School Branch in Hendricks County in central Indiana, is a small stream with a variety of agricultural and suburban land uses that drains into the Eagle Creek Reservoir, a major source of drinking water for Indianapolis, Indiana. The School Branch watershed has become the focus of a collaborative partnership of Federal, State, and local agencies; a university research center; and agricultural producers to understand the effects of land use and management practices on water quality and water quantity in the watershed. The U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, contributed to the School Branch partnership with the operation of three streamgages (03353415 School Branch at Maloney Road near Brownsburg, Indiana; 03353420 School Branch at County Road 750 North at Brownsburg, Indiana; and 03353430 School Branch at Noble Drive at Brownsburg, Indiana) and the operation of a continuous water-quality gage (also known as a supergage) at County Road 750 North that measured dissolved oxygen, pH, temperature, specific conductance, turbidity, nitrate, and orthophosphate. Additional efforts included the use of passive samplers to identify wastewater indicators; assessment of fish and macroinvertebrate communities and stream habitat to identify ecological impairment; sampling for nutrients and sediment to estimate loads; and using major ions, stable isotopes and nested groundwater monitoring wells at County Road 750 North to determine hydrologic connectivity between the groundwater and surface water. The objectives of this study were to collect surface and groundwater data to analyze the hydrology and water quality within the watershed. Total nitrogen yields were highest at the upstream site, Maloney Road, and indicated a mixture of nitrogen sources in the watershed. Differences found in total nitrogen loading patterns throughout the watershed may be linked to differences in hydrology and land-use management from site to site. The groundwater and surface water were shown to be highly connected, and except for some low-flow periods, the water was flowing from groundwater to the stream for most of the study period. Fish and macroinvertebrate communities show improvement from upstream to downstream, with increases in diversity, richness, and species sensitive to poor water quality and habitat. These increases were most likely due to improved habitat quality at the downstream station.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215061","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Bunch, A.R., McCausland, D.R., and Bayless, E.R., 2021, Hydrologic and ecological investigations in the School Branch watershed, Hendricks County, Indiana—Water years 2016–2018: U.S. Geological Survey Scientific Investigations Report 2021–5061, 61 p., https://doi.org/10.3133/sir20215061.","productDescription":"Report: x, 61 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-114931","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":390195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5061/coverthb.jpg"},{"id":390196,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5061/sir20215061.pdf","text":"Report","size":"4.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5061"},{"id":390197,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QCIDBV","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Data and rloadest models for daily total nitrogen load for the School Branch watershed, Hendricks County, Indiana—Water years 2016–2018"}],"country":"United States","state":"Indiana","county":"Hendricks County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-86.3267,39.9238],[-86.325,39.8662],[-86.328,39.8662],[-86.3281,39.8526],[-86.3268,39.6318],[-86.4648,39.6297],[-86.4642,39.6006],[-86.574,39.6002],[-86.6546,39.6001],[-86.6522,39.6087],[-86.6463,39.6128],[-86.6403,39.6201],[-86.6404,39.6305],[-86.6654,39.6305],[-86.6858,39.63],[-86.6853,39.6884],[-86.6849,39.7773],[-86.6845,39.8648],[-86.6929,39.8643],[-86.6937,39.9228],[-86.3267,39.9238]]]},\"properties\":{\"name\":\"Hendricks\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Theodore Roosevelt National Park is in western North Dakota and was established in 1978 under the National Wilderness Preservation system to preserve and protect the qualities of the North Dakota Badlands, including the wildlife, scenery, and wilderness. The park is made up of three units (North, Elkhorn Ranch, and South) that are connected by the Little Missouri River, which was identified by the National Park Service as a significant resource essential to fulfilling the park's purpose. The development of oil and gas (OG) resources has expanded in the past two decades in the region surrounding Theodore Roosevelt National Park. This expansion of OG development outside park boundaries increases the potential for adverse environmental and economic effects inside the park boundaries, especially for the hydrologic processes within Theodore Roosevelt National Park.
This report assesses the vulnerability of critical components that contribute to supporting plants and wildlife of the Northwestern Great Plains ecological region and Theodore Roosevelt National Park’s mission of preservation. Critical components include land cover, slope, soil saturated hydraulic conductivity, distance to Ovis canadensis (Shaw, 1804) (bighorn sheep) critical habitat, distance to springs, distance to rivers and streams, and distance to surficial aquifers. The study area included all the 12-digit hydrologic units within the watershed boundary dataset that intersect Theodore Roosevelt National Park or are within the 12-digit hydrologic units for Little Missouri River tributaries that flow into the park. Critical components that had existing publicly available geographic data were assessed and assigned vulnerability index values. These values were then summed to develop a vulnerability score and mapped. OG development and associated transportation infrastructure, referred to as “stressors” in this report, with publicly available geographic data were mapped, and then flow paths were generated starting from the stressor locations to assess their likelihood to contaminate vulnerable areas within the study area.
The North Unit had the most area with moderate, high, and very high vulnerability. These areas occurred all across the southern and eastern parts of the North Unit where the Little Missouri River, surficial aquifer, wetland type land covers, and bighorn sheep critical habitat are present. Several stressor flow paths from pipelines and highways cross these areas and may pose the most risk to the vulnerable areas identified. In the Elkhorn Ranch Unit, areas with moderate, high, and very high vulnerability were in the southeastern part of the unit, where the Little Missouri River, surficial aquifer, wetland type land covers, and bighorn sheep critical habitat are present. The stressor flow paths in the Elkhorn Ranch Unit follow the length of the Little Missouri River and all its tributaries in the study area. The stressor flow paths originated from crude oil wells and pipelines. In the South Unit, one area had moderate, high, and very high vulnerability. This area is where the Little Missouri River and bighorn sheep critical range are present. The stressor flow paths in the South Unit follow the length of the Little Missouri River and nearly all its tributaries in the study area. Several stressor flow paths cross the one identified vulnerable area that originated from crude oil wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3479","collaboration":"Prepared in cooperation with the Inland Oil Spill Preparedness Project","usgsCitation":"Valseth, K.J., 2021, Vulnerability assessment in and near Theodore Roosevelt National Park, North Dakota: U.S. Geological Survey Scientific Investigations Map 3479, pamphlet 9 p., 1 sheet, https://doi.org/10.3133/sim3479.","productDescription":"Pamphlet: vi, 9 p.; 1 Sheet: 23.50 x 31.10 inches; Dataset","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-122274","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":390167,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3479/sim3479_sheet1.pdf","text":"Sheet 1","size":"9.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3479 Sheet 1"},{"id":390169,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3479/sim3479.xml","size":"53.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3479 Pamphlet xml"},{"id":390165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3479/coverthb.jpg"},{"id":390168,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":390166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3479/sim3479_pamphlet.pdf","text":"Report","size":"2.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3479 Pamphlet"},{"id":390170,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3479/images"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.72467041015625,\n 46.751153008636884\n ],\n [\n -103.14788818359375,\n 46.751153008636884\n ],\n [\n -103.14788818359375,\n 47.11873795272715\n ],\n [\n -103.72467041015625,\n 47.11873795272715\n ],\n [\n -103.72467041015625,\n 46.751153008636884\n ]\n ]\n ]\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
To assist resource managers and planners in developing informed strategies to address nitrogen loading to coastal water bodies of Long Island, New York, the U.S. Geological Survey and New York State Department of Environmental Conservation initiated a program to delineate areas contributing groundwater to coastal water bodies by assembling a comprehensive dataset of areas contributing groundwater, travel times, and groundwater discharges to streams, lakes, marine surface waters, and subsea discharge boundaries. Steady-state, 25-layer regional, three-dimensional finite-difference groundwater-flow models of average regional hydrologic conditions were used for particle-tracking analysis to delineate areas contributing groundwater to 843 water bodies. Two steady-state conditions were simulated: recent conditions from 2005 to 2015 and predevelopment conditions of about 1900. About 14 million particles were evenly distributed across the water table and tracked forward to discharge zones. Using a uniform porosity of 25 percent, simulated recent condition travel times ranged from less than 2 years to greater than 10,000 years and were visualized in 11 travel time intervals. About 85 percent of particle travel times from the water table to points of discharge are less than 100 years. Simulated particle-tracking ending zones represented 843 receiving water bodies, based on the New York State Department of Environmental Conservation water body inventory and priority water bodies list. Areal delineation of travel-time intervals and areas contributing groundwater to water bodies were generated and are summarized with total groundwater outflow for each water body.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215047","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., Casamassina, N.A., and Walter, D.A., 2021, Delineation of areas contributing groundwater and travel times to receiving waters in Kings, Queens, Nassau, and Suffolk Counties, New York: U.S. Geological Survey Scientific Investigations Report 2021–5047, 61 p., https://doi.org/10.3133/sir20215047.","productDescription":"Report: iv, 61 p.; 3 Tables; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108532","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389890,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5047/sir20215047_table1.3.csv","text":"Table 1.3","size":"27.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Marine subsystems, estuaries, and number of receiving water bodies on Long Island, New York, associated with New York State priority water bodies"},{"id":389888,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5047/sir20215047_table1.1.csv","text":"Table 1.1","size":"12.2 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Association of receiving water body index to New York State priority water body list database for water bodies on Long Island, New York"},{"id":389874,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5047/sir20215047.XML"},{"id":389876,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DKILJY","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to delineate areas contributing groundwater and travel times to receiving waters of Kings, Queens, Nassau, and Suffolk Counties, New York"},{"id":389889,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5047/sir20215047_table1.2.csv","text":"Table 1.2","size":"9.48 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Sum of groundwater outflows to receiving water bodies simulated by a flow model of regional hydrologic conditions from 2005 to 2015 for Long Island, New York"},{"id":389875,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5047/images/"},{"id":389872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5047/sir20215047.pdf","text":"Report","size":"92.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5047"},{"id":389871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5047/coverthb2.jpg"}],"country":"United States","state":"New York","county":"Kings County, Queens County, Nassau County, Suffolk County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.20166015624999,\n 40.51379915504413\n ],\n [\n -71.7572021484375,\n 40.51379915504413\n ],\n [\n -71.7572021484375,\n 41.21998578493921\n ],\n [\n -74.20166015624999,\n 41.21998578493921\n ],\n [\n -74.20166015624999,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Between July 2011 and February 2013, the City of Cedar Rapids observed water level declines in their horizontal collector wells approaching 11 meters. As a result, pumping from these production wells had to be halted, and questions were raised about the reliability of the alluvial aquifer under future drought conditions. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, completed a study to better understand the effects of drought stress on the Cedar River alluvial aquifer using a numerical groundwater flow model. Previously published groundwater flow models were combined with newly collected airborne, waterborne, down-hole, and land-based geophysical survey data and provided a detailed three-dimensional lithologic model of the Cedar River alluvial aquifer and surrounding area. An improved conceptual model for the groundwater flow system and a lithologic model were used to build and inform a numerical groundwater flow model capable of simulating water levels observed in the City of Cedar Rapids horizontal collector wells during the 2012 drought. Model performance was assessed primarily on the ability of the model to simulate water table elevation at six monitoring wells. Statistical tests were used to assess the numerical model during the calibration period, and results varied from satisfactory to unsatisfactory, likely because of stage changes in the Cedar River and production well withdrawal rates near monitoring wells. Simulated water levels during the 2012 drought indicated a depression near the horizontal collector wells, although simulated elevations at these locations and at monitoring wells were generally overestimated compared to measured values. The numerical groundwater flow model was modified to account for a decrease in seepage rate caused by low flow in the Cedar River and increased production. With seepage rate modification, model results improved; the simulated water table elevations were like those observed in horizontal collector and monitoring wells. Results demonstrated the ability of the model to simulate water levels observed in the horizontal collector wells during the 2012 drought when accounting for a decrease in infiltration from the Cedar River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215065","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Haj, A.E., Ha, W.S., Gruhn, L.R., Bristow, E.L., Gahala, A.M., Valder, J.F., Johnson, C.D., White, E.A., and Sterner, S.P., 2021, Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013: U.S. Geological Survey Scientific Investigations Report 2021–5065, 59 p., https://doi.org/10.3133/sir20215065.","productDescription":"Report: ix, 59 p.; Appendix; 3 Data Releases; Dataset","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-118762","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5065/coverthb.jpg"},{"id":390067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065"},{"id":390069,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS882S","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Airborne electromagnetic and magnetic survey data and inverted resistivity models, Cedar Rapids, Iowa, May 2017"},{"id":390070,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96CF4L5","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Cedar River alluvial aquifer near Cedar Rapids, Iowa"},{"id":390071,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXJDHX","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Geophysical data collected in the Cedar River floodplain, Cedar Rapids, Iowa, 2015–2017"},{"id":390072,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":390068,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065_appendix.pdf","text":"Poster","size":"3.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065 Appendix","linkHelpText":"— Geophysical methods used to better characterize surface water, alluvial aquifer, and bedrock aquifer interaction in the Cedar River Valley, Iowa"},{"id":390073,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.xml","size":"367 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5065 xml"},{"id":390074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5065/images"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.76759719848633,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 41.99139471889533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Los Angeles Coastal Plain (LACP) covers about 580 square miles and is the largest coastal plain of semiarid southern California. The LACP is heavily developed with mostly residential, commercial, and industrial land uses that rely heavily on groundwater for water supply. In 2010, the LACP was home to about 14 percent of California’s population, or about 5.4 million residents. The LACP is also a major commercial and industrial hub with industries including manufacturing, aerospace, entertainment, and tourism.
There has been a heavy reliance on groundwater from the LACP for many years. An average of 305,000 acre-feet per year (acre-ft/yr) of groundwater was used annually from the LACP from 1971 to 2015. The need to replenish the groundwater basins within the LACP was recognized as far back as the 1930s, when spreading grounds were first used to replenish groundwater basins and store water underground during times of water surplus to meet demands in times of shortage. Seawater intrusion resulting from freshwater pumping was first observed in the 1940s. As a result, injection of imported water through wells at what is now the West Coast Basin Barrier Project began on an experimental basis in 1951. Managed aquifer recharge from the spreading grounds and barrier wells is now a substantial component of the LACP’s groundwater supply. The average annual recharge from water spreading from 1971 to 2015 was about 120,000 acre-ft/yr, and the average annual injection into the barrier wells was about 33,000 acre-ft/yr. Other inflows include areal recharge, underflow from San Gabriel and San Fernando Valleys, and onshore flow from the ocean. The average annual recharge from these sources was 100,000 acre-feet (acre-ft) from 1971 to 2015. Additionally, cross-boundary flow from Orange County into the western Orange County subareas of the LACP was simulated as 48,000 acre-ft from 1971 to 2015.
This study, conducted in cooperation with the Water Replenishment District of Southern California (WRD), involved an assessment of the historical and present status of groundwater resources in the LACP and the development of tools to better understand the groundwater system. These efforts were built upon results from previous studies and incorporate new information and developments in modeling capabilities to provide a more detailed analysis of the aquifer systems.
This study includes a comprehensive compilation of geologic and hydrologic data (Chapter A), development of a chronostratigraphic model that provides a detailed description of the LACP aquifer systems (Chapter B), characterization of the groundwater hydrology of the LACP, including a down-hole analysis of grain size using lithologic and geophysical logs (Chapter C), and development and application of the Los Angeles Coastal Plain Groundwater-flow Model (LACPGM) to simulate past groundwater conditions, estimate groundwater-budget components and flow paths, and approximate future groundwater conditions under different scenarios (Chapter D).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215088","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Paulinski, S., ed., 2021, Development of a groundwater-simulation model in the Los Angeles Coastal Plain, Los Angeles County, California (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2021-5088, 489 p., https://doi.org/10.3133/sir20215088.","productDescription":"Report: xiii, 489 p.; Data Release","numberOfPages":"489","onlineOnly":"Y","ipdsId":"IP-023155","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389755,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H15ZAX","linkHelpText":"MODFLOW-USG model used to evaluate water management issues in the Los Angeles Coastal Plain, California"},{"id":389754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5088/sir20215088_v1.1.pdf","text":"Report","size":"66 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5088/covrthb_.jpg"},{"id":416877,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5088/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":436182,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJD4IE","text":"USGS data release","linkHelpText":"MODFLOW-6 model to update and extend the Los Angeles Coastal Plain Groundwater Model"},{"id":500446,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111785.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Los Angeles County","otherGeospatial":"Los Angeles Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90802001953125,\n 33.59860671494885\n ],\n [\n -117.59490966796875,\n 33.876116579321206\n ],\n [\n -117.82012939453125,\n 34.14249823152873\n ],\n [\n -118.20327758789062,\n 34.23337699755914\n ],\n [\n -118.53973388671874,\n 34.03672867489511\n ],\n [\n -118.41476440429686,\n 33.80083235326659\n ],\n [\n -118.24722290039061,\n 33.72776616734189\n ],\n [\n -117.90802001953125,\n 33.59860671494885\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 2021; Version 1.1: May 2023","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
A numerical groundwater flow model of the Hualapai Valley Basin in northwestern Arizona was developed to assist water-resource managers in understanding the potential effects of projected groundwater withdrawals on groundwater levels in the basin. The Hualapai Valley Hydrologic Model (HVHM) simulates the hydrologic system for the years 1935 through 2219, including future withdrawal scenarios that simulate large-scale agricultural expansion with and without enhanced groundwater recharge from potential new infiltration basin projects. HVHM is a highly parameterized model (75,586 adjustable parameters) capable of simulating grid-scale variability in aquifer properties (for example, conductivity, specific yield, and specific storage) and system stresses (for instance, natural recharge and groundwater withdrawals). Parameter estimation and uncertainty quantification were performed using an iterative ensemble smoother software (PESTPP-IES) to produce an ensemble of models fit to historical data. Results via the future withdrawal scenario from this ensemble indicate that mean groundwater level will decline at wells in the Kingman subbasin 87 to 128 feet by the year 2050 and 204 to 241 feet by the year 2080. Mean groundwater level is expected to decline at wells in the Hualapai subbasin between 44 and 210 feet by 2050 and between 107 and 350 feet by 2080. The enhanced recharge scenario results show potential for these declines to be partially mitigated in the Kingman subbasin by between 8 and 23 feet in 2050 and between 23 and 43 feet in 2080. The enhanced recharge scenario has no simulated effect on groundwater levels in the Hualapai subbasin. All planned enhanced infiltration projects are located in the Kingman subbasin, which is simulated to become hydraulically disconnected from the Hualapai subbasin owing to groundwater-level declines before 2050. Mean depth to water in the Kingman subbasin as simulated in the future withdrawal scenario will exceed 1,200 feet between the years 2155 and 2214 (median year 2171). In the future withdrawal plus enhanced recharge scenario, mean depth to water in the Kingman subbasin exceeds 1,200 feet between the years 2163 and 2207 (median year 2180), except for one model realization in which the subbasin does not reach an mean depth to water of 1,200 feet by the end of forecast simulation (year 2220). Simulated dewatering of the basin margins reduces scenario pumping rates by as much as 7 percent in 2029 and 12 percent in 2079 below specified rates. Forecasts of groundwater-level declines are based on the reduced simulated pumping rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215077","collaboration":"Prepared in cooperation with Mohave County and the City of Kingman","usgsCitation":"Knight, J.E., Gungle, B., and Kennedy, J.R., 2021, Assessing potential groundwater-level declines from future withdrawals in the Hualapai Valley, northwestern Arizona: U.S. Geological Survey Scientific Investigations Report, 63 p., https://doi.org/10.3133/sir20215077.","productDescription":"Report: vii, 63 p.; Data Release","numberOfPages":"63","onlineOnly":"Y","ipdsId":"IP-118946","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436183,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJRMSQ","text":"USGS data release","linkHelpText":"Repeat microgravity data from the Hualapai Valley, Mohave County, Arizona, 2008-2019"},{"id":389758,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20125275","text":"Scientific Investigations Report 2012-5275","linkHelpText":"— Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona"},{"id":389739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5077/sir20215077.pdf","text":"Report","size":"26 MB"},{"id":389759,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20135122","text":"Scientific Investigations Report 2013-5122","linkHelpText":"— Preliminary groundwater flow model of the basin-fill aquifers in Detrital, Hualapai, and Sacramento Valleys, Mohave County, northwestern Arizona"},{"id":389740,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9017DI9","linkHelpText":"Data release for transient groundwater model of the Hualapai Valley Groundwater Basin, Mohave County, Arizona"},{"id":389738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5077/covrthb.jpg"},{"id":389756,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20075182","text":"Scientific Investigations Report 2007-5182","linkHelpText":"— Ground-Water Occurrence and Movement, 2006, and Water-Level Changes in the Detrital, Hualapai, and Sacramento Valley Basins, Mohave County, Arizona"},{"id":389757,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20115159","text":"Scientific Investigations Report 2011-5159","linkHelpText":"— Groundwater budgets for Detrital, Hualapai, and Sacramento Valleys, Mohave County, Arizona, 2007-08"}],"country":"United States","state":"Arizona","otherGeospatial":"Hualapai Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.5,\n 36\n ],\n [\n -113.5,\n 36\n ],\n [\n -113.5,\n 35\n ],\n [\n -114.5,\n 35\n ],\n [\n -114.5,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Nutrients, harmful algal blooms, and synthetic chemicals like per- and polyfluoroalkyl substances (PFAS) and 1,4-dioxane threaten Long Island’s water resources by affecting the quality of drinking water and ecologically sensitive habitats that support the diverse wildlife throughout the island. Understanding the occurrence, fate, and transport of these potentially harmful chemicals is critical to protect these vital resources. The U.S. Geological Survey (USGS) is collecting and analyzing data to support informed water-resource management decisions. This fact sheet introduces ongoing efforts and future areas of study aimed to help water professionals develop a comprehensive science strategy to address contamination of the Long Island aquifer system, the sole source of drinking water for nearly 3 million people. These studies include surface and groundwater collection and groundwater flow modeling. Funding for the data collection has been provided by the USGS, New York State Department of Environmental Conservation, New York City Department of Environmental Protection, Suffolk County Water Authority, Nassau County Department of Public Works, State and local agencies, and Tribal and Federal partners. Without the foresight and long-term commitment of these funding partners, evaluating sustainability and planning for future water needs would not be possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213044","usgsCitation":"Breault, R.F., Masterson, J.P., Schubert, C.E., and Herdman, L.M., 2021, Managing water resources on Long Island, New York, with integrated, multidisciplinary science: U.S. Geological Survey Fact Sheet 2021–3044, 4 p., https://doi.org/10.3133/fs20213044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131602","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389579,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3044/fs20213044.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3044"},{"id":389578,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3044/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.538851525354666\n ],\n [\n -73.7677001953125,\n 40.538851525354666\n ],\n [\n -73.1304931640625,\n 40.60561205826018\n ],\n [\n -72.5537109375,\n 40.76806170936614\n ],\n [\n -71.9549560546875,\n 40.97575093157534\n ],\n [\n -71.83959960937499,\n 41.10832999732831\n ],\n [\n -72.1417236328125,\n 41.24064190269475\n ],\n [\n -72.8338623046875,\n 41.10005163093046\n ],\n [\n -73.5205078125,\n 40.96330795307353\n ],\n [\n -73.883056640625,\n 40.83043687764923\n ],\n [\n -74.06982421875,\n 40.713955826286046\n ],\n [\n -74.0863037109375,\n 40.61812224225511\n ],\n [\n -74.0478515625,\n 40.538851525354666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length (PRL) of at least 36 years, 183 streamgages were selected for use in the study.
Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage (AVPnew, which corresponds to the mean square error [MSE] of 0.11) was selected. The AVPnew corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215015","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2021, Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2021–5015, 38 p., https://doi.org/10.3133/sir20215015.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114558","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5015/coverthb.jpg"},{"id":389080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5015/sir20215015.pdf","text":"Report","size":"6.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5015"},{"id":389081,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGAL0D","text":"USGS data release","linkHelpText":"Regional flood skew for parts of the mid-Atlantic region (hydrologic unit 02) in eastern New York and Pennsylvania"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.56396484375,\n 39.740986355883564\n ],\n [\n -75.03662109375,\n 40.04443758460856\n ],\n [\n -74.86083984375,\n 40.34654412118006\n ],\n [\n -75.16845703124999,\n 40.713955826286046\n ],\n [\n -74.970703125,\n 41.062786068733026\n ],\n [\n -74.619140625,\n 41.393294288784865\n ],\n [\n -74.1796875,\n 41.65649719441145\n ],\n [\n -73.54248046875,\n 42.17968819665961\n ],\n [\n -73.23486328124999,\n 43.02071359427862\n ],\n [\n -73.32275390625,\n 43.628123412124616\n ],\n [\n -73.45458984375,\n 43.77109381775651\n ],\n [\n -73.80615234375,\n 43.35713822211053\n ],\n [\n -74.28955078125,\n 43.14909399920127\n ],\n [\n -74.77294921875,\n 42.79540065303723\n ],\n [\n -75.34423828125,\n 42.73087427928485\n ],\n [\n -75.82763671875,\n 42.68243539838623\n ],\n [\n -76.35498046875,\n 42.68243539838623\n ],\n [\n -76.88232421875,\n 42.68243539838623\n ],\n [\n -77.23388671874999,\n 42.45588764197166\n ],\n [\n -77.607421875,\n 42.19596877629178\n ],\n [\n -77.607421875,\n 42.01665183556825\n ],\n [\n -77.6513671875,\n 41.409775832009565\n ],\n [\n -77.80517578125,\n 41.1290213474951\n ],\n [\n -77.9150390625,\n 40.53050177574321\n ],\n [\n -78.11279296875,\n 40.16208338164617\n ],\n [\n -78.46435546875,\n 39.67337039176558\n ],\n [\n -75.56396484375,\n 39.740986355883564\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey mined data from a variety of national and state agencies including USGS, Oregon Water Resources Department, National Oceanic and Atmospheric Administration, Washington Department of Ecology, Pacific Northwest National Laboratory, Portland State University, and U.S. Army Corps of Engineers. A comprehensive dataset of streamflow, stage, and tidal elevations for the Lower Columbia River basin was compiled. Data were compiled from gaging stations in Oregon and Washington along the Columbia River from Astoria to The Dalles and along the Willamette River from Salem to Portland. Tidal gages along the Washington, Oregon, and California coasts were also compiled. Seasonal maximum values were calculated for both streamflow and stage for the winter (November–March) and spring (April–July) flow seasons, as well as for the full water year when underlying data were available. The aggregated datasets are available at https://doi.org/10.5066/P9R6RT0Z.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201138","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","usgsCitation":"Boudreau, C.L., Stewart, M.A., and Stonewall, A.J., 2021, Historical streamflow and stage data compilation for the Lower Columbia River, Pacific Northwest: U.S. Geological Survey Open-File Report 2020–1138, 50 p., https://doi.org/10.3133/ofr20201138.","productDescription":"Report: viii, 50 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101122","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":389696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1138/coverthb.jpg"},{"id":389697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1138/ofr20201138.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1138"},{"id":389698,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R6RT0Z","text":"USGS data release","description":"USGS Data release","linkHelpText":"Historical streamflow and stage data for the lower Columbia River basin and the coasts of Washington, Oregon, and northern California"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Lower Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.63964843750001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 41.672911819602085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Avian populations must mount effective immune responses upon exposure to environmental stressors such as avian influenza and xenobiotics. Although multiple immune assays have been tested and applied to various avian species, antibody-mediated immune responses in non-model avian species are not commonly reported due to the lack of commercially available species-specific antibodies. The objectives of the present study were to advance methods for studying wild bird immune responses and to apply these to the evaluation of cytological responses after exposure of American kestrels, Falco sparverius, to a commercial flame retardant mixture containing isopropylated triarylphosphate isomers (ITP). Hatchlings were gavaged daily with safflower oil or 1.5 ug/g bw/day of ITP suspended in safflower oil, then bled on days 9, 17, and 21. The ITP treatment group (n = 18) and a subset of controls (Poly I:C treatment group; n = 10) were injected on days 9 and 15 with a synthetic analog of viral double-stranded RNA, polyinosinic:polycytidylic acid (Poly I:C), a toll-like receptor ligand and synthetic viral mimic, and responses compared to a sham injected control group (n = 8). The hypotheses tested whether kestrels showed immunological differences among treatment groups, genetic sex, and/or white blood cell (WBC) subpopulation type over time. A flow cytometry (FCM) gating strategy categorized heterophils (H), lymphocytes (L), and monocytes (M) and their proportions, and measured relative fluorescence in response to anti-chicken CD4 binding. Fluorescent cell surfaces and some granular/vacuolar inclusions were visualized by epifluorescence microscopy. A fourth subpopulation with higher levels of granularity than M but less than H became increasingly apparent with time and was gated along with the H subpopulation; its frequency of occurrence was lowest in the ITP group (P = 0.0023). The percentages of cells differed among treatment groups, days, and sexes (P = 0.0001). For both sexes, percentages of H and L were higher than M in control and Poly I:C. In the ITP group, L percentages were higher than H and M (P = 0.0457), and H and L were higher than M on days 9 and 21 (P = 0.0001). The ratios of H:L and H:WBC, indicators of robust immunity, were also higher on days 9 and 21 than on 17 (P = 0.0079). For each sex, the highest levels of activity measured by FCM geometric means (GEO) of fluorescence (indicative of antibody binding) were observed on day 9 (P = 0.0001 female, and P = 0.0011 male) in H over both L and M (P < 0.0001 for each). In males, GEO of the Poly I:C group was higher than that of the ITP group (P = 0.0374), with no difference observed among females over all days. By using a FCM algorithm for population comparisons of fluorescence to investigate binding within H, the T(x) scores indicated higher fluorescence in control and Poly I:C groups over ITP (P = 0.0001). Unlike chickens, Gallus gallus, which express CD4 primarily on L, kestrels bound the commercial antibody primarily within the gated H subpopulation, suggesting an immunophenotypic difference between taxa, despite a ~60% identity of Falco CD4 amino acid sequences with chicken CD4. The emergent cell subset within the gated H presented dendritic-like cell (DLC) morphological and functional properties, apparently serving as an effector cell. This study adds interpretive context to ecological investigations of infection and of potential immunomodulation by emerging compounds, whereby the early innate responses are mediated by the various cell subsets serving as useful quantitative markers of immunological condition. Data showed that dietary exposure to ITP was immunosuppressive for male and female kestrels over the course of the experiment, reducing DLC frequency compared to the Poly I:C controls. Heterophils and DLC were important in facilitating innate immunological responses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envint.2021.106779","usgsCitation":"Jenkins, J., Baudoin, B.A., Johnson, D., Fernie, K.J., Stapelton, H.M., and Karouna-Renier, N., 2021, Establishment of baseline cytology metrics in nestling American kestrels (Falco sparverius): Immunomodulatory effects of the flame retardant isopropylated triarylphosphate isomers: Environment International, v. 157, 106779, 15 p.; Data Release, https://doi.org/10.1016/j.envint.2021.106779.","productDescription":"106779, 15 p.; Data Release","ipdsId":"IP-116785","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450748,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envint.2021.106779","text":"Publisher Index Page"},{"id":436196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P7ZTMU","text":"USGS data release","linkHelpText":"Laboratory analysis assessing immune response after flame retardant exposure in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":436195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SGX37F","text":"USGS data release","linkHelpText":"Discerning innate immunity in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":390889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417862,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9sgx37f"}],"volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":206575,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baudoin, Brooke A 0000-0003-2874-1604","orcid":"https://orcid.org/0000-0003-2874-1604","contributorId":267938,"corporation":false,"usgs":true,"family":"Baudoin","given":"Brooke","email":"","middleInitial":"A","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernie, Kim J.","contributorId":211241,"corporation":false,"usgs":false,"family":"Fernie","given":"Kim","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":825645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stapelton, Heather M. 0000-0002-9995-6517","orcid":"https://orcid.org/0000-0002-9995-6517","contributorId":267940,"corporation":false,"usgs":false,"family":"Stapelton","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":825646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":825647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224266,"text":"sir20215062 - 2021 - Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","interactions":[],"lastModifiedDate":"2021-09-21T11:32:14.387182","indexId":"sir20215062","displayToPublicDate":"2021-09-20T09:49:44","publicationYear":"2021","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":"2021-5062","displayTitle":"Development of Regression Equations for the Estimation of the Magnitude and Frequency of Floods at Rural, Unregulated Gaged and Ungaged Streams in Puerto Rico Through Water Year 2017","title":"Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","docAbstract":"The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area.
The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection.
Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study.
The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats (https://streamstats.usgs.gov/ss/). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215062","usgsCitation":"Ryan, P.J., Gotvald, A.J., Hazelbaker, C.L., Veilleux, A.G., and Wagner, D.M., 2021, Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017: U.S. Geological Survey Scientific Investigations Report 2021–5062, 37 p., https://doi.org/10.3133/sir20215062.","productDescription":"Report: v, 37 p.; Appendix Tables: 3; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123614","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":389343,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91XT14B","text":"USGS data 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analysis"},{"id":389340,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.xlsx","text":"Appendix 1 (.xlsx format)","size":"30.9 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389338,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.xlsx","text":"Appendix Table 2.1 (.xlsx format)","size":"19.6 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389339,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.csv","text":"Appendix 1 (.csv format)","size":"20.7 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389341,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.csv","text":"Appendix 3 (.csv format)","size":"80.4 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"},{"id":389342,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.xlsx","text":"Appendix 3 (.xlsx format)","size":"134 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"}],"country":"United States","otherGeospatial":"Puerto 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U.S. Geological Survey
4446 Pet Lane, Suite 108
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The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.
This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.
Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.
The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.
Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.
Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.
The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.
More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.
The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were investigated and updated. The MLLR models of drought stream-flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215084","usgsCitation":"Austin, S.H., 2021, Forecasting drought probabilities for streams in the northeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5084, 11 p., https://doi.org/10.3133/sir20215084.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"11","ipdsId":"IP-113685","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":388741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5084/sir20215084.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5084"},{"id":388740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5084/coverthb.jpg"},{"id":388742,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.
To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.
The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [R2]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.
The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.
The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (R2=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (R2=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (R2=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.
The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215093","usgsCitation":"McShane, R.R., and Eddy-Miller, C.A., 2021, A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17: U.S. Geological Survey Scientific Investigations Report 2021–5093, 29 p., https://doi.org/10.3133/sir20215093.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-117330","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388893,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCP1AE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17"},{"id":388895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.xml","text":"Report","size":"219 kB","linkFileType":{"id":8,"text":"xml"}},{"id":388896,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5093/images"},{"id":388894,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5093/coverthb.jpg"},{"id":388892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5093"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.90972900390625,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.09618442380296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The Mars volcano, Olympus Mons, is probably the best known extraterrestrial volcano. The summit forms a nested caldera with six overlapping collapse pits that collectively measure ~65 x ~80 kilometers (km). Numerous wrinkle ridges and graben occur on the caldera floor, and topographic data indicate >1.2 km of elevation change since the formation of the floor as a series of lava lakes. The paths of lava flows on the south and southeast flanks do not conform to present-day topography. Mapping at a scale of 1:200,000 shows that the summit area displays a complex volcanic history that has numerous similarities to terrestrial shield volcanoes. Pangboche crater is a large (~10-km-diameter) crater of impact origin that lies on the south flank of the caldera and, because of the elevation and lack of volatiles, it displays numerous features more similar to fresh lunar craters than to impact craters on Mars.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3470","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","productDescription":"Report: iv, 6 p.; Metadata; Read Me; 1 Sheet: 38.06 x 40.11 inches","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-107079","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436209,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C2UHD","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3470 Geologic Map of Olympus Mons Caldera, Mars"},{"id":388840,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_gis_files.zip","text":"SIM 3470 GIS Files","size":"260 MB","linkFileType":{"id":6,"text":"zip"}},{"id":388595,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_sheet.pdf","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388596,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.txt","size":"20 KB","linkFileType":{"id":2,"text":"txt"}},{"id":388592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3470/covrthb.jpg"},{"id":388593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_pamphlet.pdf","text":"Pamphlet to Accompany Map Sheet","size":"1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388594,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_readme.docx","text":"Read Me","size":"25 KB docx"},{"id":405427,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P95C2UHD","text":"Interactive map","description":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","linkHelpText":"- Geologic Map of Olympus Mons Caldera, Mars, 1:200K. Mouginis-Mark (2021)"},{"id":388598,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.xml","size":"20 KB","linkFileType":{"id":8,"text":"xml"}}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR.
A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report.
An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet.
The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data.
With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205150","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Koczot, K.M., Risley, J.C., Gronberg, J.M., Donovan, J.M., and McPherson, K.R., 2021, Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013: U.S. Geological Survey Scientific Investigations Report 2020–5150, 61 p., https://doi.org/10.3133/sir20205150.","productDescription":"Report: ix, 61 p.; 1 Figure: 16.0 x 10.0 inches; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-028665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":388739,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KFR","linkHelpText":"Archive of Merced River Basin Precipitation-Runoff Modeling System, with forecasting, climate-file preparation, and data-visualization tools"},{"id":388738,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150_fig11_sheet.pdf","text":"Figure 11 (16\" x 10\" sheet)","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Physical architecture of the Merced River Basin Precipitation-Runoff Modeling System."},{"id":388737,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5150/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Merced River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.3055419921875,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 36.88401445049676\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Two datasets containing the first complete estimates of reach-scale nutrient, water use, dissolved oxygen, and pH conditions for the Pacific drainages of the United States were created to help inform water-quality management decisions in that region. The datasets were developed using easily obtainable watershed data, most of which have not been available until recently, and the techniques that were used provide a framework for integrating watershed data to assess water-quality impairment across other large hydrologic regions in the United States. These datasets were used to summarize regional nutrient and water-use conditions within impaired water bodies and to summarize regional dissolved oxygen concentrations and pH conditions for free-flowing stream reaches. Two examples are also presented that show how the datasets can be applied to specific water-quality management issues: (1) nutrient conditions in water bodies that have recently experienced problems with harmful algal blooms; and (2) dissolved oxygen and pH conditions in stream reaches likely to be populated by steelhead trout (Oncorhynchus mykiss irideus) during their summer run. The nutrient and water-use estimates could help inform actions aimed at managing water-quality conditions in impaired water bodies while the dissolved oxygen and pH predictions could be useful as screening tools to identify water bodies experiencing potential impairment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215087","programNote":"National Water Quality Program","usgsCitation":"Wise, D.R., 2021, Using regional watershed data to assess water-quality impairment in the Pacific Drainages of the United States: U.S. Geological Survey Scientific Investigations Report 2021–5087, 29 p., https://doi.org/10.3133/sir20215087.","productDescription":"vii, 29 p.","onlineOnly":"Y","ipdsId":"IP-123766","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436221,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B3BQOW","text":"USGS data release","linkHelpText":"Reach-scale estimates of nutrient, water use, dissolved oxygen, and pH conditions in the Pacific drainages of the United States"},{"id":388699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5087/coverthb.jpg"},{"id":388700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5087/sir20215087.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5087"}],"country":"United States","state":"California, Idaho, Montana, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 49.03786794532644\n ],\n [\n -123.04687499999999,\n 48.545705491847464\n ],\n [\n -124.541015625,\n 48.48748647988415\n ],\n [\n -124.93652343749999,\n 48.28319289548349\n ],\n [\n -124.23339843749999,\n 46.76996843356982\n ],\n [\n -124.18945312500001,\n 45.42929873257377\n ],\n [\n -124.71679687499999,\n 43.83452678223682\n ],\n [\n -124.76074218749999,\n 42.5530802889558\n ],\n [\n -124.49707031249999,\n 41.47566020027821\n ],\n [\n -124.71679687499999,\n 40.34654412118006\n ],\n [\n -124.18945312500001,\n 39.639537564366684\n ],\n [\n -123.96972656249999,\n 38.993572058209466\n ],\n [\n -123.00292968749999,\n 38.03078569382294\n ],\n [\n -122.03613281249999,\n 36.27970720524017\n ],\n [\n -121.025390625,\n 35.02999636902566\n ],\n [\n -120.4541015625,\n 34.34343606848294\n ],\n [\n -118.91601562499999,\n 34.05265942137599\n ],\n [\n -117.68554687499999,\n 33.46810795527896\n ],\n [\n -117.20214843749999,\n 32.54681317351514\n ],\n [\n -116.4111328125,\n 32.62087018318113\n ],\n [\n -116.54296874999999,\n 33.211116472416855\n ],\n [\n -117.6416015625,\n 34.08906131584994\n ],\n [\n -118.0810546875,\n 34.23451236236987\n ],\n [\n -118.21289062499999,\n 35.96022296929667\n ],\n [\n -118.91601562499999,\n 36.70365959719456\n ],\n [\n -119.970703125,\n 37.92686760148135\n ],\n [\n -120.41015624999999,\n 38.61687046392973\n ],\n [\n -120.58593749999999,\n 39.470125122358176\n ],\n [\n -120.58593749999999,\n 40.27952566881291\n ],\n [\n -121.11328124999999,\n 41.07935114946899\n ],\n [\n -121.6845703125,\n 42.35854391749705\n ],\n [\n -121.59667968749999,\n 42.94033923363181\n ],\n [\n -121.46484375,\n 43.96119063892024\n ],\n [\n -121.11328124999999,\n 44.77793589631623\n ],\n [\n -120.2783203125,\n 44.33956524809713\n ],\n [\n -119.4873046875,\n 43.8028187190472\n ],\n [\n -118.69628906249999,\n 42.5530802889558\n ],\n [\n -118.0810546875,\n 41.83682786072714\n ],\n [\n -117.158203125,\n 41.07935114946899\n ],\n [\n -116.1474609375,\n 40.78054143186033\n ],\n [\n -114.2138671875,\n 41.343824581185686\n ],\n [\n -113.466796875,\n 42.00032514831621\n ],\n [\n -111.884765625,\n 42.35854391749705\n ],\n [\n -111.005859375,\n 42.4234565179383\n ],\n [\n -109.6875,\n 42.5530802889558\n ],\n [\n -109.4677734375,\n 43.26120612479979\n ],\n [\n -109.951171875,\n 44.05601169578525\n ],\n [\n -110.7861328125,\n 44.55916341529182\n ],\n [\n -112.06054687499999,\n 44.59046718130883\n ],\n [\n -112.8515625,\n 44.43377984606822\n ],\n [\n -113.73046875,\n 45.24395342262324\n ],\n [\n -114.0380859375,\n 45.460130637921004\n ],\n [\n -113.203125,\n 45.73685954736049\n ],\n [\n -112.236328125,\n 46.40756396630067\n ],\n [\n -112.19238281249999,\n 47.39834920035926\n ],\n [\n -113.64257812499999,\n 48.980216985374994\n ],\n [\n -122.958984375,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Black River flows through southeast Missouri and northeast Arkansas to its confluence with the White River in Arkansas. The U.S. Army Corps of Engineers operates Clearwater Dam on the Black River and a series of dams in the White River Basin primarily for flood control. In this study, the hydrology and geomorphology of the Black River are examined through an analysis of annual mean and peak discharges at streamgages, a specific stage analysis of stage and discharge at streamgages, and an examination of bathymetric data and aerial imagery. Five streamgages on the Black River were analyzed, in addition to four streamgages on Black River tributaries and one streamgage on the White River, located just downstream from the Black River confluence. The analyses indicated that regulation of discharges at the flood-control dams caused a decrease in the magnitude and variability of the peak discharges at several of the analyzed gages on the Black and White Rivers. Conversely, peak discharges on the Black River have been increasing since water year 2000, though this is not matched by an increase in peak discharges on the White River for the same time period. The specific stage analyses and the available morphologic data generally did not indicate pronounced changes in stage-discharge relations at streamgages on the Black River, with the exception of the gages nearest to Clearwater Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215067","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"LeRoy, J.Z., Huizinga, R.J., Heimann, D.C., Lindroth, E.M., and Doyle, H.F., 2021, Historical hydrologic and geomorphic conditions on the Black River and selected tributaries, Arkansas and Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5067, 72 p., https://doi.org/10.3133/sir20215067.","productDescription":"Report: ix, 72 p.; Appendix; Dataset","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114034","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":388646,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5067/images","description":"SIR 2021–5067 Images"},{"id":388645,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.xml","text":"Report","size":"298 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5067"},{"id":388644,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5067/coverthb.jpg"},{"id":388642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.pdf","text":"Report","size":"7.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5067"},{"id":388643,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5067/downloads","text":"Appendix Tables 1.0 through 1.10 (.csv and .xlsx formats)"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Black River and selected tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.51611328125,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 35.585851593232384\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.
The coefficient of determination (R2) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.
Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.71484375,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 25.005972656239187\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated.
Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method.
Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun.
Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours.
Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215066","collaboration":"Prepared in cooperation with Eagle River Watershed Council","usgsCitation":"Richards, R.J., and Henneberg, M.F., 2021, Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5066, 36 p., \nhttps://doi.org/ 10.3133/ sir20215066.","productDescription":"Report: viii, 36 p.; 3 Databases","onlineOnly":"Y","ipdsId":"IP-116765","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":388128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5066/coverthb.jpg"},{"id":388129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5066/sir20215066.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5066"},{"id":388130,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":388131,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS 09065500 Gore Creek at upper Station, near Minturn, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":388132,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS 09063000 Eagle River at Redcliff, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"Colorado","county":"Eagle County","otherGeospatial":"Eagle River 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107381","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Roman, D., Mastin, L.G., Anderson, K.R., Damby, D., Itikarai, I., Mulina, K., Saunders, S., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea: Journal of Geophysical Research, v. 420, 107381, 14 p., https://doi.org/10.1016/j.jvolgeores.2021.107381.","productDescription":"107381, 14 p.","ipdsId":"IP-131054","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":451112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://hdl.handle.net/11603/26623","text":"Publisher Index Page"},{"id":389733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Papua New 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}\n ]\n}","volume":"420","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, 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regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011","interactions":[],"lastModifiedDate":"2021-09-21T11:36:03.273884","indexId":"sir20205142","displayToPublicDate":"2021-08-19T15:56:48","publicationYear":"2021","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":"2020-5142","displayTitle":"Regional Regression Equations Based on Channel-Width Characteristics to Estimate Peak-Flow Frequencies at Ungaged Sites in Montana Using Peak-Flow Frequency Data through Water Year 2011","title":"Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011","docAbstract":"The U.S. Geological Survey, in cooperation with the Montana Department of Transportation, developed regression equations based on channel width to estimate peak-flow frequencies at ungaged sites in Montana. The equations are based on peak-flow data at streamgages through September 2011 (end of water year 2011), and channel widths measured in the field and from aerial photographs.
Active-channel width and bankfull width (channel widths) were measured in the field at 64 sites across Montana in 2017. Channel widths also were measured near 515 streamgages from aerial photographs. These new channel-width data, along with more than 438 historical channel-width measurements, are published in a separate data release.
Regression equations were developed using generalized least squares regression or weighted least squares regression. The channel-width regression equations can be used to estimate peak-flow frequencies (peak-flow magnitudes associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites in each of the eight hydrologic regions in Montana. Methods are presented for weighting estimates from the channel-width equations with estimates from equations using basin characteristics. The weighting technique can be used to reduce the standard error of prediction relative to that obtained using a single method. Several example problems covering a range of estimation scenarios also are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205142","collaboration":"Prepared in cooperation with Montana Department of Transportation","usgsCitation":"Chase, K.J., Sando, R., Armstrong, D.W., and McCarthy, P., 2021, Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011 (ver. 1.1, September 2021): U.S. Geological Survey Scientific Investigations Report 2020–5142, 49 p., https://doi.org/10.3133/sir20205142.","productDescription":"Report: vi, 49 p.; Data Release; Dataset; Version History","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-102009","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":436235,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D7MEB1","text":"USGS data 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\"}}]}","edition":"Version 1.0: August 19, 2021; Version 1.1: September 20, 2021","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
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