":425306,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5123/images/"},{"id":425305,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5123/sir20235123.XML"}],"country":"United States","state":"New York","county":"Westchester County","otherGeospatial":"Hillview Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.87575164367507,\n 40.91924473969948\n ],\n [\n -73.87575164367507,\n 40.901369445530406\n ],\n [\n -73.85851786410133,\n 40.901369445530406\n ],\n [\n -73.85851786410133,\n 40.91924473969948\n ],\n [\n -73.87575164367507,\n 40.91924473969948\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Since 1952, wastewater discharged to infiltration ponds (also called “percolation ponds”) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy (DOE), maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in both the aquifer and perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer and perched groundwater wells from the USGS groundwater monitoring networks during 2019–21.
From March–May 2018 to March–May 2021, water levels in wells completed in the ESRP aquifer increased in the northern part of the INL and decreased in the southwestern part. Water-level increases ranged from 0.02 to 1.04 feet in the northern part and decreases ranged from 0.03 to 2.94 feet in the southwestern part of the INL.
Detectable concentrations of radiochemical constituents in water samples from wells in the ESRP aquifer at the INL generally decreased or remained constant during 2019–21. Decreases in concentrations were attributed to radioactive decay, changes in waste-disposal methods, and dilution from recharge and underflow.
In 2021, tritium was detected above reporting levels in water samples collected from 46 of 105 aquifer wells and ranged from 150±50 to 4,280±150 picocuries per liter (pCi/L). Tritium concentrations from eight wells completed in deep perched groundwater near the Advanced Test Reactor Complex (ATRC) generally were greater than or equal to the reporting level during at least one sampling event during 2019–21, and concentrations ranged from 160±50 to 2,097±107 pCi/L. Concentrations of strontium-90 in water from 12 of 45 aquifer wells sampled in 2021 exceeded the reporting level, and concentrations ranged from 2.5±0.7 to 299±6 pCi/L. During 2021, concentrations of strontium-90 from five wells completed in deep perched groundwater at the ATRC equaled or exceeded the reporting levels, and concentrations ranged from 3±0.9 pCi/L to 27.8±1.3 pCi/L. Concentrations of cesium-137 were less than the reporting level in all but one aquifer well, and concentrations of plutonium-238, plutonium-239, -240 (undivided), and americium-241 were less than the reporting level in water samples from all aquifer wells sampled during this study period.
Dissolved chromium concentrations in water samples from 64 ESRP aquifer wells ranged from less than (<) 0.5 to 76.4 micrograms per liter (μg/L). During 2019–21, dissolved chromium was detected in water from wells completed in deep perched groundwater above the ESRP aquifer at the ATRC, and concentrations ranged from <1 to 82.1 μg/L.
In 2021, concentrations of dissolved sodium in water from most ESRP aquifer wells in the southern part of the INL were greater than the western tributary groundwater background concentration of 8.3 milligrams per liter (mg/L). During 2021, dissolved sodium concentrations in water from 15 wells completed in deep perched groundwater ranged from 11.7 to 122.5 mg/L. Variations in sodium concentrations in aquifer wells and perched groundwater zones are attributed to either migration of remnant water from the former chemical-waste ponds or disposal volume and composition variability in percolation ponds installed in 2008.
In 2021, concentrations of chloride in most water samples from ESRP aquifer wells south of the Idaho Nuclear Technology and Engineering Center (INTEC) and at the Central Facilities Area (CFA) exceeded background concentrations. Chloride concentrations in water from wells south of the INTEC have generally decreased because of discontinued chloride disposal to the legacy percolation ponds since 2002 when the discharge of wastewater was discontinued. During 2019–21, dissolved chloride concentrations in deep perched groundwater above the ESRP aquifer from 18 wells at the ATRC ranged from 8.15 to 231 mg/L.
In 2021, sulfate concentrations in water samples from ESRP aquifer wells in the south-central part of the INL that exceeded the background concentration of sulfate, ranged from 21 to 141 mg/L. The greater-than-background concentrations in water from these wells are attributed to sulfate disposal at the ATRC infiltration ponds or the legacy INTEC percolation ponds. In 2021, sulfate concentrations in water samples from aquifer wells near the Radioactive Waste Management Complex (RWMC) were mostly greater than background concentrations. The maximum dissolved sulfate concentration in shallow perched groundwater near the ATRC was 575 mg/L in 2021. During 2021, dissolved sulfate concentrations in water from wells completed in deep perched groundwater near the cold waste ponds at the ATRC ranged from 22.3 to 519 mg/L.
In 2021, concentrations of nitrate in water from most ESRP aquifer wells at and near the INTEC exceeded the western tributary groundwater background concentration of 0.655 mg/L. Concentrations of nitrate in aquifer wells southwest of INTEC and farther away from the influence of disposal areas and the Big Lost River, in intermittent source of surface water recharge to the aquifer, show a general decrease in nitrate concentration over time. Two aquifer wells south of INTEC show increasing trends that could result from wastewater beneath the INTEC tank farm being mobilized to the aquifer.
During 2019–21, water samples from several ESRP aquifer wells were collected and analyzed for volatile organic compounds (VOCs). Twelve VOCs were detected, and 1–4 VOCs were detected in water samples from 10 wells. The most frequently detected VOCs include carbon tetrachloride (tetrachloromethane), trichloromethane, tetrachloroethene, 1,1,1-trichloroethane, and trichloroethene. In 2019–21, concentrations for all VOCs were less than their respective maximum contaminant levels (MCLs) for drinking water, except carbon tetrachloride in one well, trichloroethene in two wells, and vinyl chloride in one well.
During 2019–21, variability and bias were evaluated from 34 replicate and 14 blank quality-assurance samples. Results from replicate analyses were investigated to evaluate sample variability. Constituents with acceptable reproducibility were major ions, trace elements, nutrients, and VOCs. All radiochemical constituents including gross alpha- and beta- radioactivity, strontium-90, cesium-137, and tritium, had acceptable reproducibility. Bias from sample contamination was evaluated from equipment, field, and source-solution blanks. Chloride and sulfate were detected slightly above their respective method detection limits in equipment and field blanks, but at concentrations well below the co-collected sample for that well. These chloride and sulfate detections in the field and equipment blanks were inconsequential because they weren’t detected above the analysis-specific variability for those constituents as determined by replicate sample result evaluation. None of the detections of nutrients and trace inorganic constituents were high enough to indicate environmental sample or analytical procedure bias.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235128","collaboration":"DOE/ID-22261Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520