Streams and rivers are undergoing rapid change as the Arctic warms and thaws. We review recent observations in Arctic stream systems to identify ubiquitous changes and the most useful tools for observing change and exploring the underlying processes.
Recent literature indicates increasingly significant trends in river hydrology and chemistry due to persistent warming in the Arctic and longer observational records for analysis. However, regional differences in the magnitude and direction of these trends persist. We also observe thresholds in ground thaw and surface–groundwater interactions that can impact river hydrology and chemistry.
Warming and thaw are occurring rapidly at high latitudes, resulting in increasing, yet variable responses in stream systems across regions and scales. These differences highlight the need for long-term records and an interdisciplinary approach to explain trends and predict future states. Stream systems respond to multiple landscape changes related to hydrology (changing precipitation and subsurface flow), geology (ground thaw dynamics), and ecology (vegetation change).
","language":"English","publisher":"Springer Nature","doi":"10.1007/s40641-025-00202-5","usgsCitation":"Koch, J.C., and O’Donnell, J.A., 2025, Observing northern high-latitude river systems to understand changes in a warming Arctic: Current Climate Change Reports, v. 11, 5, 11 p., https://doi.org/10.1007/s40641-025-00202-5.","productDescription":"5, 11 p.","ipdsId":"IP-173848","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":490673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s40641-025-00202-5","text":"Publisher Index Page"},{"id":490362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2025-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":939950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, J. A.","contributorId":195376,"corporation":false,"usgs":false,"family":"O’Donnell","given":"J.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":939951,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70265985,"text":"dr1198 - 2025 - Critical minerals in orogenic (gold) and Coeur d’Alene-type mineral systems of the United States","interactions":[],"lastModifiedDate":"2025-08-07T21:28:11.2103","indexId":"dr1198","displayToPublicDate":"2025-05-30T11:45:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1198","displayTitle":"Critical Minerals in Orogenic (Gold) and Coeur d’Alene-Type Mineral Systems of the United States","title":"Critical minerals in orogenic (gold) and Coeur d’Alene-type mineral systems of the United States","docAbstract":"Orogenic and Coeur d’Alene-type mineral systems are produced by metamorphic devolatilization of thick volcanic or siliciclastic sedimentary rock sequences and the focused flow of hydrothermal fluids upwards along crustal-scale faults. Most orogenic systems are found along the Cordilleran orogen, stretching from California northwards into Alaska, whereas most Coeur d’Alene-type systems occur in the Proterozoic Belt Basin in Idaho and Montana. Although the deposit types in these systems are exploited for precious and base metals, potential exists for the production of critical minerals necessary for current (2025) societal needs in the United States. Publicly available geochemical data compiled for these mineral systems, coupled with mineralogical characteristics, indicate that several critical minerals could potentially be recovered from unmined resources and processed mine waste: arsenic, antimony, tellurium, cobalt, and tungsten from orogenic gold deposits and zinc, antimony, arsenic, and manganese from Coeur d’Alene-type systems. These critical minerals reside primarily in arsenopyrite (arsenic), scheelite (tungsten), siderite (manganese), sphalerite (zinc), tetrahedrite (antimony and arsenic), stibnite (antimony), and telluride (tellurium) minerals.
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Denver, CO 80225
The glacial deposits underlying southeastern Michigan, northeastern Indiana, and northwestern Ohio are a substantial source of water to communities, agriculture, and industry in the region. Previous efforts to characterize aquifer materials in the area cited a need for additional information about the underlying hydrogeologic characteristics and related groundwater availability as well as improved mapping of the extent and properties of the glacial deposits.
Recent U.S. Geological Survey multi-State compilations of water-well drilling records have greatly increased access to high-resolution geologic data, particularly in glacial depositional environments. This study by the U.S. Geological Survey, in cooperation with the Ohio Environmental Protection Agency, uses processed data from the State-managed collections of well records to characterize the glacial deposits in the study area using two methods. The first method creates two-dimensional maps of basic hydrogeologic information commonly required for assessments of groundwater availability, including (1) total thickness of glacial deposits, (2) total thickness of coarse-grained deposits, (3) specific-capacity-based transmissivity and hydraulic conductivity, and (4) texture-based estimated equivalent horizontal and vertical hydraulic conductivity and transmissivity. The second method builds a hydrogeologic framework of the complex glacial aquifer through construction of a volumetric geologic model by using three-dimensional kriging.
Results of the volumetric model indicate that aquifer materials are primarily concentrated in the western parts of the study area near the Indiana-Ohio border. Coarse-grained sediments are also present as surficial deposits in the north of the study area where intermixing glacial advances created complex distributions of unconsolidated deposits. Two-dimensional maps of hydrogeologic properties support the volumetric model, showing thicknesses of coarse-grained deposits that reach up to 250 feet in the western sections of the study area and progressively thin to near absence in the east. Visualization of the aquifer materials with a volumetric model generally shows a highly discontinuous distribution of coarse- and fine-grained materials, with no clearly defined boundaries to delineate the extent of the aquifer. Comparisons of cross sections derived from the volumetric model with existing published maps support previous near-surface hydrogeologic interpretations while filling gaps where data are sparse, particularly in deeper parts of the aquifer. Both the two-dimensional maps and the volumetric model provide data that can directly inform assessments of groundwater availability, in addition to having future applications to studies of groundwater flow and transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255008","collaboration":"Prepared in cooperation with the Ohio Environmental Protection Agency","usgsCitation":"Riddle, A.D., Arihood, L.D., Naylor, S., and Lampe, D.C., 2025, Hydrogeologic mapping and three-dimensional geologic modeling of glacial deposits in a multicounty area of southeastern Michigan, northeastern Indiana, and northwestern Ohio: U.S. Geological Survey Scientific Investigations Report 2025–5008, 47 p., https://doi.org/10.3133/sir20255008.","productDescription":"Report: viii, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146138","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":486276,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5008/images/"},{"id":486275,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5008/sir20255008.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5008 XML"},{"id":486274,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5008 HTML"},{"id":486273,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5008/sir20255008.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5008 PDF"},{"id":486272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5008/coverthb.jpg"},{"id":499040,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118589.htm","linkFileType":{"id":5,"text":"html"}},{"id":486277,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13BO3GJ","text":"USGS data release","linkHelpText":"Hydrogeologic framework of the glacial deposits in a multicounty area of southeastern Michigan, northeastern Indiana, and northwestern Ohio"}],"country":"United States","state":"Indiana, Michigan, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.45494559947987,\n 42.39763689085959\n ],\n [\n -85.45494559947987,\n 40.651853498464675\n ],\n [\n -83.24314510969089,\n 40.651853498464675\n ],\n [\n -83.24314510969089,\n 42.39763689085959\n ],\n [\n -85.45494559947987,\n 42.39763689085959\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
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To improve estimates of the frequency of annual peak flows for ungaged locations on non-urban, unregulated streams in Tennessee, generalized least-squares multiple linear-regression techniques were used to relate annual peak flows from streamgages operated by the U.S. Geological Survey to physical, climatic, and land-use characteristics of their drainage basins. Geospatial data acquired since the previous study in 2003, annual peak-streamflow data through the 2013 water year, and Bulletin 17C methods for frequency analysis of annual peak-streamflow data were used in the study. Generalized least-squares regression equations were developed for four hydrologic areas with distinct hydrologic, geologic, and topographic characteristics. Drainage area was used as an explanatory variable in equations developed for each hydrologic area. In addition to drainage area, a 2-year recurrence-interval climate factor was used for hydrologic area 1, a 10–85 channel slope was used for hydrologic area 2, and percent imperviousness was used for hydrologic area 4. The regression equations can be used to estimate annual exceedance probability streamflows for ungaged locations on non-urban, unregulated streams in Tennessee. The term “unregulated” indicates that streamflow is not appreciably influenced by regulation from reservoirs or other impoundments. Average standard errors of prediction for the regression equations ranged from 44.4 to 51.4 percent for hydrologic area 1; 30.4 to 42.9 percent for hydrologic area 2; 35.7 to 42.6 percent for hydrologic area 3; and 32.5 to 47.4 percent for hydrologic area 4.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Frequency analysis on annual peak streamflow (hereinafter, peak flow) is essential to water-resources management applications, including critical structure design (for example, bridges and culverts) and floodplain mapping. Nonstationarity is a statistical property of a peak-flow series such that the distributional properties (the mean, variance, or skew) change either gradually (monotonic trend) or abruptly (shift, step change or change point) through time. Not incorporating or accounting for observed nonstationarity into peak-flow frequency analysis might result in a poor representation of the true probability of large floods and thus misrepresent the actual flood risks to life and property. This report summarizes how hydroclimatic variability might affect the temporal and spatial distributions of peak-flow data in the State of Montana (and northern Wyoming) and is part of a larger study to document peak-flow nonstationarity and hydroclimatic changes across a nine-State region consisting of Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flows. This study was completed in cooperation with the Montana Department of Natural Resources and Conservation.
The purpose of this report is to characterize temporal and spatial patterns of nonstationarity in peak flows and hydroclimatology in Montana and northern Wyoming. In this evaluation, peak-flow, daily streamflow, and model-simulated gridded climatic data were examined for monotonic trends, change points, and other statistical properties that might indicate changing climatic and environmental conditions. This report includes background information on the study area, the history of U.S. Geological Survey peak-flow data collection and frequency analysis in Montana, and the review of research relating to hydroclimatic variability and change in Montana. This study might help provide a framework for addressing potential nonstationarity issues in peak-flow frequency updates that commonly are completed by the U.S. Geological Survey in cooperation with other agencies throughout the Nation.
The analytical structure of this study includes analyses of monotonic trends and change points in numerous hydroclimatic variables in assigned 30-, 50-, 75-, and 100-year analysis periods. For Montana and part of Wyoming, the 30-, 50-, 75, and 100-year analyses included 157, 70, 48, and 12 streamgages, respectively. For those streamgages, nonstationarities were analyzed in the following variables: (1) climatic variables, including annual and seasonal (winter, spring, summer, and fall) temperature and precipitation; (2) daily streamflow variables, including the annual center of volume duration, annual center of volume median, and peaks over threshold with a mean of four events per year; and (3) annual peak-flow variables, including peak-flow timing and magnitude. A likelihood approach was used to express statistical confidence and assign the nonstationarity results as likely upward or downward (highest statistical confidence), somewhat likely upward or downward (less statistical confidence), or about as likely as not (little statistical confidence; hereinafter, neutral). For the nonstationarity analyses of the climatic, daily streamflow, and peak-flow variables, the results are presented in detail and discussed with respect to statewide patterns and geographic variability. For each of the 30-, 50-, and 75-year analyses, peak-flow change-point and monotonic trend analyses were compiled for streamgages classified with likely downward or likely upward trends. For those streamgages, the associated basin characteristics and nonstationarity results for peak-flow timing, daily streamflow, and climatic variables were investigated and statistically compared to discern associations among other variables that might contribute to the peak-flow nonstationarity results.
The 50- and 75-year peak-flow nonstationarities identified in this study are mostly downward, in association with mostly upward temperature and potential evapotranspiration:precipitation monotonic trends. For the 50-, 75-, and 100-year analyses, the peak-flow change points are predominantly downward and are concentrated in the 1970s and 1980s, which indicates general consistency among the longer trend periods. These findings are in association with substantial research documenting globally rising temperature and atmospheric greenhouse gas concentrations that might be largely attributed to anthropogenic activities. Anthropogenic effects might represent long-term (on the order of several decades to more than a century) climate changes that might happen within highly variable natural climate fluctuations. Several paleo studies in the north-central United States have indicated that hydroclimatic extremes (that is, low- and high-streamflow conditions) before European settlement have been outside of extremes since the 1900s. Depending on the interactions of anthropogenic effects and natural climate variability, extreme high-streamflow conditions might occur in the future, even in the presence of long-term downward peak-flow trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064G","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., Barth, N.A., Sando, R., and Chase, K.J., 2025, Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020, chap. G of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 129 p., https://doi.org/10.3133/sir20235064G.","productDescription":"Report: x, 129 p.; Data Release; Dataset","numberOfPages":"144","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159092","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":486055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.pdf","text":"Report","size":"19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5064-G"},{"id":486054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/coverthb.jpg"},{"id":486056,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.XML"},{"id":486057,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/images/"},{"id":486059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064G/full"},{"id":499038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118575.htm","linkFileType":{"id":5,"text":"html"}},{"id":486073,"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":486058,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1921–2020"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.16658108866669,\n 49\n ],\n [\n -116.16658108866669,\n 43\n ],\n [\n -104.0598539498924,\n 43\n ],\n [\n -104.0598539498924,\n 49\n ],\n [\n -116.16658108866669,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Lahars, or volcanic mudflows, are the most hazardous eruption-related phenomena that will affect communities living along rivers that originate on Mount Baker. In the past 15,000 years, the largest lahars from Mount Baker have affected the Middle Fork Nooksack River drainage and beyond. Here we use the physics-based D-Claw software package to model nine lahar scenarios that are initiated as water-saturated landslides between Sherman Crater and the Roman Wall on the Mount Baker edifice and flow down the Middle Fork Nooksack River. The scenarios range in volume from 1 to 260 million cubic meters and have an initial hydraulic permeability from 10−12 to 10−10 meters squared. Model output includes data such as flow depth, velocity, runout distance, area inundated, arrival time, and sediment concentration as well as information that allows scientists to calculate other important hydrologic characteristics such as lahar discharge. These data are important to officials who have the responsibility to plan for, or take mitigation measures against, future Mount Baker lahars. To check the validity of the D-Claw results, we compare the scenarios to known geologic information. We also compare D-Claw results with empirical models that have been used in the past to determine potential inundation areas, runout distances, and arrival times. These comparisons highlight similarities and differences between empirical and physics-based models. We also present D-Claw scenario-based animations to help scientists, officials, and lay people alike to visualize how future lahars could affect communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245133","usgsCitation":"Gardner, C.A., Benage, M.C., Cannon, C., and George, D.L., 2025, Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5133, 47 p., https://doi.org/10.3133/sir20245133.","productDescription":"Report: vii, 47 p.; 9 Animation Videos; Data Release","numberOfPages":"47","ipdsId":"IP-151680","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":485743,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioC2.mp4","text":"Appendix 4 - Scenario C2","size":"35.9 MB","description":"Scenario C2","linkHelpText":"- Scenario C2"},{"id":485742,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB3.mp4","text":"Appendix 4 - 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Scenario E2","size":"22.1 MB","description":"Scenario E2","linkHelpText":"- Scenario E2"},{"id":485744,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioD2.mp4","text":"Appendix 4 - Scenario D2","size":"26.5 MB","description":"Scenario D2","linkHelpText":"- Scenario D2"},{"id":485732,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245133/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5133 HTML"},{"id":485738,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA2.mp4","text":"Appendix 4 - Scenario A2","size":"41.7 MB","description":"Scenario A2","linkHelpText":"- Scenario A2"},{"id":485848,"rank":16,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118573.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Middle Fork Nooksack River, Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.667,\n 49\n ],\n [\n -122.667,\n 49\n ],\n [\n -122.667,\n 48.6667\n ],\n [\n -121.667,\n 48.6667\n ],\n [\n -121.667,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"David A. Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Building 10, Suite 100
Vancouver, WA 98683
Email: askCVO@usgs.gov
","tableOfContents":"Captive breeding and release programs aimed at recovery of rare species can be informed by genetic data to help select high-diversity source populations, make pairing decisions to minimize inbreeding, and manage release strategies. We developed a set of 54 microsatellite loci to assess genetic structure and diversity across the United States range of the Light-footed Ridgway’s Rail (Rallus obsoletus levipes), a federally endangered marsh bird for which populations have been augmented by a captive breeding program annually since 2001. We identified three regional genetic clusters, with the highest genetic diversity reported in the central cluster, which included all sampled wetlands in north San Diego County. Recent (2019–24) captive-breeding adults all clustered within the northernmost cluster (Orange and Ventura Counties), which was expected given that this cluster included the source wetland for the captive breeding program. Gene flow rates, which approximate the proportions of individuals in a population originating from other populations, were relatively high among clusters (4–24 percent) and may have been enhanced through the release of captive-bred rails. Based on the genetic data analyzed in a genetic rescue decision framework, sourcing new breeding birds from the north San Diego County cluster could provide the greatest genetic diversity benefits. The northernmost cluster, which included Mugu Lagoon and all sampled Orange County wetlands, was considered the most in need of genetic rescue. Recent breeding pairs in the captive breeding program have comparatively low diversity and high interrelatedness. Sourcing birds from wetlands with high genetic diversity and population sizes, assessing genetic relatedness before pairing, and focusing releases in areas that have low estimates of genetic diversity could improve the distribution of genetic diversity across wild populations in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251011","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Vandergast, A.G., Smith, J.G., Mitelberg, A., Wood, D.A., Sawyer, K.A., and Conway, C.J., 2025, Genetic structure and diversity in wild populations of the Light-footed Ridgway’s Rail reflect 20 years of augmentation through captive breeding and release: U.S. Geological Survey Open-File Report 2025–1011, 24 p., https://doi.org/10.3133/ofr20251011.","productDescription":"Report: viii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169671","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":485012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1011/coverthb.jpg"},{"id":485013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1011"},{"id":485015,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14CYDJC","text":"USGS data release","description":"USGS data release","linkHelpText":"Microsatellite genotypes for light-footed Ridgway's rail (Rallus obsoletus levipes) sampled in southern California"},{"id":485017,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.XML"},{"id":485014,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1011"},{"id":485016,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1011/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.10331263380175,\n 34.10687099022617\n ],\n [\n -118.03807861620402,\n 33.46101273577156\n ],\n [\n -117.15926055168606,\n 32.54485080859107\n ],\n [\n -116.83812382579282,\n 32.56519890177364\n ],\n [\n -117.45219990652568,\n 33.65951802680661\n ],\n [\n -118.60202603728568,\n 34.09630540323134\n ],\n [\n -119.10331263380175,\n 34.10687099022617\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251021","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with the University of Minnesota","usgsCitation":"Livdahl, C.T., 2025, Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota: U.S. Geological Survey Open-File Report 2025–1021, 15 p., https://doi.org/10.3133/ofr20251021.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-166630","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":493763,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118543.htm","linkFileType":{"id":5,"text":"html"}},{"id":484783,"rank":6,"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":484781,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1021/images/"},{"id":484780,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.XML"},{"id":484782,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251021/full"},{"id":484779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1021"},{"id":484778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1021/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Lake Nokomis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.23325811481614,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.91617052326589\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
The richness and abundance of freshwater mussels in the Big Darby Creek Basin has declined in recent decades, according to survey results published by the Ohio Biological Survey. In October 2016, a major mussel die-off of undetermined cause reportedly affected over 50 miles of Big Darby Creek; however, fishes and other wildlife were not noticeably impacted. Pollution, habitat destruction, climate change, and hydrologic modification have all been theorized as potential reasons for the widespread declines in freshwater mussel populations in North America. To better understand potential stressors to mussels and other aquatic organisms in the Big Darby Creek Basin, the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority, evaluated water quality and temporal changes in hydrology at selected locations. In addition, environmental deoxyribonucleic acid (eDNA) quantitative polymerase chain reaction (qPCR) assays were developed to detect the presence of selected mussels and macroinvertebrates using stream water.
Time-weighted average concentrations of pesticides, organic wastewater compounds (OWCs), and polycyclic aromatic hydrocarbons (PAHs) were determined for selected locations within the Big Darby Creek Basin. Passive samplers designed to mimic the respiratory exposure of aquatic organisms and the bioconcentration of organic contaminants into their fatty tissues were deployed three times annually at three sites within the Big Darby Creek Basin in 2020 and 2021. Analyses were done for 204 pesticide compounds, 38 OWCs, and 33 PAHs. Of the 204 pesticide compounds, 70 were detected in at least one sample; 30 were detected in all samples. Herbicides and herbicide degradates were the pesticides most frequently detected and also had some of the highest concentrations of the pesticides detected in this study. Three herbicides (atrazine, ametryn, and metribuzin) were detected in at least 88 percent of samples and two fungicides (azoxystrobin and propiconazole) were detected in all samples. Of the 38 OWCs, 24 were detected in at least one sample; however, only one (N,N-diethyltoluamide [DEET]) was detected in all samples. Of the 33 PAHs, 29 were detected in at least one sample; 12 were detected in all samples.
A continuous water-quality monitor was operated seasonally on Big Darby Creek above Georgesville, Ohio, from 2020 to 2022. Dissolved oxygen concentrations generally followed a daily cycle, peaking in early evening and troughing around sunrise. There were occasional 24-hour swings in dissolved oxygen concentration that had a range exceeding 10 milligrams per liter. However, dissolved oxygen concentrations never fell below Ohio’s aquatic life criteria for warmwater habitats (outside of mixing zones) of 4.0 milligrams per liter as an instantaneous minimum and 5.0 milligrams per liter as a minimum 24-hour average. The Ohio water-quality criteria for temperatures are 29.4 degrees Celsius as an instantaneous maximum and 27.8 degrees Celsius as a 24-hour average maximum. In 2020, there were 10 days when the maximum instantaneous value for temperature was exceeded and 3 consecutive days when the maximum 24-hour average temperature was exceeded.
Streamflow time-series data from three gaging stations within the Big Darby Creek Basin were evaluated for trends in annual flow statistics and daily nonexceedance probabilities over time. In general, the evaluation of streamflow conditions at the Big Darby Creek gage (with 97 years of record) indicated that streamflow changed between water years 1922 and 2021. During that time span, flows in general increased, the number of high-flow pulses became more frequent, and low-flow pulses and extreme low-flow periods became less frequent. The only strong indication of trends over time in annual flow statistics for the relatively short records for the other two gages (on Little Darby Creek, with 25 years of record, and Hellbranch Run, with 29 years of record) was that as time went on, reversals between rising and falling periods became more frequent.
The U.S. Geological Survey Ohio Water Microbiology Laboratory developed eDNA qPCR assays to detect Epioblasma rangiana (northern riffleshell mussels), Chimarra obscura (a species of caddisfly), Maccaffertium pulchellum (a species of mayfly), and optimized a preexisting eDNA qPCR assay to detect for Ptychobranchus fasciolaris (kidneyshell mussels). The assays were validated by using environmental sampling methods. Assay sensitivity was established by determining the limits of detection and quantification. Water samples were collected at 12 sites in the Big Darby Creek Basin between 2020 and 2022 and analyzed for eDNA with the qPCR assays developed for this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255005","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Huitger, C.A., Koltun, G.F., Stelzer, E.A., and Lynch, L.D., 2025, Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22: U.S. Geological Survey Scientific Investigations Report 2025–5005, 59 p., https://doi.org/10.3133/sir20255005.","productDescription":"Report: ix, 59 p.; 2 Appendices; 2 Data Releases","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-161896","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":484334,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13GN45M","text":"USGS data release","linkHelpText":"Pesticide, organic wastewater compound (OWC) and polycyclic aromatic hydrocarbon (PAH) data determined from samples collected with instream passive samplers in the Big Darby Creek Basin, Ohio, 2020–21"},{"id":484333,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WELW7W","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages on Big and Little Darby Creeks and Hellbranch Run, Ohio (through water year 2021)"},{"id":484331,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_csv.zip","text":"Tables 1.1–1.17 (CSV)","size":"34.6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484330,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_tables.xlsx","text":"Tables 1.1–1.17","size":"131 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5005/images/"},{"id":484328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5005 XML"},{"id":493757,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118527.htm","linkFileType":{"id":5,"text":"html"}},{"id":484327,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255005/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5005 HTML"},{"id":484326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5005 PDF"},{"id":484324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5005/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Big Darby Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.8333,\n 40.333\n ],\n [\n -83.8333,\n 39.5\n ],\n [\n -83,\n 39.5\n ],\n [\n -83,\n 40.333\n ],\n [\n -83.8333,\n 40.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
In May 2021, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center acquired airborne multispectral high-resolution data for the Colorado River in the Grand Canyon, Arizona. The image data, which consist of four spectral bands (red, band 1; green, band 2; blue, band 3; and near infrared, band 4) with a ground resolution of 20 centimeters, are available as 16-bit unsigned-integer GeoTIFF files in Sankey and others (2024) (available online at https://doi.org/10.5066/P9BBGN6G). The image files are projected in the State Plane Coordinate System, using the central Arizona zone (202) with the North American Datum of 1983 National Adjustment of 2011. The assessed spatial accuracy for these data is based on 47 ground-control points that were independent from the ground-control points used by the contractor for aerotriangulation and is reported at the 95-percent confidence level as 0.514 meter (m) and a root mean square error of 0.297 m. The intended uses of this dataset are primarily in support of scientific research and monitoring applications. Examples of these applications include high-resolution spatial and temporal change detection of the river channel, geomorphic landforms, riparian vegetation, and backwater and nearshore habitat, as well as other ecosystem-wide mapping. These imagery data also serve as reference material for field science mission planning, as base data for field data collection including community science activities, and as a highly detailed guide for technical boat operation during science activities such as reconnaissance for nighttime missions and navigating rapids during low flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1202","collaboration":"Prepared in cooperation with Northern Arizona University","usgsCitation":"Sankey, J.B., Bransky, N.D., Pigue, L.M., Kohl, K.A., and Gushue, T.M., 2025, Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2021 airborne image acquisition: U.S. Geological Survey Data Report 1202, https://doi.org/10.3133/dr1202.","productDescription":"Report: HTML Document; Data Release","onlineOnly":"Y","ipdsId":"IP-162668","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":483089,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1202/dr1202.XML"},{"id":483585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":483091,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1202/full"},{"id":483090,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1202/images"},{"id":483526,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1027","description":"Durning, L.E., Sankey, J.B., Davis, P.A., and Sankey, T.T., 2016, Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2013 airborne image acquisition: U.S. Geological Survey Data Series 1027, https://doi.org/10.3133/ds1027.","linkHelpText":"- Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2013 airborne image acquisition"},{"id":483066,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/780/","description":"Davis, P.A., 2013, Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection: U.S. Geological Survey Data Series 780, https://pubs.usgs.gov/ds/780/.","linkHelpText":"- Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection"},{"id":483527,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"http://pubs.usgs.gov/of/2012/1139/","description":"Davis, P.A., 2012, Airborne digital-image data for monitoring the Colorado River corridor below Glen Canyon Dam, Arizona, 2009—Image-mosaic production and comparison with 2002 and 2005 image mosaics: U.S. Geological Survey Open-File Report 2012–1139, 82 p. (Available at http://pubs.usgs.gov/of/2012/1139/.)","linkHelpText":"- Airborne digital-image data for monitoring the Colorado River corridor below Glen Canyon Dam, Arizona, 2009—Image-mosaic production and comparison with 2002 and 2005 image mosaics"},{"id":483065,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P93Y4FMJ","description":"Sankey, J.B., Bransky, N.B., Kohl, K.A., Gushue, T.M., Bedford, A.F., and Durning, L.E., 2025, Digital elevation model (DEM) and digital surface model (DSM) data for the Colorado River corridor in Grand Canyon National Park and Glen Canyon National Recreation Area (2002, 2009, 2013 and 2021), including accuracy assessment data: U.S. Geological Survey data release, https://doi.org/10.5066/P93Y4FMJ.","linkHelpText":"- Digital elevation model (DEM) and digital surface model (DSM) data for the Colorado River corridor in Grand Canyon National Park and Glen Canyon National Recreation Area (2002, 2009, 2013 and 2021), including accuracy assessment data"},{"id":483064,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BBGN6G","text":"USGS Data Release","description":"Sankey, J.B., Bransky, N., Pigue, L., Kohl, K., and Gushue, T.M., 2024, Four Band Image Mosaic of the Colorado River Corridor in Arizona—2021, including Accuracy Assessment Data: U.S. Geological Survey data release, https://doi.org/10.5066/P9BBGN6G.","linkHelpText":"Four band image mosaic of the Colorado River Corridor in Arizona—2021, including accuracy assessment data"},{"id":483525,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F7TX3CHS","description":"Durning, L.E., Sankey, J.B., Davis, P.A., and Sankey, T.T., 2016, Four band Image mosaic of the Colorado River Corridor in Arizona--2013, including accuracy assessment data: U.S. Geological Survey data release, https://doi.org/10.5066/F7TX3CHS","linkHelpText":"- Four band Image mosaic of the Colorado River Corridor in Arizona--2013, including accuracy assessment data"}],"country":"United States","state":"Arizona, Nevada, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.25503634497424,\n 37.07331588090071\n ],\n [\n -115.01927028989792,\n 37.07331588090071\n ],\n [\n -115.01927028989792,\n 35.44030487638608\n ],\n [\n -111.25503634497424,\n 35.44030487638608\n ],\n [\n -111.25503634497424,\n 37.07331588090071\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The history-matching approach to parameter estimation with models enables a powerful offshoot analysis of data worth—using the uncertainty of a model forecast as a metric for the worth of data. Adding observation data will either have no impact on forecast uncertainty or will reduce it. Removing existing data will either have no impact on forecast uncertainty or will increase it. The history-matching framework makes it possible to perform this quantitative analysis leveraging the connections among observations, model parameters, and model forecasts. We show this behavior on a specific groundwater flow model of the Mississippi Alluvial Plain and show where the analysis can be informative for considering the potential design of an observation network based on existing or potential observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255007","usgsCitation":"Fienen, M.N., Schachter, L.A., and Hunt, R.J., 2025, A model uncertainty quantification protocol for evaluating the value of observation data: U.S. Geological Survey Scientific Investigations Report 2025–5007, 12 p., https://doi.org/10.3133/sir20255007.","productDescription":"vi; 12 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171702","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":483399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5007"},{"id":483398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5007/coverthb.jpg"},{"id":483404,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255007/full","text":"Report"},{"id":483400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.XML","text":"Report","description":"SIR 2025–5007"},{"id":483403,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5007/images"},{"id":492790,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118495.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, Wisconsin 53726
The Arbuckle-Simpson aquifer is divided spatially into three parts (eastern, central, and western). The largest groundwater withdrawals are from the eastern part of the Arbuckle-Simpson aquifer, which provides water to approximately 39,000 people in Ada and Sulphur, Oklahoma, and surrounding areas. The Arbuckle-Simpson aquifer, including the eastern part, is designated a sole source aquifer for its service area. Based primarily on data collected between 2003 and 2008, a series of comprehensive hydrologic studies of the Arbuckle-Simpson aquifer was published to provide the information necessary to perform groundwater-flow model simulations so that the Oklahoma Water Resources Board could determine how much water could be withdrawn from the aquifer while maintaining flow to springs and streams. As part of the Phase 1 studies, an aquifer water budget was developed from a numerical model for the period 2003–08. For this report, Phase 1 refers to the 2003–08 data collection period, although for some of the analyses, data collected prior to 2003 were used to inform model development work. Allocation of water from this aquifer was then established by the Oklahoma Water Resources Board in 2013. Additional well-spacing rules were also established by the Oklahoma Water Resources Board for sensitive sole source groundwater basins. To determine how the water budget for the eastern part of the Arbuckle-Simpson aquifer has changed over time, recently collected hydrologic data (2018–23) were compared to data collected during 2003–08. The analysis of changes in the aquifer water budget from 2003–08 to 2018–23 could help resource managers better understand changes in the overall balance of water in storage and the potential effects on streamflow, changes in groundwater levels, and the effects of different water uses in the aquifer area on available water in the eastern part of the Arbuckle-Simpson aquifer and streams overlying the eastern part of the Arbuckle-Simpson aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and the Wyoming Water Development Office, has developed standard methods of peak-flow frequency analysis for studies in Montana, North Dakota, South Dakota, and Wyoming. These methods describe the implementation of national flood frequency guidelines described in Bulletin 17C (https://doi.org/10.3133/tm4B5) for the four States and deviations from Bulletin 17C standard procedures to accommodate unusual hydrologic conditions. A U.S. Geological Survey data release accompanying this report (https://doi.org/10.5066/P1WHRK8H) provides example peak-flow frequency analyses for selected streamgages in the study area. The methods described in this report can be used to publish similar data releases for other streamgages in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255019","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wyoming Water Development Office","usgsCitation":"Siefken, S.A., Williams-Sether, T., Barth, N.A., Chase, K.J., and Cedar Face, M.A., 2025, Methods for peak-flow frequency analysis for streamgages in or near Montana, North Dakota, South Dakota, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5019, 19 p., https://doi.org/10.3133/sir20255019.","productDescription":"Report: vii, 19 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148119","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483328,"rank":4,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Excessive nitrogen discharge is a major concern for the Long Island Sound. Programs have been implemented to reduce point sources of nitrogen to the sound, but little is known about the nonpoint sources. This study aims to better understand the current groundwater contributions of nitrogen from nonpoint sources in the Long Island Sound watershed.
During the spring and summer of 2022, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, collected water-quality samples to analyze nutrients (nitrogen and phosphorus), chloride, and bromide at 45 stations in the Long Island Sound watershed in Connecticut, New York, and Rhode Island. The stations were in small drainage watersheds (5 to 30 square kilometers) in the southern part of the Long Island Sound watershed. During two separate synoptic sampling events, water-quality samples and instantaneous streamflow measurements were collected under base-flow conditions (where the streamflow is dominated by groundwater inputs rather than overland flow or runoff flow). One sampling event was in the nongrowing season (April 24–25, 2022), and the other was in the growing season (June 30–July 1, 2022). To calculate instantaneous nitrogen loads and yields, streamflow was measured at the time of sample collection.
Nitrogen concentrations, loads, and yields varied among sampling stations and by season. Total filtered nitrogen concentrations were generally lower in the nongrowing season (from less than 0.14 to 1.9 milligrams per liter) than in the growing season (from less than 0.23 to 3.0 milligrams per liter). Nitrate plus nitrite concentrations showed little variation between the nongrowing and growing seasons. Unfiltered ammonia plus organic nitrogen concentrations were generally lower in the nongrowing season (from less than 0.07 to 0.83 milligram per liter) than in the growing season (from 0.11 to 0.98 milligram per liter). In contrast, total filtered and unfiltered nitrogen loads and yields were higher in the nongrowing season than during the growing season, likely because streamflows were higher during the nongrowing season. Total unfiltered nitrogen yields during the nongrowing season ranged from less than 0.15 to 5.0 kilograms per square kilometer per day. Total unfiltered nitrogen yields during the growing season ranged from less than 0.12 to 2.5 kilograms per square kilometer per day. Total filtered nitrogen yields during the nongrowing season ranged from less than 0.13 to 5.2 kilograms per square kilometer per day. Total filtered nitrogen yields during the growing season ranged from less than 0.06 to 2.5 kilograms per square kilometer per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1206","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Laabs, K.L., Barclay, J.R., and Mullaney, J.R., 2025, Base-flow sampling to enhance understanding of the groundwater flow component of nitrogen loading in small watersheds draining into Long Island Sound: U.S. Geological Survey Data Report 1206, 23 p., https://doi.org/10.3133/dr1206.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161925","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":492783,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118484.htm","linkFileType":{"id":5,"text":"html"}},{"id":483188,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1206/full","linkFileType":{"id":5,"text":"html"},"description":"DR 1206 HTML"},{"id":483186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1206/coverthb.jpg"},{"id":483187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1206 PDF"},{"id":483189,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1206 XML"},{"id":483190,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1206/images/"},{"id":483191,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99IAXUI","text":"USGS data release","linkHelpText":"Nitrogen loads, yields, and associated field data collected during baseflow conditions and site attributes for small basins draining to Long Island Sound"}],"country":"United States","state":"Connecticut, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91277546117404,\n 40.875888202775144\n ],\n [\n -73.5833363685162,\n 40.98693216297761\n ],\n [\n -72.40741106161018,\n 41.2504046305547\n ],\n [\n -71.4646381344502,\n 41.35702020040523\n ],\n [\n -71.40893145316154,\n 41.57350712794573\n ],\n [\n -71.49014822027934,\n 41.724945464467424\n ],\n [\n -72.43277552970126,\n 41.78157938825299\n ],\n [\n -72.757106016586,\n 41.53934120640511\n ],\n [\n -73.08115837726446,\n 41.93608782758082\n ],\n [\n -73.30909534441503,\n 41.8870827192506\n ],\n [\n -73.51694453574096,\n 41.73992349657644\n ],\n [\n -73.50192428145502,\n 41.280885844515296\n ],\n [\n -73.88228764759546,\n 40.963994488494365\n ],\n [\n -73.91277546117404,\n 40.875888202775144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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Ratings are used for several reasons in water-resources investigations. The simplest rating relates discharge to the stage of a river (the stage-discharge relation). From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because flow becomes unsteady as a flood wave passes. The stage-discharge relation is unable to represent hysteresis. However, a dynamic rating method can capture hysteresis, which is driven by the variable energy slope of a flood wave.
A dynamic rating method called DYNPOUND, which accommodates compact and compound channel geometry, was developed by simplifying the one-dimensional Saint-Venant equations. The DYNPOUND method was developed in the Python programming language and computes discharge from stage and stage from discharge. Stage and discharge time series computed with this dynamic rating method were compared to the U.S. Geological Survey (USGS) published stage and discharge time series. The results from the DYNPOUND method were also compared to in-person field measurements of stage and discharge made at 10 USGS streamgages.
DYNPOUND was calibrated for 10 USGS streamgages using published discharge time-series data computed with a simple rating method. The calibration objective was to minimize the mean squared logarithmic error (MSLE) of the DYNPOUND-computed discharge with respect to the discharge time series computed by a simple rating method. For each site, the calibration process also included comparing all field measurements within a selected water year to the corresponding DYNPOUND-computed discharge data points. The MSLE of the DYNPOUND-computed discharge time series for the 10 sites ranged from 8.51×10−4 to 1.36×10−1. For each site, an event-based period was selected to compare the discharge time series computed with the dynamic rating method to discharge field measurements made at the streamgages; the range of MSLE for the 10 DYNPOUND-computed discharge sites was from 4.79×10−4 to 2.30×10−2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245129","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Domanski, M.M., Holmes, R.R., Jr., Heal, E.N., and Knight, T.M., 2025, Dynamic rating method for computing discharge and stage from time-series data: U.S. Geological Survey Scientific Investigations Report 2024–5129, 92 p., https://doi.org/10.3133/sir20245129.","productDescription":"Report: ix, 92 p.; 3 Data Releases; 1 Software Release","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-142872","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This report chapter summarizes the effect of hydroclimatic variability of annual peak streamflow in Minnesota and is part of a larger U.S. Geological Survey multistate study to assess potential nonstationarity in annual peak streamflows across the Midwest. Spatial and temporal patterns were examined for nonstationarity in annual peak streamflow, daily mean streamflow, and modeled climatic data in four analysis periods: (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. Upward trends in annual peak streamflow were detected in northwest to southeast and north to south directions. Downward trends in annual peak streamflow were detected in northeastern and southeastern areas. Trends in peak-flow timing indicated that peak streamflows are being detected later in the water year (the period from October 1 to September 30 designated by the year in which it ends) mainly in the southern areas and earlier in the water year mainly in the northern areas.
Changes in climate data point to wetter conditions in southern areas and drier conditions in northern areas. Annual precipitation was determined to be increasing in a northwest to southeast direction and in the east. In contrast, some areas in the north and northwest indicated decreasing annual precipitation. Annual snowfall was determined to be decreasing except in the extreme northeast, where annual snowfall was determined to be increasing. Decreases in annual potential evapotranspiration were detected in the south, and increases were detected in the north. Annual soil moisture increased in southern areas and decreased in northern and eastern areas. The potential spatial and temporal nonstationarity violations detected in the four analysis periods have important implications for flood-frequency analysis and point to the need for guidance on how to incorporate nonstationarities into future flood-frequency analysis in Minnesota.
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\"}}]}","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
Standardized guidelines for completing flood-flow frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C, https://doi.org/10.3133/tm4B5. In recent decades (since about 2000), a better understanding of long-term climatic persistence (periods of clustered floods or droughts, or wet or dry periods) and concerns about potential climate change and land-use change have caused a reexamination of the stationarity assumptions underlying methods in Bulletin 17C. Bulletin 17C does not offer guidance on incorporating nonstationarities and further identifies a need for flood-frequency studies that incorporate changing climate or basin characteristics. As part of that reexamination, a study of annual peak streamflow (peak flow) has begun in the Midwest. This chapter of the study summarizes how hydroclimatic variability affects peak flows in North Dakota.
In this analysis of peak flow, daily streamflow, and climate metrics, four periods were selected: (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. Output from a monthly water-balance model was used for the climate data. Statistical analysis of peak flow consisted of evaluations of autocorrelation, trends, and change points and was augmented with analyses of seasonality and daily streamflow. The long-term pattern of decreasing peak flow in the west and increasing peak flow in the east is a pattern of opposing signals on either side of the 100th meridian. Analyses indicate that a key factor in changing hydroclimatology is the increase in fall precipitation. The trends in soil moisture closely match the trends in annual precipitation. Nonstationary flood-frequency analysis necessitates detailed exploratory data analysis and additional data and information about climate, land use, and other factors. This study provides extensive exploratory analysis for peak flow, daily streamflow, and climate data for North Dakota, setting the stage for informed nonstationary flood-frequency analysis.
Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2025.109308","usgsCitation":"Ji, L., Senay, G.B., Friedrichs, M., and Kagone, S., 2025, Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches: Agricultural Water Management, v. 309, 109308, 17 p., https://doi.org/10.1016/j.agwat.2025.109308.","productDescription":"109308, 17 p.","ipdsId":"IP-163904","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2025.109308","text":"Publisher Index Page"},{"id":481548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"High Plains","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.057698,44.997431],[-104.043814,45.868385],[-103.668479,45.945242],[-96.571871,45.871846],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.41932,40.048442],[-94.916918,39.836138],[-95.113077,39.559133],[-94.615834,39.160003],[-94.617919,36.499414],[-94.431822,35.397652],[-94.485528,33.663388],[-94.386086,33.544923],[-94.070395,33.574561],[-94.0427,32.056012],[-93.523248,31.037842],[-93.765822,30.333318],[-93.702436,30.112721],[-93.922744,29.818808],[-93.852868,29.675885],[-94.731047,29.369141],[-94.532348,29.5178],[-94.767246,29.525523],[-94.724616,29.774766],[-94.965963,29.70033],[-94.894234,29.338],[-95.16525,29.113566],[-94.73132,29.338066],[-94.803695,29.279237],[-96.341617,28.417334],[-95.983106,28.641942],[-96.221784,28.580364],[-96.287942,28.683164],[-96.473694,28.57324],[-96.664534,28.696904],[-96.481836,28.407844],[-96.790235,28.383926],[-96.898123,28.152881],[-97.21535,28.076575],[-97.040618,28.028708],[-97.183455,27.833231],[-97.354614,27.849572],[-97.296598,27.613947],[-97.399398,27.344735],[-97.640111,27.270943],[-97.485149,27.250841],[-97.552325,26.867633],[-97.145567,25.971132],[-97.36542,25.849826],[-99.110855,26.426278],[-99.452316,27.062669],[-99.556812,27.614336],[-99.841708,27.766464],[-100.280518,28.267969],[-100.785521,29.228137],[-101.441059,29.753451],[-102.341033,29.869305],[-102.698347,29.695591],[-102.944911,29.18882],[-103.227801,28.991532],[-104.46652,29.609296],[-104.924796,30.604832],[-106.158218,31.438885],[-106.381039,31.73211],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.050076,41.000659],[-111.046723,40.997959],[-111.055199,45.001321],[-104.057698,44.997431]]],[[[-97.240849,26.411504],[-97.383531,26.875521],[-97.366771,27.333276],[-96.946988,28.026522],[-96.403206,28.371475],[-96.929053,27.99044],[-97.276091,27.472145],[-97.370731,26.909706],[-97.161471,26.088705],[-97.240849,26.411504]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"309","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":925791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":925792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":925793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":199091,"corporation":false,"usgs":false,"family":"Kagone","given":"Stefanie","affiliations":[],"preferred":false,"id":925794,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263262,"text":"ofr20241076 - 2025 - Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2022","interactions":[],"lastModifiedDate":"2025-07-21T18:11:15.378927","indexId":"ofr20241076","displayToPublicDate":"2025-02-04T14:11:40","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1076","displayTitle":"Continuous Stream Discharge, Salinity, and Associated Data Collected in the Lower St. Johns River and Its Tributaries, Florida, 2022","title":"Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2022","docAbstract":"The U.S. Army Corps of Engineers, Jacksonville District, deepened the St. Johns River channel in Jacksonville, Florida, to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection sites in the St. Johns River and its tributaries.
This report contains information collected during the 2022 water year, from October 2021 to September 2022. Data at each site were compared for the length of the project and on a yearly basis to show the annual variability of discharge and salinity.
The countywide annual rainfall for the 2022 water year was above the average yearly rainfall in four of the five counties. Annual mean discharge at 8 of the 10 tributary monitoring sites was lower for the 2022 water year than for the 2021 water year, and the annual mean flow at Broward River below Biscayne Boulevard near Jacksonville, Florida (USGS site number 02246751), was the lowest recorded at that site over the 7 years of data collection. The annual mean discharge for each of the main-stem sites was lower for the 2022 water year than for the 2021 water year.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek above Buckhorn Bluff near Jacksonville, Fla. (USGS site number 302657081312400), the site closest to the Atlantic Ocean, and was lowest at Durbin Creek near Fruit Cove, Fla. (USGS site number 022462002), the site farthest from the ocean, for all years. Annual mean salinity data from the main-stem sites indicate that salinity decreased with distance upstream from the ocean, which was expected. Annual mean salinity at all monitoring locations was higher for the 2022 water year than the 2021 water year, except at St. Johns River at Buffalo Bluff near Satsuma, Fla. (USGS site number 02244040) and St. Johns River at Dancy Point near Spuds, Fla. (USGS site number 294213081345300), which remained the same. St. Johns River Shands Bridge near Green Cove Springs, Fla. (USGS site number 295856081372301) and Durbin Creek near Fruit Cove, Fla. (USGS site number 022462002) had the highest annual mean salinities at their respective sites since data collection began.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241076","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Carson, J.N., and Benacquisto, M.T., 2025, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2022: U.S. Geological Survey Open-File Report 2024–1076, 51 p., https://doi.org/10.3133/ofr20241076.","productDescription":"Report: x, 51 p.; Data Release","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-159934","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":492684,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118424.htm","linkFileType":{"id":5,"text":"html"}},{"id":481631,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS NWIS Data Release","linkHelpText":"- USGS water data for the Nation"},{"id":481630,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241076/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1076 HTML"},{"id":481628,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1076/ofr20241076.pdf","size":"6.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1076"},{"id":481626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1076/coverthb.jpg"},{"id":481629,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1076/ofr20241076.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1076 XML"},{"id":481627,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1076/images"}],"country":"United States","state":"Florida","otherGeospatial":"Lower St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.07550333942321,\n 30.37984516308761\n ],\n [\n -82.07550333942321,\n 29.26001508937391\n ],\n [\n -81.32685861157174,\n 29.26001508937391\n ],\n [\n -81.32685861157174,\n 30.37984516308761\n ],\n [\n -82.07550333942321,\n 30.37984516308761\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Contact Us- USGS Publications Warehouse
","tableOfContents":"In 2022, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 11 volcanic centers in Alaska and in the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; the eruption of a thick lava flow within the summit crater at Great Sitkin Volcano; and weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano. Uplift and an increase in seismicity were detected at Mount Edgecumbe, a long-dormant volcano in southeastern Alaska. Anomalous seismicity was also detected at three other volcanoes, including Trident Volcano, Takawangha volcano, and Davidof volcano. Other activity documented in 2022 includes ash resuspension events at Mount Katmai and Aniakchak Crater, and Mount Cleveland had a period of unrest, but no eruptive activity took place. In the Commonwealth of the Northern Marianas Islands, hydroacoustic detections and a submarine plume observed in satellite data at Ahyi seamount indicated underwater eruptive activity there.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245108","collaboration":"The Alaska Volcano Observatory is a consortium between the U.S. Geological Survey, the University of Alaska Fairbanks Geophysical Institute, and the Alaska Division of Geological & Geophysical Surveys","usgsCitation":"Orr, T.R., Dietterich, H.R., Grapenthin, R., Haney, M.M., Loewen, M.W., Saunders-Shultz, P., Tan, D., Waythomas, C.F., and Wech, A.G., 2025, 2022 Volcanic activity in Alaska and the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory: U.S. Geological Survey Scientific Investigations Report 2024-5108, 46 p., https://doi.org/10.3133/sir20245108.","productDescription":"ix, 46 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-152943","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":492678,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118410.htm","text":"Mt. Edgecumbe","linkFileType":{"id":5,"text":"html"}},{"id":481523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5108/covrthb.jpg"},{"id":481524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5108/sir20245108.pdf","text":"Document","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":492676,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118408.htm","text":"Mt. Katmai, Trident Volcano, Aniak Crater, Pavlof Volcano, Mt. Cleveland, Takawangha Volcano, Great Sitkin Volcano, Semisopochnoi Island, Davidof Volcano","linkFileType":{"id":5,"text":"html"}},{"id":492677,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118409.htm","text":"Northern Mariana Islands and Ahyi Seamount","linkFileType":{"id":5,"text":"html"}}],"country":"Commonwealth of the Northern Marianas Islands, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.53816786826286,\n 57.38098900209502\n ],\n [\n -136.05611606682763,\n 57.38098900209502\n ],\n [\n -136.05611606682763,\n 56.83684431579326\n ],\n [\n -135.53816786826286,\n 56.83684431579326\n ],\n [\n -135.53816786826286,\n 57.38098900209502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -141.35385989880953,\n 60.14025333267071\n ],\n [\n -141.19757367571614,\n 63.76977206547613\n ],\n [\n -158.28314177162866,\n 59.80039757333148\n ],\n [\n -159.58076765733466,\n 58.614262518125656\n ],\n [\n -158.3486276218774,\n 57.657199413717876\n ],\n [\n -163.92181500362653,\n 55.48998666266456\n ],\n [\n -170.96840796656934,\n 53.54354331847691\n ],\n [\n -179.35526720020715,\n 52.41913115813213\n ],\n [\n -179.9,\n 50.40814181877161\n ],\n [\n -156.32990077575613,\n 54.72335821881029\n ],\n [\n -150.75655120881962,\n 57.04792418079805\n ],\n [\n -141.35385989880953,\n 60.14025333267071\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n 52.416621580476686\n ],\n [\n 176.6106954228785,\n 52.416621580476686\n ],\n [\n 176.6106954228785,\n 51.16946152120039\n ],\n [\n 179.9,\n 51.16946152120039\n ],\n [\n 179.9,\n 52.416621580476686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 143.7848438037919,\n 21.03459968883182\n ],\n [\n 143.7848438037919,\n 12.571176082090219\n ],\n [\n 146.78274675227806,\n 12.571176082090219\n ],\n [\n 146.78274675227806,\n 21.03459968883182\n ],\n [\n 143.7848438037919,\n 21.03459968883182\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
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In 2017, the U.S. Geological Survey, in cooperation with the Upper Arkansas Water Conservancy District, began a study to provide a comprehensive analysis of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado. The study included collection of data pertaining to groundwater hydrology, groundwater and surface-water interactions, and water quality in the alluvial aquifer. In addition to providing foundational information on the hydrology of the alluvial aquifer, a numerical groundwater-flow model was developed to estimate the potential effects of additional storage of groundwater in the alluvial aquifer.
Groundwater-level elevation data from 30 wells were used to estimate groundwater-flow directions in the alluvial aquifer, which were generally from the southwest to northeast, away from the Sangre de Cristo Mountains and towards perennial streams in the center of the valley. Although some seasonal variation was apparent in groundwater-level elevation records, no statistically significant seasonal trends were indicated. Statistically significant long-term trends were indicated in groundwater-level elevation records for 8 of the 30 wells, and of these wells with statistically significant trends, all but 1 indicated a negative trend of groundwater-level elevations. Spatial evaluation of wells with statistically significant negative groundwater-level elevation trends showed many are in areas of denser well drilling for domestic or other uses, indicating increasing groundwater use could potentially be causing groundwater-level elevation declines. There were instances of wells with no statistically significant groundwater-level elevation trends also located in areas of greater density of well completions. Additional investigations may be necessary to more fully characterize the processes responsible for negative groundwater-level elevation trends.
Streamflow gain or loss calculations were completed for low flow in 2017–19 and for high flow in 2018 in nine reaches of streams within the study area. Stream reaches of the upper Texas Creek, upper Grape Creek, upper-middle Grape Creek, and Taylor Creek displayed consistent streamflow loss in each period from 2017 to 2019. These stream reaches represent long-term sources of recharge to the alluvial aquifer. Streamflow gain or loss varies through time in other stream reaches (lower Texas Creek, lower-middle Grape Creek, lower Grape Creek below Westcliffe, and lower Grape Creek above DeWeese Reservoir). The temporally variable behavior indicates these stream reaches may be sources of groundwater recharge or areas of groundwater discharge, likely depending on temporal dynamics between the elevation of the water table and the stream.
Water-quality samples were collected from 10 groundwater wells and 10 stream sites during September through November 2019. All groundwater and stream samples were analyzed for major and trace elements and stable isotopes of water. A subset of groundwater samples was also analyzed for the environmental tracers sulfur hexafluoride, tritium, and noble gases. Comparison of water-quality results to U.S. Environmental Protection Agency drinking water-quality standards indicated no constituents exceeded primary standards for human health. Spatial evaluation of water quality indicated the concentrations of various constituents are likely controlled by groundwater and surface-water interactions and by spatial variability in bedrock geology underlying the alluvial aquifer. Specifically, streams shown to gain from groundwater had water chemistry constituent compositions similar to groundwater, whereas streams exiting the Sangre de Cristo Mountains tended to have compositions consistent with snowmelt. Groundwater geochemistry appeared to be partially controlled by oxidation-reduction processes and by proximity to igneous rocks in the Wet Mountains. Environmental tracers used to estimate groundwater age indicated all sampled groundwater contained tracers representing modern recharge (approximately less than 65 years old) but mixing of premodern recharge (approximately more than 65 years old) also occurs. Spatial evaluation of environmental tracers indicated large faults may be conduits for upwelling of older groundwater. No trends were observed in groundwater age with well depth, indicating all sampled wells are located within the zone of active groundwater flow. The presence of modern groundwater in wells with statistically significant negative groundwater-level elevation trends indicates groundwater storage depletions may be partially offset by capture of modern recharge. Repeated sampling of groundwater age would be necessary, however, to determine if any trends in groundwater age exist, which may indicate changing groundwater recharge, storage, or discharge. Additional investigations could also consider quantifying groundwater age in deeper wells to more fully define the depth of active groundwater flow.
A numerical groundwater-flow model was developed to estimate components of the water budget, simulate groundwater and surface-water interactions, and evaluate the potential effects of aquifer storage and recovery. Simulated groundwater-level elevations from the calibrated groundwater-flow model are similar to the observed pattern of groundwater-level elevations with higher elevations in the western part of the study area along the Sangre de Cristo Mountains. Simulated water-budget components indicate most of the recharge to the alluvial aquifer is derived from streamflow losses, which is consistent with observations of losing streams along the mountain front. The largest groundwater discharge component of the alluvial aquifer was to streams in the center of the valley, where observations of stream gain or loss indicated the predominance of gaining conditions. Comparison of groundwater and surface-water interactions between the calibrated groundwater-flow model for 2000-19 (the base-case model) and a simulation including additional recharge, representing potential aquifer storage and recovery operations, indicated the additional recharge distributed throughout the area had minimal effects on streamflow in the nearby Grape Creek. An analysis of subregional groundwater budgets showed approximately 54 percent of the additional recharge flowed back to nearby Grape Creek, and the other 46 percent was distributed laterally into adjacent cells in the alluvial aquifer. The comparison of simulations and subregional water budget show the additional recharge did not substantially alter groundwater-level elevations or basin wide groundwater storage. Although the analysis of additional recharge provided in the numerical groundwater-flow model considers only one of many possible recharge scenarios, the model provides a useful tool that could be modified for various scenarios to understand potential effects of managed aquifer recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245105","collaboration":"Prepared in cooperation with the Upper Arkansas Water Conservancy District","usgsCitation":"Newman, C.P., Russell, C.A., Kisfalusi, Z.D., and Paschke, S.S., 2025, Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2024–5105, 62 p., https://doi.org/10.3133/sir20245105.","productDescription":"Report: vii, 62 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-125470","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":481114,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5105/coverthb.jpg"},{"id":481115,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5105"},{"id":481144,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9342SSP","text":"USGS data release","linkHelpText":"Environmental tracer model for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2019"},{"id":481145,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AAUGNY","text":"USGS data release","linkHelpText":"Groundwater-flow model of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado"},{"id":481407,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5105/images"},{"id":481408,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.xml"},{"id":481417,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245105/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5105"}],"country":"United States","state":"Colorado","county":"Custer County, Fremont County","otherGeospatial":"Upper Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1667,\n 38.5\n ],\n [\n -105.1667,\n 37.9167\n ],\n [\n -105.9167,\n 37.9167\n ],\n [\n -105.9167,\n 38.5\n ],\n [\n -105.1667,\n 38.5\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 (USGS), in cooperation with the Wyoming Water Development Office, developed streamflow statistics for streamgages in and near Wyoming. Statistics were computed for active (through September 30, 2021) and discontinued USGS streamgages with 10 or more years of daily mean streamflow record. Streamflow at each streamgage was assessed for degree of human alteration owing to dams and diversions before streamflow statistics were computed. Streamflow records from 615 streamgages were used to compute basic, seasonal, and flow-duration statistics; streamflow records from 387 streamgages were used to compute n-day statistics, which are streamflow statistics describing streamflow over a number of days (n), and statistics that can be used for regional regression. The streamflow statistics are provided in a USGS data publication that accompanies this report and through the USGS StreamStats web-based application (https://www.usgs.gov/streamstats).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245104","collaboration":"Prepared in cooperation with Wyoming Water Development Office","usgsCitation":"Armstrong, D.W., Lange, D.A., and Chase, K.J., 2025, Methods to determine streamflow statistics based on data through water year 2021 for selected streamgages in or near Wyoming: U.S. Geological Survey Scientific Investigations Report 2024–5104, 10 p., https://doi.org/10.3133/sir20245104.","productDescription":"Report: v, 10 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148941","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":492669,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118406.htm","linkFileType":{"id":5,"text":"html"}},{"id":481019,"rank":6,"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":481017,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20245104/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024–5104."},{"id":481016,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5104/sir20245104.XML","linkFileType":{"id":8,"text":"xml"}},{"id":481015,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5104/images"},{"id":481014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5104/sir20245104.pdf","text":"Report","size":"5.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5104"},{"id":481013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5104/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10935768359315,\n 45.03387726366623\n ],\n [\n -111.10935768359315,\n 40.99279378165886\n ],\n [\n -104.05893115935177,\n 40.99279378165886\n ],\n [\n -104.05893115935177,\n 45.03387726366623\n ],\n [\n -111.10935768359315,\n 45.03387726366623\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
In the arid western Snake River Plain around the City of Mountain Home, Idaho, declining groundwater levels concern agricultural, municipal, and other water users who rely on groundwater for sustenance because surface-water resources are limited. The U.S. Geological Survey developed this hydrogeologic framework to provide an updated characterization of groundwater resources in the western Snake River Plain around the City of Mountain Home. The hydrogeologic framework comprises: (1) a conceptual description of hydrogeologic units, (2) a three-dimensional hydrogeologic model and borehole database, (3) a map of groundwater levels and change, and (4) a discussion of groundwater occurrence and movement within the study area. Hydrogeologic units were defined based on existing literature and the borehole database compiled for this study; the five hydrogeologic units are granite, rhyolite, basalt, fine-grained sediments, and coarse-grained sediments. Each unit can bear water, but the main regional aquifer in the study area occurs in the basalt and fine-grained sediment units with depth to water ranging from 150 to 765 feet. A perched groundwater zone near the City of Mountain Home is primarily hosted in basalt and used domestically with most depths to water ranging from 30 to 100 feet. Interflow zones, scoria, and vertical fractures create heterogeneity within the basalt hydrogeologic unit that exerts strong control on groundwater movement, creating horizontal perching conditions and zones of enhanced vertical conductivity that facilitate downward groundwater percolation. In the fine- and coarse-grained sediments and rhyolite units, inferred faults both impede and enhance groundwater movement. The borehole database was constructed by digitizing 540 well-driller reports and was used to build a three-dimensional hydrogeologic framework model which reasonably represents the spatial distribution of hydrogeologic units in the study area. Generally, fine-grained sediments underlie much of the study area, with basalt concentrated in the central and western study area and rhyolite and granite in the uplands to the north. Groundwater levels were measured in 180 wells in March and November 2023; these data were used to develop water-table contour maps and describe groundwater-level change over an irrigation season. Groundwater generally flows south-southwest to the Snake River and groundwater levels declined across most of the study area (from 0.03 to 22.01 feet) between spring and autumn 2023, which is consistent with long-term declines in the Cinder Cone Butte Critical Groundwater Area and Mountain Home Groundwater Management Area. Groundwater levels rose (0.6 to 15.44 feet) over the irrigation season in most wells in the perched groundwater zone near the City of Mountain Home and near the Snake River, indicating the importance of surface-water recharge to groundwater in areas where surface water irrigation occurs. In aggregate, this hydrogeologic framework provides an updated characterization of and new insights into groundwater resources in the study area to help inform water resources management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245132","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Zinsser, L.M., and Ducar, S.D., 2025, Hydrogeologic framework of the Mountain Home area, southern Idaho: U.S. Geological Survey Scientific Investigations Report 2024–5132, 47 p., https://doi.org/10.3133/sir20245132.","productDescription":"Report: vii, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-140356","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":492032,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118332.htm","linkFileType":{"id":5,"text":"html"}},{"id":466551,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5132/sir20245132.XML"},{"id":466550,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5132/images"},{"id":466549,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HK5XWS","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrogeologic framework of the Mountain Home area, southern Idaho - three-dimensional hydrogeologic framework model, borehole database, well data, water-level contours and groundwater storage change"},{"id":466548,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245132/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5132"},{"id":466547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5132/sir20245132.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5132"},{"id":466546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5132/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Mountain Home area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.5,\n 43.5\n ],\n [\n -116.5,\n 42.833\n ],\n [\n -115,\n 42.833\n ],\n [\n -115,\n 43.5\n ],\n [\n -116.5,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4250