Water insecurity poses a complex challenge for the western United States. Large populations are exposed and susceptible to physical and social factors that can leave them with precarious access to sufficient water supplies. Consideration of social issues by water managers can help ensure equitable supply. However, how social factors affect water insecurity conditions remains unclear. This paper reviews literature on how social vulnerability influences water insecurity in the western United States. Through a meta-analysis, indicators measuring how dimensions of social vulnerability influence water insecurity were classified and hierarchical clustering was used to characterize the relationships among these vulnerability dimensions for the largest water-users—the agricultural and municipal sectors. The study then assessed uncertainty associated with social vulnerability dimensions and their indicators. There is greatest evidence for the influence of demographic characteristics, socioeconomic status, and exposure. Indicators of these determinants were mainly significant and exacerbated conditions of water insecurity. Evidence for indicators of social dependence and special needs populations was limited, although studies assessing these factors showed significant agreement on their influence on water insecurity. Conceptual framings of social vulnerability and water security determined which indicators were measured, whereas studies of the water-use sectors focused on differing associations of social vulnerability. These findings indicate the importance of recognizing the different contexts posed by water-use sectors and diverse conceptual framings. Further, some determinants such as living conditions remain important but underexplored drivers of a community's experience of water insecurity. Understanding the uncertainty associated with these measures has implications to equitable decision making.
No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Bulletin of the American Meteorological Society, State of the Climate, 2023 report","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-24-0101.1","usgsCitation":"Burgess, D., Wolken, G., Wouters, B., Andreassen, L., Florentine, C., Kohler, J., Luks, B., Palsson, F., Sass, L., Thomson, L., and Thorsteinsson, T., 2024, Glaciers and ice caps outside Greenland, v. 105, no. 8, 4 p., https://doi.org/10.1175/BAMS-D-24-0101.1.","productDescription":"4 p.","startPage":"S307","endPage":"S310","ipdsId":"IP-162903","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466959,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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Washington","indexId":"sir20245026v2","publicationYear":"2024","noYear":false,"chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1}],"isPartOf":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"lastModifiedDate":"2026-02-03T18:14:38.644212","indexId":"sir20245026v2","displayToPublicDate":"2024-08-21T14:09:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5026","chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","docAbstract":"Groundwater flow in the active model area (AMA) was simulated using a groundwater-flow model. A steady-state model version of the model simulates equilibrium conditions, and a transient model version simulates monthly variability. The model corresponds to the physical and temporal dimensions of the conceptual model and groundwater budget. The steady-state model version represents average conditions for an 11-year period (January 1, 2005–December 31, 2015), and the transient model represents monthly hydrologic variability within that period. The 13-layer model was constructed using MODFLOW-NWT with a uniformly spaced grid consisting of 416 rows, 433 columns, and cells with a horizontal dimension of 500 feet (ft) on a side.
The model was calibrated to measured values of water levels in wells and lakes and estimated base flow for selected streamflow measurement stations, commonly referred to as streamgages. Model calibration was accomplished using a combination of manual and automatic methods, including the Model-Independent Parameter Estimation (PEST) program that adjusted model input parameters with the aim of minimizing the difference between estimated and model-simulated values of hydraulic head and base flow.
Model boundary conditions consist of all simulated groundwater inflow to and outflow from the AMA. For example, a stream reach that simulates a gain from or loss to groundwater is a boundary condition that allows water to exit or enter, respectively, the groundwater system. Other boundary conditions include springs, seeps, precipitation recharge, groundwater exchange with lakes and Puget Sound, and groundwater pumping. A comparison of the estimated groundwater budget to that simulated by the steady-state model version indicates that the relative percentages of total inflow or total outflow for six major categories of boundary conditions are similar for the two budgets.
The model was used to simulate three suites of scenarios of potential drought and water-use changes. Scenario 1 suite consisted of the steady-state model version that was run with 0, 15, 20, and 25 percent reduction of precipitation recharge to assess the corresponding reductions in base flow with decreasing recharge. The last simulation for the scenario 1 suite consisted of the transient model version simulating 3 years of consecutive seasonal drought, defined by the months of May through September, to assess the corresponding base-flow reductions. Scenario 2 suite consisted of the steady-state model version with all simulated groundwater use removed, compared with a simulation that includes current groundwater use to evaluate changes to potentiometric surfaces and base flows. Scenario 3 suite consisted of a transient model version of the model that simulated pumping increases for four different categories of water-supply wells (compared to no pumping increases) to evaluate resulting reductions in base flow. Although, these scenarios provide examples of model applications and useful insights, many other scenarios could be simulated. A description of how to download the model is described in the body of this report.
Uncertainty is associated with most model inputs. Groundwater levels, lake levels, and land-surface altitudes are relatively certain; other model inputs are far less certain, including precipitation recharge, base flow, hydraulic properties, water use, and the three-dimensional structure of subsurface hydrogeologic units. Models are useful not because of high levels of accuracy of all model inputs, but because they combine the best information and estimates available, thereby providing the best predictions available related to physical processes.
The model described in this report simulates groundwater flow on a regional scale, which has inherent limitations for simulating hydrologic scenarios at local scales. Model structures and inputs were generalized to be consistent with this regional scale. For example, the actual groundwater system has much greater heterogeneity of hydraulic conductivity than is possible within the model’s degrees of freedom. Variations in hydraulic gradients over distances less than 500 ft cannot be simulated. The distances between model features, such as a pumping well and a stream, must be placed at 500-ft intervals and are co-located if both features are within the same model cell.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v2","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Long, A.J., Wright, E.E., Fuhrig, L.T., and Bright, V.A.L., 2024, Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington, v. 2 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 2 chap. (D–E): U.S. Geological Survey Scientific Investigations Report 2024–5026–D–E, [variously paged; 103 p.], https://doi.org/10.3133/sir20245026v2.","productDescription":"Report: 103 p.; 14 Tables; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140115","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499452,"rank":22,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117217.htm","linkFileType":{"id":5,"text":"html"}},{"id":432082,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.14.csv","text":"Table 1.14","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.14","linkHelpText":"- Groundwater use applied to scenario 3 for the Spanaway Water Company and the City of Sumner, near the southeastern part of Puget Sound, Washington"},{"id":432079,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.11.csv","text":"Table 1.11","size":"8 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.11","linkHelpText":"- Supplemental hydraulic-head targets for the steady-state model version set equal to the land surface to prevent groundwater flooding and corresponding simulated values, near the southeastern part of Puget Sound, Washington"},{"id":432074,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.6.csv","text":"Table 1.6","size":"637 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.6","linkHelpText":"- Time-series records of measured and simulated hydraulic-head values (transient model version) for selected wells used, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432087,"rank":21,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5026 Vol 2 XML"},{"id":432066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.jpg"},{"id":432067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.pdf","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 2 PDF"},{"id":432069,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.1.csv","text":"Table 1.1","size":"995 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.1","linkHelpText":"- Streamflow-Routing (SFR) Package specifications by reach"},{"id":432070,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.2.csv","text":"Table 1.2","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.2","linkHelpText":"- Estimated monthly average base flow estimated for Coal, Boise, and Scatter Creeks where they enter the active model area, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432071,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.3.csv","text":"Table 1.3","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.3","linkHelpText":"- Estimated monthly average base flow estimated for selected streams where they enter the active model area, the Buckley diversion (inflow to Lake Tapps), and outflow from Lake Tapps, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432072,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.4.csv","text":"Table 1.4","size":"6 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.4","linkHelpText":"- Monthly average water levels for American, Gravelly, Steilacoom, and Spanaway Lakes, and Lake Tapps, derived from measured and estimated values, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432073,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.5.csv","text":"Table 1.5","size":"6.7 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.5","linkHelpText":"- Measured water levels for American, Gravelly, and Spanaway Lakes, near the southeastern part of Puget Sound, Washington, 2000–18"},{"id":432075,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.7.csv","text":"Table 1.7","size":"398 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.7","linkHelpText":"- Averages of measured hydraulic-head values for selected wells and corresponding simulated steady-state values, near the southeastern part of Puget Sound, Washington, 2005-15"},{"id":432076,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.8.csv","text":"Table 1.8","size":"291 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.8","linkHelpText":"- Estimated and simulated monthly average base flow for selected stations, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432077,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.9.csv","text":"Table 1.9","size":"9 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.9","linkHelpText":"- Estimated and simulated base-flow values for the steady-state model version for stations with continuous records, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432078,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.10.csv","text":"Table 1.10","size":"63 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.10","linkHelpText":"- Estimated and simulated vertical hydraulic-head differences for the steady-state model version between an upper and lower model layer for selected locations, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432080,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.12.csv","text":"Table 1.12","size":"199 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.12","linkHelpText":"- Model calibration parameters showing input to the control file for the Model-Independent Parameter Estimation (PEST) program"},{"id":432081,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.13.csv","text":"Table 1.13","size":"12 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.13","linkHelpText":"- Simulated groundwater budget for the calibrated transient model version, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432083,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Tables1.1-1.14.xlsx","text":"Tables 1.1-1.14","size":"5.2 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2024-5026 Vol 2 Table 1.1-1.14"},{"id":432084,"rank":18,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432085,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LU1PMQ","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model to simulate the groundwater flow system near Puget Sound, Pierce and King Counties, Washington"},{"id":432086,"rank":20,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/images"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.8989970759464,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 47.52614250846048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This report presents information used to characterize the groundwater-flow system and the development of a numerical model in the AMA. Included are descriptions of the geology and conceptual hydrogeologic framework, groundwater levels and flow directions, groundwater recharge and discharge, numerical groundwater-flow model construction and results, and model limitations. The study area encompasses the western part of Pierce County and the southwestern part of King County, Washington. The study area extends south to the Nisqually River, southwest to Tanwax Creek, northeast to the Green River, and north through the valley near Auburn and adjacent uplands. It is bounded on the east by foothills of the Cascade Range, and on the northwest by Puget Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v1","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Welch, W.B., Bright, V.A.L., Gendaszek, A.S., Dunn, S.B., Headman, A.O., and Fasser, E.T., 2024, Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington, v. 1 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 3 chap. (A–C): U.S. Geological Survey Scientific Investigations Report 2024–5026–A–C, [variously paged; 71 p.], 1 pl., https://doi.org/10.3133/sir20245026v1.","productDescription":"Report: 71 p.; Data Release: 1 Plate: 50.67 × 37.33 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135626","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499451,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117216.htm","linkFileType":{"id":5,"text":"html"}},{"id":432059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.jpg"},{"id":432065,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_plate.pdf","text":"Plate 1","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 1 Plate 1"},{"id":432064,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.XML"},{"id":432063,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/images"},{"id":433528,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_versionHist.txt","description":"SIR 2024-5026 Vol 1 Version History"},{"id":432062,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.pdf","text":"Report","size":"57.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5026 Vol 1"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.8989970759464,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 47.52614250846048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The partners of the Chesapeake Bay are investing billions of dollars in the restoration of critical habitats to improve conditions for people and living resources throughout the Bay and its watershed. However, the recent proliferation of invasive Ictalurus furcatus (blue catfish) in the Chesapeake Bay’s major rivers has the potential to disrupt these restoration efforts and limit the full potential improvement of the ecosystem. The U.S. Geological Survey can help respond to this management challenge in the Nation’s largest estuary by leveraging its leadership and technical capabilities to work with resource managers, academics, and other stakeholders.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243033","usgsCitation":"Robertson, E., Malpass, J., Ottinger, C., Griffin, J., Densmore, C., Hyer, K., 2024, Invasive blue catfish in the Chesapeake Bay: A risk to realizing Bay restoration investments: U.S. Geological Survey Fact Sheet 2024–3033, 4 p., https://doi.org/10.3133/fs20243033.","productDescription":"3 p.","numberOfPages":"3","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-169320","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499129,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117223.htm","linkFileType":{"id":5,"text":"html"}},{"id":432932,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3033/fs20243033_print.pdf","text":"Report","size":"1.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3033","linkHelpText":"Printer-friendly version"},{"id":432930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3033/coverthb2.jpg"},{"id":432931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3033/fs20243033.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3033"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.96493103491717,\n 40.67063909676753\n ],\n [\n -78.96493103491717,\n 36.23363948406978\n ],\n [\n -74.8780169724172,\n 36.23363948406978\n ],\n [\n -74.8780169724172,\n 40.67063909676753\n ],\n [\n -78.96493103491717,\n 40.67063909676753\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Eastern Ecological Science Center
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More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This multichapter volume documents the development of the conceptual and numerical groundwater-flow models of groundwater flow. Chapters A, B, and C provide an overall introduction to the multichapter volume (Chapter A), the conceptual hydrogeologic framework (Chapter B), and the groundwater budget (Chapter C). Chapters D and E describe numerical groundwater-flow model construction and calibration (Chapter D) and the numerical groundwater-flow model results (Chapter E). Collectively, these reports present a characterization and simulation tool for groundwater resources near the southeastern part of Puget Sound, Washington.
","doi":"10.3133/sir20245026","usgsCitation":"Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5026, https://doi.org/10.3133/sir20245026.","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.74462161441157,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 49.512370286103504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure.
The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data.
Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019.
The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions.
Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix.
Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions.
Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent.
The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In 2016, the U.S. Geological Survey began a multiyear cooperative study with the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system through hydrogeologic mapping, compilation of groundwater chloride concentrations, and groundwater flow modeling. In the initial phase of the islandwide study, the hydrogeologic framework and extent of saltwater intrusion in aquifers in Kings, Queens, and Nassau Counties on western Long Island, N.Y., were investigated. The aquifer system underlying western Long Island has been under stress from pumping of public, irrigation (golf course), and industrial supply wells. Saltwater intrusion has occurred from surrounding embayments (East River, Long Island Sound, Jamaica Bay, and the Atlantic Ocean) due to pumping.
Eighteen boreholes were drilled and cores taken during 2019–21 to collect hydrogeologic, geochemical, and geophysical data to delineate the complex subsurface hydrogeology and extent of saltwater intrusion within the study area. Evaluation of the new cores, reexamination of legacy core descriptions, and analysis of borehole geophysical logs was used to refine the previously published hydrogeologic framework of Pleistocene and Cretaceous unconsolidated sediments in the area, including delineation of a previously undefined hydrogeologic unit between the Magothy aquifer and the Raritan confining unit, herein named the “upper Raritan aquifer.” The upper Raritan aquifer was first recognized in southeastern Nassau County from an analysis of about 50 closely spaced boreholes with high-resolution core descriptions and gamma-ray (gamma) logs. Further analysis of borehole logs across the study area indicated that the upper Raritan aquifer was also present in Kings and Queens Counties.
Nuclear magnetic resonance (NMR) logging was used for the first time on Long Island to provide estimates of the hydraulic properties of the major aquifer and confining units. Unlike other geophysical logs that record responses to the rock matrix and fluid properties and are strongly dependent on mineralogy, NMR logs record responses to the presence of hydrogen protons in the formation fluid to determine water fraction and pore-size distribution. NMR log analysis provided estimates of the clay-bound, capillary-bound, and mobile water fractions and hydraulic conductivity of aquifers and confining units penetrated by five wells in Nassau County.
Pumpage for public-supply and industrial wells on Long Island began in the 1870s with small, localized suppliers of populated areas in Kings and Queens Counties. By 1904–16, pumpage for public water supply in Kings County averaged 21 million gallons per day, and averaged 37 million gallons per day in Queens County, mostly from the upper glacial aquifer. Saltwater intrusion was reported as early as the beginning of the 20th century and included the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifer systems. By 1936, pumping in central Kings County created a major cone of depression in the water table extending to the south shore of much of Kings County and into southwestern Queens County. Saltwater intrusion has caused the shutdown of public-supply wells in Kings, Queens, and Nassau Counties. A large saltwater intrusion wedge in the Lloyd aquifer was indicated in southern Queens County in the early part of the 20th century, and the saltwater interface may have been onshore predevelopment. Most of Kings and Queens Counties are intruded with saltwater in both the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifers systems. Saltwater increased during the 20th century and continues to increase to the present (2023) in the Lloyd-North Shore aquifer system in Great Neck and Manhasset Neck in northern Nassau County. A major wedge of saltwater intrusion in the upper glacial-Jameco-Magothy aquifer in southwestern Nassau County appears to be increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245048","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Finkelstein, J.S., Williams, J.H., and Lange, A.D., 2024, Hydrogeologic framework and extent of saltwater intrusion in Kings, Queens, and Nassau Counties, Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2024–5048, 83 p., https://doi.org/10.3133/sir20245048.","productDescription":"Report: ix, 83 p.; 3 Data Releases; Interactive Geospatial Data Viewer","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-147125","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":431252,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WN72B","text":"USGS data release","linkHelpText":"Hydrogeologic framework and 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Salinity, or total dissolved solids (TDS), of the Colorado River is a major concern in the southwestern United States where the river provides water to about 40 million people for municipal and industrial use and is used to irrigate about 5.5 million acres of land. Much of the salinity in the Colorado River Basin is derived from natural interactions of surface water and groundwater with various geologic materials (rocks, soils, and alluvial deposits). The Dolores River in southwest Colorado is a major tributary of the Colorado River that historically accounts for about 6 percent of the salinity load to the Upper Colorado River Basin with the Paradox Valley being the primary source of salinity to the Dolores River. The Paradox Valley, one of several salt-anticline valleys in the region, is a fault-bounded topographic basin aligned with and exposing an underlying salt-anticline core. Salt deposits in the Pennsylvanian Paradox Formation of the Hermosa Group form an elongated salt diapir oriented northwest to southeast that is up to 12,000 feet (ft) thick beneath the present valley floor. Surface erosion, groundwater circulation, and weathering during Tertiary and Quaternary valley formation contributed to development of a cap rock, collapse features, breccia, and brine at the top of the exposed salt diapir. Today (2023), brine occurring in the brecciated cap rock and underlying salt deposits is in hydraulic connection with an overlying freshwater alluvial aquifer, and depending on seasonal river stage and hydrologic conditions, the brine discharges to the Dolores River causing the observed increase in salinity as the river crosses the Paradox Valley.
To reduce salinity concentrations in the Dolores River, the Bureau of Reclamation (Reclamation) operates the Paradox Valley Unit (PVU). The PVU project consists of nine shallow brine pumping wells near the Dolores River and one deep disposal well where the brine is injected for disposal. When operational, the PVU pumping wells extract brine from the base of the alluvial aquifer that is piped and injected into a deep disposal well about 3 miles southwest of the PVU. The PVU became fully operational July 1, 1996, and by 2015, operation of the PVU had reduced salinity concentrations in the Dolores River by as much as 70 percent compared to pre-PVU conditions. In response to a 4.5 magnitude earthquake, injection operations, and thus PVU pumping, were ceased from March 2019 to June 2022. A trial period of PVU operation began in June 2022 with a reduced injection rate, and thus PVU pumping rate, of about two-thirds capacity to gather additional information and guide future operational decisions.
In cooperation with Reclamation, the U.S. Geological Survey (USGS) developed this report to present the current (2023) understanding of groundwater and brine occurrence and discharge to the Dolores River in the Paradox Valley. Results from the compilation of spatial datasets, groundwater sampling and age dating, and aquifer tests are presented to provide improved understanding of the Paradox Valley hydrogeology, to supply datasets for a numerical groundwater-flow and brine-transport model, and to support future operations of the PVU. The hydrogeologic data provided herein, along with the most recent loading analysis for the Dolores River in the Paradox Valley, and a previous conceptual model for brine discharge to the river are used to present a conceptual understanding of groundwater occurrence in the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235094","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Paschke, S.S., Mast, M.A., Gardner, P.M., Newman, C.P., and Watts, K.R., 2024, Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5094, 58 p., https://doi.org/10.3133/sir20235094.","productDescription":"Report: x, 54 p.; 2 Data Releases; Database","onlineOnly":"Y","ipdsId":"IP-125569","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":432678,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245038","text":"Simulation of Groundwater Flow and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"},{"id":432677,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the nation: U.S. Geological Survey National Water Information System database"},{"id":432676,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJQDDU","text":"USGS data release","linkHelpText":"Geospatial datasets developed for a hydrogeologic conceptual model of brine discharge to the Dolores River, Paradox Valley, Colorado"},{"id":432675,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NV5U6F","text":"USGS data release","linkHelpText":"Water-level and pumping data, water-level models, and estimated hydraulic properties for the Paradox Valley alluvial aquifer in Montrose County, Colorado, 2013"},{"id":432674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FMWX2J","text":"USGS data release","linkHelpText":"Recharge temperatures and groundwater-age models for the Paradox Valley alluvial aquifer, 2011, Colorado"},{"id":432669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5094/sir20235094.pdf","text":"Report","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5094"},{"id":499380,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117218.htm","linkFileType":{"id":5,"text":"html"}},{"id":432668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5094/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.23469392755278,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.62004006715256\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, Colorado 80225
Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU.
In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET).
Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells.
The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate.
Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought.
The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245038","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Paschke, S.S., Mast, M.A., and Watts, K.R., 2024, Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5038, 47 p., https://doi.org/10.3133/sir20245038.","productDescription":"Report: viii, 47 p.; Data Release; 3 Databases","onlineOnly":"Y","ipdsId":"IP-130109","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":432768,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09171100/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09171100 Dolores River near Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432764,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5038/coverthb.jpg"},{"id":432763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZW0FH5","text":"USGS data release","linkHelpText":"MODFLOW-6 model of variable-density groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Colorado"},{"id":432766,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09169500/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09169500 Dolores River at Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5038/sir20245038.pdf","text":"Report","size":"9.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5038"},{"id":499462,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117219.htm","linkFileType":{"id":5,"text":"html"}},{"id":432769,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235094","text":"Hydrogeologic Conceptual Model of Groundwater Occurrence and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.10372373203981,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.51805273423872\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, Colorado 80225
The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2024.104590","usgsCitation":"Birchler, J.J., Palmsten, M.L., Doran, K., Karwandyar, S., Pardun, J.M., Oades, E.M., Mulligan, R.P., and Whitehead-Zimmers, E.S., 2024, Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane: Coastal Engineering, v. 193, 104590, 19 p., https://doi.org/10.1016/j.coastaleng.2024.104590.","productDescription":"104590, 19 p.","ipdsId":"IP-154794","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2024.104590","text":"Publisher Index Page"},{"id":462596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"193","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karwandyar, Sharifa 0000-0002-1531-2360","orcid":"https://orcid.org/0000-0002-1531-2360","contributorId":343816,"corporation":false,"usgs":false,"family":"Karwandyar","given":"Sharifa","email":"","affiliations":[{"id":82201,"text":"Department of Earth Sciences, Binghamton University","active":true,"usgs":false}],"preferred":false,"id":915010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pardun, Joshua Michael 0000-0003-4633-3970","orcid":"https://orcid.org/0000-0003-4633-3970","contributorId":335148,"corporation":false,"usgs":true,"family":"Pardun","given":"Joshua","email":"","middleInitial":"Michael","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oades, Elora M.","contributorId":343817,"corporation":false,"usgs":false,"family":"Oades","given":"Elora","email":"","middleInitial":"M.","affiliations":[{"id":82202,"text":"Department of Civil Engineering, Queen’s University","active":true,"usgs":false}],"preferred":false,"id":915012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mulligan, Ryan P.","contributorId":194423,"corporation":false,"usgs":false,"family":"Mulligan","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":35723,"text":"Queen's University - Kingston, Ontario","active":true,"usgs":false}],"preferred":false,"id":915013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehead-Zimmers, Eli Sawyer 0000-0002-8925-3498","orcid":"https://orcid.org/0000-0002-8925-3498","contributorId":339930,"corporation":false,"usgs":true,"family":"Whitehead-Zimmers","given":"Eli","email":"","middleInitial":"Sawyer","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266827,"text":"70266827 - 2024 - Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire","interactions":[],"lastModifiedDate":"2025-05-14T13:20:58.690457","indexId":"70266827","displayToPublicDate":"2024-08-19T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2414,"text":"Journal of Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire","docAbstract":"With the intensity and frequency of wildfires increasing rapidly, the need to study the ecological effects of these wildfires is also growing. An understudied aspect of fire ecology is the effect fires have on parasite–host interactions, including ectoparasites that might be pathogen vectors. Although some studies have examined the impacts of fire on ticks, studies on other ectoparasites, including pathogen vectors, are rare. To help address this knowledge gap, we examined the abiotic and biotic factors that predict the likelihood and extent of parasitism of deer mice (Peromyscus maniculatus) by fleas within a landscape of unburned and recovering burned (>9 yr postfire) mixed conifer forests. We sampled 227 individual deer mice across 27 sites within the Jemez Mountains of northern New Mexico in 2022 and quantified measures of parasitism by fleas (primarily Aetheca wagneri). These sites were distributed in both unburned areas (n = 15) and recovering burned areas (n = 12), with the latter derived from 2 large fires, the Las Conchas fire (2011) and the Thompson Ridge fire (2013). Using these data, we tested for differences in prevalence, mean abundance, and mean intensity of fleas on deer mice, focusing on the predictive importance of host sex and fire history. We also created generalized linear mixed-effects models to investigate the best host and environmental predictors of parasitism by fleas. Approximately a decade postfire, we found minimal evidence to suggest that fire history influenced either the presence or intensity of fleas on deer mice. Rather, at the current forest-regeneration stage, the extent of parasitism by fleas was best predicted by measures of host sex, body condition, and the trapline's ability to accumulate water, as measured through topography. As host body condition increased, the probability of males being parasitized increased, whereas the opposite pattern was seen for females. Male mice also had significantly greater flea loads. Among potential abiotic predictors, the topographic wetness index or compound topographic index (a proxy for soil moisture) was positively related to flea intensity, suggesting larger flea populations in burrows with higher relative humidity. In summary, although fire may potentially have short-term impacts on the likelihood and extent of host parasitism by fleas, in this recovering study system, host characteristics and topographic wetness index are the primary predictors of parasitism by fleas.
","language":"English","publisher":"BioOne","doi":"10.1645/23-45","usgsCitation":"Padilla, C., Martin, J., Cain, J.W., and Gompper, M., 2024, Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire: Journal of Parasitology, v. 110, no. 4, p. 375-385, https://doi.org/10.1645/23-45.","productDescription":"11 p.","startPage":"375","endPage":"385","ipdsId":"IP-153592","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Ranger District of Santa Fe National Forest, Valles Caldera National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.30445858820687,\n 36.653454747968524\n ],\n [\n -107.30445858820687,\n 35.654968715553636\n ],\n [\n -105.9993414935517,\n 35.654968715553636\n ],\n [\n -105.9993414935517,\n 36.653454747968524\n ],\n [\n -107.30445858820687,\n 36.653454747968524\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Padilla, Colton J.","contributorId":353982,"corporation":false,"usgs":false,"family":"Padilla","given":"Colton J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Jessica T.","contributorId":355088,"corporation":false,"usgs":false,"family":"Martin","given":"Jessica T.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gompper, Matthew E.","contributorId":353984,"corporation":false,"usgs":false,"family":"Gompper","given":"Matthew E.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261644,"text":"70261644 - 2024 - Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","interactions":[],"lastModifiedDate":"2024-12-18T14:28:08.044274","indexId":"70261644","displayToPublicDate":"2024-08-17T15:55:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3562,"text":"The Holocene","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","title":"Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","docAbstract":"Stable isotopes of water preserved in geologic archives, primarily as oxygen (δ18O), have proven critical for documenting Earth’s climatic and hydrologic
systems past and present. However, timescale differences of water isotope inputs to proxy systems and the signal embedded in long paleorecords often
confound translation to observed hydroclimatic metrics. Here, a unique 20-year dataset of meteorology, hydrology, and the isotopic composition of
weekly meteoric and surface water samples (δ18O, δ2 H) are combined with paleoclimate δ18O data from tree-ring cellulose and lake carbonate to better
understand proxy signals of Upper Colorado river basin drought. Annual tree-ring cellulose δ18O from Picea engelmannii growing within a glacier-fed creek
and a spring discharge area were used to derive annual source water δ18O using a cellulose source-water isotope model. Comparisons with the monitoring
record indicates that tree-ring cellulose δ18O tracks variations in wet and dry hydroclimatic extremes. Source water isotopes are shown to reflect the
hydroclimate of the current year and some number of previous years as an effective moisture-discharge proxy rather than a precipitation isotope proxy.
Results contextualize Holocene lake carbonate δ18O data. The contemporary-to-paleo comparison identifies changes in seasonal precipitation extremes
during recent millennia and several earlier arid and monsoon-dominated Holocene periods that exceed the arid maximum of the calibration period.
Broad infestations of invasive, non-native vegetation have transformed wetlands around the world. Ludwigia hexapetala is a widespread, amphibious invasive plant with a creeping growth habit in open water and an erect growth habit in terrestrial habitats. In the upper San Francisco Estuary of California, L. hexapetala is increasingly terrestrializing into marshes and this expansion may be facilitated by allelopathy. We conducted the first field-based study on L. hexapetala allelopathy to determine whether (1) three allelochemicals known to be exuded by L. hexapetala are expressed in situ, (2) the allelochemicals are detectable in leaves, soil, and water, and (3) allelopathic expression varies by season, salinity, and growth habit (open water “patch” vs. terrestrial marsh “interface” locations). Water, soil, and L. hexapetala leaves were collected in two freshwater sites and two oligohaline sites in the upper San Francisco Estuary in summer 2021, fall 2021, and spring 2022. Myricitrin and quercitrin, known allelochemicals, and salipurposid, a newly identified polyphenol, were detected in water, soil, and leaves. There were significant differences in allelochemical concentrations under fresh versus oligohaline conditions in water and soil, but not leaves. All three allelochemicals generally had higher concentrations in patch versus interface locations, suggesting that L. hexapetala allelopathy plays a greater competitive role in open water than terrestrial habitats. Leaf concentrations of each allelochemical varied seasonally; however, both myricitrin and salipurposid had heightened concentrations in spring. These results suggest that herbicide application in early spring may be most effective in controlling L. hexapetala terrestrialization from open water to marshes.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03412-4","usgsCitation":"Drexler, J.Z., Gross, M., Hladik, M.L., Morrison, B., and Hestir, E., 2024, In situ allelopathic expression by the invasive amphibious plant, Ludwigia hexapetala (water primrose) across habitat types, seasons, and salinities: Biological Invasions, v. 26, p. 3811-3828, https://doi.org/10.1007/s10530-024-03412-4.","productDescription":"18 p.","startPage":"3811","endPage":"3828","ipdsId":"IP-160888","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":432937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.89839069902723,\n 38.06121335326662\n ],\n [\n -120.11889157934385,\n 36.328833706314626\n ],\n [\n -119.45382625178553,\n 37.03516328439403\n ],\n [\n -121.50294770892147,\n 40.68679922497094\n ],\n [\n -122.32978568372388,\n 40.63907726889312\n ],\n [\n -122.2668740986846,\n 39.26816562329512\n ],\n [\n -121.89839069902723,\n 38.06121335326662\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2024-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gross, Michael 0000-0002-2433-166X","orcid":"https://orcid.org/0000-0002-2433-166X","contributorId":343411,"corporation":false,"usgs":false,"family":"Gross","given":"Michael","affiliations":[{"id":81579,"text":"California Department of Food and Agriculture","active":true,"usgs":false}],"preferred":false,"id":910994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Bailey 0000-0001-5824-8605","orcid":"https://orcid.org/0000-0001-5824-8605","contributorId":343414,"corporation":false,"usgs":false,"family":"Morrison","given":"Bailey","email":"","affiliations":[{"id":54780,"text":"UC Merced","active":true,"usgs":false}],"preferred":false,"id":910996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hestir, Erin","contributorId":343417,"corporation":false,"usgs":false,"family":"Hestir","given":"Erin","affiliations":[{"id":54780,"text":"UC Merced","active":true,"usgs":false}],"preferred":false,"id":910997,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258722,"text":"70258722 - 2024 - Global assessment of aquatic Isoëtes species ecology","interactions":[],"lastModifiedDate":"2024-09-25T12:06:12.189538","indexId":"70258722","displayToPublicDate":"2024-08-17T07:00:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Global assessment of aquatic Isoëtes species ecology","docAbstract":"A Decree of the Supreme Court of the United States, entered June 7, 1954 (New Jersey v. New York, 347 U.S. 995), established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes the diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs owned by New York City be made under the supervision and direction of the River Master. The Decree stipulates that the River Master provide reports to the Court, not less frequently than annually. This report is the 64th annual report of the River Master of the Delaware River. The report covers the 2017 River Master report year, from December 1, 2016, to November 30, 2017.
During the report year, precipitation in the upper Delaware River Basin was 47.85 inches or 108 percent of the long-term average. On December 1, 2016, combined useable storage in the New York City reservoirs in the upper Delaware River Basin was 110.115 billion gallons or 40.7 percent of combined storage capacity, the lowest combined storage of the 2017 report year. The reservoirs were at about 100 percent of useable capacity on May 31, 2017. Combined storage remained above 80 percent of combined capacity until September 2017.
A lower basin drought watch issued by the Delaware River Basin Commission in 2016 extended from the beginning of this report year to January 18, 2017. The drought watch was ended on January 18, 2017, due to increased precipitation in December 2016. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Programs.
Diversions from the Delaware River Basin by New York City and New Jersey fully complied with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey (N.J.), on 52 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were made during the report year. Excess Release Quantity and Interim Excess Release Quantity Bank releases were also made during the report year.
The water quality in the Delaware River estuary between the streamgages at Trenton, N.J., and Reedy Island Jetty, Delaware, was monitored at various locations. The data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241022","isbn":"978-1-4113-4580-5","usgsCitation":"Russell, K.L., Andrews, W.J., DiFrenna, V.J., Norris, J.M., and Mason, R.R., Jr., 2024, Report of the River Master of the Delaware River for the period December 1, 2016–November 30, 2017: U.S. Geological Survey Open-File Report 2024–1022, 109 p., https://doi.org/10.3133/ofr20241022.","productDescription":"xi, 109 p.","numberOfPages":"109","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-153026","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":499249,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117174.htm","linkFileType":{"id":5,"text":"html"}},{"id":432667,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1022/images/"},{"id":432666,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1022/ofr20241022.XML","description":"OFR 2024-1022 XML"},{"id":432665,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241022/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1022 HTML"},{"id":432664,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1022/ofr20241022.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1022 PDF"},{"id":432663,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1022/coverthb.jpg"}],"country":"United States","state":"New Jersey, New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76,\n 43\n ],\n [\n -76,\n 39.31354002356349\n ],\n [\n -74,\n 39.31354002356349\n ],\n [\n -74,\n 43\n ],\n [\n -76,\n 43\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
Water temperature is a primary control on the occurrence and distribution of cold-water species. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support several cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer base flow between August and September, and the climatic, hydrologic, and physical processes regulating this temperature distribution are not well understood. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and seven-day average daily maximum water temperature for August and September for the White River basin located with Mount Rainier National Park. The SSN models were calibrated using water temperature measurements collected between 2010 and 2020. Significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. In addition, models of mean August and September water temperature had better predictive performance than those of seven-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of main-stem White River compared to cooler water temperatures in the high-elevation headwater streams. Estimated water temperatures for the upper White River model are three to four degrees Celsius warmer for tributaries but one to two degrees cooler for the main stem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale SSN model are attributed the upper White River SSN including water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream of the Mount Rainer National Park boundary were used in the regional-scale model.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X5712P","usgsCitation":"Gendaszek, A., Leach, A.C., and Jaeger, K.L., 2024, Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington: EarthArXiv, https://doi.org/10.31223/X5712P.","productDescription":"33 p.","ipdsId":"IP-166723","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433007,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439208,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.31223/x5712p","text":"External Repository"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":343378,"corporation":false,"usgs":false,"family":"Gendaszek","given":"Andrew","middleInitial":"S.","affiliations":[{"id":82076,"text":"King County","active":true,"usgs":false}],"preferred":false,"id":910876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leach, Anya Clare 0000-0001-7828-8858","orcid":"https://orcid.org/0000-0001-7828-8858","contributorId":339960,"corporation":false,"usgs":true,"family":"Leach","given":"Anya","email":"","middleInitial":"Clare","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910878,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257561,"text":"70257561 - 2024 - Simulated sea level rise in coastal peat oils stimulates mercury methylation","interactions":[],"lastModifiedDate":"2024-09-23T16:20:31.643081","indexId":"70257561","displayToPublicDate":"2024-08-16T08:36:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5615,"text":"ACS Earth and Space Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Simulated sea level rise in coastal peat oils stimulates mercury methylation","docAbstract":"Coastal wetlands are vulnerable to sea level rise with unknown consequences for mercury (Hg) cycling, particularly the potential for exacerbating neurotoxic methylmercury (MeHg) production and bioaccumulation in food webs. Here, the effect of sea level rise on MeHg formation in the Florida Everglades was evaluated by incubating peat cores from a freshwater wetland for 0–20 days in the laboratory at five salinity conditions (0.16–6.0 parts-per-thousand; 0.20–454 mg L–1 sulfate (SO42–)) to simulate the onset of sea level rise within coastal margins. Isotopically enriched inorganic mercury (201Hg(II)) was used to track MeHg formation and peat-porewater partitioning. In all five salinity treatments, porewaters became anoxic within 1 day and became progressively enriched in dissolved organic matter (DOM) of greater aromatic composition over the 20 days compared to ambient conditions. In the four highest salinity treatments, SO42– concentrations decreased and sulfide concentrations increased over time due to microbial dissimilatory SO42– reduction that was concurrent with 201Hg(II) methylation. Importantly, elevated salinity resulted in a greater proportion of produced Me201Hg observed in porewaters as opposed to bound to peat, interpreted to be due to the complexation of MeHg with aromatic DOM released from peat. The findings highlight the potential for enhanced production and mobilization of MeHg in coastal wetlands of the Florida Everglades due to the onset of saltwater intrusion.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsearthspacechem.4c00124","usgsCitation":"Cook, B.A., Peterson, B.D., Ogorek, J.M., Janssen, S., and Poulin, B., 2024, Simulated sea level rise in coastal peat oils stimulates mercury methylation: ACS Earth and Space Chemistry, v. 8, no. 9, p. 1784-1796, https://doi.org/10.1021/acsearthspacechem.4c00124.","productDescription":"13 p.","startPage":"1784","endPage":"1796","ipdsId":"IP-163692","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439209,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsearthspacechem.4c00124","text":"Publisher Index Page"},{"id":432933,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":434914,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P139NMHU","text":"USGS data release","linkHelpText":"Mercury Methylation Assay Along a Salinity Gradient in Coastal Peat Soils in the Florida Everglades"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.26748408478383,\n 26.417153260709895\n ],\n [\n -81.55705618538097,\n 26.417153260709895\n ],\n [\n -81.55705618538097,\n 25.075472168285998\n ],\n [\n -80.26748408478383,\n 25.075472168285998\n ],\n [\n -80.26748408478383,\n 26.417153260709895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Bryce A.","contributorId":340463,"corporation":false,"usgs":false,"family":"Cook","given":"Bryce","email":"","middleInitial":"A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Benjamin D.","contributorId":328487,"corporation":false,"usgs":false,"family":"Peterson","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulin, Brett A.","contributorId":328488,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910843,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263690,"text":"70263690 - 2024 - The use of conceptual ecological models to identify critical data and uncertainties to support numerical modeling: The northern Gulf of Mexico eastern oyster Crassostrea virginica example","interactions":[],"lastModifiedDate":"2025-02-20T22:09:37.912852","indexId":"70263690","displayToPublicDate":"2024-08-15T16:06:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The use of conceptual ecological models to identify critical data and uncertainties to support numerical modeling: The northern Gulf of Mexico eastern oyster Crassostrea virginica example","title":"The use of conceptual ecological models to identify critical data and uncertainties to support numerical modeling: The northern Gulf of Mexico eastern oyster Crassostrea virginica example","docAbstract":"Increasing reliance on numerical simulation models to help inform management and restoration choices benefits from careful consideration of critical early steps in model development. Along the northern coast of the Gulf of Mexico, the eastern oyster Crassostrea virginica fulfills important ecological and economic roles. Using the eastern oyster as an example, we draw on several recent frameworks outlining best practices for model development and application for restoration, conservation, and management.
We identify priority model questions, outline a conceptual ecological model (CEM) to guide numerical model development, and use this framework to identify uncertainties and research needs.
The CEM uses a nested design, identifying explicit vital rates, processes, attributes, and outcomes for the species (oysters), population, and metapopulation (i.e., network of populations) levels in response to drivers of species, population, and metapopulation changes and changing environmental factors. Most management actions related to oyster restoration and harvest affect population attributes directly, but many coastal management actions and changes (i.e., climate change and coastal and water resource engineering) impact environmental factors that alter vital rates and attributes of oysters, populations, and metapopulations.
Investment in studies targeting individual oyster‐ and population‐level multi‐stressor responses (filtration, respiration, growth, and reproduction) and improving hydrodynamic and environmental models targeting drivers that influence metapopulation vital rates and attributes (i.e., connectivity and substrate persistence) would contribute to reducing uncertainties. Development of numerical models covering the entire oyster life cycle and connectivity of populations using hydrodynamic models of current and predicted conditions to provide key abiotic and biotic factors influencing larval movement, recruitment, and on‐reef oyster vital rates would assist in balancing the goals of conservation, restoration, and fisheries management of this foundational estuarine species.
Rockdale County Department of Water Resources has a directive to update estimates of the reservoir storage capacity of Randy Poynter Lake, located in northern Georgia, and to assess recent sedimentation and associated storage capacity loss. In 2022, the U.S. Geological Survey completed a multibeam bathymetric survey of Randy Poynter Lake to update storage capacity estimates and to quantify storage capacity change since the first multibeam bathymetric survey in 2012 in consideration of estimated errors inherent to bathymetric surveys. Data from the 2022 survey were used to generate contours of the reservoir as well as compute storage capacity at regular increments of water-surface elevation. Storage capacity comparisons between 2012 and 2022 at Randy Poynter Lake show minimal changes that are within the estimated uncertainties, with consistent or slightly increased storage capacities observed at most water-surface elevations and reductions observed at the remaining few elevations. Comparison of the multibeam bathymetric data collected in 2012 with data collected in 2022 further allowed for a formal geomorphic change detection analysis to map, quantify, and infer causation of morphological change over time with respect to a level of detectable change. The volume change in Randy Poynter Lake for the decade between 2012 and 2022 was slightly net-depositional and within the estimated uncertainty. The spatial distribution of sediment deposition was primarily concentrated in the northern portion of the lake, where the principal tributary flows into Randy Poynter Lake. The results of the geomorphic change analysis were used to further understand the future implications to storage capacity change. Despite the challenges of confirming systematic biases because of uncertainties exceeding the observed changes, insights from the study help predict long-term reservoir sediment accumulation, indicating a reservoir half-life extending about 650 years from 2022 on the basis of the current sediment yield estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3523","issn":"2329-132X","collaboration":"Prepared in cooperation with the Rockdale County Department of Water Resources","usgsCitation":"Whaling, A.R., and Bolton, W.J., 2024, Estimation of reservoir storage capacity and geomorphic change detection analysis from a multibeam bathymetric survey of Randy Poynter Lake, Rockdale County, Georgia (ver. 1.1, 2025): U.S. Geological Survey Scientific Investigations Map 3523, 2 sheets, https://doi.org/10.3133/sim3523.","productDescription":"2 Sheets: 47.00 x 32.23 inches; 2 Data Releases","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-157799","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":494128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118630.htm","linkFileType":{"id":5,"text":"html"}},{"id":432656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3523/sim3523.pdf","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3523"},{"id":432655,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3523/coverthb2.jpg"},{"id":432657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G8HZTY","text":"USGS Data Release","linkHelpText":"Watershed characteristics and streamwater constituent load data, models, and estimates for 15 watersheds in Gwinnett County, Georgia, 2000–2021"},{"id":432658,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G9YVDU","text":"USGS Data Release","linkHelpText":"Bathymetric and supporting data for estimation of reservoir storage capacity and geomorphic change detection analysis from a multibeam bathymetric survey of Randy Poynter Lake, Rockdale County, Georgia"},{"id":489473,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3523/sim3523_verHist.txt","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Georgia","county":"Rockdale County","otherGeospatial":"Randy Poynter Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.9579460131726,\n 33.762755823898075\n ],\n [\n -83.9579460131726,\n 33.72795567856667\n ],\n [\n -83.92619350646511,\n 33.72795567856667\n ],\n [\n -83.92619350646511,\n 33.762755823898075\n ],\n [\n -83.9579460131726,\n 33.762755823898075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 15, 2024; Version 1.1: June 03, 2025","contact":"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":"Milford Lake, the largest reservoir by surface area in Kansas, has had confirmed harmful algal blooms every summer since reporting began in 2011, except 2018–19. Milford Lake has been listed as impaired and designated hypereutrophic under section 303(d) of the 1972 Clean Water Act. In 2014, the Kansas Department of Health and Environment established a total maximum daily load for eutrophication and dissolved oxygen impairments. In 2018, the Natural Resources Conservation Service funded the Regional Conservation Partnership Program for the Milford Lake Watershed to focus on best management practices in the Lower Republican River Basin. The U.S. Geological Survey, in cooperation with the Kansas Water Office, completed this study to assess and quantify water-quality constituent concentrations and loads for total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) using previously published models for the Republican River near Clay Center, Kansas (U.S. Geological Survey station 06856600), about 15 miles upstream from Milford Lake, during August 1, 2018, through July 31, 2023. TN, TP, and SS concentrations and loads were monitored because of their relation to water supply and water-quality issues in Milford Lake, including nutrient and sediment transport, taste-and-odor events, potentially toxic cyano-harmful algal bloom events, and subsequent downstream transport of contaminants. Data from this report can be used to evaluate changing conditions, provide science-based information for decision making, and help meet regulatory requirements.
The study mean annual loads for TN and TP were greater than the reported mean annual total maximum daily load and exceeded the watershed reduction goals as well as Kansas nonpoint source reduction goals defined by the Watershed Restoration and Protection Strategy for the Lower Republican watershed. TN and TP annual loads during 2019–20 were greater than the defined mean annual total maximum daily load. During 2022, TN and TP annual loads were less than the Kansas nonpoint source reduction goal and during 2023 were less than the watershed reduction goal. SS loads were less than the mean annual sedimentation rate computed from the total maximum daily load for the entirety of the study period, and the study mean annual load was 72 percent less than the designed annual reservoir sedimentation rate for Milford Lake.
Data collected during the study period represented a wide range of streamflow and water-quality conditions at the Clay Center site, ranging from low-flow with less frequent runoff during 2023 to high-flow with frequent runoff during 2018. Nutrient reduction goals were only met in the final 2 years of the study period when annual mean flow conditions were lower than normal, indicating that goals may be unattainable during average or high-flow conditions. In all years except 2019, the annual mean SS load was less than the 20-year sediment load reduction target. Although annual SS loads at the Clay Center site generally decreased over time, corresponding reductions in annual streamflow indicated that these reductions may primarily be related to less frequent runoff from the upstream basin. Continued water-quality monitoring and tracking of best management practices are necessary to understand the success of Regional Conservation Partnership Program efforts to reduce nutrient transport in the Milford Lake Watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245072","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Kramer, A.R., and Abel, J.R., 2024, Water-quality constituent concentrations and loads computed using real-time water-quality data for the Republican River, Clay Center, Kansas, August 2018 through July 2023: U.S. Geological Survey Scientific Investigations Report 2024–5072, 21 p., https://doi.org/10.3133/sir20245072.","productDescription":"Report: v, 21 p.; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160554","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":432611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5072/coverthb.jpg"},{"id":432612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5072/sir20245072.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5072"},{"id":432613,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5072/sir20245072.XML"},{"id":432614,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5072/images/"},{"id":432615,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245072/full"},{"id":432616,"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"}],"country":"United States","state":"Kansas","otherGeospatial":"Clay Center, Republican River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.25,\n 40\n ],\n [\n -98.25,\n 39\n ],\n [\n -96.5,\n 39\n ],\n [\n -96.5,\n 40\n ],\n [\n -98.25,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Understanding and quantifying soil erosion from rangelands is a high priority for land managers, especially in areas underlain by Cretaceous Mancos Shale, which is a natural source of sediment, salinity, and selenium to surface waters in many areas of western Colorado and eastern Utah. The purpose of this report is to present the results of a U.S. Geological Survey study that assessed sediment, salinity, and selenium yields after the Dead Dog wildfire (fire began June 11, 2017) in northwestern Colorado. Two methodologies were used to quantify erosion, with different data requirements and analytical complexity. The first approach was the use of a process-based erosion model, the Watershed Erosion Prediction Project, which uses inputs of climate, topography, vegetation, and soils data from existing datasets to predict erosion, making this approach easily extensible to other areas. The second approach required more complex data collection and was used to measure erosion and deposition by differencing digital elevation models created from uncrewed aerial vehicle imagery collected in 2016 (pre-fire) and 2021 (post-fire). Sediment, salinity, and selenium yields were calculated from the volumetric estimates of erosion from both methods, and a discussion of factors that may have contributed to overall findings, including vegetation, fire effects, and soil characteristics, is included.
The two approaches yielded different outputs. Results from the Watershed Erosion Prediction Project model indicated that almost no erosion occurred after the Dead Dog fire. However, morphological changes in the study basin after the Dead Dog fire were visible in the pre- and post-fire imagery and measured in the digital elevation model differencing technique, with net erosion occurring in channel and landscape extents, though calculated erosion rates and salinity and selenium yields were relatively small. Visible and measured morphological changes consisted primarily of incision and deposition within stream channels and rill incision and expansion on steeper slopes. Widespread sheet erosion was not evident. Much of the new erosion originated within, and immediately below, previously vegetated areas that were then burned by the wildfire. Greater erosion rates and salinity and selenium yields were measured in the channel extent relative to the landscape extent. Calculated erosion rates ranged from 0.24 to 0.45 megagrams per hectare per year. These results indicate that the Dead Dog fire resulted in increased erosion in the study basin, yet these effects were relatively small based on the overall magnitude of modeled and measured erosion from the Watershed Erosion Prediction Project and the digital elevation model differencing technique. Minimal erosion in the basin is likely due to local site characteristics typical of soils derived from Mancos Shale, including the presence of robust physical crusts and biological soil crusts, and limitations of the methods based on data availability. Focusing uncrewed aerial vehicle flights on key areas (individual steep slopes, high-intensity burn areas, specific stream reaches) could likely increase understanding of erosional process with less effort and error than doing landscape-level flights.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245043","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Day, N.K., Preston, T.M., and Longley, P.C., 2024, Wildland fire effects on sediment, salinity, and selenium yields in a basin underlain by Cretaceous marine shales near Rangely, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5043, 31 p., https://doi.org/10.3133/sir20245043.","productDescription":"Report: vi, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-144341","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":433027,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245043/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5043"},{"id":432498,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5043/coverthb.jpg"},{"id":432499,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5043/sir20245043.pdf","text":"Report","size":"21.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5043"},{"id":432500,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91KRAAD","text":"USGS data release","linkHelpText":"Orthoimagery, digital elevation, digital terrain, final surface, and vegetation classification models for four stream catchments in western Colorado 2016"},{"id":432501,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZA125K","text":"USGS data release","linkHelpText":"Erosion rates and salinity and selenium yields in a basin near Rangely, Colorado following the 2017 Dead Dog wildfire as modeled by WEPP and measured from UAV"},{"id":432610,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5043/sir20245043.xml"},{"id":432609,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5043/images"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.99103575578066,\n 40.20515709262918\n ],\n [\n -108.99103575578066,\n 40.015728339502004\n ],\n [\n -108.68461550797593,\n 40.015728339502004\n ],\n [\n -108.68461550797593,\n 40.20515709262918\n ],\n [\n -108.99103575578066,\n 40.20515709262918\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, in cooperation with Erie County, New York, the New York State Department of Environmental Conservation, and the Great Lakes Restoration Initiative, collected water-quality samples in nine selected New York tributaries to Lake Erie, computed estimates of suspended sediment and nutrient loads using the R scripting package rloadest and used the Soil and Water Assessment Tool (SWAT) to simulate hydrology and suspended sediment and nutrient loads from these tributaries. This project was undertaken to better understand the water quality of New York’s inputs into eastern Lake Erie.
Water-quality samples for suspended sediment, nitrogen, and phosphorus were collected at 19 sampling sites in the Lake Erie Basin in New York. Daily and monthly suspended sediment and nutrient loads were computed with regressions of streamflow and suspended sediment and nutrient concentrations using rloadest.
SWAT models of nine watersheds were created using publicly available data; and the loads calculated by rloadest. Twenty-six SWAT model scenarios were created to explore the effects that best management practices (BMPs; 21 scenarios), point source discharges (4 scenarios), and green infrastructure (1 scenario) can have on the water quality of the nine tributaries to Lake Erie. BMP scenarios for the watershed models included combinations of agricultural BMPs applied at varying implementation levels across the study watersheds, including cover crops, reduced tillage, nutrient management plans, and filter strips. The BMP scenarios showed small reductions of total nitrogen and total phosphorus. The scenarios have variable suspended sediment load results, with both increases and decreases of sediment modeled. The point source scenarios result in lower total phosphorus loads. The green infrastructure scenario shows only minimal reduction of suspended sediment and nutrient loads from the Buffalo River watershed but shows substantial reductions locally.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245022","collaboration":"Prepared in cooperation with Erie County, New York, the New York State Department of Environmental Conservation, and the Great Lakes Restoration Initiative","usgsCitation":"Merriman, K.R., Fisher, B.N., Nystrom, E.A., Bunch, A.R., Welk, R.J., and Kappel, W.M., 2024, Monitoring and simulation of hydrology, suspended sediment, and nutrients in selected tributary watersheds of Lake Erie, New York: U.S. Geological Survey Scientific Investigations Report 2024–5022, 152 p., https://doi.org/10.3133/sir20245022.","productDescription":"Report: xii, 152 p.; 2 Data Releases","numberOfPages":"152","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-142479","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":432349,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5022/images/"},{"id":432347,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245022/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5022 HTML"},{"id":432346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5022/sir20245022.pdf","text":"Report","size":"49.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5022 PDF"},{"id":432345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5022/coverthb.jpg"},{"id":432350,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N6IQZT","text":"USGS data release","linkHelpText":"Data and rloadest models used to estimate sediment and nutrient loads in selected New York tributaries to eastern Lake Erie"},{"id":499450,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117172.htm","linkFileType":{"id":5,"text":"html"}},{"id":432351,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U2IF5I","text":"USGS data release","linkHelpText":"SWAT Model Archive for Simulation of Hydrology, Suspended Sediment and Nutrients in Selected Tributary Watersheds of Lake Erie, New York"},{"id":432348,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5022/sir20245022.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5022 XML"}],"country":"United States","state":"New York","otherGeospatial":"Lake Erie watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.05117923155397,\n 43.180030390880034\n ],\n [\n -79.03978515463244,\n 43.13847304512555\n ],\n [\n -79.06582875902379,\n 43.119465987668804\n ],\n [\n -79.05606240737677,\n 43.10639520955428\n ],\n [\n -79.07233966012183,\n 43.07667851399174\n ],\n [\n -78.99420884694702,\n 43.056462925290475\n ],\n [\n -79.0235079018874,\n 43.00648994913769\n ],\n [\n -79.01048609969206,\n 42.9779156948116\n ],\n [\n -78.95514344035962,\n 42.9540936681897\n ],\n [\n -78.91933348432123,\n 42.94932815586105\n ],\n [\n -78.90305623157617,\n 42.9243031624776\n ],\n [\n -78.89817305575336,\n 42.889728119559265\n ],\n [\n -78.87212945136129,\n 42.800219981426636\n ],\n [\n -79.75923746535221,\n 42.27370078552943\n ],\n [\n -79.75435428952869,\n 42.04444584745826\n ],\n [\n -79.2155772236776,\n 42.08553142608457\n ],\n [\n -78.44891922533873,\n 42.52492114004454\n ],\n [\n -78.15592867593357,\n 42.82529655474477\n ],\n [\n -78.1005861716743,\n 43.11709104352599\n ],\n [\n -78.66540684191676,\n 43.21444355835797\n ],\n [\n -79.01536777592817,\n 43.19308678489239\n ],\n [\n -79.05117923155397,\n 43.180030390880034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349