Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 10 bridges at 9 highway crossings of the Missouri River between Kansas City and St. Louis, Missouri, from May 22 to 31, 2017. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,550 to 1,840 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were observed at most surveyed piers. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although exposure of parts of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 4 and 5 at structure K0999 on Missouri State Highway 41 at Miami, Mo.; piers 2 and 3 at structure G0069 on Missouri State Highway 240 at Glasgow, Mo.; and pier 5 at structure A4574 on Missouri State Highway 5 at Boonville, Mo. At these structures, the bed-material thickness between the bottom of the scour hole and bedrock was less than 6 feet.
Pier size, nose shape, and alignment to flow had a profound effect on the size of the scour hole observed for a given pier. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several structures had piers that were skewed to primary approach flow, and scour holes near these piers generally indicated deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow. A riprap blanket constructed in 2015 around pier 4 of structures L0550 and A4497 on U.S. Highway 54 at Jefferson City, Mo., effectively mitigates the scour observed near those piers in previous surveys.
Previous bathymetric surveys exist for all the sites examined in this study. Bathymetric surfaces from a nonflood survey in 2013 and a flood survey in July 2011 at most of the sites are compared to the 2017 survey surfaces. The average channel-bed elevation at structure A4574 was remarkably similar in all three surveys and higher than what might be implied by a trendline along the reach between Kansas City and St. Louis, which may indicate this site is at or near a local feature that controls sediment deposition and scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205018","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 22–31, 2017: U.S. Geological Survey Scientific Investigations Report 2020–5018, 104 p., https://doi.org/10.3133/sir20205018.\n","productDescription":"Report: x, 104 p.; Data Releases","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-110170","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":373939,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L6GW57","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, March 2010 through May 2017"},{"id":372633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5018/coverthb.jpg"},{"id":373938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5018/sir20205018.pdf","text":"Report","size":"23.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5018"},{"id":373940,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94M4US7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, January 2010 through May 2017"}],"country":"United States","state":"Missouri","city":"Kansas City, St. Louis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.142822265625,\n 38.805470223177466\n ],\n [\n -91.12060546875,\n 38.92522904714054\n ],\n [\n -92.16430664062499,\n 39.08743603215884\n ],\n [\n -93.2958984375,\n 39.04478604850143\n ],\n [\n -94.119873046875,\n 39.12153746241925\n ],\n [\n -94.68017578125,\n 39.198205348894795\n ],\n [\n -94.63623046875,\n 38.91668153637508\n ],\n [\n -94.04296874999999,\n 38.865374851611634\n ],\n [\n -93.109130859375,\n 38.79690830348427\n ],\n [\n -92.274169921875,\n 38.85682013474361\n ],\n [\n -91.91162109375,\n 38.81403111409755\n ],\n [\n -91.29638671875,\n 38.69408504756833\n ],\n [\n -90.648193359375,\n 38.659777730712534\n ],\n [\n -90.186767578125,\n 38.57393751557591\n ],\n [\n -90.142822265625,\n 38.805470223177466\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Groundwater in eastern Abu Dhabi in the United Arab Emirates is an important resource that is widely used for irrigation and domestic supplies in rural areas. The U.S. Geological Survey and the Environment Agency—Abu Dhabi cooperated on an investigation to integrate existing hydrogeologic information and to answer questions about regional groundwater resources in Abu Dhabi by developing a numerical groundwater flow model based on MODFLOW–2005 software. The groundwater flow model developed in this investigation provides an improved understanding of groundwater conditions in the eastern region of the Emirate of Abu Dhabi. The flow model simulates steady-state predevelopment conditions from before the rapid growth of modern pumping in the 1980s and was calibrated with 1,342 groundwater-level observations by use of automated and manual calibration techniques. The calibrated model provides good accuracy, with a mean error of 0.50 meters and a standard error of 5.92 meters for simulated groundwater levels. The results of the regional water budget simulation show that gap recharge, which is groundwater inflow through mountain-front gap alluvium, is the greatest source of water to the aquifer. In the base simulation scenario, gap recharge represents 80 percent of total inflow (119,470 of 149,403 cubic meters per day) and the greatest outflow from the aquifer is from evapotranspiration (93 percent of total outflow). Model scenario and sensitivity results reveal a need for data that more thoroughly and more accurately describe aquifer hydraulic conductivity, inflow to the aquifer from the Oman Mountains, and recharge from precipitation on the piedmont. Additional long-term aquifer pumping test observations would improve understanding of aquifer hydraulic conductivity, which would also improve model accuracy. Future studies can modify the model to understand the effect of land-use change and water use on groundwater supplies and simulate more complex groundwater flow conditions in a predictive mode.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185158","collaboration":"Prepared in cooperation with the Environment Agency—Abu Dhabi","usgsCitation":"Eggleston, J.R., Mack, T.J., Imes, J.L., Kress, W., Woodward, D.W., and Bright, D.J., 2020, Hydrogeologic framework and simulation of predevelopment groundwater flow, eastern Abu Dhabi Emirate, United Arab Emirates: U.S. Geological Survey Scientific Investigations Report 2018–5158, 48 p., https://doi.org/10.3133/sir20185158.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088658","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":373295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5158/coverthb.jpg"},{"id":373296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5158/sir20185158.pdf","text":"Report","size":"6.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5158"},{"id":373297,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZWZISB","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 Groundwater Flow Model to Simulate Predevelopment Groundwater Flow in the Eastern Abu Dhabi Emirate, United Arab Emirates"}],"country":"United Arab Emirates","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[51.57952,24.2455],[51.75744,24.29407],[51.79439,24.01983],[52.57708,24.17744],[53.40401,24.15132],[54.008,24.12176],[54.69302,24.79789],[55.43902,25.43915],[56.07082,26.05546],[56.26104,25.71461],[56.39685,24.92473],[55.88623,24.92083],[55.80412,24.2696],[55.98121,24.13054],[55.52863,23.9336],[55.52584,23.52487],[55.23449,23.11099],[55.20834,22.70833],[55.0068,22.49695],[52.00073,23.00115],[51.61771,24.01422],[51.57952,24.2455]]]},\"properties\":{\"name\":\"United Arab Emirates\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey’s (USGS) Modular Ground-Water Flow Model (MODFLOW-2005) is a computer program that simulates groundwater flow by using finite differences. The MODFLOW-2005 framework uses a modular design that allows for the easy development and incorporation of new features called processes and packages that work with or modify inputs to the groundwater-flow equation. A process solves a flow equation or set of equations. For example, the central part of MODFLOW is the groundwater-flow process that solves the groundwater-flow equation; the surface-water routing process is an additional process that solves the surface-water flow equation. Packages are code related to the groundwater-flow process. For example, the subsidence package modifies the groundwater-flow process by including aquifer compaction effects on flow. With the development of new packages and processes, the MODFLOW-2005 base framework diverged into multiple independent versions designed for specific simulation needs. This divergence limited each independent MODFLOW release to its specific purpose, so that there was no longer a single, comprehensive, general-purpose hydraulic-simulation framework.
The MODFLOW One-Water Hydrologic Flow Model (MF-OWHM, also informally known as OneWater) is an integrated hydrologic flow model that combines multiple MODFLOW-2005 variants in one cohesive simulation software; changes were made to enable multiple capabilities in one code. This fusion of the MODFLOW-2005 versions resulted in a simulation software that can be used to address and analyze a wide class of conjunctive-use, water-management, water-food-security, and climate-crop-water scenarios. As a second core version of MODFLOW-2005, MF-OWHM maintains backward compatibility with existing MODFLOW-2005 versions, with features that include the following:
MF-OWHM is a MODFLOW-2005 based integrated hydrologic model that can simulate and analyze varying environmental conditions to allow for the evaluation of management options from many components of human and natural water movement through a physically based, supply and demand framework. The term “integrated,” in the context of this report, refers to the tight coupling of groundwater flow, surface-water flow, landscape processes, aquifer compaction and subsidence, reservoir operations, and conduit (karst) flow. Another benefit of this integrated hydrologic model is that models developed to run by MODFLOW-2005, MODFLOW-NWT, MODFLOW-CFP, or MODFLOW-FMP can also be simulated with MF-OWHM. At the time of this report’s publication, MF-OWHM version 2 (MF-OWHM2) does not include a direct internal simulation of snowmelt, advanced mountainous watershed rainfall-runoff simulation, detailed shallow soil-moisture accounting, or atmospheric moisture content. Atmospheric moisture may be accounted for indirectly by, optionally, specifying a pan-evaporation rate, reference evapotranspiration, and precipitation. These features are not included to ensure that simulation runtime remains short enough to enable the use of automated methods of calibrating model parameters to field observations, which typically require many simulation model runs. The MF-OWHM approach is to include as much detail as possible to simulate hydrological processes, providing the simulation runtimes remain reasonable enough to allow for robust parameter estimation and model calibration.
To represent both natural and human-influenced flow, MF-OWHM integrates physically based flow processes derived from MODFLOW-2005 in a supply and demand framework. From this integration, the physically based movement of groundwater, surface water, imported water, and precipitation serve as supply to meet consumptive demands associated with irrigated and non-irrigated agriculture, natural vegetation, and urban water uses. Water consumption is determined by balancing the available water supply with water demand, leading to the concept of a demand-driven, supply-constrained simulation.
The MF-OWHM Supply-and-Demand Framework is especially useful for the analysis of agricultural water use, where there are often few data available to describe changes in land-use through time, such as crop type and distribution, and the associated changes in groundwater pumpage. This framework attempts to satisfy each land-use water demand with available water supplies—that is, groundwater uptake, precipitation, and irrigation. An option provided in MF-OWHM2 is to automatically increase groundwater pumping for irrigation, which often is unknown, by the calculated residual between demand and the other available sources of supply. From large- to small-scale applications, the physically based supply and demand framework provides key capabilities for simulating and analyzing historical, current, and future conjunctive-use of surface water and groundwater.
To achieve the physically based supply and demand framework, the MODFLOW-2005 standard of no inter-package and -process communication was relaxed for MF-OWHM2. Traditional MODFLOW simulation models required that all packages and processes interact through the groundwater-flow equation or by removing the water flow from the simulation domain. For example, the MODFLOW-2005 representation of a groundwater well extracts water from the groundwater-flow equation (by subtraction) and removes it from the simulation domain. This feature is available in the MF-OWHM framework, but options have been added to allow the specification of a use or destination of pumped groundwater within the model domain, for example, it can be used for irrigation, managed aquifer recharge, or return-flow to streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A60","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Boyce, S.E., Hanson, R.T., Ferguson, I., Schmid, W., Henson, W., Reimann, T., Mehl, S.M., and Earll, M.M., 2020, One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software: U.S. Geological Survey Techniques and Methods 6–A60, 435 p., https://doi.org/10.3133/tm6A60.","productDescription":"Report: xvii, 435 p.; Application Site","numberOfPages":"435","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071159","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437036,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K2IQ6Y","text":"USGS data release","linkHelpText":"Batteries Included Fortran Library (BiF-lib), version 1.0.0"},{"id":437035,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8I8GS","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) Conjunctive Use and Integrated Hydrologic Flow Modeling Software"},{"id":374113,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix8.pdf","text":"Appendix 8","size":"300 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process (CFP2) Input File Documentation for New Capabilities of CFP2 Mode 1—Discrete Conduits"},{"id":374112,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix7.pdf","text":"Appendix 7","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process Updates and Upgrades (CFP2)"},{"id":374111,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix6.pdf","text":"Appendix 6","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Farm Process Version 4 (FMP)"},{"id":374110,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix5.pdf","text":"Appendix 5","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Landscape and Root-Zone Processes and Water Demand and Supply"},{"id":374109,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix4.pdf","text":"Appendix 4","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Consumptive Use and Evapotranspiration in the Farm Process"},{"id":374108,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix3.pdf","text":"Appendix 3","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Modflow Upgrades and Updates"},{"id":374107,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix2.pdf","text":"Appendix 2","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Separation of Spatial and Temporal Input Options"},{"id":374106,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix1.pdf","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- New Input Formats and Utilities"},{"id":374105,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix0.pdf","text":"Appendix 0","size":"500 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Report Syntax Highlighting and Custom Font Styles"},{"id":374104,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_body.pdf","text":"Main body","size":"3 MB - Main body","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Main body"},{"id":373682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a60/coverthb.jpg"},{"id":373683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60.pdf","text":"Full report","size":"30 MB - Full report","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Full report"},{"id":373696,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://www.usgs.gov/software/modflow-owhm-one-water-hydrologic-flow-model"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The lakebed in Wapato Lake National Wildlife Refuge (NWR) in northwestern Oregon was farmed for decades prior to the establishment of the refuge in 2013. Planning for restoration of these lands required extensive data collection and construction of a water budget and tools to design and evaluate potential restoration strategies. The U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service worked together to monitor streamflow and water levels in and around Wapato Lake NWR, apply the USGS Shoreline Management Tool (SMT), then construct and apply a water-budget-based Water Management Scenario Tool (WMST). The SMT was used to determine the spatial availability of different water depths (as potential habitat for different species) as a function of water level and other factors, based on topographic data. The WMST uses a water-budget approach to predict daily water levels, inflows, outflows, and areas of specific categories of water depth in the refuge over the course of a water year in response to a range of hydrologic and meteorological conditions and potential water-management strategies. In this study, two hypothetical water-management strategies were simulated to predict their effect on water levels and areas with specific water depths as an indicator of potential habitat. In the first scenario, several tributaries that had been diverted around the lakebed since the 1930s were reconnected to the lake, and an outflow weir was used to control lake level and to create a lake and seasonal wetlands of specific depths. In the second scenario, an outflow weir was combined with pumps to help meet target lake levels. Results showed that reconnecting the largest three tributaries to Wapato Lake would provide sufficient water to create a range of aquatic conditions in most years. For a median water year, rainfall and tributary flows in these scenarios provided 99 percent of total inputs to the lake, whereas pumping, weir outflows, and open-water evaporation
accounted for 95–97 percent of losses. Management of lake levels could be accomplished with a variable-elevation outflow weir or a combination of a weir and pumps. The lake would take longer to fill to a higher seasonal target level during a dry year. Without an outflow weir or other means of allowing water to flow out of the lake, the largest of two existing pumps would need to be used during late spring or early summer to attain a lower seasonal target water level in summer. High-water conditions downstream of Wapato Lake may prevent the use of a simple outflow weir, as historical downstream water levels in winter and spring sometimes were higher than the target water levels used in these scenarios. Water-budget-based methods applied in this study have proven to be valuable for the design and evaluation of potential restoration strategies at Wapato Lake NWR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205013","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Joint Water Commission","usgsCitation":"Rounds, S.A., Freed, T.Z., Snyder, D.T., Smith, C.D., Doyle, M.C., Holmes, E., Mykut, C., Mayer, T., Stockenberg, E., and Pilson, S.L., 2020, Evaluation of restoration alternatives using water-budget tools for the Wapato Lake National Wildlife Refuge, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2020–5013, 26 p., https://doi.org/10.3133/sir20205013.","productDescription":"vi, 26 p.","onlineOnly":"Y","ipdsId":"IP-110975","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":373658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5013/coverthb.jpg"},{"id":373659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5013/sir20205013.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5013"},{"id":399634,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109890.htm"}],"country":"United States","otherGeospatial":"Wapato Lake National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1417,\n 45.4\n ],\n [\n -123.1083,\n 45.4\n ],\n [\n -123.1083,\n 45.4431\n ],\n [\n -123.1417,\n 45.4431\n ],\n [\n -123.1417,\n 45.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
This study was developed to assess if groundwater from the western Hualapai Plateau could be used to supply developments in the Grand Canyon West area of the Hualapai Indian Reservation and to collect hydrogeologic data for future use in a numerical groundwater model for the reservation. Ground-based geophysical surveys; existing well, spring, and other hydrogeologic information from previous studies; and new well and spring data collected for this study were used to provide a better understanding of the hydrogeology of the western Hualapai Plateau.
Surface geophysical data provided information on the depth and geologic structure of lower Paleozoic rock units and Proterozoic crystalline and metamorphic rocks that underlie the western Hualapai Plateau. The surface geophysical data and discharge information from springs were used to select a site to drill and develop the U.S. Geological Survey Hualapai Test Well.
The Hualapai Test Well was drilled to understand the geophysical properties of geologic formations at depth. These data were used to verify the results of surface geophysical data and to evaluate if sufficient water was present in the Hualapai Test Well for potential groundwater development. The Hualapai Test Well was drilled to a depth of 2,468 feet and bottomed in Proterozoic granite. Water was expected in the lower part of the Muav Limestone, but water was not observed until the Tapeats Sandstone at a depth of 2,400 feet. The Tapeats Sandstone was determined to be confined with a hydrostatic head of over 900 feet. A 48-hour pumping test was conducted to determine aquifer properties. Low specific capacity indicated that although groundwater is present in the Tapeats Sandstone, well yields are likely to be small. A water-quality sample indicated the sample had a calcium, magnesium-bicarbonate water type with a total dissolved-solids concentration of 371 milligrams per liter. Alpha radioactivity of the sample, 18.3 picocuries per liter, exceeded the U.S. Environmental Protection Agency maximum contaminant level of 15 picocuries per liter for drinking water. Concentrations of iron and manganese in the water sample also exceeded the U.S. Environmental Protection Agency secondary maximum contaminant levels for drinking water.
An inventory of wells and springs provided insight into the occurrence of groundwater on the western Hualapai Plateau. Data from 56 springs on and adjacent to the western Hualapai Plateau were compiled for this study, and new data were collected at 31 springs. Discharge from springs visited for this study ranged from dry to about 345 gallons per minute. The temporal data from springs, where repeat measurements were available, indicated that spring flow is highly variable and likely related to seasonal and annual precipitation. Water levels from 36 wells on and adjacent to the western Hualapai Plateau were compiled for this study, and new water levels were collected at 5 wells. The spring and well data in conjunction with the Hualapai Test Well results indicated that on the western Hualapai Plateau, bedrock aquifers have limited discrete flow paths that make extensive groundwater development unlikely.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
A three-dimensional, numerical groundwater flow model of the Hualapai Plateau and Truxton basin was developed to assist water-resource managers in understanding the potential effects of projected groundwater withdrawals on groundwater levels and storage in the basin. The Truxton Basin Hydrologic Model (TBHM) is a transient model that simulates the hydrologic system for the years 1976 through 2139, including hypothetical low-, medium-, and high-groundwater withdrawal scenarios beginning in 2020. The simulated effects of these withdrawal scenarios are presented as groundwater-level changes from the year 2020 to 2070, and from 2020 to 2140. Hydrologic properties in the TBHM are derived from calibration of a steady-state model of the predevelopment (before 1976) groundwater system. The future pumping scenarios are each simulated with three different interpretations of basin depth supported by geophysical data. For each of the resulting nine transient models, a Monte Carlo approach is used to produce a range of possible and probable groundwater-level changes at points throughout the basin given probabilistic ranges of hydrologically reasonable aquifer property values supported by the model calibration results. The ensemble of models that simulate the future pumping scenarios include pumping from the existing well field (three wells) plus additional pumping from a proposed new well. Simulated high future pumping increases progressively to 1,840 acre-feet per year in 2120 and produces a range of drawdowns between 20 and 39 feet (ft) near the pumping center, with a median drawdown of 28 ft. The low future pumping scenario, which increases progressively to 650 acre-ft per year in 2120, produces a range of drawdowns between 5 and 15 ft, with a median drawdown of 10 ft at the same location over the same period of time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205017E","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Knight, J.E., 2020, Simulation of groundwater-level changes from projected groundwater withdrawals in the Truxton basin, northwestern Arizona, chap. E of Mason, J.P., ed., Geophysical surveys, hydrogeologic characterization, and groundwater flow model for the Truxton basin and Hualapai Plateau, northwestern Arizona: U.S. Geological Survey Scientific Investigations Report 2020–5017, 39 p., https://doi.org/10.3133/sir20205017E.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-108383","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":399689,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109887.htm"},{"id":373648,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O2WGLS","linkHelpText":"MODFLOW-NWT groundwater model used for simulating potential future pumping scenarios and forecasting associated groundwater-level changes in the Truxton aquifer on the Hualapai Reservation and adjacent areas, Mohave County, Arizona"},{"id":373647,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5017/e/sir20205017_chap_e.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5017 Chapter E"},{"id":373504,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5017/e/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Truxton basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.05,\n 35.2403\n ],\n [\n -113.18,\n 35.2403\n ],\n [\n -113.18,\n 36.1656\n ],\n [\n -114.05,\n 36.1656\n ],\n [\n -114.05,\n 35.2403\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter referred to as Clear Lake; fig. 1), California, are experiencing long-term declines in abundance. Upper Klamath Lake populations are decreasing because juvenile suckers are not surviving and recruiting into the adult population. Most juvenile sucker mortality occurs within the first year of life in Upper Klamath Lake. Annual production of juvenile suckers in Clear Lake appear to be highly variable and may not occur at all in very dry years. However, juvenile sucker survival is much higher in Clear Lake, with some suckers surviving to join spawning aggregations. Long-term monitoring of juvenile sucker populations is needed to 1) determine if there are annual and species-specific differences in production, survival, and growth; 2) better understand when juvenile sucker mortality is greatest; 3) help identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake; and 4) monitor for successful juvenile survival in Upper Klamath Lake.
The U.S. Geological Survey (USGS) began a summer juvenile sucker monitoring program in 2015 to track cohorts over time in Upper Klamath and Clear Lakes. The juvenile sucker monitoring program involved using trap net data at fixed sites to determine the status of juvenile suckers. Annual variability in apparent age-0 sucker production, juvenile sucker survival, and growth were tracked. Using genetic markers, suckers were classified as one of three taxa; shortnose (combinations of shortnose and Klamath largescale suckers), Lost River, or suckers with genetic markers of both species (Intermediate [Prob]). By using catch data, we generated taxa-specific indices of year-class strength, August–September apparent survival, and overwinter apparent survival. We also examined the prevalence and severity of afflictions such as parasites, wounds, and deformities.
The Upper Klamath Lake year-class strength indices for both Lost River and shortnose suckers were slightly lower in 2015 and 2017 than in 2016. The ratios of age-0 Lost River suckers to age-0 shortnose suckers captured in August in Upper Klamath Lake were low in 2015 and 2017, given that adult Lost River suckers are more abundant and more fecund than adult shortnose suckers. This may indicate lower egg, larval, or juvenile survival or poorer spawning success for Lost River suckers than shortnose suckers in these two years. Apparent relative age-0 survival indices for Lost River suckers from August to September in Upper Klamath Lake were greater in 2015 (0.29) than in 2016 (0.16) or 2017 (0.14). Age-0 shortnose sucker catch rates increased between August and September in 2015, possibly indicating new individuals of this species were still recruiting to the lake between the two sampling periods. August to September relative survival indices for Upper Klamath Lake shortnose suckers were 0.35 in 2016 and 0.00 in 2017.
We predicted year-class strength would be greater in Clear Lake in years when high spring-time lake elevations and instream flow allowed adult suckers access to spawning habitat in the Willow Creek drainage. Instream flows and lake elevations were sufficient to allow adult suckers to access Willow Creek during the 2016 and 2017 spawning seasons, and age-0 suckers were detected in Clear Lake both years. Higher lake surface elevations and instream flows in 2017 than in 2016 were not associated with higher year-class strength indices in 2017 than in 2016. Low lake surface elevations appeared to limit access by adults to Willow Creek during the 2014 and 2015 spawning seasons and age-0 suckers were not detected in Clear Lake during these years. Nineteen shortnose suckers from the 2014 cohort were captured in Clear Lake in 2017. A 2015 cohort of shortnose suckers was captured as age-1 in 2016 and as age-2 in 2017. The most likely explanation for increasing catch rates of the 2015 cohort is that the higher Willow Creek flows in 2016 and 2017 facilitated the movement of stream-resident suckers, spawned in 2014 and 2015 downstream into Clear Lake. Due to uncertainty in the genetic identification of non-Lost River suckers, these fish are equally likely to be Klamath largescale or shortnose suckers (Hoy and Ostberg, 2015).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201025","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Bart, R.J., Burdick, S.M., Hoy, M.S., and Ostberg, C.O., 2020, Juvenile Lost River and shortnose sucker year-class formation, survival, and growth in Upper Klamath Lake, Oregon and Clear Lake Reservoir, California—2017 Monitoring Report: U.S. Geological Survey Open-File Report 2020–1025, 36 p., https://doi.org/10.3133/ofr20201025.","productDescription":"v, 36 p.","onlineOnly":"Y","ipdsId":"IP-112875","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373492,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1025/ofr20201025.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1025"},{"id":373491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1025/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.77197265625,\n 40.763901280945866\n ],\n [\n -121.22314453124999,\n 40.763901280945866\n ],\n [\n -121.22314453124999,\n 43.08493742707592\n ],\n [\n -123.77197265625,\n 43.08493742707592\n ],\n [\n -123.77197265625,\n 40.763901280945866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
This geologic map depicts and briefly describes geologic units underlying Petroglyph National Monument and immediately adjacent areas in Bernalillo County, New Mexico. The Monument is underlain dominantly by Quaternary basalts of the Albuquerque Volcanoes volcanic field, a series of basin-filling volcanic flows and associated vents from a monogenetic volcanic highland along the eastern margin of the Llano de Albuquerque. This compilation builds on data of previously published geologic maps and reports but includes new interpretive synthesis of volcanic stratigraphy and a unified representation of Quaternary surficial deposits overlying volcanic deposits within the Monument and areas immediately adjacent. This geologic map emphasizes the distribution of Quaternary volcanic vent areas and lava flow deposits which were incompletely mapped on previous publications. Surficial deposits are simplified, but uniformly mapped and described in contrast to varying map unit distributions, names and descriptions presented in the references above. Underlying deposits of the upper Santa Fe Group are exposed in the western part of the map area and described briefly.
North-trending, syn- and post-eruption faulting is well preserved in the volcanic field and reflected in the subsurface models of aeromagnetic data. These faults are dominated by dip-slip displacement and are interpreted as extensional faults of the central Albuquerque Basin of the northern Rio Grande rift. Elongate distribution of vents for most of the volcanic deposits are spatially associated with the easternmost of these faults and are interpreted to reflect eruptions from fissures paralleling the regional extensional fault trends of the rift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3447","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Thompson, R.A., Chan, C.F., Gilmer, A.K., and Shroba, R.R., 2020, Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3447, scale 1:24,000, https://doi.org/10.3133/sim3447.","productDescription":"2 Sheets: 50.50 inches x 40.00 inches; Data Release; ReadMe","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102605","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":373216,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LW817K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico"},{"id":373215,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447_georeferenced.pdf","text":"Sheet—Georeferenced geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"},{"id":399520,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109803.htm"},{"id":373213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3447/coverthb.jpg"},{"id":373222,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3447/ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3447 Read Me"},{"id":373214,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447.pdf","text":"Sheet—Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"}],"scale":"24000","country":"United States","state":"New Mexico","county":"Bernalillo County","otherGeospatial":"Petroglyph National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.79946899414062,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.097439809364204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195127","usgsCitation":"Brakebill, J.W., Schwarz, G.E., and Wieczorek, M.E., 2020, An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset: U.S. Geological Survey Scientific Investigations Report 2019–5127, 49 p., https://doi.org/10.3133/sir20195127.","productDescription":"Report: vii, 49 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098180","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372768,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P986KZEM","text":"USGS data release","linkHelpText":"E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 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-94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"NAWQA Science Team
National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20191-0002
Groundwater flow and development were characterized in four groundwater basins of the Death Valley regional flow system in Nevada and California with calibrated, groundwater-flow models. Natural groundwater discharges in the Furnace Creek, Lower Amargosa, and Saratoga Spring areas were defined and distributed consistently with a revised hydrogeologic framework. This simplified hydrogeologic framework was limited to four hydraulically unique, hydrogeologic units: (1) basin fill; (2) carbonate rocks; (3) volcanic rocks; and (4) low-permeability granitic and siliciclastic rocks. Hydrogeologic units and division of carbonate and volcanic rocks between shallow and deep were supported by results from 271 aquifer tests and specific-capacity estimates. Greater than 90 percent of field-estimated transmissivity occurred within 1,600 feet (ft) of the water table. Pumping in the study area from 1960 to 2010 averaged 46,000 acre-feet per year (acre-ft/yr), which is 80 percent of the predevelopment discharge. The central Amargosa Desert and Pahrump Valley were the two primary pumping centers and measurably affected water levels across 900 square miles in 2018.
Water levels in Devils Hole were a special focus because endangered Devils Hole pupfish (Cyprinodon diabolis) are affected by water-level declines. Pumping 42,100 acre-ft by Cappaert Enterprises, formerly Spring Meadows, Inc., caused a 2.3-ft water-level decline in Devils Hole, which temporarily reduced habitat of Devils Hole pupfish by 85 percent in 1972. If no pumping occurred, water levels in Devils Hole would have risen naturally about 1 ft between 1973 and 2018 from temporal variations in recharge. The 2.6-ft range of measured water-level changes in Devils Hole was simulated with a root-mean-square error of 0.2 ft during the 70-year period of record. Simulated water-level declines from pumping totaled 1.4 ft in 2018, with 25 and 34 percent attributed to pumping by Cappaert Enterprises and the central Amargosa Desert, respectively. Water levels in Devils Hole will decline at rates of 0.1–0.2 ft per decade if pumping from Ash Meadows groundwater basin and the central Amargosa Desert continue at current rates. Effects of future natural water-level fluctuations remain unknown.
Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins are hydraulically connected near well AD-4, about 5 miles south of the town of Amargosa Valley, Nevada. About 40 percent of the discharge from the Furnace Creek area is recharged in the Ash Meadows groundwater basin. Basin fill in the central Amargosa Desert hydraulically connects carbonate rocks east of well AD-4 with saturated carbonate rocks in the Funeral Range. About 7 percent of the 960,000 acre-ft pumped from Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins prior to 2019 was captured discharge from springs and phreatophytes. Greater than 40 percent of the 2,080,000 acre-ft pumped from Pahrump Valley between 1910 and 2019 was capture that primarily discharged from Bennetts and Manse Springs.
Simulated advective-flow distances and velocities from underground nuclear tests are within the range of advective transport calculations from tritium data and previous radionuclide transport investigations. Boundary conditions and flow rates from the regional model in this study are plausible for local-scale flow and radionuclide transport models. Simulated 165-year groundwater-flow paths do not extend into pumping areas and effects of regional pumping on advective transport are negligible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1863","collaboration":"Prepared in cooperation with the U.S. Department of Energy Office of Environmental Management, National Nuclear Security Administration, Nevada Site Office, under Interagency Agreement DE-EM0004969","usgsCitation":"Halford, K.J., and Jackson, T.R., 2020, Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole: U.S. Geological Survey Professional Paper 1863, 178 p., https://doi.org/10.3133/pp1863.","productDescription":"Report: xvi, 178 p.; Data Release","ipdsId":"IP-105994","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":372815,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"},{"id":399508,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109738.htm"},{"id":372814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1863/pp1863.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1863"},{"id":372813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1863/coverthb2.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley, Devils Hole","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35.6464\n ],\n [\n -115.0611,\n 35.6464\n ],\n [\n -115.0611,\n 37.7214\n ],\n [\n -117,\n 37.7214\n ],\n [\n -117,\n 35.6464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
A study was conducted by the U.S. Geological Survey (USGS) in cooperation with the Washington, D.C., Department of Energy & Environment to estimate the loads of suspended-sediment-bound chemical compounds in five gaged tributaries and four ungaged tributaries of the Anacostia River (known locally as “Lower Anacostia River”) in Washington, D.C. Tributaries whose discharge is measured by the USGS are the Northeast and Northwest Branches of the Anacostia River, referred to in this report as “Northeast Branch” (NEB) and “Northwest Branch” (NWB), respectively; Watts Branch (WB); and Hickey Run (HR). A USGS streamflow-gaging station was established in 2016 on Beaverdam Creek (known locally as “Lower Beaverdam Creek” [LBDC]) to support this study. The ungaged streams studied include Nash Run; Pope Branch; an unnamed stream at Fort DuPont, referred to in this report as “Fort DuPont Creek”; and an unnamed stream at Fort Stanton, referred to in this report as “Fort Stanton Creek.” The gaged streams were sampled during four to five storms and two low-flow events during January, March, May, and July 2017. The ungaged streams were sampled during one storm and one low-flow event during July 2017. Storm sampling involved collecting large-volume (60- to 70-liter) composite samples, then removing sediment by filtration in the laboratory. Low-flow samples were obtained by filtering streamwater directly in the field. Continuously recording data sondes were deployed throughout the study to measure turbidity and other water-quality characteristics. During sampling, multiple discrete samples of streamwater were collected to determine suspended-sediment concentration (SSC) and particulate organic carbon (POC) concentration. Shortly after each storm, bed sediment was collected for chemical analysis.
Sediment samples were analyzed for 209 polychlorinated biphenyl (PCB) congeners; 35 polyaromatic hydrocarbon (PAH) compounds, including 20 nonalkylated and 15 alkylated species; and 20 organochlorine pesticide (OP) compounds. Sediment from one storm was analyzed for 23 metals.
Relations were developed among turbidity, discharge, and measured SSC by using multiple linear regression of log-transformed data. These relations were used to estimate SSC from continuous records of discharge and turbidity and were subsequently used to estimate sediment loads for the 2017 calendar year. USGS continuous records of turbidity in NEB, NWB, Watts Branch, and Hickey Run were available for 2013–17, which allowed sediment loads to be calculated for these years. Sediment loads for the ungaged streams were estimated by using loads measured in Watts Branch adjusted on the basis of stream-basin areas.
Sediment loads for 2017 total 3.10×107 kilograms (kg), with 1.02×107 kg (33 percent of total) from the NEB, 1.55×107 kg (50 percent) from the NWB, 4.45×106 kg (14 percent) from LBDC, 5.62×105 kg (2 percent) from Watts Branch, and 2.82×105 kg (1 percent) from Hickey Run. Sediment yields were highest from NWB and LBDC (3.13×105 kilograms per year per square mile [kg/yr/mi2] and 3.01 kg/yr/mi2, respectively). As a result of gaps in turbidity and discharge data, the load for LBDC reported here was calculated from measurements representing only 88 percent of the year (2017), and thus underestimates the actual load. All other gaged tributaries had datasets covering 100 percent of the year and are considered to fully represent actual loads. Estimated sediment loads for the ungaged streams during 2017 total 3.5×105 kg, with 1.2×105 kg from Nash Run, 6.2×104 kg from Pope Branch, 1.1×105 kg from Fort DuPont Creek, and 5.6×104 kg from Fort Stanton Creek.
Concentrations of PCBs, PAHs, and chlorinated pesticides in streamwater are presented for stormflow and low-flow conditions. Average concentrations (in stormflow and low-flow samples) of total PCBs (sum of all congeners, including coelutions) are 5.9 micrograms per kilogram (µg/kg) for NEB, 6.6 µg/kg for NWB, 130 µg/kg for LBDC, 34 µg/kg for Watts Branch, and 69 µg/kg for Hickey Run. Average concentrations of total PAHs (tPAH) (total of nonalkylated and alkylated species) are 2,000 µg/kg for NEB, 3,300 µg/kg for NWB, 2,200 µg/kg for LBDC, 2,400 µg/kg for Watts Branch, and 18,000 µg/kg for Hickey Run. tPAH concentrations among the ungaged streams were highest in Nash Run (5,500 µg/kg); concentrations in the other ungaged streams were less than (<) 700 µg/kg.
The general magnitude of tPCB and tPAH concentrations in streamwater samples was low-flow samples greater than (>) stormflow samples greater than or equal to (≥) bed-sediment samples. PCB congener profiles in the three types of samples were nearly identical in each stream and were similar in all streams except for LBDC, where the dominant PCBs shifted to the lighter di- through tetra- homologs. LBDC showed higher tPCB concentrations and a distinct congener profile from the other streams. The similarity in congener makeup supported that averaging PCB concentrations in stormflow and low-flow samples was appropriate for calculating chemical loads.
Loads of tPCB, tPAH (total of alkylated and nonalkylated forms), and pesticides were estimated for each stream by multiplying average contaminant concentrations by the respective sediment loads. Total PCB loads for 2017 were estimated to be 820 grams (g) with 8 percent (60 g) from NEB, 12 percent (95 g) from NWB, 75 percent (590 g) from LBDC, 3 percent (25 g) from Watts Branch, and 2.5 percent (19 g) from Hickey Run. PCB toxicity totaled 3.8×10−3 µg/kg, with the largest contribution (47 percent) derived from LBDC. Total PAH loads (sum of alkylated and nonalkylated forms) for 2017 were estimated to be 89,000 g, with 23 percent (20,000 g) from NEB, 59 percent (52,000 g) from NWB, 11 percent (9,800 g) from LBDC, 2 percent (1,400 g) from Watts Branch, and 6 percent (5,200 g) from Hickey Run. These results indicate that the largest contributor (75 percent) of PCBs to the Anacostia River is LBDC, although it contributes only 15 percent of the sediment and its basin area represents only 10 percent of the area of the Anacostia River watershed. The majority of the PAH load originates from NWB (59 percent of total) and NEB (22 percent). The ungaged tributaries contribute extremely small loads of PCBs and PAHs, totaling 8.1 g and 765 kg, respectively. More than 94 percent of the total load from the ungaged tributaries is derived from the Nash Run Basin.
Various organochlorine pesticides were present in suspended and bed sediment from all gaged and ungaged tributaries; however, elevated detection levels associated with the analytical methods resulted in numerous unquantifiable concentrations in the suspended-sediment samples. Only the pesticide chlordane was found in measurable concentrations in all gaged tributaries. As a result, in this report, a combination of analytical data from suspended-sediment and bed-sediment samples was used to estimate the maximum pesticide loading for each tributary. Chlordane was the principal compound present in the gaged tributaries; the highest average concentration (average of stormflow and low-flow samples from each stream) was 62 µg/kg in sediment from Watts Branch. Chlordane loads for 2017 totaled 1,100 g, of which 7 percent (430 g) was from NEB, 28 percent (320 g) was from NWB, 28 percent (310 g) was from LBDC, 5 percent (56 g) was from Watts Branch, and 1 percent (11 g) was from Hickey Run. Chlordane was not present in suspended or bed sediment from any of the ungaged tributaries. Loads of the other pesticides were estimated by using the highest concentration measured in the combined suspended-sediment and bed-sediment data for each stream. Notable loads include dieldrin (860 g from NWB), methoxychlor (205 g from LBDC), endrin aldehyde (150 g from NWB), and 4,4-DDT (79 g from Watts Branch). Compared with pesticide loads from the gaged streams, those from the ungaged streams were minimal, with only the Pope Branch contribution exceeding 1 gram per year for 4,4-DDE (1.05 g) and 4,4’-DDT (1.3 g).
The results of this study show that the dominant source of PCBs and chlordane is LBDC, despite its relatively small basin area. PAHs are ubiquitous throughout the study area, with the largest sources being NEB and NWB; this finding is a result of the large sediment load originating from these basins. The small, ungaged streams supply only minimal PCB and PAH loads, with Nash Run being the largest contributor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195092","collaboration":"Prepared in cooperation with the Washington, D.C., Department of Energy & Environment","usgsCitation":"Wilson, T.P., 2019, Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5092, 146 p., https://doi.org/10.3133/sir20195092.","productDescription":"Report: x, 146 p.; Data Release","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099743","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":399540,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109730.htm"},{"id":372690,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RUZSMV","text":"USGS data release","linkHelpText":"Discharge and sediment data for selected tributaries to the Anacostia River, Washington, District of Columbia, 2003–18"},{"id":372692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5092/sir20195092.pdf","text":"Report","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5092"},{"id":372691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5092/coverthb.jpg"}],"country":"United States","state":"District of Columbia","county":"Washington","otherGeospatial":"Anacostia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.0797,\n 38.8447\n ],\n [\n -76.7689,\n 38.8447\n ],\n [\n -76.7689,\n 39.1611\n ],\n [\n -77.0797,\n 39.1611\n ],\n [\n -77.0797,\n 38.8447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
As runoff flows over the land or impervious surfaces (paved streets, parking lots, and building roofs), it accumulates debris, chemicals, sediment, and other contaminants that can adversely affect water quality if the runoff discharge remains untreated. Pathogens, commonly measured using fecal indicator bacteria such as Escherichia coli, enterococci, or fecal coliform, are the most-frequent cause of water-quality impairment in rivers and streams in the United States. Rapid Creek originates in the western Black Hills area and flows east through Rapid City, South Dakota, to its mouth at the Cheyenne River. The water quality of Rapid Creek is important because the reach that flows through Rapid City is a valuable spawning area for a self-sustaining trout fishery, is actively used for recreation, and is a seasonal municipal water supply for the City of Rapid City. These uses (fishery, recreation, and water supply) are considered beneficial uses by the South Dakota Department of Environment and Natural Resources. Numerical criteria have been established for total suspended solids and Escherichia coli concentrations, among other water-quality constituents, for these beneficial uses. The objectives of this study were to improve the method by which fecal indicator bacteria and total suspended solids are quantified in the urban drainages within Rapid City and to provide information that helps identify origins of fecal indicator bacteria and total suspended solids. This information can be used in hydrologic models to estimate fecal indicator bacteria and total suspended solid loading from certain infrastructure elements in urban environments.
Stormwater samples analyzed for Escherichia coli, total suspended solids, specific conductance, and pH were collected in three drainage basin flowpaths within Rapid City: Jackson, Wildwood, and the Eco Prayer Park. Data-collection activities for this study focused on upgradient urban flowpath elements during rainfall events. This approach builds upon previous stormwater assessments that characterized the water quality in urban basin outlets near the downstream end of the stormwater flowpaths. Within each flowpath group, 4–6 sites were selected to represent the various infrastructure elements of the runoff process. These elements included roof downspouts, parking lots, street curbs and gutters, open channels, underground storm sewers, and stormwater ponds or best-management practice facilities.
In general, the concentrations of Escherichia coli and total suspended solids increased in the downstream direction for all flowpath sites. The wash-off process after the first flush is evident for total suspended solids and specific conductance; however, Escherichia coli concentrations did not necessarily follow the same pattern. Escherichia coli concentrations in the latter part of the runoff period were similar to or greater than the initial concentrations of the first set of samples. Stormwater-quality data were summarized by infrastructure type (roof downspout, parking lot, street curb, and channel/storm sewer) to provide information about approximate water-quality concentrations originating at the upper end of urban flowpaths. Escherichia coli and total suspended solid concentrations were lowest in samples collected from locations most isolated from human influence (roof downspouts); the median concentrations at these sites were 4 most probable number per 100 milliliters and 15 milligrams per liter, respectively. The delivery potential of fecal indicator bacteria and sediment from parking lots and street curbs was similar; median concentrations of Escherichia coli and total suspended solids were around 150–220 most probable number per 100 milliliters and 56–86 milligrams per liter, respectively. The downstream receiving channels and storm sewers where stormwater was aggregated typically contained the highest Escherichia coli concentrations (median was 1,800 most probable number per 100 milliliters), but the total suspended solid concentrations were similar to upstream elements in the flowpath (median was 69 milligrams per liter). The data collected from this study demonstrate that stormwater is contaminated with fecal indicator bacteria upon initial contact with impervious surfaces and highlight the importance of controlling the volume of stormwater discharges into receiving waterbodies via storage structures and pervious elements. Diluting stormwater with high concentrations of Escherichia coli with the receiving water’s (Rapid Creek) lower concentration of Escherichia coli is likely the primary mechanism for meeting the beneficial-use criterion threshold of 235 most probable number per 100 milliliters. Although total suspended solid concentrations in the upper parts of the basin (parking lots and street curbs) also begin at concentrations (56 to 86 milligrams per liter) above the beneficial-use criterion for Rapid Creek (53 milligrams per liter), current stormwater-control practices (storage ponds, swales, and wetlands) may be able to reduce suspended-sediment concentrations to meet this threshold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205004","collaboration":"Prepared in cooperation with the City of Rapid City","usgsCitation":"Hoogestraat, G.K., 2020, Stormwater quality of infrastructure elements in Rapid City, South Dakota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5004, 24 p., https://doi.org/10.3133/sir20205004.","productDescription":"Report: vii, 24 p.; Appendix; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":399627,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109723.htm"},{"id":372437,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372436,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004_appendix1.csv","text":"Appendix 1","size":"12.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5004 Appendix 1"},{"id":372434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5004/coverthb.jpg"},{"id":372435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5004"}],"country":"United States","state":"South Dakota","city":"Rapid City","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.32,\n 44.0111\n ],\n [\n -103.1364,\n 44.0111\n ],\n [\n -103.1364,\n 44.125\n ],\n [\n -103.32,\n 44.125\n ],\n [\n -103.32,\n 44.0111\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
Groundwater levels and stream stage were monitored by the U.S. Geological Survey, in cooperation with the Friends of Herring River, at 19 sites in the Mill Creek Basin, a tributary of the Herring River in Wellfleet, Massachusetts, on outer Cape Cod, to provide baseline data prior to a proposed restoration of tidal flow to the Herring River estuary at the Cape Cod National Seashore. Tidal flow in the Herring River has been restricted by a tide-control structure since 1909. Baseline data are necessary to understand current conditions and provide information on water levels for comparison to future water levels under the proposed Herring River restoration, which includes restoration of salt marshes by enhancing tidal flow to the Herring River and construction of a tide-control structure on Mill Creek to prevent the flooding of upstream private properties, including a golf course.
Analysis of data collected during monitoring-well installation at eight locations on or near the golf course and Mill Creek, along with analysis of existing information, determined that parts of the study area are underlain by salt marsh deposits up to 18 feet (ft) thick. These marsh deposits are directly underlain by estuarine sediments, and adjacent upland areas are underlain by medium to very coarse sand. The freshwater lens on the golf course is 70 ft thick or more.
Groundwater levels at individual wells in the study area fluctuated by 1.3 to 2.6 ft during the study period (June 1, 2017, to June 14, 2018). Total precipitation during this period was 60.8 inches, about 10 inches greater than the long-term (2000–17) annual average (50.3 inches). Groundwater levels on Cape Cod generally were normal to above normal during the study owing to the higher than normal precipitation. Tidal amplitudes of groundwater levels caused by daily fluctuations at nearby tidal waterbodies (M2 tidal harmonic) were as large as 0.12 ft at a well 105 ft from the tidally restricted Herring River and as large as 0.06 ft at a well 575 ft from Wellfleet Harbor. Tidal fluctuations in groundwater levels were generally limited to areas about 1,500 ft from the nearest tidal waterbody. Under the initial proposed restoration, where mean tides would be maintained similar to current conditions, tidal fluctuations would be restored to parts of Mill Creek, and subsequent tidal fluctuations in groundwater levels could increase at some of the areas closest to the proposed tide-control structure, but the fluctuations would be less than about 0.06 ft in magnitude.
Regression models were used to describe the variability of daily mean tidally filtered groundwater levels and daily maximum stream stage in Mill Creek. Significant independent variables for the groundwater-level model included daily tidally filtered Wellfleet Harbor stage with a lag time of zero to 2 days, 7-day precipitation, the growing degree days (50 degrees Fahrenheit), and the quartile of groundwater levels relative to a long period of record at a nearby observation well.
Significant independent variables to predict the Mill Creek stage included daily mean groundwater levels in nearby wells, 7-day precipitation, growing degree days (50 degrees Fahrenheit), and a binary indicator of either a flooded or nonflooded condition on the golf course near Mill Creek. Flooding in Mill Creek occurred primarily when groundwater levels at nearby wells reached certain thresholds, when the precipitation in the preceding 7 days was at least 0.92–1.04 inches, and during the nongrowing season.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195145","collaboration":"Prepared in cooperation with the Friends of Herring River","usgsCitation":"Mullaney, J.R., Barclay, J.R., Laabs, K.L., and Lavallee, K.D., 2020, Hydrogeology and interactions of groundwater and surface water near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18: U.S. Geological Survey Scientific Investigations Report 2019–5145, 60 p., https://doi.org/10.3133/sir20195145.","productDescription":"Report: viii, 60 p.; Data Release; Project Site","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103306","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437103,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P903HI9K","text":"USGS data release","linkHelpText":"Data on Models to Describe Groundwater Levels and Stream Stage near the Herring River, Wellfleet, Cape Cod, Massachusetts, 2017-2022"},{"id":399619,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109683.htm"},{"id":372270,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/new-england-water/science/groundwater-and-surface-water-monitoring-mill-creek-watershed","text":"Project site","linkHelpText":"- Groundwater and Surface-Water Monitoring in the Mill Creek Watershed, Wellfleet and Truro, Massachusetts"},{"id":372269,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T167II","text":"USGS data release","linkHelpText":"Data on Tidally Filtered Groundwater and Estuary Water Levels, and Climatological Data Near Mill Creek and the Herring River, Cape Cod, Wellfleet, Massachusetts, 2017–2018"},{"id":372451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5145/sir20195145.pdf","text":"Report","size":"6.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5145"},{"id":372267,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5145/coverthb2.jpg"}],"country":"United States","state":"Massachusetts","county":"Barnstable County","city":"Wellfleet","otherGeospatial":"Mill Creek, Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.06719589233398,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.92412111618309\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, New Hampshire 03275
Post-collisional potassic volcanic rocks on the Tibetan Plateau are widespread, but geologically young (<375 ka) volcanism suitable for 238U-230Th geochronology is rare on the plateau. The geologically young Ashikule volcanic field from northern Tibet offers an excellent opportunity for studying high-resolution timescales of magmatism in continental collision zones. Here we report U-Th crystallization ages of zircons from Ashishan volcano and Wulukeshan volcano within the Ashikule volcanic field. In this study, we have identified 3 pulses of zircon crystallization at circa 70 ka, 105 ka, and 290 ka for Ashishan volcanic rocks and 1 pulse of zircon crystallization at circa 115 ka for Wulukeshan. Comparison of high-resolution zircon crystallization ages of 70 ka and 105 ka with respective eruption ages indicate that the zircon crystal residence time for the Ashishan volcano is short (<5 kyr). The presence of 290-ka zircon in a different Ashishan lava flow suggests the 270-ka volcanic pulse previously reported for other volcanoes in Ashikule volcanic field also occurred at Ashishan. The zircon crystallization age of ~115 ka for Wulukeshan volcano suggests that Wulukeshan volcano erupted later than previously inferred. Similar zircon age spectrums of ~105–115 ka for Ashishan and Wulukeshan volcanoes suggest a common interconnected subsurface magma reservoir for these two young volcanoes during Pleistocene time. Our new high-resolution U-Th zircon age data reveal that post-collisional potassic magmas below northern Tibet erupted soon after their formation (<5 kyr), in spite of their passage through thick continental crust. The high abundance (~60%) of geologically old (>375 ka) zircons demands for crystal-scale isotope studies of the widespread post-collisional lavas in continental collision zones, as the complexities cannot be resolved by bulk analysis methods alone.
The U.S. Geological Survey, in cooperation with the Ute Mountain Ute Tribe (UMUT), initiated a study in 2016 to increase understanding of the hydrogeology and chemistry of groundwater within select areas of the Ute Mountain Ute Reservation (UMUR) in Colorado and Utah, identify vulnerabilities to the system and other natural resources, and outline information needs to aid in the understanding and protection of groundwater resources. The results presented for this study can be used to support the UMUT’s goal of protecting their vital groundwater resources on the UMUR.
Hydrogeologic conditions were characterized for the surficial aquifer contained in Quaternary-age unconsolidated surficial deposits and the Dakota aquifer contained in the Cretaceous-age Dakota Sandstone. In the surficial aquifer, median depth to water ranges from about 5.4 to 17.2 feet below land surface in the Farm and Ranch Enterprise area and 11 to 34 feet below land surface in the Towaoc area, and the water table slopes generally southwest or south. A map of depth to the top of the Dakota Sandstone was constructed from existing well data. Depths range from zero in outcrop areas to more than 3,000 feet below land surface on mesas in the southeastern part of the UMUR.
Groundwater-chemistry data were collected by the UMUT from 13 springs and 31 wells from 1996 through 2017. Specific conductance was much lower for samples from springs than from wells; median values were 512 and 6,024 microsiemens per centimeter at 25 degrees Celsius, respectively. Spring samples were well oxygenated. A few well samples were anoxic (dissolved oxygen concentrations less than 0.5 milligrams per liter [mg/L]), indicating reducing conditions in the aquifer. About 75 percent of spring samples had fresh water (total dissolved solids concentrations less than 1,000 mg/L), and about 85 percent of well samples had brackish or highly saline water (total dissolved solids concentrations greater than 1,000 mg/L). Water type for springs on the Ute Mountains was calcium bicarbonate. Lower-altitude springs had a calcium-sulfate water type. Most well samples had sodium as the dominant cation, and sulfate, bicarbonate, and chloride as the dominant anions. Fluoride concentrations in about 45 percent of well samples were greater than an agricultural-use standard of 2 mg/L.
Nitrate plus nitrite concentrations in most spring and well samples were less than about 1.6 mg/L per liter. Concentrations in samples from wells in the irrigated agricultural area were elevated; the maximum concentration was 78.5 mg/L. About one-half of the trace-element samples had concentrations that were less than laboratory reporting limits. Only aluminum, arsenic, and selenium in spring samples, and boron and selenium in well samples, were detected at concentrations greater than surface-water standards or water-quality standards for agricultural use of groundwater.
Only three organic compounds, the pesticides alachlor and atrazine and the volatile organic compound di(2-ethylhexyl) phthalate, were detected in well samples. The Escherichia coli bacteria was detected in 47 and 23 percent of samples from wells and springs, respectively. The E. coli detections included samples from three culturally significant springs, which did not meet the UMUT cultural-use standard of total absence of E. coli.
Tritium and carbon-14 were the primary environmental tracers used for interpreting groundwater ages for Lopez 2 Spring and five wells (AP–1, 5000 Block, Cottonwood Spring, Goodknight, and SE Toe). Water from the AP–1 well contained a mixture of pre- and post-1950s recharge. Tritium and carbon-14 recharge ages for Lopez 2 Spring (post-1950s in age), Goodknight and SE Toe wells (pre-1950s in age), and Cottonwood Spring well (primarily pre-1950s in age) are supported by helium-4 data. The helium-4 data for the 5000 Block well are inconsistent with the tritium and carbon-14 age of pre-1950s recharge because of interference caused by high methane concentrations in the water.
Springs and surficial deposits are more vulnerable to contamination from anthropogenic chemicals than deeper bedrock wells. Bedrock aquifers are vulnerable in areas where the geologic formations containing the aquifers are exposed at the land surface. Groundwater in deep bedrock aquifers is likely thousands of years old and is not currently affected by present-day land uses. Both shallow and deep groundwater are vulnerable to naturally occurring salts and minerals, such as of total dissolved solids, major ions, nitrate, and trace elements.
Effects of a changing climate on water resources and other ecological characteristics of the UMUR could include changes in evapotranspiration, a decrease in snowpack, decreased aquifer recharge and flow of springs, a decrease in soil moisture, and increased occurrence of wildfires and forest mortality. Of particular interest for the UMUT are possible effects of a changing climate on medicinal and culturally important plants and springs
Several information needs were identified during this study that would aid in the understanding and protection of groundwater resources on the UMUR. These include well-completion information for bedrock wells, the collection of environmental tracer data at additional wells, the addition of methane and hydrocarbon analysis to well sampling plans, and the resampling of springs and wells that were last sampled in 2002 or earlier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195122","collaboration":"Prepared in cooperation with the Ute Mountain Ute Tribe","usgsCitation":"Bauch, N.J., and Arnold, L.R., 2020, Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2019–5122, 76 p., https://doi.org/10.3133/sir20195122.","productDescription":"Report: ix, 76 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-095027","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":399604,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109676.htm"},{"id":372110,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4MOB6","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for estimating depth to the top of the Dakota Sandstone, Ute Mountain Ute Reservation, Colorado, 2017"},{"id":372108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5122/coverthb.jpg"},{"id":372109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5122/sir20195122.pdf","text":"Report","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5122"}],"country":"United States","state":"Colorado","otherGeospatial":"Ute Mountain Ute Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0333,\n 37\n ],\n [\n -108.2667,\n 37\n ],\n [\n -108.2667,\n 37.3564\n ],\n [\n -109.0333,\n 37.3564\n ],\n [\n -109.0333,\n 37\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
In 2014, groundwater samples from residential and public supply wells in the vicinity of two former U.S. Navy bases at Willow Grove and Warminster, and an active Air National Guard Station at Horsham, Bucks and Montgomery Counties, Pennsylvania, were found to have concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which are per- and polyfluoroalkyl substances (PFAS), above U.S. Environmental Protection Agency (EPA) provisional health advisory (HA) levels for drinking water. Five supply wells near the bases were shut down because of PFAS contamination. In 2016, after EPA established a Lifetime HA for PFAS in drinking water that is lower than the provisional HA in place in 2014, at least 13 additional supply wells near the bases were shut down because of PFAS contamination. At the request of the U.S. Navy, and in consultation with other Federal and State agencies and local stakeholders, the U.S. Geological Survey used historical and recent data on well withdrawals, recharge rates, aquifer properties, groundwater levels, and stream base flow to evaluate regional groundwater-flow paths from identified areas of PFAS groundwater contamination or potential PFAS sources at the bases. Groundwater withdrawals near the bases from public supply and other large wells decreased substantially from the 1990s to 2017, increasing the proportion of groundwater recharge that discharged to local streams. A preliminary groundwater-flow model, calibrated using 1,009 groundwater levels and 17 stream base flow estimates, simulated regional flow paths from the bases and showed that recharge at the bases discharged to withdrawal wells and local streams, generally within a mile or two of the bases. Supply and remediation wells at the bases captured some of the recharge on base areas of possible PFAS contamination, whereas other base recharge was simulated to flow to nearby public supply wells and streams, depending on water use and aquifer recharge conditions between 1999 and 2017. The locations of many residential wells near the bases that were identified by the Navy and Air National Guard as having elevated PFAS concentrations were generally consistent with the simulated flow paths from possible sources at the bases. However, there are some areas of observed PFAS contamination where no flow paths from base sources were simulated. Additionally, no data were available on PFAS concentrations in groundwater in some areas of simulated flow paths from base sources. Data and models used for this study are provided in this report and in digital data releases to support further investigations and model revisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191137","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Goode, D.J., and Senior, L.A., 2020, Groundwater withdrawals and regional flow paths at and near Willow Grove and Warminster, Pennsylvania—Data compilation and preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017: U.S. Geological Survey Open-File Report 2019–1137, 127 p., https://doi.org/10.3133/ofr20191137.","productDescription":"Report: x, 127 p.; 2 Data Releases","numberOfPages":"138","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113639","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":399427,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109664.htm"},{"id":371906,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZGEI67","text":"USGS data release","linkHelpText":"Groundwater levels, groundwater withdrawals, and point-source discharges to streams in the vicinity of Willow Grove and Warminster, Bucks and Montgomery Counties, Pennsylvania, for selected years during 1999–2017"},{"id":371905,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K36P5S","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used to evaluate groundwater flow in the vicinity of Horsham and Warminster, Bucks and Montgomery Counties, Pennsylvania—Preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017"},{"id":372113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1137/ofr20191137.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1137"},{"id":371903,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1137/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Bucks County, Montgomery County","city":"Warminster, Willow Grove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.3536,\n 40.0678\n ],\n [\n -74.9167,\n 40.0678\n ],\n [\n -74.9167,\n 40.2967\n ],\n [\n -75.3536,\n 40.2967\n ],\n [\n -75.3536,\n 40.0678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
To compare the effectiveness of proposed alternative water-supply scenarios on future water availability in the Mississippi Delta, the U.S. Geological Survey and the Mississippi Department of Environmental Quality are collaborating on the update and enhancement of an existing regional groundwater-flow model of the area. Through this collaboration, the model has been updated to include boundary conditions through March 2014 with the most recent water-use data, precipitation and recharge data, and streamflow and water-level observation data. The updated model has been used to evaluate selected alternative water-supply scenarios to determine relative effects on the Mississippi River Valley alluvial aquifer. Alternative water-supply options evaluated in this report include: (1) irrigation efficiency, (2) on-farm storage and tailwater recovery, (3) instream weirs to increase surface-water availability, (4) intrabasin transfer of surface water, and (5) groundwater transfer and injection. A relative comparison approach was used to calculate the simulated water-level response caused by each scenario. Water-level response is the difference between water levels simulated by the alternative water-supply scenario and those simulated by a base or “no action” scenario. Water-level response in the alluvial aquifer varied for each scenario based on the location, magnitude, and (or) adoption rates of the simulated alternative water-supply option. The groundwater transfer and injection scenario showed the largest water-level response.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195116","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Haugh, C.J., Killian, C.D., and Barlow, J.R.B., 2020, Simulation of water-management scenarios for the Mississippi Delta: U.S. Geological Survey Scientific Investigations Report 2019–5116, 15 p., https://doi.org/10.3133/sir20195116.","productDescription":"Report: iv, 15 p.; Data Release","onlineOnly":"N","ipdsId":"IP-088687","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399601,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109661.htm"},{"id":371205,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9906VM5","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 model used to evaluate water-management scenarios for the Mississippi Delta"},{"id":371202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5116/coverthb.jpg"},{"id":371203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5116/sir20195116.pdf","text":"Report","size":"5.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5116"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.69238281249999,\n 36.659606226479696\n ],\n [\n -90.318603515625,\n 35.7019167328534\n ],\n [\n -91.746826171875,\n 33.60546961227188\n ],\n [\n -91.109619140625,\n 32.20350534542368\n ],\n [\n -90.318603515625,\n 32.37996146435729\n ],\n [\n -89.659423828125,\n 33.37641235124676\n ],\n [\n -89.05517578125,\n 34.6241677899049\n ],\n [\n -88.857421875,\n 35.85343961959182\n ],\n [\n -89.69238281249999,\n 36.659606226479696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, Tennessee 37211
Since the 1990s, increased chloride concentrations of water pumped from wells (much of which is used for drinking water) and the effects of withdrawals on groundwater-dependent ecosystems have led to concerns over groundwater availability on the island of Molokaʻi, Hawaiʻi. An improved understanding of the hydrologic effects of proposed groundwater withdrawals is needed to ensure effective management of the groundwater resources of Molokaʻi, plan for possible growth, and accommodate cultural, social, and economic concerns. To address the information needs of managers and community stakeholders on Molokaʻi, the U.S. Geological Survey developed a numerical groundwater model capable of simulating salinity change and reduction in groundwater discharge in coastal areas of central and southern Molokaʻi. Estimates of groundwater recharge needed as input to the numerical groundwater model were made using a daily water budget for each decade during 1940−2012 (the period 2000−12 spanned 13 years) and the most current available data, including the distributions of monthly rainfall and potential evapotranspiration. Total island recharge during the decadal periods ranged from a low of about 189 Mgal/d during the 1970s to a high of 278 Mgal/d during the 1960s. These recharge estimates were used to develop an island-wide numerical groundwater model with simplifying assumptions (sharp interface between freshwater and saltwater; two-dimensional flow). The island-wide model provided estimates of groundwater inflows to the main area of interest simulated with a three-dimensional numerical groundwater model. Simulated withdrawal scenarios were selected in consultation with water managers and stakeholders and consisted of: (1) a baseline scenario using average recharge (1978−2007 rainfall and 2010 land cover) and average 2016−17 withdrawals; (2) a scenario using average recharge and withdrawals from existing wells at pending (as of January 2019) water-use permit rates; (3) six scenarios using average recharge and selected withdrawals from existing and proposed wells; and (4) a scenario using reduced recharge and selected withdrawals from existing and proposed wells. Results of the simulated withdrawal scenarios indicate that wells may be capable of producing groundwater with chloride concentrations below 250 mg/L at withdrawal rates exceeding average 2016−17 rates. However, the quality of water withdrawn from production wells is dependent on the rate and distribution of the withdrawals. For all nonbaseline scenarios, simulated groundwater discharge to the nearshore environment is reduced relative to the baseline scenario. Areas of discharge reduction may correspond to areas used for cultural or subsistence purposes. The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the hydrologic effects of additional groundwater withdrawals in central Molokaʻi. The model has several limitations, including its nonuniqueness and inability to account for local-scale heterogeneities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195150","collaboration":"Prepared in cooperation with the State of Hawai‘i Department of Hawaiian Home Lands, State of Hawai‘i Office of Hawaiian Affairs, and County of Maui Department of Water Supply","usgsCitation":"Oki, D.S., Engott, J.A., and Rotzoll, K., 2020, Numerical simulation of groundwater availability in central Moloka‘i, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5150, 95 p., https://doi.org/10.3133/sir20195150.","productDescription":"Report: ix, 95 p.; Data Release","numberOfPages":"95","onlineOnly":"Y","ipdsId":"IP-032683","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":399622,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109628.htm"},{"id":371721,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRQASS","linkHelpText":"Central Molokaʻi, Hawaiʻi, SUTRA model"},{"id":371719,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5150/coverthb.jpg"},{"id":371720,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5150/sir20195150.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5150"}],"country":"United States","state":"Hawaii","otherGeospatial":"Moloka‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.77352905273438,\n 21.179289725795993\n ],\n [\n -156.8572998046875,\n 21.163922551671376\n ],\n [\n -156.92184448242188,\n 21.167764494849468\n ],\n [\n -156.97265625,\n 21.211299542246586\n ],\n [\n -156.99737548828125,\n 21.189533621502626\n ],\n [\n -157.1429443359375,\n 21.199776807250093\n ],\n [\n -157.21298217773435,\n 21.220261047755002\n ],\n [\n -157.25830078125,\n 21.218980865996457\n ],\n [\n -157.25555419921875,\n 21.17672864097083\n ],\n [\n -157.2967529296875,\n 21.14599216495789\n ],\n [\n -157.30499267578125,\n 21.097313035028538\n ],\n [\n -157.18826293945312,\n 21.090906697412837\n ],\n [\n -157.08801269531247,\n 21.103719096296263\n ],\n [\n -157.03582763671875,\n 21.090906697412837\n ],\n [\n -156.90811157226562,\n 21.051181240269393\n ],\n [\n -156.84906005859375,\n 21.047336278183312\n ],\n [\n -156.77215576171875,\n 21.08450008351735\n ],\n [\n -156.70074462890625,\n 21.15879980561845\n ],\n [\n -156.77352905273438,\n 21.179289725795993\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus) are a large-bodied anadromous fish that historically supported important fisheries along the east coast of the United States. Following years of overharvest and habitat degradation, populations experienced severe declines. In 2012, the National Marine Fisheries Service listed Atlantic Sturgeon under the Endangered Species Act (ESA; 61 FR 4722). Their listing named five Distinct Population Segments (DPSs), predicated on genetic groups composed of geographically proximate populations.
Federal management of Atlantic Sturgeon presents challenges, as sturgeon from each of the five DPSs mix extensively in coastal and marine habitats yet take and recovery progress must be evaluated separately for each unit. Genetic assignment testing based on mitochondrial and microsatellite markers allows individuals to be assigned back to their natal river and DPS. However, this approach is not perfect and some individuals may be incorrectly assigned. Recent advances in genomics offer the potential of a higher resolution approach to genetic assignment testing, and thus may reduce uncertainty associated with assignment testing. In addition, genomics allows a greater number of markers to be examined from across a broader portion of the sturgeon genome, thus may provide an enhanced perspective of population structure for the species, and potentially allow other previously intractable questions to be addressed (Bernatchez et al. 2017, Supple and Shapiro 2018).
We used next-generation sequencing to develop a draft genome for Atlantic Sturgeon and identify single nucleotide polymorphisms (SNPs) that could be used to resolve the natal river and DPS of individual Atlantic Sturgeon. We identified 1,210 candidate SNPs within the nuclear genome as well as 49 SNPs within the mitochondrial genome. After filtering and review, we selected 161 nuclear SNPs and 39 mitochondrial SNPs for further testing and evaluation. We used genotyping-in-thousands by sequencing (GT-seq) to simultaneously sequence nuclear SNP loci, mitochondrial SNP loci, and the existing panel of twelve microsatellite loci. This effort required a pilot sequencing run on a single sturgeon sample to test marker amplification and refine primer strengths, followed by a series of sequencing runs to generate baseline data for 288 individuals representing nine populations of Atlantic Sturgeon in four DPSs.
Using baseline data from the nine populations, we ran a series of genomic analyses to characterize diversity within and among populations, providing a benchmark for this species using the new SNP markers. Allelic richness was similar for all populations, although there was a general trend of more northern population containing greater levels of allelic richness. Interestingly, we observed linkage disequilibrium among many pairs of loci within many populations. This might be the result of physical linkage but could also suggest these populations are recovering from genetic bottlenecks and/or are effectively small, leading to specific haplotypes to be favored by chance. Pairwise differentiation among populations varied among the populations (FST range: 0.010-0.098) and was significantly correlated (r = 0.771; P < 0.001) to pairwise FST observed using microsatellite markers). Population clustering and ordination techniques using the new genomic data both support an overall population structure that is similar to the current DPS management units (which were developed primarily based on microsatellite genetic data). Overall, this suggests that existing microsatellite markers and the panel of SNP markers developed in this study provide similar information about the populations structure and ecology of Atlantic Sturgeon. Given the observed differences in allele frequencies among populations, our genomic baseline supports previous assertations that Atlantic Sturgeon show natal homing, despite mixing extensively in marine waters during non-breeding periods. Lower levels of differentiation between populations in the South Atlantic DPS suggest that populations in this region may have greater levels of gene flow relative to their more northerly conspecifics, which has also previously been suggested based on microsatellite data. The observed differentiation among populations provides the necessary foundation for determining the natal river and DPS of Atlantic Sturgeon using assignment testing.
We tested the utility of our new genomic baseline for resolving the population and DPS of Atlantic Sturgeon. Our nuclear SNP markers showed utility for identifying the origin of unknown Atlantic Sturgeon samples, as 86.5% were assigned to the correct DPS and 66.3% were assigned to the correct natal river. However, since this study was funded the Conservation Genetics and Genomics Laboratory at Leetown Science Center has made significant improvements to their microsatellite genetic baseline, which now performs more effectively than our new genomic approach (the genetic baseline includes 12 populations and 5 DPSs, and correctly assigns 95.8% of individuals to DPS and 84.9% of individuals to their natal population using 12 microsatellite loci). We conducted an ad hoc exploration of how additional microsatellite or nuclear SNP loci may further improve the accuracy of assignment testing. We found that additional microsatellite markers are likely to result in greater improvements in assignment efficiency than additional nuclear SNPs. However, a much larger number of SNP loci (which if identified could be sequenced using other methods that are now available; e.g., the RAD-capture approach published by Ali et al. 2016) could produce assignment efficiencies that are greater than what is currently feasible using microsatellites. In the absence of further research and development of additional SNP markers for Atlantic Sturgeon (possibly using an approach other than GT-seq), the existing microsatellite loci are the most effective means available to determine the natal river and DPS of Atlantic Sturgeon encountered in offshore waters.
Because our new genomic markers were less effective than the existing panel of 12 microsatellite markers, we chose to use the existing microsatellite markers to assign Atlantic Sturgeon captured in another BOEM-funded study (cooperative agreement M16AC00003; Monitoring endangered Atlantic Sturgeon and commercial finfish habitat use offshore New York) following consultation with our project officer. Using this approach, we genotyped and assigned 186 Atlantic Sturgeon captured in coastal waters off the Rockaway Peninsula, New York. The vast majority of these sturgeon were assigned to the New York Bight DPS (94.62%), and most appear to belong to the Hudson River population (87.10%) with smaller contributions from the Delaware River population (7.53%). Smaller contributions (2.15%) were observed from six other populations, including those from the James, York, Kennebec, Ogeechee, and Edisto rivers. Although most of the fish we assigned were assigned to the nearest spawning rivers (Hudson and Delaware), the contributions from distant rivers is consistent with the propensity of this species to move long distances and form mixed stock aggregations along the continental shelf. This finding indicates that spawning populations (and their corresponding DPS) from distant locations may potentially be impacted by offshore activities. In fact, activities in this region of the New York Bight could negatively impact Atlantic Sturgeon population from at least four different DPSs. Genetic or genomic assignment testing remains an essential tool to characterize potential impacts to Atlantic Sturgeon populations and should be applied more broadly to better characterize potential impacts of activities in other locations.
","language":"English","publisher":"Bureau of Ocean Energy Management","usgsCitation":"Kazyak, D.C., Aunins, A.W., Johnson, R.L., Lubinski, B.A., Eackles, M.S., and King, T.L., 2020, Using advanced population genomics to better understand the relationship between offshore and spawning habitat use for Atlantic Sturgeon: OCS Study 2020-062, vi, 70 p.","productDescription":"vi, 70 p.","ipdsId":"IP-106640","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417553,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2020-062.pdf"}],"country":"United States","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.78806562180561,\n 30.580780235117913\n ],\n [\n -77.30336432135029,\n 29.69193789019691\n ],\n [\n -64.22711779996946,\n 41.900389189548946\n ],\n [\n -67.3512701866663,\n 45.015746962117504\n ],\n [\n -69.19972084180974,\n 45.07365899620572\n ],\n [\n -71.17441845739293,\n 43.73099181927631\n ],\n [\n -71.83104405363528,\n 41.84410310133748\n ],\n [\n -74.31268565149895,\n 41.0826339553866\n ],\n [\n -77.28914013501216,\n 39.074309300310176\n ],\n [\n -76.84797677722777,\n 36.18896372481389\n ],\n [\n -80.79266974786641,\n 32.79868499554179\n ],\n [\n -82.1343310450048,\n 31.38292965735748\n ],\n [\n -81.78806562180561,\n 30.580780235117913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874144,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874145,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874258,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205087,"text":"sir20195094 - 2019 - Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania","interactions":[{"subject":{"id":85811,"text":"sir20085102 - 2008 - Regression Equations for Estimating Flood Flows at Selected Recurrence Intervals for Ungaged Streams in Pennsylvania","indexId":"sir20085102","publicationYear":"2008","noYear":false,"title":"Regression Equations for Estimating Flood Flows at Selected Recurrence Intervals for Ungaged Streams in Pennsylvania"},"predicate":"SUPERSEDED_BY","object":{"id":70205087,"text":"sir20195094 - 2019 - Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania","indexId":"sir20195094","publicationYear":"2019","noYear":false,"title":"Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania"},"id":1}],"lastModifiedDate":"2020-12-09T12:44:17.28198","indexId":"sir20195094","displayToPublicDate":"2020-12-08T10:55:00","publicationYear":"2019","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":"2019-5094","displayTitle":"Development of Regression Equations for the Estimation of Flood Flows at Ungaged Streams in Pennsylvania","title":"Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania","docAbstract":"Regression equations, which may be used to estimate flood flows at select annual exceedance probabilities, were developed for ungaged streams in Pennsylvania. The equations were developed using annual peak flow data through water year 2015 and basin characteristics for 285 streamflow gaging stations across Pennsylvania and surrounding states. The streamgages included active and discontinued continuous-record stations, as well as crest-stage partial-record stations, and required a minimum of 10 years of annual peak streamflow data for inclusion in the study. Explanatory variables significant at the 95-percent confidence level for one or more regression equations included the following basin characteristics: drainage area, maximum basin elevation, mean basin slope, percent storage, and the percentage of carbonate bedrock within a basin. The State was divided into five regions, and regional regression equations were developed to estimate flood flows associated with the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (which correspond to the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively). Although the regression equations can be used to estimate the magnitude of flood flows for most streams in the State, they are not valid for streams with drainage areas generally greater than 1,500 square miles or with substantial regulation, diversion, or mining activity within the basin. The regional regression equations will be incorporated into the U.S. Geological Survey StreamStats application (https://water.usgs.gov/osw/streamstats/).
Additionally, annual peak flow data for 356 streamgages initially considered for inclusion in the analysis for development of updated flood-flow regression equations were analyzed for the existence of trends; estimates of flood-flow magnitude and frequency were also computed for these streamgages. Estimates of flood-flow magnitude and frequency for streamgages substantially affected by upstream regulation are also presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195094","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the Pennsylvania Department of Transportation","usgsCitation":"Roland, M.A., and Stuckey, M.H., 2020, Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania (ver. 1.1, December 2020): U.S. Geological Survey Scientific Investigations Report 2019–5094, 36 p., https://doi.org/10.3133/sir20195094. [Supersedes USGS Scientific Investigations Report 2008–5102]","productDescription":"Report: vi, 36 p.; Appendices 1-3; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104380","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":437232,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YHIU6G","text":"USGS data release","linkHelpText":"Data in support of Development of Regression Equations for the Estimation of Flood Flows at Ungaged Streams in Pennsylvania"},{"id":381091,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5094/versionHist.txt","size":"1.09 KB","linkFileType":{"id":2,"text":"txt"}},{"id":368468,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094.pdf","text":"Report","size":"15.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5094"},{"id":368467,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094_appendix3.xlsx","text":"Appendix 3","size":"40.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5094","linkHelpText":"- Magnitude, variance, and confidence intervals of annual exceedance probability floods for select streamgages in Pennsylvania substantially affected by upstream regulation"},{"id":368466,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094_appendix2.xlsx","text":"Appendix 2","size":"389 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5094","linkHelpText":"- Magnitude, variance, and confidence intervals of annual exceedance probability floods for select unregulated streamgages in Pennsylvania and surrounding states"},{"id":368462,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5c1aa7a4e4b0708288c5b35c","text":"USGS data release","linkHelpText":"Data in support of development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania"},{"id":368465,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094_appendix1.xlsx","text":"Appendix 1","size":"81.4 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5094","linkHelpText":"- Unregulated streamgages considered for the development of updated flood-flow regression equations for Pennsylvania streams"},{"id":368463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5094/coverthb2.jpg"}],"country":"United 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\"}}]}","edition":"Version 1.0: October 2019; Version 1.1: December 2020","publicComments":"Scientific Investigations Report 2019-5094 supersedes Scientific Investigations Report 2008–5102.","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships.
The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model.
The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period.
The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013.
Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).
While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie.
Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful:
❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data.
❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge.
❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Parsen, M., Juckem, P.F., Gotkowitz, M., and Fienen, M.N., 2019, Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture: Wisconsin Geological and NaturalHistory Survey Bulletin B112, 74 p.","productDescription":"74 p.","ipdsId":"IP-093476","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":375174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/pubs/b112/"}],"country":"United States","state":"Wisconsin","county":"Chippewa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8511962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 44.859762688042736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsen, Michael","contributorId":216283,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gotkowitz, Madeline","contributorId":216284,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207454,"text":"sir20195135 - 2019 - Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the southeastern United States","interactions":[],"lastModifiedDate":"2020-02-04T06:09:00","indexId":"sir20195135","displayToPublicDate":"2020-02-04T07:20:00","publicationYear":"2019","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":"2019-5135","displayTitle":"Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States","title":"Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the southeastern United States","docAbstract":"Spatially Referenced Regression On Watershed attributes (SPARROW) models were applied to describe and estimate mean-annual streamflow and transport of total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) in streams and delivered to coastal waters of the southeastern United States on the basis of inputs and management practices centered near 2012, the base year of the model. Previously published TN and TP models for 2002 served as a starting point and reference for comparison. The datasets developed for the 2012 models not only represent updates of previous conditions but also incorporate new approaches for characterizing sources and transport processes that were not available for previous models.
Variability in streamflow across the southeastern United States was explained as a function of precipitation adjusted for evapotranspiration, spring discharge, and municipal and domestic wastewater discharges to streams. Results from the streamflow model were used as input to the water-quality SPARROW models, and areas with large streamflow prediction errors—urban areas and karst areas—were used to provide guidance on where additional data are needed to improve routing of flow.
Variability in TN transport in Southeast streams was explained by the following five sources in order of decreasing mass contribution to streams: atmospheric deposition, agricultural fertilizer, municipal wastewater, manure from livestock, and urban land. Variable rates of TN delivery from source to stream were attributed to variation among catchments in climate, soil texture, and vegetative cover, including the extent of cover crops in the watershed. Variability in TP transport in Southeast streams was explained by the following six sources in order of decreasing mass contribution to streams: parent-rock minerals, urban land, manure from livestock, municipal wastewater, agricultural fertilizer, and phosphate mining. Varying rates of TP delivery were attributed to variation in climate, soil erodibility, depth to water table, and the extent of conservation tillage practices in the watershed.
Variability in SS transport in Southeast streams was explained by variable sediment export rates for different combinations of land cover and geologic setting (for upland sources of sediment) and by gains in stream power caused by longitudinal changes in channel hydraulics (for channel sources of sediment). Sediment yields for the transitional land cover (shrub, scrub, herbaceous, and barren) varied widely depending on geologic setting and on agricultural land cover. Varying rates of SS delivery, like those for TP, were attributed to variation in climate, soil erodibility, and the extent of conservation tillage practices in the watershed, as well as to areal extent of canopy land cover in the 100-meter buffer along the channel. Relatively large uncertainty, compared to the other three models, for almost all the SS source coefficients indicates the need for caution when interpreting the results from the sediment model.
TN, TP, and SS inputs to streams from sources were balanced in the models with losses from physical processes in streams and reservoirs and with water withdrawals. The losses in streams and reservoirs along with withdrawals removed 35, 44, and 65 percent of the TN, TP, and SS load, respectively, that entered streams before reaching coastal waters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195135","collaboration":"National Water Quality Program","usgsCitation":"Hoos, A.B., and Roland, V.L. II, 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5135, 91 p., https://doi.org/10.3133/sir20195135.","productDescription":"Report: xi, 87 p.; Data Release; HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101532","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":370725,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":371973,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5135/sir20195135.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5135"},{"id":370724,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370723,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370721,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A682GW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 base year"},{"id":370722,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370726,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":371031,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5135/coverthb3.jpg"}],"otherGeospatial":"Southeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n 37.19533058280065\n ],\n [\n -81.34277343749999,\n 37.47485808497102\n ],\n [\n -83.9794921875,\n 34.95799531086792\n ],\n [\n -88.8134765625,\n 33.8339199536547\n ],\n [\n -90.1318359375,\n 33.43144133557529\n ],\n [\n -90.2197265625,\n 30.44867367928756\n ],\n [\n -88.154296875,\n 30.29701788337205\n ],\n [\n -85.69335937499999,\n 29.878755346037977\n ],\n [\n -84.72656249999999,\n 29.49698759653577\n ],\n [\n -84.111328125,\n 29.84064389983441\n ],\n [\n -82.96875,\n 27.955591004642553\n ],\n [\n -82.5732421875,\n 26.54922257769204\n ],\n [\n -80.9033203125,\n 25.20494115356912\n ],\n [\n -80.0244140625,\n 25.3241665257384\n ],\n [\n -79.8486328125,\n 27.527758206861886\n ],\n [\n -80.85937499999999,\n 30.031055426540206\n ],\n [\n -80.771484375,\n 31.615965936476076\n ],\n [\n -78.57421875,\n 33.50475906922609\n ],\n [\n -77.16796875,\n 33.97980872872457\n ],\n [\n -76.11328125,\n 34.95799531086792\n ],\n [\n -75.41015624999999,\n 35.96022296929667\n ],\n [\n -75.8056640625,\n 36.80928470205937\n ],\n [\n -76.2890625,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
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In this report, SPAtially Referenced Regression On Watershed attributes (SPARROW) models developed to describe long-term (2000–14) mean-annual streamflow, total nitrogen (TN), total phosphorus (TP), and suspended-sediment (SS) transport in streams of the Midwestern part of the United States (the Mississippi River, Great Lakes, and Red River of the North Basins) are described. The nutrient and suspended-sediment models have a base year of 2012, which means they were developed based on source inputs and management practices similar to those existing during or near 2012 and average hydrological conditions detrended to 2012 (2000–14), whereas the streamflow model has base years of 2000–14, which means it was developed based on the average input precipitation minus actual evapotranspiration from 2000 to 2014. In developing the models, several updates and improvements were made to the data inputs and statistical approaches used to calibrate/develop the models from those used in the previous 2002 SPARROW models. The 2012 SPARROW models were constructed using a higher resolution stream network, which resulted in a mean catchment size of 2.7 square kilometers compared to 480 square kilometers in the 2002 models; more detailed and updated wastewater treatment plant contribution estimates; inputs from background phosphorus sources that were not included in the 2002 model; and more accurate loads for calibration that were computed using a modified Beale ratio-estimator technique whenever no trend in load was determined. Statistical approaches were added to compensate for the unequal effect of each monitoring site during the calibration process by adjusting for the fraction of the basin included in other upstream monitored sites (nested share) and thinning the calibration sites if a negative statistical correlation between nearby sites was determined.
Results from 2012 SPARROW models describe how much of each water, TN, TP, and SS source was delivered to the stream network, and the major landscape factors that affected their delivery. Atmospheric deposition and natural (background) sources of TN and TP, respectively, were the dominant sources in anthropogenically unaffected areas (especially in the Rocky Mountains and north-central areas of the Midwest), whereas fertilizers, manure, and fixation were dominant sources in agricultural areas, especially in the Corn Belt and near the Mississippi River. Urban sources of TN and TP were typically localized, but they were still important for some large areas, especially the Lake Erie Basin. All of the land-to-water delivery variables in the nutrient and sediment SPARROW models, such as runoff, soil erodibility, basin slope, and the amount of tile drains, are commonly included in process-driven models. In the SPARROW TN and TP models, best management practices (BMPs) reduced the delivery of these nutrients to streams.
Long-term mean-annual flows and nutrient and sediment loads were simulated in streams throughout the Midwest. The simulated flows from the SPARROW flow model were used in the SPARROW TN, TP, and SS models to help describe nutrient and sediment transport from the watershed and through the stream network. Outputs from the TN, TP, and SS models describe loads and yields of these constituents throughout the Midwest, and from major drainage basins throughout the Midwest. Highest TN, TP, and SS yields and delivered yields were from the Lake Erie, Ohio River, Upper Mississippi River, and Lower Mississippi River Basins, whereas lowest yields were spread over most other areas. Losses during downstream delivery resulted in part of the TN, TP, and SS that reach the stream network not reaching the downstream receiving bodies: 14, 15, and 28 percent of the TN, TP, and SS, respectively, are lost during delivery to the Great Lakes and 19, 23, and 52 percent of the TN, TP, and SS, respectively, are lost during delivery to the Gulf of Mexico. The largest losses of nutrients and sediments during transport were in the Missouri and Arkansas River Basins.
Information from these SPARROW models can help guide nutrient and sediment reduction strategies throughout the Midwest. Model results provide information on what may be the most appropriate general type of actions to reduce total loading by describing the relative importance of each source, and where to most efficiently place the efforts to reduce loading by describing the distribution of nutrient and sediment loading. By implementing management efforts addressing the major sources of the loads in areas contributing the highest loads, it may be possible to reduce nutrient loading throughout the Mississippi River Basin and thus reduce the size of the hypoxic zone in the Gulf of Mexico; reduce nutrient loading into lakes, and thus reduce the occurrence of harmful algal blooms; and reduce sediment losses, and thus improve the benthic habitat in streams and rivers throughout the Midwest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195114","collaboration":"National Water Quality Program","usgsCitation":"Robertson, D.M., and Saad, D.A., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Midwestern United States: U.S. Geological Survey Scientific Investigations Report 2019–5114, 74 p. including 5 appendixes, https://doi.org/10.3133/sir20195114.","productDescription":"Report: ix, 74 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103244","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370714,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":370711,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370712,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":371971,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5114/sir20195114.pdf","text":"Report","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5114"},{"id":370371,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-midwest-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370713,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":370369,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93QMXC9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 base year"},{"id":370914,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5114/coverthb3.jpg"}],"otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 44.213709909702054\n ],\n [\n -79.27734374999999,\n 43.389081939117496\n ],\n [\n -79.541015625,\n 42.61779143282346\n ],\n [\n -82.529296875,\n 41.44272637767212\n ],\n [\n -82.177734375,\n 43.068887774169625\n ],\n [\n -82.705078125,\n 45.02695045318546\n ],\n [\n -84.287109375,\n 46.6795944656402\n ],\n [\n -90,\n 47.931066347509784\n ],\n [\n -94.5703125,\n 49.210420445650286\n ],\n [\n -95.361328125,\n 49.26780455063753\n ],\n [\n -95.44921875,\n 48.922499263758255\n ],\n [\n -114.2578125,\n 49.095452162534826\n ],\n [\n -113.90625,\n 46.73986059969267\n ],\n [\n -112.236328125,\n 45.336701909968134\n ],\n [\n -110.12695312499999,\n 42.48830197960227\n ],\n [\n -106.69921875,\n 38.06539235133249\n ],\n [\n -101.42578124999999,\n 32.99023555965106\n ],\n [\n -97.20703125,\n 28.536274512989916\n ],\n [\n -92.900390625,\n 29.305561325527698\n ],\n [\n -89.912109375,\n 33.063924198120645\n ],\n [\n -79.541015625,\n 39.774769485295465\n ],\n [\n -76.11328125,\n 44.213709909702054\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
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12201 Sunrise Valley Drive, MS 413
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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 station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate 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 greater than 36 years, 368 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 historic/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. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 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/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great Lakes and Ohio River basins that were used to estimate regional skewness of annual peak flows"},{"id":371681,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5105"},{"id":371680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5105/coverthb.jpg"},{"id":399546,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109629.htm"},{"id":371688,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig05.pdf","text":"Figure 5","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing residuals from constant model of skew for 368 streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371687,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig03.pdf","text":"Figure 3","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing unbiased station skew of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371686,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig02.pdf","text":"Figure 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing the pseudo record lengths of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"}],"country":"United States, Canada","otherGeospatial":"Great Lakes and Ohio River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 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12201 Sunrise Valley Drive
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