StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.
The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.
StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1514","usgsCitation":"Ries, K.G., III, Steeves, P.A., and McCarthy, P., 2024, StreamStats—A quarter century of delivering web-based geospatial and hydrologic information to the public, and lessons learned: U.S. Geological Survey Circular 1514, 40 p., https://doi.org/10.3133/cir1514.","productDescription":"viii, 40 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-102663","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":499073,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116170.htm","linkFileType":{"id":5,"text":"html"}},{"id":426017,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.XML","description":"CIR 1514 XML"},{"id":426018,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1514/images/"},{"id":426016,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1514/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1514 HTML"},{"id":426015,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.pdf","text":"Report","size":"7.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1514 PDF"},{"id":426014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1514/coverthb.jpg"}],"contact":"National Coordinator, StreamStats
U.S. Geological Survey
1728 Lampman Drive, Suite D
Billings, MT 59102
The Las Cienegas National Conservation Area was established by the Las Cienegas National Conservation Area Establishment Act of 1999 (Public Law 106–538) and is managed by the Bureau of Land Management. Located in southeastern Arizona, the conservation area contains more than 45,000 acres of rolling grassland, wetlands, and woodlands surrounded by isolated mountain ranges that are part of the Madrean archipelago. This report describes the surface-water and groundwater resources within, and hydrologically connected to, the conservation area.
Two primary aquifers have been identified within the Las Cienegas National Conservation Area: a Quaternary alluvial aquifer and a Miocene to Pliocene basin-fill aquifer. The Quaternary alluvial aquifer consists of Quaternary saturated stream alluvium along Cienega Creek and its major tributaries. This aquifer provides the water necessary for base flow in the perennial stream reaches that support aquatic life and for wetland and riparian habitat along the stream courses. Wells and piezometers completed in the Quaternary alluvial aquifer show both seasonal and daily water-level fluctuation patterns, as well as responses to flood flows in Cienega Creek. The basin-fill aquifer, in contrast, consists chiefly of Miocene to Pliocene alluvium within a sedimentary basin that is at least 4,800 feet deep. This aquifer is developed for anthropogenic uses more often than the Quaternary alluvial aquifer is developed. Generally, water levels in wells completed in the basin-fill aquifer have gradually declined a few feet between 2011, when measurements began, and 2022, when this report was written. Most water-chemistry samples available from the basin-fill aquifer had either a sodium-bicarbonate or calcium-bicarbonate water type. Previous research has shown that most recharge to the basin-fill aquifer likely comes from mountain-front and mountain-block recharge. Research further shows that this aquifer likely provides most of the recharge to the Quaternary alluvial aquifer. Because no production wells completed in bedrock exist within the conservation area, little is known about the hydraulic properties of the bedrock therein, but usable quantities of water can likely be produced from places where the bedrock has highly developed joint or fracture systems.
During 2006–2021, the average combined length of measured perennial stream reaches within the main part of the Las Cienegas National Conservation Area was 6.35 miles. The average annual base flow of Cienega Creek during 2002–2021, estimated with the Standard Base-Flow Index method using data from a streamgage within the conservation area, was 0.62 cubic feet per second. Monthly mean streamflow measured at this streamgage for the same period ranged from a low of 0.29 cubic feet per second (in June) to a high of 9.8 cubic feet per second (in July). The July average is heavily influenced by a flood that occurred in July 2021; the median July streamflow for 2002–2021 is just 0.84 cubic feet per second. Periods with no daily flow are not uncommon at this gage during late May and June.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235131","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Mason, J.P., 2024, Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2023–5131, 31 p., https://doi.org/10.3133/sir20235131.","productDescription":"vii, 31 p.","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-144415","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":432298,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X94P5B","text":"USGS Data Release","description":"Mason, J.P., 2023, Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9X94P5B.","linkHelpText":"Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona"},{"id":423631,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5131"},{"id":499394,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116143.htm","linkFileType":{"id":5,"text":"html"}},{"id":425761,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5131/covrthb.jpg"},{"id":423634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.xml"},{"id":423633,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5131/images"},{"id":423632,"rank":2,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235131/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Las Cienegas National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.778891765439,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.89756957809155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
In support of a preliminary analysis performed by New York State Department of Environmental Conservation that found elevated nutrient levels along selected reaches of the Mohawk River, a one-dimensional, unsteady hydraulic and water-quality model (Hydrologic Engineering Center River Analysis System Nutrient Simulation Module 1 [HEC–RAS NSM I]) was developed by the U.S. Geological Survey for the 127-mile reach of the Mohawk River between Rome and Cohoes, New York. The model was designed to accurately simulate within-channel flow conditions for this highly regulated, control-structure dense river reach. The model was calibrated for the period of May through September 2016 using available streamflow, temperature, and water-quality data. Nitrogen, phosphorus, dissolved oxygen, and water column algae were balanced within the model; however, the nutrient model calibration was focused on phosphorus.
The HEC–RAS hydraulic model simulated streamflow adequately at the calibration locations with observed and simulated daily flows demonstrating coefficient of determination (r2) values ranging from 0.91 to 0.97, mean absolute error ranging from 15–20 percent, and bias ranging from −7 to 16 percent. The water temperature model within HEC–RAS NSM I demonstrated remarkable ability to simulate water temperature: typical water temperature errors were less than 1.0 degree Celsius (°C). Simulated water temperature results closely tracked observed continuous water temperature data at three locations on the Mohawk River, with mean absolute error for the 2016 study period ranging from 0.87 to 0.90 °C, and a root mean square error of 1.00 to 1.07 °C.
Performance criteria for the water-quality (nutrient) model were applied differently than the water temperature model because of the temporally coarse discrete samples collected for the project. The average difference between final simulated concentrations and observed concentrations of organic phosphorus for all sample locations was within 0.01 milligrams per liter (mg/L) and within 0.09 mg/L for orthophosphate using all locations except Rome, which was within 0.25 mg/L.
The calibrated model was used to implement nine phosphorus reduction scenarios by applying reductions to wastewater treatment plant effluent concentrations within the model. Monthly mean differences were computed for five comparison locations. Scenario results were generally linear and predictable; scenarios implementing the highest reductions showed correspondingly larger differences in Mohawk River concentrations downstream from the wastewater treatment plants associated with those reductions. The largest monthly mean differences were realized from reduction scenario nine and ranged from −0.018 to −0.076 mg/L for organic phosphorus and from 0.001 to −0.138 mg/L for orthophosphate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245005","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Suro, T.P., Niemoczynski, M.J., and Boetsma, A., 2024, Development and calibration of HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River, New York: U.S. Geological Survey Scientific Investigations Report 2024–5005, 90 p., https://doi.org/10.3133/sir20245005","productDescription":"Report: xii, 90 p.; Data Release","numberOfPages":"90","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127136","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":425874,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FRAYLT","text":"USGS data release","linkHelpText":"HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River between Rome and Cohoes, New York"},{"id":425872,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5005/images/"},{"id":425873,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.XML"},{"id":425869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5005/coverthb.jpg"},{"id":425870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.pdf","text":"Report","size":"20.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5005"},{"id":425871,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245005/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5005"},{"id":499420,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116141.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.4,\n 42.0\n ],\n [\n -73.2,\n 42.0\n ],\n [\n -73.2,\n 43.4\n ],\n [\n -75.4,\n 43.4\n ],\n [\n -75.4,\n 42.0\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Lawrenceville, NJ 08648
Increasingly, genomic data are being used to supplement field-based ecological studies to help evaluate recovery status and trends in endangered species. We collected genomic data to address two related questions regarding the Least Bell’s Vireo (Vireo bellii), an endangered migratory songbird restricted to southern California riparian habitat for breeding. First, we sought to delineate the range limits and potential overlap between Least Bell’s Vireo and its sister subspecies, the Arizona Bell’s Vireo, by analyzing samples from the deserts of eastern California, southwestern Nevada, Utah and Arizona. Second, we evaluated genetic structure among Least Bell’s Vireo populations in coastal California and estimated effective population size. Clustering analyses based on 10,571 single nucleotide polymorphisms (SNPs) from 317 samples supported two major groups that aligned closely to the previously defined subspecies ranges. The first cluster included birds in the Central Valley, all coastal drainages, and westernmost deserts of California, with no further sub-structuring among coastal drainages. Almost all birds from the Amargosa River in eastern California and eastward assigned to the second cluster; however, low levels of gene flow were detected across the subspecies groups, with greater rates of gene flow from Arizona Bell’s Vireo to Least Bell’s Vireo than the reverse. Admixed individuals occurred in the California deserts; and although smaller than coastal populations, desert populations may be important for maintaining and replenishing genetic diversity and facilitating the movement of potentially adaptive genes between subspecies. Within Least Bell’s Vireo, local populations in coastal drainages comprised a single genetic population, with some evidence of close relatives distributed across drainages, suggesting these could function as a well-connected metapopulation. These results are consistent with previous Least Bell’s Vireo banding studies that reported high rates of dispersal among drainages. Effective population size for both subspecies was high, suggesting that adaptive potential has been maintained despite previous declines.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/ornithapp/duae009","usgsCitation":"Vandergast, A.G., Kus, B., Wood, D.A., Mitelberg, A., Smith, J.G., and Milano, E., 2024, High inter-population connectivity and occasional gene flow between subspecies improves recovery potential for the endangered Least Bell’s Vireo: Ornithological Applications, v. 126, no. 3, duae009, 13 p., https://doi.org/10.1093/ornithapp/duae009.","productDescription":"duae009, 13 p.","ipdsId":"IP-156012","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":440316,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae009","text":"Publisher Index Page"},{"id":426117,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitelberg, Anna 0000-0002-3309-9946 amitelberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3309-9946","contributorId":218945,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Julia G. 0000-0001-9841-1809","orcid":"https://orcid.org/0000-0001-9841-1809","contributorId":221086,"corporation":false,"usgs":true,"family":"Smith","given":"Julia","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Milano, Elizabeth R.","contributorId":334415,"corporation":false,"usgs":false,"family":"Milano","given":"Elizabeth R.","affiliations":[{"id":80134,"text":"former USGS employee; currently USFS","active":true,"usgs":false}],"preferred":false,"id":895595,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251592,"text":"sir20235146 - 2024 - Comparison of longitudinal stream temperature profiles and significant thermal features from airborne thermal infrared and float surveys of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, King and Snohomish Counties, Washington, summer 2020 and 2021","interactions":[],"lastModifiedDate":"2026-01-30T20:02:03.582545","indexId":"sir20235146","displayToPublicDate":"2024-02-16T15:38:21","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5146","displayTitle":"Comparison of Longitudinal Stream Temperature Profiles and Significant Thermal Features from Airborne Thermal Infrared and Float Surveys of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, King and Snohomish Counties, Washington, Summer 2020 and 2021","title":"Comparison of longitudinal stream temperature profiles and significant thermal features from airborne thermal infrared and float surveys of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, King and Snohomish Counties, Washington, summer 2020 and 2021","docAbstract":"Summer water temperatures in the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers in western Washington have in recent decades exceeded the water temperature criteria for aquatic life uses set by the Washington Department of Ecology. This temperature increase is of particular concern because these rivers provide critical habitat for several salmonid populations, including Endangered Species Act-listed Chinook salmon (Onchorhynchus tshawytscha), steelhead trout (O. mykiss), and bull trout (Salvelinus confluentus), thus helping sustain Endangered Species Act-listed Southern Resident orcas (Orcinus orca). To inform salmonid restoration efforts within these rivers, this study used high-resolution thermal infrared (TIR) and three-band red, green, blue imagery acquired from repeated airborne surveys conducted in August 2020 and 2021 to (1) quantify longitudinal stream temperature profiles (LTPs) and (2) identify and characterize significant thermal features (STFs), including cold-water anomalies that could represent thermal refuges and serve as salmonid habitat. In addition, drag-probe water temperature surveys (“float surveys”) were performed on the Skykomish and Middle Fork Snoqualmie Rivers during August–September 2020 and on a segment of the Middle Fork Snoqualmie River in August 2021. These float surveys were intended to evaluate this thermal profiling method in comparison to airborne TIR surveys, by employing a novel method of processing float survey data to adjust for diurnal heating.
The Middle Fork Snoqualmie River warmed about 7 degrees Celsius (°C) from upstream to downstream in the 2020 airborne TIR survey and 9 °C in the 2021 airborne TIR survey, and the Snoqualmie River warmed about 4 °C in both surveys. The water temperature of the Skykomish River cooled in the 2020 and 2021 surveys, primarily because of cold inflow from the Sultan River. The overall shapes of airborne TIR LTPs of the same river were similar in the 2020 and 2021 surveys, with increasing and decreasing gradients in temperature tending to be nearly parallel over the same reaches and abrupt changes in temperature typically identified at the same locations. A total of 854 STFs were identified in the 2020 TIR imagery, and 732 STFs were identified in the 2021 TIR imagery. Interannual persistence was detected in 36.4 to 61.3 percent of lateral groundwater, side channel, and small tributary STFs, depending on the river surveyed, and in 14.8 to 28.7 percent of hyporheic and diffuse groundwater STFs. Hyporheic flow was commonly detected at the downstream end of a riffle, but not often detected directly downstream from large woody debris. Shade from riparian vegetation did not reduce water temperatures but rather maintained the water temperature recorded just upstream from the shaded section.
The adjusted average water temperature profiles from the float surveys were comparable to the LTPs derived from the airborne TIR surveys, with differences in temperature gradient primarily because the surveys were performed under different streamflow, radiation, and shading conditions. Though float surveys were found to be a valuable means of obtaining thermal profiles comparable to profiles obtained by airborne TIR surveys, one key advantage of airborne TIR surveys is that they may be used to precisely locate STFs over long distances, during a short survey duration, and in areas inaccessible to most watercraft.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235146","collaboration":"Prepared in cooperation with the Tulalip Tribes","usgsCitation":"Restivo, D.E., Diabat, M., Miwa, C., and Bright, V.A.L., 2024, Comparison of longitudinal stream temperature profiles and significant thermal features from airborne thermal infrared and float surveys of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, King and Snohomish Counties, Washington, summer 2020 and 2021: U.S. Geological Survey Scientific Investigations Report 2023–5146, 31 p., https://doi.org/10.3133/sir20235146.","productDescription":"Report: viii, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-139968","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":428021,"rank":3,"type":{"id":39,"text":"HTML 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Data Release","description":"Restivo, D.E., Diabat, M., Miwa, C., Bright, V.A.L., Seguin, C.M., Boucher, C.D., David, J.E., and Pouley, M., 2023, Water temperature mapping of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, Washington— Longitudinal stream temperature profiles, significant thermal features, and airborne thermal infrared and RGB imagery mosaics: U.S. Geological Survey data release, https://doi.org/10.5066/P9FJCM8N.","linkHelpText":"Water temperature mapping of the Skykomish, Snoqualmie, and Middle Fork Snoqualmie Rivers, Washington— Longitudinal stream temperature profiles, significant thermal features, and airborne thermal infrared and RGB imagery mosaics"}],"country":"United States","state":"Washington","county":"King County, Snohomish County","otherGeospatial":"Skykomish, Snoqualmie, and Middle Fork Snoqualmie 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Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The recent destruction of thousands of homes by lava flows from La Palma volcano, Canary Islands, and Nyiragongo volcano, Democratic Republic of Congo, serves as a reminder of the devastating impact that lava flows can have on communities living in volcanically active regions. Damage to buildings and infrastructure can have widespread and long-lasting effects on rehabilitation and livelihoods. Our understanding of how lava flows interact with buildings is limited and based upon sparse empirical data. Often a binary impact is assumed (destroyed when in contact with the flow and intact when not in contact with the flow), although previous events have shown this to be an oversimplification. Empirical damage data collected after past events provide an evidence base from which to better understand lava flow impacts across a range of building types, environments, and eruption styles, as well as to explore the temporal and spatial trends in these impacts. However, information on lava flow impacts is scattered across literature, reports, and maps; no comprehensive dataset of lava flow impacts exists. In this study, we compile and standardise lava flow impact information from previously compiled data, eruption records, and published literature to create the first comprehensive global dataset of impacts on the built environment from lava flows. We found that since the first recorded event between 5494 yr B.P. and 5387 yr B.P., lava flows from at least 155 events have impacted buildings or infrastructure (e.g., roads, electricity pylons, ski-lifts), with most (47%, n = 73) recorded as located in Europe. Over the last century, there have been approximately seven lava flow impact events per decade (n = 71 total). This greatly expands on the past compilations of lava flow impact events. Since ca. 1800 CE, impacts have been consistently documented for less than 14% of recorded eruptions with lava flows globally; prior to 1800 CE, impacts were recorded much more variably (between 0 and 70% of lava flows in any 10-year time bin). The most destructive recorded events were the 1669 CE lava flows at Etna volcano, Italy, which destroyed up to 12 villages and part of the city of Catania, and the 2002 CE lava flows at Nyiragongo volcano, Democratic Republic of Congo, which destroyed up to 14,000 buildings. We found that few studies in the dataset report building typology, damage severity, or hazard intensity at the building-level scale, limiting our ability to assess past building-lava interactions. Future collection of building-level hazard and impact data, supplemented with non-English language records, can be used to inform models that forecast future impacts, support lava flow risk assessments, and develop potential mitigation measures.
In 2001, the New York City Department of Environmental Protection installed 25 wells on the southern embankment of the Hillview Reservoir in Westchester County in an unsuccessful attempt to locate the source of a large seep (seep A) that began flowing continuously in 1999. In 2005, the U.S. Geological Survey began a cooperative study with the NYCDEP to characterize the hydrology of the local groundwater system and identify potential sources of seep A and other seeps on the embankment.
At least two groundwater-flow zones—one shallow and the other deep—overlie the bedrock at the Hillview Reservoir in southern Westchester County, New York. Analyses of slug tests of wells drilled into the southern embankment of the reservoir were used to determine the three-dimensional distribution of hydraulic conductivity of the embankment materials. The wells with the minimum and maximum hydraulic conductivity values are in the deep saturated zone on the southern embankment, where hydraulic conductivity ranges from 0.0012 to 2 feet per day. Hydraulic conductivity ranges from 0.0026 to 1 foot per day in the shallow saturated zone and from 0.021 to 0.27 foot per day in the toe of the embankment. A hydraulic conductivity of 0.016 foot per day was determined for one toe well partially screened in the crystalline-bedrock aquifer. In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment of the Hillview Reservoir in southern Westchester County, New York.
Long-term hydrologic data indicated that water levels trended downward in 29 of 41 sites, including the reservoir basin that was monitored during the 12-year study period; data from a National Weather Service precipitation gage at Central Park indicated annual precipitation also trended downward during the same 12-year period. Of the seven wells in which water levels trended upward during the study, two of the wells are on the west side of the southern embankment, proximal to a major water supply conduit, whereas the five remaining wells are screened in the toe. These data indicate an increasing hydrostatic pressure within the deep system and the toe of the dam, which could result in future seeps on the southern embankment near these wells.
Results of 11 suspended-sediment samples collected from seeps along the southern embankment at 234.1- and 221.6-feet elevation, and another drainage outflow point between 2007 and 2015 indicate a poor correlation between suspended-sediment concentration and discharge. From the flowing seep at 234.1 feet, suspended-sediment concentrations ranged from 1 milligram per liter at a flow of 2.6 gallons per minute (that is, 1 milligram per 0.26 gallons) during March 2008 to 16 milligrams per liter at 12 gallons per minute during July 2014. At about 12 gallons per minute discharge, suspended-sediment concentration from samples collected at that seep during different sampling events, ranged from 3 to 16 milligrams per liter. From the seep at 221.6 feet elevation, the suspended sediment concentration was 2 milligrams per liter at a discharge of 3.4 gallons per minute and 2 milligrams per liter at a discharge of 1.1 gallons per minute. Only one sample was collected at the drainage outflow point, for which the suspended sediment concentration was 2 milligrams per liter at a discharge of 2.4 gallons per minute.
Anomalously high-water levels were recorded in deep-system wells between June 5, 2013, and January 14, 2014. The period for the increase and the decrease back to more typical water-level elevations occurred rapidly during a 13-hour period in each instance. The sudden and rapid changes, in addition to the spatial distribution of magnitude of water-level response indicate that leaky water infrastructure was the source of recharge to the affected wells.
A major water supply conduit was drained for repairs between July 7 and 10, 2010. The seeps indicated an immediate response and a substantial hydraulic connection to the water supply conduit. Approximately 10.5 hours after the water supply conduit was drained, flow from a seep on the southern embankment decreased from about 20 gallons per minute to less than 1 gallon per minute. This seep is located at about the same elevation and within the vicinity of the water supply conduit. A travel-time of about 10.5 hours from the source to the seep at 234.1 feet elevation was estimated from the dewatering timeline. During the 3-month shutdown of the water supply conduit, the previously flowing seeps remained dry until precipitation resulted in discharge of about 0.7 gallon per minute at the higher elevation seep, indicating a minor contribution from precipitation to the total seepage discharge. Discharge from the seeps resumed almost immediately coincident with the refilling of the water supply conduit, supporting the hydraulic connection observations during the drainage stage. In addition, during the refilling of the water supply conduit on September 21, 2010, a new seep (I) was observed on the southern embankment. Discharge from this new seep remained relatively constant until it became inaccessible under construction stone from subsequent embankment repairs by the New York City Department of Environmental Protection. Precipitation after the refilling stage of the shutdown seemed to have induced a rise in water levels in the toe wells and an increase in discharge from the seep at 234.1 feet elevation. The post shutdown discharge was less than 12 gallons per minute, compared to a discharge of about 20 gallons per minute before the repairs. The lower discharge rate measured during the period of historically higher discharge rates for the fall season indicates that the repair of the major water supply conduit may have contributed to a reduced discharge from the seeps. There were no definitive responses to the shutdown in any of the wells near the major water supply conduit.
The more transmissive deep system of the southern embankment near the major water supply conduit and its associated infrastructure seems to be the preferential flow path for leaking infrastructure. The wells screened in this system showed a response during the deep system anomaly and have some of the highest hydraulic conductivities of the tested wells. All the seeps are in the elevation range of the deep system from approximately the crystalline bedrock surface around 200 feet elevation to the contact between the deep and shallow saturated zones of the reservoir at about 250 feet elevation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235123","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Chu, A., Noll, M.L., Capurso, W.D., and Welk, R.J., 2023, Hydrologic analysis of an earthen embankment dam in southern Westchester County, New York: U.S. Geological Survey Scientific Investigations Report 2023–5123, 41 p., https://doi.org/10.3133/sir20235123.","productDescription":"Report: vii, 41 p.; Data Release; Dataset","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099377","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":499388,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116022.htm","linkFileType":{"id":5,"text":"html"}},{"id":425302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5123/coverthb.jpg"},{"id":425303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5123/sir20235123.pdf","text":"Report","size":"8.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5123"},{"id":425304,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235123/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5123"},{"id":425308,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":425307,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J404KW","text":"USGS data release","linkHelpText":"Data and analytical type-curve match for selected hydraulic tests at an earthen dam site in southern Westchester County, New York"},{"id":425306,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5123/images/"},{"id":425305,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5123/sir20235123.XML"}],"country":"United States","state":"New York","county":"Westchester County","otherGeospatial":"Hillview Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.87575164367507,\n 40.91924473969948\n ],\n [\n -73.87575164367507,\n 40.901369445530406\n ],\n [\n -73.85851786410133,\n 40.901369445530406\n ],\n [\n -73.85851786410133,\n 40.91924473969948\n ],\n [\n -73.87575164367507,\n 40.91924473969948\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, began monitoring the Yellowstone River fish bypass channel according to the specifications of the Lower Yellowstone Adaptive Management and Monitoring Plan. The fish bypass channel was constructed to provide upstream migrating fish with a route around a diversion dam. The objective of this study is to monitor the physical and hydraulic characteristics of the bypass channel, including flow split, minimum depth for the deepest continuous 30 cross sectional feet, and mean channel velocity. Data are collected through several sets of measurements within the bypass channel at varying times during the field season. Physical and hydraulic data collected during this study can be used to ensure the hydraulic design criteria of the bypass channel are being met.
This report presents the methods used to monitor the physical and hydraulic characteristics of the bypass channel. Examples of the types of data collected and summarized as part of this study are provided using three figures and one table. Data collected for this study are summarized and published in an accompanying U.S. Geological Survey data release. The monitoring data can be used by the cooperating agencies to help describe the preferred hydraulic conditions for Scaphirhynchus albus (Forbes and Richardson, 1905; pallid sturgeon) passage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1189","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Stephens, J.B., Alexander, J.S., and Siefken, S.A., 2024, Yellowstone River fish bypass channel physical and hydraulic monitoring, Montana: U.S. Geological Survey Data Report 1189, 8 p., https://doi.org/10.3133/dr1189.","productDescription":"Report: iv, 8 p.; Data Release; Dataset","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-156045","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":425226,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q1TR1U","text":"USGS data release","linkHelpText":"Physical and hydraulic monitoring on the Yellowstone River fish bypass channel, Montana, May 2022 to August 2023"},{"id":499104,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116024.htm","linkFileType":{"id":5,"text":"html"}},{"id":425222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1189/coverthb.jpg"},{"id":425223,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1189/dr1189.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1189"},{"id":425224,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1189/dr1189.XML"},{"id":425225,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1189/images/"},{"id":425227,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":425228,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1189/full"}],"country":"United States","state":"Montana","otherGeospatial":"Yellowstone River Intake Diversion Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.56423730516126,\n 47.292078436200825\n ],\n [\n -104.56423730516126,\n 47.254544294660235\n ],\n [\n -104.50580646209615,\n 47.254544294660235\n ],\n [\n -104.50580646209615,\n 47.292078436200825\n ],\n [\n -104.56423730516126,\n 47.292078436200825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Adverse ecological and water-quality effects associated with industrial land-use changes are common for littoral wetlands connected to river mouth ecosystems in the Grand Calumet River-Indiana Harbor Canal Area of Concern. These effects can be exacerbated by recent high Lake Michigan water levels that are problematic for wetland restoration. Wetlands in the adjacent Clark and Pine Nature Preserve and Pine Station Nature Preserve are intended to mitigate wetland destruction in the area of concern by restoring residual dune-and-swale wetlands and preserving habitat for endangered and threatened plant species. Physical hydrology and water-quality monitoring of restored wetland cells at the preserves were initiated during 2019 to evaluate changes after wetland restoration efforts in 2015 and near record-low water levels in early 2013. Lake Michigan water levels rose steadily between late 2013 and 2018 to record-high water levels in 2019 and 2020. In this report, precipitation, evapotranspiration, and groundwater and surface-water levels are analyzed to better understand wetland inundation controls and flow directions in restored northern dune-and-swale wetland settings relative to the Grand Calumet River. Continuous specific conductance data and discrete water-quality samples were collected and analyzed to provide a synoptic view of water quality for the restored wetlands.
High Lake Michigan water levels affected Grand Calumet River stage and shallow groundwater elevations in the study area after the onset of peak lake levels in June 2019, that persisted through summer 2020, before finally receding in September 2020. Grand Calumet River stage peaked soon after lake levels in July 2019, whereas groundwater elevations in the study area peaked in October 2019. Specific conductance values in closed-basin wetland cells in the western and central parts of the nature preserves indicated a dilution trend and contrasted those of interconnected wetland cells along an eastern corridor, where alterations to wetland cells were more pronounced. Monitoring results indicate that varying seasonal wetland inundation trends with low stands in autumn have returned after high water table conditions owing to high water levels on Lake Michigan. Wetland water balance results during the study period indicated that the wetland ecosystem partially moderated flooding during high lake levels through summer evapotranspiration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235122","collaboration":"Prepared in cooperation with the Indiana Department of Natural Resources","usgsCitation":"Naylor, S., and Gahala, A.M., 2024, Hydrology and water quality of a dune-and-swale wetland adjacent to the Grand Calumet River, Indiana, 2019–22: U.S. Geological Survey Scientific Investigations Report 2023–5122, 29 p., https://doi.org/10.3133/sir20235122.","productDescription":"Report: vii, 29 p.; Dataset","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":499387,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116023.htm","linkFileType":{"id":5,"text":"html"}},{"id":425295,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":425294,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5122/images/"},{"id":425293,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5122/sir20235122.XML"},{"id":425292,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235122/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5122"},{"id":425291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5122/sir20235122.pdf","text":"Report","size":"3.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5122"},{"id":425290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5122/coverthb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Grand Calumet River-Indiana Harbor Canal Area of Concern","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.4167,\n 41.6278\n ],\n [\n -87.4167,\n 41.6056\n ],\n [\n -87.35,\n 41.6056\n ],\n [\n -87.35,\n 41.6278\n ],\n [\n -87.4167,\n 41.6278\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Generalized potentiometric surfaces of the Fort Union, Hell Creek, and Fox Hills aquifers were constructed to assess the groundwater resources of the Standing Rock Reservation. Additionally, this information can provide water managers with tools and data to effectively manage water resources in the future. Previous studies that mapped the geology and hydrogeology of the area at differing scales were used to confirm in which aquifer the study wells were completed. Water-level data from wells are provided by the U.S. Geological Survey Groundwater System Inventory database, the South Dakota Department of Agriculture and Natural Resources, and the North Dakota Department of Water Resources. Hydrographs were constructed for five selected observation wells to evaluate historical water-level fluctuations and trends. Hydrographs for the Hell Creek aquifer showed a flat trend with a rise in 2020. Hydrographs for the deeper Fox Hills aquifer showed that water levels fluctuated in response to climatic conditions and demonstrated an increasing trend in water-level elevations starting in 2010. Hydrographs were not constructed for any wells completed in the Fort Union Formation because none of the wells had continuous long-term measurements.
Generalized potentiometric surfaces, constructed from interpolating water-level elevations, gave insight into groundwater flow directions. Groundwater in the Fort Union aquifer likely flows radially outward from the northwest part of the study area to the northeast and south-southeast parts. Groundwater in the Hell Creek aquifer generally flows from higher elevations in the northwest towards lower areas, where surface-water tributaries have incised into the aquifer. Groundwater in the Fox Hills aquifer likely flows from higher elevations in the west, southwest, and central parts of the study area towards the valleys of the Grand River and Missouri River.
Most wells used for constructing potentiometric maps had only one recorded water-level measurement from drillers at the time of well construction. These measurements are often subject to error because the well is still recovering and because of spatial limitations of data availability. Also, because single water-level measurements were recorded at different points in time, additional uncertainty is introduced by fluctuating climatic conditions effect on water levels. Potentiometric map interpretation limitations are a result of areas with sparse data. Limitations also arise from potentiometric surfaces generalizing a complex and dynamic hydrogeologic system; however, the generalized potentiometric surface maps can be used to assist water managers and can help prioritize locations for future monitoring in areas with high uncertainty from sparse existing data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3516","collaboration":"Prepared in cooperation with the Standing Rock Sioux Tribe of North & South Dakota","usgsCitation":"Anderson, T.M., and Lundgren, R.F., 2024, Generalized potentiometric maps of the Fort Union, Hell Creek, and Fox Hills aquifers within the Standing Rock Reservation: U.S. Geological Survey Scientific Investigations Map 3516, 4 sheets, includes 13-p. pamphlet, https://doi.org/10.3133/sim3516.","productDescription":"Pamphlet: vii, 13 p.; 4 Sheets: 42.00 x 36.00 inches or smaller; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-152317","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":499283,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116009.htm","linkFileType":{"id":5,"text":"html"}},{"id":425195,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KG3KOE","text":"USGS data release","linkHelpText":"Datasets used to create generalized potentiometric maps of the Fort Union, Hell Creek, and Fox Hills aquifers within the Standing Rock Reservation"},{"id":425214,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3516/full"},{"id":425194,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3516/images/"},{"id":425193,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3516/sim3516.XML"},{"id":425192,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3516/sim3516_sheet04.pdf","text":"Sheet 4","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425190,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3516/sim3516_sheet02.pdf","text":"Sheet 2","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3516/sim3516.pdf","text":"Pamphlet","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425189,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3516/sim3516_sheet01.pdf","text":"Sheet 1","size":"45 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425191,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3516/sim3516_sheet03.pdf","text":"Sheet 3","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3516/coverthb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Fort Union, Hell Creek, and Fox Hills aquifers, Standing Rock Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.98993073804536,\n 46.05081356032207\n ],\n [\n -101.9946729762572,\n 45.47849897658867\n ],\n [\n -100.35385855485926,\n 45.47517373072063\n ],\n [\n -100.49612570122335,\n 45.57152619514463\n ],\n [\n -100.31592064916218,\n 45.684281010407375\n ],\n [\n -100.33963184022313,\n 45.77035224703667\n ],\n [\n -100.4107654134049,\n 45.90581051622172\n ],\n [\n -100.62416613295073,\n 46.126459491422025\n ],\n [\n -100.54354808334469,\n 46.24465356412793\n ],\n [\n -100.5814859890418,\n 46.297102696693855\n ],\n [\n -100.55303255976898,\n 46.33640660935069\n ],\n [\n -100.5814859890418,\n 46.42473720136911\n ],\n [\n -100.71426865898158,\n 46.39530954851841\n ],\n [\n -100.88498923461792,\n 46.408390465486036\n ],\n [\n -101.02251414277018,\n 46.29382609633299\n ],\n [\n -101.1031321923762,\n 46.238093893437394\n ],\n [\n -101.16952352734609,\n 46.19215423847203\n ],\n [\n -101.20271919483075,\n 46.15274680265421\n ],\n [\n -101.3023061972859,\n 46.126459491422025\n ],\n [\n -101.36395529404341,\n 46.08700499239785\n ],\n [\n -101.47302677292281,\n 46.05410467109428\n ],\n [\n -101.54890258431698,\n 46.034355064645865\n ],\n [\n -101.64374734855916,\n 46.03106277714136\n ],\n [\n -101.75281882743856,\n 46.04752225343029\n ],\n [\n -101.85714806810553,\n 46.07055728318318\n ],\n [\n -101.98993073804536,\n 46.05081356032207\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. This work is part of that reexamination.
The stationarity assumption is a concern because flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done to assess potential nonstationarity in peak flows in the north-central United States.
This report summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Ryberg, K.R., comp., 2024, Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, [variously paged], https://doi.org/10.3133/sir20235064.","productDescription":"1 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":424953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/sir20235064.pdf","text":"Abstract","size":"522 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5064"},{"id":424911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/coverthb.jpg"}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. Flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done by the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, to assess potential nonstationarity in peak flows in the north-central United States.
This chapter summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flow. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The climate data consist of monthly time series estimates of temperature, precipitation, potential evapotranspiration, actual evapotranspiration, snowfall, soil moisture storage, snow water equivalent, and runoff on a 3.1-mile by 3.1-mile grid for the conterminous United States.
Statistical and graphical analyses were used to investigate potential changes in hydrology and climate. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. To examine potential causal drivers of changes, the climate data were analyzed graphically and statistically. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Peak Streamflow Trends and Their Relation to Changes in Climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064A","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Ryberg, K.R., Over, T.M., Levin, S.B., Heimann, D.C., Barth, N.A., Marti, M.K., O’Shea, P.S., Sanocki, C.A., Williams-Sether, T.J., Wavra, H.N., Sando, T.R., Sando, S.K., and Liu, M.S., 2024, Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin, chap. A of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 27 p., https://doi.org/10.3133/sir20235064A.","productDescription":"Report: vi, 27 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-145865","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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\"}}]}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
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 specific reservoirs owned by New York City to 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 61st annual report of the River Master of the Delaware River. The report covers the 2014 River Master report year, which is the period from December 1, 2013, to November 30, 2014.
During the report year, precipitation in the upper Delaware River Basin was 42.40 inches or 95 percent of the long-term average. On December 1, 2013, combined useable storage in New York’s Pepacton, Cannonsville, and Neversink Reservoirs in the upper Delaware River Basin was 200.133 billion gallons or 73.9 percent of the combined capacity of 270.8 billion gallons. The reservoirs were at about 99.7 percent of usable capacity on May 31, 2014. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs decreased below 80 percent of combined capacity in late August. The lowest combined storage was 151.730 billion gallons or 56 percent of combined capacity on November 24, 2014. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and the State of 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, on 94 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 also made during the report year.
Water quality in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231084","isbn":"978-1-4113-4543-0","programNote":"Water Availability and Use Science Program","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, 2013–November 30, 2014: U.S. Geological Survey Open-File Report 2023–1084, 98 p., https://doi.org/10.3133/ofr20231084.","productDescription":"xii, 98 p.","numberOfPages":"98","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123859","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499192,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115976.htm","linkFileType":{"id":5,"text":"html"}},{"id":422830,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1084/ofr20231084.XML"},{"id":422831,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1084/images/"},{"id":422832,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231084/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1084"},{"id":422833,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1084/coverthb.jpg"},{"id":422834,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1084/ofr20231084.pdf","text":"Report","size":"9.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1084"}],"country":"United States","state":"New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.3534603634281,\n 39.372074240175664\n ],\n [\n -74.00,\n 39.372074240175664\n ],\n [\n -74.00,\n 43.02029898998293\n ],\n [\n -76.3534603634281,\n 43.02029898998293\n ],\n [\n -76.3534603634281,\n 39.372074240175664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Delaware River Master
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Survival of out-migrating juvenile salmonids (Oncorhynchus spp.) through the Sacramento-San Joaquin River Delta averages less than 33 percent, depending on water flow through the delta, and is partially governed by the distribution of fish among three Sacramento River distributaries: Sutter, Steamboat, and Georgiana sloughs. Behavioral altering structures in the junctions of the distributaries can effectively increase entrainment into favorable routes, thereby increasing through-delta (Verona to Chips Island, California) survival. The effectiveness of these structures, hence forth called “behavioral barriers,” are dependent on shape, length, location, barrier type, and water velocity, which is governed by Sacramento River discharge (hereinafter referred to as “flow”).
We developed a machine learning tool to optimize behavioral barrier designs at up to three junctions within the Sacramento-San Joaquin Delta for improving through-delta survival of juvenile winter-run Chinook salmon (Oncorhynchus tshawytscha). This barrier optimization tool (BOT) works by evolving barrier solutions in one to three junctions by repeatedly simulating survival of populations of Sacramento River origin fish as they pass through the Delta. Over approximately 6,000 simulations per junction, the BOT converges on barrier designs that result in the greatest average survival given simulated environmental conditions. Survival at each iteration of the model is simulated using a modified version of the salmon travel time and routing simulation (STARS) model. In the BOT, STARS is modified by replacing probabilistic route determinations with an individual based model (IBM) that simulates fish behavior to predict the entrainment rates in each junction. The IBM allows the flexibility to explore how entrainment changes with evolving barrier designs. We used juvenile winter-run-sized Chinook salmon catch data collected at Knights Landing from 1997 to 2011 to create realistic arrival and spatial distributions of simulated fish within the BOT that varied among water years (hereafter years). We demonstrated the capabilities of the BOT by comparing optimized barrier solutions and their resulting simulated improvement in survival among three scenarios that differed in the number of junctions with barriers (Georgiana Slough, Steamboat Slough, or both) and the barrier operational period (early: November 1–March 15, or late: January 1–April 30). In this initial demonstration of the BOT we only considered a bioacoustic fish fence (BAFF) at Georgiana Slough and a floating fish guidance structure (FFGS) at Steamboat Slough.
The increase in simulated through-delta fish survival ranged from 1.0 to 6.3 percent among the optimized barrier designs. The most effective Georgiana Slough barrier design predicted improved survival by 6.3 percent and was chosen by the California Department of Water Resources (DWR) as the Georgiana Slough salmon migratory barrier planned for operation annually from 2023 to 2030 at Georgiana Slough in response to the 2020 California Department of Fish and Wildlife’s (CDFW) Incidental Take Permit Minimization Measure 8.9.1 (California Department of Fish and Wildlife [CDFW], 2020). When barriers were simulated in both junctions, the percentages of simulated winter-run Chinook salmon interacting with a barrier at Steamboat or Georgiana sloughs were 95 percent given the early operational period and 48 percent given the late operational period. When barriers were simulated at both sloughs, the optimal barrier at Steamboat Slough effectively routed fish into the Sacramento River. This is because the Georgiana Slough barrier reduced routing into Georgiana Slough where survival is low, which resulted in higher survival for fish routed down the Sacramento River at Steamboat Slough than fish routed down Steamboat Slough. Whereas when no barrier was simulated at Georgiana Slough, the optimized barrier at Steamboat Slough routed fish into Steamboat Slough. This is because survival was higher through Steamboat Slough than the Sacramento River and Georgiana Slough combined. The greatest improvement in survival (6.3 percent) was predicted over the earlier operational period with only a barrier at Georgiana Slough.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231095","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Swyers, N.M., Blake, A., Stumpner, P., Burau, J.R., Burdick, S.M., and Anwar, M.S., 2024, A machine learning tool for design of behavioral fish barriers in the Sacramento-San Joaquin River Delta: U.S. Geological Survey Open-File Report 2023–1095, 38 p., https://doi.org/10.3133/ofr20231095.","productDescription":"ix, 38 p.","onlineOnly":"Y","ipdsId":"IP-151594","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":424660,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1095"},{"id":424362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1095/images"},{"id":424363,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.XML"},{"id":424359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.jpg"},{"id":424360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1095"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.4,\n 38.5\n ],\n [\n -122.4,\n 38.0\n ],\n [\n -121.8,\n 38.0\n ],\n [\n -121.8,\n 38.5\n ],\n [\n -122.4,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Global resource extraction raises concerns about environmental pressures and the security of mineral supply. Strategies to address these concerns depend on robust information on natural resource endowments, and on suitable methods to monitor and model their changes over time. However, current mineral resources and reserves reporting and accounting workflows are poorly suited for addressing mineral depletion or answering questions about the long-term sustainable supply. Our integrative review finds that the lack of a robust theoretical concept and framework for mass-balance (MB)-consistent geological stock accounting hinders systematic industry-government data integration, resource governance, and strategy development. We evaluate the existing literature on geological stock accounting, identify shortcomings of current monitoring of mine production, and outline a conceptual framework for MB-consistent system integration based on material flow analysis (MFA). Our synthesis shows that recent developments in Earth observation, geoinformation management, and sustainability reporting act as catalysts that make MB-consistent geological stock accounting increasingly feasible. We propose first steps for its implementation and anticipate that our perspective as “resource realists” will facilitate the integration of geological and anthropogenic material systems, help secure future mineral supply, and support the global sustainability transition.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.3c03088","usgsCitation":"Simoni, M.U., Drielsma, J.A., Ericsson, M., Gunn, A.G., Heiberg, S., Heldal, T.A., Nassar, N.T., Petavratz, E., and Muller, D.B., 2024, Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources: Environmental Science and Technology, v. 58, p. 971-990, https://doi.org/10.1021/acs.est.3c03088.","productDescription":"20 p.","startPage":"971","endPage":"990","ipdsId":"IP-145992","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":440814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c03088","text":"Publisher Index Page"},{"id":431350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Simoni, Mark U.","contributorId":340251,"corporation":false,"usgs":false,"family":"Simoni","given":"Mark","email":"","middleInitial":"U.","affiliations":[{"id":81520,"text":"Norwegian University of Science and Technology, Norway","active":true,"usgs":false}],"preferred":false,"id":906749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drielsma, Johannes A.","contributorId":340252,"corporation":false,"usgs":false,"family":"Drielsma","given":"Johannes","email":"","middleInitial":"A.","affiliations":[{"id":81521,"text":"Drielsma Resources Europe, Germany","active":true,"usgs":false}],"preferred":false,"id":906750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ericsson, Magnus","contributorId":340253,"corporation":false,"usgs":false,"family":"Ericsson","given":"Magnus","email":"","affiliations":[{"id":81522,"text":"Luleå University of Technology, Sweden","active":true,"usgs":false}],"preferred":false,"id":906751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gunn, Andrew G.","contributorId":340254,"corporation":false,"usgs":false,"family":"Gunn","given":"Andrew","email":"","middleInitial":"G.","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heiberg, Sigurd","contributorId":340255,"corporation":false,"usgs":false,"family":"Heiberg","given":"Sigurd","email":"","affiliations":[{"id":81523,"text":"Petronavit AS, Norway","active":true,"usgs":false}],"preferred":false,"id":906753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heldal, Tom A.","contributorId":340256,"corporation":false,"usgs":false,"family":"Heldal","given":"Tom","email":"","middleInitial":"A.","affiliations":[{"id":35509,"text":"Geological Survey of Norway","active":true,"usgs":false}],"preferred":false,"id":906754,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":906755,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petavratz, Evi","contributorId":340257,"corporation":false,"usgs":false,"family":"Petavratz","given":"Evi","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906756,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Muller, Daniel B.","contributorId":340258,"corporation":false,"usgs":false,"family":"Muller","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":39348,"text":"Norwegian University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":906757,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250996,"text":"70250996 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","interactions":[],"lastModifiedDate":"2024-02-02T14:47:45.280372","indexId":"70250996","displayToPublicDate":"2024-01-01T08:26:24","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","docAbstract":"Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgages and period of historical flow observations prior to the gage installation provides an informative means of estimating flood flow frequency. The U.S. Geological Survey contributed to the InFRM team’s efforts by performing the statistical analysis of the gaged record and authored this Appendix to the Lower Watershed Hydrology Assessment. Flood flow frequency is defined by values or quantiles of streamflow for selected annual exceedance probabilities (AEPs) (England and others, 2019). The annual peak streamflow data collected as part of the systematic operation of a streamgage provides the foundation for a detailed analysis of peak streamflow, but additional historical information pertaining to peak streamflows that predates the installation of a streamgage also can be used. An annual peak streamflow is defined as the maximum instantaneous streamflow for a streamgage for a given water year, and annual peak streamflow data for USGS streamgages can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2022). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of a multifaceted analysis, InFRM team members from the USGS analyzed annual peak streamflow records for the 45 USGS streamgages (gages) and 21 Lower Colorado River Authority (LCRA) streamgages (gages) in the lower Colorado River Basin listed in Table A.1 and Table A.8. The locations of USGS gages are also shown on Figure A.1, Figure A.2, and Figure A.3, and the locations of LCRA gages are shown in Section 1.4. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE-Fort Worth District, FEMA Region 6, NWS West Gulf River Forecast Center","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 246 p.","productDescription":"246 p.","ipdsId":"IP-133413","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424560,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252622,"text":"70252622 - 2024 - Dam removal cost databases and drivers","interactions":[],"lastModifiedDate":"2024-04-01T14:26:56.740565","indexId":"70252622","displayToPublicDate":"2023-10-31T09:21:30","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ST-2023-21084 and ENV-2023-002","title":"Dam removal cost databases and drivers","docAbstract":"The United States (U.S.) has over 90,000 dams listed in the National Inventory of Dams that provide vital infrastructure to support water management for municipal and industrial uses including irrigation, hydropower, flood control, navigation, recreation, and habitat, among other uses (NID 2023). The Bureau of Reclamation (Reclamation) and U.S. Army Corps of Engineers (USACE) operate and maintain approximately 489 and 740 dams, respectively, as well as associated structures which provide flood risk management, navigation, water supply, hydropower, environmental stewardship, fish and wildlife conservation, and recreation benefits. As dams age, structural and operational maintenance investments increase until a time when decisions on whether to rehabilitate, replace, or decommission the dam need to be made. While most dams continue to provide important value even with maintenance requirements, at least 2,000 dams have been removed in the U.S. during the past 110 years, with an upward trend in the last few decades (American Rivers 2023). Decommissioning a dam may be considered when the purpose of the dam is no longer needed or other factors such as dam safety, fish passage, recreation safety, or river restoration goals take higher priority and are more economically feasible for the dam owner long-term.
Dam safety programs, river restoration programs, and asset class management programs need cost estimating methods to consider dam decommissioning when appropriate. Traditional cost estimating approaches in planning stages focus mainly on dam removal construction and may leave out or have uncertainty on important complexities that can have substantial effects on total costs and be critical for project success. As the numbers of dam removal case studies increase, a growing set of cost data has become available (Duda et al. 2023a; Tullos and Bountry 2023; American Rivers 2022). However, total costs vary over five orders of magnitude for similar size dams, and it was unclear why. We evaluated three sets of cost data that had varying level of details regarding elements contributing to dam removal costs reported by project managers working on the dam removal studies and construction means and methods.
We created planning-level cost estimating tools to assist with projects needing to consider the dam removal alternative: (1) new databases of case studies (Duda et al. 2023a; Tullos and Bountry 2023); (2) scoping questions to help determine if complexity cost drivers will be present; (3) machine learning based regression trees to estimate a potential cost range; and (4) a Computation Guide for Cost Estimating that can be used to inform discussions on potential dam removal cost items, quantities, and unit costs (appendix A). The collected data showed that dam height is important but is not a reliable predictor of the removal cost without considering other elements. However, knowing some basic characteristics about the average annual flow and geographic location of the dam site, in addition to dam size, can improve the ability to use past case studies for planning-level cost estimating. By additionally incorporating scoping questions related to sediment removal, mitigation, or other infrastructure, the likelihood of complexity cost drivers and the initial uncertainty of a cost estimate can be further reduced especially for small dams. Applying the Computation Guide for Cost Estimating requires more robust information but helps users reduce cost uncertainty. This step further refines the dam removal objective, removal approach (partial or full; phased or instantaneous), engineering design, construction means and methods, quantities, and unit costs, and results in a quantitative cost estimate.
","language":"English","publisher":"Bureau of Reclamation","collaboration":"Army Corps of Engineers, Bureau of Reclamation, Oregon State University","usgsCitation":"Bountry, J.A., Randle, T.J., Jansen, A., Duda, J.J., Jumani, S., Tullos, D.D., McKay, K., and Bailey, S., 2024, Dam removal cost databases and drivers: Final Report ST-2023-21084 and ENV-2023-002, 60 p.","productDescription":"60 p.","ipdsId":"IP-156982","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":427267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427257,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://data.usbr.gov/catalog/7975/item/128527"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bountry, Jennifer A.","contributorId":30114,"corporation":false,"usgs":false,"family":"Bountry","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":897732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Randle, Timothy J.","contributorId":90994,"corporation":false,"usgs":false,"family":"Randle","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":897733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jansen, Alvin","contributorId":317292,"corporation":false,"usgs":false,"family":"Jansen","given":"Alvin","email":"","affiliations":[{"id":68995,"text":"Technical Service Center, Bureau of Reclamation, Denver, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":897734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jumani, Suman 0000-0002-2292-7996","orcid":"https://orcid.org/0000-0002-2292-7996","contributorId":305995,"corporation":false,"usgs":false,"family":"Jumani","given":"Suman","email":"","affiliations":[{"id":66338,"text":"Network for Engineering with Nature, Georgia, USA","active":true,"usgs":false}],"preferred":false,"id":897736,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tullos, Desiree D.","contributorId":176667,"corporation":false,"usgs":false,"family":"Tullos","given":"Desiree","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":897737,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKay, Kyle","contributorId":335212,"corporation":false,"usgs":false,"family":"McKay","given":"Kyle","email":"","affiliations":[{"id":80343,"text":"Engineer Research and Development Center – Environmental Laboratory, U.S. Army Corps of Engineers, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":897738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Susan","contributorId":317293,"corporation":false,"usgs":false,"family":"Bailey","given":"Susan","email":"","affiliations":[{"id":68996,"text":"Engineer Research and Development Center - Environmental Laboratory, U.S. Army Corps of Engineers, Vicksburg, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":897739,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70251032,"text":"70251032 - 2023 - Predicting large hydrothermal systems","interactions":[],"lastModifiedDate":"2024-01-19T00:54:41.01672","indexId":"70251032","displayToPublicDate":"2023-12-29T18:53:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting large hydrothermal systems","docAbstract":"We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from measured thermal gradients in wells by comparing the total estimated heat flow at the wells to the modeled background conductive heat flow. That is, the reported convective signal is the difference between the background conductive heat flow and the well heat flow. The reported convective signals contain outliers that may affect upflow prediction, so the influence of outliers is tested by constructing models for two cases: 1) using all the data (i.e., -91 to 11,105 mW/m2), and 2) truncating the range of labels to include only reported convective signals between -25 and 200 mW/m2. Because hydrothermal systems are sparse, models that predict high convective signal in smaller areas better match the natural frequency of hydrothermal systems. Early results demonstrate that XGBoost outperforms linear regression. For XGBoost using the truncated range of labels, half of the high reported signals are within < 3 % of the highest predictions. For XGBoost using the entire range of labels, half of the high reported signals are in < 13 % of the highest predictions. While this implies that the truncated regression is superior, the all-data model better predicts the locations of power-producing systems (i.e., the operating power plants are in a smaller fraction of the study area given by the highest predictions). Even though the models generally predict greater hydrothermal upflow for higher reported convective signals than for lower reported convective signals, both XGBoost models consistently underpredict the magnitude of higher signals. This behavior is attributed to low resolution/granularity of input features compared with the scale of a hydrothermal upflow zone (a few km or less across). Trouble estimating exact values while still reliably predicting high versus low convective signals suggests that a future strategy such as ranked ordinal regression (e.g., classifying into ordered bins for low, medium, high, and very high convective signal) might fit better models, since doing so reduces problems introduced by outliers while preserving the property of larger versus smaller signals.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Mordensky, S.P., Burns, E.R., DeAngelo, J., and Lipor, J., 2023, Predicting large hydrothermal systems: Geothermal Resources Council Transactions, v. 47, p. 1763-1796.","productDescription":"34 p.","startPage":"1763","endPage":"1796","ipdsId":"IP-154718","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":424578,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034861"},{"id":424600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":892809,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206270,"text":"pp1862L - 2023 - Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia","interactions":[{"subject":{"id":70206270,"text":"pp1862L - 2023 - Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia","indexId":"pp1862L","publicationYear":"2023","noYear":false,"chapter":"L","displayTitle":"Magnetotelluric Investigation of Northern Harrat Rahat, Kingdom of Saudi Arabia","title":"Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia"},"predicate":"IS_PART_OF","object":{"id":70250730,"text":"pp1862 - 2023 - Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia","indexId":"pp1862","publicationYear":"2023","noYear":false,"title":"Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia"},"id":1}],"isPartOf":{"id":70250730,"text":"pp1862 - 2023 - Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia","indexId":"pp1862","publicationYear":"2023","noYear":false,"title":"Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia"},"lastModifiedDate":"2024-01-02T16:47:09.58641","indexId":"pp1862L","displayToPublicDate":"2023-12-29T14:29:15","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1862","chapter":"L","displayTitle":"Magnetotelluric Investigation of Northern Harrat Rahat, Kingdom of Saudi Arabia","title":"Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia","docAbstract":"Volcanism within the harrats (Arabic for “volcanic field”) of the Kingdom of Saudi Arabia includes at least one historical eruption occurring close to the holy city of Al Madīnah in 1256 C.E. As part of a volcanic- and seismic-hazard assessment of northern Harrat Rahat, magnetotelluric (MT) data were collected to investigate the structural setting of the area, the presence or absence of melt within the crust, and the mantle-derived magmatic source. Collected MT data were modeled in both two dimensions, where anisotropy can be estimated, and three dimensions. Interpretation of the preferred resistivity model includes a shallow sediment-filled graben beneath northern Harrat Rahat lavas, a melt-free upper crust, and a region of decompression melting in the asthenosphere below 60–70 kilometers depth. Models in two dimensions image the lower crust as anisotropic, demonstrating that a series of elongate conductivity anomalies with a strike of N. 10° E. within the lower crust of the three-dimensional model are artifacts of inverting anisotropic data with an isotropic modeling algorithm. Careful examination of the resistivity models, in combination with regional geological and geophysical data, suggests an anisotropic lower crust that is free of large zones of melt. Azimuthal anisotropy in the lower crust extends well beyond the limits of Harrat Rahat volcanic rocks, with a conductive direction oriented N. 10° E. and an anisotropy factor of 2–5 between the most and least conductive directions. Enhanced conductivity is likely caused by interconnected grain-boundary graphite, where the direction of anisotropy reflects either frozen-in fabric from the Neoproterozoic stabilization of the Arabian Shield or ductile deformation driven by channelized asthenospheric flow coupled with a thin rigid mantle lid. Asthenospheric melt is interpreted to transect the crust largely through diking, with limited melt storage and short residence times within the crustal column.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862L","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Peacock, J.R., Bedrosian, P.A., Al-Dhahry, M.K., Shareef, A., Feucht, D.W., Taylor, C.D., Bloss, B., and Zahran, H.M., 2023, Magnetotelluric investigation of northern Harrat Rahat, Kingdom of Saudi Arabia, chap. L of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 111 p., https://doi.org/10.3133/pp1862L.","productDescription":"Report: vi, 111 p.; Data Release","numberOfPages":"111","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-105223","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":424018,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/l/covrthbl.jpg"},{"id":424020,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99H8HJ7","text":"USGS Data Release","description":"Bedrosian, P.A., Peacock, J.R., and Feucht, D.W., 2023, Magnetotelluric data from northern Harrat Rahat, Saudi Arabia, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P99H8HJ7.","linkHelpText":"Magnetotelluric data from northern Harrat Rahat, Saudi Arabia, 2016"},{"id":424019,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/l/pp1862l.pdf","text":"Report","size":"38 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"Kingdom of Saudi Arabia","otherGeospatial":"northern Harrat Rahat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39,\n 25\n ],\n [\n 39,\n 23.75\n ],\n [\n 40.5,\n 23.75\n ],\n [\n 40.5,\n 25\n ],\n [\n 39,\n 25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
Mafic volcanic fields are widespread, but few have erupted in historical times, providing limited observations of the magnitudes, dynamics, and timescales of lava flow emplacement in these settings. The Harrat Rahat volcanic field in western Saudi Arabia offers a good opportunity to study eruptions in such a setting, with a historical eruption in 1256 C.E. (654 in the year of the Hijra) and numerous well-preserved late Pleistocene lava flows. We combine historical observations and rheological and morphological analyses of the youngest flows with analytical models to reconstruct eruptive histories and lava flow emplacement conditions in Harrat Rahat. Petrologic analysis of samples for emplacement temperatures and crystallinities show cooling trends from vent to toe of ~1,140 to ~1,090 degrees Celsius (°C) at rates of 2 to 7 °C per kilometer, crystallinities increasing from 0.5 to 60 volume percent, and apparent viscosities increasing from 102 to 109 pascal seconds. High-resolution topographic data facilitate quantitative analysis of morphology and interpolation of preeruptive surfaces to measure flow thicknesses, channels, and levees, and enable calculation of eruptive volumes. Analytical models relating flow morphology to emplacement conditions are applied to estimate effusion rates. Within the suite of studied flows, minimum volume estimates range from 0.07 to 0.42 cubic kilometers dense rock equivalent, with effusion rates on the order of tens to hundreds of cubic meters per second and durations from 1 to 15 weeks. These integrated analyses quantify past lava flow emplacement conditions and dynamics in Harrat Rahat, improving our understanding and observations of fundamental parameters and controls of effusive eruptions in Harrat Rahat and other mafic volcanic fields.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862E","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Dietterich, H.R., Downs, D.T., and Stelten, M.E., 2023, Lava flow emplacement in Harrat Rahat with implications for eruptions in mafic volcanic fields, chap. E of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 49 p., https://doi.org/10.3133/pp1862E.","productDescription":"vi, 49 p.","numberOfPages":"49","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-111360","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":423979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/e/coverthbe.jpg"},{"id":423980,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/e/pp1862e.pdf","text":"Report","size":"6.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-E"}],"country":"Kingdom of Saudi Arabia","otherGeospatial":"northeastern Harrat Rahat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.675,\n 24.6\n ],\n [\n 39.675,\n 24.2\n ],\n [\n 40,\n 24.2\n ],\n [\n 40,\n 24.6\n ],\n [\n 39.675,\n 24.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
The northernmost part of the Harrat Rahat volcanic field contains early Pleistocene to Holocene mafic eruptive products within the vicinity of the city of Al Madīnah, Kingdom of Saudi Arabia. A detailed geologic investigation into the eruptive history of a 570 square kilometer (km2) area covering Al Madīnah and the surrounding area has yielded 33 mapped Quaternary volcanic units consisting of lava flows, scoria cones, and shield volcanoes. These eruptive products consist of continental, intraplate alkalic and minor transitional basalts, hawaiites, and a single mugearite that were emplaced from at least 1,014±14 thousand years ago (ka) to a single Holocene eruption in 1256 C.E. Lava flows are generally 10 to 15 kilometers (km) long (but can reach 23 km long), 1 to 3 km wide, and at least 10 meters thick. Most of the mapped units erupted episodically between 400 and 340 ka and 180 and 100 ka. Despite small individual volumes (less than 1 cubic kilometers dense rock equivalent), each unit represents eruption of a distinct magma batch that was strongly influenced by clinopyroxene, olivine, and plagioclase fractionation. Some of these units are interpreted to have undergone magma mixing pre- and (or) syneruptively. Integrating eruption ages, geochemistry, and paleomagnetic data yields evidence that some eruptions were temporally and (or) spatially clustered. Aligned scoria cones and elongate vent edifices were constructed atop fissure vent systems that reflect the local stress field, which controls dike ascent through the middle and upper crust.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862C","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Downs, D.T., Stelten, M.E., Champion, D.E., Dietterich, H.R., Hassan, K., and Shawali, J., 2023, Eruptive history within the vicinity of Al Madīnah in northern Harrat Rahat, Kingdom of Saudi Arabia, chap. C of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 41 p., https://doi.org/10.3133/pp1862C.","productDescription":"Report: vii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-104009","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":423975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91HL91C","text":"USGS data release","linkHelpText":"Major and trace-element chemical analyses of rocks from the northern Harrat Rahat volcanic field and surrounding area, Kingdom of Saudi Arabia"},{"id":423974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/c/pp1862c.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-C"},{"id":423973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/c/coverthbc.jpg"}],"country":"Kingdom of Saudi Arabia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 37.797830302240726,\n 26.450894948969335\n ],\n [\n 37.797830302240726,\n 23.09993673871402\n ],\n [\n 41.6650178022403,\n 23.09993673871402\n ],\n [\n 41.6650178022403,\n 26.450894948969335\n ],\n [\n 37.797830302240726,\n 26.450894948969335\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
The Humboldt River Basin is the only river basin that is contained entirely within the State of Nevada. The effect of groundwater pumping on the Humboldt River is not well understood. Tools are needed to determine stream capture and manage groundwater pumping in the Humboldt River Basin. The objective of this study is to estimate capture and storage change caused by groundwater withdrawals in the lower Humboldt River Basin that can provide the Nevada State Engineer with data and information needed to manage groundwater and surface-water resources.
A numerical groundwater flow model was developed for the purpose of estimating stream capture from pre-2016 and future pumping as well as for any location of potential future pumping within the lower Humboldt River Basin. This model was developed using MODFLOW-NWT to represent the lower Humboldt River Basin hydrologic system, including Humboldt River; Rye Patch Reservoir; groundwater evapotranspiration; pumping from municipal, agricultural, mining, and domestic wells; and agricultural drains. Aquifer properties were calibrated using results from numerous single- and multi-well aquifer tests (Nadler, 2020) and through the process of model calibration.
Historical capture was estimated for 1960–2016 and predictive capture for the system was projected 100 years into the future (2017–2116) based on historical pumping patterns. Stream capture and drain capture are relatively low for the historical and predictive periods. During the historical period, increased pumping during dry years caused increased connections with capture sources and less water sourced to wells from aquifer storage. Storage and groundwater levels generally recovered during subsequent wet years. Overall, storage change has been the main source of water to wells in the lower Humboldt River Basin, followed by groundwater evapotranspiration capture. During the predictive period, pumping is projected to remain constant and capture 9 percent of stream water after 100 years.
Capture and storage change maps were created to visualize spatial variability in potential capture and storage change through time and to provide a database of results that can be used to manage groundwater and surface-water resources. These maps show that potential stream capture would be a minor source of water to wells located across most of the simulated area, except for locations close to the Humboldt River and Rye Patch Reservoir. Drains also would be a minor potential source of water to wells except for those directly adjacent to the drains. In general, the potential supply of water to wells is storage-dominated and over time groundwater evapotranspiration-dominated in the agricultural area.
Capture difference maps were generated to visualize where potential capture results might have greater limitations associated with nonlinear flow processes, such as head-dependent boundary conditions. Higher capture differences indicate larger capture map bias and therefore greater capture map uncertainty due to the inability of capture maps to account for nonlinear flow processes. Stream capture differences are highest directly adjacent to the river but are otherwise minimal. Drain capture differences are highest in the region of the agricultural drain network but are otherwise minimal. The Humboldt River, Rye Patch Reservoir, and drains introduce very little nonlinearity to the model, and their associated capture map bias is minimal. Potential groundwater evapotranspiration capture introduces a fair amount of nonlinearity to the model and has the potential to result in significant, localized groundwater evapotranspiration capture map bias over time. Groundwater evapotranspiration capture differences are the result of higher pumping rates lowering the water table below the root zone faster than lower pumping rates and essentially removing groundwater evapotranspiration as a potential source of capture faster than lower pumping rates. Wells that can no longer source their supply through groundwater evapotranspiration capture then generally source more of their water from storage. Thus, storage change bias increases over time as well.
Capture prediction uncertainty due to parameter estimation was evaluated using a covariance matrix adaptation-evolution strategy. One hundred Monte Carlo realizations of model parameters were applied to the model to assess capture uncertainty at 13 grid cell locations within the model domain. In general, results indicated that greater capture uncertainty for a given source (river, drains, or evapotranspiration) is associated with proximity of a pumping well to that source. The magnitude of maximum capture fraction uncertainties after 100 years of pumping for stream capture, drain capture, groundwater evapotranspiration capture, and storage change were plus or minus (±) 0.17, ±0.10, ±0.20, and ±0.22, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235110","collaboration":"Prepared in cooperation with the Nevada Division of Water Resources","usgsCitation":"Nadler, C.A., Rybarski, S.C., and Pham, H., 2023, Evaluation of stream capture related to groundwater pumping, Lower Humboldt River Basin, Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5110, 77 p., https://doi.org/10.3133/sir20235110.","productDescription":"Report: x, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-093899","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":499384,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115938.htm","linkFileType":{"id":5,"text":"html"}},{"id":423640,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99DN2R1","text":"USGS Data Release","description":"Nadler, C.A., Rybarski, S.C., and Pham, H., 2023, MODFLOW-NWT model and supplementary data used to characterize effects of pumping in Lovelock Valley, Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P99DN2R1.","linkHelpText":"MODFLOW-NWT Model and Supplementary Data Used to Characterize Effects of Pumping in Lovelock Valley, Nevada"},{"id":423639,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235110/full"},{"id":423635,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5110/covrthb.jpg"},{"id":423637,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5110/sir20235110.xml","linkFileType":{"id":8,"text":"xml"}},{"id":423636,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5110/sir20235110.pdf","text":"Report","size":"26 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":423638,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5110/images"}],"country":"United States","state":"Nevada","otherGeospatial":"Lower Humboldt River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.0,\n 40.5\n ],\n [\n -119.0,\n 39.5\n ],\n [\n -118.0,\n 39.5\n ],\n [\n -118.0,\n 40.5\n ],\n [\n -119.0,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
The entire Lower Fox River and inner bay of Green Bay, in northeastern Wisconsin, have been listed as impaired by the Wisconsin Department of Natural Resources (WDNR) for low dissolved oxygen and degraded habitat, with total phosphorus (TP) and total suspended solids (TSS) concentrations listed as the likely causes of these impairments. To restore the Fox River and Green Bay, total maximum daily loads (TMDLs) were developed for TP and TSS, and actions were taken throughout the Fox River Basin to improve water quality. In this study, we estimated concentrations and loads of TP, dissolved phosphorus (DP), and TSS at the Lake Winnebago outlet, De Pere, and the mouth of the Fox River from water year (WY) 1989 to WY 2021; described changes in concentrations and loads through time during this period; and compared the concentrations and loads for the most recent 5-year period (WYs 2017–21) with the WDNR criteria for TP impairment and the TMDL loading goals.
TP, DP, TSS, and total suspended sediment concentration data were obtained from NEW Water (the brand of the Green Bay Metropolitan Sewerage District), the WDNR, and the U.S. Geological Survey and combined into one dataset. All the TSS and total suspended sediment data were used together with no adjustment factor and are referred to as simply “TSS.” During WYs 1989–2021, mean annual TP concentrations increased from 0.089 milligram per liter (mg/L) at the Lake Winnebago outlet to 0.128 mg/L at the mouth of the Fox River, and concentrations decreased at all three sites from WY 1989 to WY 2021. The most recent (WYs 2017–21) median May–October TP concentrations were just less than the 0.1-mg/L WDNR criterion for TP impairment at the two upstream sites (Lake Winnebago outlet and De Pere) but were slightly greater than the criterion for impairment at the mouth of the Fox River. Mean annual DP concentrations increased from 0.024 mg/L at the Lake Winnebago outlet to 0.036 mg/L at the mouth of the Fox River. DP concentrations increased from WY 1989 to WY 2021 at the Lake Winnebago outlet but not at the other sites. Mean annual TSS concentrations increased from 13.5 mg/L at the Lake Winnebago outlet to 23.9 mg/L at the mouth of the Fox River and have decreased at all three sites from WY 1989 to WY 2021. The recent median May–October TSS concentrations were less than the 20-mg/L WDNR criterion for impairment at all three sites. Streamflow and TP, DP, and TSS loads increased from the Lake Winnebago outlet to the mouth of the Fox River (TP loads increased from 360,000 to 557,000 kilograms per year [kg/yr], DP loads increased from 114,000 to 162,000 kg/yr, and TSS loads increased from 60,400 metric tons per year [t/yr] to 122,600 t/yr).
At the Lake Winnebago outlet, DP concentrations and TP and DP loads increased from WY 1989 to WY 2021 because of an increase in DP concentrations in Lake Winnebago resulting from the lake becoming nitrogen limited as a result of biological processes not consuming the DP in the lake and an increase in streamflow leaving the lake. Although TP and TSS concentrations decreased at De Pere and the mouth of the Fox River, there was little change in the loading because of an increase in flow. Flow-normalized TP and TSS loads at De Pere and the mouth of the Fox River decreased possibly because of implementation of agricultural conservation management practices, reductions in point-source discharges in its drainage basin, and deposition of sediment and phosphorus in recently dredged areas of the Lower Fox River. Additional studies are needed to determine the relative importance of each of these actions and whether the decrease in concentrations and flow-normalized loads will continue to be observed in the Fox River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235112","collaboration":"Prepared in cooperation with NEW Water, the brand of the Green Bay Metropolitan Sewerage District","usgsCitation":"Robertson, D.M., Diebel, M.W., Bartlett, S.L., and Fermanich, K.J., 2023, Changes in phosphorus and suspended solids loading in the Fox River, northeastern Wisconsin, 1989–2021: U.S. Geological Survey Scientific Investigations Report 2023–5112, 29 p., https://doi.org/10.3133/sir20235112","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150822","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":423702,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5112/sir20235112.XML"},{"id":423701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5112/sir20235112.pdf","text":"Report","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5112"},{"id":423700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5112/coverthb.jpg"},{"id":423703,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5112/images/"},{"id":501154,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115890.htm","linkFileType":{"id":5,"text":"html"}},{"id":423706,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235112/full"},{"id":423705,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":423704,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P950EOGH","text":"USGS data release","linkHelpText":"Concentrations and loads of phosphorus and suspended solids in the Fox River, northeastern Wisconsin, 1989–2021"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.27750277664309,\n 45.85677045828476\n ],\n [\n -90.27750277664309,\n 43.06901481985196\n ],\n [\n -87.32441235531145,\n 43.06901481985196\n ],\n [\n -87.32441235531145,\n 45.85677045828476\n ],\n [\n -90.27750277664309,\n 45.85677045828476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
In humid regions with dense stream networks, surface water exerts a fundamental control on the water levels and flow directions of shallow groundwater. Understanding interactions between groundwater and surface water is critical for managing groundwater resources and groundwater-dependent ecosystems. Representing streams in groundwater models has historically been arduous and error prone. In recent years, however, all the information needed to numerically describe stream boundary conditions for a model area has become readily available online, as have robust open-source software tools for translating that information to a model grid. The SFRmaker Python package leverages geospatial capabilities in the scientific Python ecosystem to robustly automate the production of input to the Streamflow-Routing (SFR) Package of MODFLOW from the National Hydrography Dataset Plus or other hydrography data. This report documents an application of SFRmaker to automate production of SFR Package input for groundwater models within the Mississippi Embayment Regional Aquifer Study area. SFR Package input was developed in three steps: (1) preprocessing to develop a single set of grid-independent flowlines from National Hydrography Dataset Plus version 2 data; (2) setting up the SFR package from the preprocessed flowlines, and (3) correcting streambed top elevations after an initial model run. Separating the hydrography preprocessing from the construction of SFR Package input was advantageous in that it minimized the need to repeat computationally expensive geoprocessing (thereby speeding model construction) and also allowed for the curation of a single set of grid-independent SFR input data that can be used for any MODFLOW model within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235051","usgsCitation":"Leaf, A.T., 2023, Automated construction of Streamflow-Routing networks for MODFLOW—Application in the Mississippi Embayment region: U.S. Geological Survey Scientific Investigations Report 2023–5051, 28 p., https://doi.org/10.3133/sir20235051.","productDescription":"Report: vii, 28 p.; 4 Data Releases; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-105069","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":417495,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971LPOB","text":"USGS data release","linkHelpText":"MODFLOW–6 models of the Mississippi embayment (MERAS 3) and Mississippi Delta"},{"id":417456,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXDIPL","text":"USGS data release","linkHelpText":"Datasets used to map the potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2020"},{"id":423683,"rank":12,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235100","text":"SIR 2023–5100","linkHelpText":"—Simulating groundwater flow in the Mississippi Alluvial Plain with a focus on the Mississippi Delta"},{"id":417488,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5051/images"},{"id":417481,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5051/sir20235051.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023–5051"},{"id":417457,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":423682,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235080","text":"SIR 2023–5080","linkHelpText":"—Updated estimates of water budget components for the Mississippi embayment region using a Soil-Water-Balance model, 2000–2020"},{"id":417455,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P84B24","text":"USGS data release","linkHelpText":"National-scale grid to support regional groundwater availability studies and a national hydrogeologic database"},{"id":423152,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235051/full"},{"id":417450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5051/coverthb2.jpg"},{"id":417454,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WQPRFB","text":"USGS data release","linkHelpText":"Waterborne resistivity inverted models, Mississippi Alluvial Plain, 2016–2018"},{"id":417451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5051/sir20235051.pdf","text":"Report","size":"46.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5051"}],"country":"United States","otherGeospatial":"Mississippi Embayment Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.88995719655937,\n 28.910338985045087\n ],\n [\n -85.96905875905937,\n 28.910338985045087\n ],\n [\n -85.96905875905937,\n 37.74314338343309\n ],\n [\n -94.88995719655937,\n 37.74314338343309\n ],\n [\n -94.88995719655937,\n 28.910338985045087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Mississippi Alluvial Plain has become one of the most important agricultural regions in the United States but relies heavily on groundwater for irrigation. On average, more than 12 billion gallons are withdrawn daily from the Mississippi River Valley alluvial aquifer. Declining groundwater levels, especially in the Delta region of northwest Mississippi and the Cache and Grand Prairie regions of eastern Arkansas, have led to concerns about future sustainability. The U.S. Geological Survey Mississippi Alluvial Plain Project is focused on quantifying the groundwater system in the alluvial plain and the response of groundwater resources to future development. A key objective of the project is to provide updated groundwater flow models supported by extensive data collection and analyses. MODFLOW 6, PEST++, and several open-source python packages were used to develop a simplified, faster running version of the Mississippi Embayment Regional Aquifer Study model that can provide boundary conditions for local inset models, including the Mississippi Delta model described in this report. An automated workflow was used for model construction, history matching, and development of baseline future climate scenarios. The models incorporate information from a Soil-Water-Balance code simulation of the terrestrial water balance, metering-based estimates of water use from thousands of wells, measured and estimated streamflow and stages, and the largest airborne electromagnetic survey flown to date in the United States. Baseline scenarios for the Mississippi Delta under potential future climates were constructed using recharge, surface runoff and irrigation pumping forcings from a future version of the Soil-Water-Balance model, driven by downscaled temperature and precipitation output from 10 general circulation model simulations, including high and moderate carbon emissions pathways.
Results indicate a complex water balance that varies in time and space in terms of the terrestrial recharge, stream leakage, and regional groundwater flow components, which are affected by seasonal forcings, human activity, and alluvial geomorphology. The general circulation model outputs indicate a continued rise in average temperatures but no clear precipitation trend. Increased crop water demand is anticipated from the higher temperatures, resulting in increased irrigation withdrawals to sustain current levels of irrigated agriculture. Simulated drawdowns in groundwater levels at the mid-21st century vary greatly. Under moderate or wet climate scenarios, and in parts of the aquifer that are well connected to surface water, little to no additional drawdown is anticipated. Under dry or warm scenarios, drawdowns of as much as 10 meters or more are possible in parts of the aquifer that are relatively disconnected from surface water. Under dry or warm scenarios, the portion of the Delta with greater than 60 feet of saturated thickness could be reduced from near 100 percent currently (2018) to 80–90 percent by mid-century. Future simulations with the model could include alternative management scenarios to identify options for improving groundwater sustainability. The automated model construction workflows are designed to facilitate regular updating, making this a “living” framework that the Mississippi Department of Environmental Quality and other stakeholders can use for adaptive management going forward.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235100","programNote":"Water Use and Availability Science Program","usgsCitation":"Leaf, A.T., Duncan, L.L., Haugh, C.J., Hunt, R.J., and Rigby, J.R., 2023, Simulating groundwater flow in the Mississippi Alluvial Plain with a focus on the Mississippi Delta: U.S. Geological Survey Scientific Investigations Report 2023–5100, 143 p., https://doi.org/10.3133/sir20235100.","productDescription":"Report: viii, 143 p.; 4 Data Releases","numberOfPages":"156","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135342","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":423184,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971LPOB","text":"USGS data release","linkHelpText":"MODFLOW 6 models for simulating groundwater flow in the Mississippi Embayment with a focus on the Mississippi Delta"},{"id":423183,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5100/images/"},{"id":501149,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115886.htm","linkFileType":{"id":5,"text":"html"}},{"id":423188,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235100/full"},{"id":423187,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97KK17G","text":"USGS data release","linkHelpText":"Model archive and output files for net infiltration, runoff, and irrigation water use for the Mississippi Embayment Regional Aquifer System, 2000 to 2020, simulated with the Soil-Water-Balance model"},{"id":423186,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BC6UB8","text":"USGS data release","linkHelpText":"Soil-Water-Balance forecasted climate model output for simulations of water budget components in the Mississippi Embayment Regional Aquifer System, 2020 to 2055"},{"id":423185,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TSDEAC","text":"USGS data release","linkHelpText":"Digital surfaces and site data of well-screen top and bottom altitudes defining the irrigation production zone of the Mississippi River Valley alluvial aquifer within the Mississippi Alluvial Plain project region"},{"id":423182,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5100/sir20235100.XML"},{"id":423181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5100/sir20235100.pdf","text":"Report","size":"59.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5100"},{"id":423180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5100/coverthb.jpg"},{"id":423680,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235051","text":"SIR 2023–5051","linkHelpText":"—Automated construction of Streamflow-Routing networks for MODFLOW—Application in the Mississippi Embayment region"},{"id":423681,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235080","text":"SIR 2023–5080","linkHelpText":"—Updated estimates of water budget components for the Mississippi embayment region using a Soil-Water-Balance model, 2000–2020"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.14337516530937,\n 32.22047635687272\n ],\n [\n -89.39679313405937,\n 32.22047635687272\n ],\n [\n -89.39679313405937,\n 35.046419541331645\n ],\n [\n -92.14337516530937,\n 35.046419541331645\n ],\n [\n -92.14337516530937,\n 32.22047635687272\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726